From 2c3050034cf6389014b21260f329bb51cf4304e5 Mon Sep 17 00:00:00 2001
From: jrobrien91 <obrienj@anl.gov>
Date: Mon, 12 Oct 2026 13:25:51 -0500
Subject: [PATCH 1/2] ENH: Updated for 2026 Summer School

---
 Summer-Schools/README.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/Summer-Schools/README.md b/Summer-Schools/README.md
index 2dc82965..7860b229 100644
--- a/Summer-Schools/README.md
+++ b/Summer-Schools/README.md
@@ -16,3 +16,8 @@ Here, we share resources from ARM Summer School events!
 - [Main Landing Page](https://arm-development.github.io/arm-summer-school-2025/)
 - [Github Repository](https://github.com/ARM-Development/arm-summer-school-2025)
 - [Project Cookbooks](https://arm-development.github.io/arm-summer-school-2025/projects)
+
+## 2026 Big Open Data Science Summer School
+- [Main Landing Page](https://arm-development.github.io/arm-summer-school-2026/)
+- [Github Repository](https://github.com/ARM-Development/arm-summer-school-2026)
+- [Project Cookbooks](https://arm-development.github.io/arm-summer-school-2026/final-projects/)

From 0e34e50549ae2a4ccd7bb172295dd3c6888833de Mon Sep 17 00:00:00 2001
From: jrobrien91 <obrienj@anl.gov>
Date: Wed, 14 Oct 2026 15:30:11 -0500
Subject: [PATCH 2/2] ADD: Initial DUSTIEAIM ACT Basics with image

---
 .../ACT-Basics-DUSTIEAIM.ipynb                | 8689 +++++++++++++++++
 images/arm-dustieaim.png                      |  Bin 0 -> 302003 bytes
 2 files changed, 8689 insertions(+)
 create mode 100644 Tutorials/AAAR-2026-ACT-Tutorial/ACT-Basics-DUSTIEAIM.ipynb
 create mode 100644 images/arm-dustieaim.png

diff --git a/Tutorials/AAAR-2026-ACT-Tutorial/ACT-Basics-DUSTIEAIM.ipynb b/Tutorials/AAAR-2026-ACT-Tutorial/ACT-Basics-DUSTIEAIM.ipynb
new file mode 100644
index 00000000..fdb4862d
--- /dev/null
+++ b/Tutorials/AAAR-2026-ACT-Tutorial/ACT-Basics-DUSTIEAIM.ipynb
@@ -0,0 +1,8689 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ec2c6e5d",
+   "metadata": {},
+   "source": [
+    "<img src=\"../../images/arm_logo.png\" width=500 alt=\"ARM Logo\"></img>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4129f9a",
+   "metadata": {},
+   "source": [
+    "# ACT Basics\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "926d344e",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition alert alert-info\">\n",
+    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Goal</p>\n",
+    "    The goal of this notebook is to showcase ARM supported open-source software that can be included within your workflows. <br>\n",
+    "    You will learn how to download, quality control and display ARM data from the DUSTIEAIM campaign\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "830e2e64-3205-41cd-8247-2fa3905f6106",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "The [Atmospheric data Community Toolkit (ACT)](https://github.com/ARM-DOE/ACT) is an open source Python toolkit for working with atmospheric time-series datasets of varying dimensions (@theisen_act_2026). The toolkit is meant to have functions for every part of the scientific process: \n",
+    "- discovery,\n",
+    "- IO, \n",
+    "- quality control, \n",
+    "- corrections,\n",
+    "- retrievals,\n",
+    "- visualization, \n",
+    "- and analysis. \n",
+    "\n",
+    "It is meant to be a community platform for sharing code with the goal of reducing duplication of effort and better connecting the science community with programs such as the Atmospheric Radiation Measurement (ARM) User Facility.\n",
+    "\n",
+    "This tutorial will provide an introduction to ACT using the Aerosol Particle Sizer to investigate haboobs during the [Desert-Urban SysTem IntegratEd AtmospherIc Monsoon (DUSTIEAIM)](https://arm.gov/research/campaigns/amf2026dustieaim) ARM Mobily Facility deployment.\n",
+    "\n",
+    "1. Intro to ACT\n",
+    "1. Instrument Overview\n",
+    "1. Downloading and Reading in Data\n",
+    "1. Quality Controlling Data\n",
+    "1. Visualizing Data\n",
+    "1. Monthly Temperature Trend Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "feae017c",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "This notebook will rely heavily on Python and the [Atmospheric data Community Toolkit (ACT)](https://arm-doe.github.io/ACT/).  Don't worry if you don't have experience with either, this notebook will walk you though what you need to know."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fb33945",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition alert alert-warning\">\n",
+    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Warning</p>\n",
+    "    You will also need an account and token to download data using the ARM Live webservice.<br><br>\n",
+    "    Navigate to the <a href=\"https://adc.arm.gov/armlive/\" style=\"font-weight:bold\" target=\"_blank\">webservice information page</a> and log in to get your token.<br>\n",
+    "    Your account username will be your ARM username.\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74e672b9-ef14-4f01-be19-a222f5ea1573",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "| Concepts | Importance | Notes |\n",
+    "| --- | --- | --- |\n",
+    "| [ACT](https://arm-doe.github.io/ACT/index.html) | Helpful | |\n",
+    "| [Xarray Basics](https://foundations.projectpythia.org/core/xarray/) | Helpful | Multi-dimensional arrays |\n",
+    "| [Matplotlib Basics](https://foundations.projectpythia.org/core/matplotlib/) | Helpful | Basic plotting |\n",
+    "| [NumPy Basics](https://foundations.projectpythia.org/core/numpy/) | Helpful | Basic arrays |\n",
+    "\n",
+    "- **Time to learn**: 15 Minutes\n",
+    "- **System requirements**:\n",
+    "    - Python 3.11 or latest\n",
+    "    - ACT v2.0.0 or latest\n",
+    "    - numpy\n",
+    "    - xarray\n",
+    "    - matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f89921ee-514a-4e11-859a-4858ed7354ab",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "355585e9-451c-4bfa-82d3-e2199431c707",
+   "metadata": {},
+   "source": [
+    "## Intro to ACT\n",
+    "           \n",
+    "The [Atmospheric data Community Toolkit (ACT)](https://arm-doe.github.io/ACT/) is an open-source Python toolkit for exploring and analyzing atmospheric time-series datasets.  Examples can be found in the [ACT Example Gallery](https://arm-doe.github.io/ACT/auto_examples/index.html).  The toolkit has modules for many different parts of the scientific process, including:\n",
+    "\n",
+    "<table>\n",
+    "    <tr>\n",
+    "        <td>\n",
+    "            \n",
+    "### Data Discovery (act.discovery)\n",
+    "The [discovery](https://arm-doe.github.io/ACT/API/generated/act.discovery.html) module houses functions to download or access data from different groups.  Currently it includes function to get data for ARM, NOAA, EPA, NEON, and more!\n",
+    "\n",
+    "### Input/Output (act.io)\n",
+    "[io](https://arm-doe.github.io/ACT/API/generated/act.io.html) contains functions for reading and writing data from various sources and formats.\n",
+    "\n",
+    "### Visualization (act.plotting)\n",
+    "[plotting](https://arm-doe.github.io/ACT/API/generated/act.plotting.html) contains various routines, built on matplotlib, to help visualize and explore data.  These include \n",
+    "1. Time-series plots\n",
+    "1. Distribution plots like histograms and heatmaps\n",
+    "1. Geographic plots for moving systems like radiosondes or aircraft\n",
+    "1. Skew-T plots for radiosonde data, built off MetPy\n",
+    "1. Wind rose plots for wind and data roses\n",
+    "1. Cross-section plots for working with 3-dimensional data\n",
+    "\n",
+    "### Corrections (act.corrections)\n",
+    "[corrections](https://arm-doe.github.io/ACT/API/generated/act.corrections.html) apply different corrections to data based on need.  A majority of the existing corrections are for lidar data.\n",
+    "\n",
+    "### Quality Control (act.qc)\n",
+    "The [qc](https://arm-doe.github.io/ACT/API/generated/act.qc.html) module has a lot of functions for working with quality control information, apply new tests, or filtering data based on existing tests.  We will explore some of that functionality in this notebook.\n",
+    "\n",
+    "### Retrievals (act.retrievals)\n",
+    "There are many cases in which some additional calculations are necessary to get more value from the instrument data.  The [retrievals](https://arm-doe.github.io/ACT/API/generated/act.retrievals.html) module houses some functions for performing these advanced calculations.  \n",
+    "\n",
+    "### Utilities (act.utils)\n",
+    "The [utils](https://arm-doe.github.io/ACT/API/generated/act.utils.html) module has a lot of general utilities to help with the data.  Some of these include adding in a solar variable to indicate day/night (useful in filtering data), unit conversions, decoding WMO weather codes, performing weighted averaging, etc...\n",
+    "        </td>\n",
+    "        <td>\n",
+    "            <center>\n",
+    "            <img src=\"https://arm-doe.github.io/ACT/_images/sphx_glr_plot_neon_001.png\" alt=\"NEON-ARM\"></img>\n",
+    "            <figcaption>ARM and NEON data from Utquivaik, AK</figcaption>\n",
+    "            <img src=\"https://arm-doe.github.io/ACT/_images/sphx_glr_plot_enhanced_skewt_001.png\" alt=\"SONDE\"></img>\n",
+    "            <figcaption>Enhanced Skew-T plot from ARM's Southern Great Plains Site (SGP)</figcaption>\n",
+    "            </center>\n",
+    "        </td>\n",
+    "    </tr>\n",
+    "</table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cfa5fcb-2220-4e50-91a3-ca74931a099f",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f33ae17a",
+   "metadata": {},
+   "source": [
+    "<div align=\"center\">\n",
+    "    <img src=\"../../images/arm-dustieaim.png\" width=\"500\">\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "424de5e0",
+   "metadata": {},
+   "source": [
+    "## DUSTIEAIM Overview\n",
+    "\n",
+    "The focus of the DUSTIEAIM project is to characterize interactions between the land surface and the atmosphere and over a desert megalopolis (Phoenix) set in complex topography that results in steep gradients. DUSTIEAIM will interrogate the interaction between storms and aerosol during the summer North American Monsoon (NAM) in the context of spatial contrasts at the urban interface with agricultural and drylands in the Sonoran Desert. The DUSTIEAIM science focus is to quantify the bimodal water cycle and energy budgets at the urban/rural/agricultural interface of the southwestern U.S. as regional air masses and meteorological patterns change throughout the year in response to the seasonal cycle, including winter Pacific storms and the NAM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b353659a-3401-492f-bcca-24963e5e6664",
+   "metadata": {},
+   "source": [
+    "## Instrument Overview\n",
+    "\n",
+    "### Aerodynamic Particle Sizer (APS)\n",
+    "The [aerodynamic particle sizer (APS)](https://arm.gov/capabilities/instruments/aps) is a particle size spectrometer that measures both the particle aerodynamic diameter based on particle time of flight and optical diameter based on scattered light intensity. The APS provides the number size distribution for particles with aerodynamic diameters from 0.5 to 20 micrometers and with optical diameters from 0.3 to 20 micrometers.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0de3c05a-6e49-4859-850b-32c989f2cc33",
+   "metadata": {},
+   "source": [
+    "## Imports\n",
+    "Let's get started with some data!  But first, we need to import some libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "a5b00201-8c78-4ef5-b02f-57e55021f5d6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as colors\n",
+    "\n",
+    "import getpass\n",
+    "import act"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3647028a-cf78-436a-be33-6a0148474bf6",
+   "metadata": {},
+   "source": [
+    "## Downloading and Reading ARM's NetCDF Data\n",
+    "ARM's standard file format is NetCDF (network Common Data Form) which makes it very easy to work with in Python!  ARM data are available through a data portal called [Data Discovery](https://adc.arm.gov/discovery/#/) or through a webservice.  If you didn't get your username and token earlier, please go back and see the Prerequisites!\n",
+    "\n",
+    "Let's download some of the APS data first!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "6dc01deb-8e97-4e45-9efb-196b37ca7386",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[DOWNLOADING] dstaosapsM1.b1.20260804.000000.nc\n",
+      "\n",
+      "If you use these data to prepare a publication, please cite:\n",
+      "\n",
+      "Singh, A., Oliveira, D., Koontz, A., Sedlacek, A., & Kuang, C. Aerodynamic\n",
+      "Particle Sizer (AOSAPS), 2026-08-04 to 2026-08-04, ARM Mobile Facility (DST),\n",
+      "Phoenix, AZ: AMF1 (Main Site for DUSTIEAIM) (M1). Atmospheric Radiation\n",
+      "Measurement (ARM) User Facility. https://doi.org/10.5439/1407135\n",
+      "\n",
+      "[DOWNLOADING] dstmetM1.b1.20260804.000000.cdf\n",
+      "\n",
+      "If you use these data to prepare a publication, please cite:\n",
+      "\n",
+      "Kyrouac, J., Shi, Y., & Tuftedal, M. Surface Meteorological Instrumentation\n",
+      "(MET), 2026-08-04 to 2026-08-04, ARM Mobile Facility (DST), Phoenix, AZ: AMF1\n",
+      "(Main Site for DUSTIEAIM) (M1). Atmospheric Radiation Measurement (ARM) User\n",
+      "Facility. https://doi.org/10.5439/1786358\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Note - recommended ARM Live token to be set as an environmental variable\n",
+    "\n",
+    "# Check for env variables, otherwise set your username and token here!\n",
+    "if \"ARM_USERNAME\" in os.environ:\n",
+    "    arm_username = os.getenv(\"ARM_USERNAME\")\n",
+    "else:\n",
+    "    arm_username = input(\"Enter ARM username: \").strip()\n",
+    "if \"ARM_TOKEN\" in os.environ:\n",
+    "    arm_token = os.getenv(\"ARM_TOKEN\")\n",
+    "else:\n",
+    "    arm_token = getpass.getpass(\"Enter ARM token (hidden): \").strip()\n",
+    "\n",
+    "# Set the datastream and start/enddates\n",
+    "aps_datastream = 'dstaosapsM1.b1'\n",
+    "met_datastream = \"dstmetM1.b1\"\n",
+    "\n",
+    "startdate = '2026-08-04'\n",
+    "enddate = '2026-08-04'\n",
+    "\n",
+    "# Use ACT to easily download the data.  Watch for the data citation!  Show some support\n",
+    "# for ARM's instrument experts and cite their data if you use it in a publication\n",
+    "result_aps = act.discovery.download_arm_data(arm_username, arm_token, aps_datastream, startdate, enddate)\n",
+    "result_met = act.discovery.download_arm_data(arm_username, arm_token, met_datastream, startdate, enddate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f0a2335-1835-40f7-95b3-4be2bc2e2017",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition alert alert-info\">\n",
+    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Note</p>\n",
+    "    Did you notice the dataset DOIs?\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "acdaa696-ebb5-4bbe-8d1e-a4c66f524964",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 51MB\n",
+       "Dimensions:                           (time: 86399, bound: 2,\n",
+       "                                       diameter_aerodynamic: 51)\n",
+       "Coordinates:\n",
+       "  * time                              (time) datetime64[ns] 691kB 2026-08-04T...\n",
+       "  * diameter_aerodynamic              (diameter_aerodynamic) float32 204B 0.5...\n",
+       "Dimensions without coordinates: bound\n",
+       "Data variables: (12/39)\n",
+       "    base_time                         datetime64[ns] 8B 2026-08-04\n",
+       "    time_offset                       (time) datetime64[ns] 691kB 2026-08-04T...\n",
+       "    time_bounds                       (time, bound) object 1MB dask.array&lt;chunksize=(86399, 2), meta=np.ndarray&gt;\n",
+       "    diameter_aerodynamic_bounds       (diameter_aerodynamic, bound) float32 408B dask.array&lt;chunksize=(51, 2), meta=np.ndarray&gt;\n",
+       "    dN_dlogDp                         (time, diameter_aerodynamic) float32 18MB dask.array&lt;chunksize=(86399, 51), meta=np.ndarray&gt;\n",
+       "    qc_dN_dlogDp                      (time, diameter_aerodynamic) int32 18MB dask.array&lt;chunksize=(86399, 51), meta=np.ndarray&gt;\n",
+       "    ...                                ...\n",
+       "    sheath_pump_voltage               (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    status_flags                      (time) float64 691kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    stack_purge_state                 (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    lat                               float32 4B ...\n",
+       "    lon                               float32 4B ...\n",
+       "    alt                               float32 4B ...\n",
+       "Attributes: (12/21)\n",
+       "    command_line:            aosapscorr -s dst -f M1 --max-runtime 86400\n",
+       "    Conventions:             ARM-1.3\n",
+       "    process_version:         ingest-aosapscorr-1.7-0.el9\n",
+       "    dod_version:             aosaps-b1-1.3\n",
+       "    input_datastreams:       dstaosapsM1.a0 : 1.10 : 20260804.000000\\ndstaosp...\n",
+       "    site_id:                 dst\n",
+       "    ...                      ...\n",
+       "    doi:                     10.5439/1407135\n",
+       "    history:                 created by user armflowprod on machine prod-slur...\n",
+       "    _file_dates:             [&#x27;20260804&#x27;]\n",
+       "    _file_times:             [&#x27;000000&#x27;]\n",
+       "    _datastream:             dstaosapsM1.b1\n",
+       "    _arm_standards_flag:     1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3704e593-1b1d-4f77-82f8-f0c5eb89c416' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-3704e593-1b1d-4f77-82f8-f0c5eb89c416' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 86399</li><li><span>bound</span>: 2</li><li><span class='xr-has-index'>diameter_aerodynamic</span>: 51</li></ul></div></li><li class='xr-section-item'><input id='section-af773c93-b7ea-4f01-8197-13a68b4e6d5e' class='xr-section-summary-in' type='checkbox' checked /><label for='section-af773c93-b7ea-4f01-8197-13a68b4e6d5e' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-08-04T00:00:00.795000 ... 2...</div><input id='attrs-a129825e-698b-4f4c-a358-6f697b4736a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a129825e-698b-4f4c-a358-6f697b4736a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55cc9ac5-5263-4a11-9123-22aa59c752a5' class='xr-var-data-in' type='checkbox'><label for='data-55cc9ac5-5263-4a11-9123-22aa59c752a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-08-04T00:00:00.795000000&#x27;, &#x27;2026-08-04T00:00:01.795000000&#x27;,\n",
+       "       &#x27;2026-08-04T00:00:02.826000000&#x27;, ..., &#x27;2026-08-04T23:59:57.363000000&#x27;,\n",
+       "       &#x27;2026-08-04T23:59:58.358000000&#x27;, &#x27;2026-08-04T23:59:59.333000000&#x27;],\n",
+       "      shape=(86399,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>diameter_aerodynamic</span></div><div class='xr-var-dims'>(diameter_aerodynamic)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.542 0.583 0.626 ... 18.43 19.81</div><input id='attrs-709bd808-c5e1-446f-8623-6bb575fc9042' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-709bd808-c5e1-446f-8623-6bb575fc9042' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3051961e-098a-4998-8eb4-5747c00ef3f1' class='xr-var-data-in' type='checkbox'><label for='data-3051961e-098a-4998-8eb4-5747c00ef3f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Midpoint of geometric mean aerodynamic diameter</dd><dt><span>units :</span></dt><dd>um</dd><dt><span>bounds :</span></dt><dd>diameter_aerodynamic_bounds</dd></dl></div><div class='xr-var-data'><pre>array([ 0.542,  0.583,  0.626,  0.673,  0.723,  0.777,  0.835,  0.898,  0.965,\n",
+       "        1.037,  1.114,  1.197,  1.286,  1.382,  1.486,  1.596,  1.715,  1.843,\n",
+       "        1.981,  2.129,  2.288,  2.458,  2.642,  2.839,  3.051,  3.278,  3.523,\n",
+       "        3.786,  4.068,  4.371,  4.698,  5.048,  5.425,  5.829,  6.264,  6.732,\n",
+       "        7.234,  7.774,  8.354,  8.977,  9.647, 10.37 , 11.14 , 11.97 , 12.86 ,\n",
+       "       13.82 , 14.86 , 15.96 , 17.15 , 18.43 , 19.81 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ee8b226-5c2e-4608-b296-a06048b2cb63' class='xr-section-summary-in' type='checkbox' /><label for='section-8ee8b226-5c2e-4608-b296-a06048b2cb63' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-08-04</div><input id='attrs-eef1aee2-8812-4734-b568-c2726f92fc7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eef1aee2-8812-4734-b568-c2726f92fc7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5a138f5-f577-4dbc-9b26-029915ac29a9' class='xr-var-data-in' type='checkbox'><label for='data-b5a138f5-f577-4dbc-9b26-029915ac29a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>string :</span></dt><dd>2026-08-04 00:00:00 0:00</dd><dt><span>long_name :</span></dt><dd>Base time in Epoch</dd><dt><span>ancillary_variables :</span></dt><dd>time_offset</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2026-08-04T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-08-04T00:00:00.795000 ... 2...</div><input id='attrs-1e08e7d3-6bb6-4e50-a070-1fe84570cb31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e08e7d3-6bb6-4e50-a070-1fe84570cb31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-deb06292-152b-4b29-8d30-897febed54a1' class='xr-var-data-in' type='checkbox'><label for='data-deb06292-152b-4b29-8d30-897febed54a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from base_time</dd><dt><span>ancillary_variables :</span></dt><dd>base_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-08-04T00:00:00.795000000&#x27;, &#x27;2026-08-04T00:00:01.795000000&#x27;,\n",
+       "       &#x27;2026-08-04T00:00:02.826000000&#x27;, ...,\n",
+       "       &#x27;2026-08-04T23:59:57.363000000&#x27;, &#x27;2026-08-04T23:59:58.358000000&#x27;,\n",
+       "       &#x27;2026-08-04T23:59:59.333000000&#x27;],\n",
+       "      shape=(86399,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bound)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399, 2), meta=np.ndarray&gt;</div><input id='attrs-0115b569-f254-4e5f-b1ef-30c94ab2b4fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0115b569-f254-4e5f-b1ef-30c94ab2b4fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c88115c1-0b81-482a-9c64-b4c3ee083963' class='xr-var-data-in' type='checkbox'><label for='data-c88115c1-0b81-482a-9c64-b4c3ee083963' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time cell bounds</dd><dt><span>bound_offsets :</span></dt><dd>[-1.  0.]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.32 MiB </td>\n",
+       "                        <td> 1.32 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399, 2) </td>\n",
+       "                        <td> (86399, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308257291242\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.41261651458248,60.0)\">86399</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diameter_aerodynamic_bounds</span></div><div class='xr-var-dims'>(diameter_aerodynamic, bound)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(51, 2), meta=np.ndarray&gt;</div><input id='attrs-723ee7f8-a6c8-49d2-bdf1-5bf9088c6cb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-723ee7f8-a6c8-49d2-bdf1-5bf9088c6cb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7bd6d2b-1a67-4146-a5d1-f861a89b0984' class='xr-var-data-in' type='checkbox'><label for='data-f7bd6d2b-1a67-4146-a5d1-f861a89b0984' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Aerodynamic diameter bin boundaries</dd><dt><span>units :</span></dt><dd>um</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 408 B </td>\n",
+       "                        <td> 408 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (51, 2) </td>\n",
+       "                        <td> (51, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"33\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 33.01282363906585,0.0 33.01282363906585,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"16.506411819532925\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"53.01282363906585\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,53.01282363906585,60.0)\">51</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dN_dlogDp</span></div><div class='xr-var-dims'>(time, diameter_aerodynamic)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399, 51), meta=np.ndarray&gt;</div><input id='attrs-e55a5717-f41a-4157-8138-17820f13c389' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e55a5717-f41a-4157-8138-17820f13c389' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52f3d8a5-e940-4a87-88fb-662ba90a5f07' class='xr-var-data-in' type='checkbox'><label for='data-52f3d8a5-e940-4a87-88fb-662ba90a5f07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Number size distribution, aerodynamic diameter</dd><dt><span>units :</span></dt><dd>1/cm^3</dd><dt><span>ancillary_variables :</span></dt><dd>qc_dN_dlogDp</dd><dt><span>comment :</span></dt><dd>dN_dlogDp is the aerosol number size distribution where the number of particles per bin (dN) have been divided by the bin-width in log10 space (dlogDp).  This simplifies comparison of size distributions from instruments with different bin spacing.</dd><dt><span>equation :</span></dt><dd>(N_TOF_input / ( (total_flow_rate-sheath_flow_rate)*1000/60 * delta_t)) / log10(upper_bound/lower_bound)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.81 MiB </td>\n",
+       "                        <td> 16.81 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399, 51) </td>\n",
+       "                        <td> (86399, 51) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308257291242\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >51</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.41261651458248,60.0)\">86399</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_dN_dlogDp</span></div><div class='xr-var-dims'>(time, diameter_aerodynamic)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399, 51), meta=np.ndarray&gt;</div><input id='attrs-b6ee0789-3945-49a5-89ac-3ada8999b266' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6ee0789-3945-49a5-89ac-3ada8999b266' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f62e942b-2faa-4f92-b2bc-b674e1478910' class='xr-var-data-in' type='checkbox'><label for='data-f62e942b-2faa-4f92-b2bc-b674e1478910' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Number size distribution, aerodynamic diameter</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>seconds_before_stack_purge :</span></dt><dd>30</dd><dt><span>seconds_after_stack_purge :</span></dt><dd>120</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>total_N_conc is less than the fail_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>total_N_conc is greater than the fail_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>status_flag not = 0</dd><dt><span>bit_4_assessment :</span></dt><dd>Bad</dd><dt><span>bit_5_description :</span></dt><dd>time interval &gt; 1.5 * integration_interval</dd><dt><span>bit_5_assessment :</span></dt><dd>Indeterminate</dd><dt><span>bit_6_description :</span></dt><dd>stack_purge_state == 1 at any time within an interval extending from seconds_before_stack_purge seconds before to seconds_after_stack_purge seconds after the sample time</dd><dt><span>bit_6_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.81 MiB </td>\n",
+       "                        <td> 16.81 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399, 51) </td>\n",
+       "                        <td> (86399, 51) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308257291242\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >51</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.41261651458248,60.0)\">86399</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_N_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-e7bfd1d7-cb0a-4feb-9bdd-693acda2467a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7bfd1d7-cb0a-4feb-9bdd-693acda2467a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4889bef-10ae-493e-aa4b-0db7e00b16bb' class='xr-var-data-in' type='checkbox'><label for='data-a4889bef-10ae-493e-aa4b-0db7e00b16bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Aerosol number concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>1/cm^3</dd><dt><span>ancillary_variables :</span></dt><dd>qc_total_N_conc</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_total_N_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-89635424-8cc5-415c-93d7-8d5f356c95f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89635424-8cc5-415c-93d7-8d5f356c95f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ac183c5-d6d5-47a8-95b1-7b898617e8ac' class='xr-var-data-in' type='checkbox'><label for='data-8ac183c5-d6d5-47a8-95b1-7b898617e8ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Aerosol number concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>fail_min :</span></dt><dd>0.0</dd><dt><span>fail_max :</span></dt><dd>1000.0</dd><dt><span>seconds_before_stack_purge :</span></dt><dd>30</dd><dt><span>seconds_after_stack_purge :</span></dt><dd>120</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than the fail_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than the fail_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>status_flag not = 0</dd><dt><span>bit_4_assessment :</span></dt><dd>Bad</dd><dt><span>bit_5_description :</span></dt><dd>time interval &gt; 1.5 * integration_interval</dd><dt><span>bit_5_assessment :</span></dt><dd>Indeterminate</dd><dt><span>bit_6_description :</span></dt><dd>stack_purge_state == 1 at any time within an interval extending from seconds_before_stack_purge seconds before to seconds_after_stack_purge seconds after the sample time</dd><dt><span>bit_6_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_SA_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-fe16ddc1-c0ab-4341-ab1e-98e5c3b1d3cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe16ddc1-c0ab-4341-ab1e-98e5c3b1d3cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56f56b01-3a87-4979-a796-dbad41270fd2' class='xr-var-data-in' type='checkbox'><label for='data-56f56b01-3a87-4979-a796-dbad41270fd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total surface area concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>nm^2/cm^3</dd><dt><span>ancillary_variables :</span></dt><dd>qc_total_SA_conc</dd><dt><span>comment :</span></dt><dd>Integrated surface area concentration assuming spherical particles with unity shape factor.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_total_SA_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-dee6e088-b132-4714-9d08-329e9d57c557' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dee6e088-b132-4714-9d08-329e9d57c557' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19d9bfa5-aff2-4717-b2cc-f02a1ed74ce1' class='xr-var-data-in' type='checkbox'><label for='data-19d9bfa5-aff2-4717-b2cc-f02a1ed74ce1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Total surface area concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>seconds_before_stack_purge :</span></dt><dd>30</dd><dt><span>seconds_after_stack_purge :</span></dt><dd>120</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>total_N_conc is less than the fail_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>total_N_conc is greater than the fail_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>status_flag not = 0</dd><dt><span>bit_4_assessment :</span></dt><dd>Bad</dd><dt><span>bit_5_description :</span></dt><dd>time interval &gt; 1.5 * integration_interval</dd><dt><span>bit_5_assessment :</span></dt><dd>Indeterminate</dd><dt><span>bit_6_description :</span></dt><dd>stack_purge_state == 1 at any time within an interval extending from seconds_before_stack_purge seconds before to seconds_after_stack_purge seconds after the sample time</dd><dt><span>bit_6_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dD_to_dSA</span></div><div class='xr-var-dims'>(diameter_aerodynamic)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(51,), meta=np.ndarray&gt;</div><input id='attrs-370f803e-5759-4088-b993-8bf126dc529e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-370f803e-5759-4088-b993-8bf126dc529e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99331f92-18dc-4ad0-b319-d63ec4419829' class='xr-var-data-in' type='checkbox'><label for='data-99331f92-18dc-4ad0-b319-d63ec4419829' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Surface area of one particle</dd><dt><span>units :</span></dt><dd>um^2</dd><dt><span>comment :</span></dt><dd>Surface area of one particle with the given aerodynamic diameter assuming spherical particles with unity shape factor.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 408 B </td>\n",
+       "                        <td> 408 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (51,) </td>\n",
+       "                        <td> (51,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"78\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,28.91939874812263 0.0,28.91939874812263\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"48.91939874812263\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >51</text>\n",
+       "  <text x=\"140.0\" y=\"14.459699374061316\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,14.459699374061316)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_V_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-4df3415d-df27-41bb-ba35-bea65faa5d70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4df3415d-df27-41bb-ba35-bea65faa5d70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13b04714-b9f6-499d-b68e-7d8e9436cec9' class='xr-var-data-in' type='checkbox'><label for='data-13b04714-b9f6-499d-b68e-7d8e9436cec9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total volume concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>nm^3/cm^3</dd><dt><span>ancillary_variables :</span></dt><dd>qc_total_V_conc</dd><dt><span>comment :</span></dt><dd>Integrated volume concentration assuming spherical particles with unity shape factor.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_total_V_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-1348c8c8-b6b6-4245-8213-a1b325292e7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1348c8c8-b6b6-4245-8213-a1b325292e7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd12a8db-50f2-4fe0-82dc-0015a762d4d0' class='xr-var-data-in' type='checkbox'><label for='data-fd12a8db-50f2-4fe0-82dc-0015a762d4d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Total volume concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>seconds_before_stack_purge :</span></dt><dd>30</dd><dt><span>seconds_after_stack_purge :</span></dt><dd>120</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>total_N_conc is less than the fail_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>total_N_conc is greater than the fail_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>status_flag not = 0</dd><dt><span>bit_4_assessment :</span></dt><dd>Bad</dd><dt><span>bit_5_description :</span></dt><dd>time interval &gt; 1.5 * integration_interval</dd><dt><span>bit_5_assessment :</span></dt><dd>Indeterminate</dd><dt><span>bit_6_description :</span></dt><dd>stack_purge_state == 1 at any time within an interval extending from seconds_before_stack_purge seconds before to seconds_after_stack_purge seconds after the sample time</dd><dt><span>bit_6_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dD_to_dV</span></div><div class='xr-var-dims'>(diameter_aerodynamic)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(51,), meta=np.ndarray&gt;</div><input id='attrs-29ffa05b-8f11-42f4-9866-ff0f875c592f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29ffa05b-8f11-42f4-9866-ff0f875c592f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd3db9d4-9ed9-436e-bb84-0ae0220ac54e' class='xr-var-data-in' type='checkbox'><label for='data-cd3db9d4-9ed9-436e-bb84-0ae0220ac54e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volume of one particle</dd><dt><span>units :</span></dt><dd>um^3</dd><dt><span>comment :</span></dt><dd>Volume of one particle with the given aerodynamic diameter assuming spherical particles with unity shape factor.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 408 B </td>\n",
+       "                        <td> 408 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (51,) </td>\n",
+       "                        <td> (51,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"78\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,28.91939874812263 0.0,28.91939874812263\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"48.91939874812263\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >51</text>\n",
+       "  <text x=\"140.0\" y=\"14.459699374061316\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,14.459699374061316)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>event_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-302583dd-2da4-46e5-a9f2-8e0ccc27f2f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-302583dd-2da4-46e5-a9f2-8e0ccc27f2f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d09767f8-74d4-401a-ae0e-ea58c0af1d9d' class='xr-var-data-in' type='checkbox'><label for='data-d09767f8-74d4-401a-ae0e-ea58c0af1d9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Single crossing event</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>This event occurs when the signal for a small particle cannot stay above the threshold and only one crest is detected. The measurement is aborted, and the time-of-flight of the particle is not recorded. However, the event is logged for concentration calculations and displayed in the &lt; 0.523 µm size channel in uncorrelated mode. (TSI APS model 3321 manual)</dd><dt><span>cell_methods :</span></dt><dd>time: sum</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>event_3</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-615f79d0-532b-4776-a120-4896e7140feb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-615f79d0-532b-4776-a120-4896e7140feb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66c1f40c-4187-436e-9f1a-ca693222337e' class='xr-var-data-in' type='checkbox'><label for='data-66c1f40c-4187-436e-9f1a-ca693222337e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Coincidence event</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>This event is caused by coincidence. Although the signal stays above the threshold, three or more crests are detected. Events of this type are logged but not recorded for concentration or time-of-flight. (TSI APS model 3321 manual)</dd><dt><span>cell_methods :</span></dt><dd>time: sum</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>event_4</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-3f4876fc-14b4-4af9-b497-1eaa0311a470' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f4876fc-14b4-4af9-b497-1eaa0311a470' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31540ecd-090e-49eb-8c5e-bf18f60e6e80' class='xr-var-data-in' type='checkbox'><label for='data-31540ecd-090e-49eb-8c5e-bf18f60e6e80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Over range event</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>This event is outside the maximum range of the timer. The signal remains above the threshold until it moves outside the timer range, and only one crest is detected. A type 4 event is normally caused by large or recirculating particles. Again, the event is logged, but no time-of-flight is recorded. (TSI APS model 3321 manual)</dd><dt><span>cell_methods :</span></dt><dd>time: sum</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dead_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-037c768f-26df-4959-86d9-bd7a1f75dfe1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-037c768f-26df-4959-86d9-bd7a1f75dfe1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-689d2070-258a-45d7-8c5a-f40b3a05ff09' class='xr-var-data-in' type='checkbox'><label for='data-689d2070-258a-45d7-8c5a-f40b3a05ff09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dead time</dd><dt><span>units :</span></dt><dd>ms</dd><dt><span>cell_methods :</span></dt><dd>time: sum</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inlet_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-1b8f7b3c-8799-4ae5-815e-4c2e10477494' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b8f7b3c-8799-4ae5-815e-4c2e10477494' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0c624fa-cbd1-455c-80b5-b8700bbcf8f8' class='xr-var-data-in' type='checkbox'><label for='data-f0c624fa-cbd1-455c-80b5-b8700bbcf8f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Inlet pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_flow_rate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-f89e0918-1ceb-4562-bc09-411ab6d22bb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f89e0918-1ceb-4562-bc09-411ab6d22bb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86a257c7-20d4-45c4-aa20-35e1bb69f35c' class='xr-var-data-in' type='checkbox'><label for='data-86a257c7-20d4-45c4-aa20-35e1bb69f35c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total flow rate</dd><dt><span>units :</span></dt><dd>L/min</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sheath_flow_rate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-2f60bde4-1201-4da0-a019-368c81312c48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f60bde4-1201-4da0-a019-368c81312c48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0800634f-89de-4ce8-a56f-b52a6cd7cbf8' class='xr-var-data-in' type='checkbox'><label for='data-0800634f-89de-4ce8-a56f-b52a6cd7cbf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sheath flow rate</dd><dt><span>units :</span></dt><dd>L/min</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analog_input_voltage_0</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-82ce2ac0-9c95-45a9-9ca9-063cbfc7f11b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82ce2ac0-9c95-45a9-9ca9-063cbfc7f11b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cd7f3da-f96d-47e8-a2b6-5dacca543348' class='xr-var-data-in' type='checkbox'><label for='data-7cd7f3da-f96d-47e8-a2b6-5dacca543348' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Analog input voltage 0</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>comment :</span></dt><dd>Analog input voltage, I/O connector pin 7 ref to pin 15 (average over sample time)</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analog_input_voltage_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-f5f3f371-cef3-4b2d-8c21-d9cb016e5523' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5f3f371-cef3-4b2d-8c21-d9cb016e5523' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3a135f9-9915-4854-a30e-8fbe55595fa3' class='xr-var-data-in' type='checkbox'><label for='data-b3a135f9-9915-4854-a30e-8fbe55595fa3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Analog input voltage 1</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>comment :</span></dt><dd>Analog input voltage, I/O connector pin 8 ref to pin 15 (average over sample time)</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>digital_input_level_0</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-ce777757-ab8b-4913-b624-f236e72e4663' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce777757-ab8b-4913-b624-f236e72e4663' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1946a8a7-aeaf-4d4d-9a5d-5be2da327b75' class='xr-var-data-in' type='checkbox'><label for='data-1946a8a7-aeaf-4d4d-9a5d-5be2da327b75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Digital input level 0</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>pin_1_not_set 5_VDC_level_set_in_pin_1</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>digital_input_level_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-35151c08-a86b-444b-b9f8-f5b63ff30e78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35151c08-a86b-444b-b9f8-f5b63ff30e78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4fa9b84-8a68-4bd9-9b21-813e75a15c38' class='xr-var-data-in' type='checkbox'><label for='data-e4fa9b84-8a68-4bd9-9b21-813e75a15c38' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Digital input level 1</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>pin_2_not_set 5_VDC_level_set_in_pin_2</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>digital_input_level_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-13d35340-b78d-405d-a231-43b53f7f894d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13d35340-b78d-405d-a231-43b53f7f894d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd9d1f73-b534-4e6a-97ca-305d450a097f' class='xr-var-data-in' type='checkbox'><label for='data-fd9d1f73-b534-4e6a-97ca-305d450a097f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Digital input level 2</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>pin_3_not_set 5_VDC_level_set_in_pin_3</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>laser_power</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-bf494f33-60ad-473a-bd2b-df5f9b750aad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf494f33-60ad-473a-bd2b-df5f9b750aad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fcb2170-179b-4944-b38d-97185da92631' class='xr-var-data-in' type='checkbox'><label for='data-7fcb2170-179b-4944-b38d-97185da92631' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Laser power</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>comment :</span></dt><dd>Percent of nominal maximum power at last calibration.</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>laser_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-5cc8ed9d-fba2-498b-9757-4c85af2af962' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cc8ed9d-fba2-498b-9757-4c85af2af962' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8244e971-a245-4f72-926a-3de3ab2fa275' class='xr-var-data-in' type='checkbox'><label for='data-8244e971-a245-4f72-926a-3de3ab2fa275' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Laser current</dd><dt><span>units :</span></dt><dd>mA</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_pump_voltage</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-22ade6fb-47fe-4f12-ae88-2e5ce2f7476d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22ade6fb-47fe-4f12-ae88-2e5ce2f7476d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d53e7a32-54da-4370-ac29-5af60409d67c' class='xr-var-data-in' type='checkbox'><label for='data-d53e7a32-54da-4370-ac29-5af60409d67c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total pump voltage</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inlet_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-8cff595b-aab3-4470-86fe-04e1d6e93ddb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cff595b-aab3-4470-86fe-04e1d6e93ddb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2624101c-222a-459b-90ea-f68711a1bca3' class='xr-var-data-in' type='checkbox'><label for='data-2624101c-222a-459b-90ea-f68711a1bca3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Inlet temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>box_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-7a722d70-d375-435f-b43e-12a194b64301' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a722d70-d375-435f-b43e-12a194b64301' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bedd6331-c388-4ba9-91e8-1d927adcbd61' class='xr-var-data-in' type='checkbox'><label for='data-bedd6331-c388-4ba9-91e8-1d927adcbd61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Box temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>avalanche_photodiode_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-f1004da3-dd09-41d7-a56a-e06c691d90d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1004da3-dd09-41d7-a56a-e06c691d90d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e726d5d-1663-4f86-9025-2403b2e99e57' class='xr-var-data-in' type='checkbox'><label for='data-5e726d5d-1663-4f86-9025-2403b2e99e57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Avalanche photodiode temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>avalanche_photodiode_voltage</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-59836fad-c042-4eaf-be67-3cfc6817941c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59836fad-c042-4eaf-be67-3cfc6817941c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cee64a9-8303-47f3-9479-2e12420298f5' class='xr-var-data-in' type='checkbox'><label for='data-3cee64a9-8303-47f3-9479-2e12420298f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Avalanche photodiode voltage</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sheath_pump_voltage</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-e70b65e8-7405-43b5-9c89-6d3fcf5ce57e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e70b65e8-7405-43b5-9c89-6d3fcf5ce57e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7783ce7b-c297-45c9-82dd-9ce2ed592298' class='xr-var-data-in' type='checkbox'><label for='data-7783ce7b-c297-45c9-82dd-9ce2ed592298' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sheath pump voltage</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_flags</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-192cc92d-88ee-4f86-a6e2-c6727cda0970' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-192cc92d-88ee-4f86-a6e2-c6727cda0970' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9582977a-fe41-4aa0-b2f1-6a550d748e3f' class='xr-var-data-in' type='checkbox'><label for='data-9582977a-fe41-4aa0-b2f1-6a550d748e3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Status flags</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_masks :</span></dt><dd>[  1   2   4   8  16  32  64 128 256]</dd><dt><span>flag_meanings :</span></dt><dd>laser_fault total_flow_out_of_range sheath_flow_out_of_range excessive_sample_concentration accumulator_clipped autocal_failed internal_temp_lt_10degC internal_temp_gt_40degC detector_v_gt_10pct_Vb</dd><dt><span>bit_1_description :</span></dt><dd>Laser fault</dd><dt><span>bit_2_description :</span></dt><dd>Total flow out of range</dd><dt><span>bit_3_description :</span></dt><dd>Sheath flow out of range</dd><dt><span>bit_4_description :</span></dt><dd>Excessive sample concentration</dd><dt><span>bit_5_description :</span></dt><dd>Accumulator clipped</dd><dt><span>bit_6_description :</span></dt><dd>Autocal failed</dd><dt><span>bit_7_description :</span></dt><dd>Internal temperature &lt; 10 degC</dd><dt><span>bit_8_description :</span></dt><dd>Internal temperature &gt; 40 degC</dd><dt><span>bit_9_description :</span></dt><dd>Detector voltage more than +/-10% Vb</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stack_purge_state</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-598011a3-fb0f-4049-8960-22b4d999b87a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-598011a3-fb0f-4049-8960-22b4d999b87a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a80576e-eba1-41cc-9c2f-79ff00f56fc5' class='xr-var-data-in' type='checkbox'><label for='data-2a80576e-eba1-41cc-9c2f-79ff00f56fc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>AOS stack purge state</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>purge_off purge_on</dd><dt><span>comment :</span></dt><dd>Data collected while stack_purge_state = 1 is not representative of ambient conditions.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d85e09d1-64e8-47a4-ac7b-59042f49ba7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d85e09d1-64e8-47a4-ac7b-59042f49ba7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2b306ae-4c92-4c48-8d33-86b2d84973a8' class='xr-var-data-in' type='checkbox'><label for='data-c2b306ae-4c92-4c48-8d33-86b2d84973a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>North latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-96f3950b-02b7-42ed-9d1e-8ad346f8b86a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96f3950b-02b7-42ed-9d1e-8ad346f8b86a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02f8f06e-f1e9-48fc-b86d-cdeb1d5e41f6' class='xr-var-data-in' type='checkbox'><label for='data-02f8f06e-f1e9-48fc-b86d-cdeb1d5e41f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>East longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ebefa91f-b8f3-43d1-800b-347ade7e9c09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ebefa91f-b8f3-43d1-800b-347ade7e9c09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9727570b-9f3c-444b-925f-3626299e7c26' class='xr-var-data-in' type='checkbox'><label for='data-9727570b-9f3c-444b-925f-3626299e7c26' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude above mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-448e096b-c182-4a22-be29-8db06674b6fb' class='xr-section-summary-in' type='checkbox' /><label for='section-448e096b-c182-4a22-be29-8db06674b6fb' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>command_line :</span></dt><dd>aosapscorr -s dst -f M1 --max-runtime 86400</dd><dt><span>Conventions :</span></dt><dd>ARM-1.3</dd><dt><span>process_version :</span></dt><dd>ingest-aosapscorr-1.7-0.el9</dd><dt><span>dod_version :</span></dt><dd>aosaps-b1-1.3</dd><dt><span>input_datastreams :</span></dt><dd>dstaosapsM1.a0 : 1.10 : 20260804.000000\n",
+       "dstaospurgeM1.b0 : 1.3 : 20260803.000000-20260805.000001</dd><dt><span>site_id :</span></dt><dd>dst</dd><dt><span>platform_id :</span></dt><dd>aosaps</dd><dt><span>facility_id :</span></dt><dd>M1</dd><dt><span>data_level :</span></dt><dd>b1</dd><dt><span>location_description :</span></dt><dd>Desert-Urban SysTem IntegratEd AtmospherIc Monsoon (DUSTIEAIM), Phoenix, Arizona</dd><dt><span>datastream :</span></dt><dd>dstaosapsM1.b1</dd><dt><span>Instrument_Description :</span></dt><dd>TSI APS 332100</dd><dt><span>serial_number :</span></dt><dd>-</dd><dt><span>configuration_file :</span></dt><dd>Used values from input file headers</dd><dt><span>integration_interval :</span></dt><dd>1 s</dd><dt><span>doi :</span></dt><dd>10.5439/1407135</dd><dt><span>history :</span></dt><dd>created by user armflowprod on machine prod-slurmnode1.adc.arm.gov at 2026-08-04 02:22:03, using ingest-aosapscorr-1.7-0.el9</dd><dt><span>_file_dates :</span></dt><dd>[&#x27;20260804&#x27;]</dd><dt><span>_file_times :</span></dt><dd>[&#x27;000000&#x27;]</dd><dt><span>_datastream :</span></dt><dd>dstaosapsM1.b1</dd><dt><span>_arm_standards_flag :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 51MB\n",
+       "Dimensions:                           (time: 86399, bound: 2,\n",
+       "                                       diameter_aerodynamic: 51)\n",
+       "Coordinates:\n",
+       "  * time                              (time) datetime64[ns] 691kB 2026-08-04T...\n",
+       "  * diameter_aerodynamic              (diameter_aerodynamic) float32 204B 0.5...\n",
+       "Dimensions without coordinates: bound\n",
+       "Data variables: (12/39)\n",
+       "    base_time                         datetime64[ns] 8B 2026-08-04\n",
+       "    time_offset                       (time) datetime64[ns] 691kB 2026-08-04T...\n",
+       "    time_bounds                       (time, bound) object 1MB dask.array<chunksize=(86399, 2), meta=np.ndarray>\n",
+       "    diameter_aerodynamic_bounds       (diameter_aerodynamic, bound) float32 408B dask.array<chunksize=(51, 2), meta=np.ndarray>\n",
+       "    dN_dlogDp                         (time, diameter_aerodynamic) float32 18MB dask.array<chunksize=(86399, 51), meta=np.ndarray>\n",
+       "    qc_dN_dlogDp                      (time, diameter_aerodynamic) int32 18MB dask.array<chunksize=(86399, 51), meta=np.ndarray>\n",
+       "    ...                                ...\n",
+       "    sheath_pump_voltage               (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    status_flags                      (time) float64 691kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    stack_purge_state                 (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    lat                               float32 4B ...\n",
+       "    lon                               float32 4B ...\n",
+       "    alt                               float32 4B ...\n",
+       "Attributes: (12/21)\n",
+       "    command_line:            aosapscorr -s dst -f M1 --max-runtime 86400\n",
+       "    Conventions:             ARM-1.3\n",
+       "    process_version:         ingest-aosapscorr-1.7-0.el9\n",
+       "    dod_version:             aosaps-b1-1.3\n",
+       "    input_datastreams:       dstaosapsM1.a0 : 1.10 : 20260804.000000\\ndstaosp...\n",
+       "    site_id:                 dst\n",
+       "    ...                      ...\n",
+       "    doi:                     10.5439/1407135\n",
+       "    history:                 created by user armflowprod on machine prod-slur...\n",
+       "    _file_dates:             ['20260804']\n",
+       "    _file_times:             ['000000']\n",
+       "    _datastream:             dstaosapsM1.b1\n",
+       "    _arm_standards_flag:     1"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Let's read in the data using ACT and check out the data\n",
+    "ds_aps = act.io.read_arm_netcdf(result_aps)\n",
+    "ds_met = act.io.read_arm_netcdf(result_met)\n",
+    "\n",
+    "ds_aps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "db80b7d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 328kB\n",
+       "Dimensions:                       (time: 1440, bound: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 12kB 2026-08-04 ... 2...\n",
+       "Dimensions without coordinates: bound\n",
+       "Data variables: (12/49)\n",
+       "    base_time                     datetime64[ns] 8B 2026-08-04\n",
+       "    time_offset                   (time) datetime64[ns] 12kB 2026-08-04 ... 2...\n",
+       "    time_bounds                   (time, bound) object 23kB dask.array&lt;chunksize=(1440, 2), meta=np.ndarray&gt;\n",
+       "    atmos_pressure                (time) float32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    qc_atmos_pressure             (time) int32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    temp_mean                     (time) float32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    ...                            ...\n",
+       "    qc_logger_volt                (time) int32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    logger_temp                   (time) float32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    qc_logger_temp                (time) int32 6kB dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;\n",
+       "    lat                           float32 4B ...\n",
+       "    lon                           float32 4B ...\n",
+       "    alt                           float32 4B ...\n",
+       "Attributes: (12/26)\n",
+       "    command_line:                met_ingest -s dst -f M1 --max-runtime 86400\n",
+       "    Conventions:                 ARM-1.3\n",
+       "    process_version:             ingest-met-4.58-1.el9\n",
+       "    dod_version:                 met-b1-7.7\n",
+       "    input_source:                /data/collection/dst/dstmetM1.00/MET_Table1....\n",
+       "    site_id:                     dst\n",
+       "    ...                          ...\n",
+       "    tbrg_precip_corr_info:       0.001 * tbrg_precip_total^2 + 0.957 * tbrg_p...\n",
+       "    history:                     created by user armflowprod on machine prod-...\n",
+       "    _file_dates:                 [&#x27;20260804&#x27;]\n",
+       "    _file_times:                 [&#x27;000000&#x27;]\n",
+       "    _datastream:                 dstmetM1.b1\n",
+       "    _arm_standards_flag:         1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0da328f3-7fcc-4ea0-9a7a-04f6163586f7' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-0da328f3-7fcc-4ea0-9a7a-04f6163586f7' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1440</li><li><span>bound</span>: 2</li></ul></div></li><li class='xr-section-item'><input id='section-6096afb0-8f35-42d1-bc8f-915f3a06ddb8' class='xr-section-summary-in' type='checkbox' checked /><label for='section-6096afb0-8f35-42d1-bc8f-915f3a06ddb8' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-08-04 ... 2026-08-04T23:59:00</div><input id='attrs-a68bcbc6-ade8-4ad8-8402-06cfe8eb73aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a68bcbc6-ade8-4ad8-8402-06cfe8eb73aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af782d0f-2bd6-433f-88e9-ce08ea02aa02' class='xr-var-data-in' type='checkbox'><label for='data-af782d0f-2bd6-433f-88e9-ce08ea02aa02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-08-04T00:00:00.000000000&#x27;, &#x27;2026-08-04T00:01:00.000000000&#x27;,\n",
+       "       &#x27;2026-08-04T00:02:00.000000000&#x27;, ..., &#x27;2026-08-04T23:57:00.000000000&#x27;,\n",
+       "       &#x27;2026-08-04T23:58:00.000000000&#x27;, &#x27;2026-08-04T23:59:00.000000000&#x27;],\n",
+       "      shape=(1440,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0ed346fe-51d9-4b28-9e05-d0ca6ff82594' class='xr-section-summary-in' type='checkbox' /><label for='section-0ed346fe-51d9-4b28-9e05-d0ca6ff82594' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-08-04</div><input id='attrs-75ed6ef8-2bfa-48f7-a7f2-a71139a68414' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75ed6ef8-2bfa-48f7-a7f2-a71139a68414' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c3d9ccf-99c8-40df-a2b9-d0309326c76e' class='xr-var-data-in' type='checkbox'><label for='data-3c3d9ccf-99c8-40df-a2b9-d0309326c76e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>string :</span></dt><dd>2026-08-04 00:00:00 0:00</dd><dt><span>long_name :</span></dt><dd>Base time in Epoch</dd><dt><span>ancillary_variables :</span></dt><dd>time_offset</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2026-08-04T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-08-04 ... 2026-08-04T23:59:00</div><input id='attrs-562c9a28-21fb-40ae-9ee4-85103e2d11ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-562c9a28-21fb-40ae-9ee4-85103e2d11ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26e87a12-3d63-4a97-9e6a-0a1b70ad9156' class='xr-var-data-in' type='checkbox'><label for='data-26e87a12-3d63-4a97-9e6a-0a1b70ad9156' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from base_time</dd><dt><span>ancillary_variables :</span></dt><dd>base_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-08-04T00:00:00.000000000&#x27;, &#x27;2026-08-04T00:01:00.000000000&#x27;,\n",
+       "       &#x27;2026-08-04T00:02:00.000000000&#x27;, ...,\n",
+       "       &#x27;2026-08-04T23:57:00.000000000&#x27;, &#x27;2026-08-04T23:58:00.000000000&#x27;,\n",
+       "       &#x27;2026-08-04T23:59:00.000000000&#x27;],\n",
+       "      shape=(1440,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bound)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440, 2), meta=np.ndarray&gt;</div><input id='attrs-2ced9e39-b7dc-4fa2-9222-eee11f8c0706' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ced9e39-b7dc-4fa2-9222-eee11f8c0706' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2e23bcc-7d81-4d5d-9aac-3e1ccdd9bc2c' class='xr-var-data-in' type='checkbox'><label for='data-d2e23bcc-7d81-4d5d-9aac-3e1ccdd9bc2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time cell bounds</dd><dt><span>bound_offsets :</span></dt><dd>[-60.   0.]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 22.50 kiB </td>\n",
+       "                        <td> 22.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440, 2) </td>\n",
+       "                        <td> (1440, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308257291242\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.41261651458248,60.0)\">1440</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atmos_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-f0ad1c53-89d2-4f3a-9d91-d08841b8e431' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f0ad1c53-89d2-4f3a-9d91-d08841b8e431' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-130d69a0-496e-461e-9054-90f2c129002e' class='xr-var-data-in' type='checkbox'><label for='data-130d69a0-496e-461e-9054-90f2c129002e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Atmospheric pressure</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>valid_min :</span></dt><dd>80.0</dd><dt><span>valid_max :</span></dt><dd>110.0</dd><dt><span>valid_delta :</span></dt><dd>1.0</dd><dt><span>standard_name :</span></dt><dd>surface_air_pressure</dd><dt><span>ancillary_variables :</span></dt><dd>qc_atmos_pressure</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_atmos_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-4484415c-564e-42d4-a141-714ad9811a9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4484415c-564e-42d4-a141-714ad9811a9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e51589f-a199-4712-bfba-f01c30272ddc' class='xr-var-data-in' type='checkbox'><label for='data-0e51589f-a199-4712-bfba-f01c30272ddc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Atmospheric pressure</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-225dcb08-16c5-44f8-ac47-e4c06d1f5de2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-225dcb08-16c5-44f8-ac47-e4c06d1f5de2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a20ceed-81bb-4a30-827f-968d3b62a22b' class='xr-var-data-in' type='checkbox'><label for='data-1a20ceed-81bb-4a30-827f-968d3b62a22b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature mean</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-40.0</dd><dt><span>valid_max :</span></dt><dd>50.0</dd><dt><span>valid_delta :</span></dt><dd>20.0</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>ancillary_variables :</span></dt><dd>qc_temp_mean</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_temp_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-8c7133c5-b483-4f6b-baa8-fa506dc6a67e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c7133c5-b483-4f6b-baa8-fa506dc6a67e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b478de7-f78b-4a4a-b4e2-1b2885fc620a' class='xr-var-data-in' type='checkbox'><label for='data-4b478de7-f78b-4a4a-b4e2-1b2885fc620a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Temperature mean</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-bad8de8e-d75c-4699-b9d5-71b2fec170ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bad8de8e-d75c-4699-b9d5-71b2fec170ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a191c0db-7cb8-4826-aead-a8765bd1f25c' class='xr-var-data-in' type='checkbox'><label for='data-a191c0db-7cb8-4826-aead-a8765bd1f25c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature standard deviation</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rh_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-f49f857a-b75f-45f0-b049-6d81cc6a019c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f49f857a-b75f-45f0-b049-6d81cc6a019c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64bd59d0-4cae-4356-8873-e84bd6460903' class='xr-var-data-in' type='checkbox'><label for='data-64bd59d0-4cae-4356-8873-e84bd6460903' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Relative humidity mean</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>valid_min :</span></dt><dd>-2.0</dd><dt><span>valid_max :</span></dt><dd>104.0</dd><dt><span>valid_delta :</span></dt><dd>30.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_rh_mean</dd><dt><span>standard_name :</span></dt><dd>relative_humidity</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_rh_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-3cc5b494-2094-45d1-8185-f00e1f4c1b30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cc5b494-2094-45d1-8185-f00e1f4c1b30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc96443b-136d-4a31-bbcc-bd42a7414354' class='xr-var-data-in' type='checkbox'><label for='data-fc96443b-136d-4a31-bbcc-bd42a7414354' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Relative humidity mean</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rh_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-c0a819ea-da1f-4cbd-ba38-f42a4c39e0a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0a819ea-da1f-4cbd-ba38-f42a4c39e0a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34076e8a-bed7-4f1f-8943-bb1c71636adc' class='xr-var-data-in' type='checkbox'><label for='data-34076e8a-bed7-4f1f-8943-bb1c71636adc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Relative humidity standard deviation</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vapor_pressure_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-57fc8ce0-02a1-4ff7-b231-d0d3a9785c53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57fc8ce0-02a1-4ff7-b231-d0d3a9785c53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8f51802-35b1-4d5b-ab36-ca5772d86cdb' class='xr-var-data-in' type='checkbox'><label for='data-c8f51802-35b1-4d5b-ab36-ca5772d86cdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vapor pressure mean, calculated</dd><dt><span>units :</span></dt><dd>kPa</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>valid_delta :</span></dt><dd>1.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_vapor_pressure_mean</dd><dt><span>standard_name :</span></dt><dd>water_vapor_partial_pressure_in_air</dd><dt><span>comment :</span></dt><dd>The calculation is done with respect to ice or water, depending on the measured temperature being below or above 0 degC, respectively</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_vapor_pressure_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-f19b4e69-4ed7-4889-a17a-4f23b6912e67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f19b4e69-4ed7-4889-a17a-4f23b6912e67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31afdec5-e6ba-42ed-8985-70b2646ddba5' class='xr-var-data-in' type='checkbox'><label for='data-31afdec5-e6ba-42ed-8985-70b2646ddba5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Vapor pressure mean, calculated</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vapor_pressure_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-9a5ba3fe-0a71-48fa-a8ae-c69c65c5c49c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a5ba3fe-0a71-48fa-a8ae-c69c65c5c49c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-000b4c43-b64b-4d0e-8b8b-f8d15068bf27' class='xr-var-data-in' type='checkbox'><label for='data-000b4c43-b64b-4d0e-8b8b-f8d15068bf27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Vapor pressure standard deviation</dd><dt><span>units :</span></dt><dd>kPa</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspd_arith_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-26c3f5c5-06c8-4aaa-938c-cd5c10314887' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26c3f5c5-06c8-4aaa-938c-cd5c10314887' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-975fd3c1-7a34-4d25-ac45-994cd67e1875' class='xr-var-data-in' type='checkbox'><label for='data-975fd3c1-7a34-4d25-ac45-994cd67e1875' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed arithmetic mean</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>60.0</dd><dt><span>valid_delta :</span></dt><dd>20.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_wspd_arith_mean</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_wspd_arith_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-4718c3e8-238a-4487-990b-02834f78c0ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4718c3e8-238a-4487-990b-02834f78c0ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dff4b17-bcac-487e-9ef9-4eb51e36cf0e' class='xr-var-data-in' type='checkbox'><label for='data-6dff4b17-bcac-487e-9ef9-4eb51e36cf0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Wind speed arithmetic mean</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wspd_vec_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-7816d703-fcb9-429d-9209-cb6bcc1c2a58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7816d703-fcb9-429d-9209-cb6bcc1c2a58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be2ca5c7-49b8-4711-b13c-2fdd0e404973' class='xr-var-data-in' type='checkbox'><label for='data-be2ca5c7-49b8-4711-b13c-2fdd0e404973' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind speed vector mean</dd><dt><span>units :</span></dt><dd>m/s</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>60.0</dd><dt><span>valid_delta :</span></dt><dd>20.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_wspd_vec_mean</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_wspd_vec_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-b2c34afa-c28a-49ce-843b-bcc7cc8c3f05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2c34afa-c28a-49ce-843b-bcc7cc8c3f05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bdd72d7-31c2-412e-8ee8-9dfb2777fee5' class='xr-var-data-in' type='checkbox'><label for='data-8bdd72d7-31c2-412e-8ee8-9dfb2777fee5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Wind speed vector mean</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdir_vec_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-3a8450bd-b122-4184-8135-783e43baae31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a8450bd-b122-4184-8135-783e43baae31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd81ba38-735f-4fb9-a9f9-162a4c4d2a37' class='xr-var-data-in' type='checkbox'><label for='data-fd81ba38-735f-4fb9-a9f9-162a4c4d2a37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind direction vector mean</dd><dt><span>units :</span></dt><dd>degree</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>360.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_wdir_vec_mean</dd><dt><span>standard_name :</span></dt><dd>wind_from_direction</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_wdir_vec_mean</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-7e389520-c7b5-4a1e-a11f-f59ec3696a6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e389520-c7b5-4a1e-a11f-f59ec3696a6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b9f4819-65e3-451a-a96e-8ab5d4d4cfcb' class='xr-var-data-in' type='checkbox'><label for='data-3b9f4819-65e3-451a-a96e-8ab5d4d4cfcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Wind direction vector mean</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wdir_vec_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-d3d4f4e6-805c-4973-a4e0-f7f3d5aceb97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3d4f4e6-805c-4973-a4e0-f7f3d5aceb97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8613a23-b801-4fc0-96be-dda2f6cee9ce' class='xr-var-data-in' type='checkbox'><label for='data-e8613a23-b801-4fc0-96be-dda2f6cee9ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Wind direction vector mean standard deviation</dd><dt><span>units :</span></dt><dd>degree</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tbrg_precip_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-57c89c7f-def4-4f0a-9508-d40c633be74a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57c89c7f-def4-4f0a-9508-d40c633be74a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b5ccfca-0b96-4366-a6a3-e5d2376bdf74' class='xr-var-data-in' type='checkbox'><label for='data-4b5ccfca-0b96-4366-a6a3-e5d2376bdf74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>TBRG precipitation total</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_tbrg_precip_total</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_tbrg_precip_total</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-c0a54a18-3306-41b3-beea-0ac42f964a4f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0a54a18-3306-41b3-beea-0ac42f964a4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-577b6592-cd5f-4350-abf2-3baafe0ff137' class='xr-var-data-in' type='checkbox'><label for='data-577b6592-cd5f-4350-abf2-3baafe0ff137' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: TBRG precipitation total</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tbrg_precip_total_corr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-99907efc-9ff9-4d01-a430-dff9e9ed134b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99907efc-9ff9-4d01-a430-dff9e9ed134b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e34c34c-2ac0-4b5f-bc0c-b29b95812be3' class='xr-var-data-in' type='checkbox'><label for='data-9e34c34c-2ac0-4b5f-bc0c-b29b95812be3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>TBRG precipitation total, corrected</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_tbrg_precip_total_corr</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_tbrg_precip_total_corr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-c75d98d1-aeaa-4cdc-9e90-0cbd6fe32b12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c75d98d1-aeaa-4cdc-9e90-0cbd6fe32b12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cc0e1f2-4548-4da7-abb3-6b98f51364ed' class='xr-var-data-in' type='checkbox'><label for='data-6cc0e1f2-4548-4da7-abb3-6b98f51364ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: TBRG precipitation total, corrected</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pwd_err_code</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-74ec2f24-0670-4f4a-9fa7-8782b23c10c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74ec2f24-0670-4f4a-9fa7-8782b23c10c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28357414-8d91-4cc2-96b5-0fb62d9e116d' class='xr-var-data-in' type='checkbox'><label for='data-28357414-8d91-4cc2-96b5-0fb62d9e116d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PWD alarm</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pwd_mean_vis_1min</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-26078b69-6672-46ce-a78c-9932559b956e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26078b69-6672-46ce-a78c-9932559b956e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cb4b6f9-8e14-4b43-bd82-74c135ce7a05' class='xr-var-data-in' type='checkbox'><label for='data-6cb4b6f9-8e14-4b43-bd82-74c135ce7a05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PWD 1 minute mean visibility</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>20000</dd><dt><span>ancillary_variables :</span></dt><dd>qc_pwd_mean_vis_1min</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_pwd_mean_vis_1min</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-a5d44631-d46c-44e8-a49d-025044ccdd5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5d44631-d46c-44e8-a49d-025044ccdd5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e1a1bf7-73cc-4e12-b3b7-56a66212e19d' class='xr-var-data-in' type='checkbox'><label for='data-4e1a1bf7-73cc-4e12-b3b7-56a66212e19d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: PWD 1 minute mean visibility</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pwd_mean_vis_10min</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-db276773-d9be-4437-be5a-197513642d9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db276773-d9be-4437-be5a-197513642d9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5325f746-f623-4514-b08b-c22e1b4b49a1' class='xr-var-data-in' type='checkbox'><label for='data-5325f746-f623-4514-b08b-c22e1b4b49a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PWD 10 minute mean visibility</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>20000</dd><dt><span>ancillary_variables :</span></dt><dd>qc_pwd_mean_vis_10min</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_pwd_mean_vis_10min</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-a8f156b9-a0ec-48ef-8450-527bc7420ff0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8f156b9-a0ec-48ef-8450-527bc7420ff0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfce72a5-8d31-4fe9-96b3-a68a8ca811c2' class='xr-var-data-in' type='checkbox'><label for='data-cfce72a5-8d31-4fe9-96b3-a68a8ca811c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: PWD 10 minute mean visibility</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pwd_pw_code_inst</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-4ebbae73-be23-4b10-b79d-5dd46d3e7cb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ebbae73-be23-4b10-b79d-5dd46d3e7cb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82751b97-c3d1-450a-ba88-b89b1d50ea20' class='xr-var-data-in' type='checkbox'><label for='data-82751b97-c3d1-450a-ba88-b89b1d50ea20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PWD instantaneous present weather code</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>99</dd><dt><span>ancillary_variables :</span></dt><dd>qc_pwd_pw_code_inst</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_pwd_pw_code_inst</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-4922c496-e1f8-4101-8e23-c55f41802908' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4922c496-e1f8-4101-8e23-c55f41802908' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1861924c-a614-4536-b26c-17a8d479af35' class='xr-var-data-in' type='checkbox'><label for='data-1861924c-a614-4536-b26c-17a8d479af35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: PWD instantaneous present weather code</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pwd_pw_code_15min</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-849b3ead-5680-4063-a631-fa001862bdf8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-849b3ead-5680-4063-a631-fa001862bdf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84836a83-7de6-4398-9312-76059e04a104' class='xr-var-data-in' type='checkbox'><label for='data-84836a83-7de6-4398-9312-76059e04a104' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PWD 15 minute present weather code</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>99</dd><dt><span>ancillary_variables :</span></dt><dd>qc_pwd_pw_code_15min</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_pwd_pw_code_15min</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-529dc688-3729-48db-b309-ca8ba9da874a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-529dc688-3729-48db-b309-ca8ba9da874a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f741d78-743c-4f68-bc85-08bb62b385d7' class='xr-var-data-in' type='checkbox'><label for='data-4f741d78-743c-4f68-bc85-08bb62b385d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: PWD 15 minute present weather code</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pwd_pw_code_1hr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-52311951-3d1f-4896-a9a2-180e9cc5970c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52311951-3d1f-4896-a9a2-180e9cc5970c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be2f3ea0-965f-444c-8565-078b4db6779b' class='xr-var-data-in' type='checkbox'><label for='data-be2f3ea0-965f-444c-8565-078b4db6779b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PWD 1 hour present weather code</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_min :</span></dt><dd>0</dd><dt><span>valid_max :</span></dt><dd>99</dd><dt><span>ancillary_variables :</span></dt><dd>qc_pwd_pw_code_1hr</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                        <td> 11.25 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_pwd_pw_code_1hr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-f39f6525-b8f3-4119-b549-f6bb02c17c14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f39f6525-b8f3-4119-b549-f6bb02c17c14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69bff8d9-e69c-45c0-bdb4-3820a5d3b343' class='xr-var-data-in' type='checkbox'><label for='data-69bff8d9-e69c-45c0-bdb4-3820a5d3b343' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: PWD 1 hour present weather code</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pwd_precip_rate_mean_1min</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-235bb250-74cf-47b9-aecb-943aa9428091' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-235bb250-74cf-47b9-aecb-943aa9428091' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1029330e-cf00-4a30-8311-d8b0d1acc403' class='xr-var-data-in' type='checkbox'><label for='data-1029330e-cf00-4a30-8311-d8b0d1acc403' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PWD 1 minute mean precipitation rate</dd><dt><span>units :</span></dt><dd>mm/hr</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>999.99</dd><dt><span>valid_delta :</span></dt><dd>100.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_pwd_precip_rate_mean_1min</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_pwd_precip_rate_mean_1min</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-8a2ee3ed-3539-497b-9a66-694d6d5a4c71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a2ee3ed-3539-497b-9a66-694d6d5a4c71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04dabd47-fab7-46cf-a624-ec26a0179682' class='xr-var-data-in' type='checkbox'><label for='data-04dabd47-fab7-46cf-a624-ec26a0179682' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: PWD 1 minute mean precipitation rate</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pwd_cumul_rain</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-07d2943d-dab5-44c3-80de-adc6d90ca37c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07d2943d-dab5-44c3-80de-adc6d90ca37c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0c47fe3-e4c4-4a10-a15b-0439982a24a6' class='xr-var-data-in' type='checkbox'><label for='data-a0c47fe3-e4c4-4a10-a15b-0439982a24a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PWD cumulative liquid precipitation</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>99.99</dd><dt><span>valid_delta :</span></dt><dd>50.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_pwd_cumul_rain</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_pwd_cumul_rain</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-d991ad47-465f-4f6a-96fe-4ccf7d3500bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d991ad47-465f-4f6a-96fe-4ccf7d3500bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35110c18-de03-4a8a-b110-34a767618194' class='xr-var-data-in' type='checkbox'><label for='data-35110c18-de03-4a8a-b110-34a767618194' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: PWD cumulative liquid precipitation</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pwd_cumul_snow</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-a1264bc9-c329-4c9d-9d58-2fcc2368e3e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1264bc9-c329-4c9d-9d58-2fcc2368e3e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9372a8b1-2ae4-49e0-b888-399e27669cf3' class='xr-var-data-in' type='checkbox'><label for='data-9372a8b1-2ae4-49e0-b888-399e27669cf3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>PWD cumulative snow</dd><dt><span>units :</span></dt><dd>mm</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>999.0</dd><dt><span>valid_delta :</span></dt><dd>100.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_pwd_cumul_snow</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_pwd_cumul_snow</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-d9559a78-c9ba-47eb-9b04-b0eda57b453b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9559a78-c9ba-47eb-9b04-b0eda57b453b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd9fcad6-cf76-4c23-9e97-54709c7e944b' class='xr-var-data-in' type='checkbox'><label for='data-bd9fcad6-cf76-4c23-9e97-54709c7e944b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: PWD cumulative snow</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>logger_volt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-95ca4705-8c8c-4cdb-a7c9-72ada6e6ae21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95ca4705-8c8c-4cdb-a7c9-72ada6e6ae21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fd89fa3-2cd8-43d0-97b4-28cddeb62ac2' class='xr-var-data-in' type='checkbox'><label for='data-8fd89fa3-2cd8-43d0-97b4-28cddeb62ac2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Logger voltage</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>valid_min :</span></dt><dd>10.0</dd><dt><span>valid_max :</span></dt><dd>15.0</dd><dt><span>valid_delta :</span></dt><dd>5.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_logger_volt</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_logger_volt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-be21a14e-2ecc-401a-b160-b4febb9ab89d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be21a14e-2ecc-401a-b160-b4febb9ab89d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afe9b1d6-c320-42cf-980f-59c060b707b0' class='xr-var-data-in' type='checkbox'><label for='data-afe9b1d6-c320-42cf-980f-59c060b707b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Logger voltage</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>logger_temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-829f5bba-bfe4-41cc-bc6d-c0bfa87217e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-829f5bba-bfe4-41cc-bc6d-c0bfa87217e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eab70e29-919a-40d7-9e17-59a27966a500' class='xr-var-data-in' type='checkbox'><label for='data-eab70e29-919a-40d7-9e17-59a27966a500' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Logger temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>valid_min :</span></dt><dd>-25.0</dd><dt><span>valid_max :</span></dt><dd>50.0</dd><dt><span>valid_delta :</span></dt><dd>10.0</dd><dt><span>ancillary_variables :</span></dt><dd>qc_logger_temp</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_logger_temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1440,), meta=np.ndarray&gt;</div><input id='attrs-fff479a3-02a8-4641-b09a-6b327f61b78b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fff479a3-02a8-4641-b09a-6b327f61b78b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd1207bf-90c9-4dbd-acf2-98197832c800' class='xr-var-data-in' type='checkbox'><label for='data-dd1207bf-90c9-4dbd-acf2-98197832c800' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Logger temperature</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>description :</span></dt><dd>This variable contains bit-packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.</dd><dt><span>flag_method :</span></dt><dd>bit</dd><dt><span>bit_1_description :</span></dt><dd>Value is equal to missing_value.</dd><dt><span>bit_1_assessment :</span></dt><dd>Bad</dd><dt><span>bit_2_description :</span></dt><dd>Value is less than valid_min.</dd><dt><span>bit_2_assessment :</span></dt><dd>Bad</dd><dt><span>bit_3_description :</span></dt><dd>Value is greater than valid_max.</dd><dt><span>bit_3_assessment :</span></dt><dd>Bad</dd><dt><span>bit_4_description :</span></dt><dd>Difference between current and previous values exceeds valid_delta.</dd><dt><span>bit_4_assessment :</span></dt><dd>Indeterminate</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                        <td> 5.62 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1440,) </td>\n",
+       "                        <td> (1440,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1440</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3a4b72e1-fcbd-4356-8ff7-8572614406a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a4b72e1-fcbd-4356-8ff7-8572614406a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef676a7-ac85-400b-8c5f-d4d02f3ef43f' class='xr-var-data-in' type='checkbox'><label for='data-1ef676a7-ac85-400b-8c5f-d4d02f3ef43f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>North latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0fb19269-c374-40cf-a4ac-b5b6c678df30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0fb19269-c374-40cf-a4ac-b5b6c678df30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007e52aa-04aa-4408-8674-f80a91118976' class='xr-var-data-in' type='checkbox'><label for='data-007e52aa-04aa-4408-8674-f80a91118976' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>East longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-680ffc71-4b40-4bc7-8211-8354a8691690' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-680ffc71-4b40-4bc7-8211-8354a8691690' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b12be08-a235-406d-814f-51b8fcb6cb88' class='xr-var-data-in' type='checkbox'><label for='data-2b12be08-a235-406d-814f-51b8fcb6cb88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude above mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b52a7a49-b2d8-4ad9-a246-db7a0cc7a868' class='xr-section-summary-in' type='checkbox' /><label for='section-b52a7a49-b2d8-4ad9-a246-db7a0cc7a868' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(26)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>command_line :</span></dt><dd>met_ingest -s dst -f M1 --max-runtime 86400</dd><dt><span>Conventions :</span></dt><dd>ARM-1.3</dd><dt><span>process_version :</span></dt><dd>ingest-met-4.58-1.el9</dd><dt><span>dod_version :</span></dt><dd>met-b1-7.7</dd><dt><span>input_source :</span></dt><dd>/data/collection/dst/dstmetM1.00/MET_Table1.20260804000000.dat</dd><dt><span>site_id :</span></dt><dd>dst</dd><dt><span>platform_id :</span></dt><dd>met</dd><dt><span>facility_id :</span></dt><dd>M1</dd><dt><span>data_level :</span></dt><dd>b1</dd><dt><span>location_description :</span></dt><dd>Desert-Urban SysTem IntegratEd AtmospherIc Monsoon (DUSTIEAIM), Phoenix, Arizona</dd><dt><span>datastream :</span></dt><dd>dstmetM1.b1</dd><dt><span>serial_number :</span></dt><dd>121</dd><dt><span>sampling_interval :</span></dt><dd>variable, see instrument handbook</dd><dt><span>averaging_interval :</span></dt><dd>60 seconds</dd><dt><span>doi :</span></dt><dd>10.5439/1786358</dd><dt><span>pwd :</span></dt><dd>Present Weather Detector</dd><dt><span>averaging_interval_comment :</span></dt><dd>The time assigned to each data point indicates the end of the averaging interval.</dd><dt><span>tbrg :</span></dt><dd>Tipping Bucket Rain Gauge</dd><dt><span>wind_speed_offset :</span></dt><dd>0</dd><dt><span>wind_speed_slope :</span></dt><dd>0.098</dd><dt><span>tbrg_precip_corr_info :</span></dt><dd>0.001 * tbrg_precip_total^2 + 0.957 * tbrg_precip_total</dd><dt><span>history :</span></dt><dd>created by user armflowprod on machine prod-slurmnode1.adc.arm.gov at 2026-08-04 01:19:03, using ingest-met-4.58-1.el9</dd><dt><span>_file_dates :</span></dt><dd>[&#x27;20260804&#x27;]</dd><dt><span>_file_times :</span></dt><dd>[&#x27;000000&#x27;]</dd><dt><span>_datastream :</span></dt><dd>dstmetM1.b1</dd><dt><span>_arm_standards_flag :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 328kB\n",
+       "Dimensions:                       (time: 1440, bound: 2)\n",
+       "Coordinates:\n",
+       "  * time                          (time) datetime64[ns] 12kB 2026-08-04 ... 2...\n",
+       "Dimensions without coordinates: bound\n",
+       "Data variables: (12/49)\n",
+       "    base_time                     datetime64[ns] 8B 2026-08-04\n",
+       "    time_offset                   (time) datetime64[ns] 12kB 2026-08-04 ... 2...\n",
+       "    time_bounds                   (time, bound) object 23kB dask.array<chunksize=(1440, 2), meta=np.ndarray>\n",
+       "    atmos_pressure                (time) float32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    qc_atmos_pressure             (time) int32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    temp_mean                     (time) float32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    ...                            ...\n",
+       "    qc_logger_volt                (time) int32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    logger_temp                   (time) float32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    qc_logger_temp                (time) int32 6kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
+       "    lat                           float32 4B ...\n",
+       "    lon                           float32 4B ...\n",
+       "    alt                           float32 4B ...\n",
+       "Attributes: (12/26)\n",
+       "    command_line:                met_ingest -s dst -f M1 --max-runtime 86400\n",
+       "    Conventions:                 ARM-1.3\n",
+       "    process_version:             ingest-met-4.58-1.el9\n",
+       "    dod_version:                 met-b1-7.7\n",
+       "    input_source:                /data/collection/dst/dstmetM1.00/MET_Table1....\n",
+       "    site_id:                     dst\n",
+       "    ...                          ...\n",
+       "    tbrg_precip_corr_info:       0.001 * tbrg_precip_total^2 + 0.957 * tbrg_p...\n",
+       "    history:                     created by user armflowprod on machine prod-...\n",
+       "    _file_dates:                 ['20260804']\n",
+       "    _file_times:                 ['000000']\n",
+       "    _datastream:                 dstmetM1.b1\n",
+       "    _arm_standards_flag:         1"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_met"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "462813d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x3396674d0>]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Since ACT is built upon xarray, we can use xarray's built in plotting to quickly visualize the data\n",
+    "ds_aps['total_N_conc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "f2c62c66",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x330bff8c0>]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# We can also use xarray's built in slicing to quickly subset the data.  \n",
+    "# Let's look at some suspect data points in the temperature data\n",
+    "ds_aps['total_N_conc'].sel(time=slice('2026-08-04T05:45', '2026-08-04T08:30')).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a93b725a-3216-4eca-8ad0-d9e9e31b1372",
+   "metadata": {},
+   "source": [
+    "## Quality Controlling Data\n",
+    "\n",
+    "ARM has multiple methods that it uses to communicate data quality information out to the users.  One of these methods is through **\"embedded QC\" variables**.  These are variables within the file that have information on automated tests that have been applied.  Many times, they include Min, Max, and Delta tests but as is the case with the [Aerosol Observing System (AOS)](https://armgov.svcs.arm.gov/capabilities/instruments/aos) instruments, there can be more complicated tests that are applied.\n",
+    "\n",
+    "The results from all these different tests are stored in a single variable using bit-packed QC.  We won't get into the full details here, but it's a way to communicate the results of multiple tests in a single integer value by utilizing binary and bits!  You can learn more about bit-packed QC [here](https://code.arm.gov/docs/QC-flag-examples/-/wikis/home) but ACT also has many of the tools for working with ARM QC.\n",
+    "\n",
+    "### Other Sources of Quality Control\n",
+    "ARM also communicates problems with the data quality through [Data Quality Reports (DQR)](https://dq.arm.gov/#/About).  These reports are normally submitted by the instrument mentor when there's been a problem with the instrument.  The categories include:\n",
+    "\n",
+    "- **Data Quality Report Categories**\n",
+    "    - Missing: Data are not available or set to -9999\n",
+    "    - Suspect: The data are not fully incorrect but there are problems that increases the uncertainty of the values. Data should be used with caution.\n",
+    "    - Bad: The data are incorrect and should not be used.\n",
+    "    - Note: Data notes are a way to communicate information that would be useful to the end user but does not rise to the level of suspect or bad data\n",
+    "    \n",
+    "Additionally, data quality information can be found in the Instrument Handbooks, which are included on most instrument pages.  Here is an [example](https://www.arm.gov/publications/tech_reports/handbooks/met_handbook.pdf) of the MET handbook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "453468f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We can see that there's some missing data in the plot above so let's take a look at the embedded QC!\n",
+    "# First, for many of the ACT QC features, we need to get the dataset more to CF standard and that\n",
+    "# involves cleaning up some of the attributes and ways that ARM has historically handled QC\n",
+    "ds_aps.clean.cleanup()\n",
+    "\n",
+    "# Query the ARM DQR Webservice\n",
+    "variable = 'total_N_conc'\n",
+    "ds_aps = act.qc.add_dqr_to_qc(ds_aps, variable=variable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "8d11ee62",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in notebooks */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "  line-height: 1.6;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "  margin-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-obj-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-group-box-contents > label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "  margin-block-start: 0;\n",
+       "  margin-block-end: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input,\n",
+       ".xr-group-box-contents > input,\n",
+       ".xr-array-wrap > input {\n",
+       "  display: block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input + label,\n",
+       ".xr-var-item > input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label,\n",
+       ".xr-var-item > input:enabled + label,\n",
+       ".xr-array-wrap > input:enabled + label,\n",
+       ".xr-group-box-contents > input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:focus-visible + label,\n",
+       ".xr-var-item > input:focus-visible + label,\n",
+       ".xr-array-wrap > input:focus-visible + label,\n",
+       ".xr-group-box-contents > input:focus-visible + label {\n",
+       "  outline: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item > input:enabled + label:hover,\n",
+       ".xr-var-item > input:enabled + label:hover,\n",
+       ".xr-array-wrap > input:enabled + label:hover,\n",
+       ".xr-group-box-contents > input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "  white-space: nowrap;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > em {\n",
+       "  font-weight: normal;\n",
+       "}\n",
+       "\n",
+       ".xr-span-grid {\n",
+       "  grid-column-end: -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.6em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details,\n",
+       ".xr-group-box-contents > label {\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-top: 4px;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in ~ .xr-section-details {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-children {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 100%;\n",
+       "  grid-column: 1 / -1;\n",
+       "  padding-top: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box {\n",
+       "  display: inline-grid;\n",
+       "  grid-template-columns: 0px 30px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-vline {\n",
+       "  grid-column-start: 1;\n",
+       "  border-right: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "  width: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-hline {\n",
+       "  grid-column-start: 2;\n",
+       "  grid-row-start: 1;\n",
+       "  height: 1em;\n",
+       "  width: 26px;\n",
+       "  border-bottom: 0.2em solid;\n",
+       "  border-color: var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents {\n",
+       "  grid-column-start: 3;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > label::before {\n",
+       "  content: \"📂\";\n",
+       "  padding-right: 0.3em;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label::before {\n",
+       "  content: \"📁\";\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked + label {\n",
+       "  padding-bottom: 0px;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input:checked ~ .xr-sections {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-contents > input + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-group-box-ellipsis {\n",
+       "  font-size: 1.4em;\n",
+       "  font-weight: 900;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  letter-spacing: 0.15em;\n",
+       "  cursor: default;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 49MB\n",
+       "Dimensions:                           (time: 86399, bound: 2,\n",
+       "                                       diameter_aerodynamic: 51)\n",
+       "Coordinates:\n",
+       "  * time                              (time) datetime64[ns] 691kB 2026-08-04T...\n",
+       "  * diameter_aerodynamic              (diameter_aerodynamic) float32 204B 0.5...\n",
+       "Dimensions without coordinates: bound\n",
+       "Data variables: (12/39)\n",
+       "    base_time                         datetime64[ns] 8B 2026-08-04\n",
+       "    time_offset                       (time) datetime64[ns] 691kB 2026-08-04T...\n",
+       "    time_bounds                       (time, bound) object 1MB dask.array&lt;chunksize=(86399, 2), meta=np.ndarray&gt;\n",
+       "    diameter_aerodynamic_bounds       (diameter_aerodynamic, bound) float32 408B dask.array&lt;chunksize=(51, 2), meta=np.ndarray&gt;\n",
+       "    dN_dlogDp                         (time, diameter_aerodynamic) float32 18MB dask.array&lt;chunksize=(86399, 51), meta=np.ndarray&gt;\n",
+       "    qc_dN_dlogDp                      (time, diameter_aerodynamic) int32 18MB dask.array&lt;chunksize=(86399, 51), meta=np.ndarray&gt;\n",
+       "    ...                                ...\n",
+       "    sheath_pump_voltage               (time) float32 346kB dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;\n",
+       "    status_flags                      (time) int32 346kB 0 0 0 0 0 ... 0 0 0 0 0\n",
+       "    stack_purge_state                 (time) int16 173kB 0 0 0 0 0 ... 0 0 0 0 0\n",
+       "    lat                               float32 4B ...\n",
+       "    lon                               float32 4B ...\n",
+       "    alt                               float32 4B ...\n",
+       "Attributes: (12/21)\n",
+       "    command_line:            aosapscorr -s dst -f M1 --max-runtime 86400\n",
+       "    Conventions:             ARM-1.3\n",
+       "    process_version:         ingest-aosapscorr-1.7-0.el9\n",
+       "    dod_version:             aosaps-b1-1.3\n",
+       "    input_datastreams:       dstaosapsM1.a0 : 1.10 : 20260804.000000\\ndstaosp...\n",
+       "    site_id:                 dst\n",
+       "    ...                      ...\n",
+       "    doi:                     10.5439/1407135\n",
+       "    history:                 created by user armflowprod on machine prod-slur...\n",
+       "    _file_dates:             [&#x27;20260804&#x27;]\n",
+       "    _file_times:             [&#x27;000000&#x27;]\n",
+       "    _datastream:             dstaosapsM1.b1\n",
+       "    _arm_standards_flag:     1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-107557f0-16cf-4cd3-95e4-94d3621a353b' class='xr-section-summary-in' type='checkbox' disabled /><label for='section-107557f0-16cf-4cd3-95e4-94d3621a353b' class='xr-section-summary'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 86399</li><li><span>bound</span>: 2</li><li><span class='xr-has-index'>diameter_aerodynamic</span>: 51</li></ul></div></li><li class='xr-section-item'><input id='section-f64dee1f-a5f4-4fe2-bd63-e593b3907754' class='xr-section-summary-in' type='checkbox' checked /><label for='section-f64dee1f-a5f4-4fe2-bd63-e593b3907754' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-08-04T00:00:00.795000 ... 2...</div><input id='attrs-27da8bd6-0d7b-4c5c-8114-64d7146ba9ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27da8bd6-0d7b-4c5c-8114-64d7146ba9ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75cc68a9-fcf2-4d03-8f65-f48ae4f72627' class='xr-var-data-in' type='checkbox'><label for='data-75cc68a9-fcf2-4d03-8f65-f48ae4f72627' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from midnight</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-08-04T00:00:00.795000000&#x27;, &#x27;2026-08-04T00:00:01.795000000&#x27;,\n",
+       "       &#x27;2026-08-04T00:00:02.826000000&#x27;, ..., &#x27;2026-08-04T23:59:57.363000000&#x27;,\n",
+       "       &#x27;2026-08-04T23:59:58.358000000&#x27;, &#x27;2026-08-04T23:59:59.333000000&#x27;],\n",
+       "      shape=(86399,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>diameter_aerodynamic</span></div><div class='xr-var-dims'>(diameter_aerodynamic)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.542 0.583 0.626 ... 18.43 19.81</div><input id='attrs-35ed8cbc-331d-43bb-873a-603c23a13a69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35ed8cbc-331d-43bb-873a-603c23a13a69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55a5ebf8-6aa6-4caa-94d0-2a9450f2d8c7' class='xr-var-data-in' type='checkbox'><label for='data-55a5ebf8-6aa6-4caa-94d0-2a9450f2d8c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Midpoint of geometric mean aerodynamic diameter</dd><dt><span>units :</span></dt><dd>um</dd><dt><span>bounds :</span></dt><dd>diameter_aerodynamic_bounds</dd></dl></div><div class='xr-var-data'><pre>array([ 0.542,  0.583,  0.626,  0.673,  0.723,  0.777,  0.835,  0.898,  0.965,\n",
+       "        1.037,  1.114,  1.197,  1.286,  1.382,  1.486,  1.596,  1.715,  1.843,\n",
+       "        1.981,  2.129,  2.288,  2.458,  2.642,  2.839,  3.051,  3.278,  3.523,\n",
+       "        3.786,  4.068,  4.371,  4.698,  5.048,  5.425,  5.829,  6.264,  6.732,\n",
+       "        7.234,  7.774,  8.354,  8.977,  9.647, 10.37 , 11.14 , 11.97 , 12.86 ,\n",
+       "       13.82 , 14.86 , 15.96 , 17.15 , 18.43 , 19.81 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0e27c26c-c1e4-4183-8d93-c3d729794ce4' class='xr-section-summary-in' type='checkbox' /><label for='section-0e27c26c-c1e4-4183-8d93-c3d729794ce4' class='xr-section-summary' title='Expand/collapse section'>Data variables: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>base_time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-08-04</div><input id='attrs-1b766add-9688-46ea-99bc-7fbbabbf80ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b766add-9688-46ea-99bc-7fbbabbf80ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c03e3dbb-30f6-42fe-bb11-133c1de30bff' class='xr-var-data-in' type='checkbox'><label for='data-c03e3dbb-30f6-42fe-bb11-133c1de30bff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>string :</span></dt><dd>2026-08-04 00:00:00 0:00</dd><dt><span>long_name :</span></dt><dd>Base time in Epoch</dd><dt><span>ancillary_variables :</span></dt><dd>time_offset</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;2026-08-04T00:00:00.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_offset</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2026-08-04T00:00:00.795000 ... 2...</div><input id='attrs-51277343-f939-4f29-9102-7924e1a93670' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51277343-f939-4f29-9102-7924e1a93670' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5de56c58-c65c-41a5-8cd0-d7cd93a6fa16' class='xr-var-data-in' type='checkbox'><label for='data-5de56c58-c65c-41a5-8cd0-d7cd93a6fa16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time offset from base_time</dd><dt><span>ancillary_variables :</span></dt><dd>base_time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2026-08-04T00:00:00.795000000&#x27;, &#x27;2026-08-04T00:00:01.795000000&#x27;,\n",
+       "       &#x27;2026-08-04T00:00:02.826000000&#x27;, ...,\n",
+       "       &#x27;2026-08-04T23:59:57.363000000&#x27;, &#x27;2026-08-04T23:59:58.358000000&#x27;,\n",
+       "       &#x27;2026-08-04T23:59:59.333000000&#x27;],\n",
+       "      shape=(86399,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bound)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399, 2), meta=np.ndarray&gt;</div><input id='attrs-d5ba4486-83d9-47c9-8a07-06e01fff7ac2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5ba4486-83d9-47c9-8a07-06e01fff7ac2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8574c563-ca5b-47c3-9d3d-43b5e53cbf08' class='xr-var-data-in' type='checkbox'><label for='data-8574c563-ca5b-47c3-9d3d-43b5e53cbf08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time cell bounds</dd><dt><span>bound_offsets :</span></dt><dd>[-1.  0.]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.32 MiB </td>\n",
+       "                        <td> 1.32 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399, 2) </td>\n",
+       "                        <td> (86399, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308257291242\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.41261651458248,60.0)\">86399</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diameter_aerodynamic_bounds</span></div><div class='xr-var-dims'>(diameter_aerodynamic, bound)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(51, 2), meta=np.ndarray&gt;</div><input id='attrs-39a55dff-9602-49fb-a452-854c3531c50d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39a55dff-9602-49fb-a452-854c3531c50d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7269a3e7-a386-4a4a-90a9-c7c8102cb04c' class='xr-var-data-in' type='checkbox'><label for='data-7269a3e7-a386-4a4a-90a9-c7c8102cb04c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Aerodynamic diameter bin boundaries</dd><dt><span>units :</span></dt><dd>um</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 408 B </td>\n",
+       "                        <td> 408 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (51, 2) </td>\n",
+       "                        <td> (51, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"33\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 33.01282363906585,0.0 33.01282363906585,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"16.506411819532925\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"53.01282363906585\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,53.01282363906585,60.0)\">51</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dN_dlogDp</span></div><div class='xr-var-dims'>(time, diameter_aerodynamic)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399, 51), meta=np.ndarray&gt;</div><input id='attrs-c89b29b7-6bac-4390-9232-b222ce642d5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c89b29b7-6bac-4390-9232-b222ce642d5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db85516d-a0ca-4d15-89b3-55eaacdaa036' class='xr-var-data-in' type='checkbox'><label for='data-db85516d-a0ca-4d15-89b3-55eaacdaa036' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Number size distribution, aerodynamic diameter</dd><dt><span>units :</span></dt><dd>1/cm^3</dd><dt><span>ancillary_variables :</span></dt><dd>qc_dN_dlogDp</dd><dt><span>comment :</span></dt><dd>dN_dlogDp is the aerosol number size distribution where the number of particles per bin (dN) have been divided by the bin-width in log10 space (dlogDp).  This simplifies comparison of size distributions from instruments with different bin spacing.</dd><dt><span>equation :</span></dt><dd>(N_TOF_input / ( (total_flow_rate-sheath_flow_rate)*1000/60 * delta_t)) / log10(upper_bound/lower_bound)</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.81 MiB </td>\n",
+       "                        <td> 16.81 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399, 51) </td>\n",
+       "                        <td> (86399, 51) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308257291242\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >51</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.41261651458248,60.0)\">86399</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_dN_dlogDp</span></div><div class='xr-var-dims'>(time, diameter_aerodynamic)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399, 51), meta=np.ndarray&gt;</div><input id='attrs-2c74d4fe-01fc-4519-b7cb-33a0980f1316' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c74d4fe-01fc-4519-b7cb-33a0980f1316' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e3aa497-6fd9-427f-83e3-5e70bb8f062d' class='xr-var-data-in' type='checkbox'><label for='data-3e3aa497-6fd9-427f-83e3-5e70bb8f062d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Number size distribution, aerodynamic diameter</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>seconds_before_stack_purge :</span></dt><dd>30</dd><dt><span>seconds_after_stack_purge :</span></dt><dd>120</dd><dt><span>flag_masks :</span></dt><dd>[np.uint32(1), np.uint32(2), np.uint32(4), np.uint32(8), np.uint32(16), np.uint32(32)]</dd><dt><span>flag_meanings :</span></dt><dd>[np.str_(&#x27;Value is equal to missing_value.&#x27;), np.str_(&#x27;total_N_conc is less than the fail_min.&#x27;), np.str_(&#x27;total_N_conc is greater than the fail_max.&#x27;), np.str_(&#x27;status_flag not = 0&#x27;), np.str_(&#x27;time interval &gt; 1.5 * integration_interval&#x27;), np.str_(&#x27;stack_purge_state == 1 at any time within an interval extending from seconds_before_stack_purge seconds before to seconds_after_stack_purge seconds after the sample time&#x27;)]</dd><dt><span>flag_assessments :</span></dt><dd>[np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Indeterminate&#x27;), np.str_(&#x27;Bad&#x27;)]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 16.81 MiB </td>\n",
+       "                        <td> 16.81 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399, 51) </td>\n",
+       "                        <td> (86399, 51) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"12.706308257291242\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >51</text>\n",
+       "  <text x=\"45.41261651458248\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.41261651458248,60.0)\">86399</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_N_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-94cef0f2-10cf-4484-8a3a-5d491a8b562c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94cef0f2-10cf-4484-8a3a-5d491a8b562c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93a7da1f-2eef-4cbd-90e7-da3bf819ffe9' class='xr-var-data-in' type='checkbox'><label for='data-93a7da1f-2eef-4cbd-90e7-da3bf819ffe9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Aerosol number concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>1/cm^3</dd><dt><span>ancillary_variables :</span></dt><dd>qc_total_N_conc</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_total_N_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-6f555ec1-4f13-49a3-a820-5a819280128e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f555ec1-4f13-49a3-a820-5a819280128e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04019e04-a0e0-43a4-867d-e655f5b4ab2d' class='xr-var-data-in' type='checkbox'><label for='data-04019e04-a0e0-43a4-867d-e655f5b4ab2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Aerosol number concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>fail_min :</span></dt><dd>0.0</dd><dt><span>fail_max :</span></dt><dd>1000.0</dd><dt><span>seconds_before_stack_purge :</span></dt><dd>30</dd><dt><span>seconds_after_stack_purge :</span></dt><dd>120</dd><dt><span>flag_masks :</span></dt><dd>[np.uint32(1), np.uint32(2), np.uint32(4), np.uint32(8), np.uint32(16), np.uint32(32)]</dd><dt><span>flag_meanings :</span></dt><dd>[np.str_(&#x27;Value is equal to missing_value&#x27;), np.str_(&#x27;Value is less than the fail_min.&#x27;), np.str_(&#x27;Value is greater than the fail_max.&#x27;), np.str_(&#x27;status_flag not = 0&#x27;), np.str_(&#x27;time interval &gt; 1.5 * integration_interval&#x27;), np.str_(&#x27;stack_purge_state == 1 at any time within an interval extending from seconds_before_stack_purge seconds before to seconds_after_stack_purge seconds after the sample time&#x27;)]</dd><dt><span>flag_assessments :</span></dt><dd>[np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Indeterminate&#x27;), np.str_(&#x27;Bad&#x27;)]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_SA_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-cf735459-8cb6-4059-8a75-def3b32e396f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf735459-8cb6-4059-8a75-def3b32e396f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a51c03c7-3001-4bdc-b911-0466c4d07bf9' class='xr-var-data-in' type='checkbox'><label for='data-a51c03c7-3001-4bdc-b911-0466c4d07bf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total surface area concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>nm^2/cm^3</dd><dt><span>ancillary_variables :</span></dt><dd>qc_total_SA_conc</dd><dt><span>comment :</span></dt><dd>Integrated surface area concentration assuming spherical particles with unity shape factor.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_total_SA_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-28c213fa-60d6-4690-b9e5-afda1c6123b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28c213fa-60d6-4690-b9e5-afda1c6123b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f51a6c9d-b031-4d04-aa2b-14d0331101a6' class='xr-var-data-in' type='checkbox'><label for='data-f51a6c9d-b031-4d04-aa2b-14d0331101a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Total surface area concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>seconds_before_stack_purge :</span></dt><dd>30</dd><dt><span>seconds_after_stack_purge :</span></dt><dd>120</dd><dt><span>flag_masks :</span></dt><dd>[np.uint32(1), np.uint32(2), np.uint32(4), np.uint32(8), np.uint32(16), np.uint32(32)]</dd><dt><span>flag_meanings :</span></dt><dd>[np.str_(&#x27;Value is equal to missing_value.&#x27;), np.str_(&#x27;total_N_conc is less than the fail_min.&#x27;), np.str_(&#x27;total_N_conc is greater than the fail_max.&#x27;), np.str_(&#x27;status_flag not = 0&#x27;), np.str_(&#x27;time interval &gt; 1.5 * integration_interval&#x27;), np.str_(&#x27;stack_purge_state == 1 at any time within an interval extending from seconds_before_stack_purge seconds before to seconds_after_stack_purge seconds after the sample time&#x27;)]</dd><dt><span>flag_assessments :</span></dt><dd>[np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Indeterminate&#x27;), np.str_(&#x27;Bad&#x27;)]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dD_to_dSA</span></div><div class='xr-var-dims'>(diameter_aerodynamic)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(51,), meta=np.ndarray&gt;</div><input id='attrs-abd9c490-7f2d-47b2-a8a9-3b8fa56cb732' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-abd9c490-7f2d-47b2-a8a9-3b8fa56cb732' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c7e2454-72e3-470f-b866-f000e2c6e28a' class='xr-var-data-in' type='checkbox'><label for='data-0c7e2454-72e3-470f-b866-f000e2c6e28a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Surface area of one particle</dd><dt><span>units :</span></dt><dd>um^2</dd><dt><span>comment :</span></dt><dd>Surface area of one particle with the given aerodynamic diameter assuming spherical particles with unity shape factor.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 408 B </td>\n",
+       "                        <td> 408 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (51,) </td>\n",
+       "                        <td> (51,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"78\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,28.91939874812263 0.0,28.91939874812263\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"48.91939874812263\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >51</text>\n",
+       "  <text x=\"140.0\" y=\"14.459699374061316\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,14.459699374061316)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_V_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-c92159e5-3d83-4d5b-886f-60f3da5c4f08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c92159e5-3d83-4d5b-886f-60f3da5c4f08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e879dcad-15fe-4b43-9214-336192053d32' class='xr-var-data-in' type='checkbox'><label for='data-e879dcad-15fe-4b43-9214-336192053d32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total volume concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>nm^3/cm^3</dd><dt><span>ancillary_variables :</span></dt><dd>qc_total_V_conc</dd><dt><span>comment :</span></dt><dd>Integrated volume concentration assuming spherical particles with unity shape factor.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qc_total_V_conc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-84fc2bd6-3880-4d99-858c-2589963cf5a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84fc2bd6-3880-4d99-858c-2589963cf5a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30a63ba6-146c-480e-b3d1-590d5cfb091c' class='xr-var-data-in' type='checkbox'><label for='data-30a63ba6-146c-480e-b3d1-590d5cfb091c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Quality check results on variable: Total volume concentration from integrated size distribution, APS</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>quality_flag</dd><dt><span>seconds_before_stack_purge :</span></dt><dd>30</dd><dt><span>seconds_after_stack_purge :</span></dt><dd>120</dd><dt><span>flag_masks :</span></dt><dd>[np.uint32(1), np.uint32(2), np.uint32(4), np.uint32(8), np.uint32(16), np.uint32(32)]</dd><dt><span>flag_meanings :</span></dt><dd>[np.str_(&#x27;Value is equal to missing_value.&#x27;), np.str_(&#x27;total_N_conc is less than the fail_min.&#x27;), np.str_(&#x27;total_N_conc is greater than the fail_max.&#x27;), np.str_(&#x27;status_flag not = 0&#x27;), np.str_(&#x27;time interval &gt; 1.5 * integration_interval&#x27;), np.str_(&#x27;stack_purge_state == 1 at any time within an interval extending from seconds_before_stack_purge seconds before to seconds_after_stack_purge seconds after the sample time&#x27;)]</dd><dt><span>flag_assessments :</span></dt><dd>[np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Bad&#x27;), np.str_(&#x27;Indeterminate&#x27;), np.str_(&#x27;Bad&#x27;)]</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> int32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dD_to_dV</span></div><div class='xr-var-dims'>(diameter_aerodynamic)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(51,), meta=np.ndarray&gt;</div><input id='attrs-df9faec7-6477-48b3-b2bf-6c9bbe4ee150' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df9faec7-6477-48b3-b2bf-6c9bbe4ee150' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44703954-e0f7-4a1a-b105-76e0c82ae396' class='xr-var-data-in' type='checkbox'><label for='data-44703954-e0f7-4a1a-b105-76e0c82ae396' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Volume of one particle</dd><dt><span>units :</span></dt><dd>um^3</dd><dt><span>comment :</span></dt><dd>Volume of one particle with the given aerodynamic diameter assuming spherical particles with unity shape factor.</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 408 B </td>\n",
+       "                        <td> 408 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (51,) </td>\n",
+       "                        <td> (51,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"78\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"28\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,28.91939874812263 0.0,28.91939874812263\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"48.91939874812263\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >51</text>\n",
+       "  <text x=\"140.0\" y=\"14.459699374061316\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,14.459699374061316)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>event_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-89fdc4f5-fc43-4471-ada2-4d25138f4880' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89fdc4f5-fc43-4471-ada2-4d25138f4880' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aa22991-6ac8-41fc-909d-8efe91a3818e' class='xr-var-data-in' type='checkbox'><label for='data-9aa22991-6ac8-41fc-909d-8efe91a3818e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Single crossing event</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>This event occurs when the signal for a small particle cannot stay above the threshold and only one crest is detected. The measurement is aborted, and the time-of-flight of the particle is not recorded. However, the event is logged for concentration calculations and displayed in the &lt; 0.523 µm size channel in uncorrelated mode. (TSI APS model 3321 manual)</dd><dt><span>cell_methods :</span></dt><dd>time: sum</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>event_3</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-59d0a5da-8b71-4d60-a62c-dc61fa8ddda1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59d0a5da-8b71-4d60-a62c-dc61fa8ddda1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-567b45fd-f8a2-431d-a680-728c206ca991' class='xr-var-data-in' type='checkbox'><label for='data-567b45fd-f8a2-431d-a680-728c206ca991' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Coincidence event</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>This event is caused by coincidence. Although the signal stays above the threshold, three or more crests are detected. Events of this type are logged but not recorded for concentration or time-of-flight. (TSI APS model 3321 manual)</dd><dt><span>cell_methods :</span></dt><dd>time: sum</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>event_4</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-1b230a33-fc69-4f37-898b-702b2cf30001' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b230a33-fc69-4f37-898b-702b2cf30001' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb3406b0-537f-47b8-8342-18803844b37e' class='xr-var-data-in' type='checkbox'><label for='data-fb3406b0-537f-47b8-8342-18803844b37e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Over range event</dd><dt><span>units :</span></dt><dd>count</dd><dt><span>comment :</span></dt><dd>This event is outside the maximum range of the timer. The signal remains above the threshold until it moves outside the timer range, and only one crest is detected. A type 4 event is normally caused by large or recirculating particles. Again, the event is logged, but no time-of-flight is recorded. (TSI APS model 3321 manual)</dd><dt><span>cell_methods :</span></dt><dd>time: sum</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                        <td> 674.99 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dead_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-41970aec-b766-4205-a68f-34260e11b4ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41970aec-b766-4205-a68f-34260e11b4ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac188ad6-ecbb-42ce-ba0a-820291622e01' class='xr-var-data-in' type='checkbox'><label for='data-ac188ad6-ecbb-42ce-ba0a-820291622e01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dead time</dd><dt><span>units :</span></dt><dd>ms</dd><dt><span>cell_methods :</span></dt><dd>time: sum</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inlet_pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-8a4bc7a7-b52f-49be-a4f6-a4b886ee2456' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a4bc7a7-b52f-49be-a4f6-a4b886ee2456' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6502c4fc-24cc-4c8c-8e33-20be06853478' class='xr-var-data-in' type='checkbox'><label for='data-6502c4fc-24cc-4c8c-8e33-20be06853478' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Inlet pressure</dd><dt><span>units :</span></dt><dd>hPa</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_flow_rate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-652ecece-03cf-481a-881c-65fbe7af83b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-652ecece-03cf-481a-881c-65fbe7af83b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abfe9741-74b8-4a46-8864-8025449ee034' class='xr-var-data-in' type='checkbox'><label for='data-abfe9741-74b8-4a46-8864-8025449ee034' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total flow rate</dd><dt><span>units :</span></dt><dd>L/min</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sheath_flow_rate</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-b0a01f3f-5f60-4635-9c11-3d0235e8a489' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0a01f3f-5f60-4635-9c11-3d0235e8a489' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2d6208f-f9d0-4fa4-a0d7-5f64e8133c3b' class='xr-var-data-in' type='checkbox'><label for='data-a2d6208f-f9d0-4fa4-a0d7-5f64e8133c3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sheath flow rate</dd><dt><span>units :</span></dt><dd>L/min</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analog_input_voltage_0</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-088a8ca8-f171-4868-a938-61fdd7ade801' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-088a8ca8-f171-4868-a938-61fdd7ade801' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62c9b5ee-5865-4b2c-a9ad-6537cc1f79b2' class='xr-var-data-in' type='checkbox'><label for='data-62c9b5ee-5865-4b2c-a9ad-6537cc1f79b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Analog input voltage 0</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>comment :</span></dt><dd>Analog input voltage, I/O connector pin 7 ref to pin 15 (average over sample time)</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>analog_input_voltage_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-0eadeb72-ffcd-4e19-b50d-276057fa3e22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0eadeb72-ffcd-4e19-b50d-276057fa3e22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-677b00c8-856d-440f-a032-00a7a5fe497e' class='xr-var-data-in' type='checkbox'><label for='data-677b00c8-856d-440f-a032-00a7a5fe497e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Analog input voltage 1</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>comment :</span></dt><dd>Analog input voltage, I/O connector pin 8 ref to pin 15 (average over sample time)</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>digital_input_level_0</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-4b9fe0bd-3029-4f2f-ad21-de00bdc69bad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b9fe0bd-3029-4f2f-ad21-de00bdc69bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-edb980df-3344-4f0d-af3d-167fcffc8845' class='xr-var-data-in' type='checkbox'><label for='data-edb980df-3344-4f0d-af3d-167fcffc8845' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Digital input level 0</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;pin_1_not_set&#x27;, &#x27;5_VDC_level_set_in_pin_1&#x27;]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(86399,), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>digital_input_level_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-7f8dd12e-badc-423f-8a0f-a60f9d9769c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f8dd12e-badc-423f-8a0f-a60f9d9769c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c3e76e4-3f6b-4993-98da-a5de5f7f4338' class='xr-var-data-in' type='checkbox'><label for='data-3c3e76e4-3f6b-4993-98da-a5de5f7f4338' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Digital input level 1</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;pin_2_not_set&#x27;, &#x27;5_VDC_level_set_in_pin_2&#x27;]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(86399,), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>digital_input_level_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-35875018-64e5-4517-a1aa-2a08dd2d9890' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35875018-64e5-4517-a1aa-2a08dd2d9890' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53fcd117-19d4-45cb-b09e-3b3b14264631' class='xr-var-data-in' type='checkbox'><label for='data-53fcd117-19d4-45cb-b09e-3b3b14264631' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Digital input level 2</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;pin_3_not_set&#x27;, &#x27;5_VDC_level_set_in_pin_3&#x27;]</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(86399,), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>laser_power</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-9e73b3da-c82f-4967-9301-7334cb000195' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e73b3da-c82f-4967-9301-7334cb000195' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e765f3b-2a9d-4829-b359-54ccdc3d80e9' class='xr-var-data-in' type='checkbox'><label for='data-2e765f3b-2a9d-4829-b359-54ccdc3d80e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Laser power</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>comment :</span></dt><dd>Percent of nominal maximum power at last calibration.</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>laser_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-37cb5642-412a-45b6-8474-bac4fe975ab0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37cb5642-412a-45b6-8474-bac4fe975ab0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac4c37e0-1c8b-4317-a696-c819017b9e4c' class='xr-var-data-in' type='checkbox'><label for='data-ac4c37e0-1c8b-4317-a696-c819017b9e4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Laser current</dd><dt><span>units :</span></dt><dd>mA</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>total_pump_voltage</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-c2237604-8a0e-477e-b573-4ef556aff253' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2237604-8a0e-477e-b573-4ef556aff253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0616dd66-18f4-4488-b043-b993cdb0ebcf' class='xr-var-data-in' type='checkbox'><label for='data-0616dd66-18f4-4488-b043-b993cdb0ebcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Total pump voltage</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>inlet_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-e9926dd9-2365-439d-841e-9b5966c72bfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9926dd9-2365-439d-841e-9b5966c72bfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-100aa523-40e0-40f9-814c-51723ec54b90' class='xr-var-data-in' type='checkbox'><label for='data-100aa523-40e0-40f9-814c-51723ec54b90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Inlet temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>box_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-fab875f1-f1a8-4221-8793-9b25f8fa6a0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fab875f1-f1a8-4221-8793-9b25f8fa6a0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43c54426-1b83-4c08-a342-667922c2c94b' class='xr-var-data-in' type='checkbox'><label for='data-43c54426-1b83-4c08-a342-667922c2c94b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Box temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>avalanche_photodiode_temperature</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-877c32fc-b024-4770-92ca-d1b7d499de70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-877c32fc-b024-4770-92ca-d1b7d499de70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f14dcf52-d434-4e49-ac61-ca17387b4967' class='xr-var-data-in' type='checkbox'><label for='data-f14dcf52-d434-4e49-ac61-ca17387b4967' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Avalanche photodiode temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>avalanche_photodiode_voltage</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-250b4bba-41e3-4a3a-91d5-e5825351da21' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-250b4bba-41e3-4a3a-91d5-e5825351da21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a45bdd4-f36d-49b5-9418-4da3b03aa570' class='xr-var-data-in' type='checkbox'><label for='data-5a45bdd4-f36d-49b5-9418-4da3b03aa570' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Avalanche photodiode voltage</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sheath_pump_voltage</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(86399,), meta=np.ndarray&gt;</div><input id='attrs-3e5d15af-2359-47e5-b2d4-b4fa71f52e62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e5d15af-2359-47e5-b2d4-b4fa71f52e62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f90aaa8b-b584-49fd-b73b-8bda84054501' class='xr-var-data-in' type='checkbox'><label for='data-f90aaa8b-b584-49fd-b73b-8bda84054501' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sheath pump voltage</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                        <td> 337.50 kiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (86399,) </td>\n",
+       "                        <td> (86399,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.41261651458248\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >86399</text>\n",
+       "  <text x=\"140.0\" y=\"12.706308257291242\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.706308257291242)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_flags</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-cc86ffbb-577f-48fd-a827-9c601a06e6ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc86ffbb-577f-48fd-a827-9c601a06e6ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cdbabbb-1033-45f3-8ff8-ee8030c522bf' class='xr-var-data-in' type='checkbox'><label for='data-9cdbabbb-1033-45f3-8ff8-ee8030c522bf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Status flags</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_masks :</span></dt><dd>[  1   2   4   8  16  32  64 128 256]</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;laser_fault&#x27;, &#x27;total_flow_out_of_range&#x27;, &#x27;sheath_flow_out_of_range&#x27;, &#x27;excessive_sample_concentration&#x27;, &#x27;accumulator_clipped&#x27;, &#x27;autocal_failed&#x27;, &#x27;internal_temp_lt_10degC&#x27;, &#x27;internal_temp_gt_40degC&#x27;, &#x27;detector_v_gt_10pct_Vb&#x27;]</dd><dt><span>bit_1_description :</span></dt><dd>Laser fault</dd><dt><span>bit_2_description :</span></dt><dd>Total flow out of range</dd><dt><span>bit_3_description :</span></dt><dd>Sheath flow out of range</dd><dt><span>bit_4_description :</span></dt><dd>Excessive sample concentration</dd><dt><span>bit_5_description :</span></dt><dd>Accumulator clipped</dd><dt><span>bit_6_description :</span></dt><dd>Autocal failed</dd><dt><span>bit_7_description :</span></dt><dd>Internal temperature &lt; 10 degC</dd><dt><span>bit_8_description :</span></dt><dd>Internal temperature &gt; 40 degC</dd><dt><span>bit_9_description :</span></dt><dd>Detector voltage more than +/-10% Vb</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(86399,), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stack_purge_state</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-dc7f3682-c2c0-4a73-8164-2869f9763ae4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc7f3682-c2c0-4a73-8164-2869f9763ae4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fa9374b-a072-4d91-add8-cf22052aadae' class='xr-var-data-in' type='checkbox'><label for='data-0fa9374b-a072-4d91-add8-cf22052aadae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>AOS stack purge state</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>flag_meanings :</span></dt><dd>[&#x27;purge_off&#x27;, &#x27;purge_on&#x27;]</dd><dt><span>comment :</span></dt><dd>Data collected while stack_purge_state = 1 is not representative of ambient conditions.</dd><dt><span>missing_value :</span></dt><dd>-9999</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(86399,), dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-124f3f82-ed16-4367-983f-e590ef7beefa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-124f3f82-ed16-4367-983f-e590ef7beefa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b763a0a6-a036-4afd-884e-c986d05648a4' class='xr-var-data-in' type='checkbox'><label for='data-b763a0a6-a036-4afd-884e-c986d05648a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>North latitude</dd><dt><span>units :</span></dt><dd>degree_N</dd><dt><span>valid_min :</span></dt><dd>-90.0</dd><dt><span>valid_max :</span></dt><dd>90.0</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d2891e15-6722-4565-be83-7294a0d4bc7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2891e15-6722-4565-be83-7294a0d4bc7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13338f78-6bc0-49d0-b703-6ff83cfcc4d1' class='xr-var-data-in' type='checkbox'><label for='data-13338f78-6bc0-49d0-b703-6ff83cfcc4d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>East longitude</dd><dt><span>units :</span></dt><dd>degree_E</dd><dt><span>valid_min :</span></dt><dd>-180.0</dd><dt><span>valid_max :</span></dt><dd>180.0</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2f0935c1-bb61-466d-b96d-401412d3c303' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f0935c1-bb61-466d-b96d-401412d3c303' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53c11c20-3ded-425a-89b8-18bb5b6ce300' class='xr-var-data-in' type='checkbox'><label for='data-53c11c20-3ded-425a-89b8-18bb5b6ce300' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Altitude above mean sea level</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>altitude</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ff7fd5f-fbc6-425f-99c8-51eadd32a7b8' class='xr-section-summary-in' type='checkbox' /><label for='section-5ff7fd5f-fbc6-425f-99c8-51eadd32a7b8' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>command_line :</span></dt><dd>aosapscorr -s dst -f M1 --max-runtime 86400</dd><dt><span>Conventions :</span></dt><dd>ARM-1.3</dd><dt><span>process_version :</span></dt><dd>ingest-aosapscorr-1.7-0.el9</dd><dt><span>dod_version :</span></dt><dd>aosaps-b1-1.3</dd><dt><span>input_datastreams :</span></dt><dd>dstaosapsM1.a0 : 1.10 : 20260804.000000\n",
+       "dstaospurgeM1.b0 : 1.3 : 20260803.000000-20260805.000001</dd><dt><span>site_id :</span></dt><dd>dst</dd><dt><span>platform_id :</span></dt><dd>aosaps</dd><dt><span>facility_id :</span></dt><dd>M1</dd><dt><span>data_level :</span></dt><dd>b1</dd><dt><span>location_description :</span></dt><dd>Desert-Urban SysTem IntegratEd AtmospherIc Monsoon (DUSTIEAIM), Phoenix, Arizona</dd><dt><span>datastream :</span></dt><dd>dstaosapsM1.b1</dd><dt><span>Instrument_Description :</span></dt><dd>TSI APS 332100</dd><dt><span>serial_number :</span></dt><dd>-</dd><dt><span>configuration_file :</span></dt><dd>Used values from input file headers</dd><dt><span>integration_interval :</span></dt><dd>1 s</dd><dt><span>doi :</span></dt><dd>10.5439/1407135</dd><dt><span>history :</span></dt><dd>created by user armflowprod on machine prod-slurmnode1.adc.arm.gov at 2026-08-04 02:22:03, using ingest-aosapscorr-1.7-0.el9</dd><dt><span>_file_dates :</span></dt><dd>[&#x27;20260804&#x27;]</dd><dt><span>_file_times :</span></dt><dd>[&#x27;000000&#x27;]</dd><dt><span>_datastream :</span></dt><dd>dstaosapsM1.b1</dd><dt><span>_arm_standards_flag :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 49MB\n",
+       "Dimensions:                           (time: 86399, bound: 2,\n",
+       "                                       diameter_aerodynamic: 51)\n",
+       "Coordinates:\n",
+       "  * time                              (time) datetime64[ns] 691kB 2026-08-04T...\n",
+       "  * diameter_aerodynamic              (diameter_aerodynamic) float32 204B 0.5...\n",
+       "Dimensions without coordinates: bound\n",
+       "Data variables: (12/39)\n",
+       "    base_time                         datetime64[ns] 8B 2026-08-04\n",
+       "    time_offset                       (time) datetime64[ns] 691kB 2026-08-04T...\n",
+       "    time_bounds                       (time, bound) object 1MB dask.array<chunksize=(86399, 2), meta=np.ndarray>\n",
+       "    diameter_aerodynamic_bounds       (diameter_aerodynamic, bound) float32 408B dask.array<chunksize=(51, 2), meta=np.ndarray>\n",
+       "    dN_dlogDp                         (time, diameter_aerodynamic) float32 18MB dask.array<chunksize=(86399, 51), meta=np.ndarray>\n",
+       "    qc_dN_dlogDp                      (time, diameter_aerodynamic) int32 18MB dask.array<chunksize=(86399, 51), meta=np.ndarray>\n",
+       "    ...                                ...\n",
+       "    sheath_pump_voltage               (time) float32 346kB dask.array<chunksize=(86399,), meta=np.ndarray>\n",
+       "    status_flags                      (time) int32 346kB 0 0 0 0 0 ... 0 0 0 0 0\n",
+       "    stack_purge_state                 (time) int16 173kB 0 0 0 0 0 ... 0 0 0 0 0\n",
+       "    lat                               float32 4B ...\n",
+       "    lon                               float32 4B ...\n",
+       "    alt                               float32 4B ...\n",
+       "Attributes: (12/21)\n",
+       "    command_line:            aosapscorr -s dst -f M1 --max-runtime 86400\n",
+       "    Conventions:             ARM-1.3\n",
+       "    process_version:         ingest-aosapscorr-1.7-0.el9\n",
+       "    dod_version:             aosaps-b1-1.3\n",
+       "    input_datastreams:       dstaosapsM1.a0 : 1.10 : 20260804.000000\\ndstaosp...\n",
+       "    site_id:                 dst\n",
+       "    ...                      ...\n",
+       "    doi:                     10.5439/1407135\n",
+       "    history:                 created by user armflowprod on machine prod-slur...\n",
+       "    _file_dates:             ['20260804']\n",
+       "    _file_times:             ['000000']\n",
+       "    _datastream:             dstaosapsM1.b1\n",
+       "    _arm_standards_flag:     1"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_aps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "a1dd0dc1-7ae2-4c81-812e-cad0a525ecfe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABS0AAANCCAYAAACDB+nKAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjExLjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvgI3uAAAAAAlwSFlzAAAPYQAAD2EBqD+naQAA+v5JREFUeJzs3Qd8G+X5wPFH3na8MuzsQSAhgxDCDhnsVXZZhQ4KtFA6oKUD6ObfQSkdUFqgjDLK3pS9KSSQEkKADEISspezvbf1/zyX95yzfJJPsmydpN/385EtnU6n94ZuPPe87xsIBoNBAQAAAAAAAACfyEh0AQAAAAAAAADAiaAlAAAAAAAAAF8haAkAAAAAAADAVwhaAgAAAAAAAPAVgpYAAAAAAAAAfIWgJQAAAAAAAABfIWgJAAAAAAAAwFcIWgIAAAAAAADwFYKWAADEyR133CGlpaWyYcMGlmmSufLKK2XkyJGSTq699loZMGBAoosBAAAAuCJoCQBAnDQ2NkplZaW0tbVF9blrrrlGBg0alPbr4cYbb7SCvvqYP3++6/I44ogjrPf333//Tu/pZ66++moZO3asNc6DDz7oeZnW1dVZ686r7nxXPNZ3PKZRX18vO3fujHk9DRs2TLZu3drp/bPOOkv23nvvbpUNu23ZskVuuOEGa9vXZb7vvvvKRRddJJ9//rnrYqqurpYf//jH1jrQbeSoo46Sl156qdvTVfPmzZMvfelLsscee8jo0aPlwgsvlJUrV8ZcBjV37lw599xzZdy4cTJkyBA55JBD5I9//KPU1tZ2a7pOf/7zn9u3WQAAkDwIWgIAkGCxBo9STUNDgxU41ODvPffc0+n9RYsWyX//+1/r/aqqqg7vvfHGG3LxxRdbgYnLL7/cmk5TU1OPlLO73xWP9Z3IbcZeT+vXr5ff/OY3nd6vqamJKgCMyI4//nj529/+ZgUI58yZI//6179kzZo1MmnSJOu1U0tLixx33HHy1FNPWeN9+OGHcvjhh8sXvvAFa1is01UPP/ywTJs2zQouvvzyy/Luu+/KscceawXQYy3DW2+9JYcddpgVjHzkkUfkgw8+sH5TmgV88sknxzxdpwULFsjPfvYzycrKYj8LAECSIWgJAAB85bTTTrMyF5ubmzsMv/vuu60q3G5ZlkceeaQVxNDsx1GjRvVo+Xrzu/xMl8Ott94aMTMP3Tdz5kwr8HbBBRdYmYIHHnigPP3005KdnS2/+MUvOox77733WgHHu+66ywowaubir371KyvY973vfU9aW1tjmq5mU2qg/te//rX10AxjzXQ8//zzrWBmrGW48847rWCkfmby5MnWuF/+8pflW9/6lhXQ/Oyzz2Kark1vJnz1q1+1sn91/gAAQHIhaAkAQAxee+0166JfL9wPPfRQeeGFF1zH0/Ytv/3tb8vEiROti+zp06fL3//+9/aAnF6ga+BHswftqtH60Iwnpe/Zw/r27SsjRoywgnqzZs3q9F3vv/++nHHGGTJ8+HDruzSTSssZTXmi+U67HUjNrLvsssus51pl9Ac/+IGVORXt99q+/vWvy7Zt2+S5555rH6aBjfvvv98KsAQCgU6fcRsWCy/z0p3v6mp9e1mP8dxmukMDRrm5uZ0y7bpDg2MasNJsvsGDB1vVf5955pmot3N729SsUH2uwWV9aHBLs1Rj+V43PVGWcNXxnQoLC61pLl26tMPwRx99VMrKyqwMRCetfq2Zse+8805M073lllus3+B3v/vdLssbTRny8/Ot31NeXl6Hce3XzuHRTNf2y1/+0npP5xUAACQfgpYAAERJ21A74YQTrHbdtKqwdsBz3333yezZszuNq+NpVp5dnfFPf/qTLFu2rD076fbbb7fakdPgz6pVq9ofdttr+p49TDPann32WenXr59VLXPx4sXt3/P666/LjBkzrACABvu0XBoY0SykBx54wHN5ovlOux3ISy+91PpuDRpo23EaXNSqnc7MJy/fa9Nya2DCWUVcg8KbN2+2gpY9ycu8dEdX69vLeozXNtNdAwcOtNoXfOyxx+R///tft6f30UcfyZQpU+STTz6xAq9aVVgDo7od2EE0r9u5vW1qVWPNsNP1ef3111tZeqFBVi/f66YnyuLVunXr5NNPP7UC66HzMn78+E7jT5gwwfr/8ccfxzRdnbd99tlHnnjiCesmTXl5uTVNzTYObXsymjJcccUVUlJSIt///vetZRQMBq39qG7j+lt3do4V7bzpdLTNTq0CT4dTAAAkqSAAAIjKhAkTgvvss0+wra2tfVhzc3Nwjz32COqhde3atdaw9evXW6//9a9/RZzeFVdcEczNzY2qDMOHD7c+Z9PyjB49OtjS0tJhvOnTpwfLysqCjY2Nnsvj9TsvvfRSa3p33nlnh/Eef/xxa/jDDz9svfb6vdddd5013qeffhq85557gllZWcGKigrrvdNPPz14+OGHW8+nTZsW3HPPPcNO59lnn7Wmc/fdd3ueN6/zEo/virS+vazHrqbhdf1dddVVwczMzGC0nOuppqYmOHjw4OCMGTPa3z/++OODAwcOjHq6hxxySHDo0KHB2trasON4XT72+rzvvvs6jKfD8/PzO3zey/f2Vlm80P3OKaecYk3zP//5T4f3dH2edtppnT6zdOlSa/yf/exnMU1X50e3t/Ly8uCTTz4ZXLNmTfCBBx4IlpSUBKdOnWrt/2Itw7x584KjRo2y3svJybH+67IJXS7RTLe6utraR5x88skdtss+ffqEnX8AAOA/ZFoCABAFreas2Wpnnnlmh2rC2smDVhN10uwezXDTrELNdtOMq2hpm2yaLaTZTVoVXav7apVOLYdmKqqNGzfKwoULrXbbMjMzO3z+vPPOs3oJ1uxGr+Xx8p1OuiycdDlou3ivvPJKzMtB50WrhmrGmpb/+eeftzoM6WldzUtP8roeuxLt+uuOPn36WJ2maPagl+rU4VRUVFjZmlrVt6CgIG7LJ/Q3qRmVWiVbqwx7/d7eKotXP//5z63fkVZnP+WUUzq8p5mKkZpP0Pdjma5WDdfmCP7617+2V4fX9ix/+9vfynvvvSdPPvlkTGXQzx599NFWFqcuL80M1uxmzd7VdeIcN5rpajV8XQeaOQsAAJIXQUsAAKKgF8JK270LFTosJyfHqtasAbvTTz/dqgapHUiEa8vRjQbq/u///s9qZ1F769WLeq32u/fee1tBBKXVppW2qRfKHqbjeC2Pl++0aWAxtF28jIwMq/qoXa5YloMGwzQgpFV0NYihVaH1dU/yMi89yet67Eo06y8etDq6XVVYg1ux2LRpk/Vfg2HxWj5FRUVWG41Ouu2pHTt2eP7e3iqLF1qt/Pe//70VGNXfTyjdft16breHaQA7lunqb1cdc8wxHYbbrzX4GEsZtM1Y/W1re5UaxNUmDrTN1uuuu86qiq5B1Ginq0FobbJD58luMgEAACQngpYAAETBDmrZwUsnt2GHHHKIlYW2detWq2de7ehDO+DQi/WuaAcw2uajdn6h7btpO3MaPNAyOLOzIpXJDpzYF/Rdlcfrd9q0cxHtvMZJM560Ix1ncCKW5aAd8mg7g3/4wx/k7LPPtgKZPcnrvPSUaNZjONGuv3jQTEMNEC1ZssTqDToW/fv37xBEjMfy0YBzOHZWnpfv7a2ydOWmm26yAsPnnHOO/Pvf/+6U4ak0Y9GtHU67F259P5bpTpo0qT2j3EmzkJWzzddoyqDZqhrw1g55nOyevrV382inq1mw6qqrrurQUZW2Qartb+rznr4BAgAA4oOgJQAAUdCOIfTx8ssvu3bQE44GME466SSr8w3tvOPVV19tf0+rpWqGWmjwQgMBbW1t7RlZNs1adGYcaXm0AxCtQh1KOwjRDK/999/fU3m8fqdT6LJ48803rQBgaC+/XS2HUNo7uwbdNAikAczeEM28xCrc+o5mPXZ3m4k37azoiCOOkF//+tedAr9eaEacBq80oB0uWzOW7Twe39tbZYlEO6bRzmo0eK9NJrgFFpX2Eq/B6fnz53cYrlX3NVin6yiW6drNJjgzKpVm8qqDDjoopjJoVqpmAod2dKUBcPv9aKer+xfNXl29enWHjqr0N6w3PvT5vffe6zqfAADAXwhaAgAQJW3Db9asWe0BmqqqKvnRj35k9abspBfX3/jGN6zeiDXwZVddXLRokZV5aNOsQ71o12k66YW4jqeBBc0w0mCUZitq1Un9jJNW7dTedbUtNw1OaUbRH//4R6t65S9/+UvrYt1LeaL5TlVcXCyPPPKIFczQcbUHX20TT8fVap7RLAe3tuo000oDEBrAjJf//Oc/1nxqFdJo5yUewq1vr+sx0jSiXX/xpO1oaoBZe22OhfbyvGLFCquKsgaWNCCrgSddFhrYimb5xPt73fREWdxo8wi6HWp24IMPPtgp29Hp0ksvlb322ksuueQSqydwnRcN0Gkbkb/73e+sJhBima4uG20jVTOj9Xeh050zZ47V+/nkyZOt9idjKYP2Hr5mzRqrZ/WdO3da26sGQnW6WmXf2cas1+lq9qczw9J+2POnz3s6axsAAMRJonsCAgAgGd1xxx3BYcOGBTMyMqyekv/xj38Eb7755g69h2vvwdpjtvZOXFBQEMzLy7N6Kf7JT35i9bpsa2pqCp511llW77yFhYVWj7yrV6+23luxYkXwuOOOs3rV1c8feeSRwWXLlgUPOOCA4NFHH92pp+t9993XKpP2tDtmzJjg7bff3v6+1/J4/U7t4VfLumHDhuCxxx5rlV97/D711FOD69ati/p7nb1SR+LWe3hDQ4NVFn1oD8E6He2Z2R6mPZjbHnvsMet9XV/Rzku03+Um0vr2sh67mobX9ReP3sNDfelLX7Lei6X3cPXuu+9aZczOzrbKr71z33jjjR16p/ayfOz1Geqhhx6yyjd//vyov9dNT5QllO5ndLyioqL2bcx+uC1n3f988YtftLYNfYwcOTJ46623dnu6O3bsCH7zm9+0xtflVFxcHLzwwguD27Zti7kM6pFHHrG2Td036LLX77744os7/e6inW4oeg8HACD5BPRPvAKgAACkG+39126PTXtt1p6xtWpuaC+3mkGkmXF2G3BudBzN3NT/mvXnbAdPP6uHbDtbSMfT73DLGNLObXQa2sFFpO/qqjxdfadmaWn7iZohZX+vvh8pYyvS92onMbo8Q+c9lJZDy6WdmzjZ5XDjXCdaTs2K0yrW2kmQ0u/V9WdXq+5qXrx+VySR1nc06zHWbUazXnWZh1Yl70qk9WQvWx2u78dK50enFWneIy2f0PUZWj7ddtyqQnv53t4qi00zuXXabnR9hlt/uu51XTkzG+MxXaX7OS+9rXdVhlA6rpdlH+10lS5rbQIg2u0dAAAkDkFLAAAQk9CgJQAAAADEC21aAgAAAAAAAPAVgpYAAABpTnvBduu8xPnQDqj8Ml0AAACkPqqHAwCAmIRrqw/Jx25fMRJtPzCaNgR7croAAABIfQQtAQAAAAAAAPgK1cMBAAAAAAAA+EpWogvgF21tbbJhwwYpKiqSQCCQ6OIAAAAAAAAgQYLBoFRXV8uQIUMkI4Ocv0QgaGlowHL48OEJWQkAAAAAAADwn7Vr18qwYcMSXYy0RNDS0AxLtXbt81Jc3CeR6wRxtnz5rnULAACSz157VSe6CADga62tDfLuuydYzw877CXJzKRzMyAeqqpqZfjwk9rjReh9BC0Nu0q4BiyLiwsTsCrQUwoLWZ8AACSr4uJgoosAAL7W2popfUzejV7PZmbmJ7pIQEqhCcHEoVI+AAAAAAAAAF8haAkAAAAAAADAVwhaAgAAAAAAAPAVgpYAAAAAAAAAfIWOeAAAAAAASFoZ0q/ftPbnAJAqCFoCAAAAAJCkMjNzZd99b0p0MQAg7rgNAwAAAAAAAMBXCFoCAAAAAAAA8BWClgAAAAAAJKnW1np5++3p1kOfA0CqoE1LAAAAAACSWFtbQ6KLAABxR6YlACSRLbXN8tHGOus/AAAAAACpikxLAEgSjy3YLnfM3db++hsH9ZdzJvVLaJkAAAAAAOgJZFoCQBJ4NCRgqe6cu03unrclYWUCAAAAAKCnELQEAJ/TquAaoHTz0Mc7rQxMAAAAAABSCUFLAPC59VWR26/UgCZtXAIAAAAAUglBSwDwubysQMT3gyKyeHN9r5UHAAAAfhKQkpL9rYc+B4BUQUc8AOBzLy2t6nqkICeoAAAA6SgzM0+mTLk90cUAgLgj0xIAfEyrfb/wWddBywkD83qlPAAAAAAA9AaClgCQxO1Z2rbVtfR4WQAAAAAA6C0ELQHAx4YWZ3sa7/01tT1eFgAAAPhPa2u9zJ59jPXQ5wCQKghaAoCPlfXJli/sXdzleE2tbb1SHgAAAPhPc/NO6wEAqYSgJQD43H6D87scZ1FFQ6+UBQAAAACA3kDQEgB8blBR11XEN9d6a/sSAAAAAIBkQNASAHxuU3XXAcmRpTm9UhYAAAAAAHoDQUsA8LllW7qu+k2TlgAAAACAVELQEgB8bkNN15mW66ubeqUsAAAAAAD0hqxe+RYAQMyGeGjTMiPAAgYAAEhPASkqmtD+HABSBUFLAPC5oIdxGlt6oSAAAADwnczMPDnggPsSXQwAiDuqhwOAz+2ob+1ynLqmtl4pCwAAAAAAvYGgJQD4XHlh10nxjW0iW2q7bvsSAAAAAIBkQNASAHxuc423ut+LK7ruZRwAAACppbW1Qd577xTroc8BIFXQpiUA+FzQU6uWIlU0bAkAAJCGgtLYuLH9OQCkCjItAcDnVm/3Vu27prHrti8BAAAAAEgGBC0BwOdW72jyNN7OejrjAQAAAACkBoKWAOBj2rmOtxYtRdZWNvZwaQAAAAAA6B0ELQHAx9ZXNcc9IxMAAAAAAL8jaAkAPja0ONvzuBkBGl4HAAAAAKQGgpYA4GNlfbJlv0G5nsatbSJoCQAAkH4CUlAw2nrocwBIFVmJLgAAILI+Obqr7rq9SmKWAAAA6SczM08OPvjRRBcDAOKOTEsA8LnPttR5Go8dOgAAAAAgVXCNCwA+t63eW7XvwhyqAwEAAAAAUgNBSwDwuRyPe+q6lp4uCQAAAPymtbVB3n//HOuhzwEgVRC0BACf++KkUk/j1TUHZUttc4+XBwAAAH4SlLq6FdZDnwNAqiBoCQA+1ycn0/O4G6oIWgIAAAAAkh9BSwDwMc2cvHPuNs/jDynO7tHyAAAAAADQGwhaAoCPLaqgXSIAAAAAQPohaAkAPlbVGF3vOosJcgIAAAAAUgBBSwDws2CgR4OcAAAAAAD4UVaiCwAACK+miSAkAAAAIglIbu7g9ucAkCoIWgKAj22Msjfwmoa2HisLAAAA/CczM0+mTn020cUAgLijejgA+FhDSzCq8VfsaOyxsgAAAAAA0FsIWgJACllfGV1mJgAAAAAAfkTQEgB8LbpMy7pm2sAEAABIJ62tDTJv3teshz4HgFRBm5YA4GMNrdEFLVvaaHwdAAAgvQSlunpx+3MASBVkWgKAj7W2RXfiubmuVbbUUkUcAAAAAJDcCFoCgI+N6pvrOnx4cXbYz2yIssdxAAAAAAD8hqAlAPjY2AH5rsPHDHAPZmYERIZECGgCAAAAAJAMCFoCgI9NHJjnOvyNFTWuw08eVyxlfQhaAgAAAACSm2+Clm1tbVJTUyNNTU1hx2lu7rrKo5dxACBZLN8WXQ+QG6kaDgAAAABIAb4JWl5xxRVSVFQkP/nJTzq995vf/Eb69+8veXl5Mn78eHnttddiGgcAks3cdXXRfYAOIwEAANJOdnap9QCAVOKLoOUzzzwj//3vf2XcuHGd3vvHP/4hN9xwgzzxxBNWJub5558vp5xyinz++edRjQMAyWhsmLYrJ5TluA6vbWnr4RIBAADATzIz82XatNeshz4HgFSR8KDlunXr5Nvf/rY88MADkpvb+eL8xhtvlIsvvliOOOIIyc/Pl1/84hdSXl4ut912W1TjAEAyysvKdB1eHqbdysWbG2XJlvoeLhUAAAAAACkctGxtbbWyIn/84x/LpEmTOr2/bds2Wb58ucycObPD8MMPP1zmzJnjeRwASFZVjS2uw5duDd/W5ftra3uwRAAAAAAApHjQ8tprr7XaoNT2LN1UVFRY/8vKyjoM19ebN2/2PI6bxsZGqaqq6vAAAL8pzs1yHd7UEr7xyp317oFOAAAApJ7W1gaZP/8S66HPASBVuF8N94J3333XaotSsyFra2vbexDX3r+1XcrCwsL2cXW4k74OBAKdhnU1jtN1111nBU0BwM8mDsxzHd4aocedhZs4WQUAAEgfQams/LD9OQCkioRlWi5cuNDKdpwyZYoMGjTIeixatEjuuOMO67lWHR8yZIg1bmjGpL4ePHiw9dzLOG6uueYaqaysbH+sXbu2B+YSALov4PK6siH8Cen2+mYWOwAAAAAgqSUsaHnJJZdYGZXOh7ZrqZ3y6PPMzEwpLS2ViRMnyuuvv94hg/KNN96Q6dOnW6+9jONGO/0pLi7u8AAAv1lf1dzpfrm+jtRHeHNrDxcKAAAAAIBU7z28K1dffbXcfffd8sgjj8iaNWvk8ssvl/r6ernsssuiGgcAktHQ4mzJCEm1DH0dqoGgJQAAAAAgySWsTUs3BQUFVgak01e+8hWpq6uz2p/UTnc0G/O1115rrxbudRwASEZlfbLlimnl8tdZm9sDlvr6jvc3S02T+2dyfH87CgAAAACAyALBYJCWekWs3sNLSkqksvItKS7e3QkQkt/SpUWJLgLQbWc+8LlUN7bJ9ScMkSlD+sgPnl0ji7Y0uo6bnynyzAVjWOoAUsLYsdWJLgIA+Fpra728884M6/mMGe9IZmZ+oosEpISqqhopKTnC6geFJgUTw1eZlgAAd6H3l7KzwtcRb+NWFAAAQFrJyMhLdBEAIO4IWgKAz724tFJqmnZFIq9+aYN8f3q5NEeITBbnUT8cAAAgXWhm5cyZsxJdDACIO65sAcDHttQ2y02zd7VnqTRUqa8PG9En7GdK8jJ7qXQAAAAAAPQMgpYA4GPrq5o7VffW12MH5Ifdgbe0Uj8cAAAAAJDcCFoCgI8NLc62egx30tdDirNlaIl7Cx9tvVM0AAAA+EBra6N88skV1kOfA0CqIGgJAD5W1idbrphW3iFgqa91eH6W+y48L8xwAAAApKI22b59tvXg9jWAVMKVLQD43IljS6Qge1e65fUnDLVeR+pBPCdCz+IAAAAAACQDgpYAkAQCgV2ByP4Fu6uE7zso33Xcgmx27QAAAACA5MaVLQAkEWcOZVGue5uWc9fVW72OAwAAAACQrAhaAkASaDVdiG+va2kfts/APNdxdcwNVQQtAQAAAADJi6AlAPjci0srpaFlV9DyRy+ut16HVhUPlUu7lgAAAACAJEbQEgB8TKt53zhrc/trDV3qax2+PkI2ZUX17oxMAAAAAACSTfg0HQBAwi2qqLcClU76evHmeplQ7t4RjyUQ+ikAAACkoszMfDniiA8SXQwAiDsyLQEgabrecQiGGW7kZEZ+HwAAAAAAPyNoCQA+NnFgXqewpb6eMDAvYvXwZVsbe7xsAAAAAAD0FIKWAOBjZX2y5fvTyzsM09c6PC9CZzsbK+k9HAAAIB20tjbKokVXWQ99DgCpgqAlACQpu0dxNyt3NvRqWQAAAJAobbJly+vWQ58DQKogaAkAPqa9hN80e3fv4Upf6/BImZZNrb1QOAAAAAAAegi9hwOAj2m7lW0hCZX6ekNVsyyqqAv7ucqGlp4vHAAAAAAAPYRMSwDwsaHF2ZIRklCpr4cUZ8uHG8IHLeuber5sAAAAAAD0FIKWAOBj2uHO0XsWdRimr3V4Tkb4XXgme3cAAAAAQBLjshYAfEzbrnx1eXWHYa8tr7aGnzKhJGHlAgAAAACgJxG0BAAfW1TRuRdwbeJycUWDTB3RMQPTKbRKOQAAAAAAyYSOeADAx6oaWyIOLyvIkC11bZ3er2/dlaWp1cgBAACQujIy8mTGjHfanwNAqiDTEgB8rKahLexwDUq6BSxtTy/a2YMlAwAAgB8EAgHJzMy3HvocAFIFQUsA8LGmNq0M3llzMOhaddzp8UU7rcAmAAAAAADJhqAlAPhYbVOr6/DqRh3uHtC0BYMiG6oIWgIAAKSytrYm+fTTX1sPfQ4AqYKgJQD42KLN9a7DF2+ul4kD87v8/JBi2rQEAABIZcFgq1RUPGc99DkApAqClgDgYyNKcsIO1052SvIit1u0rc69Ix8AAAAAAPyMoCUA+Fj/gizX4f3M8IGF7u/b7pm7tUfKBQAAAABATyJoCQA+tqHKPVNyoxk+vixyFfHPd0burAcAAAAAAD8iaAkAPjamv3v18DEDdg3f2kX176wAu3kAAAAAQPLhahYAfKyxzb2H8IbWXcMLcyLvxjMDkdu8BAAAAADAjwhaAoCPzV1X6zr8AzO8qx7Et9fTgyQAAAAAIPlE7sEBAJBQew/Ik+Xbml2Hq5om90xMW0tQ5L011TJ1RFGPlREAAACJk5GRJ4cd9mr7cwBIFWRaAoCPHT+2xHX4cWb4PgO7PjF9Z2VN3MsFAAAAfwgEApKT09d66HMASBUELQHAxzZVu3e0U2GG9y/oOmF+XWVT3MsFAAAAAEBPImgJAL4Wpvp3YNfw9VWdq46HWrK1SV5cWhnvggEAAMAH2tqaZOnS662HPgeAVEHQEgB8LFxHOxPKdw2vb/bW0c6NszfLltquA5wAAABILsFgq2zY8Jj10OcAkCoIWgJAEltf5V59PFQwKLLBQ1YmAAAAAAB+QNASAHxsUUW96/DFm+s9d8RjW7q1IW7lAgAAAACgJxG0BABfC9MDZHDX8HFl+bJn3xxPU7pj7jaqiAMAAAAAkgJBSwDwsYlhMiknOIb/33FDPE/vwY+2xaVcAAAAAAD0JIKWAJDkyvpky7gBuZ7G/WRjXY+XBwAAAACA7iJoCQBJ3KalbVBRlqfptbbFpVgAAAAAAPQob1e5AICEqGpwjzJW1e8evqW2Wf67stbT9DbUtFrja3YmAAAAkl9GRq4ccsh/2p8DQKogaAkAPlac554QX5y/e/j6qmYJRjHNDVUELQEAAFJFIJAh+fne2zgHgGRB9XAA8LGJA/Ndh08o3z18aHG2ZITpZNxNblYUIwMAAAAAkAAELQEgyYSGHLWq9xXTyj1/vqK6Je5lAgAAQGK0tTXL55/fZD30OQCkCoKWAOBjWvU7VNBU8XY6cWyJjCzx1uLHsq3unfsAAAAg+QSDLbJ27b+thz4HgFRB0BIAfCwvTFVutyreJXmZnqa5YFNdt8sFAAAAAEBPImgJAD7W0OLexU6jy/C9y/I8TXNHfWu3ywUAAAAAQE8iaAkAPqad7ITSHMshLsPHDPAWtIym0x4AAAAAABKBoCUAJJlglD2NhwqTvAkAAAAAgG8QtAQAH1tU4d5pzuLNnYdrL+KHjSjocpqba9vkxaWVcSkfAAAAAAA9gaAlAPhamLrcQffho/rleprqjbM3y5bazj2TAwAAAAAg6R60rK2tlT/+8Y9y9NFHyyGHHCIXXXSRLFy4sNN4zz//vBx//PGy3377yVe/+lVZsWJFTOMAQLIZVJTlOnxgmOF7D/AWtAwGRTZUEbQEAABIdhkZuXLQQY9YD30OAKkioUHLSy+9VOrq6uTXv/613HjjjdLa2iqHHXZYh4DjCy+8IKeffroce+yx8o9//EPq6+tlxowZsmPHjqjGAYBU7z1c5Wdnep62W2c+AAAASC6BQIb06bOn9dDnAJAq3FN1esndd98t2dm7L5oPOuggefDBB+Wtt96S0aNHW8M0oHn++efLj370o/ZxBg0aJLfddptcc801nscBgGTtPVwrggdDev8OF3DcVN3kedrb6lqsdjABAAAAAPCbhN6GcQYs7YxJO+ioampq5IMPPpATTjihfZycnBw55phj5M033/Q8DgAkKw0qjgmp8n30nkVhg41LtzZ6nvb7a2q7XT4AAAAkVltbs6xc+U/roc8BIFUkPHd81qxZMm7cOBkyZIhccMEF8uyzz8qkSZOs99atWyfBYFAGDx7c4TOaRanveR3HTWNjo1RVVXV4AIDfaGc5y0ICka9/Xh22E52DhnXde7gtiprkAAAA8KlgsEVWr77DeuhzAEgVCQ9aTpkyRZ5++ml59NFH5dRTT7UCl8uXL7fe0zYu7cxJp9zcXGlpafE8jpvrrrtOSkpK2h/Dhw+P+7wBQHetr2ruUDVctUXoRGfqiCKZUO6tAfbapjZWEAAAAADAlxIetOzTp4+VaTl9+nS55557pF+/fnLzzTdb7/Xv39/6v3Xr1g6f0df2e17GcaNtXVZWVrY/1q5dG/d5A4B4tWkpHtu0VDeePEKuPWaQ5IZ+MMQ7q2pYQQAAAAAAX0p40NIpEAhYWY92VW2t4j1s2DCZM2dOh/Hee+89OfDAAz2P40YzMYuLizs8AMBvtO3K6aMKOwQsr5hW3mUHOppxObp/xwz0UBtrWsNWMwcAAAAAIC2DlvX19fKLX/yiPUCp7VLed999MnfuXDnttNPax7v00kvlzjvvlGXLllmv//Wvf1nVx7/5zW9GNQ4AJKtxZXnW/zH9c+TGk4fJiWNLPH3uxHFdjxeumjkAAAAAAImUlagv1kxHzaocO3as1R6lVtHWquF33HGHnH766e3jXX311bJmzRrZZ599rPG1nUqtRr7vvvtGNQ4AJKslWxqs/8u2NckVz66T708v9xS4zMvq+r5UpGrmAAAAAAAkSiCoKY4Jtn79eqtty9LS0rDjaEbmtm3brKrg2dnZMY8Tafoa8KysfEuKi3dXxUTyW7q0KNFFAGKm1be//MiqDsO0qcr7zx3VZRXxt1ZUy+/f2hRxnFcuGsPaAeBrY8dWJ7oIAOBrra318s47M6znM2a8I5mZ+YkuEpASqqpqpKTkCCvJjiYF0yzT0mno0KFdjuOl3UnapgSQahZV7MqydNI7TYsrGuTw0ZGDlhMH7qpWHsmSLfUyrowTWwAAgGSVkZEj++9/b/tzAEgVvghaAgDCCZMMH+g6SV4zMbWCeFuEcd5cXk3QEgAAIIkFAplSXDwx0cUAAH8ELZuamuTDDz+UdevWWa+HDx8u+++/f9RVsgEAkeVmaWXwznIy3YeH0tHaIsQ3V+1oZBUAAAAAAJI7aKmByhtvvFGeeOIJqaurk0Bg10WzNoupbVKeddZZcsUVV8iUKVN6qrwAkFbWV7W4Dt8YZni0KmroPRwAACCZtbU1y7p1D1nPhw07TzIySCYCkBq67lrWuPDCC+Xoo4+WvLw8eeihh2TDhg3S3NxsPfT5Aw88YGVaHnXUUXLRRRf1bKkBIE3sE6Zdygke2qtUxXmRMzIbWlpjKhcAAAD8IRhskRUr/mY99DkApF3QcsSIEbJ69Wq5/fbb5dRTT5XBgwdLZmam9dDnp512mtxxxx3WOFpdHADQfdpJzl79OjaofuxeRZ7boZw+qiji+9sbRB5dsL1bZQQAAAAAIGHVw6+99lpP42kP3l7HBQB0bcYeRbJ8+zbZe0CufGdqWVQd5wSk67Yv75y7zRrr7En9WB0AAAAAgOTKtAQAJEbQdKSzR7/cGHr67rqXcXXH3G2ypZb2LQEAAAAASRq0vOuuu+Sb3/ymvPDCC9brf/7zn7LvvvvK5MmT5aabbuqJMgIArKzJ6B21V7HnceesqWE5AwAAAACSL2j517/+Vb773e/KokWL5Nxzz5W//OUvcuWVV8rMmTPl0EMPlZ/85CdWJz0AgPjxlivpTjMztQ1ML9btJNMSAAAAAJBkbVoq7YRHewn/4he/KM8884yceeaZ8vDDD8tZZ51lvX/IIYfIzTffLOedd15PlRcA0k6wW2FLka8f0F9eXV7d5Xg76lusKuJlfbK79X0AAAAAAPRqpqX2DH7CCSdYz4877jhpbW1tf620V/HPPvus24UCAHQWiKV+uIisr/KWQfnWqlr56qOr5MWllSx+AACAJJGRkSOTJ99mPfQ5AKRl0HLo0KGydetW63l+/q7OIAoLC9vfz8zMlECsV9UAAHfdS7SUocXeMyfbgiI3ztpMpzwAAABJIhDIlL59D7Qe+hwA0jJoqe1YarVw244dOzq8P3fuXKt9SwBA/GOW2+p2Vd+Ollb3HlacGdX3Pb1oZ9TfAwAAAABAQoKWP/zhD6Wtra39dWlpaYf3tROeX/3qV3ErHABAZNm2Rmsx/G9tXczVt2ubdu+7vXhs4U6yLQEAAJJAW1uLrF//qPXQ5wCQlkHLvn37yhVXXBH2/bvvvlsmT54cj3IBAESswOGcNbUdqm/fNDuW6tvRN91BtiUAAID/BYPNsmzZH62HPgeAtAxaAgB611Mu1bQ1cLnBY+c6tpLc6IOWjy8i2xIAAAAAkGRBy9D2LAEA8aXZlE8s7By0zAiIDImicx2Vnx19o+xBExzVcny0sY7q4gAAAACAXpMVy4c2btwoxx9/vHzyySfxLxEAwLK+qtm14/AzJ5ZanetEo7wwWz7d2hT1kn1swQ75YH2dld2pwdIrppXLiWNLWEMAAAAAAH9lWq5YsUKmT59uBS0BAD1naHG2FSh00penT+zYCZoXkwbnx1SG99ftClgq/X/jrM2yZEt9TNMCAAAAAKBHgpYLFy60ApbHHnus3HDDDdF8FAAQJc2m1MxGp+9PL486y1JNHVEYl+Wv8csrnl0XUw/mAAAAAAD0SNBy5syZcuSRR8ott9wSzccAADEKrYoda9VsDXTuNygvboFLzbiMvgdzAAAAAAB6IGjZ0tIihYWFkpFBp+MA0BtCMxq7k+F40UEDJF40cLl4M9XEAQAAEi0QyJZJk260HvocANKyI5433nhDTjjhBCkqKpI//elPPVcqAICVyXjT7M0dloRmOB44tCCmKuL9C2Lqey28YEiDmwAAAOh1GRlZ0r//dJY8gJQTVcrkgQceKO+88448/PDD8n//9389VyoAgNV7uN0Jjk1fPr1oZ8zTi6f6lta4Tg8AAAAAAFvU9bzHjx8vs2fPlvvvvz/ajwIAopCX5Z7J+PiinTG1J6m9kcczN/L9tbVxnBoAAABi0dbWIhs3Pms99DkApIqYGqccOXKkzJo1K/6lAQC021TtftIZDIpsiCFrUquUn7lPadyW8KzVdfQiDgAAkGDBYLN89tm11kOfA0CqiLlHnfLy8viWBAAQIqRuuKHZkkOKY2tk/YyJ8Qtaqr/O2ixLttAhDwAAAAAgvugGHAB8alCRe2DyoGGxdcSj9HM/mB7fm05XPLuOjEsAAAAAQFzF3JXs66+/blUR37FjR6f3brzxxu6WCwDS3tKtDa7LYO66OqtNy1gDlyeOLbF6IH/wo+3y/GdVcckH1V7OY+3VHAAAAACAuAQtf/GLX8j1118vBx98sJSWxreqIQBgl2VbG8MGCbVNy+4ECPWzV0wbKMePLZbLn13X7UWuvZx3t0wAAAAAAHQraHnbbbfJa6+9JjNnzozl4wAAD/KzA3Fv0zLUuLJ8OWBInszb4J7VGY14lQkAAAAAgJjatGxqapIpU6aw9ACgB43ul+s6/Ji9CuOa0Xj82PhkzD+3pDIu0wEAAAAAIKag5amnnioPPPAASw8AelBNk3vv4Xv2y4vr90wcGJ/pPfTxDqutTQAAAPSeQCBbJkz4g/XQ5wCQ1tXDb7jhBpk4caI8/PDDsueee0og0LEK45133hmv8gFA2goG21yHt4YZHivN2jxrn1J5fOHObk9r8eZ6OXwPTpYBAAB6S0ZGlpSXH8MCB5ByYgpaXnXVVVJfXy95eXlSW1sb/1IBAGRjdYvrUtgUZnh3nDGxVJ5YuNPq5Kc7qurjG1AFAAAAAKSnmIKWTzzxhLzxxhty6KGHxr9EAADL2AHubVqOCTO8u9mWFx/UX+6cu61b0ynOj6nVEQAAAMSora1Ftm59y3o+YMARVuYlAKSCmPZmRUVFMmHChPiXBgDQblBRjuvSGBxmeHeNHdD9ti0HFlI1HAAAoDcFg82yePHV1vMZM96J9TIfAHwnppSYo48+Wu666674lwYA0K6+udV1adSFGd5dQ4uzJaNjE8VRe+WzqngVBwAAAACQxmK6BVNZWSlXXnml1RHPXnvt1akjnvvvvz9e5QOAtLW+yr3tyo1hhsejivgV08rlptmbpS3Gxi3fX087xwAAAACABAUt+/fvLxdccEEcvh4AEM4+A92ra08IMzweThxbIgcOLZDFFQ3yu7c2Rf35LbWtsqW22QqAAgAAAADQq0HLe+65J+YvBAB4M64sXyaU58rizY3tw/S1Du9JGnAsyW+O6bOaoLmhiqAlAAAAAKB76OYVAHxKMxadAUulr3V4T+tO+5ZDismyBAAAAAAkIGj57LPPyte+9rVOw3XYc889180iAQDUq8sqXRfEa2GG90T7lt3tmAcAAAAAgF4LWv70pz+Vq666qtNwHfbzn/88poIAADr6cH19VMN7on3Lf58zSn52xCCJJnY5Z01ND5YKAAAAToFAtuy996+shz4HgLQOWi5btkyGDx/eafiwYcPks88+i0e5ACDt1TW1ui6D2jDDeyrj8vDRRfL96eWeP/P8kp09WiYAAADslpGRJYMHn2I99DkApHXQcq+99rKqiId65plnZI899ohHuQAg7ZXmu5909i3o/ZNRzbr82ynDPI27YkdLr7S7CQAAAABIXTFd+V555ZVy6aWXWlmVM2fOlGAwKG+//bb85S9/kZtvvjn+pQSANDRzdKF8sKFzVfAZexQmpDzaa/l5k0vloY+7zqScs7pWTplQ2ivlAgAASGdtbS2yY8cc63nfvoeSbQkgvYOWF110kTQ2Nsrvfvc7+c1vfmMNGzp0qPzpT3+y3gMAdN+gohzX4YPDDO8NRbneDhtz19cQtAQAAOgFwWCzLFjwfev5jBnvxHqZDwC+E/Pe7LLLLrMeFRUVEggEpLzce3tnAICuDS3OtnrvbgvuHqavhxQnroH1fQbmeRpvQxXVwwEAAAAAvdSm5Zlnnil33323Fai0DRw4kIAlAPRQJzjjynI7DNPXOjxRtIr4AUPyuxyvqcURaQUAAAAAoCeDlgcccIDcfvvtVi/hhxxyiFU1fP78+dF+JwDAgyVb6mXx5sYOw/S1Dk+kK2cM7HKcml7s4RwAAAAAkOZBy5/+9Kfy3nvvyYYNG+Tb3/62fPLJJ3LEEUdYQUztmEd7FK+rq+u50gJAGllY0eA6fHGY4b1FMz0PGBq5mngNtcMBAAAAAL0VtLSVlZXJBRdcII899phs3bpV7r33XikoKJAf/vCH0r9/fznppJPk1ltv7U65ACDthWs/coLHdiV70qnju+4ZPNEZoQAAAACANAtaOmVnZ8vRRx8tf/3rX2Xp0qXy8ccfy1FHHSWPPvpofEoIAGmqf0FWVMN70/qqli7HeXN5da+UBQAAAACQerodtAw1duxYK+PyzTffjPekASCtvLemxnX4nDDD/daL+I566ogDAAD0tEAgW8aM+Yn10OcAkCrinq5zwgknyEsvveRp3JaWFnniiSfk3XfflaysLJk+fbqcfvrpEggEOoz3+eefy7/+9S+r1/JJkybJJZdcIvn5+VGPAwDJZHu9e2c2O8IM7+1exPvmBWRHQ/hewj/f0bETIQAAAMRfRkaWDB16DosWQMqJe6blyy+/7Gm8trY2mTBhgjz99NOy5557Wu1kXnbZZfKlL32pw3ja2c+UKVNkxYoVVjBSA5OHH364NDU1RTUOACSbvQfkug4fE2Z4bzt4WJ+I72+qSnxwFQAAAACQBpmWf/jDH+L2xZpN+corr8ioUaPah02bNk1mzpwpV111ley///7WsKuvvlqmTp0qDz30kPX6vPPOk5EjR1qd/3zzm9/0PA4AJJv87EzX4QVhhve2MWV58vLy8FXVO+bMAwAAoCcEg62yc+d863lp6RQJBPxxrggAvRq0vOaaa2To0KESr6ClM2CpRo8ebf3XHsmVZkq+9tprHXoiLy8vtzr6ee6556yApJdxACAZDS12b5NoSJjhvW3qiEL5+3u79tdusjIJWwIAAPS0trYm+fjjb1nPZ8x4RzIzaSYNQBpWD9c2Jx944AFZt25d2Ed33HLLLVJUVCQHH3yw9XrNmjXS3NxsZU066WutCu51HDeNjY1SVVXV4QEAfrJ8W0NUw3tbWZ9s+cZB/cO+X9cSlCVb6nu1TAAAAACANAxaapuT//znP3ukINq25fXXX29lTJaWllrD6ut3XewWFhZ2GFcDm/Z7XsZxc91110lJSUn7Y/jw4XGfJwDojrnr6lyHfxBmeCKU94mc9bm4wh8BVgAAAABACgctzzrrLDnjjDMijvPpp59GXQjtbVw74Pnzn/8sX/7yl9uHazBR7dixo8P427dvb3/PyzjhqrpXVla2P9auXRt1uQGgJw0ucm/BY1CY4YkRvvdw1Rps67WSAAAAAADSNGiZk5MjZ599dsRxxo0bF3Vv4xoI1czHK664osN7mv2oWZcLFy7sMHzBggVWL+Fex3GTm5srxcXFHR4A4Ce1ze4BwfowwxNh4sDIbSY992llr5UFAAAAAJCGQcuPPvrI80Tnz9/Vc1lXXn31VTn99NPl97//vfzgBz9w7axHMzDvuusuqa6utoa9++678v7778v555/veRwASEZNLe5Zio1hhieqXctINta0ypba5l4rDwAAAAAgzYKWp556qpVl+frrr0tra2un91taWqxq3meeeaacdtppXU5PA4w6nmY4zps3T77yla+0P95666328TSgqe1T7rPPPvKFL3xBjj/+eCvAedxxx0U1DgAkm5ws9110bpjhfkW7lgAAAACAaHluGE3bqvzjH/8o5557rtXz9n777ScDBw6UYDAomzZtsjIxCwoK5Nvf/rbcd999nqqa33777a7vOTvF6du3r8yZM0dmz54tFRUV8pe//KVTFXQv4wBAstl7QK7r8DFhhieKhlAj5X5WNbb0YmkAAADSSyCQJaNHX97+HABSRSCoUccoaI/cWq1bA4TaeY1Wzx42bJhMnz5djj32WMnLy5NkVFVVZXXcU1n5lhQXd+yJHMlt6dKiRBcBiMlHG+vkJy+u7zT8hhOHyuTBBb5Zquc+uFx2NIQ/lHxv6gA5ZXzfXi0TgNQxduyu5n8AAAB6U1VVjZSUHGF13kw/KIkR9W2Y/Px8q6q4PgAAPScvK+A6PDfM8EQZXpojOzY1hn2/oopMSwAAAABAdJKrYTQASCNLtzS4Dl+21X14opy5T+QsygUVdb1WFgAAgHQTDLZKVdUi66HPASBVELQEAJ9aV9kU1fBEmToichMMq3f6q7wAAACppK2tST788ALroc8BIFUQtAQAnxpW4t7hzrBif3XEo7IjHE3qqR0OAAAAAIgSQUsA8KmpI/u4Dj80zPBEKskN385mFkcaAAAAAECUuJQEAJ8q65MtFx7Qr8OwH0wvt4b7zZAI2Z8Z/uo3CAAAAACQBAhaAoCPzdxjV3uRuZkBeeDcUXLi2BLxozMnlYZ9r7FVZEttc6+WBwAAAACQ3AhaAkASyMwI+DLD0mtnPIsr/NXjOQAAAADA3whaAgC6ratMyqVb61nKAAAAAADPsryPCgDodcHkWObrqyIHLVdtb+q1sgAAAKSTQCBLRo78ZvtzAEgV7NEAIAkEfN6ZzdDiyFXXKxtae60sAAAA6SQjI1v22OPSRBcDAOKO6uEA4GNJkmhptbdZECFuua2upTeLAwAAAABIcmRaAkAS8HmiZZd3wepa2nqxJAAAAOkjGGyTurqV1vOCgj0kECA3CUBqYG8GAOhx9S1dd9YDAACA6LW1NcrcuedaD30OAKmCoCUAJEH18Ja2oO+Dfn1yMiO+v6GLznoAAAAAALARtAQAH3tnZbX1v6ElKF99dJW8uLRS/GpIUeTOeHKzkqGSOwAAAADADwhaAoBPaWblvR9ub3/dFhS5afZm32ZclhdGDlou29rQa2UBAAAAACQ3gpYA4FPrq5o79R6ugUu/VrMeMyA34vuPL9jRa2UBAAAAACQ3gpYA4FNDi7M79RqeERAZUhw5ozFRpo4sjPj+xprWXisLAAAAACC5EbQEAJ8q65MtX9u/X4eA5RXTyq3hfqTl4qACAAAAAIiHrLhMBQDQI2aMKrLatSzIDsgdXxzp24ClLTdLpL4l0aUAAABIH4FAlgwf/tX25wCQKtijAYCPbatrbs+y9HvA0i5nJEu21Mu4svzeKg4AAEDKy8jIlj33vCLRxQCAuKMmHwD41ItLK+XqlzZYz2uagtZrvyvKyYz4/gtLdvZaWQAAAAAAyYugJQD40JbaZrlp9uYOvYfrax3uZ6X5kRP4l29t6rWyAAAApINgsE3q6zdYD30OAKmCoCUA+ND6qmZpc0YsRazXG6r8HbQcWZoT8f36FnoQBwAAiKe2tkb53/9OtR76HABSBUFLAPChvCz3xiFzwwz3i5PGl0R8Pz8rcvVxAAAAAAAUQUsA8KFN1e4ZlRU1/s601E52ciIcWYryOOwAAAAAALrG1SMA+FKYjMqgvzMt1f5DwvcOXt1A9XAAAAAAQNcIWgKAD00cmOcatpwwME/87rBRhWHfq6NNSwAAAACABwQtAcCHyvpky/enl3cYpkHMD9bXid/VNIX0IOTQ0Bz+PQAAAAAAbAQtAcCnDhxa0OG1hvtumr1ZttT6u13LfSJkg25vIGgJAAAAAOhalodxAAAJsL6qc3CyLSiyoarZysT0c2c8kSzZUt/lOAAAAPAmEMiUIUPObn8OAKmCoCUA+NTQ4s6ByYyAyBCX4cnk/bW1BC0BAADiJCMjR8aOvYrlCSDlUD0cAHwqNJtSA5ZXTCv3dZalLVIf5x9v8n+7nAAAAACAxCLTEgCSxL/PGZUUAUtVlCNS1eT+3rKtjb1dHAAAgJQVDAaluXmn9Tw7u1QCgUi3jwEgeZBpCQBJIlkCliozI/zhpaFFfN+ZEHqetm36+MId1n8AABC7trYGeffdY62HPgeAVEGmJQAg7saX58q7a8IHoxZvrpfD90ieICzi64a3N8mry6vbX88Y1UdOGV9qteOaTMF5AAAAAD2HoCUAIO7GDsiLGLSsqm9jqacYzZ7VHu/dAo/O95Zva+gQsFTvrKq1Hna7rSeOLenl0gMAAADwG4KWAIC429lFUHLFDqouJXsg0unFpZVy46zNEjSvv3FQfzlnUj/r+d3ztspDH+/w9H1tQZGbZm+WA4cWkHEJAAAApDmClgCAuCvNj9xksmbbwZ9BykUVum6CsqW2Re76YJsVSFQzR/WRsyb1lf4FWR0Cme+tqZa/ztrcYTp3zt0mtU2t8vaKGllf3RJVGfT7NlQ1E7QEAAAA0hxBSwDwKc1ec3p0wfb27DW/G1KcG/H9rAD9wPnNYwu2yx1zt4V9/+1VtdbDplW5x5XlyuLN7r3BP/Txrl5MY5GbRa+nAAAAQLrjqhEAfJrxptVkQ7PXNLCUDCYOzIv4fpZGvOAbj3YRsAyXERkuYNldjS12RXMAAAAA6YpMSwDwIa1+a1fLDQ1cHjG6yPdVZ7V8elcsXMuWVY3RVRlGzwTG31tdKws21cl/HRmUfvCfT3fK5MEFiS4GAABJIRDIlIEDT25/DgCpgqAlAPhQXpjqsRrHTJb2/vYfkicfbHBvu3LtToKWiexMR5seCG2H0k+0J/ElW+plXFl+oosCAIDvZWTkyPjxv050MQAg7ghaAoAPNYSpHquhzCHF/g9Yqq8dMEA+2LDO9T1Clr1DmxPQ7Nyg2XYuPqi/HDm6yNcBS9vlz67r0As5AAAAgPRC0BIAfEiz4rTZx9Aq4hrESYYsy0iBV0XFpZ53w9sb5dXlNe2vdW1oAPPf86JruzKRtLwabD2bwCUAAGEFg0Fpa9tVuyUjI08CAdoOB5Aa6IgHAHxIA5NXTCvvFLBMpuCNBl7DyeDo06POfnB5h4ClU2O4hkZ9SjsI0iruAADAnQYs33lnhvWwg5cAkAq4bAQAnzpxbEmH18lWTVYDr2Ga5pRMjj495rh/LZPKhtTqfXtxBRdgAAAAQLrhshEAkoR2TJJshpe6t0IyuIjWSXrCqfctk1T00UZ/9W4OAAAAoOcRtASAJKEdk9zw9iZJJv0L3KuIDwgzHNHTqtMfbayTxxZsk4YU7eFowabkC9gDAAAA6B5SXQAgiTIrX11eLaeML5FxZfmSDHLC9LiTTU88cdk+/vm/LbJoc6Okuh11KRqNBQAAABAWQUsA8KnHF+wI275fsgQtN1W7B5sqwgyHN5pxqwHsdFFDPzwAAABA2qF6OAD4tMrv26vc2/EbXJw895vC9MMjgXBvwFOGZToFLFVqdSsEAAAAwAuClgDgQ+urwqeWNSVRkuJe/fPch/dzH47dbVTqfzdz1qRGpzRHji6UPUq9tW3KyQoAAJGPlGVlR1sPjpoAUknypOsAQBoZWhwhmBNInryzEX3d52N4mOHp7sWllXLjrM1WZqEmo565T6mcMbFUyvrsXl6rtjeIX00amCcLKsKXL3SeNDD75UdWdTndXNpABQAgrMzMXJk48XqWEICUk/CgZVtbm7zyyiuyZMkSOeuss2TYsGGdxqmvr5eXXnpJKioqZNKkSTJt2rSYxgGAZKEBnW8c1F/unLut03sTypOjPUv1zkr3rMBZK2vl7En9e708fqYBPDtgqfT/4wt3yhMLd8rFB/WXsQPyrGD2u2t7viftvfrlyF79c61SDC7KlsLcTKlpbJUHP94hja3unzlsRIH8+pih1nzMWV0r66qapDQvQ4aU5MjAwmxpbAnKkOLsDgFYff6FvYvlhc+qIpYnk1RLAAAAIO0kNGj55JNPyo9//GMZNGiQvPvuu7Lffvt1Clpu2rRJDj/8cMnJybHe//nPfy4nn3yy3HPPPVGNAwDJZt9B7sHJbXUtHQI/flbT1BrV8HT21KKdrm036jC34HVPunxauWtnT1OG9pHLn13n+pnvTC23/uu2ecqEUs/f9eX9+nUZtCzMI2oJAAAApJuEBi1LS0vl1VdftYKNw4cPdx3n6quvlvz8fJkzZ47k5eXJJ598IlOmTJEzzjhDTjvtNM/jAECyWRimmm0y9R5+4t7Fcsfc7Z2Gn7B3cULK41eanahZlX4wY1SfsNuXDj92r6JOHQH9YHp5zIF0/dxxexXKK8trwo7TNzc5gvQAACRCa2u9vPPODOv5jBnvSGZmcpwnAkBXEpq6cNRRR8no0aMjVh1/4okn5Otf/7oVjFT77ruvVfX70Ucf9TwOACSjYLDNdXhrmOF+NGlQQVTD09UDH3UO7CbCeZNL5RdHDYk4zo9nDpK/nTJMvrJfX/ne1AHywLmj5MSxJd363h/NHCz988OfkpREeA8AAABAakp4m5aRrFmzRmpqamTcuHEdhuvruXPneh7HTWNjo/WwVVVFrpoGAL1txfYm1+Erwwz3o3A9XWvbjbedMbLXy+PXLMuuqkf3lJ8dOTBse5ORaMZlvLN9rzpisPzkxfWu71U3JE+gHgAAAEB8+Dp1obq6ur0auVPfvn3b3/MyjpvrrrtOSkpK2h/hqqcDQKL0y3PvMrlffvJ0pZyTof1Fd7ZiR5Ms2dLzHcokg/dWuwd2e1LAVOk+fI9iK/g4eXBBwttJ1U6GwqkK1/sPAAAAgJTl60zLgoJd1QdDg4+aFWm/52UcN9dcc41ceeWVHcYncAnAT8ZYmWyVnYcPSJ52ioaUhA9Evfl5VdK0zdmTZq8Jf4MtHo4aXSgn7l1iZVKqDVXNUWVV9hYtz9CiTFlf3TlAWZpEgXoAAAAAaZBpOWLECMnNzZXPP/+8w3B9PWbMGM/juNHPFBcXd3gAgJ8MKnK/rzQwzHA/mjgwfFByVRJVc+9JH21w73ApXi4+aEB7JqU+/JBVGU55kXu5sjPdM3YBAAAApC5fBy2zs7PlC1/4gjz44INWhzt2G5ZvvfWWnH766Z7HAYBktKm6xXV4RZjhfqTBsZJc9/cqapsl3d3w9iYJ9uD0pwzO922A0s24sl0d6oXaO8xwAAAAAKkroek6ixcvlldeeUUqK3dVf3z88cflo48+kkMPPdR6qOuvv14OO+wwOfHEE+WQQw6xgpMzZ86U888/v306XsYBgOQTJpwV6MkwV/z1K8iSysbOgda8rPTOntM2PV9d3rNVwy88sL8kk/pm9227IcxwAACgMqRfv2nJkJcEAFFJ6B5Ne/1etWqV7NixQ6644grJysqyXu/cubN9HK3ivXDhQjn++OOt3r5//etfy0svvSSZmZlRjQMAySZc1eoJ5cnVDuQxe7k3v3F0mOHp4o04Biz75nU+nB+7V1ESthkaLjhJ0BIAgHAyM3Nl331vsh76HABSRUIzLQ8++GDr0ZWBAwd26DQn1nEAIJks39YQdngyVfkt65PjOrw8zPB08fHG+PUa/u2pZZKTGZBXl1VJn5wMOWlcaRIGLEWmDCmQpxdXdRq+35DwHesBAAAASE3J05sDAKSZt1fWuA5/Z2WNTB1RJMliQ1VjhOHJMx+91WZptAIm+1YD2cm0XbhpbHHPqGxqJdMSAAAASDc0eAEAPhWuzcdkawvyow317sM3ug9PFxlxWo3fn16eVJm3kYVZKMHk2uYBAOhNra318vbb062HPgeAVEGmJQD4VEme+y66NMxwv1pf1eQ+vNJ9eLoIxCEOp5M4cGjqVJ2eODDPmqdgaCbpQHoPBwAgkrY292aFACCZkWkJAD61d5l7Q+pjwgz3q5Jc907RisMMTxctbd2fhgb3NlQ1S6rQjFHNHE3dTFIAAAAAXhG0BACfys92D+oVhBnuV1PCZAKGG54uWqMMWk4dUdCp8rRWMR9SnFoBvRPHlsjMUX2s58fvVWy9BgAAAJB+kquOIQCkkU3V7tWnN1Y3yeTByRPwG1joHlQbVJRawbZobKltluYu+pb52ynDpEI76wkE2zvaeXFppdw0e7O0BXcFLK+YlnpZiDqPb6/a1bP6y8urZMKgPAKXAAAAQBoiaAkAPrV0q3uv28u2NsoJYyVp1DS6pxTWNMShfnSSem/1rqBcOEOLs2RcWb6MK+s4XLMOtQ1LrRKuGZapFrDUYK4GZZ30tc5zqs0rAAAAgMioHg4APpUZpqOWA4clT5almrvePUD3fpjh6WB7fUvE979+QP+w72nwTjNtUzGIt76q2coiddLXqdRuJwAAAABvCFoCgE8zzp5dUtVp+Jj+OTJ1RJEkk3VheglP597DDx2xq83GcLQ6eDoaWpxtVXtP9XY7AQCIr4CUlOxvPfQ5AKQKqocDQJJknKn9kqgtS1vA6uPaTReNOqZwQLqhJSh9skRqXRIuywoyUzKL0gudb22n88ZZm62tQy+7UrHdTgAA4ikzM0+mTLmdhQog5RC0BACfZpwFXMJ6TyzaKadPLE2qIE5DmJrQNWmYaOnsSCeckaXJs257grbb+dGGOnlzRY0cMbqQTngAAACANEX1cADwIQ1KnrlPaafhydi+X2aYI01LcFfWYbqwO5mJFLBUDa3pmYHqDOxqwFLpf30NAAAAIP0QtAQAnzpjYuegZTK27ze6X27Y9xZvrpd0r/IfakJZnqSrcL2Hp1NwGwCAaLW21svs2cdYD30OAKmCoCUA+DjbcnxZboeAZTK271eclxn2vQ1p1BmPXeW/K5nhuo1PA/QeDgBAbJqbd1oPAEglBC0BwMeGFOdY/08eVyz/PmdUUrbvN7gofJD1rRXVkk68VPx+6/P0WiZO9B4OAAAAwEbQEgCSIMilwctky7C0DSrcFXh1s64yTC89KWhRRYOn8ZrTuE1Lu/dwO9eU3sMBAACA9EXQEgCSQDJXGK6oDl8FvDmN4nMfb6hN+XUdD5pNfOr4XRnFx+5VlJTZxQAAAAC6j6AlAPhYMAWCep9tbUx0EXxh1mpv1b4bW9sk3RXm7moHNS+b0xQAAAAgXXE1AAC+titqGUji9LvNNeF7fs5O4vmKVqXH2G2/guRsBiCeAikUtAcAAAAQm6wYPwcAgCd1TeEzBwlKdXbwsIK037JqzTazrqpJttQ2J217rgAA9I6AFBVNaH8OAKmCTEsA8DE7qJfMp5/FeeFLTyJdZ2MG5Es6e3FppTy1eKf1fP6GevnKI6usYQAAwF1mZp4ccMB91kOfA0CqIGgJAEkhecOWEwf2Cfte/4L0OAwt2VLvedwJA9P3YkOzKm+ctblTYPum2Zut9wAAAFLVxspWeffzRus/gF2oHg4APpYKmYgnjS+Rl5e7d0IztCRH0sHzn3rLFJw2oiCtq0Kvr2p23ebbgiIbqqgmDgAAUtMjc+vkmierrHOejIDIdV8slnMPoskgID1SXAAgySVzRzzjyvKlNEwV8bY06Sjbc8/hLWmyQMLIywq/oedGeA8AgHTW2tog7713ivXQ50gumllpByyV/v/pk1VkXAJUDwcAf7ODWDWNyV1NJCfMLbKFFQ3y0ca6lK/663X2Fm9O7wuNTdXhF1RjSyrkHQMA0BOC0ti40XqkRj2d9LJya0t7wNLWGhRZtbUlUUUCfINMSwDwKe18ZM7aOuv5v+dvT+rOSLbXuZ9Aayj2Jy+up7MVIztT0hyZlgAAIL3sMSDLqhLulBkQGTWA1vwAgpYA4EOaeaidj6RKZyRdBeN0/rQDlmSdv67089i3Tlmf9GjjM5yJETohqqgm2wAAAKSewSWZVhuWTr//YrE1HEh3BC0BwKcdkoRWE7E7I0lG48q6jtrp7C6uSM3q0VNHFnkab0BBep+caidEh41wb3R+fVVjr5cHAACgN4R2ukMnPMAuBC0BwIeGFmd3qiirr4cUJ2fP0pOH5Hsar6oxNbPpskPr/IQxuCi9My1VeaH7Nl7ZkN6dFAEAAADphqAlAKDHDSnO9TRecW5qtt0zrMTb/O/Rn6BldpgzkyzOWAAAQIp6ZG5dxNdAuuISAAB8Wj08tOsafZ2s1cMjtVXoNLAoNYOWY8u8BS1Xbm+SdDdrVXVUwwEA6WljZau8+3mj9b87n/94bVO3puMPASkoGG09InVql+66u830FC3PNU9WdRj20yerfFdOIBFS8+oQAFKgerjWKHa2a6mvk7V6uLZV6MX8dXUyrsxbVfJk8uH6Wk/jba9Lzerx0dhY0xbVcABAatAAzcqtLVZPys99XC9Pz6+TsQOzZf9ROVJZ3yZNLSJHj8uVycNz5Nf/qZR73q1vPz/STky0DUANQD4wp1bWbG+VsqJM2XtQltUD8wEjc6xOTfT91z5tkKWbWuTlxZ1vFO47NEP69cmUYX0zJTsrIKMHZMkxE/LaO0TRMn6wqkkCAZHhfTOltikofXIC1n8td6I6TsnMzJODD340Id+dLDRzUQODem7t3Gb8QLf70LbsW4Miq7a20BkP0h5BSwDwaZDvimnl8tdZu3oQ13vm+tpr8C9ZvbO6Ws6b0l9SzUKPHQz1K+CwDABIzWCkW3DPfu/d5U3y9zc73+BbtLFRnvpod0dsf3u98zga7Lnqiar2gNRuLfLsJ9F14vbJer1Bpo/dNVt+8Uy1jBuYIcdMyHctoy2egTBnAJcepOOXyWhvH/pfMxlnjs31xfLV34abgjDDgXTC1REA+NSJY0tk1spqmbu+Xr5+QD/rdTIrLwjI5rrQSu8dLd/eLC8urUz6eQ2V5fGccz+PHRYBAOB3mtX4x5eqZPbnnWsRDC8VyczIkFXb45dFH5qpFk9LKtpkSUXkWhN28PTvb1TL16b2kdzsgGytaWvPDvUaiPRzRmCy8nsmowbz3dSFGQ6kE4KWAOBjOab3kaLcxJ9Qdddho4rk6cUd2+txc9PszXLg0IKUyirN9Ri1XLipXqaOKOrx8iSrJVvqU7L5AABINqEBuNc/bZA3ljRIc3NQ5q5ulLU7RFoixCPX7tS/qdnsx9odQfndCzURs0OLcnd1MDdqQKZ896giOXp8XnvV86ufrJJglBmBra0NMm/e16znBxxwn1VdHLvpdqpnYs4QoFbx16YD/FK+0Gah1Cfrm2Xqnt7aRQdSlT9+pQAAV43mjL+6MflP7I/as9hT0FJP2LTDoVQKWnptFP+TTfQUGcmbn1cTtASABAcqn/qwXh6bt7vZk8zArqw1eFdtaq3vWNsqF99rRXDD8pYRGJS6uhXtz53riyrmYReZb+i6veqEQrnuxd3BbvXHF2vk1Mn5vsgGBRKFoCUA+JRWk/5g/a5G5u+Zt01K8zOTutq0ZsgNLsySjTUtcctMTDV98zgs52eK1IfpLPOdVTVy2aHlvb1akKS4YAfi99v559s1cp0je9CJgGXPizYjkCrmHen2HBqj1Nd+qR6uJg3rfLPeT1XYgUTh6ggAfGhLbbNVTdp5YpUK1aavOXKQXP7sui7Hq6hukXFlkjLysr0FYfvk7GoOIJ31L8iUddXuUcutda1UEYfnC3ZtW872hYk5cstX+7H0gC4ClQvWN8v1L9a0V1MdVqrVmOPb9iSit3hDs+esyY2VbXL1E9XtQTpdl1f7qNMZv1S/zvRR9XC7jKH07NFPZQQSgV8AAPjQ+qrmTu3apEK1ac22PHavInl1eXXE8aoau87GTCZLNnvrvbSiNrXmOxZ5XWTZvvBpJVXE0WXwxRmwtLabRU2y37UV8tGvBrL0kDLZwY99UCcvLayXE/bJl7MPLPDUMc7T87UZkoBMGJIta7a3yPbaNvlgVbN8VuF+s2hdCrc9mUycVcjPmpIrPzyh2Gr/cmd9m/QtyJAhJS3y6bZJkptZLx/MbeycVRgU+XB1k5y0b3q2C62/J+3QyHls+P0Xi/0fxE3PikdABwQtAcCHhhZnd7ojrK+HFCdvwNL245mDugxa3vzeVrn3w63SNz9LxpXlyUnjSn0TqNIsWA0q6zryEkDWzmNWVzZ7mjZ5liKl+dkiO8IHb99fp9vOoKjWGdLL9Ou2uA7fWR+UO96ukW/OLOz1MgFd+dPL1fL3N3d32HLNiYVy6eGF7cHMx+fVyYotrXLyvnlWwPG4v2xpbxfx9SXN8qdXquQbMwrl4FE5Vk/V9ufswNbD79fJog3OwOSu5meQfB6f3yiPz3fbz11n6uY0uX5OA9TpTHtgdwYt/dYju96wEJdgM9XDke4IWgKAD2kw7Og9O2Yk6utkzrIMPfh0lVNY1bgr43L1zhp5eVmNDCzIkJmji6S5VQNbGTKkOEcGFWXLpuoWqWrYNbWV2xtl9ppqaWgSGdUvR0rzMmXFjkbrHH5gUbY0tgalb16mZGbsylqdODBfzt+vv+flqu2MajV9DSZrEPmKaeVdtjP64PxtnpfLhkr3C410csqEEvlgQ/iL6UpvSatIU2N/uknCNIlquf4Fgpbwn8vu3yEvLuy4c9MOOd5drr1wt8mKrbu36qfm7+4Ax6miSuR3z+9uczK0p2Ski/Cpedpbdro3GxL6OhGBy3AZ1W7Vw/1WhR1IBH4BAOBDms33+ucdsxH19dcP8B5g87Ox5bmy2GOVaVtFXZs8trDS8/hLtnYMAG6u6/x9q3ZWy/OfVUu/vICU5GfK+PJ8Gd03V4rzMq2Api7r99ZUy9x1ddaJ4zOf7r5Dr4HLv87aLA/M3yoluVmSkx2Q48cUy6i+ufKGte6CVo/pn2zyns1S1cgl5tQRRSKyKewyyvF5TS70Lq0eq9VdszMC8tayrjOaaYABifL6pw3y73drrZjSV6f2kaPH57VX2Q4NWNr+62GbDoejCULd+U6NfOXQPmnZ+ZmW7eqQZkM067K32/nUjOdrnqqyMij15rdWWbcDp6HlCCRLFXaghxG0BAAfStU2LW3fOqTMU4c8vWV7Q1C2N7TIyh0abKyOKlNlc22bbK7dFSBdVNGxutbTizueIHelhavMLpe7nugjPdlVXTVbKD87IJc/tFPMTw9oDwy+saRBjhqX1x4U9INDf18hm6p277zeWrpT9hmSKUNLM+XlxWzEbqYMy5Tykkx5eVHH5TOkWKSkIENqGoMydY8cWbChST7d1PHAMLY8Q8YMzJbnF7gHg7VzoSkjcuX0Kfny7Mf18sxHu9uAnDIiSw7ZI1uK8jLksbn1KdUB0aptQZl23SaZfU38m1jpqd7KNaj//qqmDs0exGLe6ibX84q/v1Ejvzsjco2ZeB6/dBk5O0j6aYQOknS891c2+a4aO9DbCFoCgA+lcpuWStun1NOzSNU4/aC342MFqbF6uy03U6QhzMbR6PeNBj3ioru3yxufNcXt4pHMldTzxVu2yodrduXSPvC/Btl/RJY8+e0BCQ2uHzAyR2b+YYs0uxxMFm5otR7o6DtHFMiPTyjusDznmeW5/8icqH67P69stdoDtKvX2s+d09Dg9lUntrq+950ji6zv1/fW7WiVJ+bVSVVDm+w/IkfGDd51wH51cYP8d2lzh9oATT5eresrRSb+YpM8eEm/bgUB3bIYvfRWHpqN6fb6189UytvLmqSlVaTZETM+c/88+fM5pTFleIa74fnQ+/Xy3aMKe/SY4Azohmp1tFmpAdpQT3zYIF+b2hRxXfk5wxWIB4KWAOBDqd6mpfrlMYPkV6+Frwacjs7br1+ii+ALOVnhg5Z0VpR8ou28yvm5V5dWyT3/2h7X8jz5YZ0VjEBqZVjaAUubvv7zy1Uyda/cThfzkS7y9TOPzauTAYWZ8oNji7rM2NRpaeBqa02brN/RIk98SMO7tn75Il+YnCfbaoISDAZlR22bDCnNlJY2kaUVLR16LL9kZoFcOK1Pp/Whr0+eHFtHfPpZ5/TCBXRCxwv3nlsP7Vrd2g5s2kFP52v12uIGeXlhg3y0rllqfLB51DaLnPaP7TKqf4Y8dEl/a1h3gl5uWYyhvZXrMrn59RorSGiPW5y7q/1yZ1BSg3Th6Ht52Tvlu0cVtQf5bn+7Vl5Y2Oha3drpwFHuQb+2Hu7oJjSg66YgZ1djo9e/5N5J5TMf1YcNWvZUhivgJwQtAcCHUr1NS7vtwjH9t8uybVSNUyW5GXL2pF0XD+muX362VDW6t+WWOhX10sPd87bKwx/vaL9gO2rPPnLxgWWyra5F5qytlX75mTJ1RKG1X1uypV6eX1Ipq3c2yqbqZtnZ0DO5zi9+Uk/QMoVoUOBnT+10fe/mN+ush7r+zGIZUJgh1/6nUtbs2L1tnb5frozonyWTh2XLN+7d2b6tbqpqlYvv3Sl980Ve+H5Zh6COtqX60sJ6ycvOCFsFOR1p1e2RA7Jk+phc+eL+BV0GgkKDfcnKLTjqfK1tmOpDaaDtjU8bpKx41zg6/xpQKy/OlHNu22Z1vNRbVm1rk6nXdWzWZmCRtqNYKvk5gU7ZkPVNQZm1TIOKAatqvR1IW7XNvbXgt5c2Sl52QB74X528saTzuZ4zYKkiBSxtmkWtD21nXLMUnTRwp+1UVtW3yTdnFnZ4T+fjmhMLrQ6uQkXT0U20WY3hqqU7aRZveXGrvPu5+3lPbUNb2LI4Mzi7qm7e3XkBEoWgJQD4UKq3aWn7x2kj5Ya3N8qryzufRKaDmaMKZGttq0wb1YeApcOgwkxZtdP95D2YIlmEqcI5/0qfz99QK89/ulOqXO5HvPF5rfVw+vt7W6U3fbqJ0HcycF5Q//zJnfLWZ82SnSVy4j658pMTd3VOoVlGGqTwItx4T3+kkZPwgccd9dIhsJOVIVamYDqbOCgg2VkZkp0ZkD55ATloVI6nIGU0GY6pSgN94bLm3rmq3MoadgbPe1tFtVjBelu4pnzufndXJ4OarZkfJqLwyAcN1qMnhAYsnX73Qo310CZ3jp+YK0dPyJPhfTOtQKybzVW7mwxxNu+gn6ltCrYH9f75do384cWa9qzOq04olEnDsl2DfvZ07njbPXvS6dG5dbK9LvxOpalN5N3PG6VPTqBDeXT/GHqt4KxuHknovlNv6pChCb8KBDVXH1JVVSUlJSVSWfmWFBd3vDOD5LZ0KVXAkJyBgK8+uqpTm5b/PmdUSgZGHluwXe6Yu03SzSsXjUl0EXzpl6+slTnrwl/ofPOg/nL2pMRXpX9xaaXcNHtze7WsK6aVy4lje75B/97eFy2q0HURlIkD89sDk/XNrfLUokr5aOOuC9dkc9cFpb7qqCVZxKtTDLfpvvZpg6zb3ipbatqsC+7H5zVEDNxonITe4HueM0irIZ+rTyyUSw/nWimZ2vFFZOccmCd/PKtUHtKevZ+MrgNF+3dx3IQcCQQCMmlolqza1iqPzeuZYK2tJFfkqPG51k2X0P3kj4/rY2U72wFOFdpuaGiGrXrvmrK0u4ngRVVVjZSUHCGVlZVSXLy7vV30HoKWBkHL1EXQEsnqhrc3dWjT8ti9iuTHM+Pf46OfgiPfemq1VDcF0ybL8udHDU10MXzp8mdXy5ItkS/WHji3dwL44TIpdfhXHlnV4WJBA5c3njxMGlqC3c68DP1eO3hY1dAixXmZ7QHE91bXygframR7Q6vs1T9X9htcILlZAZm/od4KLI4ZkNde/To0CKnjvbu6VrbVNcsefXNlYGG2LK6ol8Vb6iU3K0MamoNSUevjHiW6IS9T5JFv7eqIIpWryHU1b3Y20M76NulbkGFV/7zljSpZv3NX27Ij+2fI2IHZomlHn25oliWONgjLikRO3jdfRg/IkmMm5HXZ4Ybbd2qbd0/P73zRjd6ne4h+RWJV59WOZnSd2h3fpEo17mSjy107/7njnVqp9HR/SH9J7tmECC87Q+Scg/KsauepTkOYffJEKl1mtTRf5Denl7R3Iub333pvHbsJWiYeQUuDoGXqImiJZJRumZZO762pllvnbJZNNald/44sy/B++vJa+WB91xcPV04vk6VbG+WgYQVWG6mx/tY08LdgU53saGiRwUXZctjIQsnPzpR75m2VxZs7VxvNy9rVBmlFbdfbqHYc1C8/Q8aU5UpBdqaM7qfZGBmyanuDfLyxXkryM6W5pU02VrdYVc6ysvQzgQ7B+3DV8xBfeqn/hzOLrfbA9EIotCpeVxId/Az9fq3KeN0LNR3abtxrYJZsq2mzAo2frGuOazZQbpZIcZ7IjprOGZCFuSKtrSL1pEbGle5fYjlSXnBonlx7einByCTi1tnPa6YDqH2HZUteVrPM/fBHcuvHV0ljGx2xoPuKckUuP7pQllU0S1VD0OqIasKQ7Pbq8/EIbMZ63IylA6JYv4ugZeIRtDQIWqYugpbwq5eW7rSynA4b2UdOGFva4b2PNtbJT15c3+kzN5w4VCYPTp+TUQ1gvrOyxsoIW7W9SZZva5RGD1do+w7MlVMmlEpOZsAKSC3f1iDVjS3S1ibS3Ba0Gn9PZEiUgGXXv42/zOpcdclL0KkoR6SpdXfv49kBkZL8gAwrzpWSvAzpW5AlR+25q3rPn/67SdZUEUVJxR6LP/zVIBl19aa4TVPbRsvN3lVVtk9uwOpZujA3IMP6avCgTf63sqlDr8DlhSLT9sq1LvQ+29QktU27Pqs0HK1twGVmiNQ1i9Q0iOTniPQvzJB+BRkyY0yujBucLR+tbZLyokyrvbWXF2mnLwFpbRNZu71N6ptEcrJF6pr8184reka4QKPdsUtja1D++1mTfLqptX1/+E1Hj9xkS6a21tZ6eeedGdbzioLX5KH3m2TJpjZp4BCHXqA3c/WxM8J9MHMItDpR0hu0ek7e6Lgje+TYbCkt2DWWHlf16LZXeZZ1Tqc3EYeUZlqdPu03PMdqj9OZ2KHTnHV1+Ort3ellnaBl4hG0NAhapi6ClvAT7R13YUWDPLlwh2yt232kHlyUJfeevUf763TOtIyGLiftnGiI6QTEfu5lGWlA9NVlVdLS2iY76tvkswi9mOdkaK+oWTK6X65srG6WxpY2GV+eb71WWqX2vytqu2xfrThH5PGv0I5lV8IF7YFIQi9E9rt2k+xMziY3kab2HZIpvzmjpL3Zgnkmo8mupu0Fwcn05AxazpjxjmRm7mpCRDv2+eur1bJwA/n6SH26l9SAaL8+IqdOLpAvTMqT5xfUy+1vdzwZ6CrI6UTQMvEIWhoELVMXQUv0RuDs1aVVsraqSSYPzpemFpF1lU3S3NYmFTXNkmWaF1q6rUm214fP7/vmQf1kzID89jbs0q1NS78EQDdWN8knG+ulb36mjC3Lkwnl+VEHinVai7X9wcYWqahulu31rTJjj8KYqzCnG7egPeCsavyVQwrkoD1y2jPOCnICndrb04t1Zy+4QKJp9fmqkEwkPUe46bySqAKTgFvQcs6cU6znhx76bHvQ0hbaXASAXfQMP0+bMAnu2kfvWZYtQ0szpW+fgOyoC0pmS5384bxT6YgngQhaRhG07KkeE9GzCFoi1s4vlHZYsaGyUVbuaJJ1VU1WELImgZ059lbnI4Afeub+66zNiS4GfOaaKHsujmcVcSBWfXJEFv3frpuOd7xdI/e/VyM52RlyycxCq504oDdoFu7Nr9fIg++Tgg541dZYJ2tvPIegZQIRtPQYtPz2AzvkhQW7Gys6cZ9cufUrfTsdCO6eXSPz1zRbve59fVofa7jd4Kv9XNtkWLO9tb2nxmTonSvenA3hqmgavNfPvmoanj56XG6XAeRIQUu33lnDvVZdPbeDSXYV4H0G5sm4svyIPdC60c8/sWCHrKlskuElOXLWpL7t04k0L3aAraltV9sfizc3WHfw9+iXK0OKc2RQUbYs3dJodThR29xq9RRbkpfVPm5ra5tUN7bJ+uomaTEJgS1amySwq52Ssj45UtfcKhurWtvbA9T24prJhuo1Z+9TIt88uLz3vhBIoAseXSEba6jSlq70TGDKkDz5xRkFUtcUjKnn4sc+qJMfP17VY2VEaho7MEOWVrTF3DP9pGFZsvfgLKvtUa2eePT4vLiXEYiVXktd+0ylvL2syTrP1zZ1te3nNTt6/oR+/+GZ8qtTS+SZ+XVWW7+ThmVb7Zbf+U6tfFbR2qEN15l751n78G21bTJvdQu1L5AQBC0TL6WClmvXrpWKigoZO3asFBfvamQ/2qDl8O8/Khm53PEE4E8aJH7+QtpERHrQGziXP7su0cVAjHIzxLo52zc/S0aU5sjEgfmyuaZFsrWN2JIcq6OsjVUtMrg4y+pZXS9cG1uCndqlHTt2dzMZsZj5x82yZnsiu95Cb/nOEQUyfUyuFeBevKFZ3vqsQd5b3iTLt+5e/4OLRU6bki+PfVAv22p3t1u8V3mG7FmeLd+Y0ce6Ia4dN2hnD+GMG5ghR4zLtZqEGV2WZQUm0y0JAanFbkf1z69UysptuzpOKckXycna1RlZQ0tAZuyVI30LM6zOn9buCHbq6EeTHHLMY8ZeuXLMhDyrwzIvN54itcfqfO/Cf22XJY4Ap60kT2RgUUAqG4LWb9tOwAC6g6Bl4qVE0LKhoUG+/OUvy4svvigjR46U1atXy/XXXy/f+973PE+DoCWAZEHP00gnoW27wl+0yd7hJVly2oRSOXREYY80X9HdoKUi4zJ5aehi78GZVi/YGpD8/sM75H8rO0ZKLnH0kh3Pzmmcn1OxdIwD9IbW1gb55JPLref77vs3yczsnezeRHX8pM22fbCqKWybxvZ+/4aXqmRzTWxtJ19xdKG8vrhBPlrXIrlZIqUFAdHISbiMVN1LFOSKNLZov9ci/QtF9i7PtmqnTR6WLZurWuX91Y3S2ByQ4yfkynH75MsrC+vlsXkN0tAs1ndosHfikGz58QlF7R1yvba4QR7/oE5Wb2uVwaV61A3I0k1tQj2U3kHQMvFSImh5zTXXyP333y/vv/++DB48WJ5++mk544wzZM6cOXLIIYd4mgZBSwDJgqAl0jHj8tpX18u2hqQ/ZekRpbkiWRli9ZTdVQ/24WhGTZv53zdPJCMzQ3IzA5KVEbAyIE8aVyoHDO1jdVYVmgnZ0+IRtLT9+eUq+fd7dbIzpDMUdKTbQGamyPbaXduF04A+IltNhmJ3aw5kZIgMKBSZPjZPDhyZI+u2t0hOdkBG9c+KGBiMtWdtIN16D8fuwKpmez79YZ0s3NAiK7e5p2GO6heQm87rG7H5sdCbGYkI2kYqm71v1AzXtdtbZfX2FlmzrdWafw3wjui/q5z2MLVgXaOs3S4EQl0QtEy8lAhaDho0SC677DL51a9+1T5s0qRJMm3aNLnttts8TYOgJYBkQdAS6e6xBdvksU+2y87dTU3HxfDiLNlU3dLeTm9upsiEgXly4QEDrHaFte3gh+Zvl4821VrVmTMzxKriPGVIgRw7psT6jPYav6GqUXY2tElpXoYU5mZaPciv3NEomQHNwAjKhupmaW0NysDiHBleki1ba1usjr4mDMyX0yf0lYrqlvZpNLe2yefbG6UtGLQChyeMLY04D1rGcIFF+71w1bD9Kp5By9CLuyfm1ckTH9bJjtqgFOWKrNu5K0Mm1fTN16yfgGyuClrbbVtQpCgvIEfsnStT98yNOvDnDAJ8srbJCjTqsIXrmmV4vyyZOTZXPt3QZFXL1yYA9GpD23uP5bsAdI2gZXTsfZgG8WJtMzlVaRartjm6YH2LrN2u+3mRqjifbyUTgpaJt+v2QBLbsGGD1Y7lAQcc0GH4wQcfLPPnzw/7ucbGRuvhDFoCgN9p7+FAujt7Un/rESlgZwcQqxpb5MP1tbJgU720BMVqT1GzCXOzM6QkL9NTINCmAb7Lpw+MOM7ho/W7w3cA58W4Mol5GlrGcIHISO+lI71A/e5RRdYjUiaNVunTaogHjsqR8uJMq6qedga477BseWNJg8xb3SSj+mXJkL6ZVtuGOl0N5G2ra7M6Yhk3KEs27myxAngtwaAcNCpHpu+Va2XAaKeMlfVtVruIR5nOBbVK42Mf1Epjs0hlfatkZ2XIPkOyJScrIH1yAzJtr9yw1SF7c9nZ3x2+kxnaiAfgT859GDrS41BXHd3Gkm3qDBTr8W/hhibZUh20jpEZAbE+r8fUBeua5fCxubL3oOz2jov/u6RBXlvSKNX1dACbjpI+aLl9+3brf//+HS9e9LX9npvrrrtOrr322h4vHwDEyw+mlxNwACIIDcrtCiCKnDK+L8sNMV/Q6n/nBdxXp/Zpfx4uYOelt+jJw92Hn31ggfUAACAZgr5eAsDOz+jx7+TJ+Z6PnSftG765A7fmQl7/tEGe/6ReCnIDsmlnmyzf3GR1GqU3CFtNywDVDZ2bP4E/JX3QMjs7u70zHqf6+nrJycmJ2A7mlVde2SHTcvjwMGePAOC2/7Ea2c6UyYPzrWwtrT4aLWd1Ta0SqllhoYrzMmVCeT4BSwAAAAAwNEgZGgDV4KeXm4deOpKqqqqRkhtZ3ImU9EFLDTRmZGTI+vXrOwzX1yNGjAj7udzcXOsBpKuAaQTftL8sBVkihTkB6ZOTJXXNrdLU0mZVnyzIzpDx5fnSJztDPtvaINvqWqz3dLzqxhZpag1K/z7ZkpelUxQp75MlAwu1GluGHDy8j/QvyLKCcjrNZVsbraqZ2sZbTWOr1V6bvm5uExlWkt3e86x2uqHVOrUtOf28PpdA0JqutsPmbI/NWQXUDu5pGZ2fD23fLVy7bpHaguuNzLBdVUIBAAAAAD2JZgKSQ0p0xDNjxgyr1/BHH33Uel1bW2u9/vWvf90hm9JLRzyVlW9JcXFhD5cYvWnp0u61LQYAAFKvIx4ASKWOeGbPPtZ6Pm3aq/QeDsSJlWlZcoRUVlZKcXExyzUBkj7TUv32t7+VY4891qryPXXqVLn55pulvLxcLrnkkkQXDQAAAACAHpOZmS8zZ85iCQNIOdqBZtI7/PDD5c0335TVq1fLTTfdJBMnTpRZs2ZJYSEZkwAAAAAAAECySYlMSzVt2jTrAQAAAAAAACC5pUSmJQAAAAAA6ai1tVE++eQK66HPASBVpEymJQAAAAAA6adNtm+f3f4cAFIFmZYAAAAAAAAAfIWgJQAAAAAAAABfIWgJAAAAAAAAwFcIWgIAAAAAAADwFYKWAAAAAAAAAHyF3sONYDBo/a+qqk3k+kAPqKkJsFwBAEhSVVU1iS4CAPhaa2uD1JrLWL2ezcxsTXSRgJRgx4fseBF6H0FLo7q62vo/fPhJCVgNAAAAAAB01wksQqAH4kUlJSUs1wQIBAkZW9ra2mTDhg1SVFQkgQCZeYheVVWVDB8+XNauXSvFxcUsQrANISHYF4FtCInGfghsQ/AD9kXoLg2XacByyJAhkpFB64qJQKaloRvgsGHDErISkFo0YEnQEmxDSDT2RWAbQqKxHwLbEPyAfRG6gwzLxCJUDAAAAAAAAMBXCFoCAAAAAAAA8BWClkCc5Obmyq9+9SvrP8A2hERhXwS2ISQa+yGwDcEP2BcByY+OeAAAAAAAAAD4CpmWAAAAAAAAAHyFoCUAAAAAAAAAXyFoCQAAAAAAAMBXshJdAMAPtmzZInPmzJH8/HyZPn265OXldRpn5cqVsnDhQunXr58ceOCBnjvc8TJtL+PA35qammT27NlSXV0tBx10kAwePNh1vI0bN8oHH3wggwYNsrajQCAQl2l7/X7428cffywrVqyQ0aNHy+TJk13X84cffigVFRUyYcIEGTNmTNym7XUc+JvuA1599VXp37+/HH744Z3eb2trkwULFsiaNWus9Txx4kTP09b919y5c6WoqEimTZsmOTk5MY0DfwsGg/Lmm2/K5s2b5ZxzzpGMjI45Di+//LLs2LGjw7A999zTOvZ0paGhQWbNmiX19fVy6KGHSllZWUzjwP8+++wzmT9/vkydOlVGjhzZ6f3m5mZrX6Hb2ZAhQ6xzotBtLRw9Dq5evdo6Bu6zzz4xjwP/am1tlU8++UTWr19v7V/Gjx/vOt7OnTvl9ddft86r9Zjj1bp162TevHlSWloqhx12mGRnZ8c0DoBeEATS3EMPPRTs06dPcMaMGcEJEyYEhw0bFly4cGH7+1u2bAmefPLJwTFjxgRPPfXU4Pjx44MDBw4MvvXWW92ettdx4G+ff/55cPTo0dY2csQRRwQLCgqCt99+e4dx2tragj/60Y+s94477jhrvGOOOSZYV1fX7Wl7GQf+1tzcHDzrrLOCffv2tbYP/a+vdbjto48+Cu65557BcePGBU866SRrnO9+97txmbaXceBvtbW1wW9961vBQYMGWY/jjz++0zivv/56cOLEicHJkydbx7WysjJrn1FZWdnl9HWfovsWHV/3NbrP0X1PtOPA32677TZrP6MPvUyor6/vNI5uP/vtt1/w3HPPbX/885//7HLaem4zdOhQ61xn+vTp1rmPngNFOw78be7cucGjjjrK2gfoNvTvf/+70zgLFiwIDh8+PDh27NjgaaedFhwxYkRw7733Dq5evTritBsaGqzj34ABA6xjVUlJSfBrX/tasLW1Napx4G8vvPCCtT1MmTLFOlb169cveMIJJ1jHOdvOnTuDF198cXDw4MHB8vLy4Jlnnul5+jfeeGMwPz8/eOSRR1rHKT2vWrduXdTjAOgdBC2R1jZt2mRdYP31r39tDyydcsopwQMPPLB9HD2BevPNNzt87stf/rJ1Qt3daXsZB/539NFHWw87wHPHHXcEs7OzgytWrGgf5/rrrw8WFxcHP/nkk/Zhul3pSVd3p+1lHPjbzTffHCwtLQ2uXLnSeq2BHt1e/v73v7ePo0GCM844o/3CS0+eNbj4yCOPdHvaXsaBv23fvj14yy23BKuqqqxjlFvQUi8ElyxZ0uEzo0aNCn7ve9+LOG3dHnSfcvfdd1uvdV9j33iJZhz4nwaely9fHnzsscciBi1vuOGGqKd9wAEHBE8//XTrXEfpNPQcqKKiIqpx4G+vvfZa8NVXX7XWYbigpSYBaFDaPp5poFGDVJdccknEaV933XVWgGr9+vXW68WLF1uBpXvuuSeqceBv//nPfzrc8NLfvwYnr7nmmvZhGzZssM53a2pqrMC316Cl3hjJyMiw9nH2tnfwwQdb+51oxgHQewhaIq3deuut1smwM9vtjTfesE6ynBd2oa699lrrDrHTE088Yd05jmbasX4//ENPmnR9PfPMM+3D9GJd7wr/4Q9/aH/dv3//4E9/+tOI05ozZ07wpZdeimraXsaB/x1yyCHBCy+8sMMwzQw59NBDrecaOAgEAsH777+/wziagfCFL3yh/bUGMjUrSU/ivU7b6zhIHuGClm50vWumv5NuQ5999lmHIIBmLbW0tLQPe/LJJ61tUvdBXsdB8ugqaKlZ3rp+//e//1kX9KHeeecdK7PX9umnn1rT++9//9s+TPdTGkzS7E6v4yC5hAta6v7p/PPP7zBMM9q+/vWvt79etWqVtS9qbGxsH6aZ4qE3Wb74xS92uDniZRwkn7PPPjt44oknur4XLmip58O6DTkDoD/72c86XcPdd999wczMzPZEAi/jAOg9dMSDtKbtemk7KdqWpG3SpEnt7zm98cYb8sADD8ivfvUrueOOO+Rvf/tbh/cvuugiefzxx6OadjTfD3/Sdk6Vs72krKwsq+0dex0uXrxYtm3bJscdd5w1/n/+8x/X9XvrrbfKVVddFdW0vYwD/9N1Fdrmlu4L7HWo7dxqe7pLlizp0N7TsmXLrHYobdo+2HnnnWe1k+t12l7HQerRNlLffvvtTutet6EXXnih/bVuB7pPyczM7LB9aExi0aJFnsdB6njuuefkrrvustq8HDdunLz77rsd3v/Tn/4k//d//9f+2t6XOLe1Pn36WO2qOs+JuhoHqeH3v/+9/O9//7POee6991759re/bbXV/POf/7x9nHfeecfaF1VVVVmvW1pa5NNPP414rPIyDpKPtm+r+5ho2ybV9nF1G9JruK7Od/ScSrcdr+MA6D10xIO0VllZaQUCnOzX2rCzkzYKr8EBDRINHz5cRo0a1eH9M888s8MBzsu0o/l++JOuQxW6HrUTDHsdaiPz6pZbbrG2IW0U/r333rM6OtEApl6UqUMOOcRqSDyaaXsZB/6mF1l1dXWu67C2ttZ6XwPRv/zlL+UnP/mJ1XmBbkNPPfWU9Tl7G1DDhg2Tc889t32b8jJt5eX7kXp++MMfyvbt2+Xqq6/uMFy3ob333rv9tduxSrePro5noeMgNfzhD3+Q448/3upITvdHF154oZx99tmydOnS9n3PjBkzrP2Hzd5P9e3bt8vjWaRxkBq04x09B3ryySetIJB2uHLkkUfKgAED2sfR82zdF9kdX9bU1FgdiUU63/EyDpKPBrV1X3PllVdG9TntOEe3IU0Qsel+Rs+VujqedTUOgN7DVQjSmp4I6QmOk32SHdqDtwYMlGaNfPe735WTTjrJ6mXXPpnSjINopx3N98OfnCfT2rugTV/bF172utTAkGZdau+YmgmnQW69+PvNb35jvX/ZZZdFPW0v48DfNCCo2Wmh+wJ9re/ZAcPLL7+8PdCtN1H0RHzKlCly5513tn9Ge199+OGHo562l3GQWjQL7u6775YXX3xRRowY0eE95zZk72ecwXFlby/O41lX4yA1nHDCCR2CAloDRWuiaC+7M2fObA+IO9nHKj3HKSws7LCNOLehrsZBatAM3aKiIqv2gB5/GhsbrW3nW9/6ljz00EPWONOnT7ceNuf5jpPbNhRpHCQXvVmrN2lfe+21Djf2vdDtwe145rZ9dHV9xvEMSByqhyOt6Z23tWvXWoFI2+rVq63/Wh3JjWYWfOUrX5ENGzbI559/3q1px/L98Bf77u2aNWs6DNf16FzP6vTTT7cClqqsrMw6QdeLvHhMO9I48D9dV27rcI899ugw7PDDD5c///nPVrDpq1/9qlW9br/99uv2tL1+P1LDb3/7W7n++uvl+eeftzLiuqL7GbftQzn3RV2Ng9RUXFxs/Xc2SxHK7Vil5z56DhTpeBY6DpKfVrGdPXu2fPGLX2xvTkKDRKeccoq8+eabYT+nTSkNHjw44vmOl3GQPK655hr55z//KS+//LJ1UzYeOJ4ByYegJdLaF77wBavqrrbpZXvkkUesgNJBBx1kvd60aVOnz2mgSbOP9MTIplVc7PYFvU7byzjwN23DTaswPfbYYx22Dw1oazau0u3kgAMO6NAeoV6IffbZZ1ZTAzYNQOmJWTTT9jIO/E/3BU8//bRV/clua1BfO9fh1q1bO3xm/vz5VjtNl1xySfuw9evXW1kFzmqZXqbtZRykTltymuGtAUsNgrvRbUir+jq3D20/9aOPPupwrNKgtu6DvI6D5Ldjxw6rnTgnPf/RG3J6nLNpNrizHTk9p9Gqv85jlb6vgU7ddryOg+SngUo9LwptG1BfO6vkaqBR90V6PLLpdqDbmwY+7bYOn3322U7Hs67Ggf/99Kc/tZpV0vNibT4pFtq8jW5DWjPOuX3ocUqPV85jldZ+smsdeBkHQC/qxU5/AF+66KKLgoMHDw7++c9/Dl5zzTXBrKwsq4c42y233GL1aPjHP/4xeNddd1k9ZmpPlr/97W87TKekpCT4q1/9Kqppex0H/vb0009b6+3KK68M3njjjcGRI0d26sVQe1ItKioK/uhHP7K2o9NPPz3Yt2/fDj0aXnDBBVavrNFO28s48LeNGzcGhw4dGjzuuOOsfc6xxx5rvdbhtkceecTqOVN70dX9j/bU/O1vf7vDdJ566imrt9aVK1dGNW0v48D/dF+gPaVOnz7d2pfo80cffbT9/X/+85/W9vGtb33Les9+PPfccx2mo+P89a9/7TBM9ymjRo2y9jE/+MEPrF5U9fuiHQf+NnfuXGub+P73v29tB3o+oq/tfcHHH38c3GeffYK//OUvrWPZd77znWBOTk6n8x/tzffwww/vMEynlZ2dbZ3r6DnPoEGDghdffHHU48DfNm3a1L5v0W1ItxF9/v7777ePo8cxXc+6nf3rX/8KXnLJJdb+Qo9hNu11XD+/ZcuW9mGrV68OlpWVBU8++WTrWKXb2B577BHctm1bVOPA3/S3r+tetw/nseqll17qMN7jjz9uDT/wwAODhxxyiPX8ySefbH+/urrams4dd9zRPqytrS14wgknBMeMGRP829/+Zm2feg79yiuvRDUOgN4T0D+9GSQF/EZ/Avfff79VJUWrlWg7cXabTLYPPvjAumurPRvqXeCzzjqrvZdv28UXXywnnnii9V400/YyDvzv/ffft9ZjdXW1TJs2zeqYwNmLrp1F8K9//cvKmNOOVL7xjW9IeXl5h97DNbNAs6CinbaXceBvun/RbUAzArQam7ZxOnDgwA7jaNaRZiFppvepp54qxx57bKd9lfbae/PNN1sZ29FM28s48DdtDy60k4CcnBy57777rOe33357h+w3m7YTduONN7a//tKXviQXXHCBdUyzadaSNkug1Tq1LTptJuXggw/uMB0v48DftH3uV1991bWaprapa2d06zalWUhDhw61mj5xZlkqbcZCM77t9sBtWrNEM5Y0+007XtFtRJvdiXYc+JfWOtImKEIdddRRHWoGaO0SbatQjz26Hel+x9mhpWbr/v3vf5c77rjD2p/YdPu77bbbrCrgei6lxyq7k5RoxoF//e1vf7N6Cw+lNYuc58hf//rXO2V+a3MVeqxT2laqHssuvfRSa19i01ol2h64boPaHryOo22EO3kZB0DvIGgJAAAAAAAAwFdo0xIAAAAAAACArxC0BAAAAAAAAOArBC0BAAAAAAAA+ApBSwAAAAAAAAC+QtASAAAAAAAAgK8QtAQAAAAAAADgKwQtAQAAAAAAAPgKQUsAAAAAAAAAvkLQEgAAAAAAAICvELQEAAAAAAAA4CsELQEAAAAAAAD4CkFLAAAAAAAAAL5C0BIAAAAAAACArxC0BAAAAAAAAOArBC0BAAAAAAAA+ApBSwAAAAAAAAC+QtASAAAAAAAAgK8QtAQAAAAAAADgKwQtAQAAAAAAAPgKQUsAAAAAAAAAvkLQEgAAAAAAAICvELQEAAAAAAAA4CsELQEAAAAAAAD4CkFLAAAAAAAAAL5C0BIAAAAAAACArxC0BAAAAAAAAOArBC0BAAAAAAAA+ApBSwAAAAAAAAC+QtASAAAAAAAAgK8QtAQAAAAAAADgKwQtAQAAAAAAAPgKQUsAAAAAAAAAvkLQEgAAAAAAAICvELQEAAAAAAAA4CsELQEAAAAAAAD4CkFLAAAAAAAAAL5C0BIAAAAAAACArxC0BAAAAAAAAOArBC0BAAAAAAAA+ApBSwAAAAAAAAC+QtASAAAAAAAAgK8QtAQAAAAAAADgKwQtAQAAAAAAAPgKQUsAAAAAAAAAvkLQEgAAAAAAAICvELQEAAAAAAAA4CsELQEAAAAAAAD4SlaiC+AXbW1tsmHDBikqKpJAIJDo4gAAAAAAACBBgsGgVFdXy5AhQyQjg5y/RCBoaWjAcvjw4QlZCQAAAAAAAPCftWvXyrBhwxJdjLRE0NLQDEvLD0QkN4FrBAAAAAAAAInVKCJ/dcSL0OsIWhrtVcI1YJnX+ysCAAAAAAAA/kITgolDpXwAAAAAAAAAvkLQEgAAAAAAAICvELQEAAAAAAAA4CsELQEAAAAAAAD4CkFLAAAAAAAAAL5C0BIAAAAAAACArxC0BAAAAAAAAOArBC0BAAAAAAAA+ApBSwAAAAAAAAC+QtASAAAAAAAAgK8QtAQAAAAAAADgKwQtAQAAAAAAAPgKQUsAAAAAAAAAvkLQEgAAAAAAAICvELQEAAAAAAAA4CsELQEAAAAAAAD4CkFLAAAAAAAAAL5C0BIAAAAAAACArxC0BAAAAAAAAOArBC39pk5EnhGR2kQXBPCJDSLygqRGOdeIyCtxmE6i+Lls6TgfTotF5D3H609E5P1e+u5NIvKaiPxHRDb20nemsjdEZFUcpvOC2dbjYZGIvGjOT9LNVjPfbYkuCDxZZX5DsVgiIrPitJx3iMibZr/4eZymiei8Z46NvUXX88tmf6HXc6ms2cznzkQXBJ5sN+urtQeXV73Zfz4rIvMTtF4Wisj/JK08/fTTcsMNNyTNdGNF0DJWDebHXxXX9SHSaH7oOn10tF5EXurFz/lB0JwEPRcSkOgOPy8Pt7Lpif/H4n+h5XSbl20isiDK6fhJLGXz4/bWG8tYpz9Xeo8u5+WO12t76UK5WkTuMifCQ0Ukvxe+M9VpgHBLnLZB3da76yMReV5ESkVkmPj/oqylB7bx+eZ4DP/bYn5DsdCbLp/FKaDzLxPQ0f1ikfS+d0RkqaS35ebY2FvB8odEpMDsJwNmf6TnfYnU2kPlaDH7RZJskkOtWV89efPtERFZISKDRaSvuWHwbg9+n9v014acC6eBDz74QJ5/Xk/SYvfkk0/KX/7yl7hPN56yujuBzz//XOrrNbTuLjc3V8aMGdOt6ZeWlkr//v09jb9u3TrJz8/3PH63Tkj0x3+oiBT37FfBcUGiF2En9NLnEk1/Vnea7aveXHxOjcN0/bw83Mo2RES+IP4XWs5Yl7Of5zeWsvl5e+tJa0ymxUEJ+v59RaSpF75HLwgzReT4XvguJIZejI+N0/GnNy7KTojH2S3QDdtMsPtEEclL0JJcYgIU+ttF7+wnNVg5I+Q6cT8R6eFL0ojafFIOpLY2c977NREZZYa9amp7HNaD5589Of008v7771tByiuvvLJ92BlnnCFTp/rnxK/bp3U/+9nPZOFCzcUVaWxslOXLl8vo0aOtwKHS5//5j9aNiM2ZZ54pX/nKV+RHP/pRxPFeffVV+c53viM7duywvvvggw+WO++80wp4dstmc8e20dw5mGSG21U83zInJHogmG5SottMDmuJiEwQkQEu011p7gTonfu9RWRkhDJ8bu4mzDTTDKfO7CBmmmlrlSad/QNEJMeMo3d9/2sCD9mOu2XPmwNtP8d0DheRT83J12QRGS4iNSIyzwTRBpnHByJycshdvYVmZ6LLZoz5rFfLHFXjRovInubO+TxzEW5XT9MTsXJHVZ4ssx4mO7J9wn1ufBzK2dM0EHC+macnPFY12daN5RHNdMQcnJaYwP0Ck3U8xARLAiZb+BXzu9DtyhnE0gyAY80daemibCVmHU0O+d4DzP9Kk8mwr5n2J+b3qttN6P2SaNZ50PwuDjbbmZg7etWOAE29mcdjzHO7nOHmxVYVZpnZ03Sb33DL2U081p+baMvWnd+fPe2DTaZXtdmW3jb7OL2L67ZN1XuYdy8ila/ZrPexjm2szVSRHmT2ravNvtWebz2pKjO/449NmXXb3sfsp212RtdAsy9sMt9jnwTaKsy2rst5D5fy7zC/wT2jnO5Gsy4zzHQbzW/M7bxlodn/2/OZaY4F9neVmWOIDj/afGaF+f5W893jQ7a3F8xxdocpS475rfcxy22TWW4H6l3RCOuvyiyfKnMMnhwy/iZzXG00v+/JLmdEFWacBnOM1uO5k5d5mWzmZb35/ikux/HF5phX5PIdXuYlkqD57EbzmQNcsr4iLYtXTdmyzDoeapZ9NPOv5yLrzDgTTZmWmN9Ilhm+l3i31mSQ6XY3wnxvreN3/4LZfoeb9+zztUyz35jkcrM5aDLs7DJNNL/lcBaYchzZxb5F53222f4XmG1hoAkgaHnEXHR9FHJDqMpULT7e7H/s6eix5kNzLneY2b9tN7+5VhM0KTDr5ijH9JrM70fPaQtFZJwpRzht5re7zuzPxoZk2XqZXpOZ5wqzvPcz49pqzHzvNL+J/UK2zSVmvseZstSabUVfO+0w898SJhO4q3lxU2G+v9bs40PPJSLN/+eO6okvmd+EvR69zvNYs+z0eHGKeW+7GVZrzqn2ixAQ/Z9ZLksc1XdPMPuA9WZ5NDqO1/a26KbeccwqNeM716OXcu0w49Q4vtNZ16+rMr1l1lnQ/EYzzLHTPjezrTC/43xzbRXtvIQz1/xOA+b3NTJkm5jtOFY8Y5ZDpeO9j8xvQPcXYuZhqdk2B5t5yXCcX7xgznc+N9vYxDDHebeaPPVmW9zX7MteDlOOl8zyDphtMdw+YY0pa6uZZ70uC2eN+Y5pXQRI7XmcZpbFZvM7ONCx7dSac6rQawW9zj7ErPtIy6rOnH/WmHFHmHP4kx3bVptZb2vNucZoD8t5pfmuNrM/cp7be5mevT9aa36P+5hzJbd9S77Ldr7RHAMON+t7p+OY4vxN1Zv5rzaf7x/lvIjL8UjMNtXXHNv1vEzMPusdM533TPmHmliE3eydmJv4Q8zv5GPzuSKzzvqHnFvWmnW20Gx7oYkPy8NM33le85njPDj0HKOrMoQyGao//elPpaysTE4//XSZPHnXxdCbb74pDzzwgPW8qKhI9t13Xyt2lZ1tB1pEHnnkEdm8ebMVn3rllVdk69atcuqpp8rRRx8tc+bMkaeeesoa70tf+pJMmTKl0+cOO+wwee6556w4l373EUccEaGwIpWVlXLffffJZ599JkOGDJGzzz47bBKhlufFF1+0yvSNb3zDGnbZZZfJ2rVrZdmyZXLSSSd1ax6iLU+PVQ9/+OGHraClPuzC6oqzh9kBy507d8qKFSukpcW93o5ma+r7Gvi0rVq1ShoaGmTTpk3t02tr65zXPHfuXGuBfutb37IW5po1a+SrX/2qrF6te8Ju0B3K7WbD7mt+3I+ZHbweZMTsKIY5djhDzetB5oD1T7MTd3rBVCEImh/86xGqsiw06dZ7dhGwdFYt/5c5uBabHdu9jnRwOxPBuRpaQ1L87encbQ6Cg0w568z8fG6mrSdDD4S0W9FsPjfPnBAEzLx6bV9CD6q6GWWb73zXPHLNziTTLN9hZnnkOl4PMDuwW83OWiJ8LpZyVpsdY6THR90YP1RODHdFY10e0U5HzLahbebdZ5ZnoTk50pMMMSceFWYZO80zB13nSUiksoVW5bW/936zHeeb6vOPme0x02w/D5sTA1u06zxgDnq6nYv5vepBeY6jWYhV5jvyQ8oZaTnXR1hmEmF+I32mJ9afm2jL1p3fnz3te82yH2L2CetdfjcfmwvSAo/z3pWuypdtjglPOC5MZplyjTbz2MdsF3ZZcs3J0S1m/9zfnGzeZsoe2h7b8+Z72szyXepybKo23/GqCUxJhOrhXqarh8w7zEl+gVmPL0aoalNiloO9foeGfNdzZjnYwR/9/Txoxu9jyvKMy7p8yATDSsw83Gl+26vN9y000wmn2izXTWY5bzNV2G3zzXbV6pjeHWa92+aZ412V2QYWhLST53Ve7P1QiVnP9jmFTS8Enjb7yxZTruoo5qUrL5myF5n18k+zfr0ui8FmG+tj1nH/GOZ/iTlHKjG/5UfNfBea38V/PLT1a1tkttugOQ9ZYLbRTJfzMOe2OcxcuOmx5x8maGFrM+dZz5rfRrYpk/6G3Lxn5neih5shdtXyO8y+osjcOH68i+Yq6s3nmkOmc5d5b4hjn/JPc6wtNPvMJ0LOKWvNOEvN+ms054n2sc3Ni+b338csj5cd0/QyvRqz3c4166nBrDc7+3un2S+vMAGe1Wbf6FwvG805oa4b+5zoiZBmN7absmwy8/8/c2PL67y42Wq27YDjXMJ5bOpq/otctsXMGOY5x7FPXW72HbUhn3X+lp36m+2jxLH9Z5rjxF1mPZSYZfXvCE0fNJttd5mZZq1Zj7VRlGup+c1VOK6lnPsKL2X61Hzmf2Y97jDf61x28815YcBc3T4S0sZyV/MSSX/HMS7TlEWv3WxlZr3b5x9ljuvEMjNsoCMA+7gJ/hSZ/ckDjvltceyXNzquwSKpML+3GrMe1juOkeHKMdi8HmKWwV1m2Ti94Ti/KzLnOaHn9M71fL+5WdTVtYtzHleZ9b7IlMG+Pm0w4+wOC+zyoeO8K9yyajLr2j72rjTz4awe3WrK+67ZR2WZ/Uvo/sNpntmuMszymOcI5nmZXovZtl8220qGGafC8f5d5mZwqTn/sK+7bTvN995jjgXFpgzOWrzN5rxpsZn/FeZ7vM5LqOyQ8+o15jdtn6vkOvZV9nbWN+Q3McwcL+394CrH7/B2U0bbWnOcfNJ8xt6GnYrCTF/M9v+U+a1mmHNKZ9NcXsoQyizfQYMGSVZWllxyySVW4E4NHDhQDj30UOsxbNgwufnmm61AXjC4eyf23nvvybXXXmt9TpPrWltb5dhjj5Wvfe1rVtJdeXm5FSvTaWhgz/m53/zmN3LuuedKTk6OBAIB63P//rduSO40DqYB1f/+979WYLCiokIOOuggef11505rt8GDB8vQoUOlpKSkfT60xnJo9fBY5yHa8oTT4xVoNCJ80UUXyWuvvWZFprds2SJXXXWV/PznP28f55e//KX8+c9/tjaEbdu2yYUXXmg1/Pm73/3OCjxqEPSll15qD1DaWZy2X/ziF1YE2pnSetppp3W/8J+ZC1Ct3mGrNAdEvVP/irmL77wztX/INPLMDusr5vVKc1J5sSNr57CQixTbXHOi9aUu7myFGu+4Y6+B7hvNj9XOjvJqf3Mnx/a6OYm6wOwIDjM7aJ0n23vmgHCRIyQ+3OwYdXq7bzq4+9TcMbNTvQ8zy7zY3AVabDJFnJyvDzY7/HlmOuE+93YM5bR32pH07cb48VAY4/KIdjriOMCe6Tig9DEBFL0zKmZZvmmyTDLMMv/YUX3GKVzZ3Npk0+89x/G9jSaY+B3HxUKtCSKN78a2Ocps3zPNRVHA3GFfaX5Pq8zrDI/zUuFhmbmJ9jPxWn9eRJpGd39/Ou3TQzLR9zUnrXbGgpj925Qo5z0SL9vKVHPB9pTZvvVC5Fzz/YXmxK4upCz3mWPH8SHHCN3Pfz2kDF93ZMjXmZNt+y74q2Y6umzEZCfoft6LSNN9zSxfe7pa9psiTGu4uWhc6rI/0XV3oSNrotqchJ7mqLGwl7mwOCAky1Zvvp5hno8z8zbWkdG/p7kw3hFmH2pny53lGFbp2C/oOdhXHdvVweaCb57JGq4xNxdPdNy5n+qYRjTzMtoxL7qe/mzOLSab6ehF4NmO7LGhIQHZSPPiRT+TsR8w83abCYoc73FZ7GMuJgsc6zia+R9mfhfOAMUGs6+2t8Px5gLoIA/HxE/Nb10zDp3rJc9kVs1yyUQN3R885si4EXNMWm7KZH//tDA3Ol43v5mvd5GJGeoQx3nNOLP9ru6ilo2bw0PO5V42+15dx+JYx60hZS4PWQ+6XbzkkrXoXM5fcGT+Tnecp3qZ3qtmu/2G40rjUEeG05tmWX/F7GOnmhtFr5vfg/Pi+6shmbEfOn6Xb5n1cL5jOd/qEvAKNy9u6s13ajBHTDlfM5lMuR7mv9zsr2abz2RHOc96PvNNRw2VVnNj40THuj/UnH/PCtP0yl7mNzvYsf03m+3lSMc52L5mH7/Q8VuWkHOWHea3kelYfpkey2Vn4R8Scl5RGUOZ+jmupcT8hhaY30SzWUfHmTKI2R/c6nFeujLaZfnebT6fa9a3fS3kXN7PmvdGOsqg5z/fc+xr7OPsZyG/x4lRNLuyzKzrk12W8b4u5RCXa0L7hoqd/LTRlPXLjmGHhenL4RNzk/KLEfYpbvYwn3Euh3lme4lG6LKa7TgHsX9/GijeVTF0l3lmXr7l2EftaW5A2LU7Qn1qjiFHuZwXeJmenbH7PUeG72GOm1Nzzb7pCscxLNvcePmuoxx6fnqSI4uzr5m/k8y+Za7Zj3zLMf+PhnRKFWleQuWHHEd1/fzb3EQ4xmw7k815096Oc4AVZj/h/Oyj5vfkDNMUmZiKltfWbKqah2sPeKB5hE7fXj4XOJJjms1xw1lb1ksZnEwM7vLLL5fi4mK5+uqrrUQ5NWHCBOth0yS6kSNHWrWAjztOd0q7aJBPA3d2LeDFixdb8S1tClEzNNX8+fPlwQcftIKDNo2NzZo1S8aOHdseZNRYmh3IDPXDH/7QCpreddfuO9ya3ai1lnX6oSZNmmRlhzY1NbVnWoYTyzxEW56EBS3PP/986du3r5Ut2adPHyvNdNq0adaCP+ecc2TBggVWcFL/6wrXTMxbb73VSiO94447rCBlpOrhzc3NVlru9ddfby1sTWXVlVlQEJrGFYMyc7dDT2jHmh9tV9mOdWbnvcPc6dkR0rPaMnNC4zypD7hUVZplfiBfc9y98MpZvazA/DBXxRC0DE2lXm12Rpkh3+UMWtrVHezAfNAcOJodWZtdLfOPzY5ohNnZdrXMt5idb5XZedWbO+WRxFLOvC4Cfd0dP15iWR6xTif0DtgAM16zWXf7mBPSZWbbWWbe15Oo7gj93n4h2Q32sI3dXOejTDC0xfyGRpnfr/17WmUuSrpT9tBlFq/PxGP99dT8eF0X2S4X9ZPMxck6ExBZZwJnk+L4G/BSvoAJRt1q7vLvH6Y6mq3J7CszzQWEPd2dJiDupMeHnJBlat+0bDV3oZ0B2FxzUdFVxkhX010XckMh1xwHYmlof3hIFcF1ZpnqxYVtqLnLvdol0GcrdVRVt9kX81VhglwDTOBxttkHlTiOIyvNvH7iyE611+8mx4m2lrVj7Zbd04h1XuxqVfZFn51h69xuxrqso3Dz4sUER5XtDPP6syiWhZto5n8vl99WhqPKoh1cCjgysSIpMxcfe5hla2eTRVJpLlYrzW94R8g2vcxMy/ndmS7VRp8zy+yikCZPoj0vKzPnOPZNr+6elzn3BRkmaLAoZJn3C9nv1Jl9T0OYKsZl5sK30JFdVhzF9JaZcjmvMpzLc5W5SLZvCtnJAP91KUdxyO9hXsj8a4DZlmmW9SKP8+Km1BGwFPO7e878JkbGuDyjmef+IdvXRrMP+NxRcytohkXT6dRWs907j5X2jcXVYYKWpWZ5vWGOcVq2fMd+oKtybTDfGXouXBJDmUIDhwMc+1J7Os7f2cCQarWR5qUrreacYqM5n2g1+8AdUd68WGbOa5w91AfNNrEpJOAXTZMZZWY7mmem0cfDfrHB7Pe3mwBXVch50jKzLkJrcIb+duaaLOPzXZqb6YpzfdnB31UxBC3d9otjQs5BJ4QELe1aJhoQtNdD0KzXLWGClmVm3zLMzGuOYzl7mZ59LeTcF9rZ/c72o5033fY1y7jG8bmMkGPHAPM9teZaaLWZTnbIfmyxx3lxs92MX2V+39VRXCfYgmYZDAvZf+q0NpvflR1jGNSNDsz0mqQgZPl8HkMZnHR/USEyb948mTlzpmRmZlrxJpsGFTVIqfEuDexpRuSnn37aIWi5//77d2i2cK+99rIS8exgnz1M+2dx0izFsSZgqTRYqUHLpUuXyj776IrdTbM7X3jhBastSg2e6mt9rF+/3qqxrHE2zRSNVbTzEM/y9GjQUlNDNfqqWZYaTLQLeswxx1jdqGvQsqamxlrxGrW2CpSVJd/7nt6C8EYzNzVYqRmZe+yxh9Xxz4YNG6w69tqmpT3dUFoN3VkVvarK5dbRvmbj/dBs2OXmjl64C9OdJo3b3gHkOU7EnQeJrtL8xZwY9w8JwnhV4PI6XBWSSEJPvOrCTDt0nLKQEz4xO1cv7cacYDIgXjHBAb0wOS7CcvjM3LXZz+yUcsznQqsTSBzKWR1SPdDNSEcQK9rx4yHW5RHrdEL3IPbFsV39Itdc2M43v5v55oSquz0Mu32v27C2bq7zEWYa683JxJ5mP/CMOXHdHMNJWlfLLB6fidf68yKWaXhdF24XfyVmvAVmX/uJeV0ax9+A1/IVmvHCXfBJyL4/aLYf5wWpXkR3POeIvC3Xm+m47Yu7Ci721HTdhK67WjMsw8Pxqavfd1fbmJ7snmdO9N826+lgcyFUZ6YVum6HOoJWduAh3BlSd+fF2VxLvksbsgUe58WLSOvTy7Lo7vy7nUcUhvnO0Pbp3Ex3XPQ/YT53TIS2iTeYTBfN5hxsjkn1IRklDR4DwSvNNGJpKr2nfldez8vGuCzzMREaiTrLNAHwjDmXGWvOxYo9Tq+rc93aCMsk6PhNdHVcDzcdr/PiJvTz9rZe243lGc08u61jMdu6c3kM9Xg94fx+ifIaodBkFb9nqptmm5s5h3ssl90URp84lKmrfWm46XiZl0jrLWiy3ysdbVAHTYJFtOfWdWb9uu3/QtuTjKYDp71N26fzTcav3c9C6LmFrcZcrw4w57X2udKKGK5X7erdkdrIDcdtfTmr/Hvl9psp97BfLHZZF3bVZjdHmWP2myYQOcJkEA/2OL2GLpZTrUvVervczqClXe053DlRrct1c0EU8+K2jv9tznMHmm2lKobtv9k87Bt3TqEZut3pwCzSviKaMoQeR/4hVu1hrUWsndT88Y9/tGoR//73v7dqDGtNYc1azMvLs6o+V1d3TOkPrSmckZHhOkyDnk6hnUsPGLCrsxQ709Oprq7OeowfP769zU2blrm7op2HeJanR4OWGmFWV1yhec4dHXDArttuWu9dg5d77723lTp61FFHWRFkZ/Q6Eg14qkcffVTeffddKx1Xo7vTp0+3otCatenmuuuu65B66ypg7sbtbzbwOaZ64JVhOqpYZHbcmkpvC+08pbiLNhNs55gsnwfN3avO2b/hVYbsrDWY2j9kjTt/D872tSIpdkkdD31dZL4j1gzDXFNN5Eizg37epLxfFmaZf2CqCTmrxzjvJEmYz8VSzmSoHh7r8ohlOl4dYC4aN5s7kXY1LjdeyharWNZ5jjkBWWkyCY42AadqEyzLjXAC0pPz0pvrr7vi9fsLvaH0hjnBWhTS4UQ85t1r+eaYm1ITTBt4lzr21W6BqExH52ix6mOmUxmyf3Fm9MeiIMx0o6mKHEmJOR42hmQRVIbpfKa77M4zWs1+51FzXCwyx/OxEe7iF5vAVn2YGyzxmpdic3HhzEpucblYDzcvXXUYYJfJye4ARDwuC4nz/BeZbJ5YfwN20zSHmXOXN8x50o/D7GvmmywcbcLCGcisDFkPbs2QhLrANPHwpKnSGE2r8JUhF5E7HZ1k6b6m4zVK/M/LnNX7ve5nTjCPnWaenzcBdC/T62qZlrjss+ymgKI5drrN/84o5sVNVUgQscpc8JZ0Y3l2Z57t3+aQKDuLDJ2mXf6dIdcIWoZIl1zDTPV1uxOch8xxbJCHctmB4XAZibGWKdz36Oec1/c7Q2qrhZuX0Kx6p+0mS+v7jhsWGuSJ9dynPkzHb921r3m0mGSbJ8y6cQvOf2a+/2uOcjrbihXzuZ2OzmXDOd000/BvM71ogk2Rjk9u16uNEdpfjWW/2BLDdeBM86gzNQa0zdsfeJxerPtF+7147hcjzUuoeSFNEomJZdRF+RvIMdtHUZxqIsZynRVrGcx++OOPP7baZNTOZn784x/LPffcI7fccov85S9/kQsu0JOE3U0Xxosm/jnZfbYMH955x6u1mjUTUtuo7Kqqt5NmhvaEWMvTIx3xRGLXs3/jjTfaO9KxH/fee2/7Qrr//vutrEztKVwj0xrA1DryXmiEWyO8GujUgKXSRlDPO+88K8MznGuuucaqgm4/QjcIy3JHm0bZ5qKhzVTzs3fKoW0eNTui+XbPZU4TzY7EWYW/3pxEO+WaNnWazAm53Xi5F+87yrDJBFzGhVS1c86u1+YE7CpHNY55Df3svo5e0WzBKIIGix1l1ztKIx3LON8sh9CTe+ednvUh1dXDfS6WctrVvSM9RnZj/HiJZXnEMh2v7IabHzU7/Ujts3otWyxi3TZHmd9xhjmpzjLzNCtMe5a9MS9exGv9dVe8fn9OE8xF/avm/4Q4z7uX8lWYtshOcZzI2VVe7fl2Hh+yTDnfDQlKNbo0fh9JwOyL5zqW6dYop+Emw2RrfOCY7vY4TNc2wgQPNNAb2kNkpGr1sVjnOEHPNMGhLLM+9jTleC0kY2ubozmJ0SYo8ZbjAqnVZBvEc15Gmu3EebFo98buZV68+Mjxe6g1N1vs8wEvy8JNd+Z/X/ObXORyEe3sCCkc53h5Zl01mvVjB5jrI+wPdoRUERSTjbQq5IZypUtgothkaq03ga9oMtKdHbksNeWwl1U/s86dyzxSJ31O48y4TY7zzoUuy3yeWa+2lpCO6kItdKkuXR/F9CaZbdlZiWmNY91NMOePdnC2wUwz2hsY40M666gJ6XChq3lxUxOyfc4JCdLFsjy7M8+DzLnHmyG/Eb15Gqm/0dBjUH8zHefvdq1ZL3bb36G2OGqLBcxvv8BM10u5BjqqLtvHlaDjtxZLmdwMMN/j/J05e07val68aHSUX5vr6EqWuXZ0Tn+C2W+ENgmwIcYMQ9tKR7ZplrkRFTTbmFs57G221TFvoUHL8Wa/4lymOl7Hmqu7pm0n1/w7ihsu4nK+sdRxfCoy0471evUzR+Cu1eV6fF8TjA7taDBSJ12fOo7PBebGoV2Lxsv0Jpl5dC7D7Y5A5gTzvr1vsXvj3iPKGmrjQ7b/FrOsvc6Lm8aQ39Lufla874PsZfC/kKBqs8fpeZm+F9GWobnjubB2JqNtQGpbk0prETuzKu+++26rmni8aIzs5Zd3X2DceOONVkanVsN28+Uvf1luuummDrEt7dj62Wft+vCd9evXT7Zv785OKLxYytPrmZbaJboGLjUL8rvfdbYgK1aVbn3P/q+BRo1Q60NXglYr1zYuNcVW260MR1NQjzzySKuauJN2w65taYaj1cj1EVG9I30+zxxkpziq9o0x2TUjzDj7mR+B/ZnV5iTX+UMvMxe5z5udbx+TgXaqy/fnmcDlv6PMuGwyZehnyjzZcUc/17T/86Q5qFV7bIRaTMbpItPI+whHb43OoI0G4raYXs1Gme/baHaGXk5G15qq4YPNznqlI2PK7hnsXnOiM9Y0uP2Q2fFkmxOd0NR6t891t5y95WVzEFlnDrrPmPXlbGzbKdblMT6G6URjf1NlpatqOG5li5dY1/koE6CcGDLMbuMyEfPSlXivv+7oid9fvtn//s9su/lxnveuytdishgmOrYLzby6y5RrnJnPd82d63yTGfYFE7z/hwlYtZiAo1vHVJEcYzoBuM0cU9Z5rFoby3TL4nR7M9cc+54w+/Vs8//YGNoH7ErQHDcLTXbCWnNM2cscQ7/kWA+DzbbS6OgQIMfUdnjU/M4HmOPddMf2EI95yTWdWDxtLnQCJshT4HFevNBzjFtNoOb/27sPMKnK8///n6Uj0kFBihRFRVFsgIoRNdgVayyxRWP9qTHGvxE0IRpb4tcSY+y9JJpEwd5bjJFioihiAxGUJr1J3/lf9+E+69lxypnZ2d2zy/t1XaPMzJnT58zMZ+/neb728GVAZDvz7Yts613s9vf26Z70P7Bu7EFCu8j3lFzme2f/nfyzcIpXitg6tPX3wWO+Ld286vo+f29uHBmBPmoLb93xqL8vbV4L0gZaCbX24PIBH4TriJjvj7neJLVFZJ3Xt/Ba/x7r51Wcvf2aEPcH6mD/MXWbV5R94+sY/d65l/8ovsPP3wa+z/vnCIY+8SrWzpHvIMcUML8f+Xfb23ya8MfwSZH1nurPd/P5t/DXFSLc/vAcn+Hn0ncxtyWT1j79h74fZ/i50LAK+7Mq21zm1yM7r2/1z9Tlfq3I9l1Q/jn0kk/byL9LH+bn+RwPhaZ4P5s9cix7lL++nV8HG3lAE2e9ynxf/zVyjfnW/1DQy/ddoeuUSQP/fP2rf1ZvFLmuxNmWXNr75/6Dvs5zC/j1vLV/9/3Uz6u9vZnpaD9vO/i1JtyXxVrl15e2fk5N9e8mnbKsxyAPXu/waaZn6CaptX8OjPbffq18n0UHp1Xks+QEP44P+/s8bsXl7X7dnurX4r6RYzrEfy9/EXlPxykI287fv3f6efRt5HM1vF5v6+fI3/w3bXO/v2na9/2oOf6eCvfrVF/Hspjz29avJ+F3y/Az//jI85/5oHY9/fNuZeS6Gde2/sebO30+czIc31zbkm6gfz494Md1Wszv1VtEWkO08M/jof49I/xsSPnxCb+XFCLT/OModB3KvDsI69L6tNOCEbKtb8tnn7XOjhUMymNVlzbGioWXVnhnuVapWEh6zjnnBEGpNQm34j/rJzJbdaSN82Ijdlt/l0OGDFF5eXnwmlzdLx544IFBdah1r2ijgNvySqWY9cmkLBUdj72KbAUs+bUh0a3Zt7F2/ldddZVGjhypvfbaK0ien3jiCe2yyy5Bs3Hr29IG3Dn11FPVq1cvjRs3Tr/85S+D6kwbEdz6B/jyyy+DvgIswLQg04LKKBusx4JLGxLeBvkZO3ZsMHCPjVJkg/jEYX1a2lDvujTtQrvSv+iv8otq9IdhWHkR/sjYyqezoHCNf4EKqxp3yPCX3GmRznTDi+kq/6vwtmnVnJ/4/HJ1+LzQR1473+czz3+g2MVTGf6qN98/uDv5X+rDzpszrUOo3Lcv7Jdjuv/F8PuB29db5F/IGvh6F9IH1BJ/rb0Xu6Y1W1vh+zz8K+9mfuGZ7l8ou/sXgLVpFX2ZXlfV9awJ9qGbntk3yNOUpSr7o5D5zPfzKNqX33L/UtQ/LQz/yj/sfhGjSXz6ujX39dghx3Ln+gfOtmkfyAsy/Igo9Jiv8eOwWaS50iL/Yr1lWvOn6Hpm2pamMfZZ+nwK2c/VdfxCxa5bMe+/TPOO+tavrd0yBHb5tj3Tscok2/ot8C949sU4+revKX6+bB1Zzgy/pkbPlZl+zm4UCXVDtp9SaU1/Z/u6RPvbWeXLa+BhxRLfv2GY9bX/Aat3Fef7sl+Ls4VYC3wfRX/4ZVpW9Pz4yj8/u2W4HkzwYxZ9/H0/dmHzqJQ3f8vVrHmtr1c4WE+3DM9P88/idn5808/5Vb6ua/w4tSnBtoQ/GqNNHxf68Wrpy/ncz+mOMbclm3D55f5Dvpkfk4YF7osv/Ud69xJsf2iZvwfL/ZoQBnhxLPf9tc7XNTr/Nf7eXOaP94ysZ7mv58oM5738sa/9Pb155L291I+J/fGtLHKNmezzzxXUTvWw43I/Bgv92Gb6LjfNj/Emfq2Y5O+rxlnWIXr8pvj+6OzvjWk+YFDUXF+Hpv7eztfPeFhx2zjyI7zQ+c3ya3Ub3/fRr+/lfg6ETULTWy7M9O3eKu16k/6dOn3712b4PpBvW9KX2cPfc8v935ma2Oba/mUePuxYgm1Of+1if22XGANhzPZ9sSbSJHmln5er/f2Tr+/8sF/vhb4fumfZplzrFQ5m+J0vM/39nm+dPvH3WbQ7nqkZBiRZ6stpHhmor3mkiXi+bcnlKz9u4bn8QdqgKt/4dkT/oFTu78VFfp70jXy2TPPpO6R9B1/r187wN1lcq/29EfbJ3SnPeqyJ/J7r5J8PU/waE/Wdvzbl+2vjHOu52n8/bppnEFn7rvIHSWf4Z81c//zrkeUcnuv7vYvv995+ruXaVyk/Zsv8mjrPCz9GpE1n19ywAKxzjD+8LfVjnfL1aV3E/MLvTRtF/lCW6brZPDJITmhRhkFAV3qw3C8ybfr2b+zfP6LXpHzbkr7d0/zzp5vfD/v3lV9/o1lCaHHkvREd8G6O35pl6J83zF3i/HE2ff7LMrw202/EfOuQzuZ/nYJsqVOnTtpzzz0rDT7z8ccf64MPPgiyJAvmLMfq3r27+vdff6DGjBkTZE3RgXneeeedoOLQukcMvfnmm0HOZYP9mAsvvFCTJ08OWiXbPKw/TZvegsWQBaiWrx18sA0f/z0b5PrDDz8MmmcPHDiwoi/MbKwS0pZhLZBt/BmrJI3Ot9htKHZ9qjW0tJ16+OGHB8OcWxoceuqpp4JSWdsZ1oT7mGOOCfoCCBPiF154IXh+6tSpwQE+66yzKnaIveayyy4LUmvbKRZQpnf4Ge7I66+/Pmjnb/OwdvMHHWR/dlPVQsu6JBpaVldV1fTID5c1HkTZOVf1vl1Rnz3pH27x/oYAoDYs9R8BbSOfKbd7ZUWuP5QAyB1a/qaAVi2FWOM/xjtHwgCrnurvVV0AkDTR0DJXuFmq36trvQq0SY6+bAHlDy0t0Ms2yHN1CEPLZ72qc0NW0ubh1qzbqi3TDRs2LLjlKkm1WybWyehDD1lNcm5W2WkVnPXapBz9i3WooWbN//YPm1aRv2BGB8HIx0ZwzNZlwlZ5Ru5C3TxnP/Tz9vTaXhkAOZV506ZW/u1gqlcz5ms+h5o3I0MfVSGr2oj/N9tkec+3LZPuCQzP861vmxp4zz7rP8abeyXMJt4dBYD8Fvhvk2wOilHRWt2+yNHnd2tv1psk+dbXmhvXxLX5Jf8j7Az/oxGBJVBnVWufligxu9Bn6yIhHM3w0AKbExTqBL/4L/APnS4FVg90TOuvK6rm/nCBmjxnt/a+8gqrAgdQ0zb2yoewidneac3xkBxh88f69s2ufY4+y/J1LZLE9W3v38uqa9jLRv4Hwa+9WeIe3tS0egYCBeofay6dq/u5JLyXwq5DMsn2myrJ69vYr4vV+UedI72Z9Tzv67BLHf9sxAbJWiZba2CUuHl4XVYvmocDAAAAAACgzjYPx/eq62+/AAAAAAAAAFAUQksAAAAAAAAAiUJoCQAAAAAAACBRCC0BAAAAAAAAJAqhJQAAAAAAAIBEIbQEAAAAAAAAkCiElgAAAAAAAAAShdASAAAAAAAAQKIQWgIAAAAAAABIFEJLAAAAAAAAAIlCaAkAAAAAAAAgUQgtAQAAAAAAACQKoSUAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmgJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgEQhtAQAAAAAAACQKISWAAAAAAAAABKF0BIAAAAAAABAohBaAgAAAAAAAEgUQksAAAAAAAAAiUJoCQAAAAAAACBRCC0BAAAAAAAAJAqhJQAAAAAAAIBEIbQEAAAAAAAAkCiElgAAAAAAAAAShdASAAAAAAAAQKIQWgIAAAAAAABIFEJLAAAAAAAAAIlCaAkAAAAAAAAgUQgtAQAAAAAAACQKoSUAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmhZqHWSvpC0agNbdinVl+0oRH3f5trYvvqyT+vLdgAAAAAAUEKElrmszRAmrJT0qKQFqnnFLDvTNtS2mtyH30iaEnPar3xfRW/zYrxuuaT5G8ixS9L7or7s09q8pgAAAAAAkFCElrms8DBhUeSxhpJ6S2qqmlfMsjNtw4bgv5L+IukxSX+N+ZpRkl6WNCZym5Zj+mX+/5mSPkx7rD4fu6S8L+rTPgUAAAAAAJU0UhUdd9xxmjhxYtbne/Xqpaeffrro+R999NE65JBDdOqpp2ad5ve//70ef/zxSo9tueWWGjXKUqgipbzyznwtaYmk5pI6SxokqYU/t8an29z/bZV5rSS19ecX+a29pJZZKr9m+7875glgGudZ9nxfRts829A1xrKj817h87Zp5vq2tE5bt6m+3DaSJvuyG/h07YqIx5f4fiyWrfMxvt3PF/A62787x5z2Ld9PPbyJ75u+vGMlNanFYxda6vNt6/O2itNukprlOL4t8xy/uO+LaBXhbL/SdEq74uTbB/lsSO8HAAAAAAA2MFUOLa+++mqtWGG/4qXJkyfriCOO0KOPPqrtt98+eKxp06qVXtk8583L3UZ3xowZ2myzzXTjjTdWPNasmSUzVZDyaj0zwUOoTTyIsEqtszyoWeb3+0j6VtLGtkKS9vLnJvtjFoYcIWnb6MZ5dd/GHsDYZh4gqX+eZqTpy+4nabqHfFb1t4ekfXJsQ9cYyw7nvZ3Pu4OkwZLGephzZGS9rFnrg5LO8JDGpin321wPao73ZcV1l4dCO0na2texELauYThViKUeOLX2Y12WY9qDJc2S9HdvIn64pCEJOHbyKtFX/DXfeVhpf1s43f+d7fi2zHP84r4vzAceGLfz5thrPdANQ8J8+yCfDen9AAAAAADABqbKoWXv3tY+84fVldttZ7/u17MqyAcffDAIH/v06aNLL7200vNjx44NAsepU6eqZ8+e+sUvfqHdd99dZ5xxhj777DPdcMMNeuCBB4Jpx48fr+bNrTyqslatWlWaZ5VZJdRRkm6QdIikTf1xC6cysQDkAn/du5Je8sAjfMyq8l6NhJaLPeyyZWzlj1kl1yOSunvQE1e5L6eh99dnzaF38dAm0zYUsuzVPu/wTLF8+il/PKwm/NCrzbr4/Z9GXm9BlRXBviHp0AK26QRJ70t6zm+Wge8YCcSqy3ivSJzrQdyRHp5mMsOPaycP0Oy1pm8tH7uF3sz9CA/wbBn/yLL89OOb7/jFfV9YdfGzkg70ytWUnzcWDJ7r2xtnHxSjPr4fAAAAAADYwFR7I8Vrr71Ww4cP10knnaRbb71V2267rQYOHBiEkWbOnDnad9991bdvX/3lL38JmpuPGDEiCDgvu+wybb755vrpT3+qxx57LLhlq9x89913NWDAAA0dOlRXXnmlvvvOystq0MDI3uyR5bGF3szUfORNUxt5SDbZA43mkSascQ2KhEC9/P+5ilMLWfZuaYFWH68+/CxtfusLa7+3xPuDnOoBTqEVj5t5JePFHjDN9+rLOyWN87Co1IZK+pVXI/7S98c/vOl3JlN9HXfycOt4DzvDY1xbx+5jr/CzwFJ+DoaVp8pzfEt1/D72Ztu2b+TnzN5+HG1wpKrsgw3x/QAAAAAAwAamypWWuSxYsCAIEF9//XXttpv92lcQLE6YMEE333yzbr/9dn3yySdau3atfvOb36hBgwbaddddNWzYMKVSKXXo0CFo5t2pU6ecVZRWZXneeedpr732CpqKjxw5Uk899ZTGjBmjxo0ztytetWpVcAstWWKpQhVslGGvZnrMwpDG3oTUwq3/pM1nkyKaQkeX09BDqlzBWSHLTq92s+e38WCmn1cbzo+ENBbwPeEVbja/Zt60NtsANfnYftvOb4u9qe3z/v/zVVrRU6yph2z3+vbZtqQbHKkybOeVdtYtQG0fu0UZ+oXMVrmbfnxLdfwW+TKjzetb+zou8r4hi90HG/L7AQAAAACADUS1hpbWlHvlypU6++yzVVZWFgSRdps9e7b69VtfBta/f3+1a9dOBx10kE4++WTtvffe6ty5sDbA11xzjRo1+n5TLBjdYostgmbpJ554YtYK0CuuuEK1polXop1UB5e9vTedXe5NYbtFQrIJXkX2y0h4ZM2mX6/C+lpz3y+9ufinHoYNUPVrHunnMlNoGa0KtVtSjl0zb64c9X0+n1upjl+zDMtcG6lgTJK69n4AAAAAAGADUK2hZdhE+5577vlBP5QtWqwf8rdNmzZB5aX1efnwww/rzDPP1JAhQ4LAMZwmn2hgaaxJud1yjWpuTdYvuuiiSpWW3bpZ2hAR9lGXrXlwVfTywVK+iQxMIg91yjOMPl2sTNtQ1WVbU3c7NBO9GXC0unCJBzbRardo09lCzPfBXCZ4c/C+HiyFze+raqoPPNPB+6RsmlYZ+IXfzxVYVqdij11Xn2ZZZLAXa/IcR5zjF+d9YevwjlfHhiNrf+5Vj9YHaG2py+8HAAAAAAA2INUaWtqgO2bx4sVBs+9sOnbsqIsvvji4zZ07N+j30kLMc889Vw0bNgyqMwuxZs0affvtt0Gz8Wysb8y8I5s39cBhrA+gs1GGZrfF2tLn+aiPbtzO+9370PtGtH7vSiHTNlR12Q28KfUb3qw2OiJ6bx+c5g0P+z7xvvwKPdMe9dDQKhh/5MsrZED42V4haX1MpnxepltkPk/6fPf3UcBf8/utPMAa6wGUBZu1odhjt5Xve5tmNw8vbXAo5RkNPe7xi/O+2MqbgD/i67nK57l7FQbY2ZDfDwAAAAAAbGCqdSAeCx9tYJwLL7wwGBnclJeX67nnntMTT1hHb9Kbb74ZVFhav5Zm3bp1wW3jjdeXiFl/ll99lX1kmtWrVwf9YS5durSir8oLLrggePyYY46p+kYc6wHDeK+iauhBRNNIn3a906qxmvhj0WCimT/WMBIeHe0DuXzrfeLZLjgxR0gSZ9nyx1rk2IY4y84279COPjrybmlVZN18tOQFPt9NfFm9cmxHJjbvcySd6SM/FxJYykPKMR5a9vR/2239abJeT6+yDP99qFcGfuSVeKdIGqLSqMljV+YVqTavSb7NP4ksN9fy4xy/TOuV6Zie4OfJp95E+iBJ+0aej7sPkrBPa/v9AAAAAADABqYsVWgZYw7WHNv6qrSRvAcNsiF8pYULFwYVk08++WTQV6UNzmP9Vt54443q3bt3MHr4JZdcEjxvAeWsWbN0/PHHB4P0WLPvF154IRhR3KoxbVAe6ycz2tTcVv+Pf/yjbrjhhqA5uVVYWn+Wt9xySzAwT1zWPLx169bSpUUEZEDSrEoLwayJ/bOSfk2VHwAAAAAAeVk3ctetbz2cqyUv6khoaVWOX3zxRRBGpvdhuWzZsiCQ7Nq16w+eC1/7zTffBMHmRhtFS5UUDOZjz9n/+/btG4wyns42Y/r06Wrbtm1RJxOhJeoVCyjtbdbZmzq/41WApaocrQm23guzPNc8rf9JAAAAAABKidCyfoWWdRmhJeoVGz18nKSZHvD18X4m6xIbLT7bWFqbStqvhtcHAAAAALDhILSsdQwHAdRH1vfiYNVtO/oNAAAAAABscKp1IB4AAAAAAAAAKBShJQAAAAAAAIBEIbQEAAAAAAAAkCiElgAAAAAAAAAShdASAAAAAAAAQKIQWgIAAAAAAABIFEJLAAAAAAAAAIlCaAkAAAAAAAAgUQgtAQAAAAAAACQKoSUAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmgJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgEQhtAQAAAAAAACQKISWAAAAAAAAABKF0BIAAAAAAABAohBaAgAAAAAAAEgUQksAAAAAAAAAiUJoCQAAAAAAACBRCC0BAAAAAAAAJAqhJQAAAAAAAIBEIbQEAAAAAAAAkCiElgAAAAAAAAAShdASAAAAAAAAQKIQWgIAAAAAAABIFEJLAAAAAAAAAIlCaAkAAAAAAAAgUQgtAQAAAAAAACQKoSUAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmgJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgEQhtAQAAAAAAACQKI1qewVQA+ZJelbSHElrI4+fL6kVRwAAAAAAAADJQmhZrGWS/iTpTEkda+B1VfGKpI0lHeu1tfMl3SUppbqhpvbZGkmvSvpEUrmk3pL2l7RRNS4TAAAAAAAApQ8t77rrLs2cOTPr8x06dNB5551X9PzvuOMObb/99tp9991jTf/RRx/piSee0KBBg3TAAQeo2qQ85CqvoddVhYWUAyU19/uNVbfU1D57RtI3Hu7aPnpK0mOSTqvm5QIAAAAAAKD6Ki2//fZb3X777Tr99NPVtWvXkszTQssTTzwxVmi5bNkyHXPMMUGIetppp1U9tLSw7C1LQiWtktRZ0o+92u8Wn+ZuSWWSukk6WdK1Hq5ZRWNrSdtL2t3vl2d53VBJ90n6laRm/vxqSddLOlVSlxzrsmmO9V8p6QYP/F6U9LKkvSVtmWHaXOsdmi3pBUlzJbX150dH1jHdIkl/kXSopP95M/VWvr0900JVqwad7mdkH5+maY59Zvu6lBZL+lDScZFtOdgrUm29upd4eQAAAAAAAKi+0PLMM63N7noTJ04MQsuf//znQaVjaP78+Xrqqac0b9489enTR4cccogaNfp+0atXrw6enzp1qnr27Bk837x5cz300EOaPXu2Xn755SCQNCNGjFCTJk0yrss555yjww8/XC++aAldCXwg6T2vvGsnaZak/0g6whbmYdrPrJw0Eu79ygPGcg/5LNSzTR3k02R63bceLKZyVBfmWpdsLAC92JJfr7Tc0ddlQYZpc613GKI+4k2mD/cm26PyVECG22Bh6zAPOm2d/y7pQg8lrY/NhyVtJunnHrSO9nkfl2OfZWJNyNefJpm183llYsGk0sLUzbw6ldASAAAAAACgfvVp+c4772jYsGHaa6+91Lt3b/3zn//UVVddpbfeekstWrTQypUrg4CzcePG2meffTRhwgRdc801evvttwtazoMPPqhJkybpvvvuK11oaRWAnbyyz2zht2gT60YevoWieaoFYHtImhAJ/7K9rirrkku4jIZ5lpdvvSdKWifpEN8GCyD38+bT+ewXqVQcIuldD2q7eeXoCg81w/Wzysx7vaKzYwH77Jw8/XRalWY2S7PMv4U/BwAAAAAAgPoRWq5du1YnnHCCrr32Wp1xxhnBY+Xl5UFIedNNN+nyyy/Xe++9p08//VSLFy9W06brE6PJkyerrKxMJ598sm688Ubtt99+uvhiKxnM7PPPP9cll1wSBKEWfsaxatWq4BZasmTJDyfqK2mcpEe9yXJvr9bLxUK4sZIWenVieaQvyaooZl0KkW+9v/Wm6NHdm6lJeCbtI/9u6gHqish8N0kLC7tEKlALGXgncwFufGUFBp0AAAAAAACoe6HluHHjNH369KDZt1VXplKp4GbB4tixlpBJnTt3DsLNBx54IOi70qovt9giTgnhehY8Hnvssbriiiu09dZbx36dBan2mpysefAFPpr0VO8TcltvHp3JVB+8xSoFe3jzbAsDX4u9WqVbl0LEWe9UhgAvWzPtOMFfKvL/BhmmLytidPOqNA9v4U3Z16QFs8v9OQAAAAAAANSP0HLWLOt4UUH/lRZMhoYOHaru3de3F7Ym448//riuv/56XXjhhdp5552DPjFPPdVGd8nvkUceCUJR6/vyd7/7XfCY/XvMmDHB/d/+9rdq0OCH6drw4cN10UUXVaq07NYtbHsdsbGkXf02wweDGRypQowGa9O8SnCHyGNWLRjVIMPrwsF3VkXmaxWPcdfF+nmsijjr3T7SRNwqJeV9X1ZVB2+GvjZyNs7x5XTIsc9K3Ty8W2RfbBHZB99JKs2YUgAAAAAAAEhCaLnJJtbuV0ET8VxVkEcddVRws+Bw1KhRweA+7du316GHHho0E8+lX79+QdhZKGuKHjZHz+otr86zEMsmnemBXQsPGht7cGd9Tcr7eRzjoZsFbp/7qNnRyr2NMryujQeSY3zUbKvue7GAdamqOOtto4m/7tWXQzzMe7UEy+4n6Q1JL/lo6BbcPu/9anbKsc9K3Ty8nTe7f9WbpDfyddrUq08BAAAAAABQP0LLgQMHqkuXLrr66quDkcDDAHLp0qWaNm2atttuO02ZMkUtW7YMAs5WrVrplFNOCZptW/Wkad26dTB9NgMGDAhuUaNHjw76zQwrL6scqD3nzYY7+OjdYTXkUA+2nvWBZk70kabv9j4hLbPdWdKHkXmWZXjdyZbcSnrG+61sLWl3SV8VsC5V3c58693Ml/eMD6TTyna+j2IeVl4WwwLYn3pQeZ1XVW7pA/7k22eldoTP/xav2LTg9PgCmsEDAAAAAAAg+aFlkyZNgqbfNnq4hYh77rln0HTb+rO8+eabg9By4cKFOuCAA7TTTjupV69eGj9+vFavXh1UXhobUfyOO+4I+q5s1qyZRowYEcy3Rlj13frVWB/mpYdXA/xmIaL8+UM9cCv3MM+aOe+b53XygOyCtOVsH6l2zLcu+ZpNR4NFCzyHRyoT4653uI5hE3ELOsNK0UzapC0n9Ou0M8/66/y5z7csy7Zl2melZgHwMb4PDGElAAAAAABA3Q8trVpy5MiR6tr1+04A99hjj6Ca8plnntHXX38dhJN/+tOfgubfZpdddtH777+v559/PqiuPP3003XYYYcFA/IYG2G8f//+mjRpklauXBlrPc4++2z16FHiNr25Aqz0AcvLIiFhwxyViI3zLCdb6/VCw7QmGdYv07zzrfe/fdRyazI9z5uwb+HNtzPJtpxsmXOcis14g8NXDWElAAAAAABArSpL2XDeCPrTtKboujQyME5dYE3G/5Pluc29yXqpfC3pBe/7son3Abl/jtASAAAAAACgLrK6ueukxYsXB90Zop41D0cN2NP7v6yJikEbYfvMIpqnAwAAAAAAAAUgtKwPR7CmjyKBJQAAAAAAAKoR8RMAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmgJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgEQhtAQAAAAAAACQKISWAAAAAAAAABKF0BIAAAAAAABAohBaAgAAAAAAAEgUQksAAAAAAAAAiUJoCQAAAAAAACBRCC0BAAAAAAAAJAqhJQAAAAAAAIBEIbQEAAAAAAAAkCiElgAAAAAAAAAShdASAAAAAAAAQKIQWgIAAAAAAABIFEJLAAAAAAAAAIlCaAkAAAAAAAAgUQgtAQAAAAAAACQKoSUAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmgJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgEQhtAQAAAAAAACQKISWAAAAAAAAABKF0BIAAAAAAABAohBaAgAAAAAAAEgUQksAAAAAAAAAiUJoCQAAAAAAACBRCC0BAAAAAAAAJAqhJQAAAAAAAIBEIbQEAAAAAAAAkCiEljVhqaSnJK2oJ8upTz6R9I42jPVZIOl1SU9LmhrzNV9IeivHfQAAAAAAgGpAaJkvAPw2y/OfS3peUnmMvbxS0vuSVqt61dRyqovt07dreP4z/fHaUJPrY+fEfX5ed5HUIubrZkv6NMd9AAAAAACAatCoqjOYMmWKVqzIXtrXtGlTbbnlllWaf5s2bdS+ffu80y5YsEDLly/XZpttpoYNG6pKWkqa7nvo4AzPvympfcJiX1vnQyU1V900ywO7Pevo/JO8PvMkLZN0oKQmBbzO3rodq3G9AAAAAAAAqiO0vOyyyzRx4sTg36tWrdLkyZPVq1cvNW++Pjmzfz/9tLVHLc5RRx2lE088URdffHHWaV577TUNHz5c06ZNU6NGjYIQ9fLLL9dFF12kKtlR0r8l7SepceTxOV4RN1TSGq+4NJaTtpG0nf8/G6veHOsBY+g7Sa9I+nFaFdzn3pTXwtHukrbKMV9bl28k9Y08NkPSZ/6cVdhtK6ksRzXefyUt9EB2aw9nw3Va5E2D95H0oU83QNImdvD9Mdu2jSVt44+HpnsVqDw0s+d2iJyBX3kF32KvcA33f/cY8/7Emz5vIekjnz49aM42/9Bcn48dh55p+znfukfXoU+O+ZRqfRRjn0RN9vPNvOD/P8DP43zbtdSns3OhUOE+6enrsMyPUR9//3wsKeXna9cC9rdVN78qqbOkfpHX/UtSMz8nAQAAAABAnVblOsHHHnssCC3tNmrUqOCxRx99tOKxMLBctGiRvvzyS61duzbjfCxotOct+Ax99dVXWrlypWbPnl0xv/LyH7bHnjBhgu677z7NmTNHM2bM0L333qtf/epXeuutKna+19+DvPTmsBaotJXUwwPArn7bxMOm2zzoycaCqv+lPbbK52tNvENPSnpJ0kZePfmipGcLaB5u/Q/eL2mdpNYeXmbLj223PuihZSsPw+5JW6flfv9eD7MsNGrqYdSdvrz2Pv29vrxQi8h+au3bb9OUR55v7fMLp2sRc94zPVz+u79+swzbl23+8m39h78bLCT7p++HuOseXYdc8ynV+sTZJ+kVuGG1ZBdfVsOY21WV5uAzvfn7KF+ebc/f/Lwe7ef1Wm+2Pitt3+Rarwa+HaN9/cwHviwLSAEAAAAAQJ1X5UrLfBYuXKjTTjtNr776qjp27Ki5c+fq17/+dVAJGfrtb3+rG264QZ06ddL8+fP1s5/9TNdff72uvvrqoHrSQtAXX7TETho/fnxFFWcovaLysMMOCyouLQTda6+9il/5jb0q7P1IRddar3Ab5IGl7cGd017XwAdTObL4RQdB0RRJ50Wae1sF558i1Y1xKt2284rQaGCayccell0YCc9elvSfDNPuIWnXyP3RHmAeE3msrQeuYYWghWvRFv4DfFs+9Uq7jj6PpWn7M868w9D3rBwVrtnmHx7Tkzzck4dj/4tMl2/d486nVOvzasx9EtrUm3n/x6s5Gxa4XVVhgfkp/l6SV15auPoLDy3l592Hvk1x12tbD22f8P3wvFdE05QdAAAAAIB6odpDyxNOOEFt27YNqiVbtGihL774QnvssYf69Omjn/zkJ/roo4+CcNL+37dv36AS8/bbb9fixYt19913ByFlvubhZtmyZUFl5pIlS3TPPfdoiy220OGHH171DdhJ0l+9aXQbD1xWeBVmaKk3S17kzbDnxhygJ5fP/ehYQCVvRit/bHbM0LKjB1UWSPb2prNWuZbJNEmbpzVN75sltNwiw7rasp6JrOtyD6hWeTWhvOn6F14pWO7TWV+LucSdd8c8TfJz2SQSEJoOVr6bNk2cdY8zn1KsT9x9Ekcxx6TQbQkDS9PO13GjtMeWFLFe1j/nHZLu8vM7GqQDAAAAAIA6rVpDy88++yyokLQqy6+//lqpVCq4/fjHP9bo0aOD0NLCRhs0p1Ura5OsoELy/PPPL3hZ1kT8rLPO0rx584Im5nfeeWcQlmZj00SbolvYmdEWHiBZteXeXvFmVWvrV/f7ZtRbeHPWph5cRpu7FuM7DxDTmzp3iVSk5TPQqz7HeJPczbw/yp5ZlhcNkpThfqhZhte2z7CufSIdENg6vOFhb0fvI9T6Lvz+EGQWZ96Z1qkq74KytNA57rrnm0+p1ifuPsmn2GNS1W0p1f4Om9Z/5BXFAAAAAACg3qjW0PKTT6x9svSLX1hb0Mp23nl9W9dBgwYF4eVWW22lfffdV/vss4+OPfZYde4cN5lbz6o3wwGBnnrqKR199NHByOXDhg3LOP21116rK664Iv+MG3h48oE3rf1S0rGR5yd4cPKTyGPz8oSWjbxyrDwSMkX7spQHpfOzNC2Oq4EHlwM98PmX9yn4/6UNLCQPYdPXOVtTcmVY1xZ51nW8pH3TBkmxPiCjyoqcd1zZBiDKJ8661+T6lGqfVNd2qYbWa5LfbJCeFz2Mj1Z1AgAAAACADXcgnlyaNLFRRKTXX3+9YiCd8Pbggzbqi1RWVqZHHnkkqMq0kcJtJHALMCdNsjSiOBZUDhw4MKjmzMZGG7cm6OHNKkGz2tEDvNEeFlmlZVQ48I1ZEqNJcFgAGl2khaJR23sz83AU5dAUb54ex+eRdWvqTWjtfqaxkLb2arYwuEx5eBTH9j6tNU8Orc0wgEu0Uu5jH308qnmGbYs77zgyzT+ufOuuGlyfUu6T6tiuUsi3Xku8ebyNbD/Mm5iHo7CHFdBPedcNAAAAAACgzqnWSssBAwYEweXf//53nXeejSjzvdWrVwfPhf/v2rWrTjnllOBm/VFas3Lr47JZs2Zas8baW2dmzc3XrVsXNCsP2f1Zs2Zpl112yfo6q8K0WyztvIprqg9CEw5kIq92sybjd3t/kdP8/9EgM10bf93fPEi0QCZ9VbpJOkjScz7/Vh5itkyr9MzFgta/+EAsjT3wtPWvPI7Rej28ovR+Xyfrn7NJzGh7b9+GOyIjqs/x/kAtDJUv9znvq3Cdh6PpfVD29sFkHvHt3DHmvOPKNP844qx7MYpdn1Ltk+rarqrKt14pH5G8c2RArCN9f4zzCs3FHvjvntY/KAAAAAAAqBOqNbTs0KGDRo4cqUsuuUTLly8PRvK2AXmeeOKJIFC0ZuPPP/98MODOqaeeql69emncuHH65ptvgmbjZptttgkCzKFDhwYBpgWZDRp8n6KtWLFCgwcP1gUXXBA8t2jRomAgH+vb8uyzzy7dxuwvaUaGKksLNK0Lzq88YBkaGYwnZKHJoWlh4aEeEi7yfvus+nJi2kA4A3yU5OleSbdXngF40pezq7/+a3/9PmmjMqcb5sHXQp/Omq/fG5lfG59/4wxn0U98m2d5ANslramuzbe7pJkehvbw9UofkOUCf3yV74s4897GQ958Ms0/02u7+fEuZN3jzKdU6xNnn6Tr4MeuQYHbtWXaiNzp93PJtC3belgbtUNa9W++9VrufVj2iTSxt/fPiZGKzE18ewksAQAAAACok8pSVqpYIpMnTw5G7P7rX/+q7be3Nqyq6GPy/vvvD5pgb7755jrmmGN03HHHBU3DzQsvvBA8P3XqVHXv3j0YUGe//fYLnrPXXHbZZUFz8ZUrVwajiTdv3vwHy73xxhv1/vvvByOU77jjjkGI2a1bnCTr+4F4WrduLV1axUFd6rLpHhbJA8tR3j/nWbW8XgAAAAAAADXJxh65TkGXguHg0ajDoWVdRmgp6VVJX3gl3Ryv/jvWq/iAdC/mGGncqiXj/80AAAAAAIBkIbSs383DUcf82JvqzvbQqWukX0sgXecsgzoprZsDAAAAAACAAhFaorKOBfRZiA2bBdwAAAAAAADVIN+40AAAAAAAAABQowgtAQAAAAAAACQKoSUAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmgJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgEQhtAQAAAAAAACQKISWAAAAAAAAABKF0BIAAAAAAABAohBaAgAAAAAAAEgUQksAAAAAAAAAiUJoCQAAAAAAACBRCC0BAAAAAAAAJAqhJQAAAAAAAIBEIbQEAAAAAAAAkCiElgAAAAAAAAAShdASAAAAAAAAQKIQWgIAAAAAAABIFEJLAAAAAAAAAIlCaAkAAAAAAAAgUQgtAQAAAAAAACQKoSUAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmgJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgEQhtAQAAAAAAACQKISWAAAAAAAAABKF0BIAAAAAAABAohBaAgAAAAAAAEgUQksAAAAAAAAAiUJoCQAAAAAAACBRCC0BAAAAAAAAJAqhZU1YLekLSWsTvJyaWse4Vvr6rNOGsT6LJX0paUaRx6smjl/SzhEAAAAAAFBvEVpms8IDGgursoVMkyWVx9jLNu2jkparelVlOTW1jpmsyhCGzff1Wb0BrM94SbdJelvSJ0Uer5o4frV5jgAAAAAAgA0KoWU2jST9U9J/szz/sqS3ErYHm0jq7etelyzyMMyC4g1xfcZI+rGkU/z/ST3WdfX8AgAAAAAAdU6V44fjjjtOEydOzPp8r1699PTTTxc9/6OPPlqHHHKITj311KzTzJgxQ7feeqv+85//qFGjRho8eLAuuugitW7duujlqrGkfpLel7RH2nPfSfpU0sHeXNia9ZqGktpIapdn3lapNkvSFpHH1kj6StLmHg5FH5/t4WjHtOfSNZc0SFLTyGNWCfqtz2eTtOfiyLf8XPOPs2yrZvza/23b30zSxmnTLJW0QFJbSa0ij8fZ9yt9/r38uGWaT6nWJ+4+C1nV5jRJS/z2he+njWNsV6ZjHVf6Ppnv2xK+XRb4+ti6t8ixzEL3LQAAAAAAQE2FlldffbVWrFhfkjZ58mQdccQRevTRR7X99tsHjzVtWkyq8j2b57x587I+v27dOv3oRz/SmWeeqd///vf67rvvNGLECL3wwgv697//rSZNcqV8eewk6T0PZrpFHv/Qw6TtPDgbE66Mh3SbSjrWA69MZkr6q6SRkceWeXXf+ZLa+2MfS3rWQ6sySQslDZO0dZ7mu7/0AMoqBh+SlJLU0l+/t29XHPmWn2v+cZdtAd8E//d/fb/avt7SH3vB938LD3qtEnG3yP7Ot+/DZt3b55hPqdan0GO2ytd/nQeWM31ezWNsV/qxLkS4T7aSNNeXZ8veX9I3vl1N/bnjvLoy0zIL3bcAAAAAAAA1FVr27h0mGpWrK7fbzhK99R5//HE9+OCDQfjYp08fXXrppZWeHzt2rG688UZNnTpVPXv21C9+8QvtvvvuOuOMM/TZZ5/phhtu0AMPPBBMO378eDVvbinLeg0bNtSkSZMqhaPdu3fXtttuq3HjxgVVl0XbTFInr7aMhpZ231Y/zENPSquee1jSO5L2LX7RQVD1lKSfevWlvL/DUR5splf/ZTLGK99OigRyn5Zw+bnmH3fZdigPkXS7pKM84FRkQJomvrwGHiY+48FnU38u7r7PNZ9SrU+hx6ylr/+1kvaUtG3kueo4p9K18lDSwtXXJL3oYePRkYD29UhomU3cfQsAAAAAABBTtffIeO2112r48OE66aSTgibcFiYOHDgwCCPNnDlztO+++6pv3776y1/+EjQ3t0pJCzgvu+wybb755vrpT3+qxx57LLhlqtxMf8yaiJvy8jij5OSxo6SJkQFYLLyak6FicJE3J7bmvh0iTYyL9YGHftZceYoP+tPYKxetGi6uNV7RF23yXsrl55p/scuO2i1ylm7h62PVi4Xu+zjzqer6lOqYFbJdVTHIA0vTI/KYIo9ZtWU+pdq3AAAAAAAArlqH1FiwYIGuvPJKvf7669ptt/XtRQcMGKAJEybo5ptv1u23365PPvlEa9eu1W9+8xs1aNBAu+66q4YNG6ZUKqUOHTqoWbNm6tSpU6XKzHx+97vfqVu3bsGyslm1alVwCy1ZYp34ZWBNX1/xZr87epWl9TvY1Z+3MPNxD5TCfhutGW1VLfB+Av+T9ni3AqLm3SU9Ien/vPKvdwEVcHGWn2v+VVl21EYZzlYLQwvd97nmU6r1KcUxq85zKs62pD8WZx+Vat8CAAAAAADURGhpTblXrlyps88+W2VlZUEQabfZs2erX7/1ZXf9+/dXu3btdNBBB+nkk0/W3nvvrc6dO1epsnPUqFF69dVXg8Az13RXXHFF/hlac+FtIk3CP5I0JPL8OK+I+1UkkHvL+73MJqxui7K+C9Ob3LZLayZcTPPfn/mgKl95k23bjnOyrEOhy881/6osO65i9n11KsUxS+J2AQAAAAAA1KfQ0gbFMffcc0+lfihNixbrhyVu06ZNUHlpfV4+/PDDwYA6Q4YMCfrBDKeJ66abbgoqO0ePHq099kgf8rsya7JuI4xHKy2tOjMjqxB8UNLb3vR1h8hz4SjLYbhkTYHXt3zPbmOfbnFkEJXpadPYiMw26Po8bxocrcJrEPPIfedVcK28YtQCtXt89PJ8fWLGWX6u+TcoYNnWhDpTcJtPMfs+jmLXpxTHrDq3q7ZM8eNvTecBAAAAAABqO7S0QXfM4sWLg2bf2XTs2FEXX3xxcJs7d27Q76WFmOeee24w0I5VZ+bzpz/9KegL06os99/fhkHOzfrBjD2yeQ8PXP7lg6VEm8NaH36PSfq3jxj9ofcDaGFdNpt4KDVa0i7e/59V10X186pOG39oD5/fXJ//6TGP3MsevPX2IO49H1woziA+cZafa/6jC1i2zbu5N6veMub6Fbvv4yh2fUpxzKpzu2rLX7062QYbAgAAAAAAqO2BeCx8HDp0qC688MJgZPBwcJznnntOTzxhHR5Kb775ZlBhaf1amnXr1gW3jTdenxRZf5ZffWXti7P785//HIxIboHlAQccUPoNsebMP/IALr2bzD4+2vJs7/fSAs7DI6NHh82Ge0dCK9vrJ/vI5OEgPyf6NOGI5A0lneCjRc/06eRNrrMVoKYv5zAP3WzXT/LnTor52jjLzzX/QpbdyEfcXuPhrVUVNvPX2Hoost/ssWYF7Ps48ynV+hRzzMIKzY2reE6l388l07Y098eiV4QWHqBmW2bcfWv3qbIEAAAAAAAFKEvFKWOMaeLEiUFfle+++64GDVo/DPHChQuDisknn3wy6KvSBuexfitvvPFG9e7dOxg9/JJLLgmet4By1qxZOv7444NBemwU8BdeeCEYUdyqMa2PSusnM9rU3Obfvn17tWzZ8gfNu62p+JFHHhlr3a15eOvWraVLc4RZAAAAAAAAqP9WSrpufevhVq3qatPHuq2koaWNxv3FF18EYWR6H5bLli0LAsmuXbv+4Lnwtd98800QbG60UbT9tYLBfOw5+3/fvn2DUcZDVpVpI5Bn0qVLF7VtG6/Ei9AS9ca3OUYbt4rO4se5AgAAAABgw0BoWb9Cy7qM0BL1xruSJmd5bnPv6gAAAAAAAGRHaFm/B+IBUAt28xsAAAAAAEAdVa0D8QAAAAAAAABAoQgtAQAAAAAAACQKoSUAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmgJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgEQhtAQAAAAAAACQKISWAAAAAAAAABKF0BIAAAAAAABAohBaAgAAAAAAAEgUQksAAAAAAAAAiUJoCQAAAAAAACBRCC0BAAAAAAAAJAqhJQAAAAAAAIBEIbQEAAAAAAAAkCiElgAAAAAAAAAShdAyl5Tfst3P9li2eaW/JtPros/FnXemZVTna0rx2mzzq8r+jPNc+vO5psv3mlyPl1KhyyjkvCnF+ufb33GWlenY51u3Qo5tqbclzuuLVejxK2Sb4763qnotKeb6FWe+KvE5VMj8474m37U97vxr4tqSvsxs61Fd+7EQpT6n4pwrNX0OZ1tmdZ/T+T5D46xj+nPVcayyff+qrWNXzPWmkOUW8t6LM21VP98yLbPY1+Zar6qsUyleU+x3weo4/vmmKeV8436Gl2o7q7qM9GtDqc7HmpxHTV4zSvG9rpQKOXbVfS7GnW9tnytVec/E+Swr5jiU4lpV6HekXK8r9lpQqusJqk2j6pt1PVCW5362x/LNK9dr4s6vVK+t6eWVan659me+fV3McSyroX1QimUUux+LVYpzu1THpCrv0fRpa/r9VMw8Cj0vy6pxG6q67+LMt9Dpqmt7iznPa/p9WahSvneqQ3WfU1Xd/kKXV8i0hb63qvqZUcxnaLHLzqeUnynVvfzauO4VMm0pr9Gl/O5YqvUq1bW8uq/vcdT2Z1fS3lu1tR7VPY+avGZU9XxJym++YuehhCyvus6Vqjxf29+zS/k7LQnvRVQLKi0BAAAAAAAAJAqhJQAAAAAAAIBEIbQEAAAAAAAAkCiElgAAAAAAAAAShdASAAAAAAAAQKIQWgIAAAAAAABIFEJLAAAAAAAAAIlCaAkAAAAAAAAgUQgtAQAAAAAAACQKoSUAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmgJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgEQhtAQAAAAAAACQKISWAAAAAAAAABKF0LImzJN0taQl9WQ5NSHp21Kq9Uv6dta39QYAAAAAAHUCoWU2FsZcK2lSludfk3SrpPIYezklaY3/vzrV1HJqQr5tmeuh2TLVjlLt67p6zOrqegMAAAAAgDqhUVVncNddd2nmzJlZn+/QoYPOO++8oud/xx13aPvtt9fuu++ec7rVq1frySef1KeffqpTTz1VPXr0UJW0ktRV0v8k9U17bp2k9yXtlLDYt4Ok4ZKaqP4rr+XQrFT7ekM6ZgAAAAAAADUVWkZ9++23uv3223X66aera1dL/KrOQssTTzwxZ2j56KOP6te//rX69eunF198UUOGDKl6aCkPJf/pVZcWYoa+8Aq/HSWtlHSDP95QUht/3a6SyrLMd4qkv3tYFVok6S+SzpHUzh9b7BWdUz0c7S5paNq6RM2XdKek832adf76Tz3g6+Kvb5/l9XGW942kFyUt8PnYdj4TWe8421bMPouyfX+3//sW//9Wko6WNEfSK5JmeBBogfM+khpnmdcsSfdJOtCD6Pm+XQf7v9/242/77tDIvihkX+d6Ln0+4focIWmMP2/77AB/XWi6pJckLcxyHHKxY7GLpIGRx5Z45fBJkjom6JwGAAAAAAAbpCqHlmeeeWbFvydOnBiElj//+c81aNCgisfnz5+vp556SvPmzVOfPn10yCGHqFGjRpWqJO35qVOnqmfPnsHzzZs310MPPaTZs2fr5Zdf1rJl69sBjxgxQk2aVC5L6927t/73v/8F8+nWrZtKZmtJzSV9IOlHkcct3Oolqa3fv9j/v87DslGSmknaPkeV4Oo8zW0t2Lvfw7jTPCx6U9Ijks7yMEl55jHGg7JjJLX0dRvjgVy6OMtb4fe394DQgssn05YZZ9uaFbHPolpI+pkHl+f4/Qa+DQ/6NthpudTnu0rSYTmOha3bex5KbiTpKUkPeRh4uK/Xkx7W/qSIfZ3rufT5hOvzL18fm/51DwQv8OPwnSX1kvr7PBdmOA652P6ZkBZafuTnelc/9kk5pwEAAAAAwAap2hs3v/POO9pqq6303HPPBaHlNddcEwSay5cvD55fuXKlBgwYoD/+8Y9BuDl69OigqjIMKeOw+W2yySalX3kLUXbwkDIMXpZ6paVVWYaa+s0Cry0lDfAQqCre93ke6OFoGw+xFnqVXRzzPYTqLGljD4sOrsLy/ufTHODPW3C7d5HbV5V9ZmFXmHk39vnY///rx+wQ34buvq62bflOpwN9P7X2dbFg8CBJnXxbrTJxWpH7upDjEDrYKyutAnGIVyha5WJ4HCxg3N/XrWeBx8GCx5k+mE7I9n2/SCVlUs9pAAAAAACwQShp8/B0a9eu1QknnKBrr71WZ5xxRvBYeXl5EDLedNNNuvzyy/Xee+8F/VAuXrxYTZtaoiFNnjxZZWVlOvnkk3XjjTdqv/3208UXh6VfpbFq1argFlqyJMswyNYs9l0PrHp4hZqt5jaRaf7rt0VeWVaeowl2XN/4YDM2GJAioekar3C0oCqf7ST9TdLjHjz18qCo2OV960FaNOoutheA6thntn6bpVXsdfdtmeuBYTbRZTfP8phVmhazrws5DrnWZ0XadkaPQ7TpeD6W728q6UNvOm/zmy3pyDpwTgMAAAAAgA1CtYaW48aN0/Tp04Nm31dddZVSqVRwa9y4scaOHRtM07lz5yDcfOCBB4K+K1u0aKEttthC1c2C1CuuuCL/hB0jA/L08Gqx7SN77mNJL0sa5tNZoDnWw82qsJCotzfDLvaoWTh2njdN/lzS894v4f5FLq88Q5+GxdTqVtc+S2VYn3B98zWbLovxWKrIfV3Icci1PqnI/6t6HLb3JvH7eHhpFaWb1IFzGgAAAAAAbBCqtXn4rFk2qoiC/istmFy3bl1QaTl06FAdccQRFf1RPv7447r//vuDkcYHDx4cBJjVbfjw4UF1Z3j7+uuvs09s1ZafeOA03++Hpnko1deb8jb1yrVcmnnwFO0D0JrIRnXyvgTLIk11w1shff+19r4Lj5N0rFeNWtNnFbG8Dl6RFzWziG0rZp9lO3OjQaKt3ywPx9LXr6pVglXd13GPQxztMxyH9Pv59PMqSjvtJ6b1VZn0cxoAAAAAANR71VrfFPYzaU3Et97aRrXJ7Kijjgpu1kR71KhRweA+7du316GHHho0E68O1hQ9bI6e13Y+CMtob4ZrTWtDbT3QXODB1EdeqdYuT/VmUw+u9vTw6NW0aXb26rZR3i9jS++D8F2vjrP7+bzilXK9/EjPioREKmJ5O/lo2m9J2t3X+80itq2YfZbO1qfMw7pw5Glbv39LesMHTlrmFYPb+nKqU659neu59V27FibcTjsWu2U5DvnYPttc0gveX6aFmHXhnAYAAAAAABuEag0tBw4cqC5duujqq68ORgIPA8ilS5dq2rRp2m677TRlyhS1bNkyCDhbtWqlU045JWi2bU3KTevWrYPpa1UTDy7/5+FK1K5ePXabV/ht5qHSV3mq0o7wwOhfHhLtnFa1uLGPsPySpFsiFXa7+WjZcezgI0+P9lGgLUM+PktVW5zltfTRs5/z4LK1b//LBW5bMfss0z60wWee8Hls5c2OT/CA+d++ndv4gDrVLde+LuQ4xA0cj/Fm5m/4cdjRl1HIPK268mkPU1vWkXMaAAAAAABsEMpS1slkiUycOFH9+vXTu+++Gwy2E44ePmzYsKAZ+J577qnZs2cH/VnefPPNOvjgg4OBeI4//njttNNO6tWrl8aPHx8MzGPTWOB55ZVX6o477ggG5WnWrJlGjBihJk0sRfzeBx98EIw6bpWaNsCPBZ89evTQkCFDglsc9loLSHWpBzDpLGxa66NUZ2pUHzZJbuDT2i1czbDZrN0vy/C6BnmmSfl0+QKpbPNIZenvMdd88i1vnT9vVXo3STo/QxPsfNtW7D5LZwO5yI9NdP0axHhtub++aYzH1sZYv1z7OtNz6fPJtGyzKsu5Fx4H6y/zH9bvQQF/igiX1TDLa5JwTgMAAAAAUBtWSrpOQZeCVmSHOl5padWSI0eOVNeu3w8pvcceewTVlM8880zQb6SFk3/605+C5t9ml1120fvvv6/nn38+qK48/fTTddhhhwUD8hgbYbx///6aNGmSVq60MyY7O4ls+dWiYZ6ApUGOacM+/HK9Ltc0ZTHDnWzzsMcLaWUfZ3lx1iffthW7z9I1LnL9wnVoGvOxJlXc15meS59PpmUrw2Nvep+TYT+jr3oz+ELe0dmWFX2+ts9pAAAAAACwQSpppWVdlrfSEpnlqrRE9ZnizfLneTBoAeZQ/7c13/8gy+usufyRHBgAAAAAAHKi0rJ+92mJDUArb5JcucU+qltvSedEmmJH7S/px1leF7eLAAAAAAAAgFpEaImqKaQZN0qvQZZ3Ne9sAAAAAABQh1F3BQAAAAAAACBRCC0BAAAAAAAAJAqhJQAAAAAAAIBEIbQEAAAAAAAAkCiElgAAAAAAAAAShdASAAAAAAAAQKIQWgIAAAAAAABIFEJLAAAAAAAAAIlCaAkAAAAAAAAgUQgtAQAAAAAAACQKoSUAAAAAAACARCG0BAAAAAAAAJAohJYAAAAAAAAAEoXQEgAAAAAAAECiEFoCAAAAAAAASBRCSwAAAAAAAACJQmgJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgERpVNsrkBSpVGr9P1bV9poAAAAAAACgVq1Ky4tQ4wgt3dKlS9f/46aaPwgAAAAAAABIZl7UunXr2l6NDVJZisg4UF5erpkzZ6ply5YqKyur7eOCOmjJkiXq1q2bvv76a7Vq1aq2Vwd1EOcQOI+QBFyLwDmE2sZ1CJxHSAKLyyyw3GyzzdSgAb0r1gYqLZ2dgF27dq2Vg4D6xQJLQktwDqG2cS0C5xBqG9chcA4hCbgWoSqosKxdRMUAAAAAAAAAEoXQEgAAAAAAAECiEFoCJdK0aVONHDky+D/AOYTawrUInEOobVyHwDmEJOBaBNR9DMQDAAAAAAAAIFGotAQAAAAAAACQKISWAAAAAAAAABKF0BIAAAAAAABAojSq7RUAkmDu3LkaM2aMmjdvrsGDB6tZs2Y/mGbq1KmaOHGi2rVrp1122SX2gDtx5h1nGiTb6tWr9c4772jp0qXadddd1blz54zTzZo1S++99546deoUnEdlZWUlmXfc5SPZJkyYoC+//FK9evXSDjvskPE4/+9//9OcOXPUt29fbbnlliWbd9xpkGx2DXjllVfUvn177bXXXj94vry8XB999JGmT58eHOdtt9029rzt+jV+/Hi1bNlSe+yxh5o0aVLUNEi2VCqlN954Q99++61+8pOfqEGDyjUOL730khYuXFjpsd69ewefPfmsXLlS//73v7VixQoNGjRIHTt2LGoaJN9nn32m999/X7vttps233zzHzy/Zs2a4Fph59lmm20WfCdKP9eysc/BadOmBZ+B2223XdHTILnWrVunDz/8UDNmzAiuL9tss03G6RYtWqTXXnst+F5tnzlxffPNN/rvf/+rNm3aaPfdd1fjxo2LmgZADUgBG7i//e1vqRYtWqT23HPPVN++fVNdu3ZNTZw4seL5uXPnpg455JDUlltumTrssMNS22yzTWrTTTdNvfnmm1Wed9xpkGxTpkxJ9erVKzhHhgwZktpoo41Sd911V6VpysvLUxdffHHw3H777RdM9+Mf/zj13XffVXnecaZBsq1ZsyZ19NFHp9q2bRucH/Z/u2+Phz744INU7969U1tvvXXq4IMPDqY577zzSjLvONMg2ZYvX546++yzU506dQpu+++//w+mee2111Lbbrttaocddgg+1zp27BhcMxYvXpx3/nZNsWuLTW/XGrvm2LWn0GmQbHfccUdwnbGb/UxYsWLFD6ax86d///6pY489tuJ255135p23fbfp0qVL8F1n8ODBwXcf+w5U6DRItvHjx6f22Wef4Bpg59DDDz/8g2k++uijVLdu3VJ9+vRJDRs2LNW9e/fUVlttlZo2bVrOea9cuTL4/OvQoUPwWdW6devUySefnFq3bl1B0yDZnn/++eB82HHHHYPPqnbt2qUOOOCA4HMutGjRotTpp5+e6ty5c2qTTTZJHXXUUbHnf/PNN6eaN2+e2nvvvYPPKfte9c033xQ8DYCaQWiJDdrs2bODH1g33XRTRbB06KGHpnbZZZeKaewL1BtvvFHpdT/96U+DL9RVnXecaZB8++67b3ALA56777471bhx49SXX35ZMc0f/vCHVKtWrVIffvhhxWN2XtmXrqrOO840SLY///nPqTZt2qSmTp0a3Legx86XW2+9tWIaCwmOOOKIih9e9uXZwsXHH3+8yvOOMw2SbcGCBanbbrsttWTJkuAzKlNoaT8EP/3000qv6dGjR+r888/POW87H+yacv/99wf37VoT/uGlkGmQfBY8T548OfWPf/wjZ2h5/fXXFzzvnXfeOXX44YcH33WMzcO+A82ZM6egaZBsr776auqVV14JjmG20NKKACyUDj/PLGi0kOrMM8/MOe9rr702CKhmzJgR3J80aVIQLD3wwAMFTYNke/rppyv9wcve/xZODh8+vOKxmTNnBt93ly1bFgTfcUNL+8NIgwYNgmtceO4NGDAguO4UMg2AmkNoiQ3a7bffHnwZjla7vf7668GXrOgPu3RXXHFF8BfiqCeeeCL4y3Eh8y52+UgO+9Jkx+upp56qeMx+rNtfha+77rqK++3bt0+NGDEi57zGjBmTevHFFwuad5xpkHwDBw5M/exnP6v0mFWGDBo0KPi3BQdlZWWpRx55pNI0VoFw0EEHVdy3INOqkuxLfNx5x50GdUe20DITO+5W6R9l59Bnn31WKQSwqqW1a9dWPPbkk08G56Rdg+JOg7ojX2hpVd52fMeOHRv8oE/39ttvB5W9oU8++SSY31tvvVXxmF2nLEyy6s6406BuyRZa2vXphBNOqPSYVbSdeuqpFfe/+uqr4Fq0atWqisesUjz9jyxHHnlkpT+OxJkGdc8xxxyTOvDAAzM+ly20tO/Ddg5FA9DLLrvsB7/hHnrooVTDhg0rCgniTAOg5jAQDzZo1q+X9ZNifUmG+vXrV/Fc1Ouvv65HH31UI0eO1N13361bbrml0vOnnXaa/vnPfxY070KWj2Syfk5NtL+kRo0aBX3vhMdw0qRJmj9/vvbbb79g+qeffjrj8b399tv161//uqB5x5kGyWfHKr3PLbsWhMfQ+rm1/nQ//fTTSv09ffHFF0E/lCHrH+z4448P+smNO++406D+sT5S//Wvf/3g2Ns59Pzzz1fct/PArikNGzasdH5YJvHxxx/Hngb1x7PPPqt777036PNy66231n/+859Kz//f//2frrzyyor74bUkeq61aNEi6Fc1+p0o3zSoH6655hqNHTs2+M7z4IMP6txzzw36ar788ssrpnn77beDa9GSJUuC+2vXrtUnn3yS87MqzjSoe6x/W7vGFNo3qfWPa+eQ/YbL933HvlPZuRN3GgA1h4F4sEFbvHhxEAREhfetY+co6xTewgELibp166YePXpUev6oo46q9AEXZ96FLB/JZMfQpB9HGwQjPIbWyby57bbbgnPIOoV/9913g4FOLMC0H2Vm4MCBQUfihcw7zjRINvuR9d1332U8hsuXLw+etyD6t7/9rS655JJg8AI7h0aNGhW8LjwHTNeuXXXsscdWnFNx5m3iLB/1z69+9SstWLBAl156aaXH7RzaaqutKu5n+qyy8yPf51n6NKgfrrvuOu2///7BQHJ2PfrZz36mY445Rp9//nnFtWfPPfcMrh+h8DrVtm3bvJ9nuaZB/WAD79h3oCeffDIIgWzAlb333lsdOnSomMa+Z9u1KBz4ctmyZcFAYrm+78SZBnWPhdp2rbnooosKep0NnGPnkBWIhOw6Y9+V8n2e5ZsGQM3hVwg2aPZFyL7gRIVfstNH8LbAwFjVyHnnnaeDDz44GGU3/DJlFQeFzruQ5SOZol+mbXTBkN0Pf3iFx9KCIau6tNExrRLOQm778ff73/8+eP6cc84peN5xpkGyWSBo1Wnp1wK7b8+FgeEFF1xQEXTbH1Hsi/iOO+6oe+65p+I1NvrqY489VvC840yD+sWq4O6//3698MIL6t69e6XnoudQeJ2JhuMmPF+in2f5pkH9cMABB1QKBawFirVEsVF2f/SjH1UE4lHhZ5V9x9l4440rnSPRcyjfNKgfrEK3ZcuWQesB+/xZtWpVcO6cffbZ+tvf/hZMM3jw4OAWin7ficp0DuWaBnWL/bHW/kj76quvVvrDfhx2PmT6PMt0fuT7fcbnGVB7aB6ODZr95e3rr78OgsjQtGnTgv9bc6RMrLLgxBNP1MyZMzVlypQqzbuY5SNZwr/eTp8+vdLjdhyjx9kcfvjhQWBpOnbsGHxBtx95pZh3rmmQfHasMh3Dnj17Vnpsr7320g033BCETSeddFLQvK5///5Vnnfc5aN+uOqqq/SHP/xBzz33XFARl49dZzKdHyZ6Lco3DeqnVq1aBf+PdkuRLtNnlX33se9AuT7P0qdB3WdNbN955x0deeSRFd1JWEh06KGH6o033sj6OutKqXPnzjm/78SZBnXH8OHDdeedd+qll14K/ihbCnyeAXUPoSU2aAcddFDQdNf69Ao9/vjjQaC06667Bvdnz579g9dZ0GTVR/bFKGRNXML+BePOO840SDbrw82aMP3jH/+odH5YoG3VuMbOk5133rlSf4T2Q+yzzz4LuhoIWQBlX8wKmXecaZB8di0YPXp00Pwp7GvQ7keP4bx58yq95v333w/6aTrzzDMrHpsxY0ZQVRBtlhln3nGmQf3pS84qvC2wtBA8EzuHrKlv9Pyw/lM/+OCDSp9VFmrbNSjuNKj7Fi5cGPQTF2Xff+wPcvY5F7Jq8Gg/cvadxpr+Rj+r7HkLOu3ciTsN6j4LKu17UXrfgHY/2iTXgka7FtnnUcjOAzvfLPgM+zp85plnfvB5lm8aJN+IESOCbpXse7F1n1QM697GziFrGRc9P+xzyj6vop9V1vopbHUQZxoANagGB/0BEum0005Lde7cOXXDDTekhg8fnmrUqFEwQlzotttuC0Y0/OMf/5i69957gxEzbSTLq666qtJ8WrdunRo5cmRB8447DZJt9OjRwXG76KKLUjfffHNq8803/8EohjaSasuWLVMXX3xxcB4dfvjhqbZt21Ya0fCUU04JRmUtdN5xpkGyzZo1K9WlS5fUfvvtF1xzhg4dGty3x0OPP/54MHKmjaJr1x8bqfncc8+tNJ9Ro0YFo7VOnTq1oHnHmQbJZ9cCGyl18ODBwbXE/v33v/+94vk777wzOD/OPvvs4Lnw9uyzz1aaj01z0003VXrMrik9evQIrjG//OUvg1FUbXmFToNkGz9+fHBOXHjhhcF5YN9H7H54LZgwYUJqu+22S/32t78NPsv+3//7f6kmTZr84PuPjea71157VXrM5tW4cePgu4595+nUqVPq9NNPL3gaJNvs2bMrri12Dtk5Yv8eN25cxTT2OWbH2c6z++67L3XmmWcG1wv7DAvZqOP2+rlz51Y8Nm3atFTHjh1ThxxySPBZZedYz549U/Pnzy9oGiSbvfft2Nv5Ef2sevHFFytN989//jN4fJdddkkNHDgw+PeTTz5Z8fzSpUuD+dx9990Vj5WXl6cOOOCA1JZbbpm65ZZbgvPTvkO//PLLBU0DoOaU2X9qMiQFksbeAo888kjQJMWalVg/cWGfTKH33nsv+KutjWxofwU++uijK0b5Dp1++uk68MADg+cKmXecaZB848aNC47j0qVLtcceewQDE0RH0Q2rCO67776gYs4GUvn5z3+uTTbZpNLo4VZZYFVQhc47zjRINru+2DlgFQHWjM36ON10000rTWNVR1aFZJXehx12mIYOHfqDa5WN2vvnP/85qNguZN5xpkGyWX9w6YMENGnSRA899FDw77vuuqtS9VvI+gm7+eabK+4fd9xxOuWUU4LPtJBVLVm3BNas0/qis25SBgwYUGk+caZBsln/3K+88krGZprWp25Y0W3nlFUhdenSJej6JFplaawbC6v4DvsDD1nLEqtYsuo3G3jFzhHrdqfQaZBc1urIuqBIt88++1RqGWCtS6yvQvvssfPIrjvRAS2tWvfWW2/V3XffHVxPQnb+3XHHHUETcPsuZZ9V4SAphUyD5LrllluC0cLTWcui6HfkU0899QeV39ZdhX3WGesr1T7LzjrrrOBaErJWJdYfuJ2D1h+8TWN9hEfFmQZAzSC0BAAAAAAAAJAo9GkJAAAAAAAAIFEILQEAAAAAAAAkCqElAAAAAAAAgEQhtAQAAAAAAACQKISWAAAAAAAAABKF0BIAAAAAAABAohBaAgAAAAAAAEgUQksAAAAAAAAAiUJoCQAAAAAAACBRCC0BAAAAAAAAJAqhJQAAAAAAAIBEIbQEAAAAAAAAoCT5/wH3GeaMsZTyxgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Next, let's take a look at visualizing the quality control information\n",
+    "# Create a plotting display object with 2 plots\n",
+    "display = act.plotting.TimeSeriesDisplay(ds_aps, figsize=(15, 10), subplot_shape=(2,))\n",
+    "\n",
+    "# Plot up the variable in the first plot\n",
+    "display.plot(variable, subplot_index=(0,))\n",
+    "\n",
+    "# Plot up a day/night background\n",
+    "display.day_night_background(subplot_index=(0,))\n",
+    "\n",
+    "# Plot up the QC variable in the second plot\n",
+    "display.qc_flag_block_plot(variable, subplot_index=(1,))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "e3c00d69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_met.clean.cleanup()\n",
+    "# Next, let's take a look at visualizing the quality control information\n",
+    "# Create a plotting display object with 2 plots\n",
+    "display = act.plotting.TimeSeriesDisplay(ds_met, figsize=(15, 10), subplot_shape=(2,))\n",
+    "\n",
+    "# Plot up the variable in the first plot\n",
+    "display.plot(\"wspd_vec_mean\", subplot_index=(0,))\n",
+    "\n",
+    "# Plot up a day/night background\n",
+    "display.day_night_background(subplot_index=(0,))\n",
+    "\n",
+    "# Plot up the QC variable in the second plot\n",
+    "display.qc_flag_block_plot(\"wspd_vec_mean\", subplot_index=(1,))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcef61c0-becf-4f59-a92a-7707997877ae",
+   "metadata": {},
+   "source": [
+    "## Visualizing Data\n",
+    "We've already worked with visualizing the data in basic ways but what other options are there in ACT?  This section will show you how to create a variety of different plots.  More plotting examples can be found in [ACT's Documentation](https://arm-doe.github.io/ACT/auto_examples/plotting/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "215d8efc-9939-43c8-a23a-260ac94af769",
+   "metadata": {},
+   "source": [
+    "### Multi-Panel Plot\n",
+    "This example shows how to plot multiple datastreams together (APS and the MET) on a multi-panel plot. The top panel is the APS number size distribution (`dN_dlogDp`), a 2D field (time x diameter). Since particle concentrations span several orders of magnitude and the aerodynamic diameter bins are log-spaced, we use a log-scaled colorbar and a log-scaled y-axis to keep both the fine- and coarse-mode particles visible. The bottom panel shows MET wind speed for comparison - watch for coarse-mode particle spikes that line up with wind gusts, a signature of blowing dust or haboob activity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "0058fb57-2950-419a-8583-c2dc43989c63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# And plot again!\n",
+    "# Create a plotting display object with 2 plots\n",
+    "# Note we have to create a dictionary of datasets to pass in\n",
+    "display = act.plotting.TimeSeriesDisplay(\n",
+    "    {'APS': ds_aps, 'MET': ds_met}, figsize=(15, 10), subplot_shape=(2,)\n",
+    ")\n",
+    "\n",
+    "# LogNorm requires strictly positive values, so mask out the zero/missing\n",
+    "# bins before plotting rather than letting them distort the color scale\n",
+    "ds_aps['dN_dlogDp'] = ds_aps['dN_dlogDp'].where(ds_aps['dN_dlogDp'] > 0)\n",
+    "\n",
+    "# Plot up the APS number size distribution (dN_dlogDp) as a 2D PSD.\n",
+    "# Need to specify the dsname so it knows which dataset to use for this\n",
+    "# data. This is helpful when datasets have similar variable names\n",
+    "display.plot(\n",
+    "    'dN_dlogDp',\n",
+    "    dsname='APS',\n",
+    "    subplot_index=(0,),\n",
+    "    cmap='viridis',\n",
+    "    norm=colors.LogNorm(vmin=1, vmax=float(ds_aps['dN_dlogDp'].max())),\n",
+    "    cbar_label=r'dN/dlog$D_p$ (cm$^{-3}$)',\n",
+    "    ylabel='Aerodynamic Diameter (µm)',\n",
+    ")\n",
+    "# Diameter bins are log-spaced, so a log y-axis keeps fine- and\n",
+    "# coarse-mode particles both visible\n",
+    "display.axes[0].set_yscale('log')\n",
+    "\n",
+    "# Plot up the MET wind speed for comparison\n",
+    "display.plot('wspd_vec_mean', dsname='MET', subplot_index=(1,))\n",
+    "\n",
+    "# Day/night background only makes sense on the 1D MET line plot -\n",
+    "# on the 2D PSD panel above it would be completely hidden behind the mesh\n",
+    "display.day_night_background(dsname='MET', subplot_index=(1,))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a1a264b",
+   "metadata": {},
+   "source": [
+    "### Cumulative Precipitation\n",
+    "Another common question is how precipitation accumulates over the course of a day. ACT's `act.utils.data_utils.accumulate_precip` turns an instantaneous rain-gauge variable into a running total, which we can overlay on the original time series using a second y-axis. The DUSTIEAIM day shown here (2026-08-04) is a dust/haboob case, not a monsoon rain day, so the accumulated total stays flat at zero - a useful contrast with what this same function would show on an active North American Monsoon storm day."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "321499d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ACT's accumulate_precip turns an instantaneous rain-gauge variable into a\n",
+    "# running total, appended to the dataset as \"<variable>_accumulated\"\n",
+    "ds_met = act.utils.data_utils.accumulate_precip(ds_met, 'tbrg_precip_total')\n",
+    "\n",
+    "# Plot the instantaneous rate on the left axis and the running total on a\n",
+    "# twin axis so both are visible on the same time base\n",
+    "display = act.plotting.TimeSeriesDisplay(ds_met, figsize=(15, 5))\n",
+    "display.plot('tbrg_precip_total', label='TBRG Precip Rate')\n",
+    "\n",
+    "ax2 = display.axes[0].twinx()\n",
+    "ax2.plot(ds_met['time'], ds_met['tbrg_precip_total_accumulated'], color='tab:red')\n",
+    "ax2.set_ylabel('Accumulated Precipitation (mm)')\n",
+    "\n",
+    "display.day_night_background()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68b3e3ea-9f8a-4bd3-ba86-9bae0d9b9609",
+   "metadata": {},
+   "source": [
+    "### Distribution Display\n",
+    "\n",
+    "For the example, we will go over some functions within ACT's distribution display. Functions such as, the stacked bar plot, scatter and groupby."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "53326c0d-df46-4ce6-b0a1-a87e6bd90641",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# First, let's plot up a histogram of the data\n",
+    "# All the ACT plotting is very similar to what we\n",
+    "# did earlier, first we create a display object\n",
+    "display = act.plotting.DistributionDisplay(ds_met)\n",
+    "\n",
+    "# And then we can plot the data!  Note that we are passing a range into the\n",
+    "# histogram function to set the min/max range of the data\n",
+    "display.plot_stacked_bar('temp_mean', hist_kwargs={'range': [30, 45]})\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "da992971",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's directly compare the two datasets using a scatter plot. \n",
+    "# First we need to clean up the datasets and average to the same time grid\n",
+    "# Once again, we take advantage of the xarray data structures and use xarray's resample function\n",
+    "ds_aps = ds_aps.resample(time='1Min').nearest()\n",
+    "ds_met = ds_met.resample(time='1Min').nearest()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af1a1189",
+   "metadata": {},
+   "source": [
+    "<div class=\"admonition alert alert-info\">\n",
+    "    <p class=\"admonition-title\" style=\"font-weight:bold\">More Information on Combining Data with Xarray</p>\n",
+    "    Find more information on combining data with xarray <a href=\"https://docs.xarray.dev/en/stable/user-guide/combining.html\" style=\"font-weight:bold\" target=\"_blank\">[here]</a>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "fa58bc20-1158-4784-b98f-cc9cf9ac9a75",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Combine the datasets together using xarray's merge function.\n",
+    "ds_combined = xr.merge([ds_aps, ds_met], compat='override')\n",
+    "\n",
+    "# Next up, let's do some scatter plots to compare some variables\n",
+    "# Scatter plots are also found in the DistributionDisplay module\n",
+    "display = act.plotting.DistributionDisplay(ds_combined)\n",
+    "display.set_xrng([25, 35])\n",
+    "display.set_yrng([25, 35])\n",
+    "\n",
+    "# And then we can plot the data!\n",
+    "display.plot_scatter('box_temperature', \n",
+    "                     'temp_mean', \n",
+    "                     m_field='wspd_vec_mean',\n",
+    "                     marker='x',\n",
+    "                     cbar_label=r'Wind Speed ($^\\circ$)',\n",
+    ")\n",
+    "\n",
+    "# Display the 1:1 ratio line\n",
+    "display.set_ratio_line()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3155d4bb",
+   "metadata": {},
+   "source": [
+    "### Distribution Heatmap\n",
+    "The scatter plot above gets crowded once a lot of points overlap - a 2D histogram (heatmap) makes it easier to see where the data actually cluster. `DistributionDisplay.plot_heatmap` bins both variables and colors by point count, using the same `box_temperature` vs `temp_mean` comparison as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e71700e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# A heatmap bins both variables and colors by point density - useful once a\n",
+    "# scatter plot like the one above gets crowded with overlapping points\n",
+    "display = act.plotting.DistributionDisplay(ds_combined)\n",
+    "\n",
+    "display.plot_heatmap(\n",
+    "    'box_temperature',\n",
+    "    'temp_mean',\n",
+    "    x_bins=20,\n",
+    "    y_bins=20,\n",
+    "    cmap='viridis',\n",
+    ")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a0b0b8e6-4bc8-495c-9adf-dabde740a2c8",
+   "metadata": {},
+   "source": [
+    "### Data Rose Plots\n",
+    "\n",
+    "If your data may be wind speed or direction dependent, these plots can help inform where the influences may be coming from (or confirm there are no blocking QC issues).  For this example, let's see if tipping bucket rain guage data are dependent on wind direction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "970a06cc-8fa4-407f-98a3-4be4c0d49018",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jrobrien/dev/ARM-Notebooks/.micromamba/envs/arm-tutorial-dev/lib/python3.14/site-packages/act/plotting/windrosedisplay.py:358: RuntimeWarning: Mean of empty slice\n",
+      "  arr.append(np.nanmean(data[idx]))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot out the data rose using the WindRose display object\n",
+    "display = act.plotting.WindRoseDisplay(ds_combined)\n",
+    "display.plot_data('wdir_vec_mean', 'wspd_vec_mean', 'total_N_conc', num_dirs=20, plot_type='line', line_plot_calc='mean')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "1433bbe8-061c-4323-af47-ed00e35a56a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/jrobrien/dev/ARM-Notebooks/.micromamba/envs/arm-tutorial-dev/lib/python3.14/site-packages/act/plotting/windrosedisplay.py:429: RuntimeWarning: Mean of empty slice\n",
+      "  mean_data[i, j] = np.nanmean(data[idx])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot out the data rose using the WindRose display object\n",
+    "display = act.plotting.WindRoseDisplay(ds_combined)\n",
+    "\n",
+    "# Let's try a different type of data rose type that is using contours amd the polar y axis is wind speed\n",
+    "# depending on wind direction and speed\n",
+    "display.plot_data('wdir_vec_mean', 'wspd_vec_mean', 'total_N_conc', num_dirs=20, plot_type='contour', contour_type='mean')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4178c3dd-4a15-4e05-839c-b5fdf96c0efd",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "edcb7199",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "arm-tutorial-dev",
+   "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": "3.14.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/images/arm-dustieaim.png b/images/arm-dustieaim.png
new file mode 100644
index 0000000000000000000000000000000000000000..f495939bc3c2c18c053f7b57091301d1521214a4
GIT binary patch
literal 302003
zcmeFYhdW$t^e#N2B@v_{2;M}Cp6DT3Bm~hJy(FU7=$%B6L`$@ZPV^b0kI_rK2%?)&
zhv-J{y_{#>-#OoP{)X>5xeOEb>}T)wtaYz@-D~YH8fuD^*BP!uAP`EWS1+_65E3kS
zF<d1Bzlky1cL6`Hxx6xPgFyIM2`?h=Tp3RY<R6IA3ppL{)HR%+d&*(J(lPZ0iR|sW
z545~g8vnIrKOX#!6LuBTN{t$ELbU(L6B)F9xNbYA<ZI>Vp%!4(t5z|iCTLx^x)wP%
zR*@z?RH2riFOTjKH1S$xbJVs@DNwaTDY1`zDf!B4K=<{oz+ckC^EskdvnLR0mcK0v
z=Mv<K54T!(&mdn&j#>^6PiuFM>%o;0{t0)i?~(k^+hC^`rG)zc|MX@5-@E^3ga79T
z|38R@zaG9<ApO?|^!;B9;$jbI_`yBdG|{p5^t@IL?8AS7$nsPJI2dw+1gl20&nmLh
z2zaS{6&Gg$$LX9j=4^IeoiI&JN`AJv>;P#dBe93nYGplKpZT>QPo+){4(d7mo>TJ5
zH}R_|1ozdPQdaLv`>dCW1Q)ADHa8^%k}2kIHmDujC@3WIbZPG$BvX@cisx?%mp<jN
z_VyC6hfi35)!grEFr}>6Kl;S|16PN^FHde0F$Na{9Li*+IneM^9(<p_bOrpr<LuR7
zXHsEjiMZ4)NI@7_umdbet>Oz^%OapxYsM=HxqXIUHF@w+i@We)4mK&LrS~Q+!N$xn
zY2lNV{u0^#f(v`X!3SN7IS$a-hVYh!*k{b0bDq8&kbb^EHdbT50fJp_wJ-!n-;8QI
z?6g4#j#x#{T_>AozCjQB8*Chy!MAH4y0%t7NXIWJMVWanXkaVC#jf4XKC-m=a+06x
zZoEQRYWg*Zd&KmQq-6gtyb>fBplc@a3ybYK_J&6hDl)i`2R;g{u8L%m#ezLIW*Giq
zwduCXO~{GY_w*I*-SWwG)XcXgg5c&=gtX&0Q`1QxH;af|;ukb}<`Ovt*)|W5OeP7~
z&xDJB6*F|t2L~o}s|;0WNzCoNPJQz15oLH<Iw{1R@s|iBnpL0p?~mtk9}BHEm*^+n
zlGvF*)1c~3mb!Q}KUapbq!djv{g7CsW!AZ!L&V)JNhu1*vchymQgZzSxbkH)ux0n(
z<ZAUu-HYAKV=^(zf~rWT=$`ztTv)uLrk-oN(ee%0<UN;Y#8&kJ6_rk8k99>?2^+go
z&A;V4g_W*62>X#NYCg7{Q1*Q5nIhaoYB~|bU5I-GB2L)B2@xi^&*YBNH23DH4NpGi
ziQrI0mvPNhnTq@f!wVAf$R@pOeMQTQ=C@M5^}f=xPE8N37&W7WtjSiBHQxdkH_puv
z5%cOKJ*Ocm(u<UMXS%-58Zm6w39iDpHV>F+NSkZDQsSN+{`RClH*&t)xpM7jS@yh<
zL?`wm5tqd59xo)5uAlF&?8L1iA{&Ga@5Rp?)~Lw+NQHTv$dtBUp$JcgGLPGWuMu&T
zCz&lKN5FXFv+ONLSyb|ec2bCW{$by1)yCyLHpm|JM-hnQov1cWwt(92W$6Q-{l(U8
z)<4z0l1_kndYX=n`Ajh0uO^pgf0H~U$DwN<@Xkq7nHQLR2`n9)w#dGol9Zh4=KBzG
z5;B6n23e*K4ZvK*@VXucv`f29pt;fIaO1aQqensPW8VEx8;-xgFLzGosi=NS-LqT&
zm(S%mU?)<cz;Klu++XFRelryuQD)o2*z|<G($-x_KW705GTH(CGr!mkK}N*_D^=-)
zV){~=IuFk{f^n#)S4PRGN;}&SYJHhPk&fT51QHyvfkRYC<d`O(lPL3$V5Axw-axIv
zpvR0$%r`oNCR81N+^UsZ?DEK#<o3ODia-LN3x=92JqoUJk(2@-NA{Dt|8hv}-i+H^
zG(l$EtPQhvh?t+Tgj+8ZD3CxZbzTfA{de+hMv7o)%D3(IJpSpPy8F|@ENN{E+rQEF
zDMLdbz;UbZglVUj2&NVVrsmW3X5FnG69@6&N=|Ml^T3^{&*nw3c{AQNlg1y1+B8$i
z6;kViKzwyyP!^>^f_n%UqkPAT_$!Y8gCQ`JrW0&PX!-urgF4b+S_w4_LooCqIOI04
zkdsnJPx}DTU8~8E2Nq2td(5J1VmSz}JyOW%B3C{qB-4vv37R3X#~=bGL$W;okUF~C
z%zS)47LV-M1O5!l$PJMSw`?R!{CGtc5%7p1g2lu4HpKT%3j<_=W~%!B6UL6){Zp0B
z9?%TmT|`xwJ%3F2O1r7BY$=CQ*SlN?EZzVjTaw}eK%DTQVCav>jDMqVXd$t!2OQS>
zh3GZP5&he>A3AW1s!a9FaO_uT?0<KQy9LKWDzAWbj9yx7hht<>42*+^jSYy3lhK6D
zao^X|#f}ZP9yxxL6nq41*LA3TQ@+_r-|)RJ8Kj>&osczrsllh~k~WZw^iic|P-+Tm
zZC52%B?;F#4_t*y3wG|*xvo&4RRlK82{vjT*QlGsGm@4b9dIQOj%BmRK;<;}-MIqc
zUW;m<B>-6P)bmoZX3<bFZogHko$84y^mxcf-&o@p=|AfxX(AW1u1fqc>Em9s<Fs=R
z@F?KL4w6=iJpO$o2f&om`NOMpIS##egE4!R7p`>x1$Jo@2}rURa*ePVXaJC<FNJAY
zG>M&c>MgPz5l}yulT_);E|!)4`UV>9$C_7%zk<Dyes&(<r><#Z)jvcu5hmMH7l7Cq
zb8rr`i@gPR#18~@Sg~pAAc<sG*TIzofV(2Q37p0p7H5}fh%`y*b5>;+PE>vruZlNI
zzoa;CKW-Yv>ipzeQpgRrGR0ym&-<GE9<xoWN@9QIo9e$^+c{OfA_D6en#eNLeEbgq
zm_v7+cwg1j4^=>dO})yuvK~Vm7{KnT^3*?hYAf0+Das8F52L5|^Q$o~Ms+t((CzI}
zU%bljJ~bk7pgK-U(lhj2i+U4(i94OApBVIbkRmCl&v2WzQSWP(VV^Khfwi~BbyBcS
z1;|W+1OGWuM8Su{Vo3j05Hz0RTyvDls%?L>O{To&P{|`Q=OHqK&JD*O!GAp_4le9o
zia^oVN4(Wy{)`>^kIVsZLEBFl{$9k1@Oy=Orn>`>-Cz(~^xfU~YKaV>fzw1$-6`fn
zzUp?r2BJuk9;D5uQr953zEx0OxTAkD#;T`8**g~;)V9Bd4<2w9+XT&iWy9zGR`s>@
zE-_`EEM9GF$?HXlr1gJiELqK36<9TET3t$M3=%N3H`Vydy@sak&Gp!voj6(ijdV<j
zkQD#N%SD!fF@bI2cK0eQ0dpXL+^b#ySagRz*6*<n8eS|QkNP?ArgJl4@ub?--m~SX
zVp?7SVw3hc^8ACkxFjV67i~`2Y^C~x5<sTogt;`>P>W6f{4O@T>812gidyRy8<*0x
z?bWm90#o|QEOxY+%=Kt!<aC`NqRjpI=^Z`g=Q(9MAI())<qSm*c};EXOQF3|_qYp9
zyOBlQO|d%sWkmo{0a_pI)_f$Y2_d!*5`72~$v(F2;s)j`nS5-YJ<02_qM?fSWHStq
z%JFR*Ug;-7B0f`qfGnGW%*M;3gJ2S4T&lBGT;<f>D(jxseFTFu<{%OG?{?*(g7EMI
z0TahHe#XYBCij;mpU`3wm_~PCCN=e}Pryc`<jNnX{kctC$GlBMip|X(Wh!T<#Y{(v
zCcEr5#kg&Bngtvqlqs%tF7{X0-z+AQMW}v;i$GEU@iG@m7h%*eeK4%NS-0(*(12l3
z9_6K;_J;Y<@h^AJd5HxDGnWU|DEwm}y139^CHkkG0XDs)KNqrg_7m`;J8;K&PbF4)
z1=5LMT0t99qILhU4{5#o?JW&~cuFuwwPmCMW<+3G%W8xh1uRaHo#_eVNO1#(;DrIR
zv*=+B!KyD?Md<*w#*?j{7L3KAf{v9H$?erTU-xml;gg4kmW@qg$_@>sIrryokLA*E
zCB;_VU`+70i83}-rh{t_(a+g;Ru>-~+L@(n`{qRH7PSS*mrPVQo_RvnMIYb`THPT1
zxmU3eeUUe!%}n$TDyVVaf-1R)UloIrI#YA4BaJ1Om8*@5>Qk%-0%nD7h;9FMvs!Lc
z3G8J@_y5H&@ctDP=k#zI%ksfJO0N6yVXY)jpzkW$cYpLKCmD~8@J)6LXnUT$i=yfz
zy7c``yLId5m322ZkPve1D1&K$1(Io_+E}2(7`sLjWQp?0^1|jyRuYU+t!?dU-^x?L
z!&=kN7uA8n8e@BzERng1?+$o3Ri|wF-qp$yh#Up%v`$GbI}v)~MsFjc!qaXE5P<q-
zsczhBWVkiH@|GPT4R#p|vL{@MZ}~(8BfZ}|ecaEjuC%f#B?ZwZK=ar9KV;qttbDlq
zl@3_(vIVTkXfQ#$T&Do*?rpUg2WvzI)onHnmFHh_Q^MEFpci{oi*`D8d-REcq7RZ{
zop{BiB6=&nJ?5YJ>r00T)H1G0>MbI-?%^L;Bg01mdboA<DA&BX`X>B}XDtsyL<WHy
zV+kI>A8<f*quB~%synd<SjIUwl>ww&59;rk(a^Pkqwz!c`S#Tw+`!`h_`=D2uJdY8
z6aL&$(wcKLr9Nx>_0KH#iXp~(m0Eyh5qRkds@t-{)yFoEMn={5RoTa$C6VV<rZ<d5
zp_$0@hENqTNdrMk<3-w$%N}_-?lFkp>w*u9#*otQJQ<s1>idw&*C3C4-;+urv5%?d
zU%7SovO(BCuCZo8G>y5@R(pxkQoGiA`(yN^x5+7NWo-u2zEyN<`hJaS6MD{+ukJM4
zb^Qnh+Y_oN5f(lOYQ%qNq`>Zs6_pQ(#n-sv@P+QMq?(H<y&moiz3Wu>9vF$~mM7?C
z&@_eBxM*CbEEQlZ^=Se?U|TCe4EYM&Q<#?_LjBc=d&83lmiwfeGLMP|8yrljoJV+O
z)+J;f!&fg_YW==}G>XyZ&LV_Mle?wV>($b~MZa~eO_#_O_9Z1ZhhoU8D&H%t({<(U
z2i0!}@uWJwOUhR<8~2R4aOjRf`J1Lal$0#_ld#&AKL~hz9h-K$)%oasw8{>R=r8Ic
ziELs^L0$c>Bz&<{f9;1p9?H;2ZczyIkIJ~1kcd)^aB!IWfZ`8Gs!jn^(pGfi$EmAQ
z0W>m|iq~Y|gD=L)-mBoYmWOO6RNxuHdU~bkVni%sge3--D-&^1ox6fU;@P9}?=YFn
z>;t<wT*ds!KxoJl86UE0Oyn3G=vKVsZ))@}Ex1=jBQILwC*?5V(5C%<Q^+>p*oxnp
z#QMDcQ-#SO{hXimo6cosm1Z(wo`-W%o1X}zmz`k4m7GX129YSP!s^qX$KNIXd4y=U
z^TvLrRu3|x?rHo93p-d&VMUAFjm1?QRc)BmXR_`(uf%k0tc;pFI`4!9ZlB0>32V4v
zj{K+}YE;O*XW^@CHypJREWX!_k%C6sRe0LxtUOLJp&orG-(GGGiIM<$Fyp5kr7SC0
z{>Rt$+0%FBBsyBQ1=#SJu@fb@8B=er`@0KDrlcVG*gaR9y1alls;A-D_Q>4Z3@V)5
zwe}lkwGSN|DlE0DxZ_n;zeG$MH~kd49Gf6y5g{oAC~e|<o=T-{D7uCBYTWqyH!^ry
ze;mJGbUC&rI5jKn!K3s`B=h|(C3Pi<nXP9KcOsDeqX36|yr{eq*yr&yt*DY+JdXF-
zpDxMxaBG^wQ5Tp?<1Tl`^O{QOfSFYWAv}rUdY)<LG$`ZBcO$xbdwKfEP7h{}Uo^e1
zyMFrjc!XF)@~9q%X}+?2zazWs=-)rX^FH~US28QMxhXP>pQFa#SG;dpxzGgZ>+d^E
z63)wRFKf*x4Wuz_`NTUVXv&)ERDZs}S<SDf*|E8L9|eVu9iX6Ye4nIVI$WV!@a=ii
zGIj$gd->z0u07=S)^)1pKNVKQRMoV5%;<)+Q(yLH6}xXH>%8TrBPZ>qiTJKA44#ZJ
zmfSti5z;(t@d~^PQ{m;w=pp1|0F=iGY;uNmH;b}h$Mss`HlJ+OysSyh7rljPgFLP5
zc3u)L-3_q%0xkf}3GV+o7*D!&4sKuTOO}WL4nVIMf+BbY%<4kZ0{?HAtw*4&)k|>R
z!Vv*+si=!Kg`;JWPqwmRDc{_OXP!cq%>lBnBt2$yX+~v+6o)3G@1%=(B-Wy%bMKra
zG0OC3I3AaIzE+6U%<^=<pgdojq!vyz6)avGc<jgu2u`tiaKKP%Y`3HovH9+Q!a~?{
zaC$Gv+fi*XS}3wiTCkzI|C`~DTcnFg3O}>Q1zM+A%&;+T?98lQ7Pw_Oo@km!1Zcx&
zr~ZA3OYt^3<lrw}&#tm%WbQcrfbE^8nkv)G6)^VnWq{PGJ~k(@bN@@?+o>J9;R$LL
z2}0Rnm@dfZ(sWu|RpU5G@n!3ejyP<PuxEkR9e(>>A%4j+>P%IiY$>Iq+0oPE+Y`Tq
z7&JjGc%Vyc9=#%*IfA2!YE%F7T2*>=?*+u28C)~X&$6RyN`5`<A`NK6aF6lHR1WpG
z7Ff|AD)=ud)58(pjrJ7>Bt_OK#DZLuPW6gy0bWoQ84oplL5s<hafEp(dua<brg)Z)
zFa(eLo3g&90Ce|SFg8VlShWGN$D$|QC(Qv-2A0_syoQ}kSHXm!(#JMmy9z!-1cZ0_
zrBXW#)bxus+@H*2k1-pX+A<ui67!LtN!5JsVSWs6AG{W2ndlDQA@A59yg!?*yD*45
zbWuAQ)V0iCU-zZB;})Uyb_@_}6e;nb`K#;hf%|+Aqx1GZ!#Q|3734e==o()Dn|pvC
z+He09kD();*47{6h@Sj1V;~v(*zcFn1Zp8muc;$;qT~)bu=@C#_@n(3(uJ$O<D6v<
zpX_Bh-`C~*4Hmv{Rm0ie^na-W65$gBRp9=Aik?!%dzsA|Ep{EZBxCLxW0b&wt&<vb
zWAKbs&yU%Em8CY<&s~1*qibV|S7)2T{JK2NO^GiP?3C2X6CF8xt)J{<g9v3o7vzKj
z>~fS^mv^hi&<~zmiz$SixF<ejtLWhAiP$rQ7ahXy*V9f=hJ(5cXjW<I%0YlcJSKLD
z9bt{4V*D`f^5ZfN@>PviI+6^c1bqHF*k5Upt!tBt&tTkejD+B`3JnD;&rDKixD%ck
zoBkM|j6VG+Qh2i%S8p`6ZbUfay?ZXcWXpQ?AI72CTtpujg0*fbtyPejQ-W*zc`Zo?
z5ml#k5`5_E^~w4mH7n2sn>Ax#q*neVJdnS-=Oj%s*y|K}JlznJn}rm;>k3XKM${x+
zWhtdW96TOPCml})0gh*nu>(c@IsEV=rS)q0)*pV_=iD8wLUr*{`Aft&bcf+n5OyED
zrg=gA26UCwbT#K4pD}yNb<G>Y?NwVrRI)-Gsp*j5UO<s$?J`r(GyODW7HS5Xv&Q58
z@i6hWnlKqp>=(&zS@IfZ#Fp22eS8E2i4W&!1|miphXclqUdz_y+cTM!e9D<*&G0!O
zGZYNPLH<4~8M*`cTl9<ZBXv^L>F2}2uo7N2LDi`?twoE?+F^zzn+}t4*V`0Jqi3x6
zP&<^}v+P?yK<omQ2FTfy{?Ctf#__fuO~KREY}#slBn@o$g`XSM`dt~Vzow+fluir@
z*DND5|K-pucleX4_D*@-S(nxWT{E5ZF)vu2$nlw|k<199#qc|*8h|!%eXU9N!fP|m
zYovNWX}Gl;X=xNT_KZ1Y$y2E{WU%-B+~wUY28iGNEIUdFjlC=jPyH?Nx1t;Gr+wxG
z*12<5n!AMTg;t`#_scL$NnrQ13=WTLA=E&FqXle0bmRd#)|s-_e+r?nu4B}eZ=~!y
zrvZFYCZmtkp!w&Gbcmttb5Mo{M43}AJNG41R!<9MzxOKN_>mW+YpdwO;bG;CEuLPz
zf1_^&_oj2G>GWRBnY7QDG?$=`Yrqq80y{5ZQz_<Q8A6lEx4ZTyoXA83ix4&4WzMYQ
z4E7WU5c>aNl6VMML>uhIr<?|>Iq6wHMXm5KYe$}|tIb<~!p?gQ-SSVo#5XtBb2FjU
zXN~*4KP|J2Sb?yqC93E3+A~md46l$J<6g>li%W=yz_dxyc>lr_!Xk^yA^p;K?EVtl
zLmIxQngw5gI^!?vlvia>Le5a0>TWEXbF-f3c8STeg{G^9-hnCUiJ{?*Nj+1(g{(ID
zF687ubH+5x`(+JwuL~w(ZvSY$aj6KYoSym>j)k1u+<X=+eHVz0@3fG#u9o||QwS*?
zAMC|>@7I>3j?K7^r38dS5Q`pa3CnIdL~j{u3%+s@Bo9UDzK{8Ex@Q`{H2^y>G*lWs
z!TwS)C+R8oh=xgR4D2<r%Q(RtJV1PQQ&T6A^#eIEfP)~jN&sdjKV^TtZdE|QUYT>l
z%j*1v$nkUH9m=DH{nN;QdY(VVZ~H84AK$$fAK%B*5Iel`E7mp1VO`S025i{+o`w-f
z4f*-z0wo;<#wk_?kKn%-q8g4q66IT8$-I2=uD&DP#hV#29+{gdd-;@WF0?7{y<S%V
z)E;UC)gJf0o${Oo-I4wG_qQ<57c}?j<DHwG`E(G_=0(p=7Uuq~=z`}1Z)yV4p(VQx
zhLTW{)?|Ot2}z6{BMmH+>;z3uwxYlOA-4}wi|PCoJ_<Rx+QQJU_#awnAP;U$OJ1+D
z(^2#=^0tC;7k(G}t7qe`A2Me&PeBS7STFLa_O(DvFH%$5*gWG764w{S*8D9xXf2S=
zCq@ia^-wIj?q18!@0W0b30J*jppulj<DLwvQltD|bPz+%huqlUM$nkpgIyB=ixExV
zm@!D(`@R02=iW6QVF@;8gSJTAP+@iNx|U6M>GSMcHIoPDSJ6-p>{-){zEx+{(^g}3
zC?bK8S@?=*Ob3XSINq%avS15l0skMAnd(3Q3;4NpC1}!NvV?T1*f(Ie2HN1~9_S6V
zHzW>`3q>o;Q;@Bw7fo&33G+QVRWZ5P3MT77>j=Ln$TyiWXqX|_D-n5S=3we%iNSMK
zDiXv2t%fiU6`r*g@@6F<g+XLb!6N5#TopuKM~NP0<^y?Ke;VtA9ph#UP_{J<%%R!0
zj>$t3T7IOtO>f<V_qnEZEFrIstQnS%!6AE`FahrPY<si1d4zkyx`7i+v!N;garb(1
zjN)dBkvq^HPey<S5{L$f*w0J?aOE>W76|z5Fxf$tzrb|$Hyy+1P3)e6x77n_!@2=F
zW({$M?-Q$Bb!D#AsE)S*^&LyPF5Ueczl2UV20y#-BQuP=J>1CU<WKSpk*S1Sb*EHL
zIZyxg^=73!7CYi)O0Y-}AjB^DK?YtYQRhD<nbBu6nPoxxNXrNl+H{Px;<+T{jc}32
z_uxU7cDu#L-&T6s#~m;FgRjWH@lf`Lx?YDNW?db&b=OsT#=LIpzyKj9wsyQRst`6B
zf;8h}iwvfOjBmc05v8Bf{y(z-t&R{Wpgt|X52f2<1%UI!U02JNV}AQ|o|^hzg5f$<
z@A$oR-MWzO1*nbi!%f-DJ#i1UH!!OTF<5u*-{0m2FL7bpRG+NdR77Ts4KIC9CU~zo
zmNnWK_g^hhWVoRuS;cT#8g+x-uUw=|=0WY`pZljsBA2dDAjkCkf?MA7!#XsRhwRp3
zj2a#O9OVuzxOUQAA1*c(8v1zNn_L?bDp_O9Pl~>wOAW_v7_H`UATSPRIB8*t$tN>O
zN!x#78(r|~g~sxJ$Mp)oMXO$pKXoE;v@nXyBX4~(R|S#~3Grfwyw~rA+%cayb%;#~
zD*;>y+iD1wr{<~uwkg7RIIvZj!;qvgBM~#r6ZGgGSY1~PWqzyBX|L8*3>MLZRlxc<
zt-W!iUv4+=X56ADDoCgpxz<}0W^MJWrt;!^p=8zUkH4M;Jsh#7&}K*sc;E{V8~i|Z
z>KCnQ!zZ;k;Uk5mR6v{P+JDY#U$f&gWe#Zo{h|z*QYkG(57dCH_Q5NzKAjyOOD%d!
zMt$gz`+W<prZTa;Ye1`6--j|c^tx#ahFEc9jT6j<*U1bm=$930K#lTi%JA6Obj)4h
z*jQ~$M`27SoWFthS=c6t7rrhl?L^H5mWY!M=fs?Am6-iwT)`ceP;BhwOKU@#p~t{z
z$@Mtc^K{cXk0XQ75ilg=A5FmNo)6})MqR6OJ!vQ#5n1k1hTks2TyrhQM+zcnML%r>
zZxyZ38*7<AV%rQ3TKCs=70~r_!aA4N-WW!`G47G0B(>LVt)(=x8lunDi&f@Ba>F{Y
zsU_>lMZS`oECWW3w@h}!JUq=`$GZ32!B(QxSg*yuYCH?uNn^<KtPLf>vH2xUSbnmX
zE;-hRly=nT1b_sv_JL^cpD9g<6zE_4hyVPv$;jJqk@mUWE`VLA)c7vqlY@A;h!<I$
zwm11%s0MO8ZT0@hNj!H9X5LPNhe<(7y5yO323Z(+f>K_*p`oswZEm3tR@&07PoZHz
zEPv76?foU{xP3#I{%Ph?*rAw6xT!)x)isp`KeeCv^E>V3{d~6$28sLmZ_#h|aNbWR
z52gi@yzh__y})0QP?M)^zbbT{Z0lFL7CgIIs2(-MYDvR{l45sf45Iux7v6Wv+%rC{
zozkCBa~c}@{=0U6d?k!!!9y&Rr><uKb|s|C&&|-pSW8ql$Y&Edr5kF}qg#VX4h0!>
zLfaxm9Tu;Yr+TT*w;<)qDpcy3$iu_GFc~Ef;C>w7dMt*X7yhrjtn?3(T!f@pmMd@$
zB=@K|d{Z-;SnZx={u-NO?BtgAq=C6}=+{yXO}W9`bVai9)H1EaGaKPzNHCV5mlJe?
zWhoFFF}9V4>v!b!uy73A?(xz48r)9!6=goo1n1VgerRij?-lu$uKxa$8SZeS0$!oI
zb>(rybc1M4lj(hphxUrfbdKKdMiVf6>AJXClL5ky1*1VxuYJoFrxL}+Of%-D0sWS_
z!$jfcM*()+u(3r!1IwW#l+>(XXC*(fdhfJ*?j&OV6~2>}4gb6~Mm~1(yPQvl!UD5{
z(-&JHuYu%kxkQ&+WKiHB7~b;jKN`AfHDg0plE2?W0W>!cjJ}gy72CvmELp3TxA|@h
zRFsF0U!|KTirkN=X@C!9#8Vf`HRq#-9j!Hg@Me;h>CLnV1WEf{v&`y5nn~8{O^irp
z2k4Yrvon>5Sxv@agT_uy#z!bc_`Y=pn)=ZELiL2+$lbMFF-;8fBMu}D<3?cMbb&SS
zbIqfl4XPAs^-uCf^d$2KZ70*PZ!YVZnSTuQeyG19FLx|Ytl))_&~@)wTh-g!y}UWF
z5Z|DRBtCU^guC({x1vRL3#~ftuXF-dz2CZ9b)SMSzDF6Rn#@-D^XE$Y$Kl_%UC=7y
z(pbh1LkX+<@~_bmxml3$g+(xtunQVR!2xUMJtV=|1UixQ$u4$pV9WiS2zs#VS~E{g
zI`1iVV9~Y!4=9u>^o4P3YW=0wy|}G=Zi5QR$V;L9k=psk9?=+m9m9q<?9DGJa>9J6
z@me(#z`<cAtd?1Z>^wv(c+!&ftYkJz>!luYnt3SHks8CsC1m1gTP5O7yndTG=6I)<
z1Hr{OXrn=Q{Xn6BgFYrVeRi-v>8X9UJ5lC)0t^N>=wH9Smv{~2@Dtd+tc%o(QYaHN
z4RNzLH!-ej!Id`Jq-aX;K+4T;(zPZ^2Bd~{4_M^Hksd74GUT{^;8-xDhbyJ9DdU^i
zUl;-_ZDyHoJDZn>K|&F;C3LERa_*zk0w;T&ZJ#eXH4K}|td-E=g5|aIsAoT(HS{OT
zmL>heEtX-^dB413cV~UP3O|=4V4~}JqJVqS8i=M+2#}~3$_&wLdKid4xZzW2U9jia
z;?JG$HZqi~zToc3ky{~To94?|7&^Sjqpf*!Ys56~-WHl;bjLd~`iGTY@xInRG_xgx
ziARa+DPPo$r119;^PcTrruNf>w)&bJAYh*X$<RrGL+#5~;8+)!%7sg2Z+Sm=HxZ5>
zkeC`NX2SPL{y625m2g)-saWHkqxx8tuBBajKQ~Ln*%bprBsNQxg5h3}x1y|eM|;XM
z7q_oO5o~E`O88%Iq51qJE0eF+d*gzdmWLG0m<LMMjj<hGqNQtpo(G!mm&y3}nr28g
z^q*aGOl>Xniv4<yNA2^7*610509!#uh#>G`=T1_U0@a?jFaF)c&ZM;;cDA3UrYWr^
z2}StMoGDK{uPV%7G)qwti;xOZ&F42N@vc7ss^{Uv!Jmm-Jz;&5`T=KV2JD^iS_^?@
z)qt3#ox!z_15BRpeqVjScXT$O+2%YwDLB=05mXl8c)7`2deRW&$$_NyKkmxwoBrJ6
z@9f^y^kjU4OGz;e44?P`+SgzKs>ieoOB`OT@K2ecCDb)R1prFnLcERziz2#%F9x);
zY%O&yR7rh2oLp;+cgXy~SC?gZS5Y4JiYPjNr=BAp|2dD8k~EsH^ojY{5k)W9Si0Vl
z<<MQ8la=F@nX#Zi&K&37<NbML#Q3ep&+mmZEA$bH-2IbK_{?PKG*WT%-f*_p4X#<V
zHT`-^fx0>i69$~bx|eq)N$47UwBIk53%?S1mx`g=I>mwc{d9B>c}ut#*;Nx>iF^C_
z?-Q-Q8<^b0bnC-Ry_l&d!-H;_oJ@r76UfD8?!ZArW2CUb%o;$$1_dnuVwJ|K`%c2&
zuGLIB9gFg_f-ZH8$`nz?l@K|=bazPn_hD}hrF>|ZGxR=WeLKtgfNDl=oZmMg8~=C*
z_3GbE=-EMO<6bkgVedPWZ;paTyM4Fmc~>{9uSd<<ZfY;Ztvb~PjrEoQxrV)N$-RHG
zK09WOE8J=_QosLoIoE6|D~4sBPGzqkmnOUO-(Om-rzMo9I2siMQv9CCSO~R8z~a{D
zFr%#s=WNamdZ*65k?3!)b(@OVqBg^Kx_6ZKx2?K+Pe%yE&zu^RA5&ZLW5Lv9OCAU>
zR67zm>Oakn?qJ^xHmi9JWp2`Ac6Q4+7?Fx<v-KfAb3J{g%c6(NBh>{2!@o+5I9p2I
zR4Tw-NBuqn*7>~0_$8^)!_`5~An~HhJqwD^fDzH<p8TAJokDkvhU4fa)|x8D!I$1J
z8w9enpZANy^&87&bk>EY^3H~@UVUM=ee58dP*8D+YblUAl{@&gW6k;UI!&D?-?;$V
zuYaN{qoV53#?pTcG`vS_yc#;+C%mt&@qWW-bK8<gW4?dmlx_0cjaS07GN4y-Tr_lt
zsPrkwpfE7V#>s}W6fO35M!}Q5wH-d$-&oxCTiAx37nGGn<RN+;QPooWb-JQV^9I8X
zLm!<A|BegIV_?jq@J>D0UZJ)d`vVoitp#e_7%8XfEk9kTFvy*%)fdr8kq~v75IE@Z
zwlUKHfxkZ*A>?s5O<W7*H)ALbC5Q9hLh;;;IKqufEo0h9Xk?&L?;ri_WZ_58KRsKU
zxit|}!-bEG?^f#k)i?dt28xjTsx1mF#n?~H?%%EDJjGs4`GJwO)vfyw2dx+^A8~-?
zF9%s(VT5D4VblEhaAotns1a)x$CCMysdXZYB1JN-d+pKs8&xL868$jk8zZz8jE1#|
z_{5t7N?;s=hk-^MbS0T0PlP=jjLGk(68F2d1+VXT6e{QcJ@vdX>IpwBFr26azEgGn
zBixIG!HL8I;efrGO`Dig^V0St%zdHOeH&iJ`FD?U?sD<@+W;;rG3C5S%TbDJb5Bh8
zpS9x|+vn7Hy*4d)Q0T1(PlNdtO3B>I{+kY(^0oL?oEm7kReRjRE6QhcBHG=}nUW$`
zk?dq>Zh<igsaRGf&z2@QKKJv)7uZ}*Qww!|gzFbe%Y5f}<B@A%U;>~Y*p~5~hN$4^
zZ-qslBuX)jo2_a`=^LKg6Ri>s@<Lyt45uD=Sp<7pv|NqnHx;>(_tqPF111=tLzMoS
zKBn#-w`si{NraGFyIaC*l=V|Y1IN!Y@9+efD>h`C-XHjNy@}EvGtyD?*!An4gmw4k
zk$QR#IGoH=PGSpny?U&iamFpWJnxQAUzS{~V!GhbwJ|8a3^Jo!0TX2PhETYo#0uTk
zY^-_a5~(udjr*TZ9B_Zq$;b{$g|150v-a1XZzk&HdKD*-*{JX|Xr*Bok{q(Ko@dpr
zr`h9ZtJV|T2f>hCDWMuP<z&YM!0vf2+@<Kl(!a65=<<}`WK5wQu9H7arrj$6;YZl#
z>Wc{2$5oZ*1K-xx{M&B#lOK{wx)QIqN)n&+$R7x_T#@)%;?AO37xI|l-ss#`Z!mME
zEq-xGQPJ_Knr@Oqez+_y%W%}wqrKo_^^au^XQ3f+EgeunLaz(d=Vdqqyj@ndz}QuZ
z@)a~VL+z7kg*ylGNJXJIkE{?)vzNbPf7kA92HkbFQNRJ_!H`IOibI~WJIAGq-yPVH
z^JUKpPtYww{d67Pu#7xkso2E%J8l17f3r?MYi+2fY#q7M6xIDolG7mqOd4d`16>J+
z=OT!^euKXMh?`lzh<l?-&YMvbSbDJLVP*Zz+Q(4p?-%*+U(c}@)2l=#Y77F3`>+oO
zM{z^JV2a@-Y8+&P@$$7bkV1TS_WU~+z#Bjm{%kt@q+tvvW@^!+FTFoyYc}`sCowNM
zsxAG&W**!o9OPFy8YkyM+A11GHePhVwT*nxziRYMzZoobR86`p7)m!e2TF5>bf>jo
zzKx$XGnr96`QPP5o=(l$p5)oKfV_Qjuz;zxpi(#ojKy3fcz5j3NpnrTB*FMFe}wqt
zs5n8DrEbL1nx}O~adXu8&SyfB>KvjlEVz{kP~3O^pftVOdUU5L2;_)#e_J`aI|(WE
zVs<+i_MyvsE4NXHgwBbGB-QXJI@(j4xNX6>5`O&p%hgNr<o7FSImiD<l5i83Ky`|k
zD$D4fH~H;FKag_c(A>2)JwK%aGh)<m5AA&)_NKn0p``?W=}uhiq4S+swaVFMh+()M
zS?1~MAC%QYO-1A%-MFb8Bv7_;JXg5Desdh0iKNv|9tmW|Ofmxs5hk`D(KeGt#58s5
z(bLTgQ|jdboX7J!a15JL&GC!Q5pY!1eb1hJSBY)ZYTo4qMPX{Ax2Gn71>v(byR%wk
znk+OjPEww#946!}nVOXQn<<@4;z<J*8(0%VeiI<~C}RJhsPYl|_-nJ%R<X5!!F550
zA-ELcELVoY2=#7PI`QK;hgN}v{lU^e>2w30TCl$j^m@y`Pgs&TNSDm-kJ3~##k9}f
z0rl#VnA=UrdYhI;W(24rvhAu3ku;}1s6t!03i684%1r!(Z;@>aFl^p)k<P1P8<#t}
zs!oFQ-t)Lz#&t8b+WnRCj=eypi})%XK*yvL_-i(@R2}|3AC0I`JwMGzQCdA3DK;v`
za$kyh{i5q-`$LY|nxqyx3~J0<m?{}g_4nu;(@?d;?HWVNuy6%pzp#3mw1hV};HSFN
zr|*5*MxB8W5yD)Th|3P2-%+qMdg~Lwg_j(`!s2M_VrgOViR5l5sfk|(#5P2xOu-w1
zKr>dmEZ^AEySOr1^<O?_c!lyVso>}^8p(s#aWpu-R~moLo!?=Wiqom4ZIUwHR+-%$
z(s?%=r^2geY`qUVBDfx6DTSJWpF@zaO^Lnq+jTq#BlhSX-j|M|pPYXG(lT?uv>(~&
zOOEBrmdwOW@^@;Vd<e!d$%*}xENGDSt5#qQNzk>;+^d-a$?Z<~ijyNnJom+<!K+sl
z6bk(SP>zNszyAuyLuyG><Ua6gdVi4m9}4x(-UpU#BD~5}(Tdopt+Z`XzmdmNtn$vp
z*D-fzVOARm0MpUI3zk{b>lBH+n+Sulc0mm#IG|uR_YL6bKhziY=JBN6yW$>C3$-`d
z_|9W5`x`TRdvq>XR+aIQ`<AJlmGR3l8b~-8`$5lK$`L9~F>Ut2`(~CJlV8s<qRe${
zH~W@yG4W;hF&3uV5n5m+sDnzTM!z?IcjC@KFliMRnUmbmNI&S77)i50s(`*w+o5em
zEPXF^2xtN#@q?Aswk^H`24W(7E||tLc2$bx;V^%^*OW*0-iy$ti}RoAmou~GO3hW@
zy6}MKh<G?m4%Sd@-iRl$4{Fl5WG|eaL+yMOK_om8NHrVVE3X$wHL1K?gep(YUtgr1
z!0{D(<qSnZ9o?(SEi>!L8~AXe7mZBBC#j;JX8B>lO^+<gb5A8n{o6-AIp#^bc}GZZ
zE%4;(C7#zYe?Ga}ggee%TYj(`5k+E8(6d`XB$(ND#6ujY2%7;W+0|dwb~_j&)iOP4
z&)j{7u1Hd2PVz{ht+k?Kk3)k5<FS`Mr>RpkyvQ?xohxX0zQIFzqd*6)HT{8oUjh^E
zHj<Y$;f0vgUZSk6hap1Ji@Ctl-)zQ@JI=(+Q#3%n);}O1BcG9?y?o9tjp}p~u=AK7
zpR6(hHWNjA$O2g79NniCho~_`?qnZDG9>}}9GSbQO+2&RH>B&aTI!E2?QMrk8nsKl
z`yDrOSy|$`d#DtVQmbbqi2A|hCn)j2eC=jU!eCD&(&=Y_XM4I?5t76mY0N=hX)>nO
z5B1t8s#Sl}oO4|`DK-&`B}U^J75+kR;PRaBVPF(0?PO~`j-^hjPyQNBgxGaf-xUjT
zuHdAi9C5hu-di!aVZEOrYQ3xmy)`2dOr;1&O!faoVt_)6+72;X`#IK_N2-d`F^fo{
zt$g^~t*X`L+y}?pwQQZ8`=N`176D_^b+?pk(2(+eU>X<CXVPfvVUTO$muj$1b$yQ{
z1O1kz*pt_zSUlYMj&5d~3s0E?iF2E|z0-TQxT5@fi!=g&r_|8hUTrZ=S*PDw8IUAH
zOewTpseH|0f0iL8b}OpQ-hCX>&vg|WOa=9RkoF&QvFT6H#WeI5aomm;1%=f&xR;C~
z?1Q0{X3E1niTtO6S`0-duix>8^NL}NdS9Uo6)Oynp$I{eW$ueEYNBz?F&)i)TWDEW
z*fZY_jkdl#MJH-)WEj@2=u3~O66eHYMo3C?ol#~*TD;ET++ZnBzP@+W2YJ-n-E#>L
z1-hI?_C`Y=;bI0z-qqqf(w=Imc)*M%_pu&FtEJ&Fs&Ah@@_lZQtKW9JM`BU?sg#?Q
zW=B(?NnD92I{sp-muRRa(+5;M1UhY=rM=9G4&P5|y?ZUI&GlFTfmH)@4(_6+Up`O)
zrY3U!X(c;k<T}PK0dDoYicHJH$w7&(hgOWwM((49^U#$gI7YPBKD_DV5}nBUhPa=r
zi@S|#v*5Nj#%0!i#ABsg^Y|14n1bJvp{>|fc#C`U^hv<LNi*<OGhcJW(2#5sz_S^m
z3jW4h^i6kOS6Zq;k`>w*`fJi-l~Ci=oJjoweWb$Bl{%^_Q+I5VD#gh|v$ym%?CN-x
zDopN=9vv*CAgf5ZXdpV%S-=YW90l0Fs7_h=qKi))iA+Wuv58|=Ru@KH(PBG*4?H6h
z;muAap)nH*rr4`v!tzrjOOdBO9^OMAtzs}lsuXMYrmpX^*hloOk0wdRNQ!4phOx6k
z4^fi$_9q6L&3D4P6D2~@QH4HN{{G-TO%@rywYT2Z^>sSp%Obpo06t-EAcEb|xiq%o
z4p0;aJ&ir5A;33PFrY6n^l2>Es2%}$m);Q^1s1!JlPv8+jP|g@m<%=Cm2juGnRq%*
zgYwfkC2hC`AakXL<In@X`1awD`#+GqPv!sEgEB8Yt9?o<=yb;pv#Q<@=ir@v_O&;Y
zIc6Yh*#C|@B+brA^J}tGw&yc2!UkqQ(w$o)A;KR(b1xiJ-Cl?fb|`f!Q^&2(Oj9o_
z#u|Egp*U2D?T07{5i?iexz*3s&9^pMG&)D1u#eJ}0tL7b|6($IXyc8_FAEsdm2C>u
zPs6%>OBP?eMDnMUxB+uvx;*iiKv-rxlPVeA9RZ2Mr<>;A=49|_jl;6X`50{<NiZp>
z5|@0sfSS>0iK8ztc|Qn%&|}<Ve6+F|TUtEjm}clP(*MM$yVEjrH4aSQr?o5yoVOiM
zz8>EZa8sWYa_EQ;zR;=4H^@$~iY@Cf6dKX1u>NgCA6;?3^4B~<1qeETL8T7s^_46G
zvM@m#EcHK<Po_33jzE2ImmN`oqt}K9Mos@i>j7|CvT=l<*7DAJ_Exmm-C2aZ&c;wO
zBJ^oxerrLRoGFiMdE;-`IC`UcHD0essl2iyiLK3<dbK5>rAO`BvW%&EoAj$gk%3#4
z_3vB8_f-Q0^n_KeYLr1YYFHA(?$X_%2Z8r~s3XAdNw#$_8+UZi#rM(5cklr?>dXIG
zy_Y~WLWtdJho<K*Uj1*@+xz=WLp2)hot&tL6A_O{WbT4j@%bP<Hx5$s5TG0W6^;TD
zj=uC1C^7IiuM2nU)kL1i-eHonw-t>q)Tipw>JRhZ*n6&x`G9%m<10s@%RNmS%S#?g
z>BK86xB}|;plXaZQNK7y3i5!l-Lb1Q>}hwfKaZv~!TgQV4MYF)?We6A%kplxSuYbk
z@9{~E9*kGO@#_)pWRTcMWNirv=`Ss3hmrr3e#vmg3?FfM_aMQ3#job4a__nz9B&Rt
zHzg^ZE)WfERd(qz^Ik&@otEW=hL9GJO6oc~;xvM?MNYn0*;BEty)L@Gg!&@eb?2(x
zeDtARcgvxjxj&4JE5&$Vc6EAWa;yn6I8l<s{0AH`ao&`<wc*t%_N><Pd}WWl=b|+t
zS>`Y;%!+pGlKJJqWzO?Dr#QBSScGBYUb0owp(@hYzrh7P9`jr1=3rvoRDO%mhKBj7
znv$09b{Leup*U$XP!-?C=#l=V2mjgsOCD+TZlpqd8vl9F!Jnj;Kumax_XdRWq+P&a
zXuHI0<Z0p)qD5{7I^jmN0Wv!C<x+7z<5UT9r8?NGOTXmpI$d8wZO+m9Ox{8Jf8`N{
z)qLtJ@dCvSnVj&zm0&8C>X)&VrS)*Si-S~-rtMg@w1A2%3;#vE7<{9o)4)a<pZ!RN
zWROAMnauRyyqKw-4gdD(<^U^;xQ<|qw)NC7UHau}^6scm<4bj!3l&Nxvsl{O2#Jd(
z|H7H=XaoJ&=Xnz%|2+a?1$teMAohO%W+fq;W$5VK@Z^o2TE@NFaetH{52!o5cRp<6
z#?OUVhMJEyVppX;WGYad#S>oq0sDs{UI)(h>6YgH=!5e?3s-XPY4aPA>w~@I7fHYB
zrgOa-4^Bmd<kbHuFzE<X&BCD*CKsu0*lZ<A3VU}mB?m3k)$S|>Y3$CBwtM39f=&eH
z4`9%1M`2aJ4=oPujyg6l2~DZPzo;*YyBa36J*@Mi3x3-BWPjNpJpDj(<55$2Bg>GG
z^=;q1=&Ue=RTs$4b}OURZ~M$xjfwxn>lTfH$Xi}}4KFNRk{#ANkutJO;)eU}?A7Oa
zMvHl5fDjp;WG!K2s`trH4?Kgpn@>l_u7=AK`_5m;|Mxn>%fJ~U34hPKz{!lb6u4%z
z5TAN3@5Y?}q++Rm86$NbE)sL=8u|X(<xv?*)^Y9`vwvTp)KU7m?*?N&g4v?)DDms*
z{-@X2t0Q5pR;-+W2I>K`FO5IpgP_VTh+Xvmdpu@gvaCOn%!F||Ua~qvk2w60<fE=U
z%C(O9j3)H39<lO7>Vrm)hGX{ci;Ur_?90kU5=5vSO~=u8#&BXCqU2u>^8b#XkhwY1
zJX$<XoJ<vJjNQPxd6S9mL`Nn1Hvj_M2`dtCj0MjW60-X5^*ra<4?Pq|xwwhsrnUPp
zh43_rZ}8N^t=@J<x*zN79xZI%JbIdZiq`#M1l)WVry>!ws<Ziy?z>B=T>-TCUL;0B
z0ST^&;JAGkD`m-OJqiq*X>Pzm#>DG&pAxT9P5oFwL#$fW8&Sw>pjP)vause!6cfmb
zP1P|g8j$yK5#8vgt{r|n&ruGpFY%#W9cAYi)Ou3g%UOqdpX_Spza~a>i;6{`f5~R=
zei6}eeZ3KBSCCjASU6jYl-bv4^N77ws|%LpJCB??mBm;H7ZG1?OMND$D%V4Jn3Gfm
zgt@op`ynPgkUZ0+CE(M~gzF3EAbfM4^xfH2FLP()cDFqdw~hC%UH$!kW&uRB%b=R;
zItMDdHA!K$M8$;#PT6mfT<VRL{acNm>gnktZT~JNGrPxvTi3Y-=i@C|nRQ<{P42rG
z@6(fN&+Z7UeW+ukZ;B~&OXwDr30%uZS^KNB?T1rOX^H4adR@4-d8qr(hv+U1fRT8v
zvdQ1S<3g$NMB<;#DO~{Z=--RWiwP^C$Jill3^msE6P=C4&Wh`l8%=#rMH&u5)$tes
ze`TSdOFecpW#oFSv$)~=pIH+dyfs$aQ9;A@m&ZdAm*E*Bt4)%c?|ZY1HjAqH%P*za
z&GPB2_uDV`o%gXU&kDOO!85NHA5iBbq73y5(d8DC{YFiP2ItE)eO%kvvDk@UVk|}8
z_feI|R+~5pPx?pLBTq*}^#&-U9G)KB34iFDm`7Hs*`{#nc@_Qlht*kb3ucBXN?@YM
z5ShqJIhee8!!Y-G6dX0)_3=-{m&o*|6*j);V;!}=RHb1lDB5Q^pDNxzU}Z9;c4@8&
zDbI507_fcpoG|GL;t-ytfBdBJBn{@#*AZk?7PL!>+g*0niw^4h@O5?ea9Vq3zYWh=
zyEHJ3J?G3AoKwHl>~w6+`jCuqdpW^;p~nu}>Coa6U$gnb&Sh`d$#6^8K6uW1<c7HY
za}JE%T)KOL9jbTpC;ilJacl{l*=b|Bl7;p{(om4@lPMN@WOcE>!NM$<)dgP34^zR1
z8t^r~&vQ82_?ncUSH@@NAAq!Tx=7gMhH>Lw*BW{l9Y4G6oUZBn9$GW693_Q;mwJ@c
ziMqMpX4<~te*e(C;HUg@O;V+!N&@pZ-LP2Tn-nTx%_!nwS8wag!kh31%rSQ>kAGNo
zJx5p_$Rlc-3=Ni=LUnrGYnBzrvm3i0slbYfdfd;y0-Ep|H26I1)F>7#7JG<m)S#y9
z2fwD%ht6WRFSjWy-^Ay@TMT{LCMc#2WZEZ2oR$&OJ~i_PA!3_F&o-(C=K@dvr1+jD
z-wIR>oMY$Xg*7VHZT>~Fn>7WjhpDCcY3KJg7`&HOlJHl=A30|{ac=L|boH?)5pm-r
zh;g)iWU@^4Cs(Mr>LJojq8r&WwsQhKixgEGOQ>V58jL;N2vtIQiK&5wXHMJ5W!e1d
z{N77ST&<~U)sq(pkD`AWt(BdO(;D?r%8uDPmYFKf#d?M8Q_qZ~{fU!l^;15%<!^9+
zDdI=RJB(VM=X8w%=_bQ1Na;4mQJ$XVd$lG_;MUEaP2ied{PH0H*}>a(cj8Omz*T3b
zct`AJ9CW-gsU8eW_%J%aS#9{uG|M>Xi6#Ldo=#_pJW+=IAUliVXIYhL`}Vf`#Pu*#
zM=e7_BtJg}8d%LqpAgoUu-TT_`$~m}y#w!QJ?(3~zS(AuFc9!xGH!dq;@@|HD(RN5
zREW`ZzC@u*a2jjV{#h%Ie(h)-%FEi`!LI}VX09!%Cw-2jURAHOP=;^=HMN&8Z64vJ
zOMN3=FH>i1^^e_h|HnL1)LjIILHqngn;j}Io^o0|=50s6AzIA$58nB_bNqdIA@?Er
zo~vk(Q3*c3WBeJcgB1+G#s?zYo0@^DglQ>gDs0GDIR-5Z99k%|!!#9HG!98Fwf;S!
zW24h+w`62=#NCvUe9WApp2)}aIzK9t6DIb&?xVArsU!UM##XPaZRz3?GQdQIXVAPL
zr+gD|xV>d2dEHAX7QeEtiz>H<L%G&pgYe^j?zGv7ND}hg>XfOvk4Fkyn#6=9jr~h7
zay{(Mhyo+C{%^sozCWWCN#>1`#$aNQ=5VpmDN(2Me=!^}lj-O63|}OBFPxi9eR_Yv
z<k2|YC(;H*HWe-H6{bgXm-;@AGbN2Pnxs{U{JXE!U}1On)6vaX+zttu@MTDocB5e?
zHj;y4bUg14$G|nWWsUUT-(0hAfu6fcaSZ6T0!MJ982PFq9}{%$YulzFv+IL^-p^7@
z;3LLKjT*nwANcW)iT+NK8ti`E*jGT0pT=}tU8(h_E=^1}if>m&<V$<3gyls?!Hm6D
z(%xCNet8GyY}`akot4;4K^+Au-JEqr@07|T?Su+_+s4|vB!m`X=7wv;9Isv8FN+nv
zm`>WvI*QK;pLom)u^hFabZG}V)y5v*s#?&SW4Q{RCq`u%dYD~(ik+X~Q)WB+D|@+=
z32*r}ewEJD{*JkP*VfYw<Cct0WUN^Ys6DpG7@ShP$n}&tTdiFmkw!SOe1>^EMm?Ed
z`)uBMtRyZI(mM~HKYR)62ku+8RN`2xy{yodo5dm{{%+z)-T3csl>rFNbFH8+RK9YV
z!a35_sG9!TS-&URSNmggN5m>Rl65RH#2D@k!(Qw1K9C055?wDWjQ>&|Wrt#<K&Bcf
zdhaa-)y}WFEeS~8ew835`YjnDF6pnhAnj^`ENj3LMqFd6yg49nKpn@6wmy3O>o7S!
zWydbjWcwj$E8R<1SoDYFA(}F+0B@P;LA7{|1hv2!ooFm}scaE-mCRZC(X<9WAJ@9s
zs8=Vmw`w_Kh|w0y!8dI9s>w)Udv>QiwJ|R1#Ab~@J9o}Mh~g{46_*(d=lAasO++IH
zs|vOcgPY#jviQCLGSg|YP5a}FE?xd?erQpGW1gAj1IvfAUiOFP1`EasmWW5%xyQ}z
zivoSA2E%PH41Oo+n^M2<8=eV4>#W&|9>WAs0nC8ZJpAhiI2&W*CHRAlxK6E%(+EE?
zpIyD7>(4eJA`Q~buOALl9y4yWe5eT{OahGrn)W~uG{p>(XBF&v)^R+^GDhtB6$JzT
zi>I%Ss_Kij2ISI8cY^}bbrFy*l}5Ttx;v%2k(6%mUb?%x5iZ?GcT354_<iraF^E67
zV{p!1d#yF+T=VP$X*s{`V@$&t^}dA`_M~H6AZWW;j%1#4rAw}OfL*kqE@C{Bz%E8$
zjs5g5-AyKIIOX~!qZ5jEq01x9p*J8vsLUwOB?X*(*NgK67Q2nuwUVEr<ch^bY3;3Q
zjq?YLqcG1NT><fmhS{dbp{waomGRSb@F4^dJf>Ersxoo@+^<8YtpxAP5kJLcMgYzR
z;NP@Kl0{Q%ZJhZQV&-}8Cn1fC>+TCnW&dPdp-bohV?Y%>{d4Qa!?WxH=Vavchcqpz
z#VQhLs(=`bWYXuT3aM5J80%7#LSaYNT9a3tis&y}S*eWY>4WcVyGagcW2Hfr`BkRx
z_GzMCN%c{F2vEnuliAvGS=^pKkoerTSpj2-YoW2nl{u^NuJN{$#eqBLkvn2%A>c{!
z&KA2Do5gSni_;4kZ);110ki7)diX~`270OUfYcAz5m;9mijutbws5Rlt5L~^^@&x$
z(+2C-cmFK=ia<YDGDT09uo<2x3XIA)!r#$itYHD-iKi$bK}`~V2zq$dTEoc;I#nvW
z`Q!U^S<m)L(EE%)(D6C<$6?k(|9(+(5=DD{twUGCgtJGTmJ%HkO9c_-?96Rx!FQHy
zOh2$nDg5z^T^!)!i#rys`UPr7yC!88qGi*6X{I79>tOpbX?#!WujNLXe+POXh}t}_
zv!&>y$dnx{>MJCzTJ>Bex9Z)$-vv00{D=1E=l32@KZ-i8>EAUpJ-tG+Ua@fTtSh%X
zlOok*QnP*<Qc4*)g*G#*aPqJ9Wp&ItgJlH)QSL<)^SPX?v>8C;m)8Vo)Fh=54wK>(
zjOcBRBnSFDI69G&FhO>g%lo0U51L7gwLp>37tpSk<xU4D``=UwZfOJ0e~f2#l$vEd
zt7-4P&=*R|=fWR{1j*{xzt+CDZy2jsd7gE<HGBKkMUyO9+1PFLc*EHMPo&m<*#O!8
z#}dnHwaP-%2#!3@@-GsPg<bcd&nC)-y2=gs<xBTj$lIhOEKU0t+0XutLyOsi$!p2(
zht@+j+g;jyLRNk6Cu=)@|N6LTu}AZg1536)%(%ZgECC1!82!K^<eEPmhKpl=|F_5R
zP@elCN!5>hl|#|kzy<s<fFOwl*`|K~Gwh&=QPs7Crb-9fS}hg299uRg@bs5WmATFP
z=+bxo#zO%6*{-go)cDXi`?;qGvUBp$-Kx(;x1(cGN;R2dj(uwLC)~WBTc-#qAaG!V
zCsDZx(tatGnQq~TBBv3|XdyIv9_PuKl@wm30@T%FWTlYF0oN`|b#shMj=YYj7T>8_
z2Xgf~dw{aQ!rvsbnXJC0QGnvkc99x5y#c?0r2v?{T_3(9by=eJ@zMacFzVc-*D$2R
z`Vd8{c4$23hjU}=PlSh3ux<T#yQ(UG*iD+HjEL}uJ3^bagrl=A?W=R+!q@087&yJ0
z$E(EJw@!I?)wuv_A@eSoL^G}R1EAj30@MmP#-?`U%a^2WTGAlEcPSQqdajzQ```g?
zdSh>}WNYL594b?;-TXJLJoCQhz|2Ca{iPZ|?D~}bIk?(<t@|%hxP+{Is4nI;8>mjh
zWzI2%^%Xe#70E7eegyEClB$j6Szk?L^Yh5Fu$LVL{TJP}=Z=HDll`AUrbTA1Zl-2z
zH_i$Mgc<~ipg$^!N*$b7CUYfNV;?H%x_XIBqUqM&?y-gW;KJihQFgtXDvdx5GiUDt
zA<i_mFpgA>tPYGGEpx57c(?nixVVvL^S3k?7w=yFnL2WY?Yhqu-<Nn^KkyP;n45E)
zk}z3zniJvN(17Aq5m}7RQx_>GjbFpfYV?R|W`Um@Rkf|{?ds}en$H(7R}B{H&7Kea
z(LfTS_3y$fo)>tg>j9N*Gv*<{Ys^N|i~yC=x7@#5B(kK}&i&%e)4+G6KqV6m1SF*V
z;XQrxkFASsP!lW*Q4qDHWY=9vi6@Je0e(S{1kPne3#?XdNMUmjn6}vGtv@uEH}|Q5
zuI4+Q^Q}-yps1qx{F+k%#Fky7p9YJ%SRzIFy}Q+Ba)?Mg*JfRH8s@o(tJxwpwN8zT
z#ZeKX`b`>helvYT+S7Mp?$)9kM`mPR*TSy-+_h@D<`+~4k7$`rNIp_U5LH9lnUT?S
ze>m8vi)cet&GKdW(~w#HQ$@RyllDnV4@E@taYZ}!{IvDRqLcL&w+c~7e3e5^;TI^)
zu`I!&;NW=Le6PW%(R>4Ipwf5O?jCKMa;lA|eIK|C(aNc3DmZF+8f|a{0pjBJa4XYV
zVA1$pk<CEX-{;q=B7t&=dd(F16;F^WfvE-KMLU9_;lR3VFXFGerCfF7e-|+0gq=oh
z5o}hN(|esi`prpCl<_T<^36SdJ(WZ+N@)gLOLfwxW9ippT~tjgB8AF5MC9gvC^HW)
zwKT6P>B3(VGP#{K9F;zC_r!{&OpCg?UQZ%rLMt`Qfp6o`WeOn34g2s(YForfscjF(
z!AyZUgjf?^9@88~AgPD`Y<-3`IpK4Lr2XSL=WaAg$8Cy4d46Z{o$2XQJcp-J%#v**
z-ke_TVl9g`!@+ej`y{XrW(5lBOiLtieU}6xk&g$jZv?q3z2<UBH2MriwKNzQ0+l2k
z+rCG_FYm+Tabes_12^k@X9$>j>(@4k@RmsvOd?YOvvuiH&89ULOPv$h{d>@ki?qTZ
zZlgERa%|fA2Mlk2nD1+vE(%Lx52x*n4rDZ3rPa%kdA)v`5Z>rPxNo!}UHy9hvF;m7
zZ!O<cxhn4!?d;u(`bUrOulTctM%GEirV=6?r&`vAgkbJk4KTO)rZYW3fn>5%G`z@V
zyXRG>du?8tw4c@=xoY=PC3H~Zx+ZpQ?Fw`G0wNzK@WBVKXq^6Gdr`Go#@s;K*KCk$
z-73@%x>ZxP$6W(isEHFtdp~Q6cKer27m^ED-X_r5OOi)4n`AcGcPZTRlr)g|6*Y!M
zJ84hJ-U&6tfi5+S*&%QK`#B<Cf#d{2o#T$}j7{r2Cxer}G`Jkqv^*xW*AL$8&yZO)
zU7F=K6Czl1E;eyLt~jTySRbu<HnbFWxKjP5uCL*lrTd-Q=%=e}CSSxXjBo*B7t^M9
zWy%!0>M&Ku^ASig^wsN*U#`&LFJ#ZRzKnM3e%>9L8Zyq#`MdqGG;6+OKUE3m`v)-*
z?_3gVG_2P?X#ZUKq%RnB!`%3-n}fUdwrcwCQL@@qnfWjG^13D>p_W2?Hz8fof1mrK
zcF)Dz9pOPe`aZeCSRf~JDnFULauh8IHQ7}ML&Ou{(>EFHt;L$W3_boWwmwjsr8KAJ
zss}c^;`+6kIab#v=hH(vhm#^E9;2>ho+IBUUfX8JgqH<eT!FbI*=X0l+czH=oX4ww
z%C1npHPQaTgV~xBjCqiGQqQe+>ultrK;NvT4CnXsMC<<dLugyG?a1}b@i#py45@2_
zwMGo7Bw#ycq8p^Ex3p*}AW@`m@%kZ$iAz#-80Pfegs!}gc3RvJWuCTOn*Cjq@w<BT
zr&-y=^Q-;BLs<kZrwLAOZ2=gZ!BJhCYb^K;&-rT<MI8~dKMibDZX0!B!~)YlR%GIj
z>QkI4J?<n=TWzkjYCD7XS+qKvj*NqH@kl(YDI@w04GMBL$BU7xcHJp%ZR$E=1AjN<
zfq>NP#SJAXWh6EB93<pcDkGqf#51&%o52^2{LC50?A3FO9v^8d<JQew+P7dL6ezD?
zmTr??$5|4syHk97bWdHr)Y|_x$41lXjY|f7)-xDp%#u)z4pVa&Wt+9k5i$OwerPLW
zyv4Vou`(TAPhitli@~wj_i&(tBvZ}8%PVjKo@s-#EzSFd(O(+UXU0wh+tRB*I^4pY
zkF&pnWjc9?TpB=Y0}5?4#A-t!&xbu+<zGoN;-ruqF6f_k7Fd0Eh~yv7vY|%e@mtU5
z;1ZChPuvgkrvZnZ;x({MWVLyhbBO=!B3e<dDy2LYR=V(9mE~SOa=$}ggM!o#_J8eV
z$INdNmFV>0VzSU~d7bKA4Ly?;hrvx6Ib=cYi~-5@>A|)6#$5?JORf8eqL<A;(sag8
zo1Oov5ZYO=ea^+rg?J&EjA+&5Gyv@>_p(p!!%$<Gj5<qi_Ud2LWrr10ooYvaNfj=q
z?QTAwknd*Y4R%(EuAD?soJWi)T_p(jgmWf($F_2q?h$6L%+b7W$R<*BA~G&K915Sk
zW&JPY5fH?}WZ*&kwccj7cv48C(}x2bt<N50-hb`bf&9~^1Ph1%+s*3Su=_p~H#h37
zGAm%Cmx_jI-+w%C)?dzM@MoLQ`um@$8Y1tFFJw=v!a)#Dl1B5c(yy7Our7IYF`nq&
z;dkplZkClEkl$K+{*mbOn5%Z6Xeg6W<pz4FlXWn9z&3Nf8lnqhqsaNUB`1MYzt7i2
z#v3^pXb+f~C^P>Cf7Q$!xA;8Llt0cBR+)9Ia9MvdLd#ye#`6^R=*aKf<}Syzbv4r-
z#YV&QKK&_TJoidww|=WAa?WV?xtZ*PwT>pv^pW}8pFWvOr|L0{AJ<9khGvMSoJ1IC
zM@z|FC2u~qkyu-h^n}pS+tsZbAxt>LjGO3mGUlkDy#Ma+M5ca|=7e>j_i(TOlRi)C
z@(%qxo!<$=&ihaZCH&hjoRYL`ZuRThm|S7u^PW%k7oo9u(JmQ1v1UKH`NNcj7lU8E
zHJZtdie4uS=r!;-Q94NxvZP8KbcG;=L=MDhG$H=pz@35Qw#8=l&sChDWHhb}+8q9N
zH#jAN;tZ$qX$Z_RmZP9Gh6ZTy3eXv&KX&W_D=7GW>gYH*?1MLrkCc7+{06bgM*4T;
z*!<gsTPwctWOKs|+AZaFh{LMGT8G5W*iNFC4PB*$#fNgO4m=x{FMj62mdM*SyQ4iR
zzUOJ`H6uU+(?)hZ_2iGr#6apHrYq$j!cI6mZow96g$XTsB8kEfDrP0Ai~s2M2cu43
z+3GW9t<#lz#Ng}v`FfE+HvtxwWE4N-;FcS$lp`Sz;^s%`*vzRweLPB6^A8X9=i#pk
z)?HT~i+hvah9HH;=-J^*qQAUI(Oe?RGrm=X&*E;sW{y|HNdyc2s^lc&R@n`zkI3<e
zmCn8kuvn$dr|uSQ!F9j#%cTUm%1qeC`B2E9g&NZj2ki9Z@qu)87BOhPS(<ONR+|04
z47JC{t2p)PP&nptcQK?u!+_ubPNJ!;JFa!u7;P%kq&So@8Uws-;Dq0bFuM6W5eq?8
zbz~!tH8;}MV?R*rbcgP<>P{H}swg?~p@?A~tK8TTBoLGujB>2mtIk@qqsv49Mv++?
z9%hQ>Bm(=>d<I=>)TknYxHm@k4XH=H#!P8VbdUl$+N`zsDJi`k2wIJ|7;8!hmX*eO
z@&7rNu?%0|*i=~40hbp16xC-S5%y2AZm>)ay<&5ARD^wqDyKas*b?a&ue7NQojRG1
z77rTmMinF&(w>1G;lg_2c1_JDmyds41?TeW{bv9o<;FDO37z{XfW^%x0wSk_5rh_k
zfV^WM6F@UUrCqxDCal+5$fSsKOJn84@oiH89ob|?I5{FsyE!0KbjcByZd0V#H+N*`
zeCHKJU02qbHLcTt-Ln6;$Fcivk?NkMj1?(^SQ-b$jK<USeQU<O#;K@Zy#$7Kv(voO
zaz;aHx4e0l6$TP5O=@MofrLbPLj}H&Bebn$n|Wud6-M*=T}BaWFBav28JuOKiH7L7
zWE`Y^km%Q3=E%4wR%&X)Vl7SfQK)xy$EL$f73v`U1NHc!A8$qAB9I_2KrmPxGrhMi
zJ|IHjn7I49_LBHmTj584`Zua-zoKR6I#C0T?L=^2N0uZg$Hiuro3r*a_igUu({&gW
z(m1BJt5h^gWT0AM^mG4+8nC|(F>$|4C$Qj)g!0ZMq4S?#Dw}Z>Hv0H515r69n{Tr+
z!<RE;H^r9)Kun%7vbQ_5jp;j&pQ+;abLK>EY?}1%iJV^&HxiW4JWg<fmp)`(;4o<^
z>SIazd(&woX|w%Fc*5delI>2!mdE0DINlbg3ckQZh@~JJ&?V*4h<oEhT*X7kg=(cE
zfRrm}W&8GQJ;8K>G~b{<%&!QM>WiP&n06~M+5~*^t;A$R4xIhU<~K<`P><U0mETF7
zS-40F)4VOqX6!|gcgMt6y4B@Jg=!{g67WDU{+`Ek>B+hM@q9QTpKlxCdQTeIbr!M>
z+y{-wt=iHC>}2Iv&FAx3{Wt_O)ktQ^v=)&*KH+7mKH`-qw=_-zoN5x(um1V`Q0iGZ
zXlL(CnWqRb8Pc+^{~O_Q^b9Jh`pS{|r+;n6xjQux4h2+60=v?&<F04FOs4~}IrdF+
zyxvSMAQhx(va-@9)}f>UhkTwaCUy}v)aTRV%g!Hs3IBVx$Ze}lcp~K;t%?{0YRK#|
zP#IqYIh{{I*|>vz)aJCy29wKOxs@&ov<ABe+R%aPM}9z!><SAZ2P}hRKbZy_!Nw&t
z`%*S}0?^GL5+a><zY>W#uj&pY(IL6il3KMnJT&TU1S}|e+x7mHVU~jLvA;6~qRY4F
zWpz$*h-;)`R>dfQ11E}d-lX(#TN=4iGGj1svO}W44nFO0gJ?MnaKbXazI7-{g6}yJ
z-AsAL^X(*M_N1E)n+iS}-Xunbrx8+I%6|j012nReZ#TjZ_Xdm_#+RLT?J@ai+0P=E
z=XaPg+SAeIxjZsT-n7~tisX9A|HnfFsH*s`E+YmipMfsT;n&i(xRO@&7N;!Oorj!8
zqLbUIT-ItT0Hy6@`5p#M&_Y$K&C%0LC9t29QWwk~&f4;*socS(PUk_jDpKT<+KLN%
z)ioUsCP1yJtgkmV_3&0|J2c{u<2GR@Qr!(OxtwwvYL0+<t99QwX`OG;nz1Jq|8ZeW
z2u`N-^kT6#Vkh~jd9VC75c@sQ{YLRkbP`B@NhO;7jT^HR&s<$dC}WgF4-l(k(s+oQ
zZp2i>L`digd4+rBtrKN%RO9LG5o#mLB#$pa92L^DS>^gAEFx!mD4;ehs?L%P{n~`x
zGJp<JmYv)bzH?Lb{930fXIi1bra1vFu&c!HzCFT>{ebK#(i{M-u?^YGPsel_W8CLa
zTf7H7Gjzju0VfzFNC#F$exLAu&8={U_>=Im8@jo^QRbLfK^ZPW(LbH7WOZ>IcGJ&e
zh@WS-{`J>PZdRYgdO(OrDZzs{iddAx`nO1GH)uNgn2WC~$N?#Tanx>Ny&qle6@kCZ
zLl4%TW+A&G)2@qY74H_4Nw2(cG!aG!*Gs}PZc$etk>Vuqe&En-GlEy`|03Xo&xdlX
zorTYWQXT8Gsw%#h;QJhP7h)q9R<J5ry7{&2e(Ay+#DFG3H%1*-FR|e=9>9Ic`;DC}
z*WFa=UytbP%HY6i1SAY8Skv8)Yja2qV=H{?Y`xk79#Vd;VK+%2HqI0J9%qH?Qgz6(
zr3*t=6RMf&7d*;pbJ6wh*NU}%JZ>(oYJ@#AY|*`Emr9qC#_z(LZZT7xd`qo!y#nzk
z7SKj~0FUZq?>iIs#NWSTU@bO=pK~oI9BM?=p^77=?<7Vm2DGp$gg!9hD-%aSjLwfB
ze?kNI|NLN$L}s3Vr@@60M_HaH!y>0vD9V4>5t&P(%j!Aa$1X>5jg4AGca^}uEKPUK
zG_>p8kb|{(8pP_Hx%mq-rV14Z1Zz8}GZzikbA#lGjmyN*T`<VX=CTWgKkgyo-AdHr
zD+yt=_~)aAw&;CXsMl}cCAKW5&A&msxA5n8@#rwW4JnA;0<j>+b8vLv4zE{^wb;nG
zHDbv_J5`trqfIL~d5;z*7ENUaTa{;*9d#UcJYI2-5Jj5J5kN)2;yIoRRRRDPQvKX-
z-00flxO8I~;*Yd(-{<ti-4f+d#zD04-wFrl*sVpMZIs<i)&C>W$md|${Hg88s|*|=
z)ikaXlzsogXq>sM-yuRuMba3lgn3XcI57*U#8p+WBtc5Wxvhz|t1`44t#<kZ#u0Z6
zLajVDBMaLp-|_4QN`Bh?uNR;uTGK7wT6y+lN{Vy<Q%zKnn9TKhl_mn=uHa5^B>`t2
z|E~ZMYYC6tM>hW<#@67n;U*qJ@z{5GYC;RAX&>c@$?AhULkLyIF6|L6^9*OOIWE17
zi&PQ8Mwj)GF!eB!z$u1SlWQaHzP`Lia2ZS_8gDFhE~B$4vmFw)rY-426crNky@!)%
z<Lq&>so4bYve#TN5`vRvkzNpyNmG`$UtI&_H3E(SNghP;8!@Dm|4%WL9Fu}86g>(;
zrbiOKfiL}TGAl8xg~vo};`(pym+4D<mWYaF7bYzgG9tET)~o5QJ4Koapp&^@bv9aP
zZ>%fzrd;_c{G9V=KtrpbKzg6LHvOIHd$9U^Wr#n*Y9G2eg$<y_D0Z8!4V!xZD~$i`
z^fgY={hEDqgkUj<)x@3%K)>}Iwpzrjh^Zc>98_o`{U=W(d=Usy3Z4y2V&{8l7Wksk
zrDJ_#2tv5CL{WoZfKVUFMM632*YZH}hB;E@d;6Vi6YieHb^sT)p`33!%$=0tBC5f=
zc|Br)_;zlofxMHZfrq6v!e*gMacjc;d~`AcZXKd4IsK_2XHvo6$=gb!Duw|uLdB1S
z%E713<E&BGpAmQW{Mw4&`mQ<POgXZ#Hm`4uztmbuwtI0al<vHVrOba<KaRj*I)b34
zv8aTh)sx7ZM-`_zEKgBfgzJUjpr<JGc^>Hq>=2_#<L2DF{+t_Bw=`RL-nq&VMbxb{
z^o`<9zXN~(+BkCn=z$QfQQD0L@!xVQv}vh|o6KMbz<ul9HN}wNc^xGON4uK4s%FT7
zyDRSX=MACC(P_owKpmL8YBIY<yBT+9;sXKNuz|!lc$bw6k2$QK#5}Q<&S+ks;`KzP
zz#+_;-W$)%n4QS<1)m{O%`MHLQ8vL=GbtRe)u0OJ%t3OZ%_>b1MTUwU1Re^w)cqD=
zd1CS3>_o?g>cgzZ-*&H*nrsTVuCddrpnQuL9Ju7=H+}Qa+Pd;DE4NLGr=HRM$l2p0
z-<b2|E|2a2HRXtuVZN8b#eI}(cku<Vt(|GLU6)cga`TG95yt*(-JuZXCLY#iBLx;2
z`f>8%X?lk|0&P3$LGm_D2gA+g3jeE7;K8rmt$AXubZNxD5gUARz3CYUZ)3fqX4xm>
zvK@Te%}_8;F4ZymFadF}3-~9mZnb5g8W~})?gGAl4D3P~eZ0X~&GUl77*{(h8q$>J
znS?rYZne@KJO1e;9v&}oRC*DjqnofJbUFfTr)wUba@F#52xCrvyvCj!!L8~8s{Jpe
z?K}_+>mmS;g$jbkr|-vYa`qMd`x~avWUG<LbhX;`KJ8{k96&-zd56gL4$FG%j!Dd(
zuW+S2W#Bza+fcv9as!yqoxHuVPS81qBdui6>?ycUQ<4EHLKwudg6hq&K6Ve>aEo0k
zuBSY-%}3yxh%39&uqXb<4fVQ~KywKQPJow_@e?Biz7voZe&y8?&P-Skya9sUKbBIC
zxz>aviC)Sbj=(cvbvf-^d?E`Vckk|YU1E&vJ$?2UWtv_v3Xf9BT%SdZN8CAFY!+3&
zmTHm}la?VJ0yGyaghDD&Rn@H>270tHH`~2l*4XrVa1$><5l9Cp*8I%FgYilW$xEXZ
zB}np6jAk(X*1mr=%YSe-y*rq`e5~(m>vpuS_y%L)sQPyT&Us<kajtD+<g#7cK7l)m
zrZ07&H;)NqobRimxEF9P^I8M}kjz!b$WJ^~6T_i%MdB44Il=v!b8{6rgQ*MOwgh~x
zPCNkiYhJ?GZpbSDN!6d$ljM{Ty|m@Be*qe|r`OnyBmrJ}B>g8R>v=cH<v~r8Uov#>
zxrmhPFAEQ+R*Js6X-5+LMSB(PN409%x0~Lwc)mPTqo-%3X?`p4krtfVWo|Ivz!bQV
z4Zuos09FpWyO}b~mw<=e)V7Hk5AdyP?G1CLzt;MRut|%w#A67Vf&bKlER@g>mo$RA
z$p2mfUXOU~=A+y%w!23mO{Ru6fCT2>mY*w*2cD>NU9RI0$0s2ykbGG}txm>jJCSZ&
zf(n+7Jgus-w`WD(c9=)xWrHtjqh50%`ioQs6cwB9Pv?zH_^OoiR=1nji#?fa{iDV<
ztOSf19L%?^+}4bf8>oE=R&u}^JRn1GO+DOank*|wwVPSGNk(e=4OAq&7{j$$SNxoI
z>LdxC_vf@(BJix`5Mr$e$GY6~g!f_2pUN9fSN&I75LV~eiE40PSiTTYG+B`B8`N8D
z7DSsHw0gqKR5RX|oT!45hU(1W>)I}bRA(8#TM~-=iz>^^Uc5R7+Cq9|El0kA3FExn
z);1rpE@}Yo-33Ij(hUO;cGMFmel3b^72nKXri5SeBScfzn#p-g-ee3KPn>&8>N&&F
zV86)(`k;H~=1|W&+){h^e4k1bc`L__2&AK$KUTeyC29iHQG%J<aV%`HU+JwqW)WAo
z6f#%QW-$6?jeNY2E}e*()8}H)lW*X1+_O~`YjeIndP4D^g&Jrhp9OgQHd?LxlbHRy
z;H_gv6_N7&UogN%Y^L7poe0iyIpiVeSJDsF%{UVgW3;0#YKvcw^FTsNxJe@M->^c{
zleHvoXK8yt_-+C=70C~uWQrbN8)$|u5#m=~ifGD`ObtBW!@7bXS}zMxvx~wV&ukvU
zQB|{|X0SeyEf`7J;icEdqZSEpRZdkZQ0<lh`vH>nM!<TOZVw1{!+*=1x*lZoTtyw_
zCqGt`9QG=q%c-7&D7yb%@^Ug^#7I;94P4jnCcf{nu#cmU%LD<$g`an(wa3iGiR#BE
zHd<Eotd;<^<|Cli@Z<T=AsM@NMi8<SQS7Z+F10Q``FIJNSRF@#{-zh+yQ)gJ5wcW@
zh0&D^d7vtOIV*&m-_NFP1VpS3$S>LQ+nq2fw!t1VYTQPXD)agTR&jw{7>s@3Pn*xF
zs)BJ)hGdTVTt=M;Xm`t(zbnOK_qWf*6Y7^ObvV1aMToD1`;1v+{ga=b@J=y@V6?r&
z3;&wjd4uK_18U9L#&L{6;zTtm-vmMqC-lQR+OD;{|3c91k3E<wt@S=8kBxY^-N?Y%
zfvt$l*MAKM{=HCR8uzvRgcSL^y>-SdgeRseRg&E>y6bG`1(T;0*;`y97KsZ$hu$g`
zQ8@u<rk-uGyc4m!&|oiT7%edXF|PzW(kwc^cIEtv>PB0vpc#kJR)2lks(Yi*VeYHf
zGZ2yLP-D55*e+!qHF#N%9ir%BD`yBJV~y%>WcymYRoU*e1d=rmEu!kqskn;-sRv$q
zwTfk>t0(bR^Ah^2pgL}()uXjCm~vLjsqY=oPE#{=Ivm~!O}o7-|L6<)(ca<v-5sjg
zBO?Bl$*zt@d-nkcw_)4!p<YdhG;nJE<#y~vpJ|##p)2)jUaJMf^jVUK0}kbw`o@U!
z5QPIvg}B@R5j=cpy#Pk0MUXs$1847-pQ4M4_M&~j<jZ0NiUg;x0S>P<@8HDPG;Dki
zT~EF<#kP^Sje#ONl<oa8!^dy>Mw8f}^{azQ@xjikx`Q=_bQ07@IS-P|t$FPY@klE*
zVrNi9BMds%sKF;fIV@A_#d$-b#@kUaf+I96FiTVum8C<F?LMcb<CdkV2vv*s^So7K
zs^(o8Z3XaGn88ajjRRwymyp;x{s=dN58_I16TOjSWV8qbv1q{|F#N!fOs+%6o477h
zIPJ2m0ai3Z8m`i5;jLOdLvHNA{Be1qhxTW4!7wZNJQ73-KpVu5Wpk|uc)9G-!A2we
z@v{DTnR0#5VG>#dfbPT6gX>t-sE6qRP0;WXiXT6gZDq(5xOmZbPNpjRjh!TZjEP`<
zyuq&PQHM_DxpUR>ZHA&6x*fP>5Lu_Da#~?-By|qB>Nfy@#6Sj;=R<dB!(z5`7LOoi
zd1#QLl4+Xzj$OaVgP1mUZGM#?wbp#`TmO>Tg9?~%DAyNlFN4z(Xo+i223&V+Y)ZfS
z2U!}Eb04M%*;wV)ZO9nVAkY6AqqopNLx{EKL97-8@PNcw{&+KFF#>J?rXql}Td&SB
z1Co@-D(YiJ>cN&_FrP<j;So*$LC=DftT<VjB>HOrmlGqiEA@-DUKQMF%aOp_Ee@>6
z^=*Ygv~mMjO#?}^#~7;h-q}0;Rnlc?B;%XTATS`*PO!yUea+v)*+%QD*=t5Dq}$X)
zX&Z83UgAknjfI%QQ!AR_M-=SAgsf<@#w0$~=_2=6oyX(;ovFEDjPsLJ$tP2pcS<Jk
zuPD#Xi?CYCJ{rhACGKYL%d$M{TvgIJKUR*(ON*2H#6w7r62`V)wXOZi?_)MaZ$15k
zmFqH)NiR68ZLNXumtQAsyS3rB0GdE-VgY20-c^eV6s_J|PIIDJ4RP(C10?xf?;|Xe
zCfQdlRpKiI{%SXStM0GH0YHqx$Q8+0?d5&~1OD|@4MJCLc#Li9?p2fylmwVu9BZhT
z%RAj!#5EP2u;^)=Rh+nkw?<?66Bc5kh5?|!z$4^{;3g#a`5N*W6%oh|f6^U123o|P
z{XO*}6>pb7U7c5c$hXl()3o%EgFe-(8Alv^AY72Is0sh+i*Z^g8{y&!^H+nYK$){w
z1=Rzq1Wa+{H@d#(-(<?F7|(De0&;>LFlv`b5`{OAN!fn-#Al0?uQcCkM7?@S15xeM
zE*nk~vFX;svIApk;c{3H-rY~x6;DtTM`<EIG(~jfywK<RS?W<-{25tz$6U{ipZ#AT
zur6rk(mm3Z+NA}K5^5MExd#b>!mFf4Dbk4u`vK`KfTIocJAwr1i%POIrN$=)|BhaK
zai&*kymygO<Zv~S?#m~oKd*X{<7GGfcWnOu2XPJ)k+gf-3sLR(EE;j;k5=|`{tg27
z7rZwa_GfRW#HsSHRBF0kM(j;QfU+7Wn|4g}zU1$t4DHLz!B%T3pgoBtH|Q19Cn=L7
ze)bq$HU-|05{Ds)g4_xWsXrPxcee$-irMJ#>WC~q*it1b2N?r7W5H5^zY|6_1qmxS
z!lh@~740CpsEMrAl4>Gd%71DmJg&RtA6?DKJo>IbHA(`y82PIfx2j4-q#gvq$H0&-
zbL*F$2ETkfTNJEbWz~gU;9dvpWw{`(oQO@$FbXx%;QV8VPQiN~+MqqmzmD74^5;iZ
zPnzB~IT@I=xVyC2(#rs*$U;a<N))kQ>K3rwA0|~_zKX>b-i>lvFne@T*|DF1ItaK9
zKkYckv4*hd$~6{z^sj45-F<xxRM&q#avG2O;R1DwzlM4(u#yXmi*qt^RTz0D6L1{9
zxFG;EOCvq<!SjiXIH?N2a|*G$-%%mnJm6zK7{K4~^aQOJ33xc#^6j;Zi>ChpBszHi
zXFTocS&0#W_a(^hrhU(v3sv1vSmFBwVd4$Oq)z!F;jGYs+25Zv^#ZnVwb#JkZ6c0b
zcDak=vzm&EtsFVNZFL1&1DwkAbR*uu;2ZN9Z_Fs(M>FgpcMQsw4IDt!N-QX-Y=S+a
zSjjhy2Dn4-s)}jsz=Vcl+?4SD4*yd9JM5(rAVR8iH@GdQt_Z>Cw+;Y=@DP6;JAEcU
zaX6i=y861;n%yEy#X@c4-7b*N*|hffvZLM7aE4=$L9D#zd{oO?eb6E7@(4KW`0t;p
z`?Ath92DDq#`T>Of!{P$rN(fE(Rq1O<ntK=-ku`8YwaWh;qRY!gXt&`@d=b^?7w3o
zyR4E4WKI}F&8S5w4$x7Ne>Az&y9{u>v~JnI^otyz8qZc%#cZRJJr;y(d`6f^eJv}C
z5J-D-+@wZDEYI!uj?tmu9cpbb)O2s?7rZA0Z`P~kT?ygrJUIIMwk6Hp@xe3N1AJ^m
z5j^e*p{!T#t4G`POd`t5$=Pf;zzID)3Afgop(At54%ce=sG2*=Ce*DkZ!{}I2{7cf
z6g5jhiD`NQoN`12+p*xE?K&j5Y+_&rP^?aiWX#j`Qeyom?dqEHqAG64rdweMU-8~#
zth)KMdbZM|PSG0_lk7X?l!+_afgeCgcOLfGl|o`XYY(1;^3vD%!!>!5U1Pc+Ynz0)
zn4~#AMmz95-C|^q(24q+zOyqWY-7jkiM5X{s@_**pp_v<hV$t=jCNsIOg3=BX5f(@
zCojg`H{s8@-8u?BSngq&$G}|S0CG`Gi7C|2-1((6$K6adO4={MEDsd~@+*(o1Cg=C
z@<$fyy#0$$3Z<r#uD&6$7sINZ6XVQ%8c~>bYfH(*_~hxR>Ak6&0wRC~ctC>*VWpST
z$EXYtK7r!JEoun<-NHV#fJd(~oEcy;2m!rN1b!f0``~;~G41Mv_XPs2rpF0Gc$A9w
zuvM>qFoL-s4W~&N0&sR&soa%gfn3$JoGO&><znie>-3GKb6DF~%SbFz=+u}hHIR%9
zh}P7^ot6k}p;i@#8WELDaCEqR?R8U#aJOJsTohc9Lp(iFb1-huBABsub>PkIJ%9Se
zBfuFC{Y3~I-UEL(N;R{w*Aw|yg)8XBS{02;0{;EyxR}g{qxrjUxHCh)KKC2Vz$L*M
z<DLY_C+b--PVJDShnWh9)RckAGaHL=){ofNccw^i>-h37$kIn&_2kb4V5lr|i6|>`
zBc!~2C7$nU2hN(_`TSw-(>NdfN8<K*hn{yDLTtM-8c=S;ndLsYK{Oi3)KS$umJ&*d
zjr_=({^~7K2zr)Yf<B-<S+%_Eocfn$CQd)Mbc^L@V`~G@4p$a|O``fQVaZn%L7T*q
zubrH5sCm;BMF?ky1H@C^9P`7>u1TtiLvpCQI#NHc6XH&Va-ilJT=Hv8(a^FWSoi~L
zw>jb!wQ05UZ?UKzl@RHmK6&6$K#y$md{Yyi4ddKjOcI`D#z|GH4`hRw;>M_vMCX5T
zAB_XQMt0h1tZc)ZtMWlO3p<YiNJ8n@oZLJ=f`g&rG#N}S@Ndr`ytfQJ`S`)XXvdoN
z=R}EJhMz+fB+SWaIs55`vy37TWaaNJ$#6kA?{kA6gC&0~_d%q?!~L||rLtaWrPalz
z4+*S@vz0{WDgN9|gCHn-JO`v<1It+cCqq_#VUC&(;g{(cSNZH<glQ~uI@_|jE2no&
zYs;&TtM%``+SM_Li|k(_<H0bZ1%vAp^R({nDv+ek=Shn<0^b3Bg8X7SuFKr{MZL=T
zQ|3AeUD@5q>@Q^5(a#Nsh^c}rxMQ$Bb@j^;aXpI@qO}Hrw|KT5nhE%ca3=zkvhS{#
z+fB$qB_KoXAv)~wR?Xcax^t=dv3HxA$2-Tt)HOL8)-{}RaRmN%UyN>~ozkc8^$NK?
zzU95bj2ippK~Yb0@CPragbEZ+!O3`5xQXr!Y3iSau^s$LR3q}=wu7=s_1DJrEDSE|
zEL<`|Hc_J`Xx@C&f+TdXU?h03-0Fk-<~6&I)%Am}BX{`Gtdt0t83gU(0zmyz{kxd|
zc2MLMJUtaSD}Wsn3RGt#KR~Q@gKM2G8w>e^*FOIp$M584k$Hd?2ddBAzXhM&qm0Bh
zW}d%qUe#T>;gMc({0hIbqRxWRe8Vk~IWJX$G)6dJ8zpQY^@_$-N6Wz`5m2mbS1oJ#
zUU@<ndPNsgXEu5~SAMp^%wK;B_p_7#b2jL1BQ8OlB#MTEh8!Dm@o20PUUHc}hPTM+
zidjlEbWi%z6L+Q*Fu=`A(Q~uONKrZNn?6deX}|J<Rt12@yGh`?Nb?i^?y7rxm}Y+f
ztZ{5l+yE&5X`V33PQ(=l+>V-im?hkq|15P$HNrbKQDA6|U-%ZHDzW#^w%dP0A?6kR
z*}A)~g;Qp%lZVQxfC^!Zldmi;*#MtVUV85qc4LA#W@w@6TTOJEqS19rCqIgl!)=99
z2+^V<{MZFJdStPH57ba7zZD-dcXso31SFumzRB@cjn$vG!UuXa&hP%l96I6ct4cd9
zhG}IzBCC({&of6OHBU=bDnDSic5<ft&DgA^$}by(S++Dn4V9YbP1)kFN@FfZm3dlN
zVqun&1cg5|QXNqR*_`YYYlxzxf?gt32|&7#-nX-7w2CHuXTjt*CyO_sGU<K~Is<<?
z&jon1=1$Md!rXbHWn0_{l!~QmzYMWpQ0>Vy{%(kx$n@zEdBPyLmqk7<X*eZ7oBt^o
z@vwwxBpLN?F#CPjCI1UPBWs~9$NUk#$D}pOH1>rmA@3B(!J6+k;Z^}T;StxYB~zb!
z>x@V)`MIlX>N8ugbXtvax7ww}MFf9QA<pccB?bHOM<3Eah-06}blVx}^ou^e2b3QY
zpsbsM?+LpAd(esHM@^qiPko9Lgs}Wad`Gxpp$Lsmhjov3W)b%D(%ilpX+ZldqyiBX
zJRV)LYGE1Ziv%1_XKb~1>bVKs*hIx{rIS}|)XxwX@Dh-4<E|&jeJ<4rnD1s4F-TFL
zmbYpaViZ(M*eOsGoQ_T{3Aa*G#Va#&m;ZRGOFpffN+m5J`Z3ufGwm}k>9{`1k8`4C
zr|nz6Y`jlCl3@K+OWR8cEQAA33^i?pAisMPLkQ~OB6*M(*T?Ue!v@?8N5iokOCE#J
zuK%nZpL0)dSK7>4P2EoQ<K@UY&oc;U4kok1%|z)OJP=P5wa-UKSozoj_fQuD(Z34a
zNb+i|J83N}O+RzPV-~xUH54msrDs+c8ha<x8E5+ghL5*uY_M+epPfA4&oby3z>vmd
z%Nqh%M6X0yrD(pV%0&jjn~xRB+9^^2yWH`WQ1Q!e=@u03szt>$WH*A!Vj|Jx2%&!W
zW6>0xh>3IuZ5~;66?Mn|qKw%|a9#TbQtS5g;UZgsQ82OnD)IgJ$3H6qyZK6ry8c_8
z=xMiU!k-xFvLtD<_^wjvUM}4NV$hxg4WYuH^k;EzG`Y$$@ozg3n`HUyp0kDZkHHJ)
zeU?YX7$UDI;GPi2o!^;&CybSA_)?P~g+I5*nf5^$%UvGV)YQnmfOPDOfxJ9=sLfU7
z&^NJ<zL)&9TeeqQF5;5{_sMK?-6Br_R}1mG$A>+7Ks`p4gRvA3nQim5&OLZoUrJRE
zGB6VyHFfm_yZd^=Ie~t)cEwh1OBFDRN<3ZLZa-Q9wP;k1*LYJC(X|P@TN|gy$HwRE
zOu|!Yrs>khIM?8yam->Lx^CqpGXmGXaL3QnPai<iok|B>`}{&o??GW0%TG+9!^52*
z?01q5W!SxMv!;WRq`S$YQJ>7Jk+nH<QDu*bS*#s-sL>W3CJtv1XBY73(!#8|9aEA6
zS?7+jbcCZyc6Q2kLM=o5NRVISA{?x3jCnt*yEf2n9Q(J00pmeau8eFAXQccoKm<KT
zv~SQ(C(!Hbancu$pid+{uTE85mFOVmS$W%XU5tWPPiigh`ypNWIjAyx3oZ{w--9ua
z?yLW(!;eL0NHh|${SOrDh<BS<I8vvW!-wOB*%q--;3?WuDdnD0JIriJbh5RNA;RL6
z$bII`nn`72ukte+eQOhV*z?D!BM@BNF^10=W;Uw!dCXinIFX`8Co2z9OFS|pS!jMC
zrlum&U5cz0YRCz8I;9n>K|F54E%an}Ms};F*7<UNp??0b)T(gWx+kYGYkISKbzDZE
zr~;kQF3V%g@o9t6pura|T#gP=3pfqeSl+Sw^}VpKi=8OVPl0#+TtusYXG9DB=*;rA
zJQ;9^ux|dqjL-Z1XzBm4nGX{`y>Z)`>1LPjctkT=Psa-Hywr8vtB!DDIqm}Fw@cNF
z7&}B$`?17KbI<~E;<7t|raxw<kli&~q_iwJ6aDJP1KUm1eE=>ZkWMJaTDDXs?sy0D
zhyZDdlWKimPS5#%Y|F$RQPEUU!=<RN>B6iKNHvwhG;lnN{v^c@fhRH235#9mU-R0y
zRfaEgwV|bO${q&JMy7537QGV3E<ddu-+0yO6d|F(UsFjdjh%kG#zU~IGgCH0V!Mi^
zs@Pq2p+Y~Vurd?LCyFA*1Po-O{EzO?G5ojQCgZt3DotGp9L^>kO4=<}*MF}awVR%v
z{u#r~m8;)DR8*Z~Q)HACK<Ac`YevlK-dgXIz!2lG!-IvFa>g>>7sahwYP}lBg&er3
zgY3Xt`uP%_Px+bd-%2xiu#J)OV&p_JGZ5Z?rb_TQUPEBPNXI5gGGDbacA-!s$MwHC
zx<ZrwQ5p{ZQBaot_7w#3L<$l>?!7wwA%U?Dc(07B7Ba<|(cWs}&KDKcI(@(m3)d=f
zQtnPs-^?UzM;%!N$JR+-LO?fa=u{BZgR&j{KTfIB#SLjGENofCU9mBk-LH1)`csoC
zvwetaB7i<@^!H4y1<d7#SJ8bewHibTqHBc8nixwN;vxfv^`whKmG2Q|T{cFWn!JBn
z5%X}u<hENSmD0%emrOzkSnM;F&fL<}&juC}=2KHUSIk;CimGRRaghtbFtWZb#4$vS
z<;&Hfi0g2|e~qJ}{HUu+Y9=61yj>&>er5KlXpL(GZNh@W@>+3&lg<*e*xQ<MN}_=Z
z1p$4#6vUxaL6-hL^j?1Fa3X;PdrnVZsE-@&b3Y=p-9(O{#b~C6xYAf4gCy7&R|?2v
z@IX-vx85FQ*XJ%(jR~<!jmy?Gz|?}eQ`MiU3T$Qygvzn9F&)wFo=d&C?515#^b=y!
zlf9i4T%Has^K@%n3?6L1pfjCI5bgv-x(=imvx{vd@3+PGaJX<$oG;kk3d2x%^z=+}
z<9{Rh{WLf;)|IT@)NroovDd;ThKXbOdp#-J_Tjo|28T_FF)lFvb#ERMQMSkUUoSwa
z<{xyele&F^d1=F+ETh_yN`euF?R}rh+7^uR_<HAdt-e1k^OTxT7y!R&!9OG8-O)9%
zBP|WvU;x}ma)<srz3d70BiThnxyd5$@g*bn!%W6FT6Gyz5J$?#HlVPI(hLON?)owq
zEZg5q30{i!)L>#HukqyZ;JdaLpp@4^H&G7^GpoBWNRv7h6;r{ZE28Q*#@foO5ZWk0
z+Y?k<CH_|Od2KP5d>g@S^2P-nhvLZl)orZv=D+xu99(u1#Iy#|o<Iq*y{HuNT{=3e
zkHWY!M0Evw!5WXHao=*NES{wu`HnuI;W(^_$FreZz-wfU7+gNemu4@86r7Bix}cpO
zlJ7{fNPwSBC1@ZX6y7LG(Qq0|$=#7=QVyQOm!V;qaP?kbAa0<SxPgJVwZB@=w6&QX
zpg#*9r@iM&a|>Gt>}zmK4Rpi!w6Ig`3$}K=Qme5p3=W<Z6G$)XL+~R(H52JG!koCo
zN;g^ubvX#=t8(k!z?QY6InCSw!krPbrB=zqww->y-({PjXv9rR^m=?|^_8DjfJF#t
zpY+eIgsE+ud?gHDt1oJHZ;)u2B{9d1^aR-=?#}%poc^#(n4Hyb3u>$;^9+e^NYo9t
z!ceLrG^i!(&{|>AJcw8@#xewHTv)^eW~_qI++q9IHyQtjflc{GjiWi3UVI6`p(RK}
zZ7}Y=4^;$W5fmrl_e3>lJRTE67JPHwUgfZScLF0E`4RBIxq$l<q4F`Gx-A?Wqd$ZJ
z4!HykUP-m2+NcGqKwc?@?=ZmSt15nQxB#N4-z45Ew$#h-u+oSkQqeY8x919ojCr_j
z1I}7nc73V|`Fy`-#oe=78iq*hf}U+%y$#G6IyK(cXqZ%`=0kov;5x|3%JgbxarD<#
zL2B^PYu-E8avK`*1n)G4fff>vJx#v;IPae%f?MDIM|~BmuyJ-q6oV0~-dFxjwzups
z#R%BFHI0G$*f4Le*1LN8#*&>(GZs&!vF|(qAQm}|$se8Z4inO|w+QnGu&^$3#MA*2
zV>LkZ^&;ss`JX_?(9*H7w^a6nZZR$o6fV;opvzxKrv;o1(#bAG_~s>0ID&$CV%$xE
z9fM_r5R|HcGcDjWqg3xdJu8!hkEIz*Go%RFhgU*B740}iq!7~yqC$fkxPt<AmkItV
z0aZV&26XrBe|XPaX<8>&Sp}oNULW?<lzz1XN0b5Z>5Ajoqn13IGEJN-Y%1sTfV?Q5
zlWX&sx$Ff<&{=-=N9V`jvA*7F<X8rCu|;RK+i#<mX~s0;*=zonEgv0x+(PAQ5stc(
zT++YrBG~8n5_Rx$_@z9A(qJ!oa<Vjj-$JXU$918FsFEO%2*mz;YHAUDbGS}b9Xtk=
zs@VV@{ct6}+OBiG>pam_c(52W82tv&TyuA4ySnEq%iE-JaSal-e-~o7Rihs&yCj)I
z|ItZC{a)MO;TKCU=}rrklXyq<hL6`Y!d)tm5Y&sqah7L(h+Ch7Sk?j5V8U+<9z!Xg
zNWZThxI{Rk#RO6v&=hv2=u>fvM(`(V7N}e-E@G=nw(`MGy*C?Sey=5RH(=U3@)cJ|
za5DJS4{;M=De+v4H{$%>M$Kae>_pR}ka0Dg=0@}s*0f@qN;G-~+j=y0R<YzeUpT*i
z;Q~G$MsNykK4h<OmHm&8hH4E4c3@f*bx7DSfi@U~6|#_>K1bbJqIPuucMAT9@&ZrQ
zmlNQNY86tzWEUX6Q6Ye4;)e2K*HQm~xm7-!&@LD^{IDl22D^kd#e8XiZa!Kj{*!no
zqADy7q0LfiaUe4qLJZR2;4a4Ln*Q{BAvLHB0={bKwb!>$eAdB}yCvlA{&Qj$EE{!2
zvwd?!@PM^N6_24BK~+uGIQ*PwlSMAk!iMvwuPEf}1m^*XB-L!w(d8vEI|S|T@MmNS
zVw-&8L366lqGJE%D&!i7F_4VYG++x6r0Q&4z+%`0Du_nckIAWUMY5-zIRf#Ypx}!Z
z{N(MyMV%tMj%%kU+#&%MH-=QInJA4-Q&8rSh_LI#ov*{|3vYTG0J?1GBS4l-838!W
z%?$``$8haCu#=6Z3gdFByBm=U76Xs#+cV4R8%(PNn`OW5v~)QM4s`M^)RLJXyz%9A
zkRGmC<0i=`CikxoDIb#47^|bY|0c?mn7{6=SuC-M(Ty<7oTVX_=Q=}(l_(14Y?nOa
z7IeTa5c5hVmMT%v$R|4XB%4VoyyWxnWQ<mL?QAIPf|6^(jacdjAIR~rXKv9Q%G35f
zF~@Yp7B_D6*$F=e&cMJC&Ff8ee=KHJXOIk9lyHl);Eq|Epd=V7mxN~J!UO?T{$kA2
z@}PwwO_N})pBo4Iw9c*)0rFVYi#$fx69kqL-x|;^YuIKMN`v{Ta+T29Qhe?T2GgGs
zb<n7Rt$+Um48&umxYR-s#Ihakrs;VMt!Dz8W2k<+Vi|<Tm>;^Pt9(5|%hEGa;wdxF
z$Z51vHZ`5K(#?vCtRozKGFG`l=v7{`_`ym2?5r^t1^iu$Yx_=pQ*pmD@)={v&33j5
zy=#m=_K!;Odm{|5pE;*@0hagPXlTlMKP<A2{!KtyeI%U*_Q<899dzF4Zu-xVimeub
zE!{Veu+I~(kMkKK9Sg$JQu6Kk{k9+v5ZWR%Nw5>48@7xR{?a4Kw$<KZgk?y6E}9h#
z7tmM(UnFK^eLnxzTTDK{R5>*X%z~#XftKfr(75!U_kV-0g1Qgtj9KuT^38`zcyZRT
z=f7u`5Pvzt3#g{e?+9YVN9JF-?mNHX!jp@>e)(&Ol2u;M8zD@Ff<iYDlO#rIR_4Bw
zFHQrqMx%eNU)$-BFI1Y)sO9rjK@z<rS>xvE(wy|4VPl|21(=F?f+UdRqj^ktqwxrT
z>A@gEPNW5#u40WXy{Std6%O$tQI?h@=^;;kDwvw-@7xpe`SKJO90qj`g79%(8U&(<
zkB>&Z2xd_dRcR?P(MHC4@=8LUcqK4F>T8>cq%`(V;GoqqM*w(d1L8y$u|IMT90BIh
zD)<j9t}8XXlF33!{Kw(36{xZYy3(RGZkd;cYy07vn(IovlwP%q&Nr~-YEzd{h+mK<
zU>Yj-n?f(_JoPfIsk{a%nRVH$jAzcv-B65e7x6K65WaWc|Ds2z{YkX*=Y1jtt5YMT
zXs0-jKt@c@<GM7a)hd`MW~r(J6MnQdB%|j((b_@#6pojy#)aX)2W_0kDpvcq**TkT
zXl0&CWAGHi+54U7K?81nbZzCoy(YUxs-I@hAN=-=;^%Iv-6aWOVR!G%vlML&Q@$v5
z0{w4uf>k$my9uLQf7KWlW?ru*uA;1p@Z|@45}Sb|W30EHm}qh?1DXn~qXg+Yv#Ki9
z2-*b*74#)wbF&Sso0mpUEeVF>?GOw`JblmfZDVUSsw?#uJ1|TW$C=lDeRd=A`}zEs
z*!0-WiT(T#zwk-vb$WNG|8JY>oxTV(VH(Xa(fH5rt~*vHBFB)@KE<cgEBUc2S^@7l
z^l?V`t<p79=N|pZnoyPC8Ty+yW1IK&mLE^dM}be2(^IVD#VkebtGk(7aH4qepXuci
zezq^k_ShHtti!E-i@fm#p1A1*>%=y9Al3zLT{-YR`4%bQO8Xuj=DEjf3AiEzt`6q@
z(M0{)UP6~q7roK_{{R3%|Gw683?QUDCud#<4#54xop^Y(&F;NM^xG+?*N5>95ahjT
z9oO`E=`v2SAM}4{{rt2E0x7>!Z0q25{8l@R?Tu}Ntc4$2^gj8Q4}whQ9ol=n;jN!}
zmUp_Zv4V_IX*(~Xp3md8Ky4Ft&4Y~Nw&qTDISu2dFLe=oj6);nxvr=6bsM9SHF0KP
zYYKn*mUEGeiMD-i_jh#R3NT3}uG{Q_js3a)f6I4}dFa~aR09B6(F8D6Rp|nf!*k=o
z8IOWEHv`83q9B0hb@9-zw;I(Rhjp;L=BgV4YW&-7&$DNS@$8vVc)+~Y7!8a4lbFGn
z;rR7d`)KC0MO1Ub`Nr5EE*;&*v@0&b`nyu;nDj;*&x3g8TXPt(8seF4lJcKIjJIub
z?B*6)7upu@b5LxNNVjMq3VLo5q6<faXt~LWNkAdxz8t{of)CFZw{gbX0K+)MG>(&X
zMFB_UI0%L@ix3c~gg_;LYEq^Qws1yUbTLc6Xj4^VUwh6K<JxeXer{A1gb;AxG%q(j
z@1JV@#n~XLaXbf}<ABmJl*OdtFXoD(9SdC*vp?Z^?HC1~(}pt&rydY}?TAXC=xb0M
zxin_UKJ9#|D4#5$*oGtE^fszWw6GS{zK)!38z*S%z;hgK%l6|PsS$j7U>;taN|q-h
zM0;OrKjFi4QVl{5$Co5NU&5GHRak}TMaOBCVs#_weU0{%4v06*54{bcv~d~R&0ak;
zsQ9;W3G^2tIKyiQ9Oq&yXR+6^7Ebn-)Ko89%3}-dS;)sum)0CbZxHJpAQetOm4<cY
zQ10t%ET3bPmWG2#EhZ?xUaakch`H9|r;g)rZ2D`Q<1BXB#`9<scFhrV9NL;2#xjVw
zgevG-Jie)Y-E85!$FV_5=h|eGuj8nL_4OPh%$m5<920TJ#cOS%S`a}^+h(?vnEwas
zQ?Gn##_JH1t~1S|nyRYEOR|1`ygb9q&uwdAv&nwSweNGFpvn*g0is@*$?tkNF@2(%
zr)AjOk-`3<35-atMa{ARc7l1XF&Yr&vBqdPa=m>I0odl&X09ON;kmDuH&{e0j<U_B
zuT4jOwTNvpHxFr(#W*c51^siS*5a8PVpAM~C{hlab6<l}W7-<{?5}O4^mjO~&~mO#
z^pBLpFO?9;iV8_lk=t}FT9=(mu~GX+DNJJU#V0X!;(#vD=r;z2aSF0N&hm5p+*5$#
zfU|r53lp#WO!w5{pb||KD%!R1x(}gk>00oD06>MR5GeU69DnK?m^%JS*%oLQaEd}8
zt17ruI~HE|Ar~Xc$Hs9Sq>+A%yzsB!ISwiSSs{>R1wC)SgL){OzW$^ziL)>42ALRw
z!1E>VEtL>RvI<F7G3U}-5X<BM>vyI7#Fy-m#|JU`vwzWYt#G!JsXeuNs}W7alN1$_
zOwe}T+tAdpP;U$5Py7~>ulzTlAD<+vm~+{7@Uf<nz&N_^hfoIJ0LK9eA&_Miq^T91
z7r&Q!?_0D(9v{T`ub&3;XCVsOI(%<Di@zP{#F)Z?C^CpF<6C`icR3jxe(>5+eD&x1
z0Nx-PQN=M8L{R}%L3c|tzIgp*rg6Tp_W-{C${Q#OR)$5=Za#E-K7Z|HSlpT}^?T{$
z0RH-!7qw$kRS;Rmh4Z`c@i%WQwK+60hL1n>tbJ@Jio%$x0E&X{bPGOn#l_e%zXQzz
z0S|&Pg~ySxDSY}TFXCs%PiX?1F02qm0TdY*bk8%{F`Sq1p~rrVl3=FDAc}%--F$;-
zE$(^tfAGSI)8+Aqs;TL!f)8$7hwbOCKzkyFG%sOPjbTbr@#8m6<F2Qk0mlm<l8pSX
zUjtA+j^DfHEx7Ld)!xT$umb>Y{F|?q>?K4S>l=4&N4B{sx-mEo5B=xQ@SUIis(j44
z-MyR7#r7*Um)CG&3fsQ?FiLM+=aXnUkDFI4!6)8*jd`3eJ#4ef+71Bz;2*z@BLip4
z=fY^eOPhN?_+BiT(;jHe51u)TkNwlPwIk5SS<v2!FW=r5Y>cmcyZl-&iB~G1s@S|@
z3I6JRH(+5dg>;OA3J&8E!Tw_d_&@*l6rO+O5K6ANfb%*!aPZ7oeBpaP#?gTx!#K3h
z1fqQ|sG42h*kOK6y!iT2eC}VL(7kp`Z-$aBzF&uiwR!xPzs3DOct)Sz|1niOhUqo#
zvL+t<#alOFK{ln&?S3y8Oi(NTVR>p3YpmOhLY7(cnO;#%l4MB53idS;)vO|xAn*`H
z(Rgt5^`P41uoe!&T6IJ<`g5y2`-dm+<ngoc!IFX9yAJtj$Y9JAn8zBUp~&_2Ery4v
z1^~CF2ORggUEa6}SblIFyHc;HDu`CTY_bXlJP$xMeW^!lR5YDT-X%c>cWn~#9QYWM
z<2W?6wqRmtOcSA0O@x`06l9ieKx)n$*C{gdKaBjyC=UMfUqIls5Xj;<1zE*Zahy)~
zaZIdx3r1gl9)cd`AS>EfsTCJu%{8}xg97a;O!Ktkd4NwQu;_*lV`AV8j(qDL&6AGP
zysKoQ9Z$zO=LfPw&CS8tJqi#PwY({^f~kUxWLwU&Z}Z#l#MqJFV(8gN!Ry{ZiY{DD
z=4GT7tOU;m^TLd0a~M)dIn2`IWuf2eYUfm+chNcLS>|1Z(ck_A0EdF49ba=tmuVcK
zbv_FH2Q{%#Q8AU5ky^4EO_?^=`4j0@v~(@U*rETy#BY88k>~M_mSJpd8pYoaw&Ib2
z77(d`i#pwJTrcWaicfA{iNAdAxA^KWehp6GVS8~GJ;_uBq>#sF-hR1l-1NrPc<kka
zl>XX{G?8TxsetR3b|KA|E6iQkxd4CtU(W;3#XzZm^_{Jz@mIH}aF?pmZL?CmaaUvz
zS;9M3o`d_Y-3)=q0Ho4qT$)#~IwRm)H(Z2AUmd_*PyG}e&y|l;l0c-q<v0_#Vfhj~
z_VR0*pdc0SiOV+I*5cUEh-L%F0Yn891w@wc%!hBnqEZSqNw-5nng?!Lmc#Yuya!i*
z^9dXup9G&uLLD8~Y>{PLcix+D@nZMa1bsP*-jJj;*0rm<Jo^P5W6k@n!|vyPSr#2t
zO-Q}(12-VS$<_fu`!U1ZMsKIjw_)LY+ws%v+OV+O*6wpRUyobwdsrLP!T#&dUyb{p
ze8x1^+s|8$Of1;v$eQ^%>?nFVDhh}q<NEX01Uh$X=I8J+qt|Uwyi#P``=Og~%SFrd
zmfCl=03ag*7cXkXcmMDveDi0oXu@1EC9}u+Sh;v{8*aXEIqu&54DNsO8I)awjTl5S
zSw>HL#<r#xF7Ci*|K)LTME^y|a!!N34mDQcD^L8F_NTtSevT=MF)rSIHST%eb$H*G
z9>w#ozM&mcfa`~w*Dpg)rln#*ZG1|`SYCfm5nxU>QEpdmraz~MHHJ1(iE2in#`-*r
zs8*09$i*}z(<G_^fUKw>L`F<6v{Wro&66sMY5=e%+l=SVjCgsi0h-s^cAr)=7&8J^
zynxYY?DT4|K0bk~n};cTTrO(-=q)}%`-0;=W@xdl8aC<h-U&>-r*L{i)QNNW+tx0G
zxEQ#<&wWBngb)*T#~lJmB`8RW9<Jz3B9q2B+dl_|Xkml|+69zM2=cQ3af)c3b0~SL
z6=W5pr436;qM9?!xlVMy{rB~xzhzOWASsaKfT}DhhO+9mC@S*a7j=4H1*x9pXuY^k
z%ekU^j>(E<OIUq4XpvlE@RZ9J?DH0dx1vYj6y^9z`&G3hE=obxVbNMNuf7I~s)7(8
z#jE&OYd`+6^Dx%&o_SLM00NQm>5G=(PcPZ1hd|nnM7z0{We`cis$AAK#)4E6^f+Ey
zSLj|LkR;oFjwrb2>PtZs8Bk<EDuBoqAqM&wUgt;mjw=dd^OFgKsOHQ~%W|e;C#njf
z$awy>gBVrJzh(aZ1*>%dPy#6wuyt{_Y5y5Xz|o<h(lHZB0?Fs`gKh6Jh-!4wyo4uj
zz7|APAhc$5kw+KiLPsi)^1r$`Q;jh)qJn#W_dVs~mSx=g;Xb>l2D)%Sot2d5BP#7Z
zVYiEx*5LA03s4H#by@?p7}{H2^Kg*z>yS9x<TVB{jG+nB67K!rdkvx*9gZlt<$`l?
z`_-40j@i79{%qlhg1@-&V%&J)hVt>)4pSSKC^EKRxy3eyKxAxQzF6<CcdK-LNd)U#
z!5A4)!FT`gCiJwodVd}nSk>8ztCr1oF^QBOG*bbBQ^Qku>Q|@m-4~AGN549yJ@%f$
zx1KwSy@!Wwf)z2WdvTk70fBN;i>PKe*TF=!^1zKEsui0kiULWNt17B_daJo%w81jJ
ze|vzdyIQ?w0)TC~-oA6an=u$O2PUjB8irhN-&XkWR(pTC-@Ku!9!Md@wrbeqr+=k3
z8QOS?Yc&$jTw7OB@O1AUzJF7?S!+UI%g5!1crt-`Z~XvN{lZ%%dM>Vj;{br1p9DcU
zgfTBGSa9X<fTxIRgtI$Hr+bz_Naad5OGR-_33zj(DoW{MuqZ&u0SjrQ7OVuC+n^c+
zI+Z}B2{S(I0rbmH%K3B>TZw)yXto#gwG_`et>Typ&2FMX5ARiiufyVVA?vy2iZ^HH
zh<W^F`-wo~d}RGX%x`J2-$)baYOLmYHLoRZ>zn2!?IsnLzJ~sqIAVXnUa@GNc7v{}
zS{NqnwdC`4P7EGm_rBw;R8dW#yskg8exW|EJ`u+OD#7=EbI{aoX{JdJ0o6R>R<@hf
zvLC&Eyl99f9!L^4b<M%*)|f9l(!7Lw-g+tc*0dHvM*t8N!{ZbF7Qg`Egz{aa!;O2C
zW88Gn3M^>NfG9G0+Oz1p*dcV)uL-0qIGFO?mG%?6U6ghSP8WEJuQAH&tN4?JHmo+z
zlob4PWJ)UM(Q~!k&lu3fK1Bv(88=_N%5FC&Xy?en-Mw$t(>qjV%in+CdVReNicEQS
zeN7c?TE2+d|EdkE4E+pB0a2-T`=Y}29U7mkXbg@h`1T)fvwu#y@WG8MsoR|^f0hNS
zf~?@V0|WTcuTJ6g@RY5sB&*nWco<Lo;-vONnMI`0MllODrPsT$sX<Vpib%S>Ry&QN
zO{JomZ2bPK15vH`Tv1f0#UEZAY#=Iuq$rRi3HgFnxinwU9#eS<g@Oc0mh~S4={ESY
zsnp6R-l7^I1U=0O^fcSQ+bP`Zduj$_2Eal=8I8nFuLkSm6W9=+fTw!l?!pwvs9=MK
z?B>U&T8A+5nt<SxZ!IcY!bXM`Lw^DdSO<b9C-4yBA_{Ur6SXKeDFeq(;J^R=(@3_@
z!KS|djokeCri=a5qE$Gd0zA*5cwt04O|rt_-#_zV7^`^l90yV4(Hs}hoDk6z7oZYA
zR<%5B*`9Mu_Q(o{=f3fG=zsm!IPbl`k45V?lqVo6au?lzQ%^mFl7okXE#Ld}?RqL2
zEh`((a}Wg%%?S}LO)+FzTeKmI56T#i|E-oQcge-q;hc-ELOgD)psAq!f*Wz>>3;(B
z;}irA&)@&2$V=J<x}p*+zW6$<ezQ4$&JX|Yk0A0KVxoZNrWjJm7?O!tuvdiPg11!k
z!C`=+m}wt^lrGS<6iW1VKl;+203cYf=^8A(=qd=j;YLuEky?8#CV%q-aNtYck+gBT
zKJ_Jl5Pb4&S7Y0SYmgSof+|N8ynn+g{Pmufz!h(LOX7u=W34BhK|&8>wDQ4?>+p}i
z{0;2)%f$62v@@n)ekO&Z1A{K@{Cp<qH_NI5$TH5G+X@(L9aRKe`Ngl{$lwsZ_JKZJ
zyR;m-%n=12Jbx|z@#p)s(0Gmq;W_->Q~!;d*WOZUmw<!|y656&2j9R2J>4eZj&K6*
z{pkyOGJgW13w*z~+5Q9n-9LO05By{gE?lw*|MsDGo5s0f(HwxN5Ifo+A3p_fJl_8|
zU(tk3Lji<=M?Y^G^ON8F5x(-+li+v}oG5~e#R1VG=uy`8liz#_U-{mXFov*$7r==z
zaIu)LF#yd&>C3m@gm>)xBL443e+L4bgsI{?-ZK`oG@q~ANSAgWx%SO??zgX+$EEbw
z3R-X=9Ea7neF{XCwGTI<;`iQh4gT_Xt}=DW-TZMaMS*d>1hKqE4mRa({y2=sZ(DOg
z)IS>(5Jkc5*KY+!%s+nYdF>2t_~hSWL1zd4e#h@(%hJx$K7q*Sy=Vh=|LlJ}*C*Ha
zaR3Cr_l|4uC;#gzWMafN&Uaqes~cJxtUMkeSi4{jw4VvgT)uWGKKm_VzTDvea2z^+
z=TBYNcSPc_<kq_auWzxR*ZO*n@&3C20QddD2hexPI@9ZUaaKf6dn=BfEfz_1el5TA
zO^cD!bIG|EDJcC_+E$QNJoT$n7#c57`xqG-FW~uC25{-Bj`El`(MJEjReSUBQlr^!
z71b;=1Qyi@At)4o6pKhys|d^SN|J<lJZ`f=QB^3V=mCnDXE;`(s!$XaJkKE}ij|9M
z#<d`pW4E4T$HOeQx~L<KqlcZ-?&w^W>+Rb*uzT0jK89y7>K^8?#%Kt1Z?((3+I=K*
zI?~X*(mo#^NEL@0Mro4|qOghMxN=oHQ;Uc_&q8#P^94u~Q(C1>;&o%@F^|)(DhZyk
zrbKxc<!XoHIK*Qj<dFe9^AC69jbHu3v|o(zFkH8}+RzjiHSsE;{hLgPNX7*uVgh0U
z4;~yiXw^IwMTIDu2Rr$j|H0_t-y$aP_@96Md#ErgCGply-8-tRgU~MEn&JYQn?$rU
ziD+&TkxU3kCPc(V0RqoK5Cqeq&~7@Kbh{5f{{((??;oO2FyFi-a^1!IVF&_`cuYhh
zE}|(eA|4YU^4jrQNybGa6EP$cA`&qHq9AA$M=8(?Sy-Ms-K!gRIo(s0ghe=e12x43
zB;q2D|Kf-E&;NTD6xIA5ZJKkA=DAm#jicN22c2V>)5PPi9)Aie?*2REiTSs>jq`H4
zN@}=KQAsJ`_;stDL(w+(%!T<yYCTZr%lsW%Ho;b5Gblu#L-(Sw3V`4L%^@5e9S0|f
z_}z#8#pW?~?a~F>ARGq>JUCv!(cxj7$(v91d)KW3k!5`3(hE%650A?@HacGZ(S&Fo
zc-bbi7r>|@;K4mFfD=T#aOg08`08=f{)}GzSm<a2I35rLaH0r^F>pc*oFD>}e3Kl<
z1R@}a001BWNkl<ZmG=vxmLSpgscT<fj^nj{1|f|TML-aIk5OdvIi0w1^Le;<>0HM)
z!SVZ=aBg??+Jz+%+p$?G!6FX`A~-?R*Hh58(FzR-mi6Kcc>rDB3&!;>3ZSMjINe)O
z_Y}Q;(^^x11tQ+@>HiO$AmT{>AU^uVf3nT7c3~GxAJUGt2;c+}UwQ0@Sn~cqhGO~s
ze*5JYYJzSF!w0_po=wN;pPdVHDTDB#SJ&kIt}hpIeL3&-^)-e7P7rX%mmk8$JO0wv
z;%yt(xWAsDRRO(fX}7DVr_npjDiG~`W5}1NR^-^=Bu)?K_5Nm&hAOHVe#nSIR4dIB
zP*ej@fvDz8Mc!(*!2rqN4X>&y3Iz$WESuJcCaRTYB*nf21xsp#>WFHTbL6sSJ<SQ6
zlk!ziWT&4-48}}=32Tf-!PA}is^l=1#w@PyeqsWrD_pROHdQ6Rn!~QLxaKZ}1s2bO
z>vOh7L<N+*jQm7F6WA!WyNwhIYc<e-V{;q_AtA!;fCY|2b6h|&E+8HgaOB1Rw2ebJ
z0^*@gavYFKh{z;krbj9vqA4aICi1%18&C`@wT8h=_8&ts9z!xNA|~<}AGH30sb9`<
z#P!^@G{ulk8Xs}A=pJ*4xClYyO|NU(xoc^Pp}9#Dz+wW2Gq3;Je7w9S&hZ?FxX7b9
zAtI%Fpfx8%#9}=4y_0H=Bi$TFDjC<t7J04WDJ@TTjo~O3v^2-j+!RAoT!bj_7#%!i
z+Ak*4_6ugpfxJB@;p>YBaL1f+bjE;RoEW57P!u5a+az7Af~w%!H#rMy=g)0(R#`ph
zT{&OR(N=-529)iljPr8;S#a?LxOf~8Mf`SPoIZx`-2y=X#|wDsmBXgpS1z3oqR7~?
z$SfZH`^#@={fa+KIE0uT8JRTL5yNu_kDB%u1wk$*Lg;8Kd7l*@nkYdDqvLr^7>ns5
z0x$Ucp@8SLvEvDF@h0uxSUlJmIvi2(;74w>c~?z00+G>s@p;~_FHND>jy+cPZqmdb
zN^`?`DGN7T`QbE8alK;+eccnLF|;)&AVj4KGQT-Fq_@}3^^yKTj7aABHZ5J?XKBj9
zlP=gk|C%|3eCb?cQh!pq!Ph<$2zoEsWIEqQVJ;)8hCV30x2Ai2{f<!-<c^%NukWh)
zb1Z_Rol6$YMKWd&G%D+zMJcK*gA_l)&W;uE@^OE8GL0M^oG6RP#*s_j(FP+-v%@B;
zIpuwG64fk&`4-iRc~%37YQ`O=zpS=}5Y_Clk{|$7MFmj_3bLf<T{Es}u<tHH2;@=<
zis=#6OoE`^__8?}KjvSS>+QSIPeTTy-eJNTW5$!~?b{AugIBv-t+~}aiQa?B*O-;|
z4?AX9rN>7{j_0%oA{5VD=M2z^0{{ZKAVJRi`8BtSBCOB;9QXg&d-ni4iZbv2TUFhC
z&YAmUW-=rpBq0QdK!^$?+yuO!=z59z)98A^U438BcNKTN?CPr7byru{cXd}?(RE*4
zcU^J4AfVy}1r>z=Dj?({AW29dx5?aR&N<yx_4}i$FV)?rPoHyUCWD>_PG(Mb)l*MB
zU8kq(bDpP)fZ4Uzlpz!-78r_-g@R2nzIRv6R_nR6<S>*9Os8C`sI`uQje>2d2reC&
zAth1FBo>jjeB*_1Y>I-zP_!8)4(@};3Aj+N@>sslrLrR$J*R9c*bIfD4N6GOD6v3?
zuCJoXC?=0Qo+u+Bb!vrzk|iCbSFjZ@OKYN|V4<UEqoZgAk$i+^PqYGQbj-{FsVixF
ziyC&O!MChnF<*)WmGXK;Uj^5$IE-N{1%Sxd?|u{*7dT^SxN$*6TA1YF*jeI``UcdH
z0$iNCaz)*VYJG`JP+~c$uh_U=N3l2{wvGD_9>pZL)Js^{Gd2n6h$TX)n(Q-%kKgiB
zL)Tz|;KZIDBaK!Ph7aHR3!O&lN5iCBc-oQ<!;X6&KV;Z(#Puis6!!9^3V2v{08wg2
ztWg$3>VR6IEp^5p(V&9_ZP)>5$*beZAa8xarFB0fgb<y2sQnR*5KN0$(|!y5MQAP%
z8TdPZW2O$n;VH0sa<-~cbc5?;A%2m9wZ!TP)2YLZr9`U(MVlttk8ZAT=>~J{x1EoV
z8t<>V{9;7Nnrh=X`!^RX_CF8I(qG`1b=E9hQdh-u)~XE)jOgwdOhfct<d;VeP1UzC
zMaHIGQ)dMJq(QZ)fpMr7m%30b(sEv?CZX0=YZ$7<XxV&2HD4;OtJYgHx{=bZp(YNs
zR7Jtl!8u|@7YeEYK)21XzPFTZlalsYYi;?xkoFpDZTWqWZS@Ll=^Vu{b6eiwvDr|s
zdJ#x1DrnT4`pva$Q_3MfvMIiRR#WCTSSe?5?s1?}g;hvdbalajXLcE8TQ!Lo0fA#N
z2q_^XgX<*$Ltcv+(<zXY=s7VlzMS{7X#KfNd1?wBUEzaNXMZ1t&bk1{cH991hDW~r
zZ+Q47fKg>*(NQ3Z*LfbaEDD>kq#cyNE)>*SMpCbvSEF+lQ<FN*C>W&}Se0A^+SjYv
zA)rKk1I}1&aA#W-j!nVzL|U=NB(BQ-Ts1a^TKUx5jH(F;XJC}WDimTRA{Qg0A&V)X
z$|wP)1j~9ljq_vWN>Z=hnGiyt%L<!1I90(^)x{YDDs_ZO59h5o0e2ld3erU6*;VI1
z4cmYFaE+*hyb?m7qh*1ih?~~;7xCh=PR9)o{uYD~yyM0n;vG_|iM}0zE(GanOs9iR
zV&7yHtGaaRvgG*M8((R(x^LPu(=Q`1>Ylw%VDqx>pe&OfKK7!^@s4lb0z!bhA3ucC
zKl*PmI~;ZYEfm0(4uBsy1`wh=LwWk&5CRo_rq_rI#z7u;5~}J3>5!%`>*<(qiLo}9
zFNdcEmUb?V?}OK$(3=XW4AK22Iu%n&sWy?QbswNiM_Wa2>^U4xs0O%^dd7_~PPdbG
z`VcILG<o;O@PhL;24MKJKlqusmRL<wsP3owgRp#w8L_f&WC|gc38AqcO?Tn?la2CU
zx%EzCt+WeHUxRCZczeV}>Px%2{v}u-|JG7LgMNb6xc(VxR`zybNvT*n1+?}r0D#d3
zQb5yn$6*VMu@F5Lw=gS8%~X@UNkMQV1F8XP=glVrR7=`bqum@(%@>bzSULhZ)uy~M
z2T;vFx@k!l9+@non!Vogs(~$AuN=Alx-T|rYFpZ}m}GH0p|qtfP0GNQt=9lpl~tyb
z2)wo{D=Mab1X9yrti=eXW~bqq0I@|5*R)Mg5QH#VKTIk!>a0ek28-9KB3us1Mayup
z^)>3U+h-{$Az|AJT-lVsq6Ajz4z2kd#ktz0Q}_>sL~4kpCyXTgoe+ZEw|v7?AO+Sv
z?^Rg7>G`mAdSz$P4mLjU(@BZe6P<sXc1U<a=v_P2#EcNN-W*~12DRE4K|L52dIvGM
zYOQhJoe5KA2-N(CYG~wxwzTW&N6S$indyTNfBIPO6@5~jOMY8(_fbGc7)o@nI0c<O
zea899L^7o_O_Rsf4yMHu*5ZtpT!Uh4{jbM%`~qFfhq}^OU+u*k)*C5bPfSeVvC#>m
z{xeTrS$85|OP+o5aO>%y8jykzeERlZnWrSjr>@wHH*J182(v&71<+y%q*x5n#E1`<
z^n&ToQ9L(2@UWrFhitxl^r!d4){64S6vc<WcMB@9??ktrHjE#<@s-LBrvO?kChYKa
zSj&b0i-kICec*yNBVa7A=(V!K30Gq}KJq``Ppr+wVd0;@^`rbY%(F0fc;BmETz`B8
zx7T|9M(kQ=l#7enE?9lO;y%xP*+>A<2xWL|WDM7S_f~xNyFbRYH{XV|sK#_#{GSq=
z*RR8>*iN^P9UY4qCBsTmU|DZie!?aA&YizB>Yu)1C_)J${#?xdjoKHFAT%37Ed+Am
zNlO#wg1;f=9}}ZoZH<kH)KD-9jST%wq<-dwY7+kD7J)^9UD0YnKdXeN|8St1gbbqJ
z(rt>h9Q8-chP+Y_szFNh*bE!{JL6T;WZS@&t$p>2wWTeKSK1M4ZF$m)z5#EmSMH^q
z$IwMnQk3R$2HQyeX310@%9kmv+B7$P@)sO#e7Dd#?X+GzsKi|l)JjXWB+csukV!T3
za6CWC1OT;I=5syK69`IjLc$bbjNt4)`?xU##&&GSu{*wL%wZISQoCp#`pwUA@T~K3
z!rBP5k^(C)xE%fKHsX>0`Ka1#K)?JbFd?#S-0+rvGVG|19>JmSeiF>Kf;oG=Iqu+j
zFNQZgfu1#Ip?h!`kmkC2;}6}V=ReUDJc+ba$*2%NI*q8U4g3ep=tM*vm(fbfe-gn(
z%*%jUy*qWE_6$e~oOJ3YI0YMstzmWS{_mSX9W~^Xm%kSPKvQ<`tIW*cIpyErnX3x8
zZfXg>Jl2azodycQaq(L7`;eO-+zo)>d8dTCYd-yi!N{z!ey@*I2!;yEiA;KU(Z(~I
zpH=#UiYU<Vx!bqndF$6=^YSnPhe(bOTzER3cj{`q{3|zwFf54l()SBRXaU9`8N-Ke
zz6EbN|0$_!hjC)z#$Au7^~rREjHD!twqX{o`R0%CuP=M1QU58u7Is|oIy~n~-^PK-
zDb>E8ju#0rC5*uu8i4o2Vcm%uel?Dh$j@wt{`Jc)!#iJmN#MV-%W>8_KGu*9fZ)2@
z@5dis^pxPpr@#C2I(6n>DuSU9d6sz5rZe%kU;0{|T7JKEpX=R=4FH+PSHQ#n`aa|D
z^n(XR@RE;y9u3_oqBkGvZ_y%y@n3HZF<<`o^PT|!_|s=^!s<RJac}v|%|8wM7^N~&
zVCCQ-3N*YY?>jny#||F_hZXFixw5Z_uwsgz=Ged84oUd;KYn%}{_gAFLqq<J0>371
z;HM4tnU>am69Xx8ASpo2Y$u6i@~3{PWA&g~j6)elMeX1`npXLT1=albT*@G7rYWda
z)28gr3KA0W%f`MA+&em5H);*QHE3^U(3U5$v?JEq@?@540Q6=R=p+JLI!CjLn34&I
z@{o_FPc~QWxZBhmB_x!9S~XU7CmFBSi|48ota(wtE8)on2A(SVFS%AraNeH><$-y%
z93}-Am7xBMNEC<HfJb+Xgo^627=g_wY({a<fBhq7re+iBx`vkGtgHVo3Oz$Q%FEyU
zpuY3sb~yb@0h_6B#Z&r}mI64u{^?k8&SmHx7)(s~grpdK;Kv~(YeuBMlbPBj)#C!L
z$5C}Ts-7@kE{E%JcwDH!s%G1)Az3hW>6sT{_^gW*REtS@dK#4@dyM)7NHvf`q(Zf7
z#l@rF{sb7K=wT9XUNVZCSM9}{`i`L!9;6W1ux!YvbMyT>@VVPec=D8iZq!Y;i^wgz
z9yQ+o*(ICuRZJBE_@6Po)8R|6{ZC^ZIA1mm74fU9w_-(aj|tVHQ8bZsP*93}lj*5h
zyGP4m8fYZSg)kPZ?R3L$cH+AGA5GNlrXKEi!wc}nbI(Q)?UphQtP=)7oaD8i3n>r}
zJ+?rPCD3DWEG?m-YX|@!1aJN1{|gp)pKuBO=0Coc@9*6_N`(s?o({+Rs{WFSz&y?p
z`dj(@j1>co`CI~0c-XXV6>fO%)wuDm-+~+8|7W=I{cpvM@Bcrz;jiC}Et{X3OVtNo
zelb4y@{1Kzi^&5Ar*Y@*-z6K@8yCD_(?;XWV@E~-LNV=z_i83RtQ=gDq#g4lMDm1x
z&pG+T#>aFE(qUUTv8OZSiAIQ;35F!h!=uwx92zg9nz9^39vPc<@!<Xm9GR#Xpd<>B
zOe-3JYC#-YE~pl^lMdA+03k(vsAf1Ss^`fM)g%DPBT;LSPRun_s~fErkpiXI*w>MA
zCL!0fqt@E;WR^BqYs-^T^o{qLdgZR}JrqQ%wVs-naxTxpw@uccPn)u&Wt$oRd6ZF=
z#&;axS!RVi56rF3@#X+{($o~sh>FO(ey&74*UKO+k`lFEna!BmNWiw>Foqv}?Cq&=
zjZ%qquXzu;PCZXORQtYe!bX2iTLUfT)+1qI_c#6tjH<pwg9{qAO{$bfmkU%}j+HNa
zA5MDNd$97Qf0cNh_>#ZE@)!RlO6x8LZzh6sjzvg{{kMJz7NbGxYcpaOODOgZsCDV6
za%MLbcvJr<zPj=NuIWF9bxXq35J?#JPfP)n8flK*)P>p!GoNpE9XxD+uhR#+fE4hT
zSBOsGB(@D=TYzQZv~B;4y;I3Zvmu+~w%1&N7oN5zO%cp=P=*ix;P%uuKfV1vosJiN
za0w-9_d(kZb^!g0Z+;h_x^q`zQw1V9KCpQm{_*mQkVS0mmCG9PMJrNwLG8Hc2)&Pd
z;}!tGN5Ao-aCg+>EZ6<y?%;PKIsWDUTzUMAsVT`6ygH>JJr8HEUV-yhFTwe1BCpj;
zaQ^CkT=~pRh~@4zBzwlHxa>XuSFgPnUd!ml1<ybCOry@vcRUObf(P~;HjZC;*=EGy
z!h%XlisO9B%HB?-fiy&q#!mal>Q}!T#jOt<4mKfZR9r5wWB(+6vg2sbp87>5p;3+0
z&v1i<I8=*wDbFSawV@gSm9AS4s-*zQ-xsLnfA)_%RExgX|C~3_h3?c$5JP&AV{X@$
zwk%dz98W53X-nhs3Dk*J8)hypFOAnx(_FMrE}aV;!IVq$Lfe$X6_ipii-D5byM|`!
zZx)_qR7g()t>2fR%mM*ZX0thkW_iS`%m9QKK#UNTHtKIiP;?jy7Q>G}_IB)h;65bQ
z=aC*(T=ojk&H?q1M`WN20A%hyrAMnZB*enqpM57VGpd17vo2M&qy)H-a6N(9iidb)
zT$*qmt{o>OG7Xro%Pon8`~U0xAeC`gj0Ef6Wy!t=?!m)%{TSt0Gx~~14<|nJPb13J
z`gT%El)!OW$21&-DPL0_33l0=sF?nG;puC2+IYT+?6)6yB>0_453hLohFtX$;zxTi
zs&@obKnu2w^FR6-eC0R$5>TrE7yt6ir($_$DFGqW&dA~Jpf}$805~yM<EX&Kjk_L>
zZCar2PinVK8^mz}xb~s%--b7T<Hw0L*8t#^rwrmRFFFTOaHRQ;c6ERa&VOJ)+(&tY
z5DH>h`0RIX#hX5N1FrqfkIeM!<0yag#s4w-^qh^!4N#7^l-&klvIHPFz%%YuEr6uM
zGXm$n?e8P&21Xi|fWL9UDJPgaV_y61TR<qqJ->a_IDXFB6BiV09d9{pFnrXF^0PQh
zQ|BXVk<GD-;vg#y)e_|+WUBspYK`sunFiG$0UnJYtqIlQefs@@Y5<Hi<`xT7^Us~P
zB-dJN9~juOb#<PCZE4G5llB^GZF$m(zTv*UUb)wHXKYw72hl=J>1>v&v}{vrU~G;n
zrq^H;L`VAsf4H0Kb`zCiRkGGsYr4t#*yMuM|868}{t?}DAk7Ak6Wsj4|E)GcVE)Df
z7D|p4bVtY3fD*vA2!v#i62WGO4}9}l9C`QxJoP14f+8|;lN@Wdz6%e1{#}XPHq+d3
z)8D=s#gYTZX3CC|gHpk6C|d32kGu_10?+@ePeMgP0gxV+tU4Vywg(pTebK@Mo+zJt
zT%hXJhiZU|scLwlm^Ga0*ni6>-;F}|04~1rU11&Rp{su>Y(53XC@6{SvRXq9Y`+0g
zN<9ASThO&^9iIB)H-YGN>nz(s-_u@(>HEJ0LRJ5`P!MtWtji2NQ{zW*R;R#krYIg6
znZVHUaM#Nhowf!y{dP~jNg^Tm_z!NytImEyaOm{GF2vW;YeWd4kbbln)Ovf85d6j0
zzm1<fbO!$UrO!ek0tuP)aN{d3$J77)n_;RpA&M63bWrGY&|OETf?ZGn;Ma#I)VVlB
zimZ<ocf<|9*@<87-G{He>D7r%5U$>^3J)G0#Z3?I0iiN6uC3t#@R3o-<~IyX$a}wX
z3$DHChal8a8z<NfU^SR_N-2ot00j8f9ruDzOMO0$KUV_?!QNvtSlzF-h+Pc3ln%V=
zU14;kWvkt28}+%AICN|r6D|cHQ+H*}1J?$&z7w0Daw@*_{x=23hB|FM?jw<y1Ui^h
z6Bi*=IsHWhho!%e5xD=+{Q%Aj^qq0yKxEcFzk_1JC0O&iced`|7G$8Kn8t(xktSW4
zKWS(!wXkI3y`iMJoj-lWQPo1Wp_=Kh#Rb(Mfzx}6xa-(7ChI>NZ3A#+y+Uni%VLw`
z|3uK1wlpr=>J`}F%ox#Xt?uyH>_ofK2&4uTG-^%*%A2g#DqN%l#G-0p{#sko2TB?U
zRm&<KhiXhk(8bNJt+)wk6?pnZGSZ`hZ-{HSC2yKv(Pk()3Z^+01M_z}j0)8(fD+gi
zMZuvc6)cqW&Yy>W^9%g=pWl;64W&e4_*qw~-8$=B!=COUI*PWk!)8rIm#I1w)rtvP
zhBrP-r!CS93z{4FqmMs^iK9m`ar7w0j~>DJ(Ie{p$WcrlJBsPiV<;clm#dk%nnGHP
z!kroc7v}g``qbwtAEklTl=0G8ve8kpQ7qV)KKL+3_CIQzFAS}Qlrq?jl?wrn3-ql%
z!zfprK8lYnJBq&>K8D{uyu&zm`an<QNx$CAk>Ih>aU7|Hwz3wWTCj5<As|daBH|Q4
z3Pljd!HxItz{MZ`LLy>p$maOV3(rxJcEOhf1b#Xw(U4)Xl1$HvZhDYzQ@Gw5u^kZG
z!TyP9od1!};4Am<HH%7)ZO_<<TIsZeG1x1XCXTlvgisKsq7VJG;u=jURyr)zM=P@4
z+;JBLM{m62zT<6(A%rQ=Lo6!<SdIFNP+b4>yRqhV@5GwdydzlOu5n{1?%eSZDkA(F
zN+ef1sQNo%5`;5vApjw;dHpHIW*pP5(AS;fTesh3G|^+}@f^(F=aVRrx$$ocGEgcc
zy6DGvNk26*jf2jyU*gSVN2#Si8vlI_sAlvh)R2T~GMVle)Q!A143E5zSQoQ#u|T!f
zMKr8%{-K)x?ped(7iSsr`gYV>Tb_*4j#z8UlS&4*Y`qkDqSd-GqSXQ+k$<yF)HpXE
z5lDI3%{rHrf|v)?%ts1iGwl(zXgC2Ng~+#qXzPJ$03ooP<f6-swAHK+8!ZLE1b``a
z_-mLo`_M)R&{ecyTNI2DSd7Bazxz>c>S0-`<(#BM_o?TBcJv0v4(;5I@xzBunHa&(
zKmTDo`|TeJqM87pW7)|N!u&QiO}aW9*o?u_cGwZfM*x*R8qtyF#e&1&dJ;z--H8>a
zg>a{9*=mfSe(Gag(KZkQ?JI^o-~SvuPw1s#lz}Qr5U?z@E?rSWOP=#WN#F5JO2J|9
zJc7|D_F!<uNx|{XWot3@>+oJt&mA2_8$tq<c;H-O{J=vPT(L4ZPbh<s5=6r_&eetv
z#cng@ixxUCc;=;e7MJK6UT&N_vBN^%$+N!`<}JJT;8hz|)h%CFg7u#yUU=GCEbT19
zkFNQ*pR39K{_!bX{P8c~jz52c0r4(fbprn3=K=;SCMq3N`WqZ1I-twM2co~8kq|g<
z`7kyN4`^s7aNqvNvHg)pRTSOVZo-<u*BLvP4%r;Rx<n}#ly;QB`g@WmniWb_L{9(^
z{T(G6&_ST0aS2sW&`*tB1VRYK-+bxoc+2xI2zIJG-a>3A+%$vIhJ8+mf~=BK5Z)S)
zA&db+;GsifICEJf^7Py@)J~cao1*Khz3S4<hMr^7WnBG7&r)TCFm?t7fGwLg;#+s$
zuWtPFJTzMMuaY>uX-tx!s7^Wk<Eg9rP~`&m?ip)powcEE2g~}4Sl%B-*4B!yj;DSm
ztcp?=|KA8yO8~T5P|Z}60<W^6n(_WPLA7jYC0Z@)NDyxx^-0{0`j1|9EcJiM2h{*z
zMbW|Xl8wh_>yF1S0=TpV^V-su1(!BhYs-^Ow$&@J!I{C?g_+s~T8*m8lqO-@97QlS
zTrro<R{PYbm=!6b00X3u>RY%5DN$ZC2&G^JJ2O(Q`7YJ>Vbj30Mmng_-|BK9vGmLf
zau}H|#R7wEQCL((h*1&)P?G3%#RRIJ#3`4&9F&CDNJ64K`dvr~%#I(yvB&pgVA%=5
zd8<%V>u=S0yem1Df@zF^QMI_fv88-ay|%9kwgm!?M}KyM0oI80P#j(hacqxSy%|_b
zpi*Zo3P9QHUyG<t0=15rZ812O0)#?_JCbtlsf`pOA~YZoQl@?*O+XT9wP{kYEfB(S
zWcRNOu*O)p%c`NJhE9u7NC2LfzS#jGJ`+;Aq;er3xP)z6h^{@?yL35vmo7(|FzMl<
zfdXzH^Ws(|g;Eg8@bO!If~{x2!H9rqSbUhpIi%;|)6Y69UI71g`(1#P*grObT_e*t
zV@V-6yRxg809u5oG}3UlPgA3@AFc1pzzz@6^YG5|&&H;qaFV<I$SS;S-vL!r2z>Z^
zx8a6Ac~RWdOtF?O$u7`<5X!=6HA3`4^~Mb&j~0aKurxTdD1@q1$(>J(8sO-7ii+wB
zQ;12!JJt}jsfPdAZzX(&GC%@f`N>`Q`>oFk4zKFdpAC~d1naAvy=H}>W_6!~5B`6b
zM2?scbIYa;xc;vD79^0db^jvlw+`9rv{jm`FJsfmjedfvktnWo)wQ8pZ>LIGwR0>R
zktR<+xepzM@C&?pYpI3rW0u8_)dNLoT}p{ki;76uxS$qPOE9r|Hq9|qo1YZWup>f9
zFh+7hwYX36N2@g~+MaX!J8{G8SiRDh3~bqY>B#lh-PT-VZE4HGOFI>`El(QJ(Q22}
zE0-@?EhgDiqqX|;nJ(CLv-8=f`2lgn-{inzMzja1^Qzrg_@b5ttKcL7){_8urtJ(W
zp%&2hbcE3GnVS=3aykl(QdpF!G)Y1MP)JE2;d288@2Z|axylpIkEtz0001BWNkl<Z
zTiq*93qX^_DC|Np(T0Q!qoisC0x(K7R0{!)vZ2NUUWyW%DI2O&BM>m7{W?!M&rC0=
zr>vF`qEccRMbWX)QMAxmw9!>^j8|vLK}XR>L8rB{*k%E<<0FRJLhm4iP}kGr0$t12
zC7R|l6S+)vYzmuEj68G~WOM<2$<ednid+$dkq!sH4WE9laVCIjku=bb;ggYPLx-q*
z?t<0$>Pb)FMZHt#A{h&_t6em(e{2%_CR<%NOht%E&x3T6x=tG$P-$UIPrB1pn6~-9
zHgwZ4?U2Ik|Hh$ifGD5{O0m2zZBL(8ViKAV13>8Z<1^RH`hGnUd%p;Ts$E?_as3aD
zw<V#dJbbl5M%?E)T>iAxOG6zR8g<&{n11xoNP6XjDJrV#+8cjhZ1O-P$5oeK95E<j
zoD^6$+@DLoFQ=~<TDW1|qJLF6p?B#O*t~vSBHHm6yB|f}KgNd2@V~&02yl_`fr@M%
zf&c+jOWW~N9rs%dz5|F@h?tL3Qa>}Gnprgiss-I#%uvlJp8ES-LA6vr%>z`6`$I<l
zsd=rmtSH%7(^*IyPmyhT3bmyzi$&UCtu0R``9!?}&nb=LY{!gNn~(IyJkoRoQXZZ2
zFww|mQ_~J>&R&wzFd;;(gJxS`K4eR^y*_TEjKV59V2;&<iHiexrfsw!CE#*_D(C22
zc`AlZKGi&rc%9l0^jt`I8j5(HfXlgtJ5n!?BLm=5ag)HL_r#NpWv03Z295KTsfk>}
znF|4~+jY5s>#3I~L?j}NWX^Lei>me6D8<OW-x=pRmaPTXKuP*8iAkoNL={j!R5Y5O
zvg+6>D$RJ=47SB!Gac>a>nEzdTZyel2LVy&MaAQ&x*R8+|3?WEX7>F$eSNgJ9~jl?
zy%aMOqsF<?^3!uihI(AU_0)Jc`LaJoXO9VWq~IF9k{}Iq@U%^J^~S~NUx9T_1%Eqy
z6t}J0kG~o|hRq%2WI>%m4#sfP0}nOaj%hr?$*}8UlA8hkFIczS@X_f5=5A*@MkZTp
z->@aLV|>OO7X`RDZ{@PUmj4q=scU9e&Xz^ntJ3(nf`L-_BtEw+CZ6Vt0+<Zny8Ujo
z!2V(osr(!IJIm(;j~+Z?R4)(@n@>Gi>5vldf9*>X;AiK7L-}kWRIfosvG<tyEcS{E
zH|qPe4}+w(5h&2c(rTCW6p^27wI~hwx0VVHDLkCGqz|9E`qhc^hezvwDUt=#V7!g`
zxHS)D0kz;-!UQvMfR=X5=tI<Q<9*mz7E}v-5^XLARFlb~a}L$wZx#xwsV@l^8B~kD
zm%xPsxpoM>WMIqIm*gqfmbNSgStL&dZD~uk3~bqYC4jT*m3v+8Xtma&h302{wfVED
z`3XvWo0?^9tuFrS@7_ov`GEdIfFIRitj$K$x9LRxq>LquY!xof%Nlic{6wwf_aj|P
zm*JeZDh|!4M0{7onWG1hCtQs&hErboE>*(cnJ};m7<=G$Og*$+KYXhc#gV;_VCkBZ
zBYopI|1Um?@q-7@bHWN^eKi1>*t-)X0AjUmTJ!REL%J1pE;9BAA>dQTFnY&VYW5@O
z52cVyfu%z`@50bYt0H|RpdI}Xm2of`_E)vi*wiHO7m3Cu#erAEpHf0n0>V`)CnW^?
z@BSeMSNy4=cioHMhS|w+IGsIUvB)jXB`EcG^URKlQqg}Bj1nB%yBnQno)et2oB}wP
z5I(QY7=I_f^t}K8N~juRu}HM(L%)MW2ww%4=o~atKF3E!aNjpRuKFM(gcMkR`RlQK
z?dd@oyK4!Al%Tp#&+DGS1+;<^ja?V2BFKmk_|SK6!CTHhqs4Sk=m|B)zKLm^(I0-p
z-c3F1eb*as_Y>ndcUf;@lZ5X*w72zdXJW=kDe;+~-G%3_xxzT}jaNP!JCDs`Wlss+
z%<S*J8ICzNBvbzM*_-hEr=O7=H~RfcKJxhmvt?d{QV>W;N*7H!=ne0=I{kA>2p&B$
zinss2uN%j{a_b%V;|tdtMQ?ofYq57!LEF`R<~pO2Fns5(oEtV!y}LJ|`1y|CVRfXP
z^(XZBL`G*sN<c_l^+(S#R7|)OYhL%xhye<~jePZE!Jz{2uySw-_8%L^d`h=NGW~n=
zy}NMj_kLDa$Ah1IABr|Zw?h*5Bn}JL-}!66N}rM`SEc_AisPeHFjeD7Ghza?6gZS<
zDrf3v@<#$%Wr7;Rtb9-{S#|EATFv(75vs)j)?$Qe5<s`ba9Vc(yC*BCBcA~9t$Kyp
z(w4;_?TEFuJXvI0y#miEjboU(3m#R~a=PGr*p#RKeA;w8;35GCqac1Xn{B}=+WO@L
zk!j4f)Vgg8VH1!|HCyx(A?X_!P;f9R6;w-9weaY7|2<grKU1WK?xCgV8d{33!69@F
z4xv-OItGT&cjiUVfnq^7cYfzf2x(`ir+0Wb0o5cJ4*v9Ofh}3CDs^|GW62;omJFby
zU%g8G11R<_LC1-wf{WyXVO$s`uq+D8qBy#9yO{<FpzrMG)=URYi=IT*A-_+lFY}xk
z>82vdCLezoQxmbMC&^Lj>PkR0066^1@4;d!T`8oWscs0O=@_MW^!9H6(WoZL(R0@G
z0$7?Q6ao|?0d4_6fa1vZujx%R!d*cNo#sOMV|yP!v0$l}!{As9V~_1J$`GR0&xu7(
z#JhJ<R~c(NM3A1@aylsS{~>tqjXz36e=+G{^9h{^s5U7qeE#nHkc6qt+MyvWA-HSL
zUSro&03ed%Dg90YssX^CfA?n#$<?sB&%ya?mf-x={pM>`AI@LZS37n4$s+#Z^_1e>
zpZ}lZZ4CgV`&;|`k2anXMDr4z>UrnRhw#7?=DmeTj@5lm0;&PPoxeMR{Uf8{O)&Fs
z8qq!?_{Xn(+c?Ffhb^0Q)LIa&Ch+`o&Gie9O_lYou$KC%cjAmk2)^KHXJD>?QO)j1
zp6TB!Hl7~9!df>>A~}|J5cJq074&cW`mI5fUz+S6pALSP#!LU^N6m#lLeX}OO}D<$
zMws@QgfdBhmOPf3`k4jQg1&`46GFlh9G<6MuFJtWk7_usP%Yx6Wc%|D)ie}a#8AzD
zb8cS;oQ!piwXD*?6>VwDqL(&UYs;dSfh}9F0kA5oOeYbzy8Cc*4P=K4wWzXHSeJIL
zRj@T5HsvVvhXxljWiD+>*+sC1NMhlk?|m73Y9vTI474;c*8M0owP3$mx2rlif?r+#
zpGmluFq`6cw|o`M-(^$-Ap;CGU`KLJlT@OBl3M?aF@o~+v~j+)Y%N6i{HN7~k!87H
zWCLRGFA@ST$D(i?ir;+e+QeFLX)?O+A<RB@uimXS)aUO$8W&1Qxb7WgaI5C-Tpc<c
zv{?uuB*i1&`Bb=WT;u}9p`bhdIJ!C<bagoBC^;xN7DgVr$5bdeiYv|vu1&4$u(6|x
zFRUKH4eO5LZNpRe+D~q4%6`=d0!Tm@!`=J#<I{KSsGC|@fsN;U;d%`N(xWeRC-NIY
zh)O}!py36d`7cy*Bph6~`ylQ*a1eEOFif$9lnRKT-`ch{Eb*y-1Ab^)qWx&0J^h(d
z5QJg-u7^<(wZ=o-cg@()N<ClnXOBRQN1e+0nvYzYVS6Zh#;SPH2S24?nZM~qhAmP5
z>_2i06R!Dp;Lo0avA!3Cp!}2*%*``)9ykJGJ0NyJ?a0Y25XvwzV}kN4p0>WmJ*wHU
z(nP&TI%MLHY7Y7DJ0HSlzyDM9_p(Oyqci0gs7Z%u2K0#Bb0yT$5Y0+j+NS|!V7ejg
zSf13+T2L)WfRz$e*F(8nLAg>vwd%t4Jh-k0&vPLn>rdu|YQEmNhH8ct3k=o#cCw&a
z_(hdaYpNRETNtPY33OQu8+uB}A=?JFZ0*Zauq|ylp3(+uZCTW!Z=~Dmm3wLDF?7-7
zH-QVDnwN)pEwD|wINW^Ll&e%m{md`LbJ)AUa4{E{nk$=p*h7e#@6`Fm&*KC~_B@Jv
zKKB7k?7mAyD=`8};4g^-N&!T@RZv`A*9O>)ySux)1qcqo-QBf;1PKJE@!;+Rm&V-*
z79_YkL4$j6pMJlYzozP3bXDJ-T4%4dA6vCK_1T}ZHb&V1Fp5lUvRlh&pRz5ihsoP^
z#oypl6%|P+3Nn5$@8$-+Q!xZ~npAVk%KwE|7JKWo{)u+A^be-Low5XAi6sN1Z|=@;
z<|b>qFB|f<HK>owy0F``QbbzMTvpYcM<Km)EGb2|`3erbHjOU7U4q_H4JZcH#BVWT
zC`FSFQ2{Oru3JmugWG0)_;G{tpUZ_CM(z)8w*Bi{GNRV!PI^`ZeidxKt=>o%Kwiva
z8ijY0x!Q6EW`6bz!kRsjP9MXuMZ@3Hfop8aGCUygF=wbBzA~*c2@^PF2Ps`&(aDob
zdx`GQut48tcsEQRzEVZo;qNINjYxcsgp0;fF|tr@tThBDM9QZ=XRqrIfjQraU;$8)
zOI6AE-^zPmP&sk)Z+{kJhCV9tJbo?nq0v<BH6#rU&4C69Hc~`}GS@4{H`s4tVwosU
z4DDgg;{I3{FXz=Lm-L?d%Bwg_y!}keNx3(|-8Csb1~kaLnTF`(5ps+)cgGK^QZ8Qe
zF%3HdJZB>~!C5|<&k`?fv+AQY>Q*#vb1zTim~<73#+;Qk==@Jf8nwA8CIy|+fc|+e
zTY@`E@?Za!x?p>}{>mR4x{?ue`wf52@tugcA+qcnLdaaA-OtND_nBsDY$_|NnONHN
z=KOARk{&#gkJjU7-J|i@xo$g19MbQa>2s#}s>E}$kX(Ocker4U{A-wj=R`X+Ow>xJ
z>yq*f_sjE(-fcM1n;qZE8x)G14|9;%AuSzvWT1=DRDx*+WLlFcMQbL{s1hyMus}m9
zmaf$~^!h|UpMNK(wFwW%=g^kkc`=q~COPB8(ncMMz#H<V78IG(sP++_otkqjc5BNO
z9`Z(0out*i-ppGt8pm%&*vk4}6W?Y3t3a8ynYQUj4CO;e!I;*mM|3dnLN{C;5P6Yg
zBHVQ%R|S}iOQY9I^;J5?4f$f(t^gUk+^1^`tp>0D-$HfFyT<2tmd!iE4aagU;}I(5
z!bZ&_eC(|%$3Wyu!C~c<Utim!GFMm_WXoc|P`pCu2qJ@7>@WA0QEC7n;8SK)#8E@U
z3QNQZ@^v3SgZ71&0gL|OEVEw>wtS$SdU}!Z9;A(oLvJ&@ru`EBa*mv`=fqZ!KzhUi
zT7G0o)xN`FGVP<>T31|JSL(NIS=uGVl-QdX(Z?nq$Wu@Vh)T9J&0BUXN>2-ob<ArI
z^$(erp;}f6aK)HTAS*{(g1PqD*2*7?BK^88edQ3+1x_`jluC+HTa)oL6E9=1<{oTw
zXBoVaG@Yh9TSia9vQQqWD+g3pU|Y#y%SusE6H&q1TQV>^Hz{3Ei5UvB8^3{>BB$Zz
zlyY-ks9YxKyWZLwwbFo^uB0xNQmcp&6$gX7S3x2@iU||L66l$VGMYyKe}KblL#WlL
zGp=piMzOT3d<(`2oDe`+(^aoK)JI7WB0xeij<;$#we)F;iW;Ua3AHjhWLkwjE;ksV
zHN)Imp4_}Ag~urJ@_1xAHI09JTZoHOAljnJlaOv?YA7^IUp>~4K{D6KVLQrFC<z1o
zN;PLBzeR)$+am^;91WZ+0y{@BJX6o06b?KcAv8@%w-ThMx)k=8f(q_!iB|xYN{~+|
zmXvb6L;|b@a3#Yxk<IjmC4fotO;|v&Zm$YJDOe2nib6G5_%y<%67K(zu6PT4!2GcS
zUw$g=CWV}H!wVtY*9Rcwavc}pYVKqB=|vF3d<7cs53-lfV%KE3{Qr>qpl@494~H^Q
z>^1))4uHW+F`u=um6acdT2e(rD-`l-LwrG(xD{hU04ocjNe?3qs<4i9E~V7Q)8ahb
zC5;M?5AEsIjGxmFj@-2WL_0Aq19janFP#Y$^_JDdM_C%`RZU5E{1qw>ICIUUL62k!
zJ`o0bvGg@H<i!J}w<mS3e?VAR>(9SU*V|99`7!v%Z~NjqC~}|3if^g6DQia6QTm%i
zl|w{&)P`>L&Vmd^F*QNHe=dFCI$owSN!B#y+dcVTvjauSo<3~k;gP>()%ERT_{dpM
zk}Ph7Dvb1WZ@t{$a(~3tJ-Oa&JBiHda2zpj4T)AgQFAE0YM$@cau`kk=xQ)&mFrS_
zIfVr0Hy|&}f^`*V=-Q&>97<S5P#2OG2uUh{n(XL67!G>41MZJWN~RCHsbDY{iOj}0
zqxQAG>(nw@fROFy@no7^fNs2T;2-^;|IwURNq773&IV0|MGMCP(pRfBv?vw)w9FD^
z?N8`=IoVGW$Kxz>O)A6(ZXP8IPuB(R6waUmu^<@wmQNaW3(m$J6m69h`>%Kssk!Av
z$r*-5<F+Uj8d6l$l1O2LL?|u6^a5HEwDc~lU|E79re(Zw&5wqfQUKX4K!*wZrZGov
zSZ{%EH|6r2LDTJI2B>TtAj=Iij1a0V?4+i`_q`|FSW(foZj4dO5fK{&OEMV3Ng25+
zfa=)?2UvrNiu+f<2**S%JD_)Hw~)V?SNKaJzZahxMSC?Z4rk1Dq;V4a4^*16Rsg7%
zWDc^@&~^rL(3Rf5k?@9sxnb5UnNrA_cOl^*ZCK&bVOfn3b{kH~KIB&kBuOBs2v!k)
znJ(1c<hXD3zT8Fw?`E<+clfJfDG8@oSF>|zO+?9RT}Fd~BIL#>yMU?cl4My~Qh6=7
zBpzD|uPGU>Wso1Mh)GcK!(3gmk@CHY=*jb)P+awpfX*2XOq+3^;E*=?n5DkimSCkL
zN~mfGAmcAyCkuw><m$44XPx8XVqGj)9pwntR2Kho8)VlTxE4?wyqgk)r-Xnf|APtl
zFp?kE$;iaLd<e(Mi2PowKgMSj!!}4zoo}vDLIhLOWxee>u@{4@6!62Qa?>H!z8TL5
zsW|KJ8jB`UXup^Pu>_Krb)uIP5?x6}9-#QLHTApN!i`-TOHW^2803C@HjfWomrt8c
zCh$#?x?Fp>xo18i>B#NzZSqNAX;y2F1Kn;*?oFdY#U>%G`bM!Fq(xAlm+^VZLYke5
zvX(Jym5K_!sQ5_)EIgh4sxJ^+XNnGP5pP0Vqg*ZWQ~eIpDE3l7ouRW762uiaOLioU
z{{Aq{cIk0xqY!&?(Te=~A&N)7)ZgjwU%KS9zrc{q>8edk>dUUsU=LljxGOo`Wo3^n
zW#pUk^Z4-JH|gOz3;nY3#Sn)?p6YPbVi<mDSF#ov#Zmp4c`L3U9kgkvjH?*i9cE!i
zfcj31Q(LJVv}g=~hIZsc%M4gV-98z6$w;tVsv;37MeaMxxczs}E31X=EMv*|$8+HJ
zQ{BDaxte$(tz|r~sn5`epwSQJSk_Q0JTp4olR|n`OQG>778~AMk-xZe4{wOUc{C|a
zizu(k#Z~O&LnZGAuw<Pqfq9=L8U{5)c?uz{Ty@i`J{`sYG%HsOBNV3swCrop4mDW(
zB^cCBkkJ#D_Rh|DHOHO_F8da8V?jBWFjdHcpZb`L4nh2wt<kGNRobf~3gZ0{g%`|E
z1?Lr@ScHixSwJ(1w53n-#<j<~sjJ9@RBvuH+6|B*d7U{^hhf3%VwRMo64#e$c3vEL
z6QQ3%o{U9w?9)lX!^Vlj6Q)7i0u;~prU1}z2nh4RQBtmKE}L?p1E;A^9riA=_@T6E
ziWd#e6}=?#3A2t5T5>5X1PhL}nN~qKFt!>$se?DbK{yY^@!&#pIRM3cTw{ybuo-Eb
z7CumcScDn?v6nLXr6r3@heU=dp|MiZo8QshQvGD5KwP{4B{d3a+A%G6m)eGt1g9#t
zx>R}j-R)L}-dKLs5F>spC1x<*${oR>JD}2pbGH?@;mhb<3N|2_hwpsnRyw*W(r07s
z<S0szNu?eSZUQY!68F-07RQYgv-?~!L1aDV8K_ZefG9POAE-tp*z6OMMEyU2gTqDO
z)WJi+ac)Tkl)<-MQXfk$NGbn5oP-SmbvxjX-_b;mJZGj`gI-aB<u|KSf@NO5Eay=<
zt_T$0(;LJiCGzmsoVjb&cE~9eE}h{@o$W+&8PbD4WA_sKON`uE&5D<c!BM@g`5fQg
zTYRMI106@Lg~vFiA8#@Yxv%$(ywQ1t2G9dzc>S|Qzh8)Yp^>KDkq+bNE`lAZt7-|H
znK2+R5?Zim#Qa+A`e#i1fJ>2D1Z^eQXaz`W|K^){zM*sHalErxXbM&?;*dU<u+3k}
z#xp3F(YiY(^pk#FfXAeE84-G&dEJ2$8D|b{K`l)-z+&{p)dnUTDD1cZFg2@PckGPg
zg9z<z(_^mv&f@zp(X~HSx-XQ2%neGn-!aB0;`y^q$K||GIzHAkEpYUmk5pT(2H0R;
zfQJ9?Vpy&GzzaS3<;y*-JE{VJ2B>sFaG<ZvxvTkH@LfV0SxQQxz_~>GlO`AW9$gq;
zVfK7ZFq}YU^fc>lIi#>p$e|_23V?Cjd|@|s;fQ}rZ<Z+tPmpt7)UOZjVmM85O`L25
z0a{A@57!pKuCYkw8YrM-Dioz#y=N+2ITxUKm=X#ymW0=~uuK1S;VLc-@S)QUhuH!d
zxuO;|2*w`{1`93+HW<rm4-2Kd1VjhL|6+pVA>^*i@HTMsg(Ms_V3&aT*o%*aw8+fV
zpd1YxCwnS-<QJ4fkGGrV%Ub7*QOFh%+gYbM+u^?_L##qriSO{xqZsYrfe+j-Mpu*q
zS3V_4T*`1Ob9~}>)MQ&egkN&B)RtDo*`aUt@A7m5g-TjpWFPx9tKKDl^s>#-;@e<f
z3cyo`sZ>VRun<!$GYTOjM%haNkEvuHlO}F{z!-!@Sk)<mHIL4Cx^kbOZG<$_l41TE
zdQ|r^JtS&4*MOuo%X>8^d=ZevDuGV3yF;y72%Ut<bvf^Sv^dmgg_0k`yF~*1kdIHw
z9rU_HGi?0c8Bf3Thc`uC`BWk24O1|rylUUiED>Hr)UsM?)tm$k`G4no$DUX_K8jke
zC1oP@kbk=}9_z6UR3_i{LZUf!gW)$j|Hv0b9AAXD2`|DP#?4?g_c@mLRwgz!OM=}^
zB<s$OApRH4yGuBR|M;yJgLd?LT63;pzBp<{32=dtSv&gGauq+2l2V$x$4ksuE9K*0
z9EtkGVWD!@2Q3?V)iai@+^ZEI4M%X4erJPeK}*w~mpo}zvrYYIix0eN%V=8{UTGPh
z3Rf1R<?YG4*y{Y(SF(=m1)=!f#>eApi)0*pq+%_~$fzA4pt2TrFVY1z*xdB58r<wS
zk~uDnsKJgj-_AS5(#gr(yA0({!|Bx+uY!caOSTh`1~7)PG>05flxK<SUHkm2Un(e|
zhaHvfimGbSOO3<)>^@lm91Qt0TpW5S2uDp~%i@)`Q=c>3uhr*~_?9EQ7SC4`3`oLK
zSD*^p&}-6!N2D6<z|gAG{K>}k!?64XOSRPBtOCy3f50r7en;)sdBz1DkSz-EI}CTG
zuFEW41OB>Djj^veIz}foi<lX=ITJttq|*odgIQL#Sk}ihk9gB<QIO*51*4)+Xp>ps
zhmMCY1!1l8>j1!82a^V<xH*;wRj~>+9y};iI6Awn+L#aU{_`4Q4zRSE9O)dJX^_H{
zr@JVNX-(|&ZpL6Yi+Z%*$sjYYX?qqR=}j*UAdwrYg~e^Zoo}cNcfK9tvyeXbL4Ohv
zl%?sh-Hc8W;Gd0P@FNnZ%o#7JOl%lxZ}RczHB|ZOAZ$q7s%UG1c~349KEa5w7wq6(
zAJ8I$g8~1(2$qs9AE;NiP~j?yZ(rwqPC!|px!}z=WVy+fgQRJDh~yh)iLNl`8w70U
zglOOk2~(<1q@zB4ybPh$8d1&o_XNf6TAQI_-UgzQ{|>9e|2UG^-`Lfj9#hn>Mpl?i
zLDo8B{LBtd*6s@gMJnDXHh1NF;n|g&6)Gy(ixnyZB6wi-+hiV6RXLz(dzO+j1o6>A
znmpOx9}WdlJo$yBjvkA-&jd3ROI=p7nrngG!;~PBP9lr^3X=5Oh&eQBe3qH-t-q&#
zHxf~+@I5v0n+N@coY2lNoJgfnp3F-^6=OT#0H$jP!QT=dHEp+-V_7mZze0xQjr#ku
z$o6KFfEEp(d{vtUhuF7R3dKmIC>XQ3{ZrQjxk6$5COG+<r~&1Tc^&k?(&cvd#i7u$
zz<<A|h)I(joCGY?2WZ(h8LTF2Xq^-|dm+J&vQY-fxf3SN7*AP7Q^u*8CmoTPhK&eK
z=F%Ha?nl4Eh#1|s*V?<p6je`|4)ZW_iHPBK(QIjYg#ILOAcVL9S>IdvCbbxEd3|C|
zdfFppL%u2Gr|SA~c)p?C5DAp7)p_L`uD9$LWCbR;*U)T5Zr-g+@dNaA8eFCN`un0H
zg#Ud4q%X40LkX&;|IIx`eubt%=#yxZp0=P|vxoVv#p9`H6(Ojq`dC!|1N91!&O1^@
zi!r;xbKMCAlzodz{z*O^kqct`6B^^qQPTdX@*@`TBF{}uE*tXc4q4)R{iDRCpq%AY
zj?CHTgNv#%nrSSV!i94M^0nCtKC0#lzI*0s4VsduKzVIZ?MjLYt<hTDz9178h{rjo
zygbmzX?eBDEpwOuj90#)30fBSj|`tTPLx>q9kssk>#DvsILdx%JIiS18eu-qY2qGX
z_MBvklM`56LwAcp|F(&n(3tv!4<#;ItQF%YxU`$<n+~@IFc3vs!ak3^2!DbhBF~dC
z`_k&&g`w=2*6e|&tzCueog~5j!Vhox2@OJgl;WoBY>Degw~CSUIvNb((Y1IhFwBa*
zMq02W$aZeUSa_k7g8XiNgobe{x=>%ve@6VUgy{JHOAK^t&7p3zga*Of+0|JBc37hp
zQ<SYFpS_ZWcBB8(0+_B58yT@*wC1YhfDG*_Z;hJDz4sT3RoMFnOX(@flXU4CeOQ-i
zjP?gh$z<_;KWgM=V9?YMr1EhiG=SteVj_kBi~{Ruk*`kqqc3=0x);g(#^DGdlPMZM
zm_t_4ROPUB>k|mr7L!EFe_5PAeiK$7PejPlTQT1Fyz~vCFjz_fzp+8W+}1M+ZDn=-
zG%;>#+*a_+rKt2x+M({F3NH7eKyK3y9%bxM186c#k-p(qoMFmr4!gyZdUiJURU(#E
z{S|{=^%>b9hIEF@K}0OL-uimvuO4Y`XN$|@!M7UBLVRX$`e;p=Y|McvS)6Z#CfqVs
z-SKM)>X}gPv;{h|`_TQF{LW9T&d%=UY+7J=YB+@mMZK5-7(hjZfwcAzPy!Ak9$U^9
z7g^OJ`sPOXfAThh%>NZ_wo|3vmQabS6H?&<ff<~ZSpR&}zr~7)XqD>zK5w{ORCK}+
z9#k|}t1@VU$KBULVSd!}#n=5W=QeTME9=_!4YQV^cBljD!Q@7wL8Aj`70HctitvKu
zpVWV9NGO)D#j4~wsuL*c9FMRuZFGE5Wpl~V-&$TbiOpeHHnR{^FKBb0?TaH0|G`Uu
zp?$2@ZG_!Vl*7;1|IrO?Bk0%ls;<$hTbGT)NMA=TM$WuW@93l@oG=}5JoIFHx+;3O
zbd2bmsCHX#M=s9f;aTXf{#X-mf)!*<8fO&}tIu}h`K_>#mb9XzuPIY8_vIg-4cBwV
zkSmB>Em9NphDc`@JQ%@oq+V^BVz|C2pdD>63!{Bqn~sMQTA`A-k5cit0`qp;)z%YQ
zOJ4CDHbD=qz;5fmPbb|v|Eo;(|KZ?#JaslFslSP<7o9rHb8kv)rGFa6&1WHI|6~5r
zU|8vLiHYIWLV&z{s33)e@`v1I48M){I-%zF8D@%rjmqh&uThp(z9hxPZWQ{|DZ%6U
z2!oC@lmbN3p<KyVfzk?%?nNiwOsYA=&)Gix<N!v&zDWmGu=5&Yj(4O}V|W_a)1ygC
zdhMStvmN_Uzfg#1;vpY8ZLl1QWiTzwjUa&!8S06TVgev8rSf6#H{DPrZkv-q9s;H!
zkQy6~dcrD(`{w>Gzq7Ka)Zc;m`;7bCL@<BG)__w1fxR@0W(g1~Qe(FWyxA01uP(ox
z*F04@msw@gR=WO^Oj_|sBU00C9KE&-r+pnLTWiNyBv@j-yS>u!aWw!&;=gHS1}Sqc
zjALy!jo`m_B?{2BN%dBBAiFs97U4-(>`xj9jSPiPilMEdq)=42R#<S*rMpTVo+-7y
z>Jm75YIhnHTr&5y$V7mmrO<;*>PwZFf{^FBGtcP$b)$<Ai&kS_mSuYh`V@f?ZR`hb
z%A=+3&x%`LQ@6h2y$<!eN1EY$IgZ<_Gxo1b+fa5Cp3}~xY<Wm*nsS!)WtO#jVz2&)
zZn{a%OW--F-{#&wgeiyWj<LE98_^pdGg&_Q-^}&fK&iuJ#)Z;{oE6B7BSE`oOp?#+
z4wNXw3c_(PL@FRUypYthqsUJ~fJm*}KHzzZbDL<Xu?yXXV&wQpEq7a3B@vf^v%cn+
z2K@GF-YUj|Nv&|S{~~zHyO29nVEEF(2e)v%x(uVOMC^k4pS>&vM_tM&93&-=#60dv
z&ov0&9U!>G1N(*OKZoxAf0Bst*8l8f4NNj}l{Qk%sx&S-_XU3=tGp%ma|-T?S@Wz_
z#<MCH)iY&T_Zu82p7~nwJ#HD!&?}ZW=Ka$5XG$4j3tPf813z@FpscS%J%Z=*XT!G4
zRdfsiHS}5_>!XATz9h|(#U-<V_QMPX5%nc*>M1=(2J^W2RGiciVlRBc!EO#KyUDhV
zDrd^s7m|6}f$3EE)Y+FG!|BbVaIf;boR#F7oz+d@&=pjr6Nn48xktAR-JB-S=|2Sn
zlarJUHe(=;7<G-`dQHq>sj;nQnKiu6r*oS^a6ZoTKbw;3G0V;l?o)k(QlJIZ#K#nG
zH7fAUCP*J!#4rhq;1H>)@RN%CPWpl(JvdGi7^3tf;ox)~%;%$b?dOweTTCz=Dcww`
zrzq8ItN}{2sxA&7`E=lt9Q^lLTm*t}h|U0+5A0EfFqD9?nkhL|vUir$UN;7jCePhm
zHnsUR#2MD|J&~Q2jN&@~d(m#||6L2k0KQN&U#hRlfks!0lCz;iA@5JwLVwLTVsTIj
zzs(_E;CP4%|0#&Rf;nBqbr(m#CI{95^0Q7^)pr8r+EQzFK=}6HSE>H%y!-&dGhIoj
zUU~nI>cw>xCu+gv;Dox;3OdIJOkbWmiHc<epygQXr?C%DgaXq?tT$^aEBJFv>9eJQ
zqnvt6LrvSpMF9}T9Fa5x9|?AZop@G7OJVte(o1SHMI2RKD=VH=IT##3$-t}O@d&yG
z<}n<fJz}5KSVc^*2M^faRi3`27B*-yG(`KG_9jfHy}oj5Y8Q#c`|Dfz6dUsRP4sgO
z(i8mx9RpYTbQ|iH+P~Q$&~omb?P;*8OE6^|0Z6X#(UFqA+~F`m-r!<O!%Hp>IHRIn
zcrUHxeH=Q|)SdWrRZ*;(DeE+9r557jb_CLy@_Vq~w$}N^{NP`;W7#F#dJ=IbOu%8f
z7~h!owFI1an2qO>ESLbN5?K^pNe_*Mx;TS=RLfJg7KV5+rva87<=>k<BnPsD7<Rhl
zc}l(>*nBD<j8A1v^()T{e^XbeK1l`ff=to{n-sOu(H9y7e7Q_6{|19*++uLBu%KZH
zp`oE%{)L;NfhWpWp#YhIn(sh->Z{mabC5+l{6Jo}R}ZLl0HkEJZ}gu|ZP)|~5Bx#c
zf;F%aj_gejr;sJwRw)juFw>Nbe8Z+t+_*U@g>Ijl46A1@_<*4v!{TnIbxC_-tmu1t
z%I`d>A+GBn<}^C%z3x=uZgR31PnL{xIjZAzh1A04;AOfnAXC-HGNE$&Zz)L~vrMX|
z7%o&>JsjuoE0QIEs(j3qI{2RlGAWR$Bp=Q^1%W`3Nt6ag32DX|ccDd`@{ZCe3^V+z
zkSso!I<Y}Ak7loPDL1-*E0T%BB<$#CPtXV6$V|C&?k6HdAq~+a0C~vIL&I;}Rp%x7
zs>tWR#k4WSkPLFwF}L8ri?k)$oiWHdJxzDP+C5l+)R2!<cEgg)=(i8p<eKb|@eo8`
z_(djqb2h&Wy^rx>z0qI@51%H&C<U`M5;?Efa0(N7O2r)#K;f%TG}f_^f5O1t{p;b9
z)zj+plKi8tnHC4t4csK>FCzF7zJK1B!#UG$#e-t{(q;X}2EZeoUb{G{7!f$_;R$XH
z=>S2)UQAhX_!W`ODVXoYXH27Vk|`FKh#7b|2`~+sVb2d1-y@FzHT__h+4lr${U&1=
zBY`ginT+hegdFvD_RTK(3$Boj0}uQQ;vOcH(Lp8BG=h{Z#A(qR7g_ZVe;TM?c3OR1
zE-vn@o~3=0?%<MY4k`{zV&?SFtQ7PR1FqIYA0|aHE#H6JLug%9NSYZxP31h>zm@!E
z5<vTE^-BUXTxsOb2jJGU6Mlp(%koG5=%2}rDt#?9MS|ZPI44Pu(Y5#zLs2voU~waj
z{6cZ)R-((Z)uXlx=<C>7YpAFV5*>KFkvj7-`p+3{ry)*aC5W&;Ebtsjs-jvy5Ndz`
zTt+qhmnKrqD0QR3vWZp**9y%a<}F>y@OmgIAq4FkiB_)H1TYnkvN(mkhO><Io=qml
z22%dCBcc@b+$(v$jI7>;i$lOF3TUIK_koA@+{cvv6ymO_T{9p(56b?uI4eK{Q2Cc*
z=AaHA$|4Q}SzxwiQP%!?Y4Q1xzc~TuutSz*B0|Lg8fmSfVy0)(6Rv`T(`6iHR3p%h
zV6+bQu9#iduDO++H`bLzNk{d{?Jci;|H#FfR47eE@jB~^E=XG(It*Wt+w1V<fs4su
zyjD~5^!ef*2oRy(_964P6D4KLanIEF{C8uG>#?_*YT%1j|1I2N&lN2dPma0nkgUt$
zz8VWs+1ajC?=74N2lv-ECGMLP6|l0ZB7HsAzY`;_$E|9TH;AONwQ+oJMS2%GIqWa6
zGDjM_NZeIt51M8sc*;B!Y7>mC7@n!WgSn|*OF1I~75F`1kBhnBW_${HlP`EO*&{3A
zC(QqL)^0)G&-K=*1_TJ|wP1)pll&SwiU>dn@QPrerK^BSByjazAJ-brB2ntO{15CT
z%CpS9DC8FQh&ukVdmM9LuDEJ@Zldk5JV3-waf~I7t^^S{x<(<oE6=FEtvYqQnauvv
zehP*zglsgnf>*-k8N&2F*(|B#pN9qqd6-A0;FyUU_uj|zyc{wOl1>SsQVPEF6^$DO
zypXQnOc$|`sLO+W6LNO9Tf0f%Ls8W?>%XCKJ{}&>QX!)BA_I})?tkvYU+y1>ptISq
z=@y6e^laNboML`Pl&4dkM+^ww4e#kdIjB9ybY?_5c)2~O3;dgrWsef~pL?M(&@HUM
z_ko?#akLaUew#n<g{4Kji!oHKJ1HZVbi<urpq3a;;n9yQ7k<O*Y>76%rX|<suIqa2
z!>-ZVMLa-!P7X8h>E~g6zb_6WL7K_!Ho#Nb#rSsJZtIJgPXScExF&{kvimZ!0{f@U
zZ!aI%@$a|S!mo`HgVW5M!GcT{44Y!wyv3zRE#Hr>ixPTXTvm!HEeSnWEd<t;@Zx+`
zb<U*F&TejV!|z{_hS9on5csyn9izNp0w+eDy>w@uKQ@T~dQu6<w_S{*yx!C^!IZj$
zM)aV>#1zI#@$b$~vTH~Cp5CKZ4A!`Tnhe=HkzNki=D_yVQCMw9hiIkVCZ;oShS2XL
z{Xava&_)BgNB*kjK7u-X3Z1sR{C=ZZGG;il1cZ74%HBgMd`qo3#{_1TRnioP`hX&Q
z!XlOGMMjkNl@gd!P2l?NR$ArO!h`g~OLE;V{1ne8DLJ5mwn>oN0x#5WD<3yIq9*q^
zW5!|*2d`_)p<ezkMQ+1*qJOR&yJk$IhNTTB{jD)C((3*lDFw^RIDf~DEpYz?4T1n+
zL*;;aA<Lg=HEP_dy!KDHyhfk2q5O=D57D|8bI#kIpyzWsl&tt3_l6T>rW$xAAXp;x
zpY8#~ePi~(Nc8h{llsrdbS=fO(@L1mM7{fuOLa11_=BBF?z_gR-Ue-Jv;4B1;2|1a
zIy`mQdFyK9J!I0bKLw*$!BHm##;zLWd?b&BH1iTnf`pKc3bWfv*E&l=;oK*X^80ZQ
zPz)$L--c^Ar93z&v5sE_8;ttETuVU+<<q^YP^AxkBFN0Q<QZZplD1ghy`4yODFB+k
zm<0S?kAAYdTvx@6KoliAGtOqZ$4nPP|8V9*l!OI#m!BFGH+3xsE&#xIWW5c5KoKS2
zh!EZV<<u7+deff{CKL7gR1|M_^ar~)_`kfK3A=$*G5Vof3AyPRzp+QI2st~@mR_Eh
z0Mu0N5hZ_26!vEG_W5nK;s;%S<@MljzA;kPJU(H8*+xXyU+ceFdE}W~U)MLmtzRD0
z4(OP*5pQ;!1UQRGL56mHKx^)2sd@k$!}!fINc_04vQOLbzi6mxG?3&${$C0wY_Uut
zH`+G>S4}-^|G2R3;C7D9jS(KtcojRCYPKVHr!JRUP1>_Y>?02+C!)G~i+o1QIW|D#
zPc54pAp#$A)L$i1!cC~lAe_R)-@Ix!eh>QcFyFC>5fZt#9g%1)$k&FZlLFL10RYEg
zk@*mZdY;(#M}-+Yl2k-UIi-U-`?EeC9yW@+h3*J`i7ks_>&MS|1uYcXN|C=ukr#Hl
z9SJJvPa};Q!RqXbZqn)#dKMTVbT40-`i3|V#VKoj)!J>L(=BHll=#m5f15_qP&dkd
zqmVju2r@y>|196@_%R-?$IdNUgc&6>2eMG^Us=a-W*qIGMVP(c7(nW^@8}uO%j52Z
z=PNwJJsmCe4H06qpew^;N?a|VOJ6y(zM`W<r=3-F!fe0LbG1^w*P$Aa6%bC>*IJvy
zx;%F}Fr-#{6DkAM(w8cKWyU7*8FEy26=LCtV7jroOleGdz0{(6J^ZNFR~Of;rl-yx
zaZ@FXzc_7(^m}X1EvVtd;_!tXrDEYMB_j9Q6n0XlqmVLDr`)G}t1eA_Xxe?_q_;g3
zi{*7@mu#;BaY^NPTM91GV-L@K|71(*6TL1Ah3!9y`tR}*WhfC=rR(yBSuQh=^Lp^z
z*f@rmYGHl>PgT=L)iss9J4t0<CPG0ocTsj-csbEj9__Z9Uwe$>f8AB+J2El~{+S;%
zGxfqKK9TC+&`m+{+B;&!*+VLVuK6Ij8^x7yLMWG<OuL@c1+YeR9d~&we;qvrKb14F
z78L%~6#jS0tESs4@1i41H~jquxiIcDY5mu}mhN6dq<q}_Q={JTjUF7=cm(J}_x%J)
z_}=t2XKkQZu^QgZ#6atTf326~XVInZZIm3r=>t-$2PT!bznKTNPrqq;Q_DDp*FIDY
zIWDpf^QN))g$GeU!4wqJ$_^#3p??8gN6d^nDryFijkGST@j1{F`Q2kkvFe-|Ueedw
z%%Gv7GgRjfdC){<$4B)h{e;!r(vXMuPjfE{PmhN}Ffy~Lk&g$AUPZ?|id@T^sEWum
zzc`7J_spcx$%|vi;!>4T93%^=hV-MgPHON=2+Wk&(eppWXZq_l6BoovHy2-%fI$Vn
zR@K>G{eNzfgn_O$=kl}64*w>_s_NcT*ATMk7ePgbpZ^0}gmN3IO@orFd7wE2t?TNI
zFVwh;dlsg||6Qz%$v#78ZXK4QfaG32Ge>t!LiZKE+B%L;yJW#OQb<+7A)01{!iYZV
zhb(<8%fmn@c$^ykNiE>zH%mI|O^z1`G$Zw1*K_w-ilXNoBZgc!W=1-K%jXiywsb6#
z$0`ED;`Gu&nM^3AyhzT)>Hbfjbak+-)F<f@`OPd|s;JkDYL}|^9mV-5Yh(s#O$2Nm
z)~P^?!sSzC;^%K-U`0i^l}mb~TM`?T{Ra&<rK#^FE;!+lR<wc-oqqt_u)CRqRH&lB
zs%@yQm*zVQs)v4tpg=J6|1F+Jh^5CJY&S*0tryMro9MeWGk0ah-d$$(K|})|6F#+P
zMvi2wVJ?0FlCIKWIc{F=@PG4ywiqIiuSe}arV}_+hW{?t&x=X=n2$~IQ$ZB3R1hVB
zez$@wHHh(^{S6rg35G<XFM_g|;uz}J&`s@Br@7|NIc$kIz?41yp&cF4rTk<)s!Dvn
zoxv#u74MtWnYYh^Wz|8=PFSHZ7v6*JxPx~wx|e%-7X#Z4jiS(ujGJN3X#mRhK##0=
zS{hiTtY&dbz7{)uQBvY)-sR|*-sf@$txp-<r}cs@PQpe<i&i+nyYF@v6T9?66(2Xv
z*fI@tT90e|pYOUM^mH>+5>ixk-Z(>GNuoI-Uy>=_eqPgI%C&~au6nzThxQ3C()hD&
ztH3%@N=z#!D<dwi&^}v4WHh88G{fmA!{ao=KkOcxYu{?=d1*H&6MbblQgu8!C+3gT
zZmUN{Tz6#tY7%b>Gs48hl5%9n>wO~X_OP93#1%J%ZZxj^;wueDg3_z=_A>8SE2rrf
zsC)J>5id{J^GN%_*E7jKCGB+%>Uq@crR(j&zmLWaK5Kpuv7;78A>6t&L-j!23^e~3
za2|$u*>E;j{<{UDY=p4kx<~PHxz}mV9sWYv^|tqn6_^_IMS*lMzZ%UNmDtdp%`iqY
zxP66KeUwKsE1p2{7hkbQg^Y4USYwbOOROgBYVlCPUx7?6jshAFlcl_GN~PvHfWD$M
zkAr6AkSr%i+P>SPx=Q$x?mz&Cb~M@VzI?+9`i-}0Fo>i_4l420HQr>o+9h|1CT!wP
zt2f3YE(?y^EBw8lSj36oqLjEX)s#E2VGwnLhA{NRp+CC+EkO-7xNB4s#!ELmc|nAv
zS`9Xr5LVI{r!-1Y{(CO$k}ju)@F+wIR@P;;xZ?Mb{`<^kJdzt(&<Oc+Q-|zNY#G+a
z5Z)mq<;lzO^fwGqqY}3}vY^*mFItREyu8h$jh;7h7exyflreKiCRvWwmlw6J!xA<*
z@>Ab4cCW*t!0__3k2lkR?Q6$r3TpLi$nectwuv9~s(U)IM1OT`_E2lDJm2rRNP>lv
zpXB8U7)~|kJ73=^XT18&_>LwepW}V$*7f&!lX&gRxtEx*+6KOiB60!ER^bjxP=wf1
z1WW7!zTjn4tNF$D2@SfN#o14avtcn<VCXcJ9oZ45MB;JwVMxzGJcb0>`+Q!oPc*eU
z<a&4uEp0@jmbi|MVq7bAZ?q`y1sA{iK#`A-;}@z~AZj7kym1>z_LLua%+pN#tPy~E
z+ub@Z55aKl`Xyz<>e~zdVxB)t4OWKOC62M{FH)=Ll}SQLv0JqSnGf++g&cwTKU{e<
zkG66t-veHD(=U3_Kb9q=-Vt%52=D_f&xhVJeU#q^!YE9i8hiH1wSofQk~e#UGF{9)
z2JaMXLg-}siHF}ee{ReA-S!Q4^?B|O{x!QvkTTy~rvqO;eSf;S4y1E=3kk#uiMa5)
zy={9v_Z9ufQGBw)UfU)BPxO9>`)&76;N!OP$J&5C>X!pdr7snt@Jq!f{WPxjZ;^<(
z{qb*il&l{$w{A9HfNUhI-h24IUA~%4kxwnSM?07su5fwJFF%&8c*tf;EOCgzO(K!!
zxh-g20h=PG@!rk9dksl{Aj$NAY++ELMZG_ZBft$LwW3U}V>FRHVk`)Uap(r|*zh&0
z`4Heb6lztMwyI0QNLdF=Kl~_2=O<j3C<NMB@Eb_5x3jO;#C9dUt%GRRv#cc*^iu>%
z7iQ#^j$UUzm6Q}1Di#q{(Eo&>EzCA}sH>RbK}Tm__Hg**?Ufw|+6~w(_D_md#A8g>
zWc31R(exM0nr%bt1N^0*>5C+36T&I`wBxwY^#kw(Sko!-2K*8G1}g**+0BT(((?Tb
z=#hbq${7;~B#p@#8F{kK705*vL750oD7367Qbb9Iw8T-2P7!E*VJAP)lzMo`i(xmi
zv=UL^%wzhu(D<VZEL{0OWc<m$%(b6OK5FpJIAvr&h!x|txHv62j@8nXKb!S7i$Aeu
zs;hXwQANh|^%uYGv|fDr!)H!RmZOp5{g<@dQ}Lo;4N<+uHb3Cp$V}qTPMvbxtzEn{
zUEyL?80)V?01YnYN^U2d{k#ev2!Snmcff>F;gk2(kqPzRt26vq1|<vu4-hUc14A~y
zGQVTSjVUfV>TMQq&br(?=_`-MhZzVfQtR6~Zkx-UwUanMDYICLP<Pr!A$}a#xzqkU
zij#>7IYM7FO!kk=1Ye!s&W83p$(J)Ub1MY*$ThZU2i3_PM6>5KKL~+yDB@(&<x$bS
z2$BnIs4H0A@22M2@)_S+Q8@W*{az%H^2NS^Ca?HFZ!Cs#Dn8&02zJF90!X>KIq(|b
zS^O@93ylsd*EJ5~>Uypf$8j{8o$PLBc(JGUx^8;CnW$Cpe%grtgWY)jh?uK<AAx{l
zJz`{bB}JA2v5{>)`aF3Bsr&lZP}^=eG%Woh1De%i7ovx>5Y>$}+MOg;Vm9)Wg#Ecg
zK+a>w7k36U;-7d1R}p`FOLDw{sk322mLpt`9u!L2+;KUzD*sgv&fF<2vqzKGBN=`(
zL}yOy^-QDes|F}RqSel^z))QiF`7@EeZ`;IJ_R5UBEfGWV*-;1kH<W7%B?-u#Ee?;
z82Gu{5>xRv<~X^Kj~4M@eT>aA4}zoBirdv^uMY%ApAK|k{}p*6sD2^y3gDmUmYjo^
z!#d*k>M&mj7AkpDgU>ZupDHr<W3ibBFXTx0cZBWtqmj5LLsV3Io%GE|Zf4jK>_TLe
zhPD#1xJvgeqhSO;uiwpIo8gP-;lSXJSi8)t(85$^ES7Z);)5FnK@1^%kk(Oq13P%-
z#?vLCpm9MYyB@br7)u0LJG{H6iW;xWB$+AZeGHS3tP=CWy@{e!H=z%1@;dVnV*7ct
zc{Yq>UMJ)4fs7Irc#}Mp*#~8}=_iM>69Y0PlGFR4VYa{eWlSE2g<y-CE^{2`(Uj*^
z=6?!Q=X%qW(7phzxSwxl&2^qCJ|_sxaB<IY>B#&T*;<YaV?R|be`95z{lr7cZ)Vo>
z`0e7AAO_Y~qRfaYWoIR|IcUA%dq}#OHv~=}mHdRh=8Dzx0<bLtb}UP8@K4gTfA*+m
zlxzd`r=m+St8zNj@Y=}q^~T&RboThu*d8ch`WvmqHSky<DXXXuq(ufd@u-in;`^4o
zKW>%pu>Nv0xFoo(M{3%WYN^uJ`0~*TFS$CBPi73x;qfS+IU_ApBf<W@qUsKP9<2}&
zl;!&kC--r!@My&qgTc!Ac*WEF1Bo{qE_`zxQv*A)LxYxhbbeNz@DC@y^?-({F$w<6
zW$|!Oq#Ch*gX6G(@lSt(bQMQ}JcY4(p8pP|I-496;k0P#;V3*)BeNm?eFOy3pab+`
z#T1Uj)ukT1wJNj|gqw1fgYx4q#4G=VjunZPL3yLU2Vus93vz;75fZq5%C&($)3%mT
z<c6I|wQ+=g-V^$fS0vA<s)N&gOZf(;Et5t|=V|gR%6)sSMm`)Dk`AADvW97H0K@j(
z9MH>zq`~|(2Ir8V02Yv}p>O<21bZrdtDO&17D1_O@Don1Au%6clBj))GC>ZC=rH^1
zoKZvfk$;5i_L~~m$2stnH!yPOMU1Or*6N2Hr6jg5HGnMy-aYR&!#0=O8vO%~5B-YN
zjDal)T4|ri;Spv(Hl_6#)*Z+>gDikbv-P0g2~X`Z_aI%SZTeG1CS08|#RF~VKnUd{
zCze#Odz38M-df@RX#pl=Djf<mSaY#@mK6}3Q%JVImv(g9_YZMitn?xhFkUKu9L})-
zPpO79lVDvRU*!Lzd|#OGx^AW5n8?x<7Ipd(O4xT#wt<O-BDGM_CjPw9GcdvFTi+VH
z(_N;@`sqD%EO?m0JRJuxksG1<I#WjPc$U(p(t?IT%h>R5Nbe^O<y&XZ(s{I8v5mho
z)<kC&x^xQKTKxpidQvFDNXzf<pLG=>fv=4BN3)WzXIj;|ZQ|z{Z^?0#B}kE~CTU_*
zN(wmr581oLbPep=u^6RZU&xkj&&`nY37Cm4l0zwBO9M*98G{VRCG5>$=XbgpMG~ib
zY5@yL_n{YGA-a{B%K{%!0fIia0WXUtn1vG1I2N~?(!sSaosIIYY*xgjF@3B0E!z8W
zWd4kba+M`{DQ!0PtxDAWl0N6Z*7DUn@h#SRwDVZfIW0OXCaMu~OQPZ@%OrywdOBfJ
zqp<W^qj^wVj!UA4fTfNf!6`1}_^!LF7*uq|!y`Fk<qbD8>0)&*VTRl8prwN4w@&fP
z=Z7FO|1Xy-S(%uc$4D<sC@@mnlKlQDumJuqggUr}&!<Wt1YZRM5qAXlj|4jbxN#-C
zQ^hTIa9;*OeXk={w87IdEETdZo}K~%>ha^r)){2arq6R;PmS;7X?@#lKL$p3kX>Zw
z{jAfFLDTEwIz0%A{oW^r?x??wbEt>)kl`GDmQWe>J(9~ddWrr8&S)mO|ChlAm+>br
zm$3RbqFL~hwSdUqm<oAewLESZJ$Wug3j>zDZ$w)@9!F#hcDB2u$iv68?)7IefcJYT
z(u?(lLA`Whv9Bm?yH!Kpqw^zsnpi`Uj#=JqL@IFr_r1MzcCVS_J=NeTziW+`lM58R
ziRSMiweNj_y|))n&R8xvhQ@t)0XwP-fiaaS%A+*Hs%Dr4c;Ig^)|%JftpOcPKb*?B
zqtYkTb}JC37DW8^0{oZ``jzEe9V84X%d}=hOQWp&Jqx`n!<?^tzi?YeUqqWm!_fhp
z_{V}Rv@`XpF*DMvijv?MqPGp&Oc3X3baCE<;*xD86a<q62%MB8%ka3Oj7aEidFyI}
zKu5d!a0|0rj%W)v7#F6?6voMo9=M!;gra>X*$N~!Ft;QUXZ448l!-%{>z$g(ST((z
z{fSY>5eo2RCtzC9F0+86T|^N%2V7;2(t;ePs+gZSc>rVZGB!-odp{PUOp?S>)t|)C
zIrc}H{79N!@(%_t*9OpT`3Kkv%cCInAC`1jKL{UZdHQk`YjcS<gQZFsj!_lLX|zI9
z>?b*?yiRG!Vz2~*!M?cnT8dP!3Kt50*ApeRc!sCNs)D8#g5UeoF#`N{e+5QeytcoS
zMHBcQ#>HC<>n!OW!h(}rz#05o^r2uJ6h(Nbu_<PUH1OKjYftUPa6I17kpyw`h9jrr
zdTP6hGe{F=Xx{oAp)5aC0;~K23zF+eMhF9_R@oladv{?$srBE-=CowAqf<aSMl=Hg
zxX6H&Wr*%Js@bb==^+{wG^ofy#^R{$J}hpN6|lL86;8upDR6bAu7{~|=JdKsr|fy`
z+X~(VDPZ?K-V(U9KI&pPdQhhW7ndyW?8fLA?KeLF=X0M~RL~S-w<}@`xnPdLehE9N
z-r2>@a9PLEk3N1G9!%A<%RMX;QcGv@^Dvt>LLX*w1%0kS(vT3^S|0<T*QG8j16<GL
zMo;gW>6MukDhyeMfXi`U7wMPhpFIP5ltL7;t{Ny<m<ap1@rf<eoz51pbUfr74t8dZ
zajmDD2+N{PYe(LW<Ss6dU0V%A#QTf6MIyCKHq2Sg`=ss0rk5v6G)ack-7oz4u?ch{
z-`5$Ddx=rMmR#yx#lqq!G8y9DetrE!#V%cjrc9bqOAkatfCG_~IeiEPDKO!E&_G*=
zDx6QUm4#XOddUP0pL8&Gfasz~=!Sz7miHdrvGWao>Pj~f$Lj~Y_WiKwU43h(mD_Ch
zdUn3x)Fd0z52v@P6G&sn<ur5-%$<b8ZnJSij$Kh8@<1&fu%1>-p=ed^g&B_&4rSf?
z>Qnb&wJ#KMghRZj82VS6-7GcI_~vV$--*A^ZtCdCK2S?Q4oHZYTl6^vP!FmPD$<cf
zcKvDpfiofr3)g@!A9L1ZO&4p@gPM#Je!=tUS_|f_i{lL?QKaEt$)7W5wRHYLxYwIV
z;b7nhWIj#a45h<vO9IZ#qN}nHU`BUT9=IO&`PSb*asmK?!CR4>fn*<9gZVdY6zw^T
zwS-YpTxDlWjQT_JI?y@2{;XLFuWt!nKIQefZ$1c~&S8SxQ6`C)N&s}<#~U$E1FvfV
z>R3#vw(>%Y@qzIC$&!EnR^c3(KTY3$Qi;d=y|EyCIUC9`JoG0JhC$;=coiC`C#eWs
zUz+4hgT@-~N75|GtT#_b4b(15>E)Z>`W@e972_k}QTLP+Ya{aQAGXuxHz7bT{NNGd
zzrHL^l{B%XZw4@SKF7mNU93Tij`?Z8#VTp1Y&eDDX0eBH9@9EjV~MRr+S*^68?N8N
z`C+iiX|R~XiqSaV2RDa4ykCkB5a`u?4~c(!rW~a7-zR;D#q?hf0l|eruImI{T=lKX
zmnTtev%Thm$gygoIc~}piiY$0wxumXPre1-qkHW{CTFeEIGkrv&@1TV<?0Dt@Cu78
zlekCHc`}4Q5$CgqI<fp*<wA3j{po999GY@?>*VC*xmC+zRBJS9IoUq*L3!@5#6ZKN
zE6q4VLQh&_)$TG7A>WN0dxjY-ljzL6r43jJPkJJDWrIq?TJfd%{WndtA!$1SgoqWA
zFl+m^n)lHwS&>WR>p^Pb1`pEn+)$MwbUatG?(*u>Z;^pE&>ymqsS<%+p6Gg~KSG5x
z<|O!&t`$*j@b+cUao6nBvq2R=VV(sg`P8xji?k_@w!NcZ*#5y$&HjBRuw6h{q}j!D
zljvcWw~-j8NoE{Lq}rp7+t?+6rhUGEP|(mgUDlf2^eT5m=1<g&4c_?0ezdIXIfxp+
zg^GaryfW7$-#6S_tfN{4;rAC(OAlv`JVYxv=o*`WmJgn0dnzK7{#5MWdK5imkUi$i
zy$-Dc5w<7v?*`Jv;V0$WS@_5eJ^K(-hamY`4qW;K!>i@bE_WkxbEExzcj;ZgxusWg
zw10SS_B1ByZTVOZ0C@AOZO#yPl2zgybR8^YYB*~@G=lR5G?d7%65F7)rnggEsy{lM
z=-;4@DPR$(#BgqF!`PJ!!LFTo^!2-gTh7Bj#1I|1yr<RSg{yM=dtA;o@MiAE=p{p9
z+-Ex^1#p}Pvr7$bfLNC!37wq>yc9(7z?#e)t!&b)SFP(_I)TnKT%J(;{U>Mt=6jDg
zdD>c3JJmCmEYe2XHwp<PO%KA6-C?x>-SeOS8cfP9L`T{v2h&h4i%@BO>Wzpa*;tZq
zqgFT`=hH1%TM|?noc>BfiWCCxzp{-<xEID9l@pM}3ZsRvKA;SkY2HTKWM$+!1#2QG
z5|cC=j#nwaU4Nl@W1YS>OZ<T>s=^JQS3I!O5i3a_BRePA?9zE}q+J{)4{t0>XmnZB
zSCqh1YRZ4>j;GE(vuU8N!q5^+Qektq1pe=*2^)j~IIhgEMJP@U_u<?~qS~t@=ha;<
zj)GN_<p0jM>Leqe@*Xo^=0MNJK_R7=u5L_6SmYyd#)Qv%n0qCMkUT}PpH4QhaHs}X
zND#3I82gA2kynt(E<ffILSH(EahF$bQ`XTayi#I0_MQ7=jo+O29^%O%k4vI@b8xvv
zt9o{pgq3WyrEt41sV&B-gyS$9wAj?M9fFsX!CL&oS$G*ePd41ytWzRDMWi%U5u7!#
z`s;kdl7`J^-9aOh2`EU1#@PK2xhnT#1n<3{=0s*mxw(F2$&(awP#+Tr;n$55YHU^{
z<Hd`k$V*tfM3M|w*H`X(7eF;3c@P%Qt@%beR`?(zrNr|HXT)^``f^UiAU*gmA2{_X
zjf?GvAJ*7?AYM)-zJ*3yJV7?b3kneCR+n?a@K0UTJR9t7RuC_yKXSGstA5~q08vaK
zh*g-Cv@M8j$E14hAv%-MC8XJOrFBZohsH~-IJ&iGAD@}rY6|0$e682EOu`74Dje94
z4kms0oqo}mz45v^gMTEs&U!bs<@mm5lAzhha`K5lwZ9z@eZGk){-&&tfq_B3o>x?Z
z=pg((E!6f?T~*$ixfwJj!AKdAds-Ix)1L;el^ls8-H<--;6eve?1B`_h82mQpGzbR
zb=x2Ioif62hQUq>=zhm~wv26ds+jZt^q%LZO48u8`xJ*d9{axXDJMUb=c%!u8?_ji
z9NQsiqgKf}$wdwJYRoDFwda5%UUQ8FSuGaQV35XMH1a}enV~tKJ`M{)&q>bq(9bmu
zsn55;ABT9-62}Dbzp~L1UW9u(CT`4xVHjqmB(Nm$t3#TPxj+o7V~z<m5Rv>T8`6-{
z|A(e)jE<~rwjCQ2+qONiC-%gc7!%vp#J26EW82Qewv&l{`@L&@YxU_rr~h=H>Uyen
z?b;gw`Aze;(B_EYu7Hba<d9&<VG(gY>3hInk3g$dyLS&8%?e(KVo(Y(tyYaz-C|i~
z&%aKyik>>dW;_;Ku(hkcpSoh!Z#8LGM^1`CDv{>I-7uP)&!OReB%e2MP3MBXxbl+Q
zi#5D;+rRF#Kv$g{a)981{#%m$EFw#ZMoO@yzpFak)j#y@!R2Xzh}6zYjWmHPDW5lA
z9vB0-w*x&|6xApB{vFA(rK5iy_kUCC?yG0|?3ZWDGW!PKUP@_3*5;i6odNMlX&KoQ
zcDgZD%*+xy7y%bG2iUWtYyZ%S#H~O(3SmM~W&^=S?n1u?yWyqW1i~o)w1a^MPLFlW
z3BIUvre?+^>;0|`Cb12KwK2)?SdoS6^Lx7|bbpKTWms`sYNkqvGOSybftv5+oi92U
z=0o0CQXh4Nd17EP+Memc&wX1m=tXelFfm1ub9g_T`J&mn4fEs7kiHC{xTmJ?%0%6X
zVfKa!Lm`A~*7LC7_ID>9GSr@LX9DM1C}zAMr)B?$`(_}mmW!HJ0=B>+OFYAnBtaV@
z(TKe&{aXPdLDy%~`&T@YdTyDEM%0lq!sjv==O4v>mj$32NbBHL7o)gjGX)Afv01gG
z;aK%Y^1Lvod=!~)s=pPjUntlThfmXx5)z<9`_{{rWXtr$?{Yh)Hv8vbh=Ql@RJfeT
zACcHY+CQcY197Zc_!AnVtRPkROBN_!V@*$$O@Y&|RXI#U=#jw<4z5&<Am+!`O)!{y
zUZ_g8WZ80$V1Hc1jh!W;U+m+k!|yqe0BKG4#O5gJxxH7%4v(k>{0SVKjnZ8dD(IZB
zE3)Eq{e)h2Iy8|CcvhZCPjW8QYeu2azwa1)nVE6HFo|dAr~WNwr2G@Y)9*$;ZC6s+
z7T&+LLbYWu{87;e*F|P<6(CjMMtX5VRx1X+p#i0f#7n!PJHFmE9j0{rzNtDngfH>k
zJ2K?>pq|uNsZ#nlgba51`N$R1vB66&_QP-{7MkI*bdJ^-u4K)bkwOx^4HDC5)(Foy
z+-?dzo=f~$u>1abDg<SY7@GfHwv4n`b;ByF7yBZ%8uob`OR5`yZ|C9GiWB6_>Nj=1
zoAq2En@kCbwkn)QeA^X)99b;KRLAnnyHJ*X>lD9ovN{d0C5h;<e92`Wxs~z4GT5-$
zlS@GZ%jvEQbkYm-_yNo{tzp0~2kDLu_h!gTPe9sg#Wnv~{<wX+eG|GjBj`FZwAlh)
zcX(3kzx=bde=?ya>G&Rw9B|>%aK0i+8>w9wyn8T)UN3>j!{H-N*7MC`o{OD+W=;Lz
z*&hwc{4_h`d;-EMv@htqkma#b(Q%1cdcp4RRCV$Si~FQ@L;Gid_Ks9TL=M<DC^KAP
zMoTU)CSW)-lv1_xnfZp*1~ws&tS~U?sW8|IcS$Y8s$8%g78FO?Q<C5ZBeiSL$Ismp
zuDid9`2xV(T&_mvo|w0Mb@KN>tzvkzs>leNN;(L60Qeiw%ufVbQI_t7r;K_?G|`Bd
zxT-L^D#=RX1aJg~6IrpOxgczCM8awq5LTV5pB1M#F6gvzX5J@TYvk0Xzd>{8we1)R
zA_=7a?7>XsIjne1y1vRY<o&&E2+^YEK}%XBdy6g_$Zcj^sSIDnai%>WTD>IA7|rH!
z_+UtNZEct+x4@9uU<sNm{i>5Y3ZSZhvNs)?p&7f=dwqg|y9vcCf>Oo{to4)!D}}N1
zLzxzgPji!Rx6pM^Wx$gc+Qy%?NwWZ2rDc@mFIDAp8Wres8~zkXGmz&VRm8L6lH<Uq
zI^%>e`<ul;*<QU`H*2MB!Nst64lNhtVVV58*X~*#F@yV;Spa)o-@rWw9J_q9tM4`l
z1FZ(Nju87hD5f?1Y)3-Q8K+q-unH|%78xO2Cc>*Y_#n5P-rED+Zlb0dpADR1cNNWY
z2B(0gIAczlAsTx@+wNsHe=1ajOw&<O$Aw$LjMD(zH#I1Jh(dR}GsUu>e{~EHOlH6*
zCmwtGnqdAW!x-9R%Cw60Hp>SoEQ(N{S?vaWlf@`A^#Q(}_QeW1w6ADLYMWODT8x{D
z*2<-;wU=CVYu%qU>MwAxDA$(LaF)I4lxv2bETQYbAFKZmP6CM#dFTFSO0eRCgkwi+
z%m;;1q?^RipF6$iB8GNR3{l`O>|<u7xKOKN6(M>tpt$0(2yn3v4@vEZ#vSvw3W;L<
z7Q=L^1ll}+G4}n0>x@x}22V<6Kaa63gAU1lIH=avj=`k8;DGA(>(1qfg-1DW50jAu
zRW7|p2?)DI<jawj6+9L=&Q;1g=(XvFlD}?Nj(18Dy!1)OX=!MRP*H1?43e<nm{JOP
z3jJMz)(A)ks~HM!#+GrtZ#Gk>0?*#VR5f8+YkCg?u!kR2!3DmmzrKA!(EKR61R{5$
zdLQcS0J0RYH~%1CBu{OOHdgf0fIUGUB~&c2!<;Gv-0$$xLIR2)Ig$KXoe8l!wr;LX
z_+mdce?uqk34Okx7=G^j4va??Zg_QuJo<$M4u{7H5jxff3rE&QQ*1c-o0^tD91Y;|
zyI5Kj_M*zmr!z#`auF#F8pdQCMz%b1{;Y7pno24bF!SA!Tbn!BaxfZyH_t9SH8{x%
z44D+tM#}K4ofxcAG04C7l3{??OK5Pu&`{q`15=VVtwlHv^|Or!a1avQ18$1To_fpl
zIW<VfbUbDb2Q}<_M=TVS>vM=;tZY`!=YvY3D?QRz3L4dU>1fn_X$n4IdLMMdfMxC!
zK79|*?Kp)d$U&ek-d?XdmAr#<gME&%tq|;h*~)>o)q=x>@761ZkD8JXcL5)Fi9J6A
z$gYH0QDj^wh|==xKhF#KV1_Y!Ii)1;d}+O~#ao*euD`~K;>YvtGk_MehH-*SQxsq1
zR_QC10lWA7&Lk9iEO$gSVC&_utFi#jY?GgsxiTyi4K<11B+4yYPQ7Das!zWF6+><Z
zAiaWWg0ht8y(^dsiI$HaAy&%gPV8Gv7H~Nwi7onV*w-D`?!#NccK>Ce6&@>FhQcI=
z!8I*ODp0ZAjnm60;rX>~+Ab4;;CIYWo5MZ)NVfbQR-r;0?eRt@IWy=hK`89L3CZqp
zma3!Y<Kv>9`|*PYiX9QPq-RL&k8tFRs;zm1aUq}tyF<R+C$&J4GD#9l;QK8xa!3+t
zP4GUv+@+7ND6MXFLi~MXaupXGEk!R)l?DVl0(;u`I*7!MBSRn_$-w(x%5OJum|Rfq
z%d|2*Xev6b8#=lGs6r|5t-9Gpy(WGx*o_tI*Bzi0P$?+DDe_OTQ~f*|eD{F}D|jyp
z4nxoY+mdVwwE2#1HH~b6Ee%f-^wYR!6rmbs&|NQ<321#u9bNQaKqR#<e~O|nyOsz#
zfJASR`Hd*qS9f)w36^x1hl%ey4E`mPmaTRl<*ENq`5VIJUKa^c6grFojmY9pj}<BM
z_Vd$l7pFH<`w*p5V~A&$kh!55Ii}rqZEVHb!|4GC&R+Dm7zoR!x)#%XiR_(+iiyV{
z7G!&5mdH0np;FfPYnvbmXEgL;7*Rks3k>LG@&q3J!QWS+LkkRI?#3Gq*kpOS2G7my
z>(e`p=#)9gk<IJ5RF&-G85k?g?e}aMWS3JA*LL<hip-L>xzYFdW@W1XbB3y3K8MK^
z1DF9fp`!fp$yocWo);f2y0G!X$(59wjZuY%osY5J52zaVm_<t(9T}82+7HR<ny8i3
z_l3@i3MJNVsU?Y9oC33C+)ZvJG$)sSL-V4Z4iiK6Y1+g9RsE|-xarzhcI>s-P6Q^M
z0gVU$BP&Dkl1!A7HXywmD<Rc|<`&^2WZm5UMedcTJpL(38Pvm7jN?@OF-=l&hv;ZN
z+-Xk{T73lE*Z@eA@&prEMa-0LNgfcP;l@jjSSJANWOpA2ooqg*9UvnC<zY5o%UY`9
zv}ij6i5*^xmvKQPPyOiLQWZAv+}EYE(zKc`71?CIV|bnzVIMk^f;YtcBxuW+IT$%R
zoH9jjWo!9+Q)a~ih}8n=#v}_s?*(UV=srA84LBaZfxB3NXbJQO5;~gyKCH9XDVU1W
zHp2H7SQ`Jo%^}VY`qu{%H>}KLa$OdH#c<7rs^{LY`RMR&K}E*X@6QPNN4tsoe5Sa&
zS<rE{>y{&r>FdZM?kqAd$=6yKG^pUte^d6}iV=*OXMu#EnDs1z3w*wH<VyYVmUI4N
z_)XwjOw=DI`je`XzO~8S8(rs+!Rdhvr~1LvWC;p={`!C~^D~0M{ig$d3b5;5@aF6S
zqS}>?9X%5a63AMCRvi-(dnK>;H0PV6N@TE_%CmTI=2cupY`uR*(gzuz&n-kUtFOh&
zooG#Ffs68!GfnmfYsaO|i<n2*Pq;9lXyC&<IHv9g&oW&qMe?R183y7A(;Q#WnAJ;=
z&g76Rh%p(KfoOO{cPhOM5E-T_4QO=AaT=H<5-tsoPqdst!z4=fBY!zWVHOK4a~+-!
z2I@GFet@TAkjVC?Q@qJ+55G!(ME7M=O#Tx(09oJ#6eiUUDKR2Q4<{#h)43Q{hyg{v
z=C8xxmX6FyjnYF~7+ujt5}IE!x9n-UN7X{#dqJ!l?qp>Uyjd~&U+7TmZMNDm%)do!
z-UbYn?fk5QpJLA5U^7~*4mQR$!F)nOghT7uR01DhV}=)?SD{YwKP=WxBG-AjQcnQ0
z)sGZJ(iQA5d^kEvsNP$9gEXPEn!=(bg$p>Jk~xIWSNTrf(A*jPa7bCj$ZT}kkEX%Q
zKxxyOr|LB@T8k!_KBYaJ1AYoobXudUIXIzk#TS0b?1$i46Ls8myPcP=)i&>vwc|@m
zxg(3YbUglCI5_y&mDc`G-SELu_tr-UH?t5Kz!z02+Y$>n?elCf8Zzs*8^5geHtDzU
zxJxWgz3d`b&aIR?m7!&qXO->i48cyxiJW<pXam~~bwW?Z1^J!(hyK8`=XmHR4$fLZ
znqLd@sr8hy@;iP<r?kTDzW=+f#oqy0T3IlfaBOTn;ybDL1yM&BFk$eeASXAnn|PiS
zJxB_fRXIAg@Ak<oUGSBEJTx5YNlr-uRb*ek7$ensnQ8dkm1zJ0i{10}m)ppIUa)pX
zEb3e&+e7%mt^3K;{Z2jA$jutu?`dBMoW5SEmpqC-A4yh(W~e+_y%lbI`(Nkgb9rhv
zYVGf(+s~G6FsG^K_W=6yYz(D^8hBY;oMXeR-RT)W<#i!pWVt9?DS`sbfX2=d1hn&U
zX=l5>A38{2g@`GZf#(`h*XU`<j5!)0fL(T=36b#Uyj%7n5}q0fyG>hvh%G+|*Sz=S
zTH7Q7z4)@3_(1PnMD5Lga%=}22w<0y)-TraM3b~%8|}8F)6Y(RC221v@?g{R=W27Q
zAy_lOEydZl5SLLctTU;XVF^*>Gk3VzNV!8ucP;5(rOq^uOI8!o7jmT3tHs|ESexJ`
zgT_0p4kcmdRG0B;`jr2MtvGiMm{$qfB2eImo!=@J23E)rlPB+gmWdKh8rl<T7LTcO
zqS0%_-$#SHr_c(?=1R_$uyIwgM_NHiAqr5cVI<J26-_P%q>`kZ$?y{WN1Z}@c(>%Y
z5`9kX*<Uts3SV;73)*VQokI!bnAc8&Duvukc5(hVAq*U}VC#{A+8@^f+XK5c@A+W7
zX#Zzl1VO++;7f~z+{5uqkbF3D4TVC|CS(z(CGY~<Ozf^-MgzM4$@JeP|AA;davi!`
zZ16xCq@M#Z{;2tB@u<*W@9>n1v6h$Q&k8I!1Ffz=xaxlv*q1sR5qffNBpLurDf8GD
z9eyPRvPS~%6#Jo|#N3(iQbGuqKm40JA;qpQf-Dv%nD*FPVKogO?md)fNkf!ILzD#E
z-bpVy_s+tdkg;mou@E%%@V#Tk_ks+-^Tj^|7~(wsp%)ZDh7{EiMm`)OSjZQ{82E8G
zp=GIVEY$2<pF>|cz<$18z1d%IGQ-)*U`y3*MHFg&>CBY6-)~?0-k!se4BK0oVp1>b
zUBn94V!7OGU3U=mKF|tL7(D}LK;mUE(LqX)zXhDYmapM`S`7Dvy3SZ`Dh6oz0XM7s
z-Dv%m#CDdi;ov*bBJeTEOX^imuo)#sXVJY)_B!{rZC<tdmG48@wYXtxzVtXIU`myv
z8L!xz-apgZ=UR9(DDM;E&D1GU{^2$<E}_81;9t!55%*NxV?9MM-v$B7D9`0=r1SH=
z^NxPb*(^NjTquH<N#Mor{K8d|*WuxomA{+`wSt4-6&%dhhOZlTO9{1lec777h6%8$
zSJ?{H<8K*gh|W6C`sM3=zVvp}Vc@~iSVYY|4Wh*?>|M5O{w(52slTWHuP;E<`)_!S
zAS+(^F)#Qbn`c^lChPfA6jxrV8Y(&Y5qhHMe_Vh&Y9|BzwhAut4jOZ)!ZTT++s9)4
z64-qPw_YB1Vq{{n<bRe$bqXf+YBd%$0r@ZUsZ&3`xqI#j(MTR^T3>dOm5>LEJM|C#
zY79LJE~aYa#%<5EX8pdkM)b~R@E2hiSNjfB%(bEe$0D?FGv->_t3BfLMVup2@m_9(
zx=~_imRq_MP>!!YVJF*he+?)X2+!^3z5&D~DU?*OVz&`i+fc<DpCP^&PR&t?GTr2B
zmByudYG2XI3oyv9PUVz9=L@(|wDL-XoVJOX0o@uIyH9eNQ;-w<KxW6~!LlH7GSyF#
zV58IBnew*1PIy_p{^Aqc=Iwq5Qi5GDyyUGPaDC*t&v*Zhja$N!!>(pm6jP}Ih>~4x
zw=@b{ALWP!ON|3AKrUY!N4Oq#H|ScAgCMPShKyACG0oh~!DWSPWs+zmlyFwm;ajn<
zeXdcq5y)N`Vh`v|pi5_#>^my@Gb__AyY9|59$N0(jnq!k`}g-%Oni_pdW5cd1$+25
z?d@<7w6ZmHw}S!S_%yR5*>6Ykd2m8;GI{UsALxNheAO59m!?{UeQkoWwm4`J!V^)F
z3-vi94K!#DDcI>pVu@8J9JD?-DuiV(dpt~@-}#|6ZAL^&mxB_6t)MJ(`J^VqfV0NR
zN!oZ7FWCuY)O$r(6;N-L)1?}SOGVL&#4Mp!XPX-rh>sU(iW~Ax+~}9?zuYPVQZLs+
z(f)n@N3Q`9@<4nm+z^cEYS+7FCRgq9?K%}U8+Q$g4K_3#cp8=6?#M$Y%G+eIrC(d*
zq((kv$NQx>5?uz{KPLQXQ^gdq)g+xBG1rNleSw{kl?{~@g2@go?TQ|6vINU?haUkk
zNfwhs#}{?WsVvIutp73T(#v!}@IZb`J=%ha2KnQ`NiD1_?HrRn>Lk8oIJnKaIg6V;
zOqJOtgwZUsr!d*F5Zr^a{2ffwVw4>lPf#DumFM>Fysq>fy1Sz_JfhD`w*eO1*1j&@
z2@IUQtUeZ+tG3H07213U7#t=6Cj$EI*P@K~+)xB&$f$2cnb<^Po4b*{fj1qb>rljy
zN9y9Rbk!ELySdz3_BjA?r&*lBo#=AkeMI~p=6i3|5r%OhahFlsX2gp>sida%w7gyg
z33}d$_YlY_LL3F&dP{e+ykMct^}oz3VO$Qqza)?A6Ked|>*w^Wtu;50sp6hU3q_3V
zBRjtKTIL$>auK+e|K6^^_@Qs;2#}qj7fWYT2czN$=t7#Dt{W_u1ifC~`LOW@St`BX
zy?vtp)%>gSs{C@+i0{4*V+Uh|D5ypr9fSiNZnU#_#`n4T^(oNKt0adO+T-`k@p-zR
zJG_n*pvh*)3|4{*c1)0kucf0ia{aOUHtTRlIyZLAbtCk(7i1V+KE(HKjU@iI`J-IB
zv)2$_*yo5nHw<eU+KC_q3<UCPAjKqmoQ*b&@v83dK=>p5o9}BR3)g@zq8vXs4V9zU
zYm7sqzpRMDE+bCxY=$6(Y9~^Pa!lCpSY*r7o}Z|2Dlb+Q;>=?lD*m<cMFSay5p^Y>
zKL4CblXXKW^uS)l25MUQsu|Rxo}66DuvS*V%bHDmk;5L}W_0K9;o(DBZ~cCE3h+J^
zYdp>n^1e>9MS>z#s6U2~-?iQcXTmMcox4KkpL2%XUJH|fR8(chsm4H~Kz>%=)=81n
zQN56-V}W99c+w~k?$Uy1x#TIG4cd$<z;ID|+S>j>wive1Kkmf`#}3g7cKg?TzR>t|
zBQBx%NOLlSkX5eA5@ynJwzY&HX12A@a}hB8TvgY=3sHq?VOjTOe_qO?>}84I_imtF
zDa2M*(B@??&`ay>F`H!rgkC^AfCLa^t8%^v3_pt5d+N2iVn6OB#?ey?r{kUepxtdp
ziy@y~)e`Xm^^)zNyC`;UNdMl+4Yzb<19XCW)5^f8q}&>VrVxHuTy>*$P06ohb9q|-
zu*hxYPJ>X8cJKb-?8P-X_9E=IUk<a&kfX+eum$g~@^a<8&hrACF*I?({Cgk}?TM~}
zCM{Fv`bj|ab+Ns-TI+-8l32)Ul&`@q7LJBH+u9{B@Gz4bL=IOqW}7F8ga%nd5BfBR
z!q$uCNlrWVLq=eHbop5QN7MTB8XFZBQ`n~Mz@^Ka4ll&hMoXU-TH4SIJ1u3faNSBU
zAb_)Ro!?B)c}58AsXvh<xEIrZA0EdkwXdFVWfM7gb7=UMIiL@kRQw@=o+L!vX$E2T
zys1l*+S_Gm;>vaBz8%#SyK9J=G)2*0Wv}=LE&tJmNNWwm@|9>iS5_m6J72}6^-RwF
zbT)GELeNQ7@Aog7w9@@n@EUr<NuuC=o`9{RvD#k${b~hS5maQ)HSFoV4fLlVPP1O?
zM3PB=$dJv(`qvAYtk?dx-U|dU9J8b&Q5jm(S>_u0*`GQhw(F)9v0i%jDKp0VgFg0u
z*DVaR29Cy|*Q^Qe+rK+^a83T@a$dLP+2rEN@rAt#DHW)b4HPL(@?Y2-jHva4&a9*_
zcXIH#6^QZs6ytfC`SQ;gTZ(K{lUSDl)Ku26;vgwihUj~tE42Jc9_fZ}d#q|#==AiA
zfvgPSdwgZ%Q{09*3gDY5-qH0s>So^SXQx)$ZpN0JdWtaiIVFhMej#nTeuM0;TpCbE
zg1IXZkCM%D=t{EuJ^OsJdVG_^e-)^n+b>=i(QcVfO{-xW$SmYT<mbIEWOx}b#bU0#
zlNF?LCgE237Om!n=FInf<94WH)&*C4!HGo<p{D4Brx#r@cVtBHL1M#Y`8x|E`UcA)
zxTroc94Se#zY!-%c|@{2zI7%tk;gp`7Z<k(EXnuLnJPGGGb5LUVXO6_)#H?#Mb*;9
zHc7H4RW!o4;hv|)-JkEcq#qZh%$0tpIrvLatj!NI@qB3u=Bov4w`w!3u(U?}Og19{
z`#e02X9ef?yFt{doRMRE0l~rc;!5Ti;Kc&7eH_2N;F(U>(jeLJ5>IJ3Kbm=*5+Y{C
zz6$w&S!IGhpTT@PlEAw&T(W*(=*794=7y>WNd?b@Sz>!*gInV8#S#II#w<4bg7!YP
zL!m*Y4<;5|cq^<zVGwVwMs{ZKyYf(LyFqpV2}O%YG0ppXgX5lS!n*qZCZx8x^eLSg
z#4y>Sm-2?$umEZNj?Q?!U4X~q(~rR`WM4-`ennGjiv55}A0x&zC0;&~*noslVpCZd
z%EinA5y7$BVhpW)M6<>k#$3uJBF;Akb<s_itQBlGQiic}u_*_c)e8twJ&s)CcFc8-
z0w=}=GZot9($?-roFch|cR?DH#=K6HOajfkb>Q69+Q}M|u4bSL_{3h>GUB2<vMgLM
zfN#qxYBL9dlp+nA7eqWR`$>>hnT$X<BGGbe*l1E{&2O&<HjKkCvB24e&0Ex)kvnhV
zb;!llq2*zRi%*YlnAFdKb4C?rO4kYi#3<1_H+vy_HfPA?Di=62LXqL9wuAl}F1pTM
zXQmzCqQUVH7<8{Au{MpJB7@?UU?A5QZ)aU~^U~wI1*aVzGJh6;a6}~Co<MY210s-&
zDj$?g=1xO=PTp*OO3p5K*C02@0_Gv|vupl|tbQ}((58lg6<KzjJzYC_&~-uN2{C0^
zv?`egn{^>@7VsmG5`LzJj>ruf<PSp(8j5Mt#?){hSr4%7AORo-K`DI$cs5tA*>;Q@
zNGji5Xr+K*fxhtipQZ`^-wd^WfG;f`eW4Y58{o5~8jkv$5vS^lRXV|-gZf}$oc$$B
z`_SpC6;QP{w&RkCPY>p@2K`lzmVsTM$GCgNPMiB&w`s{^t6r~mW`LvI;#cMvE`zE>
zF0O!_5z_hH+X8<qB9^X>itIfBc6LorPT{nhWW;e#ehnp7zvH^{$0|_oo?tns+6quk
z*0b38tHjSIjSf6s<sqAy;>J04_Y`?(AQPjM4|0T`03Y0a9`3klUB-XeA<qbh1xZze
zbGUO<7F@Vm>7t$MA_romo>Z3_jWB{FAzB%Fnip{alADKTzYhgagKB?4bfJ&Zye|x&
zU!d6rw!pFEjHc3GZ@bcrG~yy$6bKn=)L$ou6ACxnNetlNR{zjMpj{Ed%MX_H1>h7M
zAf~?tTEOW9^>PU7Nj2|_%6?mD`)c}=?CRhut@5bAevyq_h*b&BnLVPznX0Xn3e7Ym
zD7B3WHAh35_ZiDKR{FKlh#X-X>njg;oyJoGvF-{TX2o}-4Ol{3xhN3j9LE6-=Pfw#
zEjXzS(<K|s*^RV&=3R9O-uk;NtjS8y|D8v*Y`a!n!-J;vh`7x9EGb6g)inNA@>I8v
z+1ZSaFv&RwIOg^R>B`B(laBCaeqq6Myxk++%jtq26@D%B9L~LA8sl1VbWMj&i^(i?
zOM=Pq%Fi5ih-a!bPMF`X?^<6G75Zq9K>%m4Fd$4W$jeDZ8I{Z^AOOIEgyqjUjqzN-
z5G`1^#f7S<>FH#G%Mrd6De3&|mu^sF)VIbSJ1vKhd92wB+vz;c;u*{eKcHmG!t-zj
zyQ}5Q#K;nf2atr7liUq|rnB;;fQ_1j8O8-`ac9w@++v<iDen#I!`0OkhDN#8f2Aii
z)GdCMIL1mGM@q{A<6I&lEna4Z*AiclU`KJVpHy>>wCXSqtIzWj0U!`yR49;A(A@d|
z>FZ0gN|k5@H_B09`8)e8TrOM9$%9mYgNdrl1qQ#2O7dCHp}tNUu`C2xQZ9@}zQt?i
zSeHh~*~(e?F)(m9tY~|f=MUq96x4UMW^aov3ftNYAqr?&fT*X<`h&a{cftxU8+k2*
zhL!{f6#?rv!kH7)xHSLvJXXes3{WJ9RpK5UHO0W%0W-nuD$^=KY|lmo?toB$d4S>2
zIeXy#(EyJ+vAS?{h11f#hW?-P;7`?S0kT2BF=7gNr@Fzw?k{_vn^wh7&S8@J8Rz=U
z)z)dWx-JVLAs>4^mW>YHkmEJgyy!qy<Y63|hn=icNe0T!x1e(2v<VVjI28J@W3t<6
zp(}|QCFrv$ddbeu5F<dazXL=Zc(9TZGgOiiy!4V(v=gi*7@jCBjVkTVcr2uabT%J_
zNrwW*({5zT8x8r*wK5u-IP!I7?Qi=OuG!RTvb3YG=e>6Du|u%H228!2s`$EACKuMr
zUsgJ}d03wj!r8iIB@n-99kGJGukjiGF3K=rd7h@~bNeCNkVD9^CsiMi=AU%3%C&aM
zL_m7)1ESB}3pXIDE!_W<DNBL0QP0CGMD%iHHq0cBPubhF1n5JT9B$I%K@Z5oxbC3G
z3aS1&`uiW5vk^q*oCv{;F=e3qh4^=v4db?8$&PW}>n(;!+om@xvFQGVLZe8&?TcQI
z73aJsP=xrOGfKh7drsrS4Z|`AX<oD^%B^-tR)4ZJ;p7plPsQ{G^qnQgUFBgyHCgJv
z^J>_C<7(37+7fYW15tY_C>}}W0Zl@ArNRE_M-sJTJIhwY&9fG=Xkg*kQ)Gy6v;(}$
z;5*3~u|N7?rZHLAkRm*%dJldP;?FRZi_{rF0mybGZfR&B%SQ2J<G|ez&)V<$M7<l{
zZ*&olvqc%?Cp7bv40BPzr`4RXNge7+$N-%lSr)B-0DuSY63D>HaNa$MwtB;>){}2b
z1bPJpVcxS8kU_-i7;DNtKO!Itw;U+*73@31|K%zV@GFd~Xe=cKkIaQxeVShdAp3!~
zqLz3Ma<K<>2-Vzut|6F?rr<Dv`@~-)_6p$h%CnPK<;lQHS8Nq<*JWffn2&I(FOgVu
zf9cZzMm&Q3y(r0tdN4fUQVU{=SoIX9yP1H|EKfOErT*SDNbE5uR-VD#Hfhu(YNh=a
zEg#8^7F5@7N)>BNWSB~#CZBJ5BIUtgaFVo$L{KyOgW55Q-3uU9yz(RkCJR(BSm~AK
zkcU6EEY^Z$Pcylw73F+tx%Nz?!C=EYBt^!JH@hb83`+tr3^$Bx5z%G)Ol{hdOD)%_
z5U^#x;?#s4ukmijm1ar=O<O0Qmh-k9UeG>izSSBU;%KO=AQlnFm9WOmHrm3}*sFRD
z$cfHRLushZjm`^x|B+$4)~&!wM>wkTL^0lf&WO6HDetLTLXkTTM_MO`&N(68u{mJY
zfy6ypI|UIVEe167<1I@$Yh(<zdeBCi;cwcGf>sda{BX1W8(y;QHBt%kJ<g_{#*(O(
z7x|+X`(W<FU@|xH9}chONWX|gr|PO*;0b>cxBF9{wEy9oA%qqaz`=BMq>c_>Kfb$i
z)PmZG0mnO~NzHOu17b#{Wa7DL$(F2~`y-tQihx1g+kQtzfIqszS(@ed&XpMV-tzvG
zn7CRItlF^ojWAAcVVo$fUPWG4(hU~Dtqs|Ctp`6|lrw)%02P(gM=x?!4lhdSF(f^z
z%(fI(cH&qIrcd6(7V4e|(&qIu#e1&!TH*;K@r47p^mspfF(N|f5)@a#O@RBX!UGU7
z4}9q_t`qZ~2`nEt|D4J06YyR`%M`X(dj<KBMiquYrE4r&D+(MM4Y+%0A}(PWKYGpa
z$ts_N0-x}z6e$d9G4?&_E%@wh9=2R`M=gSX7x+;uM(c)Rf5Rb)szXT_DTs#jxge&e
zm|=b}U=d1}`g-^H=h8ms)O+pb$5fZLO>xi96GlE~Rll#;=&c&_eC!pkct#FZ#yyZO
z_pH97y27!ybPk;2-}s)oQv>`S7aat%><in{Z{~GejnYLUOaj3#OWs<STni@tsHlK;
zk@)G0CfQJar_#rHnj4k``GMX<=l|Pi>f0S2?OLvdSbai5)ZiGK#6<Ks|4*!vm-d+R
zeuu0yJF0!~o%;uyVz**CB~{afSsLyHS)C9CW;h43hap$XokX|qD$`BJ=BsbUM`L=1
zdHUQ&8B`j}e~tKETg8@-@yawj4i)pr)FuLogM@(xZdSR%)h9fhh@r%to<f2kcQB}o
z{W1_TsW@zC%K$L}n&-d(-nHYm6tAWOtT26tFM7m7L@6GnY7_Ro?Ak`X%qTQ>$j+8+
zmCfv;SCoLdA>;`amNC)nt++jO@r#+d)t?llTIJ2r{iPz5U%v?RZzG4s?~P1b=(jWj
z!F~x)1{Yxrgbkd$BtVUUXU6~FN`)C74{<>qX6f=Gsw|xuI9+3PE7)N^vYKx;0E<Xk
zvKa3DW2S`Jh)%N*EQ(=_YWwpcg(@n!9So9$qKsZys>!bQx)6ub*IoLT-{VVar%+Kx
zEQJvCh$=kXax-aB@VXO*uoI?0VxJES1L38M(5r%%T+n`OG|{MyLjLne<gBq*`M$Q2
zTe1_YUJllB>P^InGukVp|Kx3D8FPf+&OpWO<)5ETM$ibc&owW2r%z{2Xk3XWAZroL
zc-sJ97H&MU4^&$0-ad=#K_6)~Ol!->ZfCvZ>r;<IiiTCSo`>WeW%YvHS1?NlAKVC)
zh1Gej<X*{Iz6b_{MV0nh2;hw(7sf^f4e%oQv@$0R>Zvr?UXtbe=HS17co(C(12P5^
zQ>9oJlc81_``of+!!!ejFpr3kOOE;GW9F;)kCXfRu|h$`u2<v7ssPY7aCUJt=zQY+
ze`GKeG!j{{gzL2I_~YBt^!gcDd7oW|k98nOh<pIsf(RnuIzpQr=mn04CaEB`+b|pV
zO~G2%d<x8_M*m7?ESLuxlhlhts^m}?%3)7dbGNw1C{kvv{qr~(cV3kC9$(iiDXd7I
zfdKr>E3mgGQkDx$30HlQ0CEHxJ1%oo<>({sXy+4qpj1$bw}Ox50=(yLQs##n-`ca}
z6WsL909rBi4IlCIKWySeMOR|l-8z^yatg7uVX=nCDyn-+`xL%Z*s8qVZ)S!9&x?Xx
z)6bi(*}fscAXXLxaIX3@;%b-@#$X%&O(d{xfVMZZ(`HQA;bWq>?Ky@D#<16eBNa!q
zkU){)kY$8lYVg;l<BLBb)oj9;%W^(lAR{h`Z{1Z<F)eW9tTh1>PB^qdVRzx6|5xFi
zeiO9cY9FElUxFm25?@FgG6w;?e{4{;YRCH)V0AcCKYwguV5DwE4%K(>j1cvsk|eG#
zE4<z0q!l@9G<F6XqN148puw=!<hY+Jt%Up3I#-Diz0A@ziI<PtpdR1P#m{JQjqyL)
zv`*(oGdiUl=y8ECE)b9_-Dv^TiM;o{oHaY@j)ItJ82W>#^41*xusKdMDTxFEb%RpS
zVlTwysU~^CcFQ}y<0~wO+KXE*kB_G?D(04jI>NVTV&rq*#5q8gyb%i|)wE)y;~8Z-
zx;}6Z{<*^2eBq>FskXW|W~33JkpK~=Q*s4M^X|i$^>%pxa?#D|*nMi#JJcu#VEzk0
z(qbhc?-kRXRjjyrv<Awjmv4TWzm+jVpi+Jzly2I#l2Fg3NWwc(6k5K61h?&j$fXPS
z!zGGaYy9_HQB{Q9h4_#lmq2RDD_AMg;J~VrCK1Do=`fL^3pKRDtl#o8xw5b7s6$l2
zYRd8+oQNHkY=oFtHp^<MLb2LfWaiaDtw6BCMdND$ZE<xbueE?E8Vtxgr-i=b=ZEa>
zzj`AQGtF~O1kU{5?>*`MwK1qIOlNZcDl^M~#yT=*MFf?DR;ENgg^mL`9^Ma&9^PJ-
zK<NjZVP(*-tn^=^3mSim!l=o1zL}T#5ECh}8Cq}*_52q_?FRc3$5Qk^j<D5HudhT%
zI&_+)F#O!{`>%Wh@>F<rR7p}71l%NCOiiM5&5J0uA*Kw)LW>2OfL?!ldtsKpZhb0T
zQoE|!H0BnUSwm;1cVdm{E`F$6MbGMm&`uMFYm;E9A6pZ21X#2b!TZ8I{ZWFAK~Jv<
zChlB=;Hi)T3uAbA?u)Cj2o1RSNw4e;GJzO~uoVe?#85pPPHGaGKFKQm&3drAqOV)X
z;7e%2j4A}4_MqA~z}0Zrl_0s{gbwGV)eZnf3N{cmZ=Th+gIHqZ&#Lj_X#Yb#m6c!&
zIr97(w@QxBLTyCm7cE9#(?MjK`k-tKG7O~EH}v#e_JE9%J>P=G#UV+@d7+ljmcwGD
zRK`hOh+{+~a6=WnxokU>%uGU?=JgW^u#39<jH_0vybQ=XrxEWqszcEE@||%d#lc|S
zHllg=i2iZsyPJ^O!Tf*G-tBk$xPHa(-zHSUf5o%PPtYi=3$FK%gr^X&v~ar~3Z+{;
zvUQ8jItY0b*Su_|={_iv)F%R6b|DJx^^IvvMBSHEw!FpJyZ%MQ-#k8`4<v4|{RC0k
z977Yr?WB1opQEzP{{y1~V|UM@MyAlLfOVn&C<HifTCqjEkpp2j<VS;ZRL;40RS7uX
z$U)kzxc6$YuDlmmo|@gpbI+hoJ@WBI*jfEM+dN5DI>aS#bQQ+<5d!!HSu_-Z2NpoV
z2EG6*%tjUnD@*|g-6;MOnshKo1u<s77n9Ag@*Jfs#4n*v4rb<Fm=Q1ihzGg-JAZf%
z7;04Kodo#FYV+E1<8BbfFpX78$dNc<v?*m~>5z*a3YN{#kVdx31Hyd5!z&AL^p6pM
z(||$2IL0z)dA^DMU#U=DN}EqF4d<61k(UzD)=R;c22QUp@c@*cSG8Gz20Lq%jC05L
zs+!-1wzlLYnaQu|pSq5RGvc^@LMDg{iHy$&f{$_p9Y~nMK;68QcNvlw(ymhvr^Hrc
z*9M!#HTnOvBYvd(w^$1uZz2KDJ-yaySCMN0oC9I<E}Z4g)AN&*BbG_*i{|WkYp93w
z1ZockG+ZQk9DdzNU;*Y2dqGGAFgX)d+mSN)lBT);HW}^Yx<L~p=%6SaK`A|62wwh)
zA3h}^Taqve%xv3huvaJw9WZ88s%t@b>=O@!)m-pe*!8r<84yzs`u*ysSx{J#M<yxW
zr5pS7$t&<Zbc+s>ll!DkYyh<2XJP-M9qoH3yqb`uH)U&HXG*FpCnZu$Em^L@SQ_Aa
z(+ol0k*$+nz?1@h%;n3s8Ih<DnZ$Jap!0UE?UljTVJe1&-d|4ODapi##G;(lw^39d
zla(4tf^@r^rH+CB8VoZA4H2+ykd*GKlA)~1+ns^0#G=d}m`N}x%0e?wF6{H?e;Gh!
zdjG{DMv8_;U2}81P_Fi#JZ)LZ#l`4uMvj#y0v3fgA-vj;Pk+E43hr^kI~2mL*zMO+
z80U}z$Nm4<GJgs|!LR;b5=#CBq8_*8i8rlvAfIS}ceUOy)Fy3?Y4etJN{GVZi3MAw
z9`#x|2hqvG>2mLvzS9l=_?C*s_kqQ5xInYUaq_}8?n~6yCSZYgA|XpoIHbP0A^0cR
zJTw&zPZ~p7r2q8ZaFG#MDb%geoS`y2-p{uiNs_#9x*AvGKU<vjr1{a?V8$-6z~#_x
zYqVtMZnhxqori|ZrxO~5rMdHaCisucz);by@r?N2tUS2YabjsEpeUXT48epW+PNdR
zFb_*rVGz}-I0F8uD1gWrWjA``$bltfXuKboq#98MWB?l4;28Z*LPPB*>6Ae64*AT_
z5WHkZDFYL3Sr_YAkQc)`!Bf`%j~B8@WW|ZVu9~<bK-hQGZij>G7GE@;Wo4=&lUOIl
zy(Hz>O<zq(%brs>KE@J|;j05|QndA{=Vk-iP8=aYHy@0ou({Yz*VWl#IV~*kcwGss
zT`J&JPy&AXDKWMg_^fcTBYl}IT<>dsQ(F+?!d^nV+Gbz9KhojXJoxJc=xmj?1T{`$
zpvI}VqO(+bSN`t%MFWMb--69IfeG}|g(#otLs(pczZ#Vwhn1HhZ3d7AVyFs~5k@%3
z?F}q`+`oE!DF)R>osrAX>cYaCE}sLwj?>~L3R)lvbb?fw5pKF{qToTzhj}Tqz>MRh
z;rayQ7{5XLyQs**j)YiuSg9+FFos&_+^&bXA>~}-xjO?<;$po1#{~$W{>xa!JBgnr
z9>xLRN^>MC9FFH-vx->x9TV<mvIGnuLebxx5%e7iKdi)aMF*=%;v7HSbxE}F<MxuF
zdEdbURl)hsx`kR8B#-W-$+z`UsL4c1K(E-IV^+MAQ8MLjhR}VqWHqH;Y8!q-2pV`l
zP7%gUBiRHv@Y#H306Y&Gka>yh>HEvZ;DQSIdtjCFpFggpQFc!RM`k5+Wy^t=RxXpz
z4+4==W!`q08z&8i3-KDOoQD#nr*woM_4Y>rtMv6SoMJ3anKvz{`-B9MjmL4v={oac
zaI93#y4F@i5|Hhk&rf(z9}!`R1417yrHoq&9skUW0vYm3{4#G8eOg~Uye9mC*i-*a
z6hVprUVgOZBWx+*|0S;1l>e|9I77c2g+!V6gDvs<WsBV8%G7_0A?N*%ecD6J9OF3A
zo_ry@^U-{j8Wul>rIW~*zw+U47|zF2N)b|w^i%T`i;pzV-L_!^j(ce{u@T%KX&=%x
zxU}&1hSt|Fdc~0hcqf3!DY5o*;JdBxMGpk6TW^B$Nb(J9ZAUB{JsX1&4|p1cP)>pO
z1c8PM)ii-{h3TTLlCX0nW4YJ%S=Q{{PW8}<G6Yr+_M6RIN9RAI=rNiKnJ+6sB)^37
zR1JnPh=vYDupxZ8z1CC+VPFu=>j=#cTo=V*qFc36kx=qNMo189B*L5_r6sC8DADjC
zAOv%Mbj=jl(QPu;)5BD#gIPgBW@g7^P&3lvDoIhnT>Vs*#q$ycpJCMRZC<72pvD97
zu`XX~U;WbNi4*9Mb0)y_p-e+i%fqYH=KQ`1Pc^2wyo2<GsDK=tKc=?A$Ep!E=;_5=
z%8Tl4*;QF^M^2BYNBHPGc-|RZ7DTNZA|$6EO);t^$;*D56#lWB8D5jbr?0fO*Mm_3
zN4Se8CLC|gfi#|LBVz}7W)#F9Ko_Y2r2PR-NC>rpt>|t79wnn)CN%Xt+gig3NuceW
z9|ml02PYh|lhu#p9iA)d^r=>jP!^?X-$ZGsn&r!%lKgR3Q^OV-No}xQJ1U_aKWoKO
zc(ucup~!O>_ng{7U}Xf&i@Qa_G&Hvr0qWyVI(11?jF06>lIs2{3=qs)dE6O|wJVP_
z{>OQuyg^H6)K=B~ge&yME&Or)fLbKqZTblgz4d@Pu{10wwV_bxRNplZ<HDFX%?zFe
zuAtl8SZU+9$uMh?qKq{hF?zgKtd{&dxfx3LlfaNLCattu-d0x~m@xDG_!a$ROgy1~
z;9>#h9u3j7wdNoF^Iz!TT)0e#Bw)_cjFRvP{_OC}1-_>$!m7&Fo(f#8YQdm~CrXzl
z)&;liGitUT6RC#KD?wmUR43&r^%okbes)=1RTbov<5VRz7m%SXtEoy@M7bbfxo1=c
zTE1%_4!irfp=&Mj*S--UE<Qpa?7!nNx1v-SU=c3YE?;KhDVWm~1u<_cKi8Pr^~H}_
zdSak~oV=@$u=cMP9u#l#q6Kz8(=yZh)hgk}-z9#|V{%jDSttOU8dXVFBJQv?ZZ2LV
zCZ|Fz^E|&E_$x#?6E;ee@9@IP9-oAMqjbOCJ6V{2z?S~{0-bLyOrTX7BiFxL!)VF>
zJ=PvadJx^I%g0<m`%)#qylsdUtR>o;@cQVt7L_5GMk8{md11#^=z3$H^~>P|FWakV
z>T6yk$R05=VYadY0aFoYGu2zD1m?lA$aZ{{7fpo&I;Svh?LZv7N<GY6V$a#OwHcw%
zED%osDhK*!lQ8_Z5*I!uU8zRY>9{v%9HD4|X2Afj6sr2Gx}EeKX`$~dkY@GL6vTC?
z;z3Ksd5rF*0oLP9fuh}KZO8-Eit;=3Gk<SGyYN@m8MLG=th;d}k!RHM19Jg^%Ec(F
zzi|3e!u9V0@Z)dc=3M4fU(0~*lRs+=r0YXv_fU*ogyrSKnS**n;g_&*155>*U|XxL
zejCxVxl!k(M}JKvm=(qxiIYRgC9s{C>ST*xg4M8LIUmS1%3xQguA$mF&^4Ev4$;#<
zEZ(m>3p1K7E=Q}!SE<4*8m}iF8b3{a4ssxBr9?<>1{$T+tEqhK8+i7_p>^q_WP)a5
zn^kR@jm59y1*eX4)+SaB5eh9)LkN}iw(Dl4O0`QWrW398wr22mjjE=bGA~K$N(f8F
ze$MOng5on#?M6FZOzR<j-WR82)G_~M*-ucG)nWRtT=)J@JtGN=S^M%YSr_r@(f|eX
z8|-SSO<!-*b^s<f(#=nZu0P1IGBu%z>M~Fx-2*#6Yh-D;SiQqJ+|?Fu6lZ^!E{%2D
z+Hu|xG`$zY3edx|Be5(5uTSLswoWSz!2s`?LWU*H)1B&)#DS2nMouvm?stJKLXA^_
z1g(L)?124JQyz+~R@}*DmNVYm?W<#kGApqELm~|UMfOYyu!0O1ug7F0z%_EJ_tdbB
zL?QaUJFABBd!q#|>S4XMklLuw-<*$ihb$Zq!rxyEZ6-o`(U8JZ9%ebA21BwMDJmlw
zmDUszwk%Rl)15?Kg8jvt4AUbeP#?axm|jGMwVM~hw}?}98s?7k6pMM3hD~%1zZQH>
zRNZG#V?Jq7Jm;{lsmunsiSEQun9-@@kt6cAVnNgWOCoVGmTyY&HGPr_2fj;F2Kr8P
z@fG8#IV0LvT7%ThSL0JjUkbr;`bsKr<D<I8;I}X{NxcIU*jkdfGBgDrK|U$aIheq1
zp+RJQH~tf;A8#u5#Ry2<|IIa6D(Ds~gLTAgGMrqx87dxc3@;Wk0Z+CF7M!BhACN)f
zFX;yH(f(%p)YY+=@~Nz2reUV8v3a=Z>(g`fW~cslERnjhp@FW)OTnyAS*lPu?E3BG
z@`)7(nX@{bDW<x1cgHZ?wIkwcP4>ix`t*mXOCp-DN3;gqjW{054TQIeHSzYQj2}g3
zsrtZ7ww7dz8FjkzFywCh$qT|x;!}3NlTf$*0kApHe$uO4oD-{cLZswjuf2Cp?5Tg~
z2f&wJ@ks4^@AdbF*y3QV%pt{vL)KiUoaB~M1*)0IomkyZN~*g`wMX-cNf%SU>eM34
zBNX)FRVriE5}wU>Uas2`8JJvkTa~j(AnjCPQ>tpYhhC(!0*T+!dB(f?ig-aoX~eER
zF~pJP=_zRs(IAUqnqzt|$`#MQXl9CcPxF;t&EGX9+X#M@2cm09>+d_-%~NRj4YrRP
z5vFVkgd4ps<wyJdJ}^967%&)4(mAZl_Ni5d;n0$0)Nf=o{?LR*P#ruXzbQ_+?ZK}T
zkO=Z1VlzaEhVpXcQ?l0&;-vYM?D*x;!WqVZ-)ghl<p<YAf7*Pf39-ae7B>V4<z-!3
z>Ng{5DEekbT~24A&-qH$RuL8G4{*%dam`v3*{NcN9z)GuJ0&Ghc2twqaxncoK@TIB
z$|=sf?rh)RKhuBtbSXy`=rpRs`lDFJYG7GGm7j6}izX||m|XEwBedQq7bzG}Iq^g5
zs!5DYTe56yOOwRkhg+#hWW=F<{k(5~l;H8Z3J__mBhp6HujGP)e|M|8weB~W5Oo&S
z?owDHd3p9l`tH^t#&HO&i%A6(0Ou7$(V#oPI_QD}T7IF|&(jwowhB1dqHwsW81fx{
z=UkKPcE;c}o=hrG4{(gFZ;fzbyj$PwkJXONlp_UJ4h`sWH4COh81-pOA96R>U{3+9
zWrHfwCAO&(3{ATqvY}G!eoVtlymr}+$kXi#e1}tO)^@s?!vbOZIW;nQe=AJtzSG(+
zy}`&rJVSj8S9e$uisK0g=;>S<()mbXIo9A3VDCU8JxF}eK*weWG)~#b^ncqgbij=t
z|K&BqdNX;5%p^!czI=Z!SkHjBd;`rJFmWJ)WQyShxiaXWOI><vc!TF_*eOJ@Z=p@V
zpBD=g>Y(kO(H}+rvlo3x-x|*>1`5-oPc|6l!#`9%4;PGgPFdGcBorKrj`HF8H;K(e
zwHd^e<oC|npZ&#Zg|gRZj0=(K>Z2(}agj^MAs!8cBFYdZ(mS^q1?}1L!AgllDK_;7
zHF?_I26BeOWvg6V?et}<%~<&wA#(fUm8&N;wMMlqv3-Vuoy)RD(Id8Pvl@YZulw?}
z<<P-7HeY*FneG-Zh#42U_Hw>N?^)^joESnbk364C*1t`x<M1mwz;`SGxs1vIP*=tl
z!<Gzn!u9kQ<(~cqJVYTx@mVv$!m!G|RGQL#rOqXAhh@&bKg=rBp8hH6ixHxMbf;BE
zOY)uYT*gfuj%KL{^DX0o_K#{OQ@$akkC7veF|Z$-gk*l$og8Djo39KK>vq6sLRSwe
z%w1^#cc|h@Dns!pKBy)o6Bx$CDNKK1hT=d$&gj~iI2leK)eDEe76!EZacZMOQ$K}9
zp6zS-Bek1Hq!+X(ycC)GUR}{{X1w%o8fe8IC4fv4J3GEVI_?!TepqaUA+DIfaLc5U
ztlr32Rn1hmWHWkgt^Fw$qKHY@p4TswOvJ=%VyHm{4aVm9le~X_AKfaey`Mvz!*{BA
z&a$Phxgpx*yppHBJ|+=gud24j(AEWa%8cC0H-VXMHIh3)R|c<G>4II>Nl_HX%KOZn
zTdaa|6LP`w*K0FP0LxbqDxZ`ntlU8F+BlnrX38BR166%2S;2z4%g)X}U%^`*P-gu2
zE0~Ei_AoAhKTkh8jt0o}JprNdijvcm2n<T$AtP(xA>n*U)KLeYS3Uy~C`2gh<VGs#
zW=i5fxkjEY^v=+>jo~z>czKhMk$O$pEV0ag&;#0Bhgb%4&5_IMwJT%m9Q0a94mrS=
zb7Fsw%i|^7Pctt<=4(dEptcoNnZtT<fYRRu+A#pebjz^ym`&<GZIk;L@4)Sjjp16q
zy3};1>a{|1zGeHwtX|jz86bOJx?pG^MQQY)COt1PX*`u@t;2EF{}J_#!Iiebwy|y7
zwr$(V#MZ=`*tTukwsveM6Wh)tnK}DARrj8%{Ml9c*Kc>PUe8+HB<#QOiXGC{w>l>&
zH^yljNm?dTfNS527kFxdn*Sh<h*T_xI_%<#qh4UnA9b_A!X2t*XO@S5m(<z^_2!~!
zz@zA-hgtT;OO>RTMwhxX{&B8QZ7`|Y9i4MTCZE8x(e6j$P(;lkWkuky<;s3x(dgVX
z*VcnM@D}<LLi+0SX=Aun9;tyYF03xbSOtq*EHF$spBA-X8}7F?=g6!bAEpx5RC<b=
z@yln=w}WsGQ-~WTh$IU<r7y?-a>m*B`$o~A{|Ar5I3+{x8rH&K-UEmJe&fRjSx`d#
zyH~M3<DQiewB`x>Y`8#;$`qkgKIr7%B|}}my%a6#&&&^3HZ5U`<wn{mLtXbF{8^q8
zEs94sxLhdSDru&<)3NpXbrvyUqjRKd;uT0m?htgG_tRHCCSte5a5yREtHl_8(#<rC
zqhy+=yi!=$tV9(qiX>7Wa|$BqW?9JhW+|Z|BLjzh^)sl2;o5rg5XGZX%cdd}d6+Tq
z6Q|rcglZWwGRkE&GBr_a1(+_1Y#Ft*MT%x~Qx_9lgagt<n{KL7S&qN+gi#|x=%U3;
zC4b<eP7?K{<E~h+lFAd6Wqw4($|)NpsEU?$z;UYEAeoBQ?b=K18D5}n_8K!NEK79s
zMBn5Q{RsgrVeLWvsRsm;j0O;ex(y@kN4#hjPebMN*(YUOYg*|}N2gf9B>U!E-8DPI
zd;+A*-rSSqE76m!xuqU8pkjdzm|f)~Nj8(C!hd2JImqW6_bb+?D|KNAJ~ke03lI-x
z@Ti2qbX^$d#6=!1BIfpqzh97}XMEW7NT2EPG<Eiw#)EbANw9a}u?s-%y^?ks@&HCk
zK0<(&M=r+67Tln6WFgAxW-_yaTG}m+E54)|Dc;p(66m&O<%D|y4m3|VmP5>!c)5Qp
zIY>I$Bd`cxo2Rn&U2UmusFQ6x9nl6czn{fjA~xri_;o{QI))sued?kfWzL*F|2p6o
z@Nf>xG0HgPq7x({`Hb5>Y$QM3%C;xNKB|MMi)OIG0-}ouHfi~<EQ&^EQWqJhmsj@8
z#c*+@IrefCl5&(!uhW?4NG!=hjjMKpDSfI=ob^h#aGArBCC`(syQ(>(xWNOB_VcK&
z1f>kyp0wC4%2hMNqm+7Jx0$%zFrsQ+RxMCKJ`uXGxbr(Vr2684zgO%$ii7T>n@?c;
zv`Y-dE0%}%_Jj`ioT_%k>c)LG9-z`GSE2#!zN7he+dX}i%J~I<-x~dX$LhI9lR3ii
zJsApZ{1^h?wfND{Moov`HuxiGjKB^a<3KHGP+Hs9*KI6kxywn;D$^bny|qRzy*U%W
zxdMXtm>+tSER!rAfytCv;SB7!{gx@h^JXbAY4Aks+qJOsutT-Fo`(NastV{cs^l`h
z+ino;dtM|!`Nw0z{dNWu3LY8KK|;Zeky!pTZ*M*xcXYbdbIP*DqV%`vif8f8mxJo&
zblc=I<Yg#B59SMB_`AiQSqbD!jD`k4g4HGpCHaV9{;B~amyP~N>RZe=!O_vR9cg7)
zpve=P_>1d1g~2$NK=pv2QO%U89Vd7EID=tHauk;dLqk&r6ou;O|B#d6e8{K$g<J>h
z2)nl9z(bK$YqlItk6sz%s}hkvG*9;uLJ>lUPwN>xA+p|;(=}4&3ZyV54&91<(q-~F
zR13CkIf31w^d*<7_0)Lg{%y=baaCzLofF9%zV!HD8QFHW?bYkpzIhN#jzM4zOQrbN
z!6CEwi2luhpE1uz(5f5zOmXC6mgTdluwAkOe6V7w`|kR_{%xnz5G(UgtpgdJ*s8-6
zf4Orp-RXLZC!G1Nz+M1CeDv8SMa=u$E#4kc>u?hadNn}FI32r~7E3hir>0&LlyeVQ
z@wK)OR-kXglWd{=_7+z><|)AIQu0w2h6z--1IcxFL!PEh+fN4l2$>3<tR}E`>UwHe
z#O6}7+$@v>fkQcfL0&hQ=F2P_imy(~%g<cGXvjTZszD<xk)f{Qu-BF~2pa;_mo!yX
zR_z?EPOnhajb!Lj4nMt0*uXqMgv6Ju<M)prL836PugJ;0oYUh?FRQgmRA#E&!|A^(
z*Bjfrb7`W^d{fYFX2Mfd)Jy3rg)hp@u|XQUcY^LdoqiC{S|Y~z2qwYPgdKI`AV{HB
z)u;@M(Uqh$;gwhD{tkAcgQAkW#&h+D^lI!0F6i|H|J~`+Qdw95>RZ~K2T$P65ZMO#
zc<+`p$V}kr+>MG6^^gfEt4TS5N;-d<%mdyE3yHU2tx`Jje2m3o`8PNA=#<%;=4nlv
zD}broJHKK9ixWmU@9WWwZ<AV?X%6p6Hh;D-p&`*`y6&iKPp=g_#oC6(0!?QUjSDqP
z9RqF3!78%*t80zK((24LroAPUuqSQJ#MRjNIAw+msvGAI1UHh{eBx|>P*H&(fc^!f
z=0)XS6qe-36!UZlWCTZPM$9ar-9m^W6(iB^viW0h=i#C)_CVk&RhB8=NKvUA&<A*Q
zIP+$kinIc7v~^_9>}jmAAhhb&ef4)t&dI*&VrmC1OldkBa>~&LpQEGOeo;=+3cCK8
zuy5etm}XO~W$38*)2pMeJHDib-+u;9V!dLApo{s<==IO!#!QwI#G3OG_-BA8U+s(+
z70RPtCJ)=jkX0UV3bRI5%nA{Ji2s6@&hLN_PJT`NOy1nKFAW_Py5?(_-QDV<O8Jy|
zi5V+*s<Rw*bZkGJY!{_KgiT2J`5a>97TG2ZMDqTxz;ipk{R@qdw{ri*ru5vf^IaF@
zEc)30rj}foB?NUo_Py&d3+q_7i(%M*|7^CiX2e%Rh$nw;s&=ZAOR-7GEIM_L$FW67
zE%eNsWwqaw2+#ui_Ar0vhK}lw3YvaxO3RfVnUun&J1n2CW!|)QpGAJVg7xDf%geaU
z3$z&D4_y0Ip?90Rk)O=Vu3;;d%su7MJm*k?elW@-&~=*j0Sd&gq{KI$qH1_5_t3zV
z&V_Mxc#_OBy$4Mt$?$<YO}31D0E~^18zI#S&<qGhgnQ!@4M;bpzXD!A<x*D`v$4VJ
zxY3a>zAVw8)jZV%7YPALReMh*1J)bFQrvOU<#E9uKq*$92RWkI84r0rgDRumpfHj5
zPfo6#T`t9qY$9h#(!w29*>bf~JySB&V+=~nez-?ziDgBmAYmM%dCQU@V7>ni0VhC^
z(miCzVP7w9dQ=Ua7g17b5r7@SKI`R!nMfPuIv~dIR*UHJazRAHH}`}KNO9A52bOmE
z1)Y_jK|Sd3rVgN(Oqj4=Te{E{Ih1{?r^)|;nP7osY-Un<@N5*jP!yEqKoc|+Q*#W;
ze1}x589@5aQ2`&4*^}xNJ?2L557)Ge4(Xo#>M<g`X>OxV_(}G(_kOE`@ya0;7PLQ<
z;+>KWe<%nAG+y-7TO7rTEMQ4xvC?(SJ_`~B*dH$2gSb7#=i6nCkI{SECeU5!-J>Jc
z`~;d0O1etVU3A2PB*gi`ji<@ilR3<l>+t@ZX({@gbjcs5&7|0<L4tu&C0pwrHIJP!
z=_OVaxPamI&;({XjIbf}*G=0MhgH}8WXP9Ywv+N^fI)!}Jx@yaI<9$6C0(ZYHRIS|
z>egYCt#iqdm5M7gjdmzU@jMk4I3+QiY@5Zq!sZhL!x_GS|AZ0k>cPb9Zmi1%gp_DD
zu!>Zq&CQ$5K(nP98>4>jjH+TA54d*t#+U1D+H@bObWM|A_jNqR9sXa>FsFaQzW=;?
zpKr0C3~!UW&wyXQko)JR6<kR-R_A9U$r{#VA+QZlVtCcKGepntkUweV3>8+Zl|y=K
z!KqWsKeBz)!<*u;IG~TB2$F_s<B7lL#j2?Y!vuVv7|vF2q)&`qn9q|IRpV=3sOULz
z<~(Yiv89K7)XX)MyTVn7W?p{t<=2p$?H?cXj1kGOUk09~3rvq}k9dgrl;vtm)T5Rj
zWJj@maN(oDiIOD<o7o6(xv`TMBzm1_L0fn>T<eoi_?9F41>fzp-tX#Lq8(=WfGHsN
zkO%+lH#^9=)8Xk?c$h5NEW+iERP2-lOBSzJL?d%9V~UvagnXncQI^yoB{EaPl^%~&
zbL2M?2IBrrFI}ae(6yo@226+IO-u^SGkMQy7{)3%nngo6$`U6fVVM0-Q4{?Y(&cwW
z&MYq2TmuIMt%KA=Y_KU9)`=A18D~K?#R3(2<?@q0?c=sQt(N#~2UELW$f9HXA!>R8
zNPtH-HC*qNipM}}7!jEdJ&Mca3e&~i^e;>qPL)C%{r9d<r2}OqBSyLpH6W2R|J(mH
zE3cV~*{QV&7?AyiJ|}0)L3K%&e*hb7d8|VI#H?vi%@Y?ntR|BA)_r~=gzaFH?6fJM
zHwK3ag#9aQ1ABx%RhT}VzVeuqalDLfncV|CKU@)WdU;BSsEfzrZ!_+MgNxZPKA#y5
zi_y3EiGrB`*3Mug4Ck=dj>uH#iFciIcU$io#5ZQ4IFs{J?F(XWyV*gT1N`qEi>bbh
z^4Mi+Zl~Klb)8EZt;v#5YV6LZxfDA#Yo$%{x4EU@Tm^L5x>%tqZrl?j*=bW)mTa@1
zMIBq2t~JhwRgB>#gB*(Wy!5K+glaD*=;m3i9Iz)Jq9F6g?~s{nKJXj~vgNZhwpbz(
zB2DvM$tH!lt&K+G`f(k!dnmG;4_=aLj1ej|f6Poa1)P67>SAeWXEe68P@^V0>s@+J
zmcnK3sQJf<5g}EREbBCIu8GEjWp%B!4hj516`XgG(8P!JzwJ-)x{dRU?p@PDiBDrr
zYjzJJPq0lfV{l4+z!*jnk5YFBe)>n2OR{s)(Mz~==2Pee`_>sK#a{$?Fo1uP&Bwkg
zC#ka(*j!ICZOIlm;h@v)#yU`uMeY7On$#0NB`}}PEck2rPPRBK|Js;SWW~8uS8Op+
zfqR}SJf8~A{4H`376xsJyhpUe8A7IUYhV3iL3>S-Dy&f)hiVu>cSiiTGU#rXFZZ|p
zop1mYvCq3dgXb1WasWu+`vd0PsEt`_&cpH2RBBos8WbIxiFXW^!n!_Vjo3-u_!_;I
z_~sPFAx8O-@Vw1j1xK+B30pz4?bHSZZ9<x-0CN46tSsl$7d;!r?u=eZ^(jt8A&U1?
z4I!4so~_Lr%^hI#wuP=L-A@VS#O|ea9ISTFr!G9LOvy!IiQnb|;m#^<=RuHJG2=u%
zqhQ@IN>f2q<@Imm9q}!ZsZiE-6kxWL(*eG~XMb<F8B_q1w*<)01pmhm!2U;>MTB>3
zH59lu8Z;Jb2g}W)1?NlOhuLun`Fg;*zw0~_3Ru5T>qyDQ#7C~<svNj!_J)@wi3e&j
z1_9lrLDuo;)t~;VmBIz<Q{;~vQ+e(%C2LW+;fj1}tEmb2QhbCi&h37Y+%5-k2B~$^
z_Kt6GWvMf2Qoh@DX4^GpzgwDa)>w+6L`HbmL{PqSm6lXNK-uWM;A%X8gez7x>CQPB
zEz;&K9Xe9>?t(Oxe5Q)%@<t;Ae1)=u40TI9HyU%qajtwVqmyin1fPsd8Ha3HqNRs-
z16w}GG{u;j92kRNx@_O7b&~(9%9?&$^dCw3&PSSz6v8sRa3{e>vAtAjBv8I%FlwcP
z&iZG((+u+M)YQBZy7iJKx=0r*9~79U>OEVuHb8*^XIE)<^eDE2MSLoYo^<=<XXmQ@
zptB%C_y`ZQgq<dmiCIL5T!N~Tp<S%Pt%2R}S7umbbW{P9>Rj$A^Rt!d1eV|TwO_^k
zPR^aFT_V5g0bC#ueYS5bK;B~ghj4TZf`O4>&PVB8rQ$*RT#Jul-fg^};2tjTdY*$B
zczK`%uA9PC0qOW7#Puc^8;*3j2ZQhAv@_!~ui0!Zny<*c{5-KVWiZNOLIt5e6pSB%
z!33nRSKUr)<}BLjHbyLoGjLMqW_h)0e-UAKO^!I-iFn&HeMZ3PR%b~zUT`>@tqdbp
zzz&Uu{C_O~V+Rs9%ODy;FEo<k6dEiuFKk)zLSe1>L80Vo)QucWFJOi_{RDlYWQSpT
z_`%HXyuQA0v64DuWzU~D-J*DPa_$O4-3GuDRepTPLo>tZ#c@9o4d9siN|K9_z@ewb
zJwNfOd0@yg4%ycn-;x}&-{gJfSyH;!LXTB;u$V#pZjmwU$vyehzC8r(<}=kKt<uVq
zNy*?t%95hpKbC5mg&`{%OV$QU(vkz7yvbH;NtK{iuGS=7sg7qi{}}c6x2TF+v<?k?
zM0B7#C<Mz&PqdH|QnZ!PNO*p&0BN7%+Iu&ounDfUUGi7ok^N&Ctt{w-{CCVVz(C_q
z3Xm*d-UXI@?zWpB`!Z&twTvJn4_d#PWp26eh~tzYV9vcEjQSl8J@={)@<<CB&Q!j3
z8?FxyM-;!b6IvU$IGXm@L{%b!C~+W2LJwE<nO>op_I=JFkZ!q8_8g?c1aek+Jb@I%
zWf%}V+2Fyzor(akG?O-_I5kU%j!p~0=SIk|@9>6QePdst@1-4i(c=~V1uE$C-a8l&
zx;OIL9)>Rm%SV|jRk<aB1}}gk4twOU>j?x;>esT*7tzr(o%e@0e{JvyJ>P#f+NTgJ
zpd0%+ND9IfSJm2cu071f#1hEC(o7Mipb{g@jwWg72G@0WWE<P{GPZEiGK&W|1Hbo$
zq~xjQKkAx&OK|F}1HKvJOcz|D8g!CUJW@`*U}_GR=6cmJX}GhQIw@!dPk{n1%A^I#
z)spTQ8pqO*=XptDY&6n8uie{wSBB;cm5=)pO@=WF1KsLdSn{-oyz^yOBh0hs;EL>%
z^y9~i<x!#f6UuE!zF#OgD{wXu>=XW^yxH$}aqo?_<&){rjr}jj7Wp3{id5~izXwtc
zU-lU{_v84KeceCz;k6xnKiZ@Gn2pxi8xZbSI|#qt5||H$-05=(5<#FK5yYJS`5=a_
zh=AaP4wDR?iU!W+#~dt3&bm4wAaPM0?`pxi0>?JXQhxbI1?X@akZ@F=2H8EGm(FIu
zfY2Ne${x*aav{1n?R-7Scqzg3Uzv2aFbjKWOtQyubzQVOIjjt|0awB2BQBED{HRK?
zuwJ_9#pJc%^-`{8;pC+vUFmdE(d3le#RY%dq-;WH_b}*Zm91_|vj7szW`6Flvr>d{
zQs5ASr#BAvch?68pYu=PTjqt7xXz%c1vqON{68f<u#(v|UdPbMIa-o#X|bhIdI1o@
zam%zvbrK<&gh{RFlI9-H5c=Z?T^-rhc8kG~%_u6g&J>2ike1>#Cn6>yL^h|;$(X|O
zC^m!UfY$2=+xcQk#h-s}Y-T&sv7z*)^c5$#Yz8QZ&g538R#Pu@hXkI~WMo{MYPDK|
z_C$&M@5pVy71U*58T$Q>BSPSsABZ43oPu`j;%27_Cn?w6oZIg9gUX<e-?Y=`Qncdu
zlc`id1|I*qzE1=^jgYbDUjxUu=NqenkNEUOcmkXM3Lg+lJ-~ttXi&UeopGF9(V~1T
z^<eco&5qlBo~+GzoefT;1ayb|`x0mT!X|bciX6u+TGx|l4*LxO1-h0))y)VFby=i9
z@j1na-PaF)>23YNv{!TQNFS#gYOgiL@tx$@=2^3u=;Pguunba$v69fcKK>ky_lv2e
z88SsIYKrS5M;^|i`Z3@rIi@AQL`hxL`7i9Cry#-3R+c|8EA??!q}?1^eF9%6mtL@V
zT&h!|t5x*JeC9(;s;F65lArc`sZFC+wD;O6Zk!b19q%vXc+Yj?O)U0AO2BKB*%j~+
zU@pme^M*l8dhnc~QYritHaZG9-)f4*ZV}rxm9%b@%wo?r3TTp-72I!zdT0)wW3ZV>
zc4kN+p8=QF<=qQxMG}K))xji%NL3#}b*N>UQXrXYwbOG_R<6lmu%_1(j~Ppwn4nyW
zPQXsGQRp&rBT>}<>nJ2k7WXTJWcaT{(or4NZ48!Nb&Ytn?DAvkxflf|?XSBs9ovq^
zmi410F&}w~<>PXd!Ua1;x$S0&oMdHss`aVB@fTxs*k2k+v@Nt3pCAH_{jtz%8BX3Y
z!Ubpoi2G4uA=o^RIw*GVAb8V|0d8Xx11)$T>DpdY2E=H-2i0-F{sq^!9DN_)-;faS
zYjr5#jeVMDlU2l8CKFdgtPmB=K|Q%#Iz|5X?=};+8}oYK5VwcZ_=vp)>41IH)te(j
zbykxT)vUW(vGlA&tCj=ecOyYq*uHgNK*PMGl+C6-Ye4<2v+@dueY8cn6Y2yac%CWF
zMQ4Sb+PxIhAN`&}Q2<x%aBi{{nSt%$>>?b~+qyH#h6&k7a#AGDzTg7Lp0+|iW6nJN
z!8%S=CJ32ETW4S<KQ!c1t)d$!cpIwRk93Q}p|{+TD)b35Fh<HH9W_Rx(1{SLWFnc{
z+!<3yRN9e8+ulHHMB&C%RHLjJTp@X`!l?(4l#LvBq!Qf{o?u4)w=DZ1Fisok7D*W_
zhQzQy%VAB(Jd^C$R`f|eDJx%*87jjFGO(INFZw9wvqSV$i+xwWy6#%pM4JznqZAu;
zXnE7w$DvC2c!`uGTi<}PtFrWDA4u~Yjv&yq>*hUg1i+dtSL6D&w1`c(LX2dMPn|_;
zlX-R_+b#lt<A)0)RMV_N_72l4ApY1O3Xf9I%QEB6Wu&iQ;2)8}-&1<YaDhEw{zYxi
z3q7To=3w(o0R!sEOZKyE0{Cz=G}7%_^YH%2h;Gzrc@##NhonX<y!Co-^mmi%Zwqq!
zJv}lfx{Kl$mbesRC^AMchk2^X{!BAuvuRKvQV3A+-b2pt*4|`tIre9s(wi%%$?d~{
z-K(maEhFC~oF0_wpy&uqf0<<5MK_nXRaV+s!L)|2T*hn8YoBF{*tip)Sxcq(UU#QE
z+G7*h76+p3dYU0H{h-i-XUVt6A6&`!FnxVou_G+U5nkla7BMo9saPCkwdo<e>VWd*
zNa)cKo1q;6p!~#SeA%<v<(7j3&)xUk@6D`gxn81rOfl%L05>{Wkb9V0I@CgjkS3Bj
zzwq#!DxAbrZHN|Ia1bZYm#!w}v0cs{D*?WQcieIuTmdBt&>G8$U_)&^gA#;BI&P^|
zp@J<%{Yzm5H@xJ+tXN`$Coz+hICc8WX0>a8TB;m2CuQ7gzz?H#R98#?f<;B~XB$FP
zI<4;Ai=0$7ZB)Cd-(&0y)3)EJdjg(9oz{rcC=U#ULAp#ipn)~cofesGG)ttOR$L57
zlJQaJ1#?UAl%^R`O+VKm*D@bb-|~hJba~XsZ2#99Z+-qNr$R;)_wdvLxsK<;T@A>v
z{m+i)c~neNEV9@982`3opx@^ZffqSb<j*&%JhM^zYVc93qzaL6DM4V0P(QGcI)%K7
zkz5Wb3_H0-n}(3M6z-U)<}Jq;(&$a;@>LbQD$Sikd=2IOTBqc*Ji0L5ZA}bvEESI9
z2%EiiLaihh0!<rtL-<ouR7^fj+?0|9%$&FH_QN}Z_5GoLiKY-I!iPN7uE&dTmCDkJ
zk8dzLnZg~)Q|5tZMM`m%@tqJ@qpRdI+A~PaH_wD>$UlSfmg3P<k*_Yb##X35XOz{M
zOC-e_%K<oab5NyJ8M0CzO?zu4tkx{2&Z9lO^Gv0tHps#Q<pd9s&4vM{SIiHgA(b(t
zs}l2U7j%UN2iM+a=8!pxBY`&AjSMLReZWerFPfhl@H<wy^uZz){Sw#cZO<Bw+82S)
z47;Jn_M~*^&4`Vk4I(={Bl}MtjlL`wWryInAKf-ZF`Hd`*nu^Mpf=hmY`VozLNPmV
zczq|gfs31_V8ab0-=Y~OUxyPp@==#endZ$}a+N=M{yRVLMnq_O^Kcs$Q_%N)&0fHa
zX0U&5Dq6cc_J0KDx9?mb;)VE$K0j6edwrC!zi0dGWkUn+`zo`;0k=?lXu@xO`M$M8
zpSq+-7J76xTa<nB3VfDHS>LNm6zXt(UF3F1Z!XOlb4+bnR3J4s0)c3aqo=(Y>wKeq
zT=6BsN=YpCaXc28=#+C0zNJ!Lz1lUS<v2I&LXYOn2dNa2TU+gl<`jVhwcY2WA^_}v
z2p!)@Og$A&+jpU`GhgPt(75Q;vLJY4V9ndkQeaaGLzwkjvep}WWsX*G4V;k?DGbAK
zMPaQ5rm?}K_$eegBm-KQR!RjjGMxR?vYl<_Dyd(XkYgrz;#-lWwDOncBba7nFkc;H
z5E^rcvXc}CaxH$fGq<(ey0tOufJ;+{gX?`H7%df1twv^7qRgr4UO?r4S9S}u8SAi(
zTJHD$^Rs%?L4{n<C-=15e=;ubrY`&tNy0qCzP0ntNdcT@a3f`55z(b*0k`@dWkF_*
zKWmm1<UvA3muB}~rJy*%>333Om%I2O>8DlpRIb{BAT3m`FV?OvSg$O<4Oaq9ARz1R
ztT=r4y>9j+VWF12Yd9{7jfx1cbz=3q9&$`9((3pp5?S#`{YGg7!#*wX33)={KMmI(
z^*(#0s^AG!fr;6JpAik=f0G-tjOf1OH&6b))I;U}RxPM`*GfjviScj0;q_h~nd2b~
zxwTP)9Jhf}x3bc4@?84XW`9sH=%>-u5omWrd=mB{3x2Y#Xnq=PsUDRn3JQ2(^XdRy
z3mqP_+s-eAHT)pRv}@-ON-1q1LV96C03St(&4g3e$Qp_<AD=16cxb%jfU*{0-Jrc!
zt1%tNxE-0kr8V$S1}%&dOod-&AX^`_uQ1Puwn=`_ldGqfT{f=MK~rXy?(dGgrvd?!
z6Gz_cibkH@ayfmL1!0XEj|g;`<RI7VL6e2l70#esZQlEl`k7wt{73yqFRnsl>BgB0
zpJY#Sdoy23aq6DAKvnsxUdn~3ENhUCNe#^<n!rkC%Sea#Er+UOfgjFhdX(O*W&X+L
ztN8S*495U+jYmi7w5FSUv{Io@2TMR+%yD(1O~g`)Mt))e=^?d>&^B{Zsa8HUhO{fH
z|A4h&n}S#M3YMYR1HU+HWFxL1Jg*)N-ugy)5Kx7YS)N52Z>nsw%woKDWxaCc^aJ_(
z03}|;UbWCg#v0SbVVV|OD1Rm9^qGZsZ>#!O*gylV!TiX4fpGktqodjR$(EPnL7Rss
za1z`t17@oiEaaXoVa%cSyw_3$pym?_7)?%BU^IOn9zl!-Zujp3d|~f&+t7j;KH>o!
zsmQo>IS>hXb|rW1_lGY+M5TRT!HOb)2<x`>*atvMKD0oG&0$ywp3sX3dil}5NlijL
zaF~*)iR_}7e`ZG+i8LetTez$ctrKRC@#Gj>B(%5FqFj>&CkZ4@284qG8Q>YXCz5!1
zCM~I#jQ8P?2(1d!SR^Q%mp=e7$S0Bfs{0>j1ohs#slCx+T(HjoPF%uqo&t;BwPEfr
z1afLXrCNh>bJeM?Ix`c>Fb5+J#R;X1f=(t?FbRyXt7J3iM0bseP5~So8{_ttjw#2E
z^55v=l~9?sT51AuDp{Cv0X0PnY;osOE;xx)uUzsCk`20Zg%j_drR8+W8#H=~GtA-g
z6|Ha{`FV9)883L06?=YyUg_7Gp3FxFA1zN&T(Q_$apS<HVo#BF2ix={@{F@AsjfO(
zzi&Oe{=JK|g?ovDe%KQkPRt@Lp)Xeda!DiAg6y3F2$m8S`>CzbXF}F4tdVv`27QOl
z{D|*wp)Znsaj~wP9I6uwF)`Y4l6AQ}D3HOEZ_?Yl74({8Y6YVvnoDR<f(cO+6%GY(
zVZZK}fzD>#($foIc@A&`Qt%$F@P_-7`$!j}*qIbSvaBA=t6Jt(`|icbPsYj5dVkdq
ze7*Pv+v8Z?c^;k%*;nA@r^h{U-#}Q*-Or$fRF;&maH@$=H_b`wT?(&jduV7wcxHpU
zqms}I{<9>3Wt0R!>zO0o`{V5WAE8Fe{kuHtVG2bYb4cN>r^Y_+VX1`)Z~iMr*NPM?
z*?0rfF&M!O8btu}@%oReRv2)hE;2+x3(=rKWDw!NJ0BJX^l)4>2ebJGD*kj=r=5>Q
zBu4WV+c0_HC=>gNv>&%KOonV@iih;u6)2?njo3vkVUmqG$yt}EQgofVKlNxu_Ay2n
z<J}R;yiBqwH{3o`8R(=VcGNw-jEwW&rkFR_-emLvY_gwDsw%T_FKuoSIr@waG0j6J
zY+un(DowszUn=)%G3^#6$>`*(<`9_-wQ9w}Nh;?RwP0Cw2D=dYx_c6pHRnWQn=|L3
zmGl5DM?=nfA{$b`w_3xhG0xYgu^~HuZGDx1a^5V-CCq$*0xRY*$MjU6;Mk{RyFBK_
z@`S>k8FeyyO=Q_P!F<@6&;2)u|1-HX!ZgC~INSC(sXnTJ-y|jjH(iD<vj`)B7P^g!
zEtx9xl{&wl9uflXxePaNN^Ed}w|RN~hWuJ{f#q`k+a}-=hXS_U+}Pb53LJ3bk4}5F
zDf>R(fv0cZQpf3`Uy10qCysXu;;&ptn?dfQes#asTy@0!FHhaXoYrC)R1yf;<l%Wp
z8_L~}Mg{7BriefAR4z!KLY444pew;~d2~MCZqWEtF(!&5-5#8gm`#rxQNX7>1pk|=
z{^#p`Wgl~!nc*ZmZhnu=ewQNn{4PfmvjVZF_jZW<h<k$q$Qy$`Pmk%41ioOx;fucb
zMhWSheo0gBCL53aqXNBflpzGnyHg38hx6SOhpE#<i#)V91jY;ayMJI{Vx&1cWRFW$
zooqJXqPEt1hGoe+)5u}v%53Ed9+E)Fn+lv1V*QJ${&P1p<M333o@AX(0Y|4;Udo?T
z9cdyt3QlgFnoe2npea}-7J3Q1J+0p{?915Hi;pa7Nnr#8GVD%qNKgx%wh6-CP27}`
za?$+pdUW|hhF&*HX-V-BB9#Xl_#9O^+2Ab0Nta5B_?dc<z;REYC8h17upo(E6KYBO
z6l!MSou}JRUsI4Yd1-|d!)I}(_#E28fFDlE_4Va%Ge^Km+G7)Aj}Uy0E89h1m{%~Z
zPNGk6N!uj+=k@H?pn_+m0{Z~Ajs#|bICfvcJadXm7^avPAn*(=-m0(2f&U^g;dE@w
z;{x$0NQ+huq$TY`O9}L&@4+Rxs8NhonS1@g8?M9ez`&UY2fiM9ynfrlTW!ZZ6Qao!
zv}JKvC)t5C?%0$F<CwA#P-2;ji;ei_n<}^<f_xrL^JHoeqbJ+yKGvFKy&rELy{8Zl
zsFxmA0SkySGZ2vr{nz*rfK1{%P{iSoPdsID&%I9QduR_1McK=ZSB87nT1{GFr-*)a
zrv0E40DI^7dY>6=e585+%U0Bpbk>3B`_K)1yK%0=-glc25fN)nLn9#(86kVWucAU&
z3Iwi&a_l<4NoZ7(=AnMD#2+APp`PcEmjz(g3(!%?n3tO`LeMGEhTZwx3nG_W3^g_n
zwA<>bUnHi_IX`6xlu9#cu}Fjdeqq@W1eOq2@lwpTI?%d<h+A9=4GqvPELugwXK!UT
zO_@qt{TeV<bt-fEstO8*X#{8%5b^i23Q;T}TNH4ia>?4ND>@?GAG%Y>;Au26ldWDT
znu(-T&Y6N$8Cw!EZ34=SQA|~aK_JuQs~g=_SJWzcxZe-`!&<Oa=}npofHeA-(3I4I
z4FV<&5+qaE131W*>j^l$A5q(RYIw;uqJt^PTRxR;>P)oM%kLD`GRo-TlxKkjo*MoQ
zsF564;Nd)Cmu#BA^`J<9z#&vSkBwP0gi2(eC*J&?Uy)gG5SmsTft{gf@-QPoKRISA
zVX~kzzG|IYynbwzUbJ3SZqb&@7!T_8GeqVs6wtO&|7`rnYsL3e;jxOa0CoLkAD-x_
zcH)cr_TsRies_3|=8Z~U^t?%nXU}lo+60IP{5nIyezui_67RX}LkNgU6$cq!wn=3@
zUjl=R2n?>4vwLHxT?drTgLGvI$QvfAB18G}-iKVme#~9w`(t^~6(v?yr^mioiNm18
zI|w{TCI{t)0@~jxw}AC<@0oB}zRpc#Cx;18TqNf;yrR0BS*`lVmLohlEQw@c^$~^<
zN5A=yyI(90@ZmN#5Aq+Y4S$1c1vo5mF5bBmUwwF>#Sz|+sH)Ymk6P>QdftYlC5F{t
zyV83vBM50ei;2+<FB2Cwgn(}ss;bFsj~C6Moy(M)bS^)`O+tyRH7vDFc}n<qSYc#g
z>H^i?`<Y+DX+p??<-^<3D61$;I#NI}?~+iEZ&E2`VW`syrpWs@9Fk;m7^=qou@h~t
zjbtOsts6KPOA1ygYkHjO&D-H#(oZUik(vx+XWJQx`7k)=O%EM>6_)7Zmq=jY87gw*
zxL21hI5q5wquq2(>jQ2^hhCwdf(3rjlmWd12Y>R@cMsWF_lOas7q1MFs>W~7fz>Y0
z5IN5SzD`PzUh}c9`VQK_YDQ>%oOIoE<%&}U1UiLc-SjilN>Z$TOj*j=q>f-x@7H3l
zdo};)+gGT@w5!Lzw|Sz8e|{f&Nj3r0k9xmaWSoLZLV&d$oAgoZ*JI;NCgmTdv5xwL
z`|~QFZXD1=1|^jF9IOguGIO^&lj2`Lp-BjFKn%co`>srm)vxdP8^Mqn4-ZIx@wEA%
zi#hp;++%Yie%<PyOmbjlbO`v~XU36`E)C!D2ylofv?s}g7jJVkF4_*p_HOpOnuUt~
z9;4+OWUblh6>WLs%aKf9lAKx2b;r0TL<W5@S-o~b;!;AU)zEhLZ*!g()BOAMnq>iQ
zJwYvcNDJ9;lk!a76wkqqUT0VfC7Fe`XdL(!Oe7ltaYQ-h5SHoU3|iOZ7>{|P1=V&a
zDQfP)3Qo9J!z{rQfC*UyQ7(!CfstQ9wz0C7TF=Zba@VSFn`M10AzFH@r@Dy?#@K>P
z`!MGCRGKY=CL39*R+_CM75+Sxtek6@ty3;pZSm65z0nOzozWz^GDcvc#EjW8?Qn9L
zwq+arJwSEJjKLwb#<Ko&@M$3+RRzrHEM|FEBuh26b??7PZiMO~di-S=eTKrhBh3&M
zob|L6yC5J01kVS*gwpwLDDo!Rs1l7f=|9DNTk(ZF$A~jakF$aP)Wx;RbrD+002T!X
z+!zD&<-_`_DM%HK8oIVz#DA#;Jrhc`2MzVV$N8bdjymN*g!BQKkhe1bm1lTm;&#x8
zAnZNo`yq7qc#@bAfv!g7;Ht3Le7$%rnjanLx^Vl0@J@c~BYzhlAADP@Vl)pAIyF&B
zU_bVGsFwS^b_Z+v9*odm3E(owCd}eCrQSi1MMfO($^+iL2&t+@NC@p;!F&DfAV!?;
z8wDdTS=+PlbP}ynCM9V1;|*}iL0c>_H-s&B$8(7tuD>kU<8^=hc3%8_OG)n^AEVn*
zxBk7nGy1Q&P|1;q<Mzz8G*tcBEMg}|!9pTv0q3bU4xAsUsN7~(oeUB10oY>B&EZKy
z*=UDce;SsQCIL*5tdA&q$Rt~`)<Cv^(|LfTE!eO+mmFn{Y9?4NbHH+bv<J(W!BU4e
zF40;&mg2!ZKXFm<TKwtiekj$``z`QY`y;q+hBImz#stG96EM<CcI=?mD+wG609S0?
zt=aq*o-Q1g$d+m}14TlHMCn<S@~w%adZ*~n-$lMAnXoxZE^gz(;z-g+k)jJW9myPR
zjAM6tY{(A?UE@M}9O7c2g{loBPrf{k?<K^a3^Jlg^7UgUXI>2sycXCuMT?3C$&_h3
z<f4g2?VXuR6??951)qEPl2Agp-gSNz|0QV<FiA81)5xHM_+K>M@F4h}jl&ecrhCn^
z1wQG?V^I!1i%X?6%XWec|5~?WkwFYqVY;j7_`zcscrYox>$*rPG$%JKH-agM_%&wP
zU3^x0-<QMG3nG`m3Fq!*EkeT#Zd_usKe`EQxe$%P*uQa$0$7Hf4~@k`6lNxZ1#&E@
zZeIY%`-pig0vfsS<k}8<F=o2-)#f!au@@^g2*CHdO-MKn7G?)Kk*kfO=}03UGoaQA
zjS(F!`fFiUBL85#^q{bNmGq!D65`@ReiBOV0Gfk+Kn^prJgGJ_^W(+fRFRq&*0*m-
zaJzY51(^fqClc49D{OL~QUzvsliU^_f1q3&>YRU<R=)XuJZ&VChY|m?I>|t}ST$yn
zA+JOsR@HbuYn6W&lhV)p4kFjTFfy$qps>J2P+y{G@dOaMY;@uNm1h*6b%$*3yzcW)
zDUHe&xY^y&ksLz2^dz%lA91k=VFG;+>3Ex(h80hlNr|ND;XtV+FH*Bf@ai=TpyjZ)
zwj*1n5HXy5BRx#24=K?p31a?X-<wEa9>`4}N}hB@dmbMzk`(wCA#|13f`jtiU;)Ka
zu&lVR|4hMQ8|XjnBi*SXa)y5)(kRzDbT##=guro&lKgGd4t%R^b^a}f|MMD4Y5RdA
zka|kLbucdy81=XPB@=un9h^>#pEo|8p1k(Au%uGn$@H+8aRZ?7zBz7_m7P<SqB3&h
zpk8;~gco(L7dhV`{NThrpGZP)yMIRk99W-7Ve+yPiK-B(Z)(ek$eg;~p)Xvp_ZwAw
zfld}>>z)F=Emw!W3)5K{b;s}vv3uL6Vf#4j;#~wBaL6xynQeL;B<kmcZ_mdMCT0yg
zyiB^({N)uXz&bo9)LZ03G|pe-@Z&+5<sy~3cgfS6*1Obr8H0J;f~FNC+(aT~7A4;c
zWC;`oR-6eDyitK1&s>{)Lt#0V%F_ym%Cc}I4=P?JB`gaKMU0mW6~@7B3eAoZdN1{n
z$j;etm?akgLRW9a^rNs*lp+ZZ2AIks)v~b6ve^;=T1M%e>X%0{%x$|{z)B6~@&}U8
zetRw#I_htwBC08g;YGIO)>|eEDq6RwQqD4EBNglV76qY-jf5eDhFS;O$@wp71V`lm
zrv)ejKtA4PG@EZN<S^P`lHaaV)oY91W;Br@S>$qG1Di5%ke2R8vxp@ojAgTlKN^1X
z%}m#oslRAF5oq89(Yb7G?Aw-qO*sn+Hi(Idhiwlvh=J{Y=Yzd5z}AmvcGn3Jh-y)x
zPI^&GIY_ZexbmtK{J)0@vLVP@-~VbgNK(hvQG~;U$sxXW#QRtTKaeL&Iwn*2V-gzc
zGD@V_cLGxcD~mm6V!0pCb*Px80Up@lA?PKlP>PyK;p;y0rXz0RWzAqFu?=#+rwM=e
zqwW4rJfi*2B8T(-GUcK}Q`8nX=S=QL1#)min%)B#AY_i!>ZU96$9t-Y!-e7#($z))
zqUA_2;X8;&2IQO}y-e~aG`infbL!(P(qrIY2Z5ozWZ|I83ZIkr-@;Uc$MhNDKmQ#J
zg9hQtjL^c89tfyMP3U9nF(=~E4o!Z6<u}?YK#ix6)Tuo$sJMj`Awu*whIVj#u_uHj
zI)Sp;co^sC5T<ZBf36auH-)CEgxeR8mydoukSI#2&`#35kU=g}t-iMcl5H<mmq}L<
z#G27V;jtyKmh3cgjpCN|9j^tP8zFGYFdVO8HJ0jK3~gMBzCioGgiO%cCyF5g;Ldp9
z-q$eKeubV-rG-{{9P=FHdPj_KgGI(rT`M&&-aVBTT&4|H>XLB6|MjQ2`y<ru@wQ;U
zvS>qlI$|z|w{C~X(zs${(%?BLh<tpkr(^3y00fpR+2ww*9z9cm!58;KhQ{CeB1USy
z6W;T3_z8qYV=+1WlDf<$Kr&6+xVz*L@)WW_*d81SUwFf99}2<|q!Yub_$N0=@A)?B
z>rukU^)J!Pb=c5e(5UAMi_TbsTyCZu#WQ<T3-C21DPhRJ4eL_x1p_~X+-l+W1n&=)
zI_rxN^GgkzVzGa+zubd?F*oO>L>+I~l<oD{nzXjZhXi^rBm-?}iJ_6QLMim6`|5nl
zp)MMH1HFZJA5P&V*0&W8k#!%yX6(Y%6{0Fut%%TKs~`FgL3M)WkuNU6^*4Z6w1%aT
z#Pl(fMUh-jlYLc&%naz7UjG;s3O*MWfl(OJpJqV-E`HPl`q;BrVp-hgC@2<l(#coC
z8O6aqx&Xq&6puLI(EvEQ&16DM=$BlKG67u702)NZ$q$v+5o@dEv2PYT8pR_%H4lC!
z{?##588ThU)RZ;)TE)lNWLq)b1J<ck!SC`ahLQ}%I~7?@mBh+fTfuefS8><Bv+$4h
z3S01y)+kA~_8}{)fOFKJ1<&O9gS84=tU3CrCNb${%4q_0?pZIPv`?OTI44Xn93k!c
zhuNv#Cp#X#AP^x3Fxx%97X6sL>(d&GDt|_bYmZq4eUYXb{+Qy}QrIReK`zSbYc4<m
zAR}&_(SH2}&c1wy37*UbU$yzCkhkVQ_d+~RG5*Jn$+q*x2GP6MulbQGO8I-5614<2
z<FCZUBKgHClS9%H;jM=UVc!;(@&Hq>H<Cf0Wd*@m5Qt1eFB(yq`WHQXucIlhxfQAo
zu|r=UsI+;G5HdQeM=2(R<p`VYU}=dlS7vfrx)WwUC$<k<6*<<es}VwTalTfTn0C$P
zSfaFVIU;w*tefk4wmAOWJS#@z|D=vH6v!fIY+|{AnQC2Uctth=CKCBSr4-C1%_FvI
zm=sljTFV$5mJeQDK-NAq7!*QC@Dhou;d>e(qza(Cc=Bdfu(pmt-o53!>NZiPtuQ$D
zT<-6<1e$tceTH7P;8oMOV~jaw4<mKeM(>5SD#gI#RAPjcoPGK^qkJ{xvpWO1S4rBV
zgc@PCNaWeZ0!NS<-1<}LW7a2g!(5u$C^$zBg{%tUkqbJwRXSlU4*&aBoLO~p8P6Kd
z!B2<YC}J<-a0ko|XhP)n)|(5`qxaIKDsmiHXbzb^G}mLAs`tn|x<^L2GaT}JUt<1W
zg0)P{w91Ol$Ka*&jFLp81Q@{6q<jSHk|+?p)a+#dWOdpl&!GD)$ts3~G@EHkkuYCM
zW-`P>Vv|fOyIa@H3B3}nal4zRlUn_KrIBH?oAV=J&t+lu@^&5dj*QB;G(65bg)y|N
z^~65|P@S7v3^+T4QvZ9$@;{pQyzk0Dy4PMO=uCafbj3+v;$lAQ!kS)EdA%_V9QbbV
zTy(X31N&U7pYYBYGqgS60{9fFW=-f_ZemTLl+E#knp8<S{ods4z1b+c7Id3$fwjmM
zMTNw5*_(kI1s4wFCm8bV6-&qwn2u(NOH^dc`+zG21f7)(v}r3;&lWqNzEm*yh|(`*
z&CYn&!RjxAMGf<adgYh~E4Iz*Vclhg1NHIY*$xk+PMgt`qmpZOh{Y;e3CD$<)R*L;
zgCqxZMV#AGb(SM+v%~)JMJ6us<q)S%=jH;dYPfoTO6C#>kU7U)5vn>+1_%Lg5}0w*
zCQ~t26KV7)hN+7MI-_amH@`tkvoTWtDU5!`&J9VU=by=6<vmECyICRw!pb&z7u~4I
zr+;97G_B0sGn0zcY>L#Vj#O~_N9vEP#OPpmguC|!c*8Rrb%pJzxcbZPy3Vy8$<nPQ
zx%4_?k5?PMdXbbB%mQieznLM!(vE+WO0-2TUlbQ?xIyo~=@MU!bniVCe8JdndMMcZ
zw3PmdfWzUbK=_0FM&6f(Pls5PhAMU@6ZU@P!8;T))XmlRsw4FKFLPOdT({xyus-7u
zJ-77x`v;dum?j@B**v>Hq>4Whr)WA{zJ<(k!;8RI3ib#<@b}Kk{b_xK@b@AJ$o*e4
zE+LQZcx3wxulZ_S+qA_X`G>duZf<ooM3C1XaKih2(`d*XAfm@%mS}uUtKv2qZ<z+@
zg6pTtPqn>@X|}B3JyVvdQPBC&6K^J4i(htnQ!a4pX4~V_MyzJ}q5L=|D_0do1ufS4
z?46Pz!KBULJXKb>pdu*^$Jhs60ctFYA)jqoD9H$y=0NrA#RVB?ZJ&WfW|#G-PbGb_
zVB)7;Ck@keP46oVX_0oG&EVVD0zNdz$g#O-OO6IlW6Smk;AmGyGuyBoIUU{A_agNl
zIYK|pPv*0v2?OqNcEy{r@zWxMu7`FR-!d}Y;h0?}Lm1K;{fgMBQoY>q<e5Z~P64{R
zmP=PsLZZ=Mvu`0zMPPC95vSKSghiW#Rkg>4Gs059wW;>gp$=gO7a;d{jDEz-j&%oV
zyOd>h-d@gjhzYe0X46LUQ$-x|*V76NN0Q~sv2LrFu)i3Z%5~eh<W{`3h@A7!jtYfk
zBAE;Kvr;Bge_?H>LvA-GTPa!+DIwmqy)VM5JqQMt)9EJM$LeeqxoA<)sCsX*d)1j7
z9aXG9?4;#LZWi1E)mYLU1%BlVeGWnVK%>V^<bRGcy1)vg?Rf9u)BXs3KjIoibwU?e
zGqo5EINZMWvjbv3GiX2UJ@EnG>9Oe!vEWJv^xlAy@PxeT1Mvw5f}gluq$L6WnBVS8
zZZ5ta(<9a$d7bWllKy23C!-h;VNJPTP?D(c`*@v`7!d=Vq4ws&Z+9>~*7YxE$++`@
z5g@jl4OacC*(;S>pDw82>(Ei75vLt<dIQ;;TwK=%YayF|WGSVakCaun^kG3H$2*QH
z%CP65RjzM>#~WqMwwIx?fN3kWw27tOc=Lucm*KbJp$TPA#hS$mE_S6!VNpaUfvCrS
z&A2u;;Imcc%|>u^bT}gQK`LI)r7LZLspZygmh9?N3)N%#pxglbN|xNpYSx6%lwy|l
zdiZ1BJwdjbT-%yDTgx<@3K~eh2{J}l-`Oj9kl#YJ2iI2^Is&xtM5kf5WUq9m0e4hM
z(<ztbWQ{f|lQtH6lh;|V9(YxUy~h^R{T>#K`|&k$=dWYqiE7x}7i}c;i_mDxU)h3C
zHitfSF{xIiy>RWxPm~JIWR;<RC%RM*i_PK;cSs-u2d6!XEMR!ZDb!~+yzDci97OlM
z0^!C3o&TDyXt)cm0XnU>wp4k!bATKK${&zcZ(%%(<-ag3o^`LBHC`aJJ7#3GJ-jgA
z$43t{ahkyaueXmh60F1D?FDy$*b5S}?ihc6-dDFWg75`b$fgz}0baaP!hnI%w<bk2
z_A|f?8XU5U>Ts~k>+c_!c_U=;5iUI{V9JLhJ8rx$gvWWrQFQ;SID30oIt`VjRD2f1
zV`3peGpBfwDv=SKr6FaZ>h-anrs4^(Y{TpF=t#C&KHDl*@ByCt1<JM}uSTkMNy^6+
zAiRAyI!SXhjm}-8I-d*gyl`%Bq+@tBfC_-g7K4Y)_!3tW33_)lIr2LYOQ%Hn`le&-
z_m4FauT@rIZE6X;vo@fIwKNgM<)Vm<`Q4>0TvVmSA(nueJO#J_*0y;Jg**Su>>tUq
zzsxeEX*l^#bD6BV%Vg`Q6fG;}cN(fKN>nRDRXSzQiUgKAgLq+C_@ub-ri;$c&RV2+
z8TEA1bM-{qokLifK&K>$V#LX=x)`ro-MJR6qDm4NjLpZi6QVq{EUdkeBJD(*>?kW_
zYW$n^$Oz@y-spdKr{L$mH2r(w9;R;D=2`<nm*ZoK%p+w)McsZhm@{OEF==Em>&NfQ
zowr2Fq@BsjuTX!uT^rZr>#30xoMRV!uHIIC`xDmyCFUvNCODOwoIH+ya#7FrV?VnZ
zzfaNa*QHN#p`k<*y|vI#%I$+Jrgd%fRf;JmgpTmjaeeCgew??xY5-j=6~OHo(*I>W
zjOjsL`8cv8!o%PL6&{yK?=l2i92`7+A&<G+vD#P4*^4YEuH&oU7a#%ZJ%z^p`71gs
z0eIR1C0aPZRVVS6PiH^&ToV=i`V5NsBC@m8b0SAcMu6NnGO~jngw@@Tv9ubnE-v`w
zYPt9lv3$kM%_30{&+|bR#7s{7@KEC#gW~YIkKRluU2UgWwP@7_WY2w>`06P}j4Y7y
z^>(yi*(dGW>PbyS&=HC#Ze6rySuYnx(iWbj7{3^-y~^b&XG1$QO5RDc#b|fy{v)d=
z)L^~Yacc^n`UifFoY8}|HKZ+79n>V5>Y0oTY7}HRHpQKcq#q|ELaW=W;=;IB7$${Z
zO%Jl1a^Mi4iZE?6vvk*LFGV^gT(XO_m>%%#$E%NpY$I<Wxg{UoCt#2g*-nL#3gE*`
zMY;_|mfKrNX$3mokav7FUVXnIRh*p(K>X%MJAO7C8az=M66USa^kiDg>s3;DmZAT^
zMbjP&zUbApfD!UL-x|77#^JsljXcIvseOF`e`%}VY%|TF3IzI<l8(&n>!cdPNRlU^
zOz{eXrMF*-%t4c3XNio)LT-Q~^3m8V;dQJFA&3@=Pc7s4g)|Cq15-Rz{`j!IV)QmN
z(ck@T{m$kaml%jqc*xNh7$<lf6%$Z!ro6&4kb$RJLWAEq_L-+SJ-~mAfPVkZ_(WgT
zhwS(xIwuD4KTLgNaAaMyc5K_WC${a0)3I&awl%SBI}_U-Pt1uYnq-pO@2&4veO0Ib
z_MfxQ+G{;pi-sV#wQzGCY1xD)QS3xvcS6wT1DIh;6;$gGBy=wKaE_n0%UJu*Go2d+
z*r~VlG%Xx;eu)T0>}fh9)RIp{_z=<|;`sePQ8w1|oM$qGJcHxpl_Dd<a|V$baigxv
zx8^GMzqi*3E~+DFq26X3xaW)|N@Xu8hgeOAv{+J6IqvK*sJ2tnhA%D6Ba|Ff`sQ6x
zBfk-b%|1q>ZD{@BkUCBOX2X6Mr&M|nRgnixN)CR3wW5e|2!E6&ca%~wcKW@^Vq)6{
zuiV}Rf{bLNdCzRqVSp(&*}%69P*Z3CCr0&~_no~b=YeC-xp^L}0C(syfVTmWr@&c&
z{FLr->IOku@Rp5|Zm`KTaMKE~NG~9~VEb-UJntXkPHVGZDopduah(DGnOD>#2V{7r
zvHcE5>V?YS+|m)2)^6bM@KPM};|sTQ-PtMOsb^`0p!3Whzas(HZuM=KLP$#2#E6?*
z$WO$sWExeP^Nq5W$E}f_(3D;u7oZP#u+W78)Ju1K&=0X8fqeSQu$D}iLgq(pee(FJ
zRyh>YA^n3$KNlZte~jlje6ZY2wN7}9ekp^#=0|llvk-}KP=_8(-t)as&|csazdt2O
z<nxnzz8-l03OQq~6MioZImipX43Z?Ae?9&a>RZYCw`0{hIu{sx5{W;TAdRWQq3LbC
zIfkqV_Vun1@P{Zi=oN$)xQ-1wef?(Fy4<{5jFfUd{ln0p|7&;?(EVt5VFKV+k5IEr
zvEX+6ej)}QbWGtah}QwV!VRs*xMd*gh%<S9oV4FdBHbT|@yyq+9`Ncg6Bc8*m^h%f
zQL#odl^I2O`jZn0jq(p(1sFu(2&(_<eF<7L|4CKSsUR@hs4xvp1!7V{9NtNH^)|})
znEP?i5fPDGBfO6Z6r7q}0WSilEw%y=>Cg!h^<h@nl&sjATKL3N=gd3nwEsk9<G9!%
zpGF<1HtrmeV8&*?C?9v6apwqg*;7zz;3zF5UrycO8_mCkowD^vM<_NQL7rY0b_=kM
zTmpG=qv7y58^5RohFzSfB>zD(7vZwQYI?~98dm@K!^>*3jg#x~WLtnWDXF!Ngeubd
zKrqnpjz(k)e&-UnJD#U^Qi-B#o2_Gucb-G*vf~TMbtuKZGVrafRNvZ6m_lw2>12eB
z>zoQ<T!DFK=^lVddkK67s1y8fWK(IyKu|*yZ(4QKZY`(azqVvI0BP6%i|OBWeljTg
zSH=64yA@UsRdPvasaTUygwEAJUw#XXAn56j9pqxsI%$Ut*S{{TXMYUgg@H4=we++6
zC-=Vcu$~^J;Nu9h|M4D*uyf56vpCpY97dJ6^w%%#=ey~-OUD;4gofL0-cDPNgWU8y
zGbLI<PA8YJng6OphM3wOB9FfRU(`<Li^{A!H!IsBs3Jvg>s^{I8L<Xp?1wsl&}o|B
z${Tcw8p1D^0|6Gr?Qj#lWws@X0L^ap`ffcabf@znLjJp5IgG2vl@8By_KpkCgCS^J
zETbnY3zc}jNcvg>lOKt$Vyc*G)TV@C7$h#s8$_T+0Rg5MHc~<Z>Jt1#dLro&$5ZT}
zE<#~uI~b@qXBqLB{NQm{B$Zj*c&)(0zNwZ?w9L{Wp|Bxi)@u=iOw^#~T8ol=NpVFH
zJM_LT?a`!rKawXkesgi*{FVy7ovKyTRBnn(dNBOA>5TDAWoY0ho=REswmqK-Aqv`S
zplkjHVTK(ClWwL$i=5i%71;&SLqoHtHU~I+9g=*O)@UI11rx<`I{PvqILP^=t0M8@
z?oEIYE3@rn@~iP^x(N}@xZTFI-bRaJokZ~a1Yib&d`Qy@@?|UUwb>1z*=1-Z3)gUH
z!AzIj_7x1%Eb?vEdMWY_A1%@gM4SjNfKt<@Vkp3(O~T^@vn$LG1IMicPFd&b#V)5V
z({t>Y$sh3$5-5QqZ8Z)(D~=;=v&%ftjKcHjDEC-UT-*O4^++-~pQ>_An%)G<vV~&d
zf#22F$_j>=0#8Ex?z(RWb*Y}sIe}YVH2NP9QU5{f;s5K)VCq|wAHJs>c(X)xa!9yq
zepJwL(qj{S!<Ha7THOED*AtcHM6~x`2~oHObq~_SS4tfIZ^cv;<5i~Qp@J<5Ls&RG
zuSQ0Klj)mobb&`Rey>TtfTN8f%ZGhHMFQW3eBt)ktj4Fi5NYskKF}lpGO~f3H9x)n
zEecGrn`{xp5i5R+PoGl`1NHx50z?+4?{u|Zxw<tS_2ie{D}%V5=luEv1#XF8Q$CP$
z0r*KlO>9WvX^52$(iyB*_*hG7Q+dwk_%Pzem~rHCYOc%;+PrF-o1`SFY4)j7NMu!v
z{sLKWRS$H_?Un9ki;i5Nj9KGt9D;U&+&O;575P|`nd8U-ru)&Yj3|ihV@W_8HTeoJ
zKC*biZYH{|y&?4y^*K=YDQy?0fA!%HYzl7inG6&-Si?fGK8Bm}bejEv34CpZZ4g4b
zO`vA)ZVZYmzJ;{fFMIe13oV*uEIyrHq)=%CZc?qujw;y>^>%}uUp*nLd%jE*&f>!+
zW8HyXe}&fpCcm85c8B_(mAIP6b#p*n=A`!0U4M!}2FJ5A0iPo$Ex;YMNhvS9pfl5E
z#AUUN92uf3edNhMIR*2{j?dFgQromh>uBu>0P@HOzYdt+NZp(Q!6>)vSHI(KsTtzc
z)GgE_<baTnpPY(BNHDYhUVKn`W2tGR;iph9_5Y81v7F$BD1f;e{lg3})&jW%WDv7Z
zwu1$*d7g<r?!BCj4$_zfU7cV(;@Avr)Ahj9(e~@NOM4?h5Io^KiSWxD^T%v}!^dcj
z7T$EsDx$-WyG4;f_7N{PEx~{Nq$qbIy(}#M++k50^>z+nkn{E_Uzm^doPHmroQhUU
zBwVy&|L2&aa>+DO?UDOmL5l$^`6dzv)s{RXqYE8}C^Ce1A|XZ~AM4X!=y+DcZwijv
zzFCg@Py2%U1cPO#gO{gn4uX89xzdL&WEiwNGhA*=1@BBmjiY7KeWGH546*@3aN<sY
zkz_klINILO%4s`hlp_svE0Z^y3jL9Ks+!Y;M-a26+#8`>8KKph(t$o+OWC0Rn-VSQ
zh)z!oPLfbhwwT!#N1U%I?%K2!uGsGcu08$Ov@;E(s*LEFh;7?eP06r8whWYR1&*u@
zQ0jLeBkN!j8e(qKO))o{HJ8M~)j%RW;jl3)fzmA(Z-I>g@$k@@a@+RJ_W0}xO7^Q)
zxeLJ6$68D8*PJ!&`}{q=-|xuEw)>jz!)J4PDD!n|#(GdZJ6s>;?-r|nU3lq`-wl~L
ze&U{pry>U*`Qjl(+I;1u4O_vTn||?(6DijxYTD8-**WODA_wpIAzwJ6Hy|7AtTZiE
zZBJ2f=ogOuRw1&7tpa%y&)f3OuTe$me{sK^1+;t3Ivci&b66&-6l@Ni#m2Nx^nxM_
zTtIrs0d~kVoNRE=eS<D`T>tzE0)H!yGQ8fs{rtiw=2;GAeT8z}5~$o7(Zl!b1tbP#
z*HV5?mcBmDynA0)gsBWV(f=`2kZrR)D1K+TTNr3H(s{PqCJ}yY^t?0r>UHox4N%HP
z|Avc(kOr2Nf4d*i8Jxsve!NA5LF6X;1Du0mp{7}^2z*~a*#FfTR#~zI|DcZmq>Md9
zWthQ3>k@|eC9@BGXMn1~4c=1G;4;c+QSFWVtDMlA%iHNKKGmoP^EUr{n_FqX0o{ZI
zKT<Wtr&;byJTAI*G>w`3CzLDmCnF;KNs%|lLX)K=z>3TAWqh!2b^QK@K#}t4MiD0Z
zo)1K9d5fmUA9<%Tf2jRH)$&29>Y^q*iZKVIbE6bzVi>uKGo#n<myc&mr7{o{7dD)k
zaT0`_c&wJ``bOQo#L5dtnWs(0)_5=dK+KcrnZtTZQ1Mu0SLy<J)5Y*Hl+fs?meZ$f
zN<cGZr_x_Y@nOD-q0)TDjTge#&*6)heq|z~j=SYGVPW5fgs`Fzhafq8lxg2;Yzoti
zRA*I!(_7R?XQ8BPpA96yK~r&;=<x-HAJ&f-{fzGQhu<k!6CknMa?4NRdq<$JJJLU}
zgq{TDO6E2dT|}krfGva51vwf$3*D7AsBpBlJmlXm@6%T`fR7@$-;a(=>E0SweK0$n
z%j56sS$99{N1zK>{^NMen{=@MobO+N`j4u8<Kv@WhI_->M%!Y)N)P0VISu(Z_3K^B
zEl4|Zq9PduP(C*w^<Jk5VLzsez(a78)oP<YKWAWh>CijwwiihEoxZLN>;*WN<uLM-
z@~`)|!)Qh-Dc@5>vFOnn(@)T}wpPK(CEs%N$@vQ5ElL8<g-%hVok1e#YvQ46)Pv_J
z5X8`z5;ydO<}D1d0GlwdnZ$!|ChG=E8Sfi_<oUexYYXIy2AdbFih^lNH}Epu-jX3~
zTz&^HyUlQIEPb$x)6Il8#lsiq<{!5SH=0T_n&Y6^)oFzHJWg|*0LVVbfKPq<#9}|O
zLu!`9KRL-3$8GBA3kRvsL5T#hOwB)0N)AJ?5~Q*d{*WG%PW!V@<OI3!Eg93NSYerK
zO_{G`|68?}rrAq^Z^-@~`>alw?KZqM>swvUq;GO=RU<G@u`#<&yM+=829mCa9iWN%
zj!*+5chNE5>?nw)gAu;UaMi(2+F1qp^Hdz)H8d0WtaB~h>k%K|VSRk0l*>o#DauX0
zsvx#ubbr*`9)_$Vf1J#RRbRfwsr^o>@u?GXFG&nhaIs+2U;CS|M89KpRgW_57ezBZ
zV?i%<TZ9UH#H;5+Rx43{my_&fHGj5-H;7@@=r$^&?=DmP8s1E8`}U7;y`$b=E#Wf%
zNYWp|q?kRsBE+&4JQ%d-3h&e>je(@f?XLhA*uGQ$Sy|%75l9;u<g?Ym#76iZ@C*h5
zo&#})C_xnO_n+<8=zCiPhyI=L;@Q{|@J)Lub5SlY#>e0NuM;5qJl(=U1p~h_vu^pI
z82BRiv3Yg!lgFs<;1KuA8FBDQUBUa%p7|jYu^&hx1RO@xh7;bxODE~m^5tH*zzir5
zTF~ipH9m~A8==MlC}E;DZoeL!!qvKmqqg-gSMo$_6@_Y#mufIcmM)eqca%^VqRun^
z>ZX5}Cz+9j!T6Z*?jly^WnQHuQEe*2zuLr1vqS$T2AAU77(-%Pw1hf;-#8EQDIUPY
zOvm@Hn+m*Qi?3j++iI#@EC3bBF+-;d)=sF2t{Q00=rMD#Hu0>I7Mx0W25{m(Thy%>
z%QeH4b1DhU!`3wzgsARS<DNMnKdeQtnMt3g99vIf_iUJo(VSq@OMa1#pqewnRBDG0
za{o?L6n0!n6<qdreIPqptC(c$G)DQp7&R6R<elOk5Y*)3$|6afiF;|X$u;)xUX@zx
zx>o%deYkq{ytSu+;vBCxY>!4*p?V#<fHCy-3x4W|=}bWOuX;0!AYQ!w_iXTgT7XtU
z#doTM5lz^C;2%R-XD1wr<95t)Ov~U7jqNq$#rH03o-TWK@uCl}3?NEF6~p5qE8bdS
z%Sj2{zR+F|hiNAC%%|GoyA(Y59)2}$D0?n9#FTs<Fp@=?2j%Bx@hkqIR_Fcsy{5mP
zTczyg3fQN$cqFrPw2pTjF|t0tJ!B#S_ltfP)(;@ycY=ri+YJp6%-HSM&#(IroPodN
z;J4*CWKV{ru1W2z{*Am)8W90ehdhHlQD66Okf7SJN*KNuo>X9C*4m$_4YLXB0Buzg
z;|HsR@Oa@<F@JYDG+MY<bA;;He)s!^eZax}fTi(pR2ue-hk&==-fSQ8#c9>$a+KY$
zQ|lhs&+ofOb*X+Zoi)qe=}<e=MBEck+=Fz$tp6||+v<l=L3`qYlzlG_Jv+E6knm07
z&NL3|7?gYYYqpR|B?Jhdkb7ONBO0WXpB=#6UwBiiOfcKkG!!RmO|_%;ZfXu=yqAFP
z-btbw1f>!stCUm%(nYTFae!e@_Q~E=&3kq~v;p>EI@}m4on`ix0up6o9c(ADhPEK(
z<r=nVP;#p6hy_}MjVV{DM4#%I5Br<zctDAE-g2vw`Y(;|#_EUtbhT-cS>O_rtXkhH
zm!~B*CGR_oEs!BU<RfLb^AVDm4Zy)ZjK4^v)fq(G={=(~ao51t0W|Xsz`L+32_h)u
z->}HTc1mCtJT?UvV8O%ASo3MpKD{2fnih>*Huygds#zKkuzWNb9`s8e@(hyYlu|l$
z=iZfbS>G_Oa`AqwrD@YvIH-N1_Udk+<R<hS7Zl~RNLB4Qz_v)0)%;OczuXSInS~*c
zigN-#qoB=!zHtVXi5q8kR(*ejp>9x)WR+><?FhU$Os*bzI@pX7VxHf;Ngglm>lPvG
z>z;sw|7E}o@1SMnV;#K~YJiPfIM6Z62*q&pc94B2jz|B4hxB&8*n^QF%edddxU@eM
z=-68X8z8vDL}!O0w18sP94u$ib&cj9035Hd8*a9*<5Fs~gIZ@_<N}KWgB1CArVEh*
z%Tx1)YNFdmFH+}e@&t`M)<;)7(mKVIeS`I%T&);m-`N$)8QHV;!fYb#{<Pg;6rivl
z;vRDn@633ovfOL6G6x#AA8Wr!rc17HEscti=YKIWh&;2!xv}lp-FqGmXJ!V^#zKbz
z0mqlzpK)!rB^lq`v!=BEi|%EXISVh;(Le3SA>N-<&;*H#lGUvnM<^58l?ozSHdES&
z#s)f~a&=fZvNL@vTp$PPuJ8Mk>7(S_h_ZCAF^z8p##2-U5o4JAG9Pf*Ce>j2zz$Fu
z-A8@m8hck)9Ac|}t5Z&myNmp7OYBLRQ7@m`T!y26c7P);9C`1xBi&yrXM8QqOPX54
z+`nvyTB``j7xQqv79#Z6*?GD9JBPk!{bRiUnWmweP)>|L4o{Wrek-=u6r`x`a-2V6
zNR8)^Bux7QP)KY(k;<F$w<}S?76OhwCTrWNKFgH5I((ikj!7`qvBMYRXiI5vo4qp`
z<3V*;j3iJX!jXfbjP0+bsvo}P0nW|p9YMTSulLP0Q7Hdt`TrSs5O%k5vW@;bGRB}I
zqdzvH=TLKwEQq9^>s{ZC<xG)O*WN2gq%nH4I5GI~%>5bkyeGJodL)ecUekN~_a@+~
zkLS^n?cJEzUSBjBOD-4-dWnMG_4vn83={h7sbn)Ic?|J|<e8+KvMHaItMdM2V!lqB
zEPn<07zW;VBzy!6u@EeHRWNYy{a~>F{^!);v7`4%?X9%8$L{*3x6i?O(Cg;v1`woa
zU|=xV7^L}B4+LV&2otR5D9H7Z<SK-Se7Kw{^L5{kr$!ll{zdjbnNXSnB<`P^gLjhR
z{Zy&@_EDy}<9$`c(tM(2HE!S2IcTc+cG8L^^5q^psKWwIsj0yT4K*shLTmj-UR?2o
zZkD}Wp|H_ug?0AB<-@o=TytTXRvXpgcHmh5pmXt|E(?x?Xm$nuuGHlp&~izMX2bMl
zl76RLLG5Of)T&0B!pn0QQ@9SQ?<991euod;AR_y(I+uQGKVRscjq_wRzM_h<^K^IM
zWsN=Zcxq*fQ74X_njphhm+53+jQ#J4cei64#<bI1-Gt@}n>N)r1g6H)@nwwq!&EnN
z5vsT5mPg54@fs-`5B>(zjN)Wt{dNT}WjI#-7(;?+F9ol!cph%p*$xxJ$1L*rXIj0Y
zDXyss&C5wxxBPRV<+^3}m*5Zw0NDDi<v!(6<B?1)Y}3A0n&0FwB3(oMYZaF7?|f@D
z8Lzoa7n9;J?M&LHL2DYQ<ClmjK+LjpD}<PR^^Q-jQN<bnp}3InFGblTmB`967p=B*
z$3f$amFvJ-%2prNw;K*K@U($xvew@fI<-&mmHWXuP#Lc9PbmzsV<W-i{b9+DcRm5m
zp6pp`1i=%3Q>>%wCl*Z|td72cndKBan(Ab*C-3T7Q0Um<{d43rh)=>YtL0*3kFH9O
zN=iZr@~93u@|(`C7bt@Bf`JYWrs<7h;f5Woy}TXR8KO<h{H8^<1aOM>LKWiH4HnWI
zVgw3gKd(3UZoC6`9GlhG1!j=)(LesM-|4Z!dOHgDeI99NEp<6yz079vDR4qWK|o_T
z3Znz+reyk|^CUuir$$Ei&^MsGZvco-MPUMVyPf3?wRW(2s1;*66{YtZs*C?p=w$5$
z=h7h=VASi@;K=E)5OQxn<)lA26%hz@SJ`TV>3&TT#Dj&N<XX2!Zsdw_ipTNWFcj2y
zkl&!x@Q5m??b?@}@C$3nG~*3~VT8d+LrIO6xnqvDUrMS_H<Hxjw$zb$Lo3bEkXMA;
z%_nH@_(WuA6ST()cr{E9_1aB_GBI+ICmhqMfLo9?!{@l8(mh5&4Wz0uZLU&r-RHy&
z+%5t-Z6HsPfQYWs#whDv5Sx5>{STN@6}w12j9igkNs;QJMmj>qlNqKa%tNv<MdyEl
z+%7E$WhxY2KO;~+PvgS^BDx3a*0&n6R&{Ors&-uNe`4;PGj>7~;_{|T$>%Px@?U#o
zspg*$B>!oxyEP^SZRF!NJpXAMqx-f?GL=j@Wq-w$W6klD?;1pGfJ+2BCSH8@5-*m%
zcjQG_IU{Q^F%D}<dw;F)s4GUA$M5oRU%a+8KbDNYQ5;>jws$|<(dq=FRlnuM4{|kg
z{AF(FsnU!kgK8?l?TK(Uah#yJ`>6lk)4?&%?9pU5iq6g01WyQNKO6nA)LTop4ds}u
z$cGGfcu@>acauv$B8@K6vo+4-fD~`+SD9?ZU)MF1HLv=y0$e@YuLf#6yL5-iI1*yR
zwFJU*CxLY?q<<%~(6@c-YS|dc$Hw)DqD1Loo>X#0?Tr~;*D`}tXGn}b3Sm<*4i?ZJ
zw1ylDg$x`F)yXnlnTY1y(BdYI&Sn#TOtC|_w>Tc|p`BFZ5Sk<5Te8sL>pI%Ns|99H
z!&i8-FC7ZVJVEK5XR(Ags9agpBwN>0_eR{*m{RrRq;rr1P{fs3B<yp>rB+X7wg!b+
z=N=`%jXH0VGy+Ytf6=Ep%iEJD`pZ0^JGk=qfh?hvJm5g+9)*^{C|M`+Y>eG=B`zzz
z(PFI3{Kf`5Pr};<qoFk5uMByHd?R-k_nyU-h9XSOd0&>lLoUWG(dCg*S&oYPnFJ!l
z8Vrcm8-tiP09%=SWtrHtJIfC&&|<k6L~4-&#k59BnQyQ+v01bYab>1G<Un`FVkB`Z
zA@9imGm7}b%t{sQx;2=)-%eX<w7Pg;%~`ml+ad|1bIeDPmXI(gW^f+w>Uk8aXpj@m
z4CnJ>N8V9-wGKF1c_jJ-Eb+f$a3)c4XJE%=B3Hybx&}^T@~~Cm@(7U#UgojxF7y~L
zrQxS75f1u$ojSrd12DapYE8J#)SUE$9o$4yVEF9ryM1C8`~?<MZIMK}*GC55uLyM<
zR~kS$tjOxDn_YglaPsPA-UGVB-4z+^0<OUkJf|rSgsNq2i>EEMgIoY~IdE>WB-lI+
zJTC?)NVg=J?K+k;BmZ$9cy)1dLrfYbMz|m+&X*Xzh>1~=M3yzUrE-q*Z-INw{MUvu
z#aP*RgxH6V>OrIQHYu!mIC76y=+mq7M48r8NlBEa#Jw&zb?9p3_O8t7>Rl)tnU((I
z;dlbDg`HGU$vm~{EWc!(r?J)MwWnaQ3JM+M826HnNpdeW(?b8ObWNh<ycpq%{cFZR
zJq0S*zDZST%&``Qd=qMBp7C(X<Zrd?J{w6{FEYPQ%nB+0yEsbdZQ>z<vx6>%!UBi0
z9-s=QPRR`ubJU70=wXBKj^LK-D+{iKGH^x-S9O>L&ro1JaP2H+5q0dN+Q7HjCVu}C
z`XH>)vmRY{!MIX!h#J1}t<6Ij%w3`;ry{r}XZlI5+_r2H*j`NpSrs=c82+;&<m9FK
zpRX%m#7Q}y*T?#(4BwRUTbs^yNHj^8OeY}G3qgyJ*aZ@j;O1BoOk*0_xV(Rj!cjoK
z=U_b3tYvJg$frF8VmRo2kG~JBIfGz^m24?}k<N%)$bGEb`>!eC*Fv5{9cM6W5Ly*3
z_XQ&WsO!$UB8_V{1|9ehI##4N*U8(}qdTl|pf6nJ2Jff$*t$;9lhg32YN&wKv~wNC
ze8X#3^VUc`wo#KQA)ar|uNw*naU7l4iqK10E|cWhH2CBZDN4JU7F8xJW8D(8W@x07
zhU_uXKW`!h+?cwJ5is^p;gaUCPW3p{7A*jU`D4U64+b@eGc_<@8?Q~5>2eB&3^fey
z7Wq;z(JE2qnoO0N&@F*lYqjY=2UNtm?kbw&oYOf|{7P=jI(pXgO0mT|dyhKRV=5?0
z=@){xSogwH$XcW<NVR2U74_YN)$5i9Tv19|D(Wp9e)wB9WSuGg_r3Hk1o#FFSR>q}
z25ch3yX}ls0?Z#%JG;|=R7tY30)7*A`VCNP^cZW|B`UG;3rBDPr863e<fPM#Ic>rK
zwe9CylI#=ZDVOB#N&au5=cbfl1w(7Btm^ut&9J<X_5t!RS6re*RxN=vp5L%;6o2-8
z2mE|l+h)OxBY11s(R@&Q1xl!w`g;|y90lLHG-6ELpWtdHc?198eSRG3XRZ;T0qOz<
zZjquk+>v71^JCQYn1`fG{J)6xm_tEAyG84FI_%%xGK<OjkkU00aa_b&+ro}*{_&X(
zj!}!@cLY?zK6SBEvCv60R0PnJOUa7x)I(Q1f*+rD%>HMlgths{)NkE`Py-+8P^A&z
z@E(}SR^M4-XZ|bZ^LAp63L=qX9;iWo4k-ekU#UES_Q>D3c|#q%=7tBB(A$J!ad8B)
z%ZQ0;+~qDD$Ay0LOE;!yv{}=W!{G6drP#7}qmlAK4|l*5ZHF+|drZNc+?#V@(7!E#
zWsn_)SIMR}dn}kjATK*}FK{XfyX?skvIx;*K1g19uodiJ)<}}p0x_&c)>zfSO4W1J
zOjg7#l1Wbg<kT9e8EH7ETvc}3FLpVg731`{wJ1>K9?qXhrKvrxTiNrd=CkL+a7@9t
ztF(=!DzI4U`W77KbQ*0XpD_oO%(}`q6|tAC#Z~2G=7TYusI|C+98X~?dQ>L7#2XHE
z;@qS^$VR&&gmx%2@1ZyY3&fC(B^7~0ea*rVa$gWEDZSmE<LKY%;j%3<&5LOtEp@3%
zk+`M;E5oPp$ujgIlli!?R0{(rhsVv+EvC-}SnAp%=E>@zPsvw#za6ety(S?}q8#?h
z58U8nuEd>`ze!AGilu$UnEBoNH5M$yzMZeeq*dY+_#f`T$a0Osn99~&vnl5DzOhBF
z+vX)^JfVz_Gc;MZg@BKE<*V;xhs@3e+trfPNa>Mv(81U>dpge!#|YHimAq6E)AOWL
z;n>x(jJ>dW-9?Gm9L`az<jvbL4xJ6iJf_*@jYivJ`&9hAu!#NTn{(LxwkN|tTzHt*
z$a2^pb=l=m0g<$bOud1CGkl+eflwA+$mEOnLbzdY%ms8*Xo5f9tk`iYig5KR1K1aj
zbJIB+%k&0~BXYWhJ(gX&UIm39KoNqkrP{$CU<Zs9EduKAeW-QqHd=(?+BDp`JR)j;
z-EcsZi&pE1Hfw;^eCBGTiDunt+5ZUgpRTkg+fj7IYit{nCtv2}5u;AuO{X`o6j!2Q
z*A$<t15;y3zR}Mcq;XPDlilVWfl3=QIZh=Ijww~z{834Mt!NKIAp;I=k}OTdGL{S6
z&eDkEBAVb-=`;5?JXz!~i(`21e9d6Ax6QsA#@l0?8R|T+ahd*X9!bLB$?Z>Qfih*F
zNgeq(onn67-%xm+iE|+O!9eX|-LaNSs;hx6>|DY0R<{nL(jfPwU;+Y&%<Fq`EUP>9
zl_+<|1~M!M>{<XO0D(D`8o4+8zZ(7a`XVzHBSwP*>+H0tq2(Q@e+E}O;NFB*)G_|P
zM#<FjastM>u^%gZ&<Ei54+cOhHCMh-t$UIvJG6t{N^)+INQd5?Ox3N|$>K9<D_D05
zEfU$)^rbmljEym;SwIyXXejb~$F+aG8hF^WLO|q&H@0%{2!eo2oIvO91TLO;=;~y<
zJkbJ&y}$JamZQ){E+MS_IRl9R9_N7A+d|!8TgIW^*5Do3dm?$^;ky29Be)vpDlW#e
zV)dfF6dQsNM7hq3&zG+UPc$S|l{wqHRL8o~N2YeNV`r*zFbGSeV6kHm2#JB`#%d%O
z)O6-ua1=LD7$N#*P}#iHtFv)2D`JoMttT=A;ocY(y{kvlNg3&N8kbM#-*d>Lu?KW;
za?FoDj@Sera_P!pN-uL14e1?!gk3t&v~mK&$X59qCS;Vi2B>~q$&p%7=x$g-V~L_&
z!)(=4D-dWbD-MuvO6p<oaCG*WI?(s{{np8k;==y(eOyq>1_^pQ(qX&F0Wq^&_bZLo
zvFpn3%Bj?`^S&PX9}<c*oe%ifNKca2uG(@WAy#G>zb!QB+?rIKh7}vY1V;)>S0J2V
zyE~HS%0m8JE7>kuLiISGz_tdlTL{@!F7>y^H_f1OR&16z$-HD3Aka}-rRDrmB>ODn
zo_NJ8^y2mW^FmO|4pj;C?cR-~sR-^?YDm~qM2>Euh@00xFKW%)D@*ngH{g$}Kql0$
zUuwG7ai0TDfl*#qdxkfXR|P52s)$B<VJ$lEs}7VK>Yx;eBJki2h5L^+?tyzt%(Kki
zpv{2}2>%<_RwiHvrO&zq4PS{MkIKE>0&G_cxYB#ZjGgz1>*rk*ZS1QGDc;p~==EN|
zW*_g_Vb#i|?Z5VX*#etV$%&^ThGayOUpTh;oN#T&fO7p%3*GPQfBTf=#l@Q<%yq;Y
z$c1S+H_0{9bxg210qq3yl9MM=o$7q_jo!yl<o^w)W8&oci$ujzO08?-zNPq;nhZFq
z3^<2ZI_MVIaL7|IaHBcTi57r5oAgHaTPzA@oUP<c+k<t|;)<LHdURW`{Wow&rbe2O
z<#h917*KjH=N#!_>vBDNzEWF`+x!o?ej8H%+4Fodo)l<!rWx^~H?*w_?>j+UL$U-l
zHFJ7i?r4iKnM`tWs1r5#j=4$B0;bSu7#BK#AuqK1CrVoYt%pN9;|$3-1J1N^5YJpQ
zo{f12p!Zn$C@m`UE-1hH2vWvw%iUee4yReiwPr21fQL}fmjVa|=OH#GoayYvHL<$L
zw*A}6P{q4k2<<Ww-i8g+)&wx33sYV3&$u{61+Jo5Yg-nAWnL+e8Tp2hsHgk{B}vO=
zLK3-=x!wX=C;~K&C5|NPZ0maa*Ft+&mhAp6l0a91rsix0xDmk)IYUC?D<HG#zQnVK
zCZLR(I<%M`VRmXAKo?{0<i)WJ1T{z88vdS3|1n`VGwV7x-G90T+A-%otgXKfEJVH2
zn4g2^hIC@~xLaGfdOap?AE=Dp<JXj&@w!{qhkRL+fUd37>zbQt<>dSxitVtLW6ztc
zL-IO6s&}s{|D6e^?FwS*IHSoizD)AY=-SM6PBaMKuHHrLFZB%6u~Ib6gyUl(b43^D
zMzg9jn_7MNiVfe`c89~NOih&*gHBH~1kmk!<2O65Y5Za-hF(Q+n`mxfyK?6qn6%O&
z_KWhQ0_hep&6=v*hPsw)I4(mzMRl<1vW@!sMmO!4$a?DW!;5KYY<>7Fsiu4Oi0?f?
zryl$zKL7gu{DG};a)d)-=BwNZPfpg4gX5H^*IHjccxkfeKBI=pchIBDilf7hqsxl@
zR%{NTLUSL!E<?Mp|MY~fA5#&G;6i6wlAyM~WgEO*K^#JM3Ei&NQwQc;%>laW`%(76
z412dmh+9v(sQT7&6&uiN1&Y1nTC2*=w`~&;(+l+#9!x9Kn+mefQv@v#j++e?MlWu2
zZgEbZ*1+C!N)AP=S+LcVkmxV~gulw|_x{@Q$DJGAxkY)Nk*|~Ff`;%`L>1xtwLLci
zQ;z=%I$VYFj}O7%_YP3|&Q5@gTkeWZwdIqbThBLkU}iqDc7o!WgwwstHzmrnuDqu{
z0f;*6o&hy#^)xuSVfm4Ssdgcv4ZHToIq*jQt)2h9agnZ#A!bZyqCysnhT-QhMq(mj
zucl7R3wZ9Ck!B$-|F3JDn`-yjb-V3k!K)}J`z4IMhnItEW8CO5jU-8WQ%L^_7voKC
zKs`4c`2mY3jIKvG#L03hNV0^JFt@gmt|}fQixyM2J8u0ifTns$fgN;K*wm`wGKoz*
zl68h#nL?{$zSq_9sK(c=nf9liFs0f57<@q9U3KoE#!HxIh^f%GT~|azWq4e_c3<EW
zPq2MNsSNHAFtMU#oSbzbgCYc*j+j$uiO~JC4xdvQdfI)E_b`%{%nU3d3EFM}16h^9
zwSWXdpk~rvM6~kAq|Q(sr-GIUF>O4ExQ5Bp$p-(+@_tv&KT+|>C4iaeC0?^du6ZX}
zc0kfTHZI@Bzv$WQW0gF#((u-};>NfoQ|Qy93zeNbro71*!Ks5>U3|>+S+M~etum70
zPhxoj(F2BkF<yU~y@;-jWkvN%(n8UXC(#-kFL!S9>6c*|dYj4@`hqaY&de(xOk!4S
z=dV9_hClS?5&rQ8lQ(xrunp~bM<b6{*W>KxCn|4CML{@<?th2iA#<h|bs$tW0a;d^
z9o8Zgz**sf9NmAzVqz067&;yRzuZZJ{v5T3q}d|H>+3ziDAlB-8)XgqMVpRXVDD!Y
zz=O~>Cl$4X13MAJ;WrCZB~t6ZP)dbGCzL(Pfr$<WB44KzpTM(qXWu1Yq|M)<{N9;t
z##QGE6Ax{ZU5XZzl~4C!^}tiXWlDo5uY;)k-un&_WHgTIac-(6X2dmD`nKtW9a+Hl
zE?11yD$c%=<YV*`2L_oAb($Vx1jcoXelmjaxG<Lr#qbI7VX<~KWo>2C%t+%lZVUu$
zdh$N6!=aBi5%rCmp!A2V9nx}_Jr^CZI$4(II4KLD1|7%kcvRd~GR1)9W+-AIohOru
za^&lCI1s#C$54(5CcGyVk@EGPhY4pSIgzIBMz|u_^@#@#u5<S8suq;$sW6=-(-1hV
z6j5lQ3rB}tH~&|xc;DF8^#^4*JJhKhGy)bgE@{3m<}X9uNM&OZ^_F+H0Is1Bc*puy
zy8ON5mJ0GH&{5a{7z|zeO?#inV-~2Sz3O8PIJGiCOrTG3omfsUWK%(ML8qpwrDYJO
zMoRj0>NHSUJXPbcYR&+vzoP$+_AU5OpsaoLB$cAlJ{SAXshv=<z>k)wDS1>mGPUS0
z=)oJ}nE9#Ld<mV=cXq>(_FLdid(vRLv9UMco5C7{dU&w^dU(9xFg@l80b<8m%ruGr
zyr}1rRC*S51$B+zB;&g?n&t%w*0XT`{T)_YnRv{4FMP;>ZDfWV{MZF!G{96MTnP6d
z9vh0R^M)FHBp9>$oV~T1SeO&vE`9xbfxTm`o=Bz+-vN09?e83f|Eqg(MC7%V`ol|w
z^@x?lqYX;0-`vDin|NV|?wNLnn`}W5{q#ew@02(MmX>B%9D<pA87%QNcHUuIx?CSA
zZb+w{PKg3>2-E(1Kv{}iXms^?+Cf>3>!Q@^`6MR`0cyIR4*&rgHp7)~KzWir+A-Cy
z4hDY`Uks=S;b!9ZVJ4rOC|auK`NN+*1!kH)NUd0dJwf{;)h;FY;6$PhY0AVsoFMIS
ze7Z*M#UGnS`$wH>Xc(qip0d{Ks<V8XKZ5(<q=YN-C6D)}$7r}P<*!HBhlAM`xC)wy
zk&CBLwvP~bAcZi$2aNWX887gMM!KJL#kab4`gMMAn%c`hA15SU&|IN34@Q@YN6z;M
z&xyFIta{#WuIg~s6hQfM(_iKhqDrb$84KDz1zc~IDRx`$R6^zYBBOqcE_lNgo?Lkl
z_qytSJT)Td`G<EspQ+Pp5L)Ox4-X#7?IgQlS!r1^fj8WU(k0(VysLv>7L-rr9#%o4
z+tI&iuF-L`Nho_aNz~=)`plzz1iw0xLf8gGVClJP5hhr^JpT1xIQ|TF{&t3GFCuA^
zUycs^qa9+ge7L`RViteDl2?!G)^$UYJUVdB9&gQ)FDT~zY<0sbeB(eJbn~^^P4D(V
z{pIZ-Wfnq%VNWWO`+NBF$Om`u-#_dR+OJ!T0hf-1F&xdqU(LOtSv%<Z4mmd_^8Xwm
zADEa&@;<?bjPu76W26XT<T@vjm_OoTy26bxnb##lnHxCLRztIFmgkMpF_~ns8V0b0
zshP(Zg||k<%k?4DMJ=c`R34mfN<J$<9>kZl!0Lj?!c5+kV`enGx<18=V_6QGzTXY0
zHbg$sZ&zK?hl0oahZYLc^9C|D4Glc|J%$u{WgL&16}A2d4p@d^YZVqqjonc`&AR5>
zo)SDzxoAo6-3^hDqCx+s1;DNE&Kzj9z!{gh0tLxpvr`ykvwa*~QaopB-Cajec9$3!
z+&6ozMw;Ev)vH{_((+fO16EI2Jw1ypJ5{NfcltX+%RNxF9o^Z}I+z&^RoLZ~1C*iI
zB`L#nooR%Pu;{~S^08bK2BZE~>h@{8^S<#Z^P{XaD&VcxtzZJD^MbRay-M6L-sOtV
zOZ0w(5^1Od1^Tnp3VKNp|AYu(cGs<g39jp*NZ8XvJ_0*LNFa+EjQK|^ik!VkCr_^6
za*HF<fjVKDwIE8v7?%hw>I-1~lce_NnHR2vYz?2E=WxG2a$9S{*@gqyxOD6t#1PGa
zfXu&+9*(;$_B*e_@>d|~wD;Q+8`~|vJZb6I$_&=R{C7bfnfu>@yX)%0t#|9<guetj
z6JLmlmg<V7_f?AkZTFMS=>Au8PrQ5jKPN=|0<i~QfKY|}T4PpDjAm`Jhp#J?C|ica
z3ryVi2QmJ3{_a7jy$QyNt4Oh3H%UKBxwUMtOSLeuYr_a^iX0=hyZ7KOt(Vl;|1C>4
zMBv6NNGN(X-_eJvFD9#&2KO<G`D3l?U@EMCk>%+sRE2#xF0)lw$Z|Vrg`;UOSj<Pz
zhn0vgU}2|f_#To-9o>dCRa<Fv_Vh~jUhp7|CJWb587T*CLdeW&X{A5K#+l|MSM<4@
z)oa1dWp*8)MRXmJhWm~D;8poo-M}VaIEO3I&8RQ<R^YMXP=>CU*R1-NELjU?;YBQL
z^p}f$xqiWI>DtZEi~p-wFd3Y&E{^?7EVGLN>&1~A>nPsH6UGd6e>;a%OLEW#uG*ws
zeFhUMX+D3L6DyIFfOG1*B6$OsG`Kk37Kw8J^N^n-J(-+^Xc1O=S<rFUsj_j93yMhb
zyUW)nf??mr=xB79{4j!o{l?@_vx7#h1t&p$b~x#*uU+WtZhbcnwERPyW1r<N7u=>C
zOC4z{HxJzIYqEco)CRe9fdKs_$a;N&oO#+i`P*t`$1Tm4|LyN9-q6es%le8SR@qyc
z3qYXC#h%{35_>)GuL5|%03UM3b2D{XW#NIlV@w_xh9NY_G0-?M4q|sAk)CjVZt|pw
zI4_CaZ-gXZT1J_;wn1gJBp{BMv2A!QR~&$Ve4`-pp|3v$H%I|~N2)L!sslOEoWzD*
zU5WR$;4&4lxw|3~q1Pe*GzHxnLMk|6yARYphA%w+`w99t#SaGb#crZA?qCg5@basV
zaUc@!Xgip#7VF6uTr48R@~-5JVFxk9<Lk!H!7dSqEP2Q_+?x{!*fOn~qNlnQ?0=KP
zWgtu0615g@Xm%5CGgk`y^Wc#uA7(!NE_$>h5Z+eB`HGTsqQ;xgtwH9^o)BU6LhhMR
zN+`$8LP)uZBk=rPmAp6J-CRlvom?ab=PT=Rfo$d2PFN23*woX};4sT%9vF%|xpOSa
zq_IGymz}}j&bq7EDQ`b0xkJWu{y_$69!pkSpxNVPy7fn7+c5xC4Gnfbq0xs=eMtZg
z9z0&_sA}axx!=^75vddv4~~!f_5?iPv-C>6YtfbA)p=0S;ES*9z%6ZY<WXK`C#ck%
zJPcT@$GKehIPGt61$v!j-Y5+1t|vg}qW=iG&$T)R7A0vEUFkU2L6=anfjVQCr<<nN
z1+~Lhp}bO<W@U9ia(6cPb~4((CL&P-as(O4k+dET!)9zXyvWz;fH?h>rBAo|N5(3t
zjH5;uQsbVELZ&yll-0JqA)B-H<?+APG;fzcXr8U=us`3ze+*S4r#VCqfToNN9+HVg
zfI~EKe2P50AsPxD1cq+Ib$rqZK+q=yLUpRWo@&8+=eJ}o%0gdvysHbw3KuL%G3fQf
zlL)GgtylpQYy<-aA}SX;o&9@)jrwY1-z^cG>71QQqU{2~&$QNEepOla{Dw)hqWN*F
zc%<ZoBit@A5{GV_dqhwls#|<9$?1tD+pYs{Y~$kkc(^he6KPKXC+p9W)#LP(i#)kG
z#3}ro=bReJmQN>4Q=q~B@LAH5OcWOZ!8C)BZX_>7!AzcM{^Fjh1W$=bA!Ze~yi)dq
z$UN`jYG4mRR9-17kTjeikHy!_LNk4kH8cTNUd0|I-aPGS+~?vwO>DrR!_~I>%GbJM
z`~0}+*{OZ2i8<7=HjM1t$p<np4O@PVd&+n_a|w_@K4<IT<OA)sdU^+|eLF{uTRxP5
zP3kqvd(XA~O@k(-M{E<lzQeSZ3%MKT2E~_vQVa1)3}2>F;kEV=xzDFR!i-~<fhS*|
z5zF44;?SINl1uawk&gmdUkJ`o-_r$8*Z30yCR8bw=?~>dwfHc8Wk_i5Uf8Ksr=B?T
z^U8p&FXb9c2*;I!aSzKR&RHL{f%fSHDe`njMAVDYL9NxVdHGMCO3=V~2*QqjT<I;6
zb3;j(I^>yNj!sE{k?h*?MdFoc$FI#4;e^QKi~(N7``LgSwo89U(s1dNSz+;*!MqZ|
zOQH7zRt}8Mh7FdBtimdHei)*_x1F7ei(lu9mQDQ!|K;nBtNh}NiTzpO9t*Xd)L5Gd
z&)>&?cE~f(8h=rf5cK7ZokYYi+zdBPYx}y=&ArF7KAfG#rMA7{_vv~C*5Iv<4x9P=
zH~!~YVm#Vah=w{GSB}0JS<DE2svX)?#}5mPi|S9&ax=m4@&_{Q`Ha$~aJX|jXUFYg
z4E)+qqHdg6Gm4Giu61N8!Z7j7a?atMc6+rB#bG5t?l9{NJM%mTYz|E?b*IWInD+WQ
zbn2w!5BRsraLoKJM&WJnWc%?~SAssbt*uDu!g^>UjD?pa9VdeN6-6B<q(C#ufuu4G
z`^GT{Jpt_wHOxkjOf~A6i3!jAF;u6WP|eQlKdl7paRv+Z&~yS&W+!5-Df#IN<UlI-
zsxXCogAD_nk<Di&qP+drJ@!CnPvTw~#Hy@T+<%kpC6)`vfNTkZZk)*Un;1c4jk5}$
zS<Sx+5>3rnRAryx&`ZylADAg?PAju)tKP#?8xe(zgfFr+4mdWAeWVl<hLs^GwgFru
zFDgj~7c+>uT6Ut=&ChS2ZbYV(Y@m;r^*`)a0%-~Ll9IYh%(NN@Ep)9Til?E@QfBYe
z+kwwNPMBwbXteY4og(yl*e28$yvr@#xDAiQPaNy|PXc*L#ZT9tKT#Lz4?}*I(k+@N
za3!hsc?S{$fj#Q;rv$>EoumzizXrC3R7e>PMjfz;_B2Oo?YKSL0bTVS_hDlBhW-s4
z35+*oRrnK70M~pXs>6ClwFFuZkU3VYig!6$m68mSV!~ToL8Yq-U6Ngq<OEfjYAhiZ
z_hUZ~tw@KCBKIFW2I_QLE)`XRY#Qu6!xGL(F7xkTGQF(ws3mlp;@+4j^?H=lNgLqB
zG{$}p3%`T*vJds)tTj?<k}+cb7$fAbOyw5vEfmp)>(x%V*b#Z;3nzRkUBbD{7MMNH
zkS-ExRVHkz7Qu|QQ(m%I*l|{qHO7PT451uzoS$oJV67eslX0B@Jv*nNpN-^ySv)J6
z2fc}{wp)v^s<Ir2a##*BW|Rb-t)UW$0|DQ3>pe;mB%o3i7XqI|8(f5UQ|<Ic555C+
z8^Iqa=TDHw)?{1czMGfS>sjjs7weX#*w6$`7haU0Mr$AnimVx>s3%+8nFS-mLI*w%
zaRz=RFzS+ndJC6_)kRweKM;rGAOV{Hd_+JTj>K>0bOtH!rCw&R;NZjdM+H5mwbT7>
z9|j_yQAs_}dlaPD8;5tq2Y=QaTUt$)ok@yAR9a;~Nu1O8-V_W0-B1Tg2}V4nFVfAn
zd-Xn?Kn4N*G$cFPBdo-YBQ?M~Q1?Nyx%M@8F5T%-oxrp0mu$}>im{9+7%~3M*#53z
zDhiI4zKp;MdxDXUygu_|Av80)B=u})ijB)814-c+?+@pqcyaQo5aF2{6ruVDnBg~`
z6#EvDFkiR#14Ru3+J6<<Ol$5?M4DkzH*Zs%o6K>lW?w+UdRd>O4-crk9=KEn+F7VP
zGD50UC3XV^>doMSXl`=v*h21q4x#N&G+X(<4QM(Mi4@xAW4Cqw2r_Efp*Od&=0f}h
z?c!RZts`&E+y)1bjJeNYOmdQuC-ep4G-m(_6ml?pn~9ZSnK5vQ2~y)gg;)pha(DW~
zeUr_l0*~w;8w_*rLDQY7F(nEr#j_L%`k0TP0TwE6G$Dq*+zU%^3MW!6hWolb+=F%U
zpR84ijg3jTq3&sedw~1#9y7>~Z^SeCHL!oo(_hy!sY*l(2Y+P&;S7RsVXv@%7&%y;
zJV=3|zR>KW5ypV99-;eJEkw3aw0}i948a>6#?7Ah)9hQ0pRB4pj9R<l&VH9Qe-HPx
zp`R1b1bI4zunVu_SF*IeGxKGWFhhVCx%mbps$Gw)!-a+RyR$QZx3d#Etz+$ewFRsf
zmR?tRYG9!+CEuznu;0bo8AFX30oT=tf}Bj<VSj|m1XD9~f!Xs&mdoe<!FiWFLzPf6
zZcS-v6$6m_v_>_+g#W7UlU#d>enu^crXt06f+S_v9%}ZJLQ-=}$2TQ%@+O6!QI&->
z<>N%njfHU0!(6upKZaAu`REV`qX5gN*yB2SLhADtLaIW4oZ$wB=A?9<on?$UVmlJ2
zd*>=&(}~53y986_$)c-NgnHvawm8)}@KcrxY>CsUAZlru6G52!A5LIb8)~hiT+1YT
znr=*cE_}bp>j+17nEsV2EO5tVNA1T?P;J#A_4GG_7U6A8%;^c6%Ehb%?Ue*ahJs*9
z(@&eM)juZa3RVRc&h+096m=RN?c(!f#u=*zjIzW(|8X|7ZFBh=joz<lCta|nxCjTh
zS0FCaR<FMZC?rpjnTbrf-9ES36ooZy26_L&8u+tSUT6IOu`yne5=dD8-KZv@8#M}C
zux-s1$>p)s`!`q}z@x|p#tzwiuP@lP9wIAJAVdueQ308$5hd_CEe}15aOh+_S>p<u
zHNQ9HWrsf^4hUSbMlF~Oc$SA<Fud5mxqu+qG8+ANghiTnLtS_yy#VZ6BBFn`tjE!-
zb^)?~n_yah2%N&^qs>7|ljVpuhZnePSw@;_UlHY50VqW}=r!5rG0K!MP2s>}MbA~4
z9&yLmq<Fm(wvFW)Rxf2SGuOq)BIZ}^&jU*_s2>@FgvAruk$n#Qq;3;gd395ibLmag
zXBUqz$yyX(mMmxY$`Vek!M3lFS6p_9WopVp&4SHq+hHdfjn^0t$1;TWdDvwowo7$v
zRf%>BbtW(B*0~?&4Sy@TjRK<Ohez%Mu=~85Ly~s1k}AR@UB9``z4+g)`=#6(PjT@X
zqqr(%O2EM#({7Z2`GzJ;L6$ale9amc(5(t{vn~U*j^;UsA$~=j!QKUsilGfz<weSx
z<XiKoPjr}UXWlh8+<NBG+TyO;>Lo~&uJvizRkF1KwmtDM?6y%WI{?At+%RQ2P))74
zHw%cB`te?n4&5XT3&`WH*3slG`)^pR1-e_#SCiVO`hDzYrk11Q{YO||k73a9IIn0z
z{+`^DF4YNFH`%6u@gQo_s&<0Hf(LLT>M<;F1+tz$M~1tci`ORg>q1amF*?rn0x3DI
z&tPOX7_`)Ak*Eu=?-ZC0a)z!qO#VQrNqi%TaQu<W^uC74wn=6se0daK@r2@C&+c@-
zh(s?r%8liAbMiV2Lrj?`dD^YxW27n9dpGXjIYTREsRm0ozlQ2em~D(oa|SItjAlr%
z-dJQoHWz%O0q??8Lk#_PjNTskZ$jexXfffblkJAz8Fj@!ed^X(JSy(@Qg~F!Nc1m9
zJ=oN}oGnJjXR`9Vf#6R^4nt34gfb1__hFPNcA}|uHn!GydJ;`jD-z|Bhd-#%llA43
z3uA=;ho)<aj)M!g6WeU;G)Cj3v8~3oZQHhO+cp~8n540j#?77o-gVcSmwB0oIp@<p
zdw=`dcy?TGE&e>YunU5x4ra~upi8gky{?Yw<iIm8q26}O!3IC>YD6s`QLT~FN|+%2
z_NL-mFM@SpEfw^KxpOWFmOgKpqudM%yk4|7iTYZAB4&<}?{!4|2Lk&@nc@7_6ygHR
z|GsX33LGbcWGyr%{05ji7yUSN4!91yi%-;r`rfyTDx^l=hD?>X`ucZ|bEklKpW0aS
z`sM?-sr`(S4@9oDJbBoxm|Q{bdZL;Y)d*{g&=9C&hK?g4dUr-d#`fe^E!qm&a6Dl~
zitkYL(F2Z`6=fL$u@`i4IBTR>sRV-pZ=sdsQlzg+{M&+lY+V9AbqmB;4?qN(u*gM&
zCY6PsaZ{93t0=^I(Q}scDN~+PuMOVl|AI*NkFc(hF>zM3dlc~eTYVLM3FP=*>#THF
zwDbSu)Mr8j+wpA(6ysv{^*^LVHe4!(P8GK|mp$KQw%=|q?mEDKcRu04E8L5a{{&=c
zqcl*v;#m;XQgaa)j#8H-hd(u1`+bwqL+Z|@ye*`Gy57|uw3;42rrd>R@!{|l*w+n#
zo_-Fm5kN(NmD8=fgFItLX-}&8yRpozTxd7aCju;gOx2vT+*4>J>Az3!Q7!RGzleno
z4^U>)w%yjy9NpaH*}OsyB2lDVQZ2dudGgM$b%nf&2g+7;g)3IYgRKaFfEGOxDbnqu
z6Um#opa`fv`Bf`KU@7?ZR_ad~u;J3;Ho`D$>n();3peCtS3@~jl8k(qAQIjo<WLow
z@FhW-!M6`258cblW?h5$6(QPE9Yf1zB!AD78y6l?e+}0_{SP-76nX7WJQUdPn;d=z
zdAg`$DV;SdZ;+;NVJaTz<Dv(JjdZav6=Vyg#3xSMT0Yyd)~zojgrhC!3bh19=g`8i
zHq+ARKV<QX5{VNu*hf4^pOsq8lI2M$m|>4(rRzi}yGk@NQy97VQu$PmC1hir&_?Jh
zT?8!oeoC<sq5t0rCeX~WDU}j9@yY4z*eaIAjRnqpEB7ch6;M2~&D}+&SWyi@idJr3
z*?gxO-uaiOy$K`OvMN1n6l_!?$gC{8&NL_;O{E5+&vOi5m&S)Eh^p0_PPhKJ(!6Av
z0UDX}WG&!@wJ`*BKd0Rtj#5#H6R1Y3<s{#{TXh0u53^K`7DG>n%?+EwxnOZnQF*}X
zs-fu^{bx+ziY;r_ItIG>^!`Gm3fNT6N!EjWGA>B1yDAWE6&QN$zn5CH@`64ENxMm6
z6`_WR*E!B0;}9)E9Sw~1=t6E+m@C}iX-U8KlCn`~<Sgrh+~gm<ez)_xG(Bwj+*>F6
zAmlk;-{U@#$*zV?<mf+WhzbM^A=|YQh}aOEp3{D(B<*Z#C_n<rDh%oCK<`XK%ow5h
z0b15p7wj&3TvuwOzl9FJ-%H#=%ya5O&g81El0wIupFqoGedW3ni!8K(wmU~m;Ctd#
zzy%p$fYu_YeV8PX8`f2f#pX__S#1qz$>EDrLcXeqAy?_Gx0R~b0Tk=5V=3^{>ttIf
z9i1DPAp&HrD*t_is{K^?aJjzkT}!r<3a|t1E9ZvwxX3^~oGloF`Y#9**_kHGMwVvo
zmPgih%#0ol6YncemlMqOPuq0u8iKmM?N2fgqOj>&?fYV=-;R_5t#m{o6_@v@Oq3)|
zboJmG?3dADsWT;wUfX+->SQBzy4N>C->dAwIxwWrhR|cw2`>qm_DiFYdcGv(YA_Cb
zMUu@80Y`X%3Kfyi^lXxVoECmM7F|;21o;Nb524cWS;3a$(a|tZUt!;@H~u`7Ei3nV
zn^teK<spZ#oMU(1i!X1sb{i<n4+Qc`<Ex6me$+be*1s`J#}8{7K2vKx+Vj2dV=~A@
zo4k=XI-0+y{$UR33Wu*l|5xGwAV@FH*kblI4xp<KbLAo!C8lUss8_8lelS2USqf=B
z$Jl{WcP&fzU06`Y)P;wUhjWODL9LeORu0`_JhZ=qZ=MY-VBO8V1P`!|4G&|s@NZHZ
zBHcV98z%Xf>^hG~qSOGG+KJzh)5v^fQW~P)_Y7;7OE8lIy@|sG6{~)0QR0K@*h6gr
zeHV~H-C8767^#X;me}-R8qGRkc*I7LDcYFi^4gIQ*TbhOPe*iiaU8J(Z>Wj=U2awE
z2byIMR`w-M_k*J;l&G5OTkq$$DA6?V(vFOXmI(z}1r?&H@#LFUubWp6TwN(?vTUOX
zn$;W|PdE<J8eED=9K50$gd{n90m@i<!g-5hGg%wdOReWD%Zw-S%^6#Q8re>a>Q)p5
z-4SOQZ<e*XvDGom$Q8W3=X;X{X*vq_G6{~pxbIFHwNe@n)1c3~e$MddEL##;2#ej8
z?pU*ZE%3Jvf<*Z=2}O=D{!180K4h0<Q#G%{w(2T#Q3p1O{9AfIXVqG^j5#dOAet=m
z&68iLQ$Y1xy&-2hXgfPX-LGX*a3AK53Idz7!3ICgv{1w0Fy6lJ#@M%)MkA^0=}9vw
zqiDj{v%X`|Dfzu_A}Qx6`+l-FumddKsJ2bY!(k56&jCbLx@Xfwg8_j#e61k#wwyz%
zhE(~#DIbOig1K!)7_m{xLATJ@t&IBMasvv)e;jC2<my3L-A3<eKXFfc!Xv$fh%zA6
zRG+E(HGGeRI;dqV645#osF`5&e6bIL`9giW5A^yt95Tb!H!8R|h`4)Th<61rl8sTW
z@&~_ed)4Pt%6PVUG{^hiK&M{?+|7)%p=*Y<v@R(XQBL<LjmWYwqM&1UfKbemtAdNX
zw;Z`6ZxYS0s!9|xnJWow7M+uveWNMR!AnX0HStS6I_x<@<vKtT3{n+JrtLcEy58uF
z%A<<2BR{iXvtsOwZ^O_vDqY_GJWe*+s6_7qj{Vnybc)5OSdWx67iC)!yYnl&)N#AX
z(y00(Dqqv$Id%;5DW#f!!&PqQb~A$}`bF*P_iOzAxTIs?#%S;UWE)?quV<){J3|#Q
zExP!9D_?9a-7Vy=bQR(HPT-x3B1`A(mu@p@Cx?o=A`|a-90ZGo8QKo72&w8CBqYG(
z2z?@<WZuGcSy&hj&jmX-uaH`8WF@ZD8RHSMtVD|=vUGm2P>&g<`;OyJkGybsHaXSo
z5d2v}y>iS_Bd|8xa5bxY7~)PFo^Q$_mtm^p@=VxoR$-1+JdG9K?bqhyH{Z+fty>XO
zY!OhdiN^I!v}=EIxE>w>LF^Ala`QzI8&nYK3!A)2&D*B>7?)y9T>N&v*B!XiPBg5r
z<BKC+jm1`KyKFu-?%?T%R-eNgZ~t{l>2p%AKY0K4hAq{@PSO?&I&BSO_S*iRKf$Wi
zLcI^pXEV(#Gz&QG0KAciM+RLuo(2jEAdWC8<Yn!z+gER7<0c)<?diB!eLg=}eUCH5
zIJxD!Xm{c<)l$sZ`bg#o;*?zQQdNp`NU<mt;dNZ4@y&@NSWd3ga_0&Ohw%ggG*~pZ
zD#U@6N4Afgq%f`ltazW_Ei?u6NK^MG(+DF^lT&DP0gY#P?cL^SSP%uMwtOba^55O_
zCFRNyWY}|!Dt?h3@}yy9Qv}H0O*P3c(Q@YWNl{R0F~*<PZZk+7x+F2A(a6i+|9&;d
zR%IqXST%jK^2-LuQN`KbbhNJ8m{m^wZ0GBQ1{8-P?+SQA`8z#jaKy4zx2D0c>Fl>v
zc66)3k)L&~o!LYY=q6a3Yc+eAd`JMUV@#ztiN$(tfNeVmUe?51tl$A40To+8)@>2}
zU8{j_S)(3sG2sFpuv}SKSWRC&>lKTo?`GU|cNU$uJ;R)6<HSY77oF^?UA%_DQx`5@
z9vqk5xSmHf=Qa+tfBrCKq2XD(tjc+2iVRTG_*<~F6BIbfC74QO3tz#(V8>jfQH|7;
zoMM`|8=C@-a-BCI^FXH|UmwpBqBB$pCe0~|0!sVZ@hTlsH7kDqAW#k$@;|w*c{~72
zJ$F?9&Yw7M=lcJ;vT429E>|C;BVXqYmJ8gH{Cl7KB%9UmjuZGdZGQK@#$?-cl#NoY
zCU$qr5Sf|<R-!C?_gCbWQ9qnB{dLsnLWst~Iy@mj1hFx2UAuZ;{aPT*X;7Rs731#i
z`pl8<-;bLE0_P%`P)((eA`0PSnvZe%@82b;CZf6>Z^sigSV&7LS1MdYS<6;sJ25BG
zel!kgXDHDmFvG?@1W_?m#!zt|1-6YW+=h@hlO4glQzbFT50&&-451)aV#EHqjnda~
z$)y@4?szAs(Wv@<rt;I2^G>fa^fcCBDLgglI?4nBaYO@W&MnO-L#oFXY|+FGes-38
zm6hK5JCvW<9|FG~)=-%lukGh?Ri))`*%PgFbeI^X=*Sq9bd79Iwb~7GXkL_S*u8i>
zfx($W=ylx~IkHm8yS!GwO;(4uDIVlFj0xfdke=pnpT`Ml|D459r9A9UvmQhj`T9`C
zHwx{PfNM)h$}Q!Fqc;sLy<hZe_XD^*5<C!%%J>7*vHo*YMEC1rkuqZOb~PZQ#p<Wv
zCfkF;-sBgc^ICHd|9{bqg9D;y>+EGBFZ}o~y8jQB_j9d2KE;XWzjyrDoa~QWW6~Yt
z%=0Gty1&@_ja9#31$=ys=JNeV_U|_H#?Z?KG2dg)`}R35XQ2Oc0pVq@|G5__&Bxl{
z*VK0+-uYhy=6i?$8-FL;aHN=TUA@GXXY*#>7M*{Q$9J`$zBWE3lxDe04VQiy=m|yr
z3k@C;r7V~_v_fYQ9vw9P&2E}#mE{0#OPQ!u<}yHND$~M6^sp47w3IY8qMnWm%JW6J
zKPON+T+?inZ|iD7ZxPF?ra_CSoy25vuF=YvS$PQ>hh<7;AqI}#D#z0@4PZ?NSq`L9
z46hl%RHmpwsJ>#)HlA%Pwr0hO4mOu13Qi!S(#W!)teVwbcK+}MVSG@;emAm;F!ot2
z3wZ-;ST5q?U)o{Ycr49$bg=Oy2ToRt+B{{U2Sw-Vaw+Bvv%B-1-GE26oeW-us40e1
z!RmG12*gzWKNsMSaf^vz5x8O(*b!cO-0$?Wnl*ss6+6GM%L3Bi*wN9$g0M)-U8h?#
zzFvV1Yr#}%V9rgZ&#IoK-I-`Q@<^jlGhX|j;;6Q-n;=pAl-4Y}niBHQ7xY`tJzn;x
z|N1iHRyX*86YpfF2q6P=MjMBR{be<Td6a8zA6WmltuNI4*ZtrZ3H{gpJEp$BNICs!
zlNI<Z?L9I6pXL5>U&m+Vjy`krdN1`~_&%q%iGy%GNCO-+r%-<%AulJhhdLa$3>ER|
zHCkyUOW6RCVe6F@TyDZuu9h*-r!eQ>Equ@`F~30UyA$%92awZD%KIp!n5gms1(JA7
zV}me1TMT)Ek?P-*PYNeFM#^9DaE{Q|6H`=#o~#Oz(bNu$wJSm2>$vvKTv1m_Dfslu
z_lIr=U*EzWF@})NsZU714jpoOHaQ00MJ$<oI8ZmS>X##~9djL@M%yPlu8-afZ6hWH
z!KPUS6q{@|&BLBq^l^dTg;w^ZJlXxn@I8Kz(k9zp5u}#r(89M1=_Nb1%nt^usNdGM
zqZAUF_!}fn9=<}0wE|KIuHDROk1;RoP+~<S!^&C~M3G<LW>)F{;+%lNL$V2W1C44I
zM7wu{P<Qr|L>x^PhcZV=$N*yKKjqoPhzj=%g+`GzrNX7{?zckE$tDtIH!QYIG@TQ-
zv;6wCO`cvq|D^v)yL^+X2jcsk3KGajMQBjqSljsU%i3kJ-ahmGWV*{-`H2G}3r>7w
z-cJ0pEG7<@ATQ1(ZG#nwZe<t(O-auX9xk9D#rhhg_JjVq+IxEjd5C}FgQD_KaLLYv
z=jD8w2^cDqe!G?bkFD?TdG(8g|KDZ7hjrF<wYd=l8f-5u4gUzzuysD4xAk32nO2Y*
zzIeZ^9q_wDu%W`>#D+vNU|uUKixM*|d~rWf{X5o06Bmg@#>5f+129FL+cpq3*|cb~
zK#ea7tkMNDI3)7BiZaF>7(hs{_zl8C1{$!?B~5Rkkw3Ytz_0KNu+b!xIl2~{w1Q<K
z#v!3W%iBO#1+6x^)_sv;L8+kWDaF?!jRh9DrL<{AB8#0gOe@zanT%l3X0`;aUhLrb
z^h~~{I#-*AlvTXp6&SBpjd8Wx^+OO=s8dxI=^;@U$B`!s9B&M4sN1qlt5}l0Wmm75
zi#74LrEJmsGEL!6a!*@WC&i4Kp$db}w$nHOKzx$5t_G{fZCc?MQau&zfN1FcZ=--M
zH%&vkrem!rzDV~t*R>9pBXf28UaCHUR~1sxZS{zi!348W?C{A42Y9Ax0#0{`T?|i2
zA>FnzetrJfJt#n0Z-OdWNI;jee?seEq$p4IKPycSbUF7KiL~BDSc@<c*nLm_o`LWi
z6~(DUvnD=er!+A^$qsG0cP^Lr7g*#~I<EZ2V!nWJFdq)Oc~t!1+y~FoKf^$62EqE5
zp}ntPAGP5EL3=Oi`gmNGW5A(Bbiaq}y^+uNzXx>vkJGpe>74ZSq+z&55q^#BYJ=Wx
z3MOF}t_%d<#)OzShf?4ej&pP$(qw+;KBzEcS|BSkG!jHHBxvZ6{Kbf%&-6$(iG?ZQ
zL!(;a^U3HyUN%muG~|-&WCN#7Nz?@1Q6w6LLe+f;mrB1*_^W;h#ccr<8l{1?-6Wdi
zuy;wU)CU?q3j&TOQ!~;uozusr8{@X2bLp)j*F?eT`ZW4vObWEqS8BZ{MZdf7%R0<>
zae>jtlQC1Ji9q`R+wyX^w0oR+z>FLI#l;MvSQXg7ge2uAlE!7AW;c&kci@>a#$J9t
z)SiH7VbXhTXIVi;{NKB(RI>PWrdde~t10I#2+_<UwpSn5khYX#?N~ShTD^ljQzgb#
zQBWCW(}}9CX8XI$pTy&7!dZ%#72>ioyjk^k;}0t<|CW>fV5!iIU(e;HpL=woI-|<B
zUz~Ef@SUOZ_T2<$C8P@xwxkJ@95gupME$5z-s#&FxPJm4tR{G)P!*%($4B41ATDOG
zH|r4QP`diY{R9tQ`T6!R&L7B|H-IvL{7ldT+Cd#Ffsp~OR*fxK5%d>GHL-x;cSFn<
z7U_i)|8=0w$uPI~If!ywNG@?Z{&nCVlK-WyKQQIs_(2-hLF4E_|8c%^@1vv3DR3fD
z<bZKZMpZ<}Nj(K6n<B$v>T22>nRWsMEqcl6Y-}rZ`r&h$rt6z*))jx9rI^6K7oOhu
zBlP8`ev6v%iJ;Lad7S%Gupl96qE#t&E`l_kM3`x^RU{WZ(m@1QutR{nm;fnaK6Uwo
zsa+0g=u>b&H&dh-iA?%4usTwIoX%0c5%ss>6*KAtyiyjmtlUrmYWd5Zt&jt-DHY2^
z$uh|*IO;AmE3S?qdf?e~RNCe%Dksbvx3i*&CEo_d*AI`g)>PYzHOr+vesOjm225*)
z<0FQ>u#H^23JVhfs;;^;66#w3+1>3#-owl477UQ4V5B7>3k(4%s<$}X)(*G#2bU6!
zlbx9$4PGq=<02kEn=9C!E=UOA8U+Uq*+9R4OEHN5046?-Ee?M$O&5QR2Qb}X7i{5+
zUMrE^SO++MD`Jay8H|1+r+}*zk6~Qy3!3H_RiH2X$9-kScjq=fo@dnOs_T7)Bm3EV
zp7%E>aW7nuSF$d`w(_D9?UYvv&`c>YK^|?fnrPLdjYo=CUhk>i%FD=8M^{E^hAN>P
z+3&-pQNJ%(8-!q;33><s@a6p2Acc+qy|%G)wXT3a1~Gkn>W_-{hWX#M+5-Slq2Rl|
zm#})=?qJq4zr%Z&e4Z~g0R#Q7Tm2yl{s-=yZ1uSjHBW;U%}8Cteefub&PA8&|A{L>
z(hL?oT7x;s%xu<iB=XWgyC%JFhC;?Dt^?8zCVUio4yYPduD7i})bGwe?hB$ZiVxR8
zSY}sfL6W0%PE~V=s#eh>NuD1@5-dd?HoPF|dRI~;_0JTrO4%q!3LH7;qD^w*Jo160
zKzbPweyVR{C0W9Ym7;RO6Q{?9jzJLs#Yi@JG?kBx?ke27C8S@zqV<wbMP}JIHc|1b
zGJSh*%;IEn$)@fmS*RkavPPw%<rNXq?P}+UL2~GHsV2$30eqs_Sz$D_zfEESyJ5Ew
zx)MfFz}d2D^WJi6L5iFe>2IYm$6S7HfB6{Mt+<*w>Ru>`<xJ4&I_*3TZZukqBylOu
zekXrl=A&#ee-y7{*R}<D9R9e#csV<3UjJobuv?oo$qslzAI!sZa0@)V{t3r0_BL(D
z!M;y%(#))Y1mz@$A{TT*x-J|plwecTap=u}G(!R)ycKPcO_K5rEY+d?=^E3{o9KuB
zE5;_*jGN;_T!Kzyw5{Vg;)N(-r;B_=)tY_Bw_xGN{-{YoXO8DUKa_8N(LTjm3b=s3
z1@zZr>VxJa9~-FA9{x{6;Sz6zxIhkSw5g2$dwVd3(W?GDXYzr@U{6$iKMKAXeYIZW
z%uzLdx!!2zV&;$Y|EN>|uKhc*&Q3a!7h0`L%jIMWsAK1l%E}EM>f3?XvkAC*<Jl%u
z9i>K|-G}g|Ivm8n*Z!l7ZL|}F6tE7+p?TyROzQwEG~GIhBOdv>Thz?CTJW-UD~ahY
z6L<7zffo)KHfE=H*P(CK2>@V8_~e$+KAh^op}Qbx-bJ=RN(+aQ*OkBU!B1*J9+6^(
zd{|ByusQC()MxB4qZ<w-P7jM#oiVH>u`SZ?t}Ef|Gg>RN1%yRALL?21La}N$g&K~~
zQdLgDvoKQy7m+SvQEGxop{yY#V!uy%DKMF6`OHA2)j<_4kqM5uQ}+3yM1gJv7)HHW
z)nyBR3Br8r!D^Rna>^p1RMkdXXreR}hc>T@2&0m)Lrq(Zc9r$ZP$p3LgV_U@Ct9bw
z4_fvYU^+_QLy7<1HHqn~@6oGbbD?R%$W^4ymB+@nhxvBn369{K=Fo>SyXSLAB1wxd
z{3Ri|h81*24}rm62IaibCZR}SqLf>*Bunj+)+J4DQyoRJK{RWsNfP0Gl1&-2b|7E8
z+3GmXQ_{Jxs9m8oU6V#LJajZ~v<ZBAylV0xER(mK)fkxY{XcXmc3qFx-cs-Q;|P4i
zn$!K=N}hjShU^Sf03Wo0!WuylL~PKr?SrG0A{>$tjtKOoqY!W1>W!{M{C>LsALEzW
z`Vanq5LG%JWKd=>G+;d%RM+zW>;Jn%JWPGlgP<?)36z`hj>twSQN}xUh6FS)fVy(G
zU5fNr&}I0&|LIMY63C7JqAcFVQWP2z>}{9@jUQ5iZes{AgxAA76M1)Ym8+SFOSy9n
ze-;hNdSUnNQ62E8tk7Tz<vyXFqevCO2!JoX)>t@%W7h*lxiX6rC3ei3ptIH|O=Do)
z`B#AuvDVWlsz{}O6O*Jh#@G_G3RR<?&UY`yNrkxjlHvFtZ$cz#)T_rPNeqaj$u>m-
z)Fh8lDYHYi%!)V8ecE#ZB0TzyyGtKQR);BsGm)2R94pDmiqbrm_8~`G10-ni{SvQ|
zZOk<~a2uld31ii!I|U!MlzS?Di3Jwzyu5L$beIIHbfy|1Ppxp3qsi8k)5CMKdE;O1
zsd8YY!dsIfb+2Hf!pZx2nctBqgrhM?{LCefw2GIsKc~iY54yiOVk{+EgS3bja3Sj(
zW01R?j^E}Ou9DAXq?w&~`AiR5FTl=CqZZJ=25lWwL;|mC*8nA`Hj^(R5%kmp+*Q6%
zm>EQ|u_W7FzKGq_OXx7Vs;@h!^^IoV=^brcy(~gDl7{=)m!F}R?_>_kP>fSDak7;=
z@~E$lXi__;qp){lFiAKf>t9Rw`FIn<HF&u|S>oisaXeZVjzv@-1h8aBOlV#07c5RU
zDW5~{Zav)Y=B>SbK75_u9O(OfW?Qsu0s@}*Jb9@uTt7}{ew=oAvDT@pGT;=uU?G&)
zAwKZV`QUs3=ROJYJTERCdRsy5J{T?&*6n_t<1Mc;$e1ip&u~JaAr#Fcn*EQOTaqQZ
zw>6gG^DL;Dh-$GL?JgU?MHv5!>|jDigWbeb?i0GE;~N>sCXEon73A-d=(C?*ewArb
z^hReVI2A9~n*B|bwVB*`Nq*tCSXCfzRS90~v>8v>foZ4cD;KxLz_NZJ`h9{;z_ts;
zJ$rmp6U{sgPM2a4Ysz?jy*g#pc3elYU76SU1RK%er{Oat^;vGR3C6L(!7nIn{J8`@
zj`~$=?XyyW3zqutTBtOIyl}@(aK}bUq-`!on-b=X5-Nh@Ub8oEJcFuG6FO9M=Qpi4
zQ~U!Mes|^g|8~F=buL<Xd$<4r%@@Fzmq*RzTn@F~q(47LH<fUU&jIh^aJyQF4$pS@
zHfs8MPI3u!QXJO;kU_EH*ycOvHXrLEQQ){W>s*>Hah?%vTpV&Y>D@@_Fn?Uwh@z4g
zjb-{&?@X|*DVj8j3G3<&rqysGTB9ggh9s{A3i^`8IGu6tR8zkJc<QKIplGsEBEP4T
zN`aNR&uT0ThCj>hXSS!aDi6?vq*;sDp#N7N{$q1EUJm3668jzjxtxz_L9iJzfXnZc
z#BQe@2f#6+b)`;~KW>{V_K`ePejC=Rj$gKL23lLAKW^{yVeYn^qUhc943>_pR+`fb
z#ZHV3_y7oj6y%9&wNdw<?zo@4_IiTMId5IIw;RLWhtAT)tpl)!fsFRG&}UiTg4l!D
z(8x=N^nMve(~@l)R^Q6`J<<g$B4L4jtvzvZHwO;B1FJFUqMrdO)D>n03^WR|PlhS+
z4P%J0=Mmy9<m4nkA!;;CjG}M5m5`E7LwBT80Y8?cV<*Q6H@Hik@a_|3sQIaiK<=qI
z=vc>+l3YJKskHi(Acb_$Q~%@HRf^&>sbk9Xnh~{l24$G*jnYMnMh{QGM^TZt!c#zr
z)=7|RZphU$H$IRQ+(Li|86nSc%C=}-l<O~Co<LI!TS3}|yhOjb9=v=fe)--#okLf6
zcW}@?G*#*ET(7DnpDXMHwM;<wxoyhFvkx<t1K0ZlQS(+eUqUWc8m&gl+EwUXi4dm=
zr@)&Nwxg_*_q*w+^z+L%NaT3>gVN)lliu0XYk<JUEnQIgP|oQ`+n=CFl&;j$6p^G(
zq?1+iX?OkklTBJhS`XJ-bkU;dj)5O#GR*^VNb5>xdbrTZ-OBgx`v9uo1^x4UQ~j&2
zW1($cwLR9Q4-;cnOYpjs-=xJhW|ACYUD>4Ay>~8n6#^qPwvWrGzkwOz+zdl;E|rKy
ztDDuqB(;308UM%t^ZGC3aLHj~z+z_V74x9p(B9{7>bvkc9~6LMwd(D)c04$MQ70e2
zqjeLeI^99r5e?vgpeu>=UYq{`8Ncnv`!~KCVZ=^-J=qw7axKUA2MD=Kn087Gb^SYo
zNdH9|HW55Wup0~U6qV)!H;0BI2w9qgM!3L!jAep{#*zTd7^4$u)Wp=m2Cwf%pJGEt
z=7{c3il#_Vqwdh5c2?P=8BE<+AbXoqpu7Y7B-Tk#S@D7Z(b-|wQOKmut@?mL8^2=Z
z^iB8SIk8%$!G&~NCM{i&(Txe%G%xCQT#;neBcM<PXX(T`=I6uX*c;_~@(!kM9zmDT
z`tw-t;h$qRdx%_>xMg|pr&PN#YWlm%wSc~Z#c&|PSUbL2|K;E2<P!dpCgCLZ$_a{`
zds%9yjyw>C5c~85y^4?2qGbzuk>uM8$APTuR)9Y~1DEzajw=4wfw%kt+EqWIumug1
z{8t{-kdm)R%U03cRCV;jDK+u=82ttB6X=Jr-NSyv1W0e8zE`YcA<WDx*XqSMAK$M`
z4?rp1Utv>qhV;d|m+EYExMM;&bp9I8ee#szuhdl>#A%n;1l=Bbj$}pU8Jf>@Dn(`W
ztiI@Y;mqwW(aslXZ!_KGlrp@uER3Qujq_yV^X|xl;;uVSj*myEwTUEB|3wjNlOQg&
z{p939kbI?P2^P$CW@O7|Bl0FPp}y>H%1)Iw`gU6he(!lWABTtrXA*}aBze<jl~<?+
z8Q5%%_Qc=Jq1VRHW6zT=|EgiK{bPj60L1YGFM|HmP~);L;S{qgzC9%;9+xrSjVcf5
zdtWX7E*>09hY$@hccb#edHA3mT%CFF^#WS8-TBPV>;NKj8%6wc+^bXiNPi|8>s2X^
zfYBS`Qc<4GR7Q4n7(xm0EX_r_4i#d=e}NC)v$!osq|NM>6!n1E7E&h)rZrYROPMTH
zC#&$99U7lC(uI8*UPCJPxA1q;ZG1b5#JRPz8IS<B0N*F~IO8Hvr=>%E9>*!<HuO~y
zJJ}K^GMB?O(ucaNwgEq3gF3)N+{T-X&L(XYJIu2hbUa?`^>J0o&J{Wix?YsmxuW(N
zSCNk8L!7M6=`5qSou@Vxe28C8)O_T1*M>K}AY#oB2<L*C1lV%^e#cVLclYV+s&((Q
za~}Wk#hN!?XSz*rYT9aBO?NR+3job3R^QSnweUxMsSk;$uH*STE^KQ$#Q6HTW}c7^
zlXJpB?WsKT58+OqWIskZXvT>7@+a_Iok^7p6HLXZwyi+biI>OUZ;)<S__(fGNj^dA
z!bNRR1bHML)`>$1T-+}&I{GB=jF8fA%ALFkZ|fyGU81gMcyIo4dkk%Uwi4O*T;>8d
zR7MQ=-0#WDbz!Md=e^u}vcj?8C)(#8(bTIEmLkEhSi;M1IByEONR8Q%st>-zGivzN
z7f3an*%oYC7HrxY_H7MaT}1uPT6bMXDev`Qq2Kd2*^UQT=w(t=7*8O67~|5JY0<=y
z!oaYM7%=C+cX4xtB3iR6(}y7B*%zN#_uoGVg!}(p5dZ@$i;Fv41b#N|8FfP`U)ytv
z;i0(!9Q<GL$}UZOi)petzmxZQ|E2$TSI!^m|FWF_mvS6c1d#>zvM$A8ua3u#xE|Cq
zJ0KiYFX_o%v;~}(hYJJ6!5ZX{HME9Qku>ijaLDD4lFC;g15u=D7F7l1KU~xcbBgR$
zF(P?Q?=6+f4mVvUH=PeWHEgoFs0gQ3Y5<PX2u#_E(7FBMlRzxKoz6T(;1W=>`Gv^Y
z()may$rid&ypzoYD!p_R$ww1*?FmitAk+<JMD5R%7hj`*zg31C<i-Zy5fP>akwpAn
zI>!*_2s`&FhVrABiuB0PVUS<0x}Yi}INpHs=W<t<%O6);lc_}B$~i8aavWhvJL}%k
z#BWAhvMv+DXPEHFJYjN8Z4k7_=$8}1A^uF0Yc=V+`ntn+ca%P_m$temSbZ47DQa7g
zgv1#&6E_lfTB(7;8@~%+9Ax6*o$hXI14s%mbK4N+6juAu%k;!!3kcV_y7}XV$vJNq
z@9n56xoVU0@$h=nsc9jrZTDke0q(yw_BmlC51ak?_cpEXco~hoEHtqH`pKSEBQ1#n
zKiE{nMk!p35y)T`y-q~>3B9}uYWCSGV;*e~;Vh-tz#(a{t(}xVJOo{2wp~l`kcN=d
zc<!_k?zyO?=r*8KIR(+j6*O;L3f)56#0@ml@+9LFo8b6c%)#~hnbWgigsWSb{^Qzp
z)&z(=hJ&z>!3+>Ah8mzh1AlV4VY;1)fqN+q#RDopL)Q`S)KrS}>CAhGk$qbz*Wtey
z#{WGWgXe6V_1(Mdbz$cFH+V_iZl$wer5wb*diw0ollpvZclMclGm5AU_*G5Yo%9wx
zUFkdMCcM$+u(b{XR;N3Bd%~0-u~Mm*Gw!J`ci{kRcwXqPfZvjsfd(?@n5o8jBZ*RN
zTt$Hv2pE#`ECsCrCJfXIis%ZOOZzD>W!tw?AejX8;7>+d){119_n{6ZZxaTTwBtYn
zRx>%!k9Gt|`%&6S)=a%T!iZMUSUN)Jn^7LS>B#@-xHtKvwTy2p7TqjaagJ(QzG6KZ
z^UiRzj^B0FSAVsk$eN+Ll(scE(~XH>FQs~?*a#Rzwf!uiEZb6lloJ|vJ@E8XNDIh@
z8gDe*((Hz&_`X5(Z)`XEWx8`m!!a6G-ym$pJ>hYWhrTV~49$6GWLpKgvu*LZ)~_0B
z$SKkUYmz-+=t-xe4(E*&nRX%>R@SkNC6IijG(tmW*WPhi)Fy9Zs81<Ky-rC-yL4Tb
zd|{ka&POo<AKNi?bdq}F`y`>Bn^1$)8Ql%UtN`(3S_keVhc6_9orlnMgV}rvoeJX4
zz|?*ToO7POek`gZ5FC(ZT#jz-JuTnSR(4Yi^n$er7(fxE2anchpht)!Bw~=+Eldpy
zaU2&JKi+#+P5s6BFLAEvjf-ZZWxkD~dKMl7aGhIo&~49T*qPkL{To-9Jb$hnflIU>
zG!)^-t7w8rHUhkzes|DO1H@j5*g)Tu=||Rxd!OxmZa!Y;k+B9Edz<mo+*h0&^<T3N
z^@L#LFjatk4rk~fN}qT5$R-CE4fg)!24#n>%ht|hgVnFnzM$5zHmX&SaLJp>y2HIG
zF4KzT8{0il(9VESe|pvW?&SydAhe+M=-9St5_zfx!cQYe8@zNOFy%fM>uF2mxqD-H
zDE!-M{ToVj##sf*wU;;4+O?#itaB19RrkqcEgee19(nTR7v~Klxi)!Y7Ic|*A}RJh
zM0BC?WUIWB!3Up8zwhNAj?cm>;lkaRd~8-KQxRUMYSx>WB|=}@Pu$MyNMW)~9)0mp
zc9C!!Qxq|fD^c?qLN5E@xKbQN^t^ddezhC2mXqt(D%8z_G-A~|8)*1Am<R_iYobP>
zev?n$R40v>*tZ`;Y=>o2{znmYOB3wUV4MXSHd-Cl>PBGX+O?M2CiLVIbx&YRyju?t
zphfRU=ho5^b-B9u<KyLf!VC8ca5lFwnEGsiH$?%qO50u2L)Ok<=afNKfxS%Zcfh_a
zgI;XvoX1KC5Rba0<rC)1595uL{bwtVfz$Cg64M3uk7xFs9*8D}&2Bk9+G<O$>e3`z
zz8*6K+nIYpwu0LVlmQWHCy=vjoFoU}Hds*PEgBM=^Y=Wq7!g9`q~h5o<VNyP+hU|&
zp2f6f>tBnO#KHf~C~=^S68K+6@dL!0h0+nMb1hb>`@Bpj@wt<3KQ*gW7N{|rkDL;=
zW2!w~5B4~kD~7Gr>5uPue!X)F*N}Pz#A)Ck{u%gh9CM2Y=1t;-=<>kD)(*a$h4-}L
zm00h3F*u3w{Y`=65L_2x>zS2k5+f5whD#iiWB>!{5ta-UJy5H~9UDR<fmq;VlxWqG
zq%7Dp2vw|9N!CqdIBO7cDT$&d=obU3w8zp!s^_p25QSs<=9-nGeSF`_G*TliS=v>V
zV<*Q5lRYr&E{30f*3lmbS@`F`91J<g);iA1vRzeWr>!!{dVVgb%-I%Uo>{bOb@=`I
z99X5h&WMobg3_-3051|=hM7#GV8IopuMb@_!*K9CDoQEZIUDIdw=>rW8EB)2^>x@5
zt3KMiRz|qV%o<Oh-G*Nsq$ZYF=I4&p>E3N%)-3$K>8Ltlj~W-KToxiNirRnST%R!6
z>b4bn;eE%xH}%33MR_Di$3gfwF@Y{$f4wM0_gMTfUTPI+<c{QF%`$MeqxOLeg_G=E
zGraA(YOVA3i3*yZi}jNjX6b@0mMC*!@EcPClFFzMZ0j_OL_a59-6UaIjTml4lVWh)
z9Z?SOS4WP=VJ~Bp@k(!m?&XEyo@wEV@$tV!OE%wxl4SA`P(mWB+ihsc$-je6N>5K`
zG_+OCDi1&ebGrco)*k=642>RqbOCm>>uv+NKYqNpGxb6O)_K`G3q5!AE`7;E#LI&l
zr^amF8g}5wK=u-P$xJe89K8;_+QPjUgW8d-6H6d-8H3t|C7!rv+2z|MZ)zo{nvD5E
z9w#~H+IMth7oi3*OZ0@0{Rxq#P&n$)ec){<5SEhzO6l95sxk{<5<SwxNjfDAv?YVx
zqLr+QlE&|rM_D!tyh_Ay|IY;|N43!jge!TI3gp>c9w|M%oo$h92kmyvj~2~}LzLMR
zIxmCC@^M)b5E`x|&N~@?;IkW-l=56|=^xRprC9)%vk~&eOMZj_Ep~_t_(*Wl+y1%C
zfALXTB6-r8iZ9i6JY!GSm<$rMG!SN!M{ym7Q#WxrUD;oDN1Wie8Ezn%t4@5n={e1m
z4=5+^pNaxj^}5N{j%YF<1lLo*TQ=>CEGJgr5=<u7(2|)EiKbjTHOCXJBx@!`Gm`W)
zLhoMtcm`vsMnmy2VaAeS+NdvDDP*pbl2hq0zr-&#qhe8TOKAka#Eax^RUh`~k4P7k
zd++(~?<EwVEqjK#Gs}Wzxw3SBB!fkX*L>yoa>geCJLVw!Cz@yLuIu_11R+QSg8<)e
zF-a3KX%=8yHN)83Sb!Upds9E`*?99x_kI)2J%l&fyNv;JcQ|u2r}I)<=N{RMYJ#y%
zrM*%_H4kZ&d%zL|fp!J>zG~{f))F-{zr9PSsU7EPsu#z9yku3xyo@F$s*m9G5A#tw
zq_pU!m&I&x?j?!WLhXz#cxjEC^vaRK7M)Fq%x-f^JAPRR{M*`ejpxCC(fPZ67oL{y
zcYYHLN!}73p>cuMG#)1axj#tK87sZ3no_oDOS{|IwyE?)f4wrRv3Qyg^5uQ@-ObCQ
zCR9u;Z2>2`P!Y-GTzg2ohr_NcxOSjM>5urisD{1=nsslGlW(P|(bcUWeExQ(?KLBY
zv9KKe5f%#-dI|rx7Q(I8HGsxq4kW(8-@&qSc5^C2P^LtdBlYg<?J@pmq>Z{kePU=}
zfrBe{g}~(;=4oh{kZk6J<nX3r?Xo4_(92s-wpTWn`vlhbxy4(91s_pqy3Io*Qd?F~
zcB+MIhCaii$${1rD{_Ij)pUACvW2%%mtL5Ca^;unTmMhIkG%^T<?VM5bd#QVI<4#w
zgH^L;wWfa4m3UFv_iT`p6(A3xAG6C|bgjzA(y*miB11W<XM|){X@uKqBfu(L=fb3g
zCI*S}GbHOz_a8Yjy3k{>I(9E9IL4H>yMcK`(IUY`4Z4lfztP}kdDi-A`^(^?f&`M$
zWk?NSG581OHVu&3;ds7KIgr_ubD;(Z%OD%6Vb&Bkj~RjAA==bg7KKx8Waw?yL?N44
zn$$Bf15nGNMJ(dX5Gd%hX26xpk}PV{Rq)C`3C-q8-a>m-V5fn~R#A($+Uo!?QK1U!
zd7iA=FB>%r#x6Rn;aOG0AA?%f)BrwA3e3Wvq9a;24rG!6lO^<7G~4Tn!H<e{Os{V`
z+6pG`Zv>X$)53oh``Ll<B~0Gtt2^bR-#|=Hdfj1{h<~}dT0@16jkX8owYo^-dP(eP
zZH%no;R^D|S(5ow?@Kv|FxK^8wdgipSwao$CB2dy<nhWGBRQg3o~$<Ve<jID<92+P
z*x=Z4442k%bF`LnR%JUjCfR)#3@Fzg@0!qUn#H;XrCOgv<kAwgXTbm!RR&fG&Aa*m
zPec%SvKi+5S<kJ%=EbH5oENr$7r<7@EvkmPUp}YIC`mJ7i;W%5<mbGUTlB8B!iRFz
z@ARAW%mi5wf7a}i8(5Mxii<+t!*FhoUO$nQtAEw912ULcaOF5YjdqxwY8xdve;;0y
zd8)axEg=W8PnF|zs#Qd4Bw-*qD~@Ljl=2rdL85BvI$sUJj~*!PWb)dux?QnnoL^H8
zn$?w=Ce@PSXRKhtIyy4R>>!{XYRk~cd03XSS2g+YF_;1X^bmOWzK3RSXPe5=C0V`V
z3d%DZNpzKC%piLuSU#9MU_ligTh$5#1K>}!JtvrJsAY`5p%JRTn;S;~fGDPA)$70T
z1OtWV7p<^G^&PMHS|Mlduq~%ObK=00QKRDr&2eno`9B*kJicv0JLJV8Yp)Pkv{e{l
z!UZ_Z=1wxfi-(J{RgHwdcm|Z)L!c8%Et`zNnOsG6px%@{jP>g()Wms8C0(Tqt&Q+y
zS)><<ZM33q$cm<*i!=j4gfgnXwt@!vG8yRxQisOevHOVT*^sKE<-+^cqG{2=2OX-;
zx=TD~V!kebyE@-D9<o_uZ7?cyf6kaIZ%0-t$cSe0!gRDA2xWv)Zd{6nUZgr{tGY_I
zX;#~RS+&Ycxppzv+meB7;~@jv(Y0==*7(`EgVV9a8x&V+xp+v|R;}KizQuUPyH_7+
zb;>Ca+wAlblwvx`wfU429^u)5mX@d8?Xvl1?=hJ)lItx;vTtRuiDf^rFFqEINWy=Y
z90<jV`b08DTKc2<Ilsf#vKyW7Z!L!ZqWn~@;cSvH!6r_a$|J#c$H%^1N4**fe1h$#
z!d$OW6ztTpZ<JCiw?8R8#EV6LoP%t@S$QS<npKY!yEox%(X4%MuBx+TEm2BA=42D1
z#wkkSuh*I2mLJb^9INDxVBs*zT$$R=9CoA005=#oF$+Sp##MaCH#K9JAK9Cj_BHi$
z(lwP=Xx8~s3P7uwN;Di&;Sl_I-?NTX0w+yTw)h1NE&wbWN^NwpR0G(Uyl?O!tV~m;
zpGW4UQgcJLW?JHPC(vfPd3Kip&QHraEX5NHN4AbV9RYMVjdKulbXQSv32d?r7}7;n
zu;ng?n*8{3jOKgxT)3n-xNd#`%(mpmb4^sd@3pnKB|DgJX%0D%p0Vum*S2(+847Am
zG$oUe;-=5liWqU@Y#!+MD7U}&TkFM*aFTJQ*=FcKC0ak5q=NyN7-P65irpq!OW}jT
zl$`JKEy<g+xC4)}Hvf1F(45*%vX}nrz5AP}brVI`X*LZG$YFW{?ha~x%nuEHBR+B1
zwDyLE-OVkZ_!SVCQ5)E$li*pgYjXja&2M@JKKW;}0fEQIW{Yw6?1Bt3v+M+h(}Z*t
z_Yl_1Ak3mqiQp>xBziWl;{!voDzm;;cO)0q`5#9Rx<++@T|P4YnZz(6^EL5I2<d9s
zf7Pw+<55Kc8cy5-iKx+*ye0aw5f~4aagSw`iFDRqjdh8K3yTdO*iUyY5i8zq7y^rr
zL^dsG0#|W2Sg$~krGyuY())`0WL&-3ON1G~6PGQ27TYh*Q0B5JX+OnoivP8;WcaUH
zn(f?b*)oZ}Wx(JJjQ+$Zzu?;vhq3Tco3qumL^GMUpR>dYD^={QW<37MCfDi`W1VtJ
z(F+CO@q6-X(EHK7le~~rQJVL(r~kLbw)fvu=KXs$aLZo;=Qc~9b1r*tA157!SK)u&
zz|Bvy@&7zW(=7b8V<zs>lYMStJoc4X0uF6XcYoTtWV~0|QT1E#!H^#PaT8B){F(sI
z>*YDix|t_q+1)?_vzmf1f%-lPDFRoUXG_TW@F&Um&bDsCZTtEZ|AuVzw$|nidE?!f
z7IJCB%d}7tL$LyGJ^ObjY*%VKe=J}CJy)kYqc}m;8KUjR0+@Q@Q#B(D68sXlaw(*8
z)HhyK5Ax*SRV8)#8w`=wcx0qYbLz4EdB!d6TX~6%M?=E|L=13`g*M2Z{@b`I7SAU*
zuG|tJnoFGa#SBv|UiHFzcG`K|{-?w1E$KU#o%LpT<1fDNu%@`#?SMf&1S@8NgjT#D
zW)e?$BN2h0ichqyx@koRW$q9-=s`fE<dSHIv`2F-F$3h#AuZHHoS;J2MbSYe9da9&
zjCsx+=w_hEiiQIg+sk%jk*NjEBtje4nNjieEJ8$ZX!mVVET-l(%&hDfaKmWFb@tCT
zTE!*i`mn_}cKS^(PqsNQ=j-bgW$0R)hxI%aSmLGq!Qp%Oh2-I17}lgith@E|$j?9X
zWQpoq{QJyPpYx|Pnw#p8?rw5K^WIvl^S!2qosb8^>E#W?$;IGt=)teu4(#hm{@jP2
zfY^S;_W9TsYa98DFOI${`st>K@sY4-j31Szq%E>v?~h09J?R(Y!ljnr=l+JV@N!0F
zVz<JXLiwPL`PPD=RBOL|tskj${C(bN=-4H)css=zSpVFyx`Pa0ySPGnD_8z>XhQBx
zamL5A_@UuOLEt#I!K~IQEN{@YzxnP3GosXcii}(w{3TEPy@EHFFb-2@d$jdS`!V=4
z*TkEt6Z;My28gF*j<k&ZnS5JAJ`<yq*P*Yi!W%axcjm@zmEKz3DpbWj3neIfq|4OI
zJ#+PWnBn}s-GMpqS^SB*AsA&&>wWnWNm2+~OUep$#YiLpYs>D|!Mmx~D;i{v?%!0@
zF}2-i45QcM00y|tf1P_-efB)Yl2z7C{g=Hh;gD)lsI%s~r^@D@<d|6^{NzfKI8b>J
zNE${i8Zi%!UDf(Fz8x`ZDXaEqT(#vjL{QjlNCZDH^QF~`*Q=K-RblFMXhge7Zztb=
z0=8^ezryyTRX#3;n4|e;v>bwKmL0BTi)PB|+!ieDJiVE8@MH@#(ZAJ4NVea?u`ESl
za~)lpaO{>VD@&M2T<IbqJZd;N;Kk^8N1Im|;J+Uf+H*~!p&GK~3<yU?TbfHtl}|3J
z0$MYD3wuHhy~Lk}|G@G3Y;EAMwAmbb8R}d^ps>^zBAdc@9W$xk45EUk84otqTx4jI
z(W_KTorX70Pr$PFxq7DGMY(l|18T-+7H4n5;e&#H8|e2JC1aKaK>L{${<(=-(P>@q
zlDI)BE#rvd^b$Nc#9uMP+dPFOFt;tyqbqlCaljT3eBfq_&#g4ts$ieP0#GeNny1m!
z)q@q&RCiWXds=*&e1H?MWxK;U8Px5uGM7EWxpHE)POwGs;yc{)FV|Mh-vs^&B^!pG
z=H@-r^IzAVik5V#o}l*@tAm-^=i{-Jt>PompthaK+JR-45YUOUmDnL!i7{5%Kd?F^
z<Z>aB&F%DWA+2m5W$ob29+N@v{F(|JZw960Mo>zw779WeA|+ex3h8Rw1PVEA-;%&?
z&1&2GDpjZ@qC#Q<elyhXBjaTHZqV=cxgl96g;<&mwfDiJ;n@dNQ$$;ufwy+S-^$ja
zNEkiXKD%;d+H~818lY`bb~pZPllb9wgl$+3d@vKT^+%ln^do>pvIzv>B4m&6-6F|W
zj{C=>n5@cb>WdXTC$^xtS1`SEO@3BL7WYz@$C7_Gs6&mJr&x<=B$;uehs>tA+NLG3
zC0Y~L9cA#QPL$^$r<_*CO3fuNoPIzYR7_fEl@Mkjl;axJt|_>1(8l8`wCvhgLw0O<
z)`f=?Q&QaQueN?N72AS1aJKAnU*YKs>{7Cx+jkR4m8P6XTITBWIs>mdcB*(AbCV~{
zo6q0T;0p{K9Go{D4nSRu7&uA2(Z!aWN9Ql|!LN7j56F&-VSm8;YW{GTv?jupTKXY_
zyL|sLugj@vq7&7zwx3ll!tYP$Y+Y;5dc*xfs=fzWL;X$pSCxPG&i>9fa0A0X=l}yD
zY0$v3ouV`Psw@DYW~roJgzj9esq-9R9=P@kT8hTB3?w1VIEr}<`~(jnTR;EFI)mjS
z#lZv~yIE%fSSX9`h_Aun#)!aX+v6#`gotF*h7h_hsq3rEY*|8Vl6zRt_`zvHJw^X&
z2bo1z`R6YJVWJiNuplNEs?i#WbCQ*OHC%dM_Q_&g9LLf%o;2`eT5L&Y7wg;U_9&b{
zA?yJfn)S{waNb@~0X1-st#3uI&jk(!B;DFPw$9MLQw!~IUt|2R@aigVGvk5PYp^x|
z)X)8PS@r|nft*vI=8-9uFJHAp5#;{7-VZdpF~|X_hBK!$5^a7hn<uR&VveMli3jq<
z9Fy!{vsG9IWPJu%Fr4c@s>Owh4*|98bcJT;*`0``v5jP9P*5yxL{iQ^493Qb1`q+I
z@Cns+DW%VEz_i|ET;j>yQ#;X!1_sB256|hl`yn3-CmWv=%(v#Z94?}GW1k`aV7FeD
zyMO+|+>XR!W&F{N26u`c*H87pbOAri_R9;RNSpmy1Ocn+?SI4V-DUX#-q0bo{+V0?
z&XsppeEqBOy_3Ik^e;GlIkTOinXcFe-a_LZ20ZXOTBIFi(gI8Y2m)&#5#WEA+yR4T
zwlKGdF7ud<Yzth}$vMOee-i8?TEBjlw_UqJ6iTu=!wjWVn}5Tzc3{zFo8S;AnrDV;
zZMT)WC0*%hE8?MCF)9M&B2u-<?V1IWD9<3PE6-?cE(#=?-bOZp`P^(axtH>g)wVK{
z-8Zg6f$H!c(*ASs5eGsgG+-`2dyJ6RjV-R8p0RW4>hk7>D`8M`!*}h27K2akzmppZ
zeF$_yBNK%UpA3o6&&M0W`5RjpZ!a#pwD3r2YJ;K`e?j9)Od;OHZ`e>?*x0>!JE}Iv
z%xfjlY$=hq1B1wZg3llDyVoKoV*GN{1$nl|%<5t9&t(k9XpXU2-I^(95;&4SU0LD(
zQ1ul+ZFj-9!QEYoTX73g+}(q_yB90&?(Wb6#XYz?rC5PL(E!DYmll_o@Bd`x&15ne
zhM8o3d+*+J&Yrv5K;4_9)}K}Z$o;dC3qr8C)**y}?0<5ew$p!dp@tQEEt;WTH47~I
zoG$Ffw}1EQpcS^im|nJu+9UXm8Ud0TFy*OQ?;vN!=CRMT(ZT4*X<_2%;v;S1v~0@*
zN83~vF1p>0UyeB;fg^klU^SuYvZ)*WRAG6syeg63d0<1UOheh}$=jyzmG8;!hEcQV
z&8L*}#(jRh!`*UnbF8gszB}_ni{0&4c|`1GW;zxCY7Ci_fp&6_-EhdkKWa`Aal{(r
zV?1BOoRc^Z-`9L4pnYe*ngtFH!{Q`L#y%N90})LQHCEBOK|~0TK@ySbtgK0TBW8px
zmvdKT@b+Qi`tKQ~uSU0mY)|MsRgif`^N%X@P?!;3-DlYn+bA5@;Py+e-~P@k6sGhf
z<Cf=IXrqm}NqBtewS>d-=dOuzHjA+K&=5$rOh=&|s)yZR4z213S<Oe-Hm#`ouL-y^
z*C4kMQ-oD<2@KUt+trTN&sM!z7bqF5dpb4dC$Uoms0}(S8^OuR6n<B8P9{IBA9KO4
zUA0`chgk4E86&tB(B`XIvAC=|C*SZpHj@P-jQ^xW0Q=pXtA*p3z4A1AZY*O9z-Up@
zO>UD>ihDu9m?CS@``bTfjNgC#gBo1t#XUb{nqHcVK;t7cuwF5_h_EfMpXNU{Z0k3H
zT0};7XYB_e;19C~#*SifBww?{p1f^+slY~0mQ~{en3xUCa!RFsn2EOR%4o6LhIQ-C
zy(nQziISn1iizC8B#Zo}u7s^XyKij{tE;v}Kiaq7WoE6$q&x&tR+P)b#rhh>Qpn}n
z67`NzoqX@Yli<kVu^zBXK>b}=Oibt~#9n@wZTF$o%Uu1B>Kk(&=nDX(7WtV43IqM0
zKIh{{fV}I5)ob%-3LbA4)5U|{))T&%Qc%rOiG)}^oP6a(LX*EzkgfNLgPn?Bq`aOR
zuebfGX6_gN@{9W(>plNU?(_`s#B**<%7QZVBIb<01m5jET@K>>9M_Pc<i&6A&h!O|
zB-VY${a$+7={mL!#CKV5gC)mY?`{@4hbUCqVQf25v=F2rw$tU*P$f2iL-2rcDeH!!
zzz~0Ub)nhs!c29#S*s65%*UJj`NU}9vS`FC;U-*dfQYJC8Vc9r>PR}7u-8YMpd`jP
zxMO>?M_0sR>h2EforX0}9s3IB7Et&nOXs{{eKsNx>v{jAb@pl4j32W!EHAGQi=FTp
z)c><?f$V@kVCZ5!u#TIG2n?hiG!FOuM1*b{Nnqb{>@z2#u^rj&_Wq_&3*#DQY95*s
zhX$qWb>RJIW;jc-z_4ek^{bJ~n1#6Lj1>=^j8V4Jcd^QWy8=nH`<%R9Ru&BS9uTbk
zi3?IE)=}-L>Di=+ax}dw{1j`+1{u1MV=P@76^aV`LtwIuhFO8*?5ohvu2BS~A|npV
zDu!;tRw!cFWJDC*Jv|-d!(^ruyejK%4P$HiwYk~-`;y1l!RXSyYXJZh%%*=8AN*uu
zi?Z)2>UG-SAM%OZIpg2%OV=MN--W8=yZ+yXV>L-!yB7542Y(pEGfU)i8UTPCr4P-a
zgZ?+O_g_fNT^{=AWaB&{EjKujRlUs$nF>peq}$2RvZVR=p=Hjg9Qep8S!I%>CP<O+
zVG-r{5P1o)a#HxmU+!77$M#`7$az*|Nj!+26Y7n?`L6^OnVy@$8?*0L+wiz00<kIu
z;=!=F3!2{+50zn$|HEWdbChKW(T-_%XWc)u@9)?0Er;7SzWeq!>LPr8GJL9RQMsSq
zQ&;JzyCsK`Z4R;IssZYBw|3UmBJDI>X1|%D30UXOC0jT%^g6cyRltg^hKXxz-grJ=
z!yMA+Lw_%(xTj+u(l>P2W|(tl+Wjlm<ZD5m2%TdpKw}f+{9a7$O^KB5WcKSTbxoDN
zoR$tDs;Ij%-waGb)Y75Fahwk+PznAh6xozbObJ}Oa)61|%(?$ZtoHve7?S_V*8ovc
zhR*z;x_q9to`gG2(}^v?%Sj6TMyN7pZ2pSw*_q$YI7QOrHCrt9?!$?r2^VqjY0Bty
z(VMBK1l4&cNf?#zGEOftavHH*pZ~S=R9ifZA<Bmb>n9sb@vj%%^*FShpb>(W%Cv&X
z`^UJu6ouF$pyZY`TV-3@0m)Wo2y+1LsSZpyUvI=-L*ziAh>#eI+cPB-iJJ?olYRNI
zZ%%OCJxT_3d8F^;MVWlN6<{acnj0)r1^%$V`gBda>~NQ(0L<QVG8<-gOYm!$Q$s;F
zX{ey#WsSk~Ua%t$yKP^sdJNCR81l-eD_lT&_sP*WqG6E*$QZR`#do*LF-{e|nd{J|
zfB1vwYY=WG(Fm7OuS1&=myTlM1o`BLxnvJ^heh|L`Z>B=X%#H_mGg9`2??Ma(tOJ&
zLVrQ+*;t1ghLviy^-8`RZNjyDLY{m=Phb&bT}Db8uMZE2>ll+c0x?h3Nyn*<?Ea<c
z6-(T2M~gu=c=yjpG)CiL2{J6}(4$~OM|*vxER1ymLc2wOqS|oJ@~hcn=`0P)dQ+zE
zc%r;Q70%@(6JXCaS#Ffs#oEjUUBIG%&9L~DJXF$f#RHu^WGdmFmt;~$6;pzqk`wnf
z2DUV&bfCrL<DGS^B?dZIV+9f38(Ta^u%+7}2Co$B6JrVgfz8j4@7YRy*dal#=<*yb
z*O}tH*Gi}Ka}Q*Fc;G<3QD$AMf~<a0yG)$gg3RI)z^wzYSy^X_ZZPmhw`62wIm^fq
zwQojnBJnJj{mrO4j3doftCKp2JLSjQdF9j@eG6wWw@M}XZF4!|v*AoW_*%M;QeJ3c
z#QG=KmnWZ=w}_jOyu-@dkCUzrO)IozQo7))ih_qy?qPJ3Z@rqHWdLz;;&1IQ!#zJV
zqXh6A>%s9by=gE3cirhZWzpwY?UK8#J<8tfflT}@J@9&kuLlQw8F~drCG0VjD<mTH
zAt6{s#{X#@*nHDh-iH^*?5%LsG-)=;&j%N7>*?WwiK3uzy`IgM3`HIMIKu(^Pq3mf
zhjf%0#>UrD0kH&4t6~dMVO?pDaBd+5Lz7<uO{E38$$Pcvo0P83{}A<?=F4I8LvnTo
z1Dg)~A2V)xGtQ}pEJC}ESJ~=>*_%GlQ?75+fa<zF1w&{Bg$2U>*@<g81lzUUzaI7c
z#JybyTv#uifUj#;UB7Q#?EAB)Ot*Nvej43IDHiAIkI;YXx1g43a(1PXxdE?b=cK(e
z7^WT+Nh#lD-D}A^S|fGsHdT?Nwgx1>nrFoDd^_R^R=NZBh2CvzzVPAtva^C)B*{d+
zt*};c&3@|GLM!-YOBh2Ac>rUk*d3LHLbu?Nmc3obLJrFv;ie|gI1j@LW6z9iI29%c
zBZZa#znjMGSS>0;;v&zQQ6#GUNH`qZ1HwGmY1mT!_9>qiDbM5mD%xQK8)?`eG7}A7
zJyL3T3o9VdwU`N9-b&*;`M@>BRjKo`NtTt+tP+l$o*wur0#Ai|(u3zd6mNYdjlULJ
zE*+{_8Ip|D0Cb5dvP6i`Sv3SB##Gs&@X^2zQU#y9e?2!3A(FTsd=<Oc=_TPZ>k7;E
zB5N}3j(EB|yXIhSW*6^sCh=7bLVwnlSkCrdK<5kFYe+D%dAtJ0ho;1$#85DO<udDw
z^Hxy-d?Z*1^gAE1e;tI4?Lnk>5xx?Umy!|Ux8Ez~$e?SForu~{r11scGA;U+aQl}o
zf*0$f1xTuEPeK#WQBUoASXOPOT0?C*LA>$GVDuFY3PULE)o^;jEUGbcWx-d~x@r)c
z0bBMu=cS%Nwqgt?*R)slt*)+5_6<c7w?pN&`n*pY?HL$ubFym5d0)3_BhT*>b!<P*
z2iLFk_qYJFm%E5y%uy3(dWi|Gphpj?oqL+_N@*m7$VbwE<mAO1{g(;Srfg-FOnym_
zLpCME=&ZDRK(p2LOpwRvvMH3WI(66nocB?PEV^Lf%Eyj*jeL?&AS4me&BZn>@KfRX
z6@S<Sk5r@Z6kV!$g`nQ<v;6*{nc+<XW&R3tVK=#RE%57f>6$}NSwM>M$118()BU+8
zTI_Fz)rNylMv3IkZpr?5PAOlA;R(C=2@*hj1!bTm;N6*EXLmobBG9{_)EwC97HreS
zh1ufE0F$V!XEY@K*`!$@GQ+}(1*12im_u1jxHBJr87C_-3PiaJ_;MpKWe-U_FEv2}
z-);s+vQ4n{y@ZR5jYgEaDXU@KGT3J#$%GN@e&GVb^lnJg!{^U761#(__Q}MQEm?_W
zSc#p}(pziyVAz4ZV*PS6R3hZG>8<uOT{3<s5}93`-i0cytIO~q*A-3~c4x1x#NxIZ
znUEBUFtQ&UBjcH9q69XGW;(gdQ%*p+|EFbWJa^y`ytw0d{w~&Sbmg>d0HeNT7cVKb
zy(;`&$6Uc)KifOYQ>c^KHNU<NC?TSPs~tN9^*B+EwYU(k?ItmKM@Hv~r*(t;>i1ub
zfz^A{oA~?A5`TX`q;A&8C6xwO$|$M$zw}D_y=Ly6LuG-5<i4cCQGii=LWlqB1*p4S
ze*lMUW*LnecjLu=Do9pvzCxQbKv0LxDU@ekeUwY+f?kr%O68L=$g&x^bU3}V|2z}(
zfY{M1x<nb1?7c%88N_<Sj+zC|C{2j%T7^sc6E7<mV>QboxfW{HVMMKHdpB&wg7`Fb
zh^{DC;c5<5zjR=V)Yx&!y<eF0?(*i27S#XFKggy|BkM7Nst&7lCQea_Y^UN5taR-T
zr?jsKC^KmJYBKjj0MBjiLGMBzQbRDzKb<g|Odm9BbMEKse7%Z)dEz0(xy;HQ<HfZ`
zOX3?Pr6%*uHmm`WuX~26*S;Hk@qNBCe_>TO9o)VS5Pl@YbH<~qr?2M7$a&ozR5l12
zy1EbhX;jKoTisJ-^zO0|#JxVbfAR5-1jct=Kqj+n2An8dzdB8Sjd4UNgB{bM@_B_f
zg7CM0S3QoZqa;4jFa7Ib#E(@LV&H$XFf%45EUQpzgo8CO<wgl+6a0~eu%M~oy+qJ<
z?OyelX_)xPXBK=T9AO;$@y%I24Z)d*Egi*sAW^``A!GJSAIYL!4FtH#j+bIs=cKMZ
z`R<(0XP9v-p8qC%oTLa3Z@HpHUfSW8p{kVWC*l~*r8h?%m07i^Xwa83O(HB&XEkro
zzDn(bEC8J8l=E3ldv*vm!Bz-t6jSjKlKAz%lF9%~zYB*1;MK=<AR15Zqn0;JV7rS^
zGk%3RlS1!yUdi9~gYS0drj-Cg1q{3SnauwkSp!m7Bhq@hePSYs_Ua|=X-<RQZ!h=y
zI}g~~#r+UZ3K&l6YT+(gCr@#0!bjHd_I&0b+3|uk)>_V4Ww-pL&w#}B>wseI3#`aV
zZK()Mp%wG{Lq!W;Rv7jgX{>y|cp&7pa={hL%8<-3>({@nhO}?&ZuECPOPiFK?RcLt
zw*GaHiBZz^=Q0PvhegTv<BIUO1{Q9!9&_>(_3%D~|3sXa{E<bkJicF6FX$=I(?ssi
zYkhC>)n{ihs~la)(SJXu%xcEPMyq1v&0&A2(`-QpLDdhvRXxM8B&&0*#uzbIxN}}Q
zYiRo+sXgn%y{QD$tV0X=T2WxE&ra6C--{y}Zs{>;!IF!^{QH|Mp3xA*c=#{wKZ>(!
z8euno+=}tIyh5b^o}f(VGpzj(#$d830>b^q-8KTCsHAkVyOQyYf_!ASg{LA4!p{S-
zAv?;+@pydn!H~LB>D-L?qN8))F!)+m;!sN#7vw4z*Zg`Pc5v;jCz<XtC{|)<I9^|G
zCB0U#lQ5egg1rUNdbmzs)MRR_v1#4!_|VWvavoSt%xh%I1QF5ZfF=D;f&!MO=lqJ2
zO&3kG@LITO${$EluML)Z_R@pV(ScN#hpmeFvOgc6x7eMVD8igy<R5C`vy{jK$K<ZY
z-jl$l6hHQ@^Ok8LJIIh}LIWzgD(p8*5#(e7)qWThna@D0ez9(UPhQ?~1b!wwZqAP_
zVAzyZoz+yeWf3>9O2ZEev37E|BcU{MIrPg+Bra5nD!^|jaQTNYb2_u(vTT-{{v)~5
z+yH+L>rxlg2l@e2yGvx}tq)!fUPs&FmNVl^ckl^yI-FH##>ADZKdCC?i~`XQN1jDR
z*S1y$5<5}_6m^l**h#MNb0_J}HonuIC4*aT2)wYj;`cd%4X0|zifD)ME(oU*gh<2I
zc^nvWCG?kKmOvKTS<z8I7VSiY6LOMz6<hki{(7fCHLSlAL_p2XE~%&cU$9Od0x+5I
zNmah`Y_G=BisP&etb+gWHZH-z=m>>fr5z??=KaWGAdflA-jeylQsWb-EpmhB$I|0}
zB@M)e?CXEB#52a4BC}ykD`iRvz^FbfW)*OpL1Ht9#k~fGbZOU%;%!5MFFlnnLL}Ux
z>r*#yv!nqUTjyu_=6097Qihrz^6TikHdZDXGRoxF_1~pr*9Poq4yK%SLVlG#cCO8#
zfT!5aqrqZa{=+|d5_TURK87?<qdetY+qsS5Z<bLY77oGdkR{C2A)Z){GAwVle1hAP
z|Lh~#`ZEy)&lNjhsa&Q#yKZqiyD*1eRb}?!o4z;ig-WXsW#_DxQRDU;16;s(*2M42
zAifrP1Ez(^0*7AR0@rrQ>XjR5Jg1A~0RE$T&=Nm)6mZ($X<fX=PJ{0t@09nSf(@Cd
zP|mP-+uYQcfM|)B?{oaY*V6T_6golDj*Hr=8CY^3pcuMDnfA1nG1fDlO?mrwIS`4X
zb~Z#E^joLPeV*zWdS+-ceS&kM+?@miUtf=oXj$DYIv-;y&wT7Cca9`ZtG!wB$C|c%
zDQ<DFcFo0K_|5u`spdps8bu+AiVB9o_ErJ?^DHvh0DLjjLqoSh=RyJ!jUZmbGyhsf
zh4%@k#zflkCu)(j8~c*os8|i3kZFmu()j>bTCK9H>ofFXdiGp~uSE5;QMS?Q=UVfl
z;g)#23^ki*vI(0#lsr=E2-SeXB9+A3V<flPf)E=xLaqiYJ@}Nf3-`wW3{Tz*x(1@Z
zS-v(=6k99WL{qb|8SjeL4hqcPA8K)aDr9==tB0s5`4EB<9F<0Q4cKl7xvCb>VdQT{
zh#!5!p@u_xwk~nl$myTaR{OFEYbAhC4oL=;H0Ff1#?EOe62#}x%$1@~g28g{jBE!G
zg@f|I^maAa!RcRwicbT-<8eFqZpe`^m&**+()CB^_-+dtW$(FwXnQCn^U<M)uP5F^
zPY&6E?GV+Zlva7;iI$R;Xza`A=vwYO_!(T$fBPiY&)sc+zsx<~c+<L)`DhbyX{8Y*
zG<7i;R&FPRDAhZtp-k)}1%p0LpCC%b@LXk4+fIxMT`gA$$W998IBHV#^;~*(_RYK^
z-d}omhla-(0Ye{cTbcjOH`Ppyp|-{+4|-;(`4jxSd)@oW!z~JF>rk;jx{mMPXThdE
zS9<YN<Q;e>*+J&xjt;#1u$(CX!Po;ELicu}GfeS+bTOVT$Cy;<YjSoGIP<mIVX<gV
z3Y)9#doUExCl&<Nli8IqdBxTQKCA}uLt>JF<@yydZg@_O5xc4q=RUOsF$oNju(};B
zR2X}#9{7_;dv;s!?z-G3{ELjEs31vo((QlB1py38`n*@<mX@uLmNqp$U!>_iw^Prn
zcNA(bt1DTGPP~Wol9(P0gp&@02by?Q!2xsuP5Z%pZr^x9;q4#&zuSBo1SG>AT$FnG
zG<w#GSDA_WqDL!eRLv9n8W*pbE=q<Ab)+G}-&{iIb;4mWi-Z_}sIXl^N*z7qQw7#@
zaZr*&9gz<<`*c9xSZX{vJZAgs@vpIQ)fIZCjU|ZV`YO!kXsX}9n1$k|KzHXm9|z83
zfqJKkhBXC0DRkMs5k5X?i#gi2h4rLq24wHe%)EwY{i(J41CG(GD)XhAO>q)S-Zh=F
zm+nJr`yd-Z<y5_*BkfcJT*sK^(}pRYC*&?smLAG+yFT)g1)9$}dgpXC$g9?T*mn3r
z*<AZZ!rXZfG+86(a$G#;-7A|kK&EooY{hCh+i9e64g*LSxnSN8NL-e_$YH}kG=+e<
zGTrxis0`OybB~N=F@hgPd1IGej8`$Ul$D_sxC`-MiBhh85)|3zACi|5U+e1+oBMuW
zDp<c=^ux$3cDB>F4%0@2MwwnY%j*D$?jwo_&UQS=_dcfm<=ep}9NTM@cNJH(%$>xh
zr?I>E6FB8`jWz8Td3#T!Q;a1IdSU6+31SS|aqSO^4BPH{=WSIxfww=u&)DW8tpn`~
zW(kl=X9g1Q2A)={7q^pF8~Mje-^K;6he~ECD$ar!p9TM%opM<v3(Y=7`BPfaSt=FQ
zJT&cJB(zPfQC@89mM<Kz^*ZDgL4c6&c#>%IkB?RV>bhXFmh&&vs^O@NoH(-svBTd>
zbChMeTt-GCO2QA<MNOWA!q?Jnm{*>kj-=q%RW(!XmhSBWe%4JQ|EDLv{`#||@e-T{
z^_@Hny}2X(X+22|e`S$IDJZb~G*?gGN<;+IL!jo((C1oYc0A^*p}*7DEh~G2kVXJY
zBo9#!Gei^8_xDjb_EKEX{p<=e&%gx<&d#<KaY>h4h|UJG4)4A`bvF7oZ8SDh+Z}AX
zX)3lRfwkXjHflHAf2+hVA8G4EN^YEIQbTmlnz3b3PE5=v_IPme09}H3Q>4oslvJ1q
zl39}Rj3&L@h_Y+l3~^KA^GN%7#(v9(S;Bwn>Fc$3H8Ih0L#=_Z;<VstexdxMj4m?9
zXi|GS_v`N=E!B;j&x`}UFXGZ)8>1grL5ro$nJnYk1u?~L16;-{CeF)0pVdr&kccBK
z`g*Y*I}%i&#_pAo)3&aV0}o(mC;mM<-z?8P*=|}DIkq&GTy4H%4~*~w@>EgR(s4(e
z8pH@0+P7eJ$YEHSWvBw*LOY(OMJ=|?5blMDNF)K>?$KYB&{>ld|Jx3ku{rZNAr>Ye
z8@HL<)nOQo6{$(98nL?IyEy;E6XTR4VLZ89xq8VzC32^KjhG-%7`&X;88nIBg(PVb
zDQNeQN0d89D#6>lJsvFjs`*|j<6BxbIX^f?g*TRuF@X(#*twg0F3U~yn$prbJpS6k
zpICX=_-Q6-3X@-&*<TNN8x$rY3|)fLE3r2Z-cI+uS=6A(em6nE^-P*OU-Vmv8i)%R
zE=;X_nDIZHmT0MX`DSe{Yi>^Nz4Vpa@76}+k;L+pep;_ZA_Plfn)<|$BAa~txgddl
z|J2>>oxtx4`B6qkLV^)5jd&nHh?f(Z&4xdr&HiX*9EcEt#?AztYcH^`cL#wmrVnB=
zb+MT1$Y1}Z{VRFp6Npi&-6aA3)M^qbz&`|_C7ZOR0#m>a&D~NAR5k@QHEy_0Vd@66
zyp$z)`1eHJNuCXdMofw-v$XqK76tf}Wgz`S=b2}|JW$)+`L+xB4m!jrkQ+wUWzr!9
z5_DFxD6n-MN<`bfa&P)d+bzh~-cxX4QXnI*olf#|;dRdU(0R>p;cCtRld)tt{Z!W@
zYwtNMY!m~ZID=AN86BMmndB*wnT||pyc`LQS-}dID53X|b!yU}h=S0=zdS)XHpGz5
z1?RsIJx`QaYDmw9joH&&lhHIzy6)9PjCxV|u`tGg;qTuOaf9UGJVC?I&tv6f_|G=k
z7L~QopOv%1i^)*z|7iQlP?fcX?42KtE|8_$;+z{H(3&#DTWGt1(@j~l&1Br!SyWub
z1~YxGG#@6{z}kBaq$?W%uQ%Io5yN9GZqyDT*fXsbhRCgF%OAm=Uus!db?t{@{Mirl
z-5{IVx<uWzQMu*rzx%bmkTxj^edkMap1VCiSS9dM<m1{N51PiNEs0u_Iafk(Tb0B`
zoND#fFWon|GPx-(b1JAaS^ERfWY5;h^ikte)74~a0)Gr8TTorR1L;QB*5*8wEge<r
zUwsnqvj~+xC~zVB`lK)q1z93`J5%oYBua8&neH22lT&=*2z>CaL;Ru?7LKgzGZ+g9
z#yo%F8){|W<Efjr%4<nGxD{|<U15b(`)#E7iawqrJq1phg%~MAr&4ahS3fV^K3QS9
zQSDtnuCQK^SEBZs9P2%u=LnW`qVYWU8dr_sxqOV?zwXGJ2mu7vAmb5!9^Q-^QdJwZ
zF?BwVO$wt!ZN#6L0S?%U?XcD60r8x%sGaS8#L|WjmwwbvULy!Lc8}FW2xE*HSLri&
zo3+?H@cD^eqlXwN7)@$ti0wQUKyPLjpm=~UYsOt{<);L6VpS}6ZrCb_-+u{$##|8q
z+;H&M=i=gkp`x&)fNdw)Q8u4|KVR0g9@N5!aCUXB2;o0tU*=w#`~nIkD<#MCTs0x;
z<eX-S!<@rHhrQo6OtD;J5@+nXDX4$JdUNBEd+Yqh!*Rzmayv3P!FykdeKBOv*S}?x
z>`@(O2pV&tPu5q)&BE6Pi(dRvhGyf3ENYrHJVlwwqt~H(=<a&DsSTg-M(TS5O|t3K
zDrezqs~0NBt81l9mq>cueibl*?LHLg@E0|)#I+$3jNJ116=v)9rL9ih*p7J!GN?w;
z=)-`)qYKNDK7udG$x<$-kj+Gl0%f}H*18g7rXpiT*TC_|5_k8n+6_G&;Ltm>`Hy@x
zS~Ystr3<1tl2>YDX@)c9+<w63=TFk*yBDsUW8nFV&B*JQLXi=3QWK%jf$Kn@>ksO0
ze*-zT%#jHzAJl6>FzK=G|6f$F0_B8I@W<8!(%SnuCF~-h4b(NL?%;L3|9?_qcONn`
z#u;JE3K<C5?*S4zS-!#k!bhL2`T<@!iY$?i4>b)EY+=?f9xG>b?XrR06cRZ6y6wpp
zY950$cnhb_C46LO`7PEpbe<-bjg6?!2b>hbVe)RvWw7|q#0{jz?un9X51$!|A7;M9
za8^c`DGE0PH3<_*5RWNq+J1eC8h?@vEhUk<(utXz<aZ~K7Mb)mgP4GSeU~NQHJ}MI
zpzuViS`aR;vR{j@p}yf^U!8wAs*gj{mD1BesJ(`0_kO<-nh9u<xn;xWSCdiG9V#RO
ze3Gpt>+<6!iZKw$fDxS*7}5E}tLV{7M+Xs2gZ+UApd;mFJlk0S2Uh=V&+u|E+%aV6
zNj@IVh=!Ej;?VgXbJ!_Ks-vEc>v!k2bcRf8mF2t>7Gt<$nupues-Meu#Dd*GH7lh^
zn?&5<r0cG*P;VQOiz(k7L(gQD&l8MV3BceO*nuM}jAO<jm31@q185@*Wy1DfSi^)I
zZL!d#M4+4KAb!^a_G#^Ln^r?tMsDd11$eWpz&zJq641fyz0M8b<4oSK4);yojAzN7
z_j_Z>v|?@yuQnas4#k!1b77Zx1{-lgTM`NGSz)1_*tz%oY*wvzv+YqvidG!*bfr0N
zG(IzMFo=aJZC&2D3}UIhpjXOp-Vo;wr$DwMrz)w1HmO%?>Y{3k{UO$-b@TwiC(aVZ
z$yMx{5dXz@pG)lWsXkP$_n{E1Wb^0BmCa_r`?8C6Fq@=vO}MT67ns6`Ta*cW4t~Gc
zc{?W*DNYPv;lw@deSSA$8E<*zs1^bJQlqE&{*|doND(+XX%uNtH+UMs50Q<{uOQ-_
zs4^o<4ieO^Rm?+%d0cpD?_=^t6r6P_^%zoX`;@?Rv;#1KDrt4+B*BUf@=3KeT>I}e
zyW4r<{)>em6EiMd^}es1Y<<AKY#KmNpq&xCvYF;GJbZhxLN0_QB<xZ=3<I}6TNAQ{
z5{skBVTVyb`uKPVoKklJ?1b7TO^r_Y>&WydOA`2kil;4_xU<QZ^ky0A9_+Wa$W7Z2
zsl#~nt-wvZHj4)GC%I1?v@i*u>4JnCZcPRt_+*B~p2LralnwouKb$0cWoI`0ku1Gq
z<3o5Yd3C*zuSP$eUdsuoBmn%2XZMe!`r?&IV?v=7C9EcS*H)w+L!Z_5f-nGwJ^~>?
zj;S)!Z5!y#ZT^RPFm8aq6x@?@T7;bBciaN~jNS)Au-m;@{l_}$E@Y*K?P-dHpOTg*
z66j@oZzK}hUE@;?Au;+)L$@Ij<|6jtvNGRU^K^c}%gx6ah3RVK$lY+X@P>}F@z%F-
zVB#<R7mk1J$qfm2xrm~o*^`2cPsJ!Xntz=!3~c4OKbs*(s`b!b@*%OW^M>Y}byB{C
z?ea(hX&BI+?c_#R$j4)~DLK2k2)g7+`!ozF%c~w;qUMARWrkg=(ORZ>eLv445MEFk
zibNHt_?-$9UbbFbCg(noy6M@XW?3mA4d^0M#ldxYaaI(ZOlr6cqi<eS;ZE|7?p1@_
zvO0Avi!+=AhUH%o=pBp9>!cw;t1CMHfok)R#~R7Hf%CXH18=GSs2}PNJ!`V&(S|xx
zcRdeU`YkLyP*N6Q5I2KS1m&@j3$NZDyErMnr-jl;IAga|MKCwR-c%4#EHOhLA2B7q
z$a`m&>z)UD8+h{J6!ls0UbJYU^5)OuQK0$*3cCs`4jernd>&Cy5}$1n-w+3~hCO;!
zcLM1j^x=T`(TMupCl$gQ{P7d7U(U_O)f`O^>av@XYmi9^*d~=yAHkv7^<&mC1!AZ%
zW|pf0P@YyV18*;z2u+s~KRV-00At2ACJL%_ce28EQs7#!)o;|0{LPX2^z;ND@(wmx
z9Qg0^QLT~5X{Fijk8Ow$c(6i!YCA*N>tsU*-AJ&i-RSjgVVe~jd;0V<nD`6hm;)Ny
zchZ>lpCz5E7~GVncqNi|yS78>wQymW@Ej8|CYkV&BmhaNFDz4pO0!R5uL7g{aeIxP
zCJQWP??qM}D;kq+e^xUf^2>hwZ3%gNITa{JKHhKtB@<C>iL_az*i{DLMKY^~#|=lY
z<D8SN)dG0CaG$rzlNRk+-^ZDpkkvrWa=^an++F<=;j4P^JGRXK3E18L+km=(h1X&y
z0}C1K@-0kI8jYmcU_lHbO$KU}uMN^!bIw{ay1gq5W_z@G+bmnSzk@{yCFidNB67E{
zztqS8Q8(=oO7>##F!-|?iKX+GT)Wb(DAT&+@&q?=Cq#Ko$dKmSw46lk@ln`&Y)O0}
zw>>#03!XYu18oCUeU>Vfp=lt|R~1b3-w-;kPe^LD0r(C{)Rr^6DW+?BnvRP_AKP}j
zi#iD|a9ja8L$JDzLP23iNnuPW3o0?nZxVw#$ZZkw^CbFJs+>jaP;Uq0eT6wFd<o7u
zWmFXInkDp$X63LeM#;<=s_48mJ1_z?Qd>FI$_|<}KOJKqy?Ms!$AJ*g)fuTu2F3$3
zq?CJGPF2u`DSl9pea(&b;X^?R6L&>RJX6n7L{=)m;cQmodFQ?AGRKFR!eG!x{DYD(
z3d(P3+UZTTU_-fRGO+cp=6jN=DVpe>Rk5r;@zrV$yoD^%KZk|eCAEoEO}<y*QMATk
zBmlHj?@t=bRf_7h(N=_P)lh0JVxZ7Ew=Q_es<e^#nt<oTu!&4KXEg8J=95(IPee}}
zn)pur87U)YvSX8Jm^bu)Hrk%&v0xH&+>kqkD{%Bz#!6TQl$yrkEg_0kg?q`qxGOn}
z5K|9SM2<&cSAxX>KGi)l%=T<CmyEafXepf>AQJ16%CkVmS4gXOoJwRL;~1bxh9*<k
z-u~pcN0R4m-|%OAb`!!O(lJ5Oq4dzl3_JG$SI4tBW179^WvRg|F|I8yOH<rvz^--D
z)SD@JFajgXeStm}XX?pAhnS*rW~H$HgY*)GioR#{{`7l6a>F=Y+xvew4`>~Sf>i^p
zSFzS23H^sPM&}oBpY4#EjmdT2U)5tiCw7Hhr)w3luUviiA~G*hv?`;jovXt!N4rM9
zCsu;ag(HghNZn^PhhvPTRh4bveWtMp8go3HZe`cRV1i(hiCp()U)R*wnlxZXobE)v
zFjc<uj*{n6c#*gHKHY*gUtRW}PS5_GfRZXT+`2XAPe?eR6#=0!s^9q6HmurD3M%W9
zr0Kj~=U=5@a%$Ee?6*ke{jD1k|9(Gxm(Rb`No>CPV8byK1h3A4Wx1nB{66*0mTTh@
zs3%0#iV{vA34QUWWXY9wh3Dm=lJzjp>>on)568RGaI%fVqN_2q-%c#6alUgVP6kS1
zl(#^fB;kji_|KyX!V}qJKTqBV3zxtdXKSSwy-sbXqlvR?8YoDm-abScpi9SHzZ!s}
z@T|^3ZFxAtYp}K_YHwcvvN6zY7j!Q69%JIXD}UHtW~PJLcVWaZ3TC|M#gjL7b#d5m
zsAF^{hw*h7GH=GW7ehci`@Xus?C5X*{WrZDh~>5(!kv3aK8%8y<uAs2JU~PSJBO8v
z#-%eNt2M>6t2MYIZPaA4t!Y{iBZ-I(_~U~bTy<O++UDKpR4t-t8U(m7`S6DTIzDbt
zj4D!g=d8)rZ<gwI9!l_Lb6VeL);oUqV*I&;Q!XTTt?0GpWvdf1*YXhRmdZ=#w9>mB
zG`Gd=Q$X5`rBJB%!4O6ViHa+E8!hdu&#yX7y(foZsi?^b{1Uq%5*hK|-UW>-9hPyx
zDEd=+odwjb5tye-t0n9*=R)qAeWbvY?6!JBSGI%%v$Z|)v4+SIb;v)AApeX^+n+-N
ztu92^#1e=`$%LEAgU0W<=+Tf!xbw>mbo=7Jx^<K)!`n8*oD?OX+(s?kpRPRk5yYrO
zOI5~XRawoB7HL@-VvY&@Vx{FD*X8gS@XoeuUX_dVWmdSV#W+1U6o`Z=(Tj%>v>oNx
zhcZ<_K4ZpZ0L-~&10?cJx_gq@-MTT$4ezIvg9Lpd>>4|vwiO;LYME-zvnD+&tCdl~
zI(}DU2UTPW@AS4Md{LUi41?WXJ!Ed*!mVL8@PxN)UqKF*>p)b7Uy4I@3cL0fh6U7@
z*a@qFkFT$N4U?Tg81_0dPp24LQ;nH~#cmgAI+J~$A3dItW$YPzOy^>HcA2=JnsPBp
zy$QCTftiic?lIA)O${!3iP~foJcF;?*zevA83m148LgvfSH!$;jQA3dpMsNhWqzA;
z4Mx!I^V28Gl`t96xLm&4q9+XPqQ2kpMNoJGsU{ewF56|k?(U<`gnc?6^A)0$LR_{R
z2TVfUa*?XZPG*0ruE*p$hZQcKQt72)uj3>%ap-x}R&Y6aCB!sw_$%zMkHVMYkS%(+
zAz+oOj-KztV4304Mo{~IhL-##>oe;^0;k=X^;Xvq>ffN>V!GiH_sV%tsIom4WQ}L}
z2b*s61t<>s+UL6UQAD^6pIOf)g7|O`=WJ!ETt37Ur0z6`73?Y3hQni+E#*I++h4a#
z;EGvf;mAt&e<9)L{m7@pm8pS#_aUi5|DIG9AXv98u0mbild4bvNSt8$*e{KqK!?_p
zc9VlUy5r@kzcg=_=_DU2AxfI?v1ktw@_)Smq3w-XgyktPV{*!4^0h-6W#{NguML|t
z56|ytQUpT4_V}_kjxtR-4R4-`u{$#thkyv@zZ=DG<k;*=Sl)}?k?`0YiaGp(pi>d5
z7DZ#%4Vv+``;piGgclZgSSxM355t7|a6(Yc;Pf}SCAP%{Up{!&XZ}eG!^Yi}k;5NX
zx``{2C_Spu9$TKeGx*D1f!0*ks7#2&ZN|&)zxZZ!e~Gr2U<DOj-@>{CN-e6bVsHi4
zFLBlm4Zeq~Fo*_qsIOzW%5_dP&wm>_N!wf+JZ@;Z%_)mPHNj1H62zuB2*7g<zWM_^
zMjGwAfW?I!^2o!6gOqhlN#czzufx9gJV94ILbs$um`w$3<&R0dEgli63_e}I*vg~}
z`P7o&5+Nnf(tXGpB1D8EAk2YIiCfk(F&2QEf|Psx+RTneyo#Fmu0Y(KSI-6?X}h*3
z^d|q)6+GWhYeasBcdzS?JyD%81;RCoq$XAY>{@T#-!!^OC0SGC6AAsbnAS$MQ7iKA
zPL$#1C!RdSWB~{Wp)->$GCJ3^x-n32>ZwevAzIrNjIU50C+-#pk*R1y?;a3gc79}-
z-6ejgxkZ%~P9W1Pb-+cDo74SEFdMHBmcRXSPqv#w%k1CllEWfo!WgKD-q0=*TCcvd
zkRVG}RlenY2p?T)PkIPm2Nf0B>F6a@zI5$*>Lo-$5FU2flCXw$d_@$2l~9RNE_oO|
zc^4EtPHmp!>Ds_YS4o0Okgd$2SDtNqB{S;|uM4amzn!<2wzwXTrx=_%NAgM_R)qCa
z=QD!ujgL?ubwB5jsJH*9!$oRW5>jAYI|OHY<aVEVWBa__*giVB?TBQ6^^kA862`jt
zw4j2Snk-FdeN4SP8RWybI<?UqNCJQ+x82|-=zY#VLgL1ZOm`vY3727dH@W*FGSp1@
zBVT+f15@e8WR*KNQjYC{gm`>L?ciNqH_{ZFclqNKv$lmayN%NU(4iJyJuaLma&`n%
zgD%%7t*Z;F9iA7RX$`N67K9i49bhR;buvtABR`Q&MG3E>TW>17G)3Z1+de3RKrn(I
z^Pn5#2b0VFR*7JnIVH&*$MxYKOz>WxZ+Iw(^ri4Dfx@)y%kR_v7Jitby)CE;eC+^N
zP6S@dE4|NLn(E1DP+{VN3Q3>*#1=HXr0P@fL%Nxq^pz=ROe_&vplX5hSK9>XG<&Sk
zfAXf~^v9G-$yl><Q~XW8F5@04*;$4F^nakdVLeR3he--T3GhfUE}hz>e42cAXo-e7
z<55)>BLePzjLUy9Pg~wMq*F=4L<)(*k)1i@9?ih^3+MB5n&I=XI0J<LBnnzUDMrTr
z#HT@oj`Sf&X%p*A84WM4%dc#_ENCQcojloBtutc?0`7O4k(#Mof8?b|X_+Ti+G)cd
z%6*M^`g>%5b%6}d<eXT?J|wcp*SHuRsD;!xndsX=cbK4*Q44eQ-ZnA@0v?Zzu_H^7
zV_%CX-(i_?8q-l4xCwlC?9Cw3!4P-+RY>jqjSk;E<S!*UEpu`|a;aK*M;|eZJ+5Pv
z_0U+f#f}Rc>?F0bU0Irk*iFFlvS94~{kj#mrGSdzvk5^uBkQe;v};up$fs@g$kxwG
z4R8gh+YRKjWrih>45vk-x{faQzpxs`hsO=tfTHdPIZQQm%b-=xBup$x!onR_EhvxZ
z??&OQd6Kl56YLb>6r{<M;GAFNUJt<P+L$kQNz$tP(?_fanl-{CBqXb0trcaW)`<&#
zw!<IGG9^W4V|gv%!@{RuaF<@fy)f)|B0wf`UJjJO`@IMU7~Z*QrTuZr;Di$u68Hn1
zX`3>H$A%Qy50Ss1xQ_}Gi-Hct8f*uW3O@&6HscMQqt$Ul!UZKDfI@!s{myR{Iz+cI
z-+xvdk?*fR=w<&~G1}mpvh1$kD;6g6(}-#B-Wd|&8WTexS(6eXW}ocAcaFjs6oCP`
zKoiV26Suiy_3mf{;O|Bh8SX<hOzI26p6{Mz%f>Tv(%9hL271*ob%dm;)uV8=A26%A
zYBQmE6Zicj8j<7D(M2_A!f(HU?HHEX2&$%jo~JoM)=-8k$XU4Yj(A`%inG{DGyo0~
znk$OzSV0QGe_neyrUR-D|GJ1}YIzy9B+ar5f+hSc3r4qKvJ3WQT%GsZR8FpbzKnBL
zFm^WY)PUDz0sCB)I$5GiW*aW)6R@KzTvgtZlMYfyi$XO<An@_Vz$PrxmT_c^)$zeX
zi{GUgLp=Lt$DOB4L$>2z07rq=kC_BT$tI#rrbH|(cJYV%nVztFcd?ba(`}9Ox;0;Q
z-EiVjz(RrKWC)KZ#O(=Z1m=_A5SAPD7Dh`=cAIsdm7G%Q!Hg)?fg?a=f+*%jDtt^3
zBq~M?G=m_Jc1a;$>5zD#0XRWKqPYkI4s;MJ#QS2QD{8h~r4C`N`?}gj@U2If_N)lC
z$;k7b@{0o`LvWL1^yCDlk@g4A-qU8|dd^4U6l$;6F^?QZnzE@S-Cgwia(klHDLa{}
zO2QQz@I8CGBC?phiVKo8D#>(-`GjoEO2I*=y26g;^*#Hmt=Rj!SMltB7`799AHl9P
zK$=X8ff(T;Y7(MG@~?Od$hQ#MsM#>ZDK>`!B}=LM#wI<?XO$6=9ls*k03UD29I@un
zrgOkEnC#AoJB`0>%`-@YOqOXNb>#Zaq&nF--(g?Is6GZu9dS;(FE#l;HfUFp+5?d@
z?tK|GozI71NF0Yl;$?XUrQ|#DWCt_k_qr1f^7M7@MBq+7R#M-TRaZUf0F$T1v>1z&
zyf%*+$iq$30oSVmYBd;EVe+5gd71Qk;HMq0sBHG$P)1UMN-@3)WjHaNi<s*DrpD(g
zfGrZ=0k0i^g(h9kII+1f&8rsC%&@L7_4m4KaaqAOTI)WI!CY{5DY4LVEQ;H>b*%5+
z&Wj7VMBz;Lw+0l{$q2Q$^5DwF0`vwtnYxea<e-jb!zWm+-R~>Xb04W&Q4UKa<2Q>q
zhn{ZIN&3aya9j<^IL0(8%8~VKzIV>OA7QvXs4Zj$5ec_?9I2T++9-pvgB`~PG4{|Z
zslBROuX5<A_LW4o{$cmW_n?gmqo{KfiH3GF%RKY(i#5@Zu1~8y?VToi=}p>{Tj29t
zP1DJG(Wt;FGKQb5%D*4o(*Cdyr+dP|PKBv4t3rq$6@Y!3m$V%Gl<85wgoRwm;c6DF
zT|;NOlV$q%I@tB^EM-^NRedDC*E#bHR7`J(!wJi9^sHTmcQ&cMDu#K5<)6=+rxxRV
znmqUL4|xSWcz9+1a)VTmljFcP%1ld*drrFmtQ5M2x9_R)A%<_*O<kSlhf!i25CChX
za7M%*^zk;*=lI)%R63a~5*mx_m_*m#k`PsG&7u9=CKH)>o3s_XmN#YZ-J0-Wx?Td&
z?5mdXn<1+}S7(dnA@yBr{ao&9Q}+rEK9M0uO8E-WZw$#A-;0qjknrQJ^Ya0KptDE0
zxOTA}Q9J>GDqjuwm)#<=UPo}a_Lv;pmr+BT#|&7U0X;;dQoEVe$yD%5)N|%@;lqTl
ze=_}`e^E%T&iq0nHl51Py^j$|))>r)T8AS5lJAlA^joG}V1pyftjnUYNXQUp2%fHH
zs6VWYQu*N2aj>E`E8&4w*rU!6$nao090nPmw>j&kr<nbr4+2?qSf%cGbRQ|$C%=S#
zq^t=qFLAEy9*%>Rszt$Wvfo$w62u}G|J3N`Ww+(w=5U+lo7{Cw{$SVp#=f1O>G5&(
zwtyagTdAIhZio9PW+aIrUg<J_MNN!2GO1N~W%DN-#4^jx$7W1lXCP_;1XlbP`lRkM
zJkLREvBaa{<vMaz13FqUivu^Ib;^>S3B2nhe$aVaWUaCL`&1GxOKSB$`z-apeU>Q|
zVNAC1IP?FC2-FF#5^tuW*_t-UL()4~xDH5ipPvNZXTaICGhQwCRGZn#OAeQr8+$CT
zqLVt1E;Pb|APEa%MF95#clE4g$re)lak^vDfTwuJEnWxq<KqWRN@-M9FRa`~Q<TON
zB}`gH=6#rJR@(dno+B8kY-I>F`?cSK38%am_%e(T=y^3{_Ix}kF{;wk7wrV@T~v$o
z$aZxNXNeph{Q-{%`%0T<P0Dd;3fT?!kbAdH(QY8HLd6R!T7L)NwL2T!l>Nf&{Uq^B
zvp1cLceSeyIQ!XlAG#C57DQW!wu<p^82q}uATIjJT?gYx9WehkyJ~c?TKZme!1HW1
zh=DWU@X88P@=2WZ0XG}~yV(U4N)nNHDP-Z~vUpsf7W@(IkDvV*xR?ePCRqAnRHoXw
z5c(RV|62(QbN6t=&Ar>?_Rs2YmLJ_q`p$T<=}EkV9)`33t;S%=&I9vc%Im;C?`G;K
zdx`tDoEg=NpNS&W0YiKegRN2DKRCWCz?<ohG;R`4{Oh{auRpq1#;`vVdhNPAfxWlg
zi@lX5-nIAk;N`Fw+H=R$*O4>XTNln0RF+UH0`<GbQ;~iY<7-wWB}9`dfJZ@!h=d;w
z_5JC|3s397lnkt*lBLDGXjD?{l|9F-Ts}W2txvJS5QhN5k}*og);ZY65x?l@F7<^u
z4*V0|&C?!bo<WL(cRzPOra^j4^@TwMMQ=<ur9>ee%%a#*Nz~}dh9p<DdfF&=FwQuC
zMP7q*!9J39=d8V{VjU?5tx|)XrEUhS#KKc;in4^%8<MV<wwRM<A2a00D{BOYfkc^I
zj(eII9}SO8-rkXOi6?-8uXL%ob1wab@Bz&G$624;(KB2jlqosAJY2Dk17H@8w^toD
ze*qvkExPi*-@n@%NZs6~S%V<BUm<jTiy7Rxtz|sZQYKxgO+$+;MIUudcLH2tD2Ok6
z<%5H;$AhjA*D>~cZ!H2Wv0F7h%Fq(a_*U?ZT)qX}3O}DY`D>rIJ>5Z57A?)mMi2+@
zY-<t@0_=NFR3)_YA)o1xSAgsA(_fW<4jFe73+8>d$l{?-u8Q$J97jPlVi9$qx^hq*
z?X){(sV3fs*q<)SIkkDcUM=T6ecQA@7d=r=B~xMoQs4}>b;tscb=bJ}8+H|V*5nJ#
zF8i)c14U>0yuHX3u+P2IoX(e5W<$V{ssJ9+30Gsm%R)QTU?C&Jv2Wv@v@QAAr%wIm
zJX%BUK^K<KC*~m(IwF(l_)4pDEzUc1IzvJuyx7_<RhqJN4DaQIh)qJ_|MV?w!2k;l
zSqNulrBU(K5BCx|B1JyQ#Q}C3llBf$d{#OUOS0#@v1>LjG<cr{nF|jd9-oS30$(;@
zQ9FS}mUJD2d|wq&UC_Rgur?JVE!PRNH7SNxe$=Rtsf>+1c7`&w5QK#}gIEwE@I&o8
zIhe>H8pIc}C73rdo`L!EZJZmt1%`BRwB*1j03v+*ia$IhF8z)vfc-GrH<_P^QZFBm
z!I6q*z5h5usUTK%e~QPNREsMz92wcD*n~inaaul*GFy|Ljt~_WFF(Q!__4kL0uRe3
z+2cgtSBe7^j9ho6P^DZv5Uu)pnbKSG;xt14Sa)60`#~u*Bw{g0ntZ__B3`P_>NQ?b
zlUi!#s2to@DiuZH<~{%HL?3r=gAj|w9*DO;oji1WgC8P!&3!{?dOd!#znqPv6rSgs
zXTBWM1)%$srCc=rwgW!V6b7f^|5S_T(9ZDj7yE4UlQM9`x=D<nt-v$N&PsrifT9H=
z|DBqzrG+PfG}V4}`&yBQ3ddIG^I5i{F>ejiIMe%t;HzVqt+U48jTk;<9tiQGcd4?U
z@g?o(!bc3FOCx^6^Iq@D(4{{F&)@Z3KH*y>-V)p#4;ThrQ@^?`UBUr=T3I)a`b!NB
zFoizdx$O+~Z>+X_3Z?ql-hVvhCCYR=zc;k0MVTjJC#>bQK`d(a%<^f<hg#{&arEp^
z(45TfC=TcD-_fr$>4dLMej$fF=T5JG-k#V*1McI_?I{;Rf2khrK|Wz}iM^Qv0AEui
zOhU|Z&Z0LqsLdvRzXWXd1|wK4nt|?cqLl~Jt%bP#+L6TkGz79?zeBLu<4Jz@--&sV
z(ysfeP+!0<5`fFXpFJTyxGgX6zWsnMOviWyGgWS;bafLl&uKo{^cHDf;B<N=^Ob!(
zQ-0&a^MWJnmm9gU<u{rSuO~QP4j?r)TLt!=c?61m^xj@8jvSo?)(8JR^7oG4@ib1p
zRbfY;s^_x!pt`#e`U)@q>xsCb>n*9FkA#dgg=@&jAW$NIjJ4Z4Gm`cFf=%V#OOX&W
z+EpDZKR^!aJ~0W5Dw7ojPM~z=Kd5c}AeBDlK#quOL1``J+=&;3{9_MTfXqTuCdI|A
zNn7toUeRDud7R4@@yClsc%5gmpc?LT6|G7}mjbY}nGqjR+)C&>@eNHcL#-5oLB>!#
zEzY*u;i=C<VY+?kQ^@o9QK9+yx%F*DGjYIeH*Q$d>c%C@*NF3-&1?=v4Hpf!+INm<
zUYLj=#N~$Hx#xLlJt<gD)O)`O<yYihUeJgb2pOh%WS5D!jqosK6`^JLSt?vtVIiO2
zxdA&tz3cpHk-63=jUB8)DRMN&&d%8rnk->EPsL3ZA(muZL6-`y(}Nwd0ckYk6aYp=
z*cgvgPu(GukIk<b?Dw0CqjA2Pk{A3Wmz>c4g9+Awf$sK%9Y^lLby!fZZrLP05)Y}y
z$(8e8cf;ibqM;n7>}8tYqXU9J`#vP=n|5Yr+Eh?<zu_V(rgRWNQW%Ln&sPFA2ky~{
z57nWOF8hh{-Y06JtzdMbp;Ky8l$rjh{nV226K@T{O-R^<=fhr|L6<KQpHA{YG5#O$
z=b3}VroHuE_YVm;cC9B2ZlV52of3WUIr}1BuQobpeJ{UNy8NDE4sPxL7fs(79q0Q+
zJFzCVZ98eqhK+68Zfu)PW7}*zv2D9a8aIuTJHLDHf35lSu9^ApKIhqIAMA515z)+x
zn7RSKIyzcO23zHw$bUKEB#PX_NRH5)w^@YGucXJ;XKd<jj8W_zJHKvgfmcu3Gji|*
zKfTy)!ys3;?F2kf|M!2#ZB4@`SDj$>{YGiK^W}ox4Qlw!6Q_|o);B$$oh7@A?>PhO
z^o4KlS0&8l0nd@*-oNLY`96N@T(phJmW-i8FHc8o+8+0ep~tdob!$TlUX8MAhTx1Z
zkomwuAI~Tf%%{0}Tk66DB(?Y9XKZpo7cN*iQ`Tto;X)WNQNkmu+d#@-mO7N3ZaGKa
z9Y{2}DzI8X%hTqju&%_Y<0z;Suq=|;b890kDI}AbkbA8kX)k`(fBCz3ur8-kdzT+W
z8+S^Aa_22#P;sh@B)+QMyWg_tXi+~h<4^p8>lB-~1nbBR;4+tij^yHp#AqF*{0dEu
zk(Uu3q*;8b87oF^T{3p5kJ>^LvS3aX&0aw!@3XRn%;LQh`rZs@_i^}q70nBkbL*j^
zT^&JpY^%4u2J+>-+MAwSz4@1Z(cx)FqBjmSw{SAxrHhfIBkETkyVh0Li+9+AW9^F&
zd2hzODG&Sy$<pglN}g=*X^HrG(f309c6XW&IorNMoJpo5gM6~a)*%Z=;MsMF-Rt`h
zn$SHPdEI;VokA^M8_W51@Y4BEk5u2Lf;pebRNeV{V+CuOy?pB854>c^(rc@YP-FFI
z6EFNYRtN=7IB_Q-oO>iAu2DEuYuT3zunGLaA+NF-eNI&@Z)j}NztyKnZ@~u9ydmZZ
zEBB*buSFV+;+#&_d)ZDROY7D5_+YCI+RJZ1y6=~wt4vqc=B6mp-hyX!f4N$mxGyu*
zTjkYJc)|{2cGc(I$iQ58$3QAG)85w9HuxG0pXMgOyP(N<E%i<NSd6g)zOu$Tlu9(R
z8=Hb);4HzGaleiLhaf;|in5?`u7JGOc18+G?%E9AhU##q78Vex0U<0WnS{Y6iM?Mg
z`6PoNNFPdTF&BoItxSlq4A`SM9{r+1AH@G4KP_OCimFBsQ<{m&$Was%R<Y%6=!`-@
zr~0_Tk3-*pzTC3#$zV8FPyuVbCj*%np?^&J@qK)#<#f$00jy<g#eD0ph5h|JnKFb&
zrw9u#3Il^pB~y_Ek3#PX4Z*CUHda88_h@VnTDK(uMxq1tPVuy(EN*8MF_<I<u8;Tb
zou3vsX>Vu)J<z9aYy>@Qadv&4IY@*(2xk4C7Zv?iipcg>9_%{&55G7X^sT59LT!Yu
zZm!Fj#hV`6n%N*y4w3^SBhY|<rClF14<CH?9&YQPxy9%X7Qp88w1Q2Q`Xk2S9%=Gv
zf3n_;;!B8|QVyuGI8q<M(kh=Xda*p=;6P97c0y^#@x?6*UoFO4KYp;B-aD7rg}UjP
z3qL?{<jZ`{*r}P0AP4vQlHF^#(U%vpfV1DBQ7F&1^}UZ!TJ`*bTiPGZhQ{h+CW97^
zVj4;I+qQrtX5<@QZ_*Tdx0yA;Y2yLX5&d8MCXW3<nw0VwDYuIDLz+o(PNHL{?Cd?L
zv?Tsh%NKoj#yIsm_r~0toh`uI7M3=J#Fs{wYBQ%UD|B;6ULKvP2r4Sm?dmo~v5}--
z(Nz2qDi)2*upyoW6Nm&a9{+)6a5nCwcuF;5E;VE}VjC7DA|UvSgm{shHk25^am%)J
zwMY8(mdDTg+Xh%tUbh!aIKqVxD|viSLBq5A!5rSt96sXH8e~N7HkN`_*DPr2Zl;2s
zpNTj(b!g99m|qH~$-@0k3a2tHZN12~w@*$&T7opjmrv@u(%N<!#trC0#&v9!u)j7E
zq(u<~M5q(P&<mU+7{oS0_uk)G+$8&>HA4ox-<RLc_aJxsc#s&q_w0^t{ek{?Ck;eZ
z1AOe+eGawdf=~R^sW+IA9e@6ZY>w|=>&_-wN<jp`t}pVneowf#%1E>s9Q)&*iQoGp
z#nWTpn-G(eGz0)~{*8Y6R**#49~L!Vm=kUNJA6#pF9O63AhejMs(MYfYpd8I+Kmr8
zHgWjuB=3X3Z*1#qakAfVmlW3#WLPe@wTxhcS^kwSq-FNgdw-ZTH1OFY$M=A0M#!bA
zu7Bj7DDQb`)a7J}{cZ1f)%ks7rfMlZ)3x-STjXq`lcyGIZ_%76&F!X=V}+i?i{9&C
z!n;2)@MFAL@v+_LskAsUnK|aE{PPfIwVr?V>#_=U3h1&Bo$1M7CziYR3ejIXwcnNj
zyhd5QV<!7-GYDdvDqY6dO(<fyRLZ_?*tQMzcsrb5gtPeiaYEc`6#BjzU(of9t$_N=
zOYgSiS<~wW*t0Z7g-se@tpNI~Ey%2e!G0cC-FkZE7=q$6`8>1~0zW<4i3k&qi4_3)
zenYSF>5r1!FGkQ|S1PL$=8wTbY-bt6Qk+JxpOZ3p-||`kRsH6v`6Lfo66i*K#DBbO
z&IYIZ=QVy?UAo1es8#!c<kUI&F#qm}ch?C7lGR+I>vt}{K+N`jbv{WR<l|b`|3*st
zZx-^u3(eEk^aMuf{$eW<kn{@%V7u+3Cvz91$q_2@w2?`?x$jM)B>Zk8NF&$b?f~(5
z`6@CzaF$#K-B$0nkH9o~AXDacbMq2z!$2?qmP%lKyvEiC0~&tqWa~#;CNrli?EQPV
zc-p(8uBokSDS3yux_#)-o92cNR_G1->Git0ER6>37l10b^K$jW-WDk{@Gs;Qv(v$d
zrdhrsT1+WJftCT1eW5bo^Ec@S#|du6ZD^+9psjsz;Ukw@<{kN|YRP9wN3e|)slp2v
zJ{&gwqU}I>!#_Pz0X3Bem-c%v-hklNxGu@f&zHmUTjL?ak_jtSx)V1%6u1H3U##7y
z*08G*W_6F5oFT<}$N-dym&OzsL@|~Ak4pbBS&S>1NAquc5dL9QF;TTlhaVup70ry~
zX>+2-S?qHpJ8Ageqz1O{S*r*zTq_tpZ!13zJ3g)miT$rVRaqA#HM#_FfAx+ZFruFH
zFA_j^c9Bbxdk|kNK^#Q`y0h;)w_LW;oWPRJpucR&9KR~QP-8V7xSG7;44Z~&-X_)L
zYc<EUHb@f`R$Ge20Jw1T2bu1KN^o?~HKBt1tD@Ky>k!~tLkYm`dMC_C)M=X~+*3X>
zM_f(hD#(BtIY<=7QkRfYd2n0AzMt*!D7Qe?&!F^*G8+PvSAM@FbTnMz;m1A08nj>K
zVXb4$v$<b$<qAxyM7F>PX=ns`eWYppF}8m=x``?6?)=F&BXWxd*bDw43&^~k2O+X_
zKalsnJ=3xgxn|n`G{esk;*M-CKXJbNr>X||Gt%l7&ghln<7D*On1O&^<nvZ7(#1Cb
z`#+FyFLP{7i9m<|dH?;xFZli6mZ<p!q=eXM%FTOBYBe(lkt7w@l3E>4L@7GnU8rm0
z_vaI%KqSzCFIj<UHf+RKzBeXe6dk)i8H;iKJOZ5IyPq3Qd(-W82iJjjo(7pY*raS%
zw2o%B4zD$32QKUhMtn#5zUH=&pHGL=8W5LSz3<KPb&vjR7!lfI63Nu?oxqfz7h_bs
zDq|K&vyP{SKR0-hfr)%AOZehN0BH^VuEDLa%<gGvXIZ4f#S3}(YKnm8b)(@=w+A^1
z&_+elVB=Bt#XH<uv8|{yG?j)#1b(C-I&>x78m~)Jw-R|Tkh~|@{*UA2&MIz2ppa_o
z%b{l3^s=#AC36Xn*_v)o;?hZ5v(-{Dxy;3Ntu=vUWmA0>th4+3>jW<9@fQ-XqtM|9
zJ5v#PA~68Smi@&19`(y4ejB?P`bWQC%19%6<3$}e1_Q_zh`@pPjXp@+4klN+qH;lY
zB|xc$irZi`zNFGs`VtNcT`FB_>)78RugFD}C5VbE$DDgBg^o!xwkX1Ex@0AZ3#Rm2
zFNbkgHXR(Y753w^0^$C>E8?!@U;Z+U`4C%~j0oM9*`NQy+1Lgkb~Oa8xl;Iw=&uZH
zOW`~uL?&OCyBkhAZF`c+Ar5~z2P{QiLk4ma|F0K-Wpz6KZ4=O|;Z(jol!viW5IB%P
zwErTZ&e@!@;(Qy5o#Wflda&^W6LoLP?5m5D1!ku>I%6XrVDC6ze`KoHB`!YP<$Z?Z
zP8|JN+$?o<5RTT_$%T0L>BPv`lq>G=tJT2Vzw+YM!GTFPc$n^|^jW_e%U6+?JGCck
z6Bt)0eu(S=rl#8Zx`WWkOy`Rc?h=e`R8;tT264Z`6@M`r0byl_FvHpU6;-*qHZU-S
zmuf3@Y`}+zlKqA{HC4Ucv12tk9MlrCQ5{a&lE=AS8pCk3q||2NGJIY@TVpZkOi&6u
zcgYdf=u<sfr~PK?=}8dyV#UhERM+WI4dyB}(_#M#QU=ASBY(OBGJoivF1M_P4(Xa{
za9om!8Jl-MUj&*uU<Z7nNsrKiw*XqqbQ{yf`BmLc%w|`Ksr;3d`R1PK=T9ns5X$?3
z0=O9)6^5WMr}()Afat%tAH3Ma*}Pu|@~mp^p+bu-0zs5!bNW%hZ)2qa5no3Nst~y}
zLdz;&Bt;kPrQ#y7Au?sX`c%Fgh38DIB2y;AW5CJJ^n4yW8A%rg-!S!+?LnwYc)NF7
zZi0w~RMg#F^UVkaJZkA&A8EiNp_>aJhPh_g+HVR4WBKqQybYM{vU^>f%n<<v6BH};
zJqb&uZsW%YApo&t@4s(W1HL`lhTTwkmHO7?^oRtN!6o!;RH)Z;%8CNV{&a|_f^YCE
z88~S8n?6ww8w-#CvgX!=HPC-IgHBhS3<i)w*8ejF=e1+wb_A_DV-wnaw7E0CJn}H=
z3BJ4II~scCh_Xg<V_S>zq9!FBL2xT{B9xh74KuT-hWXDnqH`!sha_tJSR^lUSqxC|
zeA$ZynH<N;H8c0ahNv^1u;uG_(48R@i#@K|nB@W;;^sH;p8E912<g)HQePxM(7uY-
z?x;}kFl8z~6CEbEQ-}L!-p&fjtkulnVAH&2Pr$K;`LTZ%J_eJB!0Bze2<7Q*=SPkw
zo`c37XkB9oo{JF*vqt#iIRrP#$J?BM?xaO-0gcaCU+t!yAMrx&QiUUiS^dbF65;Er
z7PrzQJ~a1*FS(R%qKWA5&8Z}4h=mp>AlApXd#6|xmIQ9%zoPo>I4gO7?f8by9H?4H
zVoj&#I3(RN3EeAiu_bhhK*=NXmWpk69nM@PrK_3^tO|$A@kAIib*<$Tv%<WXfw#{v
zrz@VQVJrR7Ti23#{wxTQZ??j!gUu4}obbTeyrH#Qr8LsS=je=BJHlu#;a}|$>Bt6r
z-!IFrgNtMYKN`*I?if2wd@P{auUSl)FhKjA?*;OZdTe_z@@mG`1Oq|1!l;qP{jtgg
zk6{^(vG&jxgMECtqG@ipmd6p#=A|8EV^o@Mo4Y<@Gz`$4o(S%=N+L&cOY59?b#2aQ
z$<Dq84kWCo`MYZEd;>j%S}qr@xT2E}S~IvvVp~&^=U1mgf2Obko{^MHD8A@=?Jlob
zsvT@#91mR7_`jOjqANwYJe`M8P|4u1x}~R<V^+7LV|{1JDUqmortILhbs|bUQscuj
zf@W<mgEXa8u_n;^CIk`gN;_rS1WAA8H=QR}e`xFnt_q`XeR*@1c=ve<%^&<(9jKJ$
z{Tp*Hvc@2=@`*-u{iF{mkNS3qGvDszL6fQX@r!0%4@AVmH7}Q9`X`MnB-7#sx7h|~
znEz=YQs3pXKtouZ$avuV@`bw2P!H*+to&;eF2XIt$ND%`ULShDcvUmY6%*tbMG)N=
z(n7Lv0`)~n9ZiPfQ+4DdENlv1$Y^564u6_<plT!h0QFR$?(QoBn#SvokEZX@$Gw}0
zOh)2pc>n00Mp9kg29%^#l4=PmZKo|O4g^Cg&qI%As57x;%zvn`=|XfjU9QZ-;MH0N
zE_)`_iZMs?JPgSe$7g~T0RWmmPy-8StEvFN#H_^lil&ssC?V<(8ZKnjDnKjU{%#z`
zll(&D@W=$&p4S=B5&{476%k>;)F9&SzQB(tO}}x=$x{&CMaV!)zc^Cxd#NhGsLYj5
zvCT85p-SaR>Fyt7IKxB|CRldy<qwq23ro$@R8qt&&_>3Z%9J3Sv})%U1tBW3QNbPa
zr(f;K=}#CxtKK@hOQR7ZzmK1}zm-@UtcdJN*_21Cr^ws-Bu7ktaYW92Z+n<&oxeeo
z9+Vivjp+#R!e^Q^KI!jZREaOFBtV~X&ckm2hARnwhX%0qbp&@kYN4atDdO_~#;UY0
zWcZUdyuYZlcXLMTe7S+__jg~zoHtuf${sOhB+xJrmi$Yijw7ZDO*q|8=``73T0+)K
zAfUNWcVN#N3SCP!SW<kg*JtV{>`=8%m7t6qqWW-vC0_z@l<jhsfy3lxU0cv@WOSp9
zyuJgL6b0K6l-en8mZHP{Y2dvBS?k`5Wz%eGNIuSOfGttc#uFk*ffo9EV&EID_lX+m
zC};lnEtlQs-|)ikm7`$FYh?FZs4N{4{K=@;c%v}U7%Gpw0$qTrqO4*rjuDG<n67ld
ztZ$^*r3QvV=C+l5Cn9R8Ub!jm_c=yJxt0E<(p;J_X3GzSxvG$Ngze=HE}>25z^y3T
zcbWcCGba6u8C%?zuU_j-l4eNt=1{T?aBKIaolJFgZp66D0J3Pd2=wcxS4e;aCfg)S
z&aq;KBnEW$_^;n45UP+<0hk(#FoFQ;&{*;h^$8JOddDfWwU#^iIPWHgU<Y5g`}||%
z?C^dt^E~jun8YV=8>HAV;tdbYHak#=$$D1RRgs)us&i3qZ`NHh3`77lX`VAR_Mk$*
z81g3oF=2|aL~T+&RK_Q+C#*wjyzAt|-Po;NFW{+k2+mQev<Qeo!Y-r`;P}a==%2Wm
zV?T?a^}A|;;I{$7YOp1*!RKF@I75qLors?=|63XkkBupL5o@P_hS)25e4BDm`=^*V
z=DdS%B(>lCtO=V&Jq!UG0si=3Z5Flp8%-|Et~;C4sW;u!Vg0og8C`<^J4R4&LmLvm
zkyB)-e~R98^g~TV=1y<qtl%%yxY!(Q$J_6Gg5TfBNQ45=2%{Z5Hb&iUzU!vG8gmJT
zCFb&l4Qmqx-cNKzLHpOiv<+*#2G34!K9WtY94w4z!iFf?`?XbwE@w}&7fx&=)GACE
z>L)*H367>>6e%EL+9Q%PB!aCmYJyU-U6?fY2U<OaIRmsQ^^yi<mcDa{0!85qTH%#A
zWPD4Rk|R~Tw49w_j&r-|k?p3OMMF9R0*`=mtB*g))7wMpa`vEb?#(^c8xc(2dN0Ye
zs03F-M4wUg6&?L8qsTLU-EK#y(fgeh@wVN6>6ln212GC6FN@j`r5Hjq1Ps=jMeNyG
zo~%tXee4F-uIR4d#$I>R)@(55CYcF!2zs;}JR2AGg%=FnfCp~_FN;D;k4ABbb~@m6
zmuXRboXk3DA{Tm8_JZum;;j33hUod<4^tt<yF*T${=DVQE)x7hmF;Lf{&q7ZVN`Ng
zB-EMnD?XE)@oLgD$~7IV=yBxVF0}{<_$l{X6Omosnmx@`X~%M1>&AzPZ6B@?1CS|7
zr3iyYL^%4vHqxS@PCK(8Dyot@%1scZDp~#>0A0u*akz}RXe|}-XF-UVLS0Z1hYblK
z9}h;bBtUR5ET%Xtti9#Sa4|e~IYJ{7xbIMeP~%oQqhF~xtkkXcjUR79V@8G3i0a+}
zeFd<`q%%|<1Gh5Jn_dxeloff28Gt-0lzw_*qY90R%*%ztyR_l<f~E5Z32c>uSizAX
z!JTb)CgFSa@|Q&<U|1`LiCk#@pG3@c(c~IhYpM8=#9agX&JIhqjXk->)ZZR~+zIY&
z|KQFZeHvRbK!Gp!qyw|bNgBvLjf=ZVUYPAXkOPtS?FbB^;Ro<IF|D4QgY!HCWfPD@
zN)uW8Je%tVdfvL#7<-a{e}%1<1I+ceV|KsD(iR|-<TKk9mwTJ`>Piu`2nQE#;QTzc
zE7q5Ac0MLQ;j20<2+7dy7NR1w<DT&6(jKm@EwT7oS))H6dkTre(Jl(}c85it^ZO3y
z>V)q99(wQlir9AeYpSuA;WU0bhmv>0R&K|VLiq4Qf5nFBp7!vq-^WCPM_g}mbKngd
zRc(qh4MjM}1=Z9ik`A366W&C_5cLGW_!t2mQ3RFS73=7X!5t`90l7gnnH3iOd8bCg
z8U3FfhKxB<f{1xP#t%1WVn=Bi>pNBQA@kZuFiGGySpQjXw29GZP$gsi^;Kf}Dwc!*
z&k?One>{9y1Sb*VAGK;2Tv{7Am@|E!{>KlzF?!(O2b9m7BI@+KxQu8JoDGHfzV%Z7
zU(t;N+Lf!r%ij=MFdhjQs{DW0^h*o<lNWFUc2qBiTXn=o7S)fPmL$$il17CHdOO>M
zaJgM;Dph5Zs#0x-fMUr{km;lIG?Xq|clAH8K4eI$E;Kwac=G?+c`q3G&fLv8XJfel
zF+OSus=Na-(p+k$^J|v{u@24=?4kr;x1OJ3XN`U<B>tK-#8@H1UUd0M=+3G}jHuf<
zEv;XTY_dg@Cu7Fl=Gdv2rcvXdT_4o0_g7AU*#o$S&Mupn(1x)MNP}s!-LJ(=;N@jY
zLMM0MXI4GkvB%k0wFRi2-Qljf?@u#LUGmiEJ8;RG`q|?NqbA-qnuO`YpYZPb5HntP
z!%vj8sEo;*S*Cp*ySQ2bEfNG=C;v?t{x5^8^!cX?JPq_67DRqA>+)x8pltKg-37V6
zxmdeM&^UWc{#W`0Sw)3EE7AW&1ZSUfI@cPwmJN|-#Y;RnTJ|P%w@Y0$q#`qFu9+i8
zBJh*6ZxljU;uQ9zsig7DCf{vH)MH~{J1QXsjUU;~D8JpdL{!YaY$4t<i1Wbo7YXC%
z4D!9ceQZRpS7;6BCWtRkXbep=uiW_nqK+ZkP&!2XI1)Y&<7GOPBKWNQb}EcUlB3{}
zp#!EL(FI$CpZm02<q<ekaCS`jf4vEB!>e&C&u=-UNgZN0cZsQzB{Qk=VW7Kk?g;EJ
zQ)-VQmhDTC2omVo+fiqmUuKzd!Q^jsCu)uS<$@H}5f7Tb_xs_JI(dO#7333N=fezU
zF@>%P;;U3plGbF7`aD(QA_x&1IP2%m8OhEW_MbkJ5`(zog1yxIN=zWEq=w%wVkl4P
zN%z{xkr8SoQH)gq-;~f015(dLRzC{Kwfe^I0W2-8ltQ39v=uOq_iEY^ey6KhF0Pn=
zip|^^9*O7y@D^~_)H;Y`SSVaC(yBvm?=S4J$eZxSkaw&jI7O_%0-C#7Ko8kZmdfNH
zl^GbCs$m`hmAd?74eUwwcY!YMzA!gZk>F&LSFW`Bz|+J+`!~?SsB9{<rL>O%DsR9}
zyQ;qL@pAiZwb4h-!|M$ZO;NFSJhzvSpYjY@P4vwwy{58a9hqpI+OrEQXS!}=f5T1m
z_Rp-Al<2i0Mia$x+`<#7(W(Me$E|997C0P7GrHWN5$h!mL9SoFe$!R?$eHYu%sgen
z947tgGu%&s=t?va$sS0K5}w!O7G=`-mx=|+5Hu|qUQ-gwuag|4kLVj~VWV%*+#5j~
zy8?+{5NojeQs63fBqKR=igI#ux*Fk`#?l|H?fBYVk2Atk*p>gaL4};={Gd=cHNa0*
zse5LTU_^fNqH`}NL83Xz+Ob=9gDL5Og7;8i7Qv3oJkA*E0&kS-%In(OHrsG~5hTQI
zIJM7<Bf=yi?CnhOskz}PE2M~67sfzB5Rf>t*Ebn3Ww+V)v)Oz;-FJ!(Q6Y_jg5k;+
zTAz>*`B9&iPfjk2OlGu+x{s%3pS3*-(Pf%Wb1$M^ng3>vXgjDUA(FzGuK(cMh)Pav
z_23_zmq5?oD7YZq&n@Gril*OLWLi7arB}YhDO813Wo}V6Ohgq%>>vdQ-&^E0&))De
zhen5pMa7jKntll9;}Aw`!MhvvBR@{)&H$i@2gb7DbNwfZx#P0y&-`*{a}biqZa1A}
zl-=j)thhk7q(Pt0s>|ySwNU~VQ)9L|L_94!MkoHlDuR@=?H~E&8gW)#sd(K6-flYo
zfirJHZqs&T3RM*TtJ^r8uJN(+Q4C~Jb?jbDcnm{f8ikoVC2Sv4h$X%=(4a0!HknF0
zqiNc!3}vVpf@Z}PYsHmBzvH?VEd{8Rx&GtdSk{NW3Ju!iJRe3QP8vaQIk%q;OYHUT
zzjj4+H*uI~LBFgpLm8>0XovmUZ{`)LNHc4We2*(~3B1inEVRio*s@WG^BTDN^)8$<
z2koSeOkZtjZz$kM0_;t(%$&96D%7HYsP$*3hxZM8h5I(D{XZEZi%Zlod(Xgrxlh(=
z6125gviI^(O??^9rV5o6#+_1DaC9E)yU!A~YKyYaFNVT5Wb?#}NzhA7>s%|kC)?+%
zGHo1^#4fLl{e1QkFUu-~wj%cj+r(kSrZ{nF!uER~c?r*6yu!NU<I|fARn9hlPY4rZ
zk_9+038H#sd<(O{!<t(w=}ND-?F!0*;&lP4qQ+Ei9{juyhyYj}`9rl4JD*(k1Vy6l
z1q0ATu~t|rTFSaMftf#(Uu#$OoxY_@2Hx#{gcetLy7ukzB<fOT<=RbuD}yeKx&T*e
z*XAWMHm$76ne&}+fA~SzVip!dz5Fb6$CzKkFrDAHcQB`nID7R%zehE*um(1TZ+dg|
zBviI|=)#J6<*GDAP3sfr32&ddQDKf!s$xV*>pqIX{_#nH)bZ5jM-bA4%+XQiI2Gaq
zF{BP(w#Bbf#bOEb{u~sKIm;HHk0<lS5-q-MOamWNTrSepRtt+dJco|w0De*Fj<5!1
zz^@<kRrjaYUrhN4Ld5^(*dfmu#4~0|6Nmvd=<B-&{UT2BlzI5+d@^dMmDcwNu(08c
zEh|=*-?%iaWt6LbYtj3$16w<J<SJpf?mMEW3MwB)W%pTtKeP<W1YXk%B4lB`mo}fg
zues<~kcTflXfDbF`RAeo{zCR}Hrugsej|K;FWu*oCY?TWT^CfX1dfbQ?md6B4tpYT
zH%BB#&IF@MB~O+pk6D3J!Q_uBP43MXmzE|VPZ2lxDxI*!Z6}z;-E9vLGWxH>A(p>O
zXgfU1&iq>~gq=GS=obgLVA_<V+px7F2euGU{hk)-)c$New<g4VTxtUkI<XiZ*OIfx
zoMjR=%=*?9yc5wJ9~cQk84<CEVLOU~B)lAdZ(>&OQKNfNE?T(6Yhr`Ra-=Lh(!$Y~
zd*>I)lv4)Ub56a-TaH%%2C%O@20DxndmsgEQ1yk(N=T_)aL6&pcMH+kQcRXrw181u
zI-ymz4_Nj$mjH#j8DP~it=xZE`;YHe6TI<|Wn+!TlG}hm$2Ie(1GVm5i>9z8<D>c3
zxe%w;WGB8AJ7$_ERSi2AOhjAWOA`=gq|6&rVzF)0J7GXlWw5jrp5zBg|KiD#cvVf1
zX&0~mC*pDz(&smq*BwbbP<D4MG$z-vwoWrC{G#<*W!q1@lk-gk%Kx8%3jg<5)&&gX
z{*5j6id~rmYkaMJLCcp{Yu8MwOqx=J29HdMMgR+_hzSuFjxACSN?QEXSpMjZ{vn|Z
zVTFzY5O*gjRCR=P<6}^giE7%UT=*t%kQ2r$VJ>e2QPKif$nJ9p(Q_W#X!Ja+?Q8ez
zB`(Sym`6vqDu?OrT+-+B=y9Gi`--{`E-h!<cA+n<gKOk%qtSIN8L`OT_c}yfb@^UI
z5}{P^f)&Ru?iW0UF+>rPNlzWwL;6_|d%|gN0}4c5{^Vi9SNvtU4b@v6m?+THQ)s0y
zScZL>F!F`b(g}=|-7`c9eDJjw!j@v=Pmh)FFn$-I<Tou5^&;8*UXIWWNv5{Yd=d%=
zHrU06FcBH{*sA!|-U%$Nl$09oI7ZELi(lM1@%_0UBq#B*1?rLa7baj<6ZD-mlc*5#
zTCYT;(6KkKu=)G~KEJR~Ll($j<V;^^j~GJR;i4zToCSr*v%@NVYxiN_JV=q7dvB}|
zRc8gDOLj(y34IaLGCHe2$T6^3xHu=^f-)|wB|d}r7t?Se(-#p88I<Ey$1(bRuJv-^
z!-d^2j?7sgOeHmd3lGADFG@?qttu*@!%`l*NZ`@IQW<+R;wEUPvS3YV>V?#2C0g!h
z8iCjx#t+gJt0TVZAg2f=r%*$;WU%2{fH&449xmcoZ*pER?%yEKFznG&bBd!uAo_9B
zCZ*2#bWkXGlB%FK4BQF}ZPU|ErNZwN4^-DDoYR#Fq2r&HaR!F_KXcLLjZ)rH$0fip
z-{%Mw+aFLaMDn-Z+Kmn9L&CYGgSyxERpZyG&7FTJn;6^^-f-F$qsus)_IRM+I$oIl
zynlT#v*h!OBhnf<<pQBgbgSa*#wklmD6>gseTznbs^RYwF+zvbW$wqHf~L-4jdmF^
z>;Kgd^z6HfLFb$nahhOh&**E3E(-siENoG(-%-!Hq|vn}lhU811^e}E)In&rpaPAK
zfeIs~cP|~?#+=Ikt&zR~{GgjBnFh;(HIExn%F)y@PE7T@2k~l@f@4S9)I~tdjAbi0
zjj3!aOH<17yA&SBWAPttMr>U&{E4<I8}313O(w*ud%Q<9UTr?^n-&-0IVW(uXn-Pn
z9fGYZ1^LCmqFGi_1#*&u?Dr0fp{OdBps<Kwubh}vhNMgpe53Z<CpKadRbxF!Rxey9
z4k{;>Q{4<jgFDMuZLhkD_J@QP;Xop@j49q8By!&hqQGdjuFMj=9jc^|0E7BoM!jIx
zFsVTkYsFqzYJ(@3=jXalVn<IE7!NoORaD3>QsK#cBIB84dk#Z@Y)McZSbn40<3ttN
zqN{jViT@E1$sLN)$}gxONJ4*j#4v9vYAWWhnL!R|YKCwtPn{*aq7v}RMvt*Zk>!%a
zqeRtiy<nb>^_8?e6C^j3QlVkPOmlXviE`@Q^xh|J4~HSiXWBdJi2A8l6AHln+9X4+
z;Td&ynyTaOP=4=1Lw9(YK5=W-BWQqBIWqclup80I92Q_abK<?*;qhbT&sTo``Q@*&
zH@7d^&J4-`37md3MDe%)Aa%O9>dI?Ms?HJi`$h-L3jpikQUGfxBb<E78?z((T*w3&
z@GVZ(+bnvxLumkQ({MlBn67|M4BJAvcY{BL1?XY)hCX${hm*ivKCuP``<D>Pq;_^*
zLJZEG&}>ev4!A)pZp_U!7-G3bFn}CHB3?y@77Ifc14&+v+UD83SgTVdp%FY1#?Pzp
zM5W*=fGc=e6>O=dS5RGLoB)j{8Ja6gB)G4Yje^kA)o|4D4tDV!0f*n34ctoT<mh6A
zn$!jR(G$m|OmHTxvl%`w)fJmFJKDxWXt@2-j3<kdRlqa!7_)Awu}z$1gSSvJ@2i5Q
zXLakEZFilrB+xHixiz|WB7`PHwe2v;zYToP8_7hma@p@~uNAKe=#}sIWho@QIYehY
z#&@s@P_n~5xJWi>e9KW$Cu3HX!G5?HGL?4XEN8P28+Ufa=Q}>*0O9tKKpdJClZ7H5
z;XAk?rpFiU;C9uxjY56ro(lKFGt?zbjcfcRKAI%w;~Rz3pY*HXN1C?`fyR8mH<M&F
zxx$JRxD;LA8lISd9AMp^t!)!be$4~0ksv|($W?}J@wGjnO${tB&;7I=FO_CXs|LXY
zxoOPV!4$m-n;`OC&F`)6H}(m@f5DrB{_iA`?;gUrvrS$nT&SX5cd=QGe`U$Es5y7{
zifiL+OSiY<7nb(7&GDe^Y{a6MHL6kYfHoFbg=@SOQlUxkuKzIHa>5~Y5FHZa*;A+D
z*NpJ6p%b@mecW=6l7UM`Sk!dLbs0D@7+A|6+~ewP;tlk2cVw%&v1}kFyFAu#gB4+|
zPN%*o^J$9enIe@|<G)WO${2_{fP&c15IIU381f3B<}jJ2PUWe%*rsCZMf~h{w)rE$
z9e4qvqwmeML-cDH6$IrOdRr%Fh|kb$Em89gSATg<&oHnskS+rJ{Fsmuz?0l%l9<#y
z-D+_m@Gvh^lCVt?jb&h%3MsJWMnyOkAb>hG&aU(X(K(<ZZC86)2AKJyt0v6k7i0sx
z#d?7VZOh305Hkd9a%mAyHNPsR?)Z!_enwm(CAq8z2Xe+{W|x81?Dc|>R-))l1cFft
zVt51?+gxLzFbkPdb{YzRqB}04tp>Kc6l8CYEQQLpWJteNL`xeEmNUi+>>mTqJ_0rX
zaMK8sea>b>Jogi?ur#Rk2G(9+$6PjyD*D%JE!D_nL<nMTgo6sjYJn8LCqO&D9&4p6
zlK+O-FyPn*`LqnCyJ_yJ|M}`IrM7{G8#{b=aNydeHYk7Uh&)CBj9bIG{5w=q!CRCX
zZQ2pA7MLUEQXa>X5VeCW3aZ3`c-^b9bg2xuUeO%%M7Kj|@l*q(8R)D{Ygl|+vp&h0
z^jivN&$W(ax|j(&Oe*;yKlO55=6S%Zc0ILo=g*x3M9^&WyT1^c?BHnC(?^eNT*TRz
z`o;EmR~>I5J3h~bMv#G60`#Cc8$sQ)P<KiJJFzm_ZCBq%7EG?ifN=ip_#UFR_<$d|
zzNc$=7G#;=FL5iJWKjTZq-F#8iyd@FcQ>>CgMj7!^x4`oUE`k+q<KhfRWVHym#Myp
z91sYMS*kDpEDfA5_Y~AYN>^@CHw+{Gm-&lmIN!z$#~U-~Y+}vFgN#w$scaDC8u@Qs
zq#!@8k()S{mWC1+e-*6ObhK%d>uHMb20?@_7R@Fj&I}{HL<hK4dJtxm^>c6u5<$gK
z?LWE}Jnay><0tUa#t}L7w=Fu5^|#`7z1f<6hZ<3iw~|pn^*Vj@@s#TyrtURa-FP7M
za=waI%h>D|W)&YKr)vMB+QyquR24O>hnd=}<`V7HB)1jR6N}z!Xu$R8dHla#fGz4w
zIkG&aJsD+EJ0gU#{Zec{n@^7JmJt75K|~G>oUN<V?l~^VJ{h<M9dq)QXZ<6a+Hxs6
z0o)9=y7mes*spfXwa&V%O1KuT-QGgftIB+UJPWan4wI*aH8a{Ad_8ePi-Fh3{gwKY
z;*ty`s(X4wtC7uONf`zh1S~M9e5K7y#Bkwh3JMp;V=AnM(S#%d*hjti3(z(jKVFzI
zO8nVjir(vjV8qo|z1!0!i$MCB?xXH?5CZjA-#I{6QvC0qHLzr|pRyQZXVdtGUa66h
zywEd5t-R7D^+z$ZOc4=|ZsLB`@KK-aMyxHY$Tv*yrD|^K5NgjXBlrFtbFvx;v{yd4
zQ4Kmep{MNE^d{?*eNh$j=f+XX0|P4fzhgb%)$&A60H*uyAM{6U5(?A%Wg*k|UC6A>
z_f8uZ`+E=CN>*bBZNIrVC)!&PD=-CX_uX68oa<Vj!!6*&=QSJhH+a`oO&l20i8~zN
z{^$a&veP9T!dOkiFdKI>BQrB@OQ9y8q!@zW<W2P{yx=GXD5a+XfR*3)QP7FZ92<=v
zj|7O;HMl+n9l2~DxvE>_lgh@{h?L#%399JE=Foy1MR9SMDhNcirXA|(Uo_{C{@6zo
zVa<)v2}cohQhKE(u1X;=T4^!zlW2z)%voe6>-nXvE!^~#0%2Hd$q#I1A2u(V`uHsb
zTIOYg%w$TLh{CN6;<$$#0c+QjgAx~}!>n?BtJ4&biee(9j%5*O#u{OGwWlDu?FJ^`
ze&Za4Uz)Z{!u1f0)lmqc$vqkt0f`*0&OOcO&-va>wjG!J7;D3UQoPVnTHhhz6%!1a
zRa_Ik6@^N6`0Nc7q}os&BF2g<w*r4Uw5PDX2TXs|_0a>dSbDlpGm{1_tV|ey(z?Ue
zWfj-<VB=#KvReRO$VuV2c&?UqtW^Y|Ly(Z=eyw;|bHWE>WD#UgmE&m`6kxukw%Z9+
z;7||<5wrFjZ$Cx@<odZ0LO#Dh6`TwUGTRvFtfYy>n_^wpgv;IFjkdcs*tJ;&08(nm
z@(9!+i5qy=vH0u}^>y4|;a;_Fu7&v^0XM#QZ%X-DP{jy%nx@9&d&vhdPpwpbwulr1
z<YLmv)DmMD<n}5mGSu^mRA2NAHd%nlk6hQ#PF(tp1n8nfqWX|z5MxeGoJdS&_9$3E
z{E2y{J2F^K@K6!PW>Jt_g{I45e%8F?WkF`a+}*iri4wh6mbZE`Dp?3^*8$hXQcPF`
z^`5qoC8`$5r<ShpB6_X8BEB{-ul_FxW>Gs4fKlasVW-}*tRQ|X9o0M0B^en84V-tT
z-ST1aazZeDQn<6(n&CFYnxMzs(xTlRMO{hAAl2*K8|kn&z%ASlAv_iA=pG^0qS`yu
z#Z)oXkZRUqv~$#%s^{)p(h3Qp;UR=Q4=~)UajVaB=H-V6(lw`eQ4_JYbP@^;82;#F
z<#U+&ck(dCtx66xvbZfs>zkWhRRHLB7M0Z|jI!D;|AMuQ+wg6}DV$%j^?*NiE4+zH
z+)*r#87h0*^VX#sj_6vf!dX7w{4&Y7{|j(Qzk`gxwChBZ6aJO~+%~S+9M4a9#j=KE
zSLloeoD{VcgPDz&{Jy72$ksok&IMT?3B%R;M_sQZap@0l%}Bp3Bq-+Nn*G)R(BI*T
z3TTi9H$M&0Z69Y86p`tmyVFAmNt~h~db7(p#yUIRxpRk{6N|IrM1fDoBAATRurV>&
zco&THDyIA`&SBF79aV%OLHoT3b5a!qDu?j4)QRCZE<&6rVwYQZWAOD{n5;=-lh9>Q
zJzYsFOy5}~?aI&ewFmUQ^_JM6sSX7pg7`nBpF7V5EiEA&vzW=~@9~bCJYsQLVvy$(
z`FEw&K)MpwhYK)TmlWpmA;<Imup~Dadot+Bs@RXY9eV)#NL7sVS#&O#T88%XT#8@`
z{l_gyOO@CH#zPa?k$3NzMrD9lhi4D6HLBk5%jzt+T*2DXN4srRKXs0q!*ikzSp?hQ
zZ~G_Z&gQF0e96m&T}Ju`;a&p{c4e8g1RjMD)U7?PF^bA10^)<0%b*OE`Ez%tvsk-p
zwT7F$<dAJ!qsHxj`K(M4Cp*oNrCwR4t<Lu10ED72a~h+-Q3$B-Sgw~)K@Vb-HbEy4
z*-&&O1k=GuNPC5>Xz0+Qm;fcsn0YL9Xg=gLo1MS^wITwH&CHOT{LBLfI6z{^x0gbT
zJertnH6wd!sW;WKz-snCnNFsB-H<d)<n;|;_GDEAGbO9LZqlssC*Dqz)i{cjP-8aG
z<IewWc_Nx%l@K7Q{r>$Q{zqH!1#*Sa`+Uq{FgKvTb;?_3u5#qc=f3SQzBgf7OqD)|
zv{UVaiIFP0zl5|nXMtU1EE<iOjMXckMg_-uS}9v{<DSyXSwH|$Wijx`5@JR|HtUwE
z|K-2}Vn$AILPv0w$#cJ88>D&!J286W88V`qFqFDzYQe1|5$BdHk17wxu#;LspJgQv
zEnBd9MC}Fv6Ea7;Zjni_FWi@{mcW%#Sip1U*GxHIXj<15p6<5|)z%#BN!qAo`KEPo
z*+7%Jwi=Qn)dN53!INWJKIXcNnszlS))Z;ckMT~6fwCqL2h{8O7YBj~$UFC@Ru-@f
z#RwV+Yc=NxmE?$b9{XO}i4D@VZ{~(Uf(2S*rBDrI;&^1K&X5h(S{$7<g%sLsIxtBD
z6=DPU-$h7+8qYPpK4R*04Lv&2omy0%GZ6P7Sl+{mD?lWM<C166yt;iLvxSAA6(a=`
z7S%Hq;HAE7tLrpkuOULtg|h<i_FdsQeQrvCtjwlaY3ve?!8GbmXAa~r0O;@tNXQUS
z5E+*@Jwi;IKgwKK44ay$_}yN0>UCy5?<calrgu$52rSIXX}Zo^rDe!u2szdkZpe)a
zhPHTES=H0j5Tds0Yd8DOCj8lEWzrfvu=4Mp=gQcXR>&>7%wh1+4mUPpB*uwDp6pr+
zLCnnbP*-AUdZM9h*qnekR$Q4JX|1Qiw>45*CrVf8z_6<F?P|lL-q^E7gMSG=0&;0Z
zCRo$4C)XGoWP4&9_)UH@0?DMd4y9Rd4HKjb){fOn=lRdL<a7V%?-h>E%s{jO2uvQ%
zGdc_DFT3^!foBQg(E&u4QWL(0_T2mCAal2e@rm?>-Bg-oY>pK1AUx}*Fk&U+erK1l
z7j17qk|?1oVhUNdr<8T}+@xFh=V2*cs@(Qtd?(+$4bg0&Ckgc;)irf+L`K<>&n`~Z
zKZ`zyW~s^Bw58DzWB=cmRKH(2P}5}fY-rqw+0vpbGD1Yk#mZ>-_R+mfvGX#BP8~bi
zs<4U`XzJix?x0ev%!J@U5$-C-A)d)SUJ;D`R<_HwNb_MW`p6m)R{Tn}8)OGbIeE1x
zM*1Vp;Dcq@UwlUjHyLw}C;PgI<@Qx}QYhNnv04DFPMF*TL9~q$qU?`)QsJ`@G&MS;
zq07CYtgv=1ZQy;CSt0N+D~ny&x0vW}?Y^J4@9lN`6HqfYcd}MH8L9S2o3U#Nz5{0v
zl-3MbwQV=7S69D;LyU<K`0?w^2^O@$a>UJt#ipJ5$^~|bIkWCvNKmlA-<*FCaRGqR
zv(%DgQKwoSu5>Gh0EBXfFzyHwwpJ&M$yi3k2P+H8`K1e3ca%Jf34S)aHWwfr;a837
zdw1mDOwu8D=V)UpEU{n|(4UOUq`jAx5|!X%r@4(VqnDSJVL+PWf`M*q9`_Y#pa7cG
z;`Q6k8wpDr%7xWasYg_bKjVR;GAYUimKS2;V6f@vT;ph~4#<(ivr-?3eZoSzqYKg>
zf(>~!N<-S(Nnnd7rAiCR->=D2-1>BnvjH)mdx(tjcKT~{)-6pxhV7=XQs04Q5U181
z5ofquLIb}bR+LEz)sZfOv*l>vV&m#|$Uid@H_|Hh$8m*LnSbl-kTF#zAjTldS}E%0
z*!q+am_|~21xk%M37I~uo(T6;NP}j)1a43cZ9y7pd<p%SkJoclq(F^k&7WzCIz<=S
z%X5M5xf92~KwnwyuAndJV$BQ?pDaxI7YNcp$HR_ZW=dpon_6b*TttFmP_XXl$x$A7
zzo)$n;pX&A3Sb3mi)dJCP`q$6h}sjSzW&5mN%<jIf)hdV2OBRNQbG?^C<lb=lhKOI
zcqBh;0t<AGCC}DYJc&nW+1Jjt&?>}XEy!9O90`nGIR4@$2GgiM?%rNtEh#ZNhL%xP
zolI~bEoQ=YsJM4I^buoQ67l*+)3HRlh9l+l=Vlr=23KLE{LCepq#N^BlPC@LAs*oq
zFYp^>OF)&c;KSE%dlO7i{QL@D^8lx*`^QL4lyXhut^@&lQXEZW-477d3OG&cn}U~z
zRmPF79!x==+STIr++A%#t3e-cf<YI=9#z70dl2c6SJ#0USP<2yWAEXM3P|vxobqnv
zqUHc=Q_oAO=2L9TYEhs(2C14~TS6?K*>pr~DL)SGaW{5TcZ};1>;iwc3>qX4A@qLI
z$bF~#9posHVlHJNDr;t(3TxgUREG)FR)TNSyoLmb@9cC5h52CLBlpt$m@?*W3r6_a
zFk9)L+HrUP$ke`GSixX=yPd2zol{;-P<|tIDroR;6MP3eCLG%rpba&om4ShfpG&V2
z*4ZJCO|<>ikP+$HodqFA@}TmvB+5`Q>ogxnYm$WL7dA-#Y1^*sA6LZ=qDOW)lyt_9
zk=`2zD}py2S)knL>eRv)`9EnW6nfvhCsM1%tKqb}cBZzzDQ6+IJ9RtZgpeRHAAJAb
zExlBMqQ#S=I0|KF0a0PN53l3km~>XCM+<zSvl%tXnr>o(W*;tZNJ52bz}>52*N;4<
z^qM3e2456>B}m+ZuNyBu5uHp6DTY(VTdPG`gNCa<z){^)aa|9MxtIe;fw4oI(cB`p
zpLqjcNB}~>zk`z7Z-Mn5MXWvI*V8#0l3?&<{jVJsrhlUhVlP2vPcrcM8LmJJW?PZY
z{~Xd}P3Yc@paBUUMdy`1)<&qiQ7gRFvO}QHoUz8o-|*?p@TlpE(buuG37g`3wF`<w
zi=m67R9+nf6fB<vntEr*8HIm|<!b{0R5b!#CN6*vSDj5V<y$1I=Bbk{oaNWm94E{a
zx&5z%Zb|A@OtuYSx?hB=%;@SmZzK&Z_lqU%vEI4)S8Bw$YV}RYzeK_baam_y$e2J1
zlqYF)+<||L%Y&LuN>~`S{JazAcK&*Y<w#3Ic%{Y|&do~g$y~oBJs&wL9(#Pf!t7Zm
z5v|m2Kve>6`?L0T3LPjHl;R@5Q}*=fL`!S?rM<nt^lh1M1-_^d1hcFmhWXB~xaHxO
zg+@l_(KmYS^}jbZ`3Ui5))mj6-cOu7MDF=$!F=S68WZ9_{%R<GrDo#L9~3Z9M?eP{
z>*TZkTh_3`bbl|;_c%nqd8zhF<8m?bwQdtrvXUtP`OX~~Z%(J|2`eFf3jz>^2LVos
zB&7+CG1{*eE-^=Dp`+MFIkn@o-LnyZ68Dt}4x6&>N+ybwJjB9lQ?wh;lYFc2#T5!i
z*jHHm(xU!DUj-sMBpgeXK{QaE4pv?R5@$LWhOpcQN;#MRDYt2K<u>?BHzjd>1~JAo
z%0mjG)@u(dXUbnB8kz+1ovgCEl)gR##dA)6t%>s**nB&}!jX+0#mZ;tTFIuCL>e2<
zeGbr(8*~@_fAJ-u{BMgWwJ;<tZ*lTDOwYCjG+D-I8!8D0T=w5$7#`5$;{#u)0efF3
zS2|&;@!;*cvYZCQw4s80(uY|U;lRD<l8cFkDjHZZDVK+i6xiputYnotUF5n0ZbUI5
zUFI2J-(;N-71|&@&VK_$$Ug7kI|-Z1`TV#7H68ww4sZ28OfGw2H|}>FsmeAG*is1r
zrG;mu4q!83fs7HbftAKHeI^%va&jczXIC1x+|Z@xA&9UiH?k4x&R;qpBa|8LT=2zZ
z>KGz*>hH9@(UHQPCB)0svd*591(dllLc}vg+S3`)=7#C!-M0W_l#3l0s1!$x2fAhd
zgny4bXtD}6>wMX#;=;3V<0Bg5JMra2irIEAT{a^Z(MLx)&p2l&us`d%#BwvicVcSH
zQc}r6UirMD&AQC7&_qK)Y4g+)Y(1zuQ<W#6DA-rEn`>M-%dvbQa(N@e!t&yvz%bpu
z|JV9oXyINUAT$WpjmYg6JOYRsHfd?-I+E3H*JKW>eb&V<gKCx23iyW7eEDFpmfu*@
zO6ePb@gFxu$ari8xbPe5&<1)ra1hu5y}X%++1M-4g}<|`9Nhh|z+oMRuUR7)Kfjqp
zeSwY88|!D{OEXf_K$xZP^kR&bH)Jav4^%W{!CW?R2QyP|8H51cq0FLHTgi6EQ}Jp~
zJ$%!-)~Sx#*vTgpVEfNoUALJgM=(_F7gK>5kc~}Oh&W|=!zK%FD&sVfC4niT#n+9t
zHMxU8fn+F|GMXu_@`e@i5S-4PILSw0xtfdboC#7wt9rD#VWcI++Q}mmiPOV4al4&5
z%FxOm(^<3C52}HL$iw#3j3DJscxlR|YRC;1;3KI7q*W0HxSoW5yTn?upS3E#EXww8
zv`;U;RYCSIXABSx2&3puTD~!!V;R1U+p>c5@e<&#hUzY{m}jblU`O4HuqVQIc~g8$
zL_%oe$9B*Tf7$rGj<5l^xOzYcgN^t-Fj|yjo9?Bs59%L{=kqc~_4fy3k=36l@M4Z1
zyvJ!);8MOFlGhdY_6JxU8Ah)0R7p|x<Q@;uyrklkgyoE2Gc$(&2Ad+ceALq9CjX=Q
z++$Hu15-K^e-uouZ~F-X^47G-)qfZ$Eq`m*m_V|cwR?3@W9mnf>i+Lm+dupA5NVE_
zsuU`<Y|=-tm4txd44b?lx1T9aLyOa&!4)pBiz~)D2!yBQj73*HC^|jb<7cST9T-L5
zFClxAnUkEn;51(WoJi%65f80GpA+5ltwz24eUGD?CNY`wx6f}&q3^N&TW=x~Sd0`x
zA;DlFb|F7+2T*pa^Pq^#B~SQvYcq-1))O&%^g(C}u47<#_s#5$pPdwYaOpsFY|y`N
zU*Wqqn(CI<Am~xgrNk6m8cS7?*0L)+h<ZEVK0jMRFh2jn%~uDzn!e@WtG`&jD%!6&
zK~DXJQDWliH}})xY6rUQWF>-JBX(W6R`h?}kOi8Q!nobU-5_#X4N&p+z;pNaAGEvV
zR!&8r0XOzeA1j*8(@Jya$2x?Bq{Q%cP%jzqsS;Vt#vw`)|A(u$ii)diqD32bg1bX-
zg1b8;xVyW%yK4g>xI=Jv*TxbcxVyW%-~Rq{&dVKRy)?~Wu=iS3HRr5aRSPmv^zqpP
zBGWQdU%zlkGMmYN>U*yKX~G;eLHxZ(UmHuwDV8D*thH8bU>MM$-1hV^!B9F~%Q{fK
zQU!PRE&o=wRU{F$@^7#XEIZVbzxY%T*eH`HrfB~*BcW?|asK1ffnVsBj)S&?n11aL
zOvQ2aTPT`i&{DQI-zF%p21^{-xI8ob&FT5__a4NLzg`?oO}mr7Jjo%O5cx%~!9aGm
zRzm>*u3cKe)}30N9vVB@!QyZK#<Um7`ls|oOsmr38>1=H$c|6;aJ>v}B}!9zh~$t?
zvzZW?^<UP^vGGCFc#nB(NnKHQ*6{9#u{E1bJ5OPsBtcJ`K|?<wd~ov(N605}aGh^c
zHb~r<B$s5I-Aq!@xOy|u9HuFDFuQw@On5CQfDEI4vX**(mJH%5KDGP=aQi1`z2@XV
zdsP^rtG%cd>^*D;V<5omKTMCgM|e>Tp9Mny<OAqSuu?ajBja{?z6(M}NGH;lk;USR
zOrIl#C=GtM!wk!gtSTGbbNdml^h+7d-0z}5q#Z@<Z)oD0=BYjQd|_5a96<ixqIA36
z?9R^woO-1tZ84Ahh4$8OFHQn=V8J=k29Z829a5^D$V+7)+p&=mi<h^pB4cM3>}j92
zeeCDwY;=-3D_4@NQ7t*Z%FdN4WPs-d>I|FX@k$jcX#7tKswfOJRy;tF{wfiJ=X*r{
z%$14@Z)x6gwK=>L7(Lmp<pOjlsyN5c771FF+zogtzh}*-yCOi+cEV0<%DO{DpCqo^
zaD6|#n<PByOsJ51pPCSG0Xvz1jzN>PN{XFhc^b_kFeUg|P26dn={u69j%O842&5t!
zW9Og`ai85^{PO0;bV=Q+g9v48uXrsFFq9@<Tv0UT_4BCk)L8X^lzmv#WEykXM6d<t
zOriRN{84e6(_s*TJd}sKXY~>;$02l>Cka3-@F<aqYu_)9{J|KNOZHFb@4a$o$MhU6
zA)VR<D>;h^-VU84yw72NVQ?*rLX@+xsHu?gCUwT9-O2kJ+wcJ&pSKWilM1@QitP|w
z6(T*}wts}oSmJjuqE#QQdS=H~M<fNq(44)Vj=WfM?64)spKOM^ZfsX76n@m2T2bhK
zjk89+zc;x=t0VC{+Q;p>@kCfVl7N}6J^HW8oZ$a5W?262H}7D%p?QyFJ^HExQ>WQu
zLC&DfXD3S?A{zfP^&<dv2uBT#hd>J?-Hxu=OGo-u<E}tm%!A#_3I!Y-FCC}6Ty&~l
z->qU0LnuTW+?HXawBWUZa@ng0)%C;a{ijB2VF6(N$Xc?DB7H`t=*`{)f>>n6!a@mb
z&93gHyqW+<$;&_eZh_@YOas-n=l9J5Lg@GR;kMNq72if$g$tY*w!Cosi5vNhL=xCB
z5SAT^%mwkw^DeuSK=Bhfks6C*ja<G*fEsXd0YoQOset|gk$wc35so4<gq8jsFSXGn
z93OB0+x4(9pn1EPeWN<l>~X$OmpM+r_GT`FMJRdOBWp($&*C;;7RE9XH}RKO-ES2k
z%_=>UpHjxd$w7mq5OW6)KfdQ8*6*4*!Qj&c#f@us^qqP6rt}|Qb(~vT!)T2B9X<9m
zzEuiWwT(h2vsTmgnm3#TiOcekXjPV3tkfE>PVAHfg{jxWs8e1AF#{tjpfxK^kh;C(
zEJG1npfN5TS$NnDN)vw;LVklO#*XeQBKFwTKG0-Phxkg1QL)zp=}^iLvO&jZ=>6z|
zcLY9*@ZZ-jO(#3%g9PcTAVmK2&FEm?u^AyAt|U7BGTbaRn)4;?h_RbxkccJ!v);98
zz0<iFB<MWp_tT++!b+0g{iZ4&xEkvK1$$<3=HiBrsRgQc6R6Yhh^PA6qo+KJ7V1M+
zs=+)hqrgqc;Se^l?YCD`e6(O+N@(;VS3FXrhjX~(`xJ1-LzOZBj)c#n`d!IuaD9BO
zn_vjmmWuD`zX8r$g;?1Y=HMXwmc+12!vtmx6)Jy20{(Iar#=Ad#hknA1LU9#b@hp+
z4%#4R60b7y{E6pC>2}7SDvnJ5bg^}{a3Y)>o(y<5b=|>@I9|+gnqX3fY11SX{v#K1
zx2p`V>usqycqv!OuS5$jRKxxk;$ri#^mZO0F>f|fl2a-QoFNor6j_9PU?Dq55JnPo
zYFh)YX1Y{wC8&!GZ^qaJTp8wgJWYQ`O#phatQD!>JfOa-1`myXS^P4D1AE340zry5
z{L+x8XL#)7xaR6z%{iZPy048C9(wiMJ!<axDmD}ZaZ%^mu6}q>;bD|R+Fpbt$`<>3
z{IPqiI%kP^7p#$9#h%fc#aB6nXH;rE#2gJLA`0Q?49wq|J$J9~t%4J6`h(K#QK#>?
zDp(~@h#q+U3mS2kN=nv0XmZul>BQ-I0#CjHj93pP4N9DP?=B~PedAh}gRxn$sR-m}
zw3tRfp5_|F`n!D<1b#_?VkIy1+i(`KrXbdDNP%<`8x>1p?mev*>tWz6Yi;DBqm$Vh
zZwvcji!9V1eKqyY{>Ylla_k3PJsHj+E>|=fQ#WN`S`5@?>5^s$`M7_NBWzvi?gsbv
z8PCO8*kKm>tOnn>_lH8Q;S|AL%*{GK|IU72VWw>GdQ}Ti4SsJlR-v+kK@a&h6BB3=
z5oa_!<x678-PX*N+K6eU)#wZ0s1M}^?rQj76SEMa_DlXr$Bk&YHIelyp(XhS@AO}0
z(WzgZsS?cp?$&Y6V)AcqP}*5jdQddg6IMp9M<NKfz3;(IN;#R5+|^;){#nVf7hW<C
zx8y#v82^dI`I9zx!XnwuL(|h;eKDARO$5^WP>2dcW-S_0DZJt%kb<v5X%zCKY&hw8
z;_~vf`lp8yq%oTQvYrs@mLXGMlx_;l`0e*u?_C7Zta=ugSVk^vgshmbDtaSTUcsRs
z$-{P7Fn1Vmd_xoYg{vH1Tt9<NoR`X)hq-q3y6vlO%Mikg;KM1~XxY<~?ZSTR_bJu6
zTwBjonycMt<Gq|wrHp7}$1J~I&O1^)es&)QfQ$6h$?B>9jdIFVg$FuY5MtOGt9vM7
ztbddA8z7A34Hir>Em^g}I{XbOM2ixrm)qx_AMTtVer?7Ro3%gG(kolr)k<cLWbjA2
z7Zb5#_H)pe-fXu%DxC0@v8PKwV*j`=ul;~kjy4k(CeLAET=g~~1h`eFdohj3H!(7o
zfwCxjgRZ7<lNs*WaKBC15m79iDSdT3J>&`K;<5_$Y;f=DVw`NUJ?F`6EQ#g>S+c)~
z-fY9q4s*C24G|9>gW9W1r$5L+KF@&0C)CH!D#eF<U#)*De^1!B7NYB<$l^jh>rNtz
zm}o0dR~EoeYGCk>jLAj@fzO2?p^Cn*u-Kw!jxmn^A6s_}oIpghLj-86@=ts;&-y_a
z{l9QTulud&8cw%VL?T!qE}ATOx-8>6n!v8n{=jV1^$+_+2oV?O;?qY%J3o|ee_YzL
zoyDGqRr^I>$gD@s-b22u?pkRWMcqgo3PH;i-}Z~E_ogy(rla^;f`4JQUTi9kK(|Li
zcERY{UVF|HKAIn6!7YzSdfhsrz6agzTipw9-j0f4>zd}Z4KRz_x>mnju@nAZEx<=W
zZSO560;c2nRhz2OwS-9!JT{r7d~-t2HOHl)i)noBkNCp!11&4GL6R@d=}+!jK=CjE
z1iejsyA5lnU1iTo2nj85hx3)w@-fW~z}3Cz>ihjWTaV|l*NO>=<T$GF1UbHKaGgkU
z?;M@D=4shtMH$vDJg>T5RAppOwOd24>wJc&^T**T#Rcw&Fv~4Fqout$=MpM!um~Os
z$l^*QdP||;)&D3?Ph4m2YBfQfg;{M?&jGGuP2gzrvAqud9iOU$DcW2BcOBG1a7x$l
zJVdOxcXrryEhXZpjsqG0@)BOF#1oU~9F+20nuMBXi#b~Ohqun}@%)eD2jUimR=XF1
z#!Y#_<6&ft-Thm;XK%Lzuj-=mKhmcj8`{IhGEIgu%`wXLPv<0c7&CPUt0+g=>EX1A
z)Q~A&ixW;KpUKFIKjKD=e&1SV8Cf9o3ZhF(emfEUgkoC5+pQv9_6;-iv$aq|rPY1X
zvm<0Q=U(-EujT)YI-WJ&deMMnvlppfRgB?FML{HvPgnS4?JjZ<VA5Kz&e@<XV>ikf
zk=NzYBlY1uQeZ#D7A(^}f1NcLlif<fT_DgKLR(z%ksJZ9@KHP9BaCwuf(d*k!O@Vj
zU6jdX60<9HH%B8}_dBY`^Q{pK0y0SC7s$v8wo3+8OaZc_0L0=}_8nVCT&a;s)69Yv
z?(krkP#1qs%k&(C<S}d1`gcp%Uhc!2pV1P<X{h<UdU+z75>0_{!|KSSYQ5m(%R?IB
zv-AeM#xxpvBkVs}n9(?A6MZh}_~R)SyApf5+0Y^;KcNmNpog$0pQHk_V};x#$V8&9
z&WIJANAZp18<ztJvuyALc4s+n+cWSp@TfC&YW33ec?Q==S_7&&r{>To`u%qP`#j&@
zl!cG01qalSK4T$#$+H2^`ljc_;ER#a{pW+-3}M%vQy)d6+a8aOO$&Li6UoJN>qy!=
zi6=n94;VbI*+xLV#kKt7E(`esEuJ~iUD;I{lK{F<pZ?eR{h(7uU70JskFgQbkOI3Z
z8O}HPV~PvTfRSVLkIOsjl`2w@>eQIlEmENT$V!xPBuGb;E*Iw4pEx&MNV&2Hm#G|!
z8b>&n%I+j^j_&1GnhOQl06}I=a~|O7Ftnw!r+UjbO`)bvirviewuy2DG4&*GyQg6<
zmF7u7a~2t@fV?i8>JgNIE1r)Z5#=ZyWv@Fm$7eSdQnO%;$0sK)tzVA%);%AEYB9*g
zA$)9Bb?M%?0AG=b^>N^VSHbK3SzKX5i!~-Q_<Cr8VH_`#j`9NY3UvX$Q#+zp;3vr_
zYT`D?+sx~+A8q&AA@>*a1x>!B|K<Wg-mywWX#)xXU%465f6D?myMioKM)31SD$EqG
z%7s#&45>L3(X@Gb`{<1%_C`i!=vT=N0Yv~%^CRJZ+)sW!IxSr|2g-BT<=OHMJlvXF
zWd=J!jHXpQ%`*!R*X#mk?a%ii1s7b!aC)(eu1Evk64Dii(c;6<kRs9IW$X>5$+3WB
zSQmz9Mw?H)fb5~6ku}!kw+${ieW(b(jY$kiO6ZAikBsVIlYtX3H#YntZgFQ(V$#d1
z%Is^G3Hbyh0I?@^7p(q?V^yW_>um@(yl{#_L`6N@M#LX#6p;?l-eAiTdsf~yuTmD2
zQumDx2|cod$vJ4V*xoBflgFM^!wRZYHTWAH3~$TSlR!T3pB+iVpiF+H#{4bMlk;@D
z%{XP`)KnTP@qGlJFZA_6Wp&bi%qE6ARDvjVx1UprMGFWw(!Mz>*ZCwh*yeH4ys)X}
z2ygk3N7%uTPYj#uoQN2&St?Z4W|T@G>YhCC>CF(gesbsGb-5WBHF%hJ?EC%}gv<pQ
z%tA<HWn9J;8?RF+?{%x^aN9NGw&Vr(QZd2xrKFp&T((u?AKTrVYQ8BE-mb9e$ACE_
z)9=1|teNSB0H)5%fP=Byb5tg{pcFsEcc5U#o4t|+v79-QoB7zg&bsK{OK$}Iw|)zW
zvsnfudsJ)>63HLKp&D~o54T^f#Kk6hn-OW<iC>G^TACaR+`i$5k9Icdg!GvR9p{QH
zuS`%nv||!xI&_%mk)98A#<#0sQB0b+|8XDn{%;FCE<bm|aaRMrRecH)og*8(?JQ<F
zw90Jbvbdv=)9|U9mV`pnf-H>kTVK(SNZ9WiM)-&mH*BYbEqt_E=4pGdHbcgbpoZK6
zFVe@4q^TR?>5sZu9Cs!n1*#HZh$zk^El@_|AReODlI?41Blst*yK3lC@#ze!al3~(
z;Zn%Mj<&=Mg!&vFV2nMawP_|4xfn#bUeTQ^o5==rLf2NB<LCAngdwj+LR(({SeRRc
zbN(&s-+qDfkAyZZV~Ai8`takK-zHa;7B-<#rB_kI=e4}!(w*h$0$}~@^o;N#hb~Fb
zopBw93E%aq({ox323zHFzoO_f46AUex#;4`NLjPcszRvwv(qPO04(nUAE9V0C#Li9
zkuZr1f8Xb0M$qe=n>gdedSDbYGTH)_wwM6EG;3hnPk6{+C%-$_c6T6eZt~4UQcEUt
zoj&WcDXC=^PNuxLI}aYtRX)4`ZOfVghh3NzR71L0RB8QSg?`~GS=Ttb>xU~quRvbw
z(|4}tPPnMyT8Ob4@zjX8AxX6A1tC`&71GjZGD@_W*Dg#&2?n*(2e~8>`fIa-Q`NVx
z>y8Z~Y}C7XLhr~}&)385{m{&wf`m_`z4vSnuKOhn|A<Mei`1$M^s0BsE@O1$Y1Upn
zR~#(eS?96NyI56&4_WdE>;q#lDD2xE20F6ZQ+lr803AQD`Npta?u#;HlSkltNVzQd
zXw<R_K66Y<a&McK8+ZrOk6Q?a8nlLqzY03>e~F&Y+J~H#vWfC?4e=E?U={>Sz3~Pe
zEWMKEK-BoTpnr*bc;tv7`PQ`Wh7d84FBD9XM7H<-Y@_%3At<TZ2M?|ghE5guPMs-C
zc%giFZb!`DEJAZvtKdyXgGf=;?20WO)*Rxn4gSLL`(&=FWqRFkJ<TBsNPwc><mcU3
zB>%UAW5~a$(7W)42i;-16bHZ&sx_NFJMNH!My&7L@^8|V5rW1qa1D=KB&h2a0p#*w
zoRHgC#!GR&A`bn;wztH4zHkp<Qzf#rO{Gb9F1xo+_kh8oA-BFHS7atx1f>&jS<$ut
zied8?6y0|9rok10v9d%w9&LS?yo<DP-Y3NelXt_yzPtV`I@pHBvGLb4P*W+7A{qA!
zUe;Bt2AMEs!LV6z6d6S8qObtSNzS3RSj)~&(yT&V)3>>I8?JD!%hPqZjGE2(2=S=!
z;)C>KBn50_@z5C*_%|#1{(Kb}fY;^vm+J+^4T57X%lMk9K+@dXxiO;Kn~#vV44>V6
z(eP&HBdh_lVL|kh=5VgoU0rSUUUtt?`O(`v=%uo+S@s<tg{i*kur-cYBBt3h!Zy}f
z8j1z>BRne_QywEaw)R1%T8%dSw^Mv0BEiR;A&?;U1(IUF=AdO8;lI`TtLZl?3Rn_a
zx6@A5@AyH&#7*n>_ArxQCBo8^Db;c~<Xrv8x0NG<<42A{Ym$@({!n}ce(G##;i!~-
zE^Ce0e5o$0SV-boVDrU_(}jI-<uLd`ornuOpe)y_LY~mx_2HO7hYy>TO=~QHkk(x)
z=aFGi?;3$*{>}D8|EVeC<c<}Gb|VGvnF4)+6KT&BV$T`VG4DeftIL=xoAdUbAzDZX
z7;S>hHAO?Iml>pt8ikzEs7s<#bUSr#Tjw=E1ju<~&hUTQ{&2g^c8R9hc2e1*2n+D~
z*BGGCR=ut_dtP!vjp|}v))5@)hre$_yl>OZYe7G_mL9&i(T$qVY4M`V`gmC?+kW8B
zYX|Q^+lGcEOY*HDC@V*+KA0n?)W7C2b{o(!EC;dGdU#=?=+1r>5uc&rv?9^qy?HJZ
zhniszZ_%>@o6q?Vu?fjIVxnn(#=G6+)8<+HZGEE=^a&si<AsBZ--a1EGZu0JR78#n
z%9B^%eq^&{+6ydG^9UYKYMrKRbD@IR7D!U0rnuQTvF-|B(=cQ(t#Bo@P7ko#6o2)&
zs=}-fWceZ*7-UrI0gYi`i{hq;Y2KnN@4@$M=&+qDVhY)HC)LHAW(uy8byUkgOu;37
zndiKS7JX2Kp!_+yUzf{Di%fVip-jqslLsbKP;ne`KiAhodj>L%@s_kUd1mg>;KNe+
zM@TC!(dGXIpQCkQW_?012kfub{Zu6HZCxL)yb;?Odp^@TWwnDnRY&k15~Tg?oLF$T
z+#{GNj9_;`vD_!hMYZ28Pk%M=>BJJ%z|2o4@*<z}=o9?Wb!SJg+<*#dQdUDY8;i7E
zRJDVMfyLt360L{K50PYu#N|K{_B}O7JHnYeUf?zOd;SKQviS@V<S`vzVM)H{7+Pc3
zuF$H42uu&b)`sChu-K8Ex(~pZutIj}^sfHBvDm(Y%CR1%WLa=*VLRS*foM~^{$YOW
z36neIrxH%q75Z%ER;3ay>bE__yi>`XZBNK>{ziZWYynmSjHc4*q``xCU7)a4tqdNx
z5p_{f2(dnfcB)&9J}oITT2>!Pbf@8si#Gly@dNdS(Z@Iv;#+WLh6D83HSGuAyP(;B
zqyH)x`Db?vKLh_ltJF&Lw(F5ETcqh?yEv*P4F}#vvR04k^bWs$Y-H!^cMet1kJuZz
zHgM<OKGAa6;y1c~0mj9Sx!9g!Va;S25Mf*6voLCmf9w&yX4CG3zyyo}CDBZhz?+&;
z{i-j_06V4*AE#2PBm}79hqZaV*qydU<TgR~V|*kv%%cW-!<~#$=B9VKSvLW%y7<S<
zcQh>L2@H6_>lMoPuz_g}{+*A=wx@R7)xd?m7b`-~0H2~u``)L{G-;h!oL5KVNWFrh
z%~8Ftd6Hmy${PsaePwS=VO65OHTpja#iPr4|GBlYxXzTbRLwT4_R!Z&h;HF3{p<Y>
z;xwKlAfi3KjcqTUycU8O^Ylbf+N?Z=mdwCauXO3rL22oc#wt{C3VH9dV~0emEYCWC
z497!Sq+1tb!4V3O`?T%|VHKZ&cv>^sy5e==aK5iojEE;-agsuR^s=qkfkME}=}x@=
zCVFez^~PXzuQRC<&ZBv`jN`M0#})e8OqHAX<x1iDPtB>OxTFB_@<=UWB`pDYb~2(C
z`j4l{XIJUor65LE)C=oKej$2Y>Bo--(<inCfe{_?H<!3+lYpOsYB4n-!3YSp&wyu}
z&@iEh60)0fBvb^(bfBqDL`i3hZD(!)8i7-{bhFUP>1K=lT;wm1+9|AouTlix+nx_X
z4SMtx1p0!{En?6mfM?YaYTgAn-&}%^bCc4+P-Y#EtGUf?hLhWOlW`jwn8Bj{_UzP1
zW^v7uRQPwvb7!O*Yar@3^%l5$rCY`4r@ygh<*8)_>jp5!L50=~!gMmC*;16DDNx2S
z8ZrPoHlI(-dFVGU&*<*M`Rmg9>d5dg*TVy9enpM0I0j6%Zf*b<Qm~U6GB$|d&L#XF
zv+Bj=eRFr`(BRA)`&~AVemKaDzhZzEr;=rS!FNHgN<TZAYBq{$HeJ}2WT5mBLSf+T
z@?A8mbK=t<_n#FFcSA)`75&z%3iN;c+P~<*RjfG!)I^a=#_FT1)JfBDe#(Y-a@RVE
zg0U{K0{8({&Jw94GpNTFC-a$QQ<QdekV03M_JQ_G8A|sFnZ*We;gjT7CY1i<y<0~-
zWry;;-Rh-}X^Geb_T0ynt)YX#VSJhgFvF)9aYI<q?^dXm8fENuV)5-Q<5nO1Guz^y
zlN7&rMcA5<$D%mKuq|L}iJQ_Y6`HkMk^2`B`C@@8!reE+w0VGCEiT%2&xPBOzte&(
zJ_+m6rP9=E#`wiXM!)2m$bijA&gcou?SZDbMg5H<tzQUH<Av6-&u`zZ5<xfypZgAp
zSsmZkY`Vz@biNSOG*<#XT&6r0J+b^)iN)CZGMZ(Lbhr@Jv0sY`{`wD)vXg!ymxp7+
z3qtnMf#)E5ve#LPl@UY;lPb7FOO*Fhc1VlHd@C>tPiPtqVj)4^C;KK>GH9A;YXA8m
ztuL&KLRhjNgt9fNeu8zvncs35RKCP=>yegtkhJaXhnAG^JHKyIj}Jl{HMoF|JC4sa
z2=1}<YAc|%3?2aMT#0TGrJ!8pD#wNeG?Dej1&}fOLG_Uj*3l`cAmb4`g#2WCGV9N3
zU7MB%HAFhBCMc4&3}(2g1yQjiLc_YULW~v5Ybp8>0?-hp!K>p#BVaq<fN6LAj4ue3
zW0%<W@HhaLP1xE0BH82}rW`EkgjDVlmiy8Yn6PKByS5XL(5@&Ktq>VO%~J#w#wdEK
zXGfk^oPO<qaqsXJOQY#|E@05b#9lx0K^?x)gcRH`2JfR+xn{-H#YE@QC07fIxh;2O
z!i9hYBhi2#J)cW?$frA|iowtjQg85WPp^6H^^9zsc|wda`;u~z7Wx<%1x0)gptI1Z
zDRT@vlAiN^@btC2tHqZB>Q=p0OWq6ftRb-G`3W5W(Z<Y&<YGB#(bTaQ+yWy(j&b>s
zUN{`<PX<QU+Zx2QbG{ouxXI~Aj3xy{;SVd)zfPLlV1~lEqj3tw`}g8M1?>yrj*j6X
z!B&i|=D!4udVj4A-O0WNxXU$V9fU7J-d_|0F2?j1r+5A=qc{3=J<rgHXkiX66SbNf
zndW{oS{2e;V%k26j!j0N|D6Z`o_=Z=YEO2;eIxc$<4!tT3eG16bk-c1(FPSjyQ&ct
z=eKlmz$x!Rj0D$YElm>TtI-y!!3VfZurO)0phV=JKN1cU1=E$(5&NRERg;t8kWmqC
zU-h61Q7iUuQ&CYy;?5z@N|i5svzX^Vidp%_k!Ayn-1y#ni97uP|1OQ_d1Et~*LpD-
zMpd_k*YW(+_3%ewhi$X*ysHe4MUHT}0hnpQrSFg@RJEncNz5(uuaHDu1Oso73e`Eo
zvo~%1^$2T?f;1(O4vsH&Q<8Sez=OSPohIF%vKzQM&zp|A?$(|ICQes(nU25ISiQ02
z(K%<kbF|5f&kMdQj?is_LEF*S4rk<ev!K<SrlXR)cj_~%)+;H^79U_8Y9X)13fkuP
zV#JH81fh=3=ByE6w>VQ@@M%de_av?SLC5mEIyD*4cxZp`<F|2YF_m6UW8fK(!?9|E
z%wSUfLo2qC9_!Z2fW~0DpGS^f({AR@rP3Q{m}zR1z=N21bp5fcZD8~T?bIv@bejOj
zFEt5m<?SWl=VP86S7>_PxORmS{6G2O;7&-K8uSQJg|f1hV@J%)JSM%Pqx=1@S2{5X
za-l$1^iBp1ny?x6GoumEOJld}O81g@)q1Rrg$XIbAq0tqFp!9<jQ$~stfm=rpo=4e
zbxO_UDp-Eo0b8MdwIId6OpJi?L#~w0E}B9p_3IuSyl<o=mKRkrYnTKCn8B)Z?zzI4
zJ<_;p=*=UK7WB7xLvGX7$+aRX)Wgu48+EP`WMEoDDJO}tr4EDrtSHp@*P*`9P@P!>
zjaj+5rfVTvQ#LqYK~^Ew*0=K+({pj(TzHGLmU`#b&vXA%q;-WH<!dA$$#O$PriEz7
z1O>G-Z5EdYHdH6En4#Rk3UsT%z`C*tpIhhHM4IKkqhy?`dsW!?38_ISeFq%sHU;h)
z&Ib$J0@Yd1x+`D|Jtwa*lO6aYXfydW+96g0CvR4*>v=&?Gyeduijle_uh&2Q*O7&!
zhUj8r-O%;JwD``pP#%ded%{pS*54E#*YPIpiYYwyp;yn$)j&_71YNktq(k{}nj$pl
z8uc2MLY^a1_VaLVg%f{4yYdj}UT3A;7Nsn72p^TF5hnid*2xpdRNz*16_a7LGvnTV
z1#b)cjh26uWnxs_mUc&me2LH*nh^~LYcH;d7Sg0h%}di$Wx_+Kod;#B$9d#`dQ$|E
z@fR>)JHJ#(tL=i=It42er?2LImos<v!#|=~JZ}FwEoK&PZ`1UO?Ts-%GO?wbdjUgB
zPE<SJjd#)mf6Y$GFNuF_1XyLlPEORwr?)LAzzXb&n9#UNvEiHx!y*C{>RYu8xlp-L
z+rJ_%VZqqN><nfKnNG8q)U)uUhL;?dEuNPBs>H;c_9-})(4rg{s+bV1hInmG4VuDO
zr5gVVnt&ZnAK&&~wuk?h%87*y^>s)@zL5Y@`}F>AJ;x5QiTD(?v>lXU?SlsbH2!=R
zZ0-`ZJ<C(GE2}`V8;pRWld2kKapr65(nO_w$f|J`8-&q)x#e~EA&EF|;M7-~xF4Df
zCQ!f@w8N9AMtZN60nF`C3-6E+cOC+`aNxG}dz#VBWIwQGz&N2)!UAhR0UhS$YM>Il
z@5%)wA}CsIX}10mPAvVi8b;Ice>NRD$8m&g*Ak%_p0U#3?6ph~>o}CajW#{ITm3&~
zb1C$ddako3$*FGITmx~RO^UB;yC)pYS;G@YDXDI}?C8P|^MoQ(h8#p|GtL+A*w<Gk
zpsWz=U%_43o5uT8!mX(0xD0&KS!YFy5reojPEr`!c8f!Y<@BAkiTF_IbAsZU1C$t{
zMw&8BQ@g{OD~5>vpnsCt+sv^l0iNEzBO~?XH<8sefPUiNQq9U*beHYS$BU>EZ{|Jd
zDDX*W5&lyL)S|2ybr+g;7w(Fgbyx~(MWCMPus5EL+Bte)<A?3BoP5tGST_wtWeMQ4
zcOG}maTY*IHSd3c{+@fdlKEpuJeN}d6+;c1mmYSWSKsnPfza951>dmV>J$CSg=_P%
zBhy^8(H>shAVEDsRBEf94smJsxqhgr-3@ZP4mz_i-<t3H5XMPNNJn9ud#7l=HG#5r
z7!_jFsC1gF7@6Ep0~10xOk)U{Fc`3jp(VHF{;RRRCCm%NXcxow<n~yR2r}$+!|A<#
zzIf=dp1pM3LI6rij#PVo5ytoyMm3(toA3<uukRZ|0QaO?Uzjf0I&f{5O%RN?JWfD!
zsnBZTBCFbjn?>`h8ccqwAP`H5p#<fIDnySakef{q7<Vo`c;QsXx18dR5AREj|7_Yf
zsDa4eGTK<NTQx}2=!37u)Xb%<_I_V>95B{3i8t)~weiu)lAJc|E8=9RnC!Lg1@`Lj
zWu(XFidTwx0kGZ<&DCn6^C(1nV-qSccgJ6^hvhWWeonkzg|#YQr_sC3@M@eOEbHD=
zXbkR%7|UC@+s#>0M%t^<W@i4fwzXRs<{+domLmSVYn+-EN>pBTj6J~9viUp);_mdD
z1K$CK>$lg-oC<NEFB?c$?pIR(<;MQA{786P@zH91U%@Eb^Ou&8Oa07Tt!-@FIoPgX
zH@33TEgsg1)T@2mZZ}Yn{i@*Yyq*r+*=t6>>sBKLaKeOo4I>5QZ+YC7CW_bh`#TbV
zEGO>1+u>c46!mRA#KCC#ab?OH5hfLs+ebd>JnDL;HIX~^!!_O+r&ODs#fD_1&503;
zmx_!cJSts^2VEfW8!X60N$svic{l*iNO>wKWWnXw%S4xB(8QX2o+vb%`i~d%=R_}2
z8$T!JpcyJ_21rFkLTv}lN~Q!ZqcEbgQ+?39X5xCWmZ#r!7n~N3^2L{E=f!e;7+k?Y
zQX}}v7mqR1M(zid*1NYMtUnoCVVTtXj1IPDW~*AtGc5AeuUJPgN57&34~Qz<h!BZK
zNL(E#PVpmq2DExltKk4axOd!u{O2}-n{f)arf-4cf%p-hK0funVGC}zf#OXN+Pi2j
zR{xX2=j!1XXHCB0OR}}|)TtG8>-7%?^gzpV!vQl81!AAgkVE=fhJSzx4i=TK3{-Qb
z?gJs8Cjvuj175$+vhr!Zf31j#`Q63FD*9UH?ABO~J@;M!rL<1N{pb#%D0dvS$p8x<
z$y1jJ(<i7<>{Df`SE2C*!5Pxqb=GZQf{*(F+s_N_Xz+fXj(J~AB&^xU53cWBlu&N$
zKU&t(V7|w2GKD^+0E>+;hq2m{1!V2@9ls=Mlw(kGAQ)paBKYjE?k=FlV69_hSN+Ed
zVll=OI?UF98nV*LtWP##D;6tJqeVqWw?L0@LEEOGtU>{gs4;r%M@1BrN|OhM|EZ^S
zWptogTg5B&>KhYJ26ymV6{{{$;P_&%#*->4fl5x2L<Z!k5F;bvW^Lc+I@QEL3rk&s
z6`<{a>*lZn=j+wd`Pu5vIg)FS+S-+E3?8JAx~Es$)29|Jj?I<6;%bu7)$YuM*KSAe
zsp=P829&N+)rr%Q{TiV0YXx~vuLRlQqPN8Im<Ghu=}Qd!6DftpIvOe=OEI1-_&x-M
zi$irTc%|?I50yUdU3q^J5ce&PDa+G1cnD>iFn9qMi?E5`KnF_6wmTJ&cSP+1f6X*~
zJ}#l@)Wz`w`$g|EKEvU>BM|p9y}Tq#zTX}U#560m;?o{n7Vbf)#YWO3;sbv0uGg*?
zwx{QLVgUzvl<RmyDDNlCS9is=Mk$6D+BGb6X2VuvK3N<<<KP`U5nFEKHC0}}d$idN
zWO-4$C{F{zlEo5y3@sL7OvEqTx`7NarkS>U$hRh~tlI|7rkSQ<&XhR{ZTY9XC~rJk
zEh`C8x=eTyT1X;b%a%I1Ng;)J+7(^Z@6;U5>tdnh^Wtb}*cUO)3q4|FgGUg{S60&J
z&QxFv+{mbCx0L4}VSe4_9l=WtCPS28#5={D)Jla}Rh1Fn?O{}Md%aI0Z&!{iPUDeU
ztsZflqt7RV%rjrEyI=VHf<j0%?=xEp5Wxr<+{?vGHp!(2r+?$v%DI#PQK)!OFo4%J
z#E0AylJYB_ILE4t$l>}rIi!^D?kC<>qG;Zz*HO@PRA{X6B1fSp1nS9B%}cs=KL)3h
zrb7N-EdZsAlsFV<<9*k$H2CwP*9V_8pdt9Yn~3D%z{f)S`P!vAm!~)EFs~b46w6s@
z#z+hGd%PZhplZ1^A3Yem%Rq*<rw3shPCe#NdG?NK{IOK7O?|acAdUdr_>?Iw7`mZ*
z#AvZ__lXZ7t(?j%`EC)T!1zF9?+QsNlHJzaDjjL*wu`0!TlFunhP9+e8eb-_6J|V7
z?|JybJB#x3jvlHrBfba=iDc}8<()q52%0ca(~?Ywrkiz_{fN_Q^@^=`+m3ZUE&aQd
zAITFc;RdE9U>?|a(Yx6iol;0E@zYx7&qcjGK!3M5$1s#ak$fzEM_c3z4k^XWxk<Vq
z(Jgf2zr2Ky9mm)QB3nuv952Q@o&wc#tD+Ju3nOuxDV#}n^QQpM+WTy`noVZ3)|D_Y
zXLqVGiK^3>V(QAN-u-cWy~%NTf#al95%15RBMJKv!Js~`R8A`o!DNM#L3WdGg-9-I
z2Ydf2&1{_sE8GvYXk$#8X>4lkZ2UBw-o<keNch-mWaStNAyK?c0J=}4!zPlOLE{UB
zx4;KZ1S2MbC#4z>!E|=QLDhn%Ch-L;$pA*`r{;<##F4}#aL8jJDO}*!0N1%9WOt?d
zcD9^9|29Q<Y4zzf=-a7&{K+HTwV_dz*&F#+e?o-BAxBu88Eis1R-LxxQQQ$7_%s-X
zH^~Ll+BV)<T`XkWn74HhW0%sh10DGDU)zp+3a}{VqF!l3IjQN<N%Ru{sQcGxGED2x
zV&KNNq`-$-)}z4P=kI?_6lxVENkJkifdiCuE1DxBa^Ty?33y!2XH~6LD{vj3!(8)0
zaNJ0@=D-**p^d0b4G5{8Yh}%gDZHIlI@$8d2q;=5nXe*b(VRFRU~`-M?yVIO;ZrU+
zqTxWq&O}tmD6t@)zs4uzOjLS`_B(HMu%06~(C=l){1MC3A8XbJh>Fo8W!}BQ6ok_8
zp3jsU^K1nS`FPSWYF!xyXXtInq~{^5+ZH~M`|?m%E?!#LTIyO-m=q<{ZjUt0^2_;7
zIGZ!Lhp!pEfVR2yXoIP=ZhEGvsrDcaIj0o)voiHCiVN+sBCHVwhTr6FT+5x0_z!lN
z<aA+fe9=}Eum(J9r;basRt0!|gT~(0sD*u@g;Pn99%aSyFqtLfkxv!|1ndybKfyaR
zY^%BU%`?^>y&c$Y_N2BiCyxOv25X#9Fx(#{u&g~nnHCRr)c3<~rWlK=PO<+J&-77=
zULIhP$6)yvKb~0>Udu_D6YPUG06`g}rdgZ=3ibT8)lfq?`5e;Cy0xqT_*p(Lps1b&
zdTcm)Y6(rp^gYp;Ot0wV@eC3BIs3~p0rP}=3Oz@7tKb$+5Dl*6nwLFF-a^{d`m?~s
zW_L7^YM&l`%wbt+zO}UxH}_0>xinAr(!+d$JpoG!g`9fu-IZ`ZD+*7+EsHu#D#06-
zS(_fz7UqH+;gOfGyWI%N-R0Bz<KJ1~wa3ms)Z|s+%NL>Sfv%;C5qt<f2p&0cDq&6-
zZ2`SOacpikRnRlgd>Eyt+HC#?!LQJ#zD+t)OnRF&JNqg9lv<&*i+9_E@ll#)eJ)J(
zj5rBoIG3m|+0+5t8Rkd%<2tv#cn%9vrApLF7n|Z7s9SsbktpaR;AtM}^$F0<)e~rH
zOmIsVK-F%Ik%a@Ux^|QZ<3yRSXbIBjKq<)fW$;})EP}3G14fU8%t;-R6_ze)MHL|t
zG9uyJr3jbGB(>it5|Xfg(j^94$eR!xxP#Mj?ImT+#t3XOVI(<|m`=;AsXefZD*FbQ
z+x`@0Z617PxiX>vi=8cM);W`7;JuEQn`+VLManJuyd>6%W+hsFm~z%=*po~JbsYen
zJ*JuZv$l8$Sfj;x7%3UAOC7rAK$!io@fkift>2BI49yUi=mde1HDU2_p@kWs1q0Me
zW7M|yv4cyGRC+Jdl1MuBd~H-eyZ@G$I|(4M<4Skp6r~t`gu_S;sZJis=Z`zO^^ga*
za&NtqjTo=#?o4Xn0@}i`bEQ7Kry~AS7C&7k<XOLIiKw(FzqFhpzdC5(%f_YZ?&?2=
z_GVk@MQRU3GU++WMBPpeIsKj+#E7<qfXwpkVoCghAe_0PpqcMpUWYjU1i@<RG0U9I
z{lTL}H!{IRR&M!%=yFX^iEZ=2V~zvcW%c-{vtn37QE1zO1#<P=?MSLQd1sz|jIQXK
zow4=Rzs>H~FXZq7Du3|6?TL)j*^?u=kZb+OD54X^_sDWY(#_SfLUo`XY}ZY6c?ONm
zS)FaZywS)Ci_S2&rP$Qawq$8`LUVg&{~E&oKnR0|b<?)(l#dZp#Lf8>vdIh&{R!40
z3QOd_efh=0i^zv`)YoGI0e4<=2X7f%9PIWhvhP-AaKGgy&Y*14-|{a*E-F|PI&3at
zAeV9c)O|Y@elsL9=#6mv{Aks?u4c9HNe7d}LH9XtFwIz0-ZJwP$V~8l-I;9BuT4WW
z%Hlg%<POs1?g;3eATG%YauSGqz@MP;@wUx8!n5)P5)HA{MPgCjxpdzT5E>H?hIskx
zNGv!U<|8ww(Drj;p-Tn9+ZNp-uBA>jzH=!t!8u;gMS6hm2$Srum@z&F{3X21XA|Ch
zeBt8Gun(PC`(n1*ux(YHZ%}RE05o>_HT||B>~b9Vir&qDzzzqt0vt6aVLwV}*T!Pi
z^Wl~6#W8Z!vFqzlRhz2=8ygTrvraIb(ZWCfP~H$xG(<%aQS3uO4<M9&ZP9K^bKMN)
zNry)gp<qO-w}*Z~a9}#j*uYwAFh|kmpvG=l@9m^%^Hu-{5BK-;_ebaB`i0-(Emi$2
zIm1sC8{@H*<J8!ob$(_dEx<{i$LXGz;k5lT!${N09}{VfZ6}PQzc@ktV3N&maMHff
z#X9>B%+6TfpMGF>y`p*5IJ%yY3G_3m!33q&Eli?F-Tz6}ugz>jo9cJRdz;A2js3@K
z<}AwmOF?rPtu&WNN$u2oRZt5}VZ>5-zV50u59J@0ol{ENyi_ESe1Z(9*#o=J5bXR*
zGYERpQOUidaFXWTu)<>yC9}@{A(&gYy>9E~6LAomm6Z2e#~D4iCM#Pl4v9%n@vHwn
z#_%|wX8#J0DWwTeCGOFO%W-IN;)qkDkDedGGVs5*T-sO$r%FJwwGT3h68*%tRR6ui
z@)1C62rO2ULp>9LNqtkIO^k5Z_WvbT4gL}v%x&J^n(VYs|7z{xb$Ankz}na&dIz&8
zJ7adV%)~3%__s)mb%iC{>Figcw(~r=6)u@_G2ixqAeHdPO8}sU{}ZW8H4t)0F>slP
z{eJj|Gzt4Tc@>I_6YsGrngnr6IjBs*fca0^I*`*cj`ily^qfXXn6a4@Q<V|LSHx{*
z*M%i|7i3ardafMSp?xqydJgAYDF`O4l;Ej_U|J&%w)+QAL>t#svxmQQf6DE2Ko;^|
z2D`Q0SsJyD2>?*lFzb;47nNlvXBG?vnu@Q*G7E+qQ%ZzJWn_P~<k>-4%nV;PJhhIf
zI78l~1<bo(gV&b}M}@ryU}h9EbNsI_Xz&SKO!d|CnKGb=K^>WJUWyjtkbsaZhK7uI
z<;m@ktVK0GT+9xM)bw%DidvA?Gh%>0@3cE9IQ=W>J|Y`>(B}N_h$a_4**Nitnhi%R
zF4Rj4Ffy$rGm<%(r2XIT|9<=+Ci3X|*=|uTmUsH-YHroB&7e6dKCcf^S~$B@aYxFo
zuP<lpPInBfhox1?_u4G*1CcK<ED1L`p_VsRYk(0<KjzAV>(}&qHVTbPnV-gI&tc8R
z4h^6!+zUWZAuW$M+d_y^bIAHI$9jOYq||gh&QvwmesQ*r!bjuY)00q8vnXL#a`o=M
z`Lj|DXKI17GhJ}81Rh*5!Oc8h6Kc5j79Knie@eZwSZwmQY?RR8cr5kd9!%kjVu6mb
zuS)Y5#8uJ||3xM=DR6kGkS)?VV`}-jgHBackPG*)5-tp`1Uo59^P{*5Yxp;<(z|5=
z#_2J^Kfz7YTfzC5klR`10&o6FU9J<k!gG<LO@F-9^{(}RAp}`l$EpZr5_-49$#n2v
zMOH(i-D3ja2#qxje6^5wYox3UG6f~QjO@sT1eli0tU7KRehjD8*)ui0uZ>v}h6fb>
zG+hP*+?>UYE_d9Me-Plhz_R%0GCj%A3&POsFWOHBrj$%YvX-VaQ=@)aXudfE1e2(C
zFeocK-nD$~od5U&@25Ym;)4jm;YK(cTs*=gp~X)H{t%c3PH&?C3`v{T!6jZ#7nqT_
zjAlupjKfY21&-)+8@ijP80+x;yypYjXgh8&uQ=!nK$vFem#ySogQ29IlrN)&B7+>@
zQai456Wz+M$|anGAjpHCDE0ACHk7lP$n57*Et%8f8n;LembS1CYky2HH@Ie$A&7pE
zQ#(hEb*<{0U~2M2kmzJ5deMp~Gs!@@mhFW7ZjK}z;FkIj9N5l~oSulO3Mp5x5HnJ{
zYutoR7Kl{WAn0+1``+z;R$p6YwmhMsAYAMJfc7lYJi^>010PAcZpY=Jk_<L7QpZ4&
zwY!86l{u~WqJR*N5LjY{9P@=(dV_U%4#>B6>)L$|*PA@Lk+J`Yg<BfRp#MsK;f0$e
zt4fO6I(5@m&L=65!8y0k?sAN6J01bK!E*YTf7^)M@RTMt`EwXbVEqNA!934PA~zHZ
z11&-3w_gw#+91R*iO@l<?t^_2MQr+yh}VcuOjuBT_YjMsQ?g?NPjaeJX2>dqPD@AT
z_wJ;bfDpo|zs{I{A!n40u0yiC-Bn^Sz1U_Djp~kuRk$d!QDohu<5d6f@T6vr+RlI1
zgVuerJ6q*NK6pFK-1HrS-+1igcfbs>&>A(<hxo0+tz`cT^6dCMkDFt1z@ZI9H0EH7
zNDPxJv}RQtD3^SoqpI@|Mr4~4pU*LZY)^DPZgm>C3X*)8O{{$gee?*md-#9aT6qM#
zt?7UM$p_4c!8Jykog9M3_TQ+D8FT}F>)GO_VJLABcY4D#9QAlheSLJ43u^?iD&i{D
zQ~hk_f^kz1&p(Ii*g>>;C^8nr{f{Y2{Dp&u9vI(>wx_Lpa$6MC!u~&55Nh;#dpU3z
z8f52Tr!oGT0nQ<}Ph!naXjlFZ{FQEjZ>@WcqBxtkQ{vhxIfoc8+w(d!s%0lOJti8z
zNO52S(oy3>6pHepg2MUXsxcFHV;nTzD7Lin8FVkKj-apEz}k+fD`DL^SAiV2{T}^0
z)T8vUa|e+L@ceH4UyV++sl;rPQRYQJ87=VB;Ls5|=a*^q`9m|1RVfu;YYqU73u1#<
ztBu|HBlNAKzIF=)^>wsFv$X4%>KF8d;X{7F#h3rYX2tbp53t+O&05|j%r!C#4((%2
zlz6md!x*x8aoR@<bMQ)U>$5AelFk<iJGqiDBSc4~_WB!!j7?G8uVm|GySQ%68=0(4
z%|idbgOttj|AS=H^HH*RIRQJ7hE5FgaY91~ik+tA^LANV3*K3|yVbXc1GQdnD=x?g
z62|EslSJGDXY4gc!)h%?Kah-WTMNMdYZnG&{?}g+eG3L83PrR=txkTZ5+ikMH;{SJ
zef4%M6p;$;Wj5_%nH4rmlWl?QqI)zHRn-hVe$5f4_DUbiEPT?;oi0;&h<GY|XiauT
zhQuE)hCJH?D+!_ImG5C~^TS{{*7T=tXG;>;wMYX#yuc~<i<D~`=HGDkY&goYB5D{4
zl1aguf6-FBMj^ihn`YK<47hC@o8&r>GCV+YA#tdUyaP7MZj7{X5T+g8{Co<ptH@RO
z@Rrb~pT1jf8H@e)$qv8EF*3~kL1tLNl~tgCWbhm`H~NFpgdkXi2c>C{3+d72BKij^
z^#EidJd%W67TG&=t42n!kcg8lXNpfzv2(`hORq64_byX*T+_Ql?M_&x1quB~yi*r4
zc)AD~m@l-IIw^yn;1lW1WJK^AA=XhKh85v^(^+nHc5a=8T%A&%`0-bTgz2T_W3Eo+
zGdvh`d@Y^N-Sy`nF|ETWYhj$hWPWGo_W<%T-FUM~c>qO~d_*N4MHRLilB4D6lZsDQ
z%%Nyi31wTew@VmjZxFQ$BQ#J{jbazKF~X1~Mg`n(i(mWk^B5_?Q+Sgv$AR8UOW{<U
zPPItX8PcrgRb0~<C^Wj^n$fHu5uN?rwAaRz_4jUl9ZUwnWtEu#1z4IiO2>)8h8g;f
zGK^<=2gC%LlSM+$?8%a$k<cO6(OQX!p5m+)lH>e9Dlcgh$0Jo_JmtJNMsRSjJ$C9p
zhglaZwh)P00rwkE{O{##=l@3UKMzx#(iVYxxMrlN2>r6qn&2xNyj|{4KU#lQQ?9sL
z*Y^;$p+qo{DM*&*W5BoOx|*kYU|zM1xz^iV+Jt0;H97F@4mTDDvgeZ?0%i_odoNeu
z`MO%fp_z1lp~ha$n=S!bLMsb^vbd55@iYq-{x&sA=&L3wY~u^8Ti2Ud|I)=0{SoFL
za^q_?E3^0ha(RXXsCeooDbz!At7FNY-s})4)mHjWfK>c9=zwu_ZtvffGh=AG1eR+w
zt=hUskD8>mGTg=4IHW-`4IL>NxO5JOsUF2k$&Hxp|H`$LCaYLSG;Qq^>C0&SD=@9G
z713FQU(f8@$v2M6rtAcAVi6LdTTVYmnOpRIs`lM(!@83-vPG<%OBtbPi67Y@llMAk
z#Iqw9;ttXmjx0?jQcR9&4#v9*ObIT<1hY6tSl*x6Cl)F#vH()`<5N;o;LHXPU5F9J
zcOrN0Cgk)HX%sookUkiEVvxZn2B^8ULTmcYMSFx(<EVLk^Y<<jcXDFnYFmoT-G^S{
zYd`iRG!Wh@th~>YXIz1eV$#_ATkt+_eqkA{2DaL-Wou+gFhB-JXFe_~HF<)0lg=fa
zLX`hS=>I@X4Xm8--yeYiYGZ<&USis%;GweGQQec<a$+HjVOd3axuEme$drQ?QRr4t
zg%0g+3S>=n=d-5*Zf=dGbtPKVZ-3_Q!V3A{fNU|I#o@*~i)3#Z7LUIkK51f1BMw?&
zh}XxVM>U~B4x1-1{zrK?ks6K}2CKPv6*kz-rT$dwGn9NY^o9}FvlX70yP7GCLt`ht
z*Tvx-AEF}qWuw~e|3y&*lLwYnHfRcOp9&2=l9D1eYLtR;RZ3DhfhhqTS`#IMqpbgX
zLQ%V^38|TL7nmT1gVCl1Rl7#<a#OwYV=)XoC**Z2I{TbYF1gNaKK9I-MlkL@<cpIf
z+F`p4*d&j8U{JWqb^{~baw14ikx)}>m243_gxtac{_4ZHqO2gsrW9O2Ji2l!1mDfv
zO$4xPvT@ioIYa1(SA{pC?%)t)@*2Q0!Ri#z<q!gNxPU=hP^i$gKlecWn|Pqs=FqGO
zG~!J_2A^z3aedp0{jCW>^NO10c{ewcfz00MWGn<U_#$*fX^1xdqd!B~VFSux1J%wL
z|GUlhYEYxe&~|^J+vdIt>#}^@^fAMM!aJButq2MBbg(bc_6?)t_{l73$d4$GG;wev
z5C1g5Pz&6{cgiL+T7Rg|p=Vv4M8fHo|2Vx1S#<mA)li3G2q7#<#z}z9=TQx6o~~*c
zxSG8zy1`M$liw9oDxJ4^hh)gH`4c<4{x6bF!lBei8}$%t9YZJ$e=fM*LZKVq2&<{F
z;Eqa(lkx4`vK@RU(4l#!Y4MiU`6ic%T@KXz;13Aowd%~Xuz51!3)}m3!vzh*Uv55#
zcf^2*7qOxN-%n|$g>V{T6`tNB(#pN`#Rq&GfIf)<S38#mLUaCs*Qc9<xV<0pW}c)w
z$zL`=(%cUD284<{kC_6p&wyYNvT+hHyefxy<;2eW|LFS4pt_nR+Jn2hTL>Q9-8Hzo
zySuxEph+OO2MF%&b_fo^-2%Z~g1$|@@7{Ock5_e06%<wM*)u)eYpw2{IcbGUqI4jq
zXOpn!hXk0jNZF)_%GkS}vE?-)DnWJ{k0&9uMWFiB<J3yTrB_k0XFL!g-GoA~z?h%r
zCog*$zHh?YTP&&NW@<^!%UR-8Xey-NU#u=8QFC7&q{LD)!(11dcgf!kot(Q!HPlo!
z3QNUU0OcoeVTdN8V39tW$(oyOpbnvVmUL&X(m*iumw+m|i9;{&%M**opJ5G<)q_KU
zE^6f|ZL_P;lGEoJo%hm7RVCGYkW;c)G@B}UKS{Vs^+tV;4OV=P1<Uo@!|{R+=MyZa
zpk$Z@BfqAQ_Q}5-EibeL;We!V;c*M!k)NG@zJPdL{+1FhxQ3_&+%4KSy^xx?Skbcj
zdfUHTda;GVNDKr|EEF<bV8({0=WB8ppxSr^R+z=U?@yv&rDYNo8qsT+ct&H6STR$+
zQF5C8rP9x#m|fjCgyay<*;Os&#?<qJi?Ld8%LjWzB5RlRhZlSh1nAd56ptAP-c^f3
zw?{uOlmsWJ867pf3RmF{$=9z=?3Is=R(N_0%lo_mx_;c7V<;*c{^<`@nr&!Ij~8Jt
zFM5at+|_4^CVobg6g>a0%c~|?y^FHFBSz;&u)*KL7cR+d{ya)D=5$4`-#?8k=)NF3
zy&M&LH#f{SWN8VJM?g~+VF37%PP~D)giv{sab={%1%g;19A-3pVYhlUzG6GH(!K}b
zTa=Tx%xiZW&iwm8t4mPhajrhldf^e2GLn-CE7xn^dmDY=%(zSf^UZ&}6k;qF2kf|u
zQ-d`$<zNT>ZIzR!Z&fwq_plb0_JX+M!+EeI)S+B8=P<i#J|%6?wMs5zAKbr8ic~hb
znhORfE<6R@-RB+$*6Q?l<wPqvkjTrrdVX{kKz5H>ZG*`5<ET}W;g@x93?%u|s@J{N
zK07RPncf%bY^m7m$~H4A1PfxE*na#uu?c+h3@bLqrO7sS+fGB-H2xA&_ylrTZEW9Y
z2S}thSSpM(;$!;N(g;tzWW=TDrovj0MMRVCP(=JNAo5UxO>G%x|64RBB4cZ1Vkm1S
zis8BU%8Wm$%YPALu{J~EvxfxqU)##(=D&R-=Ch@()kz2h=6Sg1lgk?8=}63Ko_->E
zK?HMbk}5~&7=?%NFJCW)fD-3@7o2XpFs^2enQFr+7mDfaxKAQHQmv~d$6xseYLZq=
zgASihR1j^vPENVHCh!=B@bumrHO+e&bB>?j$ZLEVmRmmU{Bu)0M30U86)(?*4|znY
zg^TA*eZvP{?BTFg%^zuaR}>`!j<adX-mrI(_d5eVf%D};_REF5_wxvI-lqxE?QNok
z^>#RM;gu&$cxcE?cjMp)6Uf=YJp>R7M}$PGG~Y1QyUSuj`=3CG*|95g&k?y%WGN8~
zGS;t{=|jt+n9|mqdB%~(mJimuSI=F<a9`ZmMkqen-=60K1RJF6?uVOdwq#&R1Ull!
zB|UYyW3G!LS|7cO;VOx=b4)F>$2Uni7zPdpA;f-<GOG1i2N4gFZ_W`LF5%0)(`Dfq
zo|6OI;!zyXA2km!18H0!hJSedMGYuP3IVpm;WA>U*9UmJ@a+)2KwKm%Qn1_Cdnw49
zUHtx<*3#`wM+R<VKjx^`(!4@z76qi`wv#lU5m)6(+LC#=CRmtnx)qzpMjc2oCf)KX
zETQl*giqxwG-N?5kAXrsA;0aEbmhS4m@6aSC7?aVdIM)8x0b)NFcKZMX_TL8QXn9|
zV~nWDM7|b4k9qU+uA%CQD|mhIc{jj*{Ruj4%#W%n4Uj(Ii?K#L6PfHBXacNbLWh1<
zZs1}iB3=qA?ToVN#XW`lw?u~d<ABbbnbF|JYY;5^XmJ>5lXYnxO>5vy&*y%~i6WDL
zYZaNkuo^<lxb*zwFprEdZIGa-3aBe1s8<RJk>gdSgEN&Th#V!UY|efEc#w4HsU4VY
zH_=@HgWAY<S9o(-vvI1C7ChD_8qvDFJJ=bDh6dTuWRP{CN@}AxSodq^A^>#ymu+Fq
z6K*b)>~o!9Yui7%VohYnZ9h(5A#(Dy03}K3CYwE;xTQq}MYbhnus3Lk)%Ha3+qwFh
z#_2ud^eCa0Eh+d?YPo51QOtc8WBTwh`J#0dhs5AW4XxbyGG|64-ESb>JPf5lj5z5Y
zmy<?}?@xUzL4V?)Zjq<etW2$o8`xDDaW>MLyl&Cbh38R-(q;4Xn?&mbD3e&s>B7@t
zS5{~@ccl>aOM>ezRo4X?|FG||2JH$c7)V=Qk^<*YhdvtS*69N1MZ`P2nxr`Cj}F+g
zlGBwMVRXoJ*<|qDL*9NQ(Otq?s7^&uer2}4SEGYcWn7YTD}ooIT`R&6ByHzY;@aH%
z>hm>%D4nGqv$;FP@=D7wfT(Hrbdw82Elkt_N)VD=YBes}yYgc=YZyUU0BysKXg(mg
zl)c9T%rglvPc)!n?h(G53~lLTse|o#Ct@F_6cfIWg&3C1Ufuan)a6acahOu0F_$Mi
z`vVL%gNJq_lmU0*;zUoiJrAn#yJQocW2>m=RikHc(!39Thytb#i_k~^J&O>tZa4S!
z5mLW20aAe>ky-bhIv?P}$oR(PWE4`uh0p5BCmwbJA_+Y#y_CXBc`z#<Q4~8#kvcIN
zoXFHPsPOd@<@orIfXfBVcZR*g1M)z}II#bt;u4&I;$|g_<JY<4A-Td@HcyhI?=^e(
z*1f@kp84s|<nlF=r6>`b5>+d!HVZkr04!&iX9^Ba%JFWwM|<RrHBo{NhlESJDt}Pj
zTh2U%b+84l#?PMjL7jw>KqbS%K#yWy?jPI3S>xXI?Ta6YvVoljs0W%4&-L>cC2nll
ztTY+_OXkwGI}pI~=phx_HUZU7WG}bE#P^-}<4+>R_^{Zk_ac^~b9?*$sRi)Yabz5!
zWNi8<5w>E|@lGjbAmR-!-lw5mxrMdyeC<-o&p)Vu4=K|2(IQO0*-r-KBnjM4L#7^v
zQZ@Mn%Ht+(c5wUZm<%IG=+LBXJ~+UKSlo<`Zfi@m*@<*{(aNwJ_=-^H4BV_LRR`Rv
zeF96pavhOo(~Q>~jS^X_Su7=(oU+AKPXa_d@_RAyPYJwblm(_kT=+BbyZ827+UOFw
zOZ21GSaW>Ci1w?{10G0rvx7Xpo9{9>p9q}7Tqi@%Y>NVnzTgs><tn~#PJDFVs2PjJ
z9i?cc&5o6tBx~>D_ov#B24W-;P@qKxs18L6|JHQ8pme`*ShoS55MvHr8dZ%yIIVT&
z+D*yW@3byJSqf&qIok9aY{B_vrcAgBt@^T)Jo++;c{?b{N<<ACKLw_*c7vBN!)vv?
z3e23n?-~W%<RzPV-1N<zJ`(ttevj!;fJjpy9Z<y?Kr4eEp(rmetZ7cnJRsb{AU%gq
zV81lMF5~0kKA}oV7w0=(t1?*}n}(7#ehtKnJ}tlme#0~Bef>V~KJ7Ns0%#L}=edNq
zGoKh5k5BFMRY%EgKPkTXY;E`EO1exV%88@kijigVy|TcvhUUg^T<egC)dCr}gvRs2
z(+e?c_q?_WozH2R@Ye^1ZPkO(#EceaAJ*}thP2x7CTkj<A;TM9Ss$%9NfmXXeFK$A
zV}h{~n${+8ic};cC*5z{wNZc>iJkg*LvHfq{z2!6>Ur?;B(H(}6xSZPm(w=MMF_+`
z55_TY_5H$1cU=WAHB}?~pP%#-z`fDnULG$?2%-#v9an1LIs?a`#t1kxHfhRgR^$kE
z1rhqUBHY-8Vk$EZ5S&J|cJJ%X_WKZp0<=XHcAZ`51UM3o_wkG<eeC~kGmfH4wDo8a
z@>po4p8$P8lsde&$iT(Vh8bgZuz+cF4E^9P9R}(q73?${TQA>Frg3lGma?Fe*ltzQ
z1_Gw>@4R=->k4oa^hQ;%dfVIVhWf7z;!ZQyzCkNm@7Ox^*lLuH;y)c%pddc$wxE^D
zt&jWYVxvD0<lkhz%NE8HS7EL8nIyc$h9<Qd&k8Hr=iylh8EV^3S+g!QGG1O8Hn!sa
znEZ=lJj*~}P}<c!nleFNsj`>j>$&p-C`|#Pw8-pf4VBd1GTxp<Kze(FJS^y@0nd1W
zgOqrty2U7LlL(2H4tA|W4<vH203eJIkyH=wX?VHc#z}Zf;hfS<AOr&#SZCsHm)6>O
zP!ox}e1(aIz@x0=fc*!zF%@M$fgVRS8@!I+=)yA<Z|kr-KOw#=_eg$sMIo>a-nMIU
z6J52fBJOd8C+}TSxj<Z*?Cz}OoOM0h%?|ylRrK|z1UWM0Tes$F?7^R9D(!wvlgI;b
z`XLURHO7$3VPnW_X^`XD2C1!fcIl}_5v8h5UHaDsjf}J!4afIIVO1`FN>tQQ0JS6g
ztC{z+J~H4M`2YB3Xd@bz3p9@I22j=l;29vx7WnD05sHL|Z=%W}v~j5Y^VQS8KD}d2
zw_08?Xxxqez<2Ekw-1)HU^F?-o<7#D1FxJKoMO-Mz(zU=x8W5;+loB2!m-MY{8mx>
zhXj&mbOMT>X6B7A{5v4CK>3P?q3%Y26HESf&9S*uV8ln*>-<yA^x9UK0oPE}_oI)w
z+CgwdM4<N;XQ91jXd81uUBd~hRb3;h^gg_bv^UjACIdROTl|yM!(`4Id07m3*(sbw
z#LA(@3@5?eae>Bf=J0r5dqXZ4Zn#gArlEl4B1cF4z-qa`Z^D82O9Ul`eT0%ZwAlK0
z{9(H*<FW(D!kYW{I&=AV*wkuJTU2$*l*Z9MJ+2Q1F?;&b!v1}J^PtdnYW}xPxArLk
zBzcmxRQ@G=>+1RR(eWFXyQO1dY_pQ+Kd)8h2N;Y|lRySpTIRLC1i*LtFwTwZTmZeN
z$-vR)U0)rPAt&bS=*K}5-Eg_-{Y<M>x-fhMjk_RalFCwK3(KK#+V=G3xBY$Wz|PI#
z_VK(@9f3Q3O>}sR#OW>SYjJ~OZyGNG=a_Blf}9YM&-=RSPT)2IM;z8PH+i((!Vn+(
zqGwe#;}QF{j3qNF^j9>@VY=KS^%EXCK|`h2w?m`XxC|Cw2gKwmBI&?o%~u3tD{^uk
zCx`Hn4g<lYe$QCy7~O+bEBb(#mjq7E*hB-iZB~i|s-qYh0Jc_Ils{;FC+!=6UJAs#
zu*HD6Ohcd=%kKD}RtgzZbWZqi9STAODnbIr>WcMc8$^3jBD9-aTHEF3ox%v#6btl}
z<x5#h9ekoFZHeCbt~>W!2kMZXfp0AD-bJXWA`9@8m~b08F$e<t&5@oOz)B``bf&xn
zy=g5cNg;+l_RC(vqP#g-3;({HiYRu(wjrd+pbmC8m&uqa2TLtT89Nmnt!ys+O*JP}
zeRt1X>#9+<orC%#RYtw=vV&MJ;lQM$=L&kDl}Y`)oHBSR;_Qet>}6IuM55t8{0*K2
z-Q+kLs9EZSdTkkK>ns3H4Kz8JZ20kkEDX942q{s^a0_ccxFYJwd!Dm;@kiJaEoz?b
zol8z{5)knw7`M`p7&c%tlaHhoc3UqNJTGna_CvmcZ#Dz8%=!75DM*gj(kT9Mp+F@0
zX`zwWtroG%LrV<jX#hvEB`QzStNR`f3&Ri5F8EOQp^9~hwb=9qck=BoZ8v*3#hmN=
zmUgx!p&&pkgmK}DXo$_<2I}0CY}V5*RN(~yH*)U$mzR0d=z+gZQ@)dkXU_N_vXOd%
z(QR^twE#H6elrF)@9N|bi=_>p6nHZ;wy)6}`$!A{U}5Qj5i@p$jGdZSx3D>BePDhm
zN2XzI$%S^N#8$l7^5dubJWq*+TLfoJo+waWRhbU3B-&w;dG}WFNw(G)?mB_zkFy&l
zPuww`TaY<s4yFaUf^&;MoQk{|qYFriR~S+74nZ5C0vT0pUEX&5LzYf}Fw!CP>l?Z+
zkoY!x-ALEKK@v{Jsdf%}J-6>LOyYpU8*D!y#{kZJZM{!l3^c*o>>TJ5%HV&sp$%*G
zb?>Y{4Mrw!KlWpy0C2<y5RSqe$O0H{Y{Kz={1}uq(<|O?@sUDqK(LFq>6u!^RhDvO
zW~{d5!~X*uC$<*4+<Gd|6Es2Mg}#yXNk&%Pp0}~weMPqTb2EFEy5F>hEcH@F<+P>;
zUtospabKxr(XGpv`ti?twSvHX1>u24+ty}gsg;hl1|}_s9q+k#68>8AAGOv7v~umi
zZ{W9FgTvfb;n2xtwp>NFHi$u8N*eU7kFwA2L%Mm5fD>PGpbL7!$AWZ+Hy?mTA$zXl
zuh(HcS~L%3i!Pg~RoD51o(Un4-TR5Rl%I$W*&H1g2-{ir&NOA9*8FCW<K;n>+<MN!
z#an@j-%2^Be-1+|6PUaC*gH|PV&hUE9&7cYumOiiaN_pOSR<>M)l>!^F9q61d<yX^
z^U-@+cx%=PMWN>)%w?ca<9~{JZAKN2&Q@*vFp4=HkJSkZB}Q9ZK#4_efi~PhQ?E#)
zMiCQ{D3I3csXqJI=F@&*zc4o+NItJ#Uc+yqj~CY9rUEg6)^NMqwb}zCnq5P};`tND
zuVZ#C-<I=MxqA1Ly6N-hO(Z-cxU-*@hikwq3k&eCd^1QAZAzlsJaX}4E<L7)v!_5@
zurF1(%OoT^SWuZLK$IcQ7S=fdH>NRd0&O%V!dxL{UgC{+Jzh9ID&;q347+aLSBo?=
z3312*NqferZ;|V+S@FeWPJenlXmErdc@2{F$N3lC`>D+mX;siIWmh|N-Kx>ADW>S6
zLd($mL2+0XJH&qXV1D!bL*XGgle_7d1nPQAV)8bn&a-Ux9d8HTbm}cBZ<qxtQ)okS
zX#Bbp?s)?SJq*oL2xQ|uz37rPW9*doj&c}$tIZoq4LE#82W<OmvkU^189~D%w1~2&
zg~quQV1C^Wz*4ZEOw#YB&+&OuBx?%AHPy}WF|Iy~3>j|-J}<2wY8P~6zqf+Q6YL?J
zEVQjC2_-}9R<Dl~9DP0vASezaw#*=oP@unts&^!T5maa(1g%sr8;ylwi3a%u%#PR2
zt9|$*8Pz0~o@+cN`Mv*f>n!495ZXqNP2<TytwIl>3$mGQ^+MmRDu1~s>O%e|DAMy;
z3t`4r=Nnl@QHFOtR#C$Nm<#82p2QbDhbQu`pB1!T25bL1wTON7)y*jbM>FVRfe;j3
zAYm+#rQ<jZvDVN6<1rnnC))}`oGtIsdvQPCidoHya}%<ebn-F#DZBS`OplLs#Lt^$
z;wTB?YfjFE(+8>pOI<lA^-rv_1O2V0%y*Yd5|d?UKV^m+#d>&w@#_NP4{)PR=#S&l
zP1Hnb&tTjD8q9KJ;B6vQ*s39^Dg&MZQ5T@Imk}mH;qM1J;iu9uyc^A)7C_3vTx^Nh
z8sIHm*g7?FN2dsMv1o7>n)EHS9VmAX8@`Vs*yIM<rl>UR^YE;F9R27V4%W+I9OLt5
z#C=myZRaWN?{RhIGNdQSApg<%`t-`A46d?SkkfnUF+14GmBv}U<306?T_MCd{S>)T
zCp4pGZ-Q=yvAL&1SJEJXt*D$!Lw3pOb?EGfB7UQ_)e(qgYewXdJq#2$^ecJzJ4xh-
zux#P+{WmlZHOb<Q6W_^*_VsG>H_xR|X{56DePrSHQ83@bP&0+|kZ$Lr$$B&)j!mPa
zO5&6omEY)=LEA_vLXe${Pv8;-j>FZ)8?B6O3k#9(oq2ILqTRr7$lrvO^$}jy+#RO%
z;iR=U*>tI%vw#({J~e05s#T;wIJY1zQ!oKL5#_6jBiBN;?R!?n_O`d%-^}20HsD=U
zImnIKmpihtlY^N$vP4}yV793!=Ajy=wEygSnintG(-H@hJ42CWFKR3&IC#kS6ri;H
z-Hxn+xYkPv$D>-E@$5!__+0fev>%UP0dEpOm&NcBD*+}nBt+kmrT%t~!f%5%GR-MP
zlO`3UXd*}!zh!HCdafw`F60^k)EAs^m-p?LU=TPWfW`GXAsn)M`NxIF?6WhI&eDTm
zy*pIO!Q<_<upkcKRlQ;xj-4T)e&RfGdrM+%-IXgnYuE8rpp4y`&lPfYR%wGgC~($(
z)#)=#I}b)-Dd!e`#m9nLy$>jo7(_9qc4k;<D_mPDWX-5THw-2OAPGCj+9~fp6JbIK
z5J8teWC{a5yO6!z7P4;rFk2t$SN2EnX@R2;a5qRY#FjMTC=WFe+f+Qa$EOf?q;p?M
zP5-Qs0K5BMj;}cP4GxG9RnZr?Vi-@6mhNfxa^!GEH7K$ZD<6%<A3{JAaaOnv8Ea5c
ziBoE)M^3Dvc9cyaRnlQ1XI70_e|f!Zl+l)jP=WCHOIP+eS?i~xjxUHhP24ui@?l7r
zUTTSYw-tWSrJ9$>h;ssz$*DmOcfA9=AojR9+Q^8e6xU|&(jY$$3@?i#PViCb-Ea6{
z40{JhQwdX-g9V_p&j%)h);T)zHszz5@-7qPgk}^ewj3;J#fbJ2?a@A1la}Dly=EoB
z;SHsUCF5i@+48~_UF`du(8^MocY@so6GC}`_JzzlNc1pv;pk%N_quNyX|$n0R8_&(
z2*y0P`U`Msvt?n(^nz2`Nz5-kP|426>tnEoiqM>h<JtIkA6@n*r9~h%o=62?)4yo6
zb<FOT=#KHzYG0=C)|co$Sl2~=EeegE|Gq|Em@^<PWen+^%I4y$(7TxCy&4>Gl^xu_
znTCZS;6@o5TYiizxBwU;yIv`&Y^4H@!%RX2+g0#=4Tc!}KDE$?HPD+VH77Az5dI?N
zopv>GWkjJMSeR)wI7_#(=`;1p1kv68kCU56ev+N@Q_C>vd*ah?OYn2~Yw&Yx%@z#m
zeqZE9yLN$4O9Tur@vlXVbL)9|NEQK+mNj<2gT4XC`Zoh@wPDv5nWUF$-E&s?q7clP
zA0SHB6HvNEuFbG~PP(_3na?*C2EqAZLNx_`%IYMMi}$JCoeOs@5}Lk;K9T5RGB2!0
zS@1_X$)%jLO95kC8YEJA(C^4YkYuXme19S~NRbZlJ>T;GnM2~=aZdyX36b%-8*%1#
znB|%Y;cg4vp3;1(jcLMjz{xXk^<^#;#4h)lpayl>G;S1(QFd}|_1QPqaVwd#uzo{E
zPj~ktawrI{c;Q`4U~0JnjohBNpLY#rpKF7*YYIB}mu)DzI=r00X2(G%`K5g&ZpCWM
zqwESu1JgYj5PkKz#9MDe(8b#0fBg=Zqxn|ndMGKVV6KyiCVl>3?>mu0^aoShW>3bt
zAB{b^iWI>i%F>f2O#lz)Y`AWdD#F%hwU_y12@SG~_bBe+pzFbSalF<`^}`<{ejvz_
z0tWpyIkk(+e{+i;)cMnXb=HDFnEx=21~k1MPSsxG0R?0K<P;>_m|Q`5QngETWA+gi
zCNmAW;Rt*)I&V}CYp-Y5_|Ba8s{&r`!CS@HNIm8gsKA<Mi|AFBs^nb__lkt5l2(KU
zkIRil&N99oZQRcE(~qN3)eGk&=wiMhJBKGz{L>HF2vXIB*h%VkrB6(D6G_TKM`795
zyW~+__+L>KC>`dX+T(MAQtAvUH2<_Zw$;4b-i@p{^BUi*NE8R+R>tmRzU^_K;=og;
zoQ7S$i@4}{w~7FBey{^@Qyr?+TX7zC0x_w%GwXujZ`Zxqf%XJCec;>S(>cY#0N_u(
zGCLbEKc55b59QIS?-o(!A8Nk2A;wUX(9lq#`;=<(#8^TY6!-EMry(1ZVoX=Lr{~HM
zpH<|UJmwIUp(KRY24dLDI`LuDFNX?*X>}3{U4TMvRp|p=xYspHxOUB0iCkYAZelvt
zY)=a@FXxHZ5P6|*$wYU80sEeZc&uM+1$@XI+Qf7T%d}3qiI_F3$IlU4s75PAiL;sW
zl4U(g)DN&Q0%WmUe?F^}JJKzrXuu16%u5fnyM}D=eavIA?DQvPHxf!%nX~lx9RgrX
zAR6N-P2(rPuws6TfdOzCEpF?4)c5&)J`yvUEWE9|6lt+PC1gt}I$5X(jXy=7W%6`?
z{oZ?UKxQcM{@zqNYv}3Cu#*Vv!AjnG_&H5PzQ8V>{&}h2MIYT2rRMCTFbpk22adr$
zi~>J47Skv)(+fk$p30{aDzJLbF*2X-Kk__W;~5--EmLtdRej8NFco7XzWc_Zi`^3|
zO<cy))2#_S{|>hb#VG$y4S46aAZwk;CM5JoaZuk;xZOqGeT9#tVbj^+FG|c2IXw+q
z9MSBagoKtL=BI-wr6Wc^!DQg3(|?CpqHY>Er8v7ZYmpYpDV9FTh?>K=>-;dq1!HfX
z051D}Q1w-dn|yEN6x*@TO<C@jmq3#RN-KhbELJpd$n=q#CWrs|o4BNsb+<4Sc#3d$
zROz1df>8*5--n9dEYFf^6hv*wNhyvZ6SzInkH+4p{xfI+ZIe57-fF6>=boy(?zx%(
zZ8((?$K=~Q=;QcQTVdAXhUyeS3;|=e=84dq0Kp+JlJXx&C35R2d5>6jeH&hgh`4ZX
z<e=<%+OvHCbM@Jra85BNX>4Wl{=;uu1n2YcdJ|olBBY@qdSLR1xSnVL=MN7ttS0s%
zz!mdi<%YuN*+)}X(u>{fTE!^~B0Kl<OckwYciepX5_8KnD^Buf<<vd#R-$1kdAZTI
z>?yN!wmq(LRFE+_b)B@+`J63SE4|`j{+GbBcR!l&-tm51e+GWV?b<g!U|_!Dva{9M
z!<s=)zg2u}i%W@Q937!R9IIkQJlan-^d0j>k>WFcuM3(ggC!~p*AIhXMS#&%-XUmq
zZI2P!9kMwBL1d-Na<N9F=_y>bZ7BsFbEu#)(UnZJ0vwaceA#T}if+??5t6h>Px`Ha
zREukc$!xl*wzt>&L3waIS9)rqFQjS^seB2veU9ML;8!|hH~qIp5?orSE<cxf?d`vm
zjnvkLDpcxo(lNlsq%WSiA@jvsY+(7^QIk@>Z>J7V>>R9l@_G7&JLhbLpM*zj=Dp*A
z@^NL&j?qCr5*;mclh;`kA;qMZ(equq?W><L|Ir`T9#6E)M>=$$6z{`@xkK={w?~D4
z+v3NR{1^GycAz(B0!Wm<gUpo!aQrKP@ev2VO#ct^nRa<1EY~Pe@A0HP{LUz6`-h}a
z^XZ3gME;lRY(}FE86k}AZL0Wy3OO0gCqXdapr3RuKMH6!`cL6qkU2aGP=uqO($Sqn
z;6s7&k4~>;!@_z&cJ^ZtX%@!_^LNo?d>eAjH#SmzFON~N+eXEhrDKsR38Jr1r6DEY
zg*2pe);IJafp%Jzo;An^04wq!OTr2jWF)Pxmv>X{%^&%xo=TJn%LglQ!U82`emt9p
zG5*Hd`09NpNeVg90|ZZPU#M{O=`Pp`Q2~mMt@806yie67b60AY%FVm=KSfEa1c*F4
zR8c^mYHgcz9pZuKLBZH{n4xx8r8AWvvqs<gL}0kg<-*$GBnKZqUvPC^%<xE2ep=SE
z&B%H-2f<h<et@R#7sm_@OcLV|8|qfUIpr3;rO&Clt>lfqwK;GUUaEpC*x*6E{rG1U
zCIyBm84m9Th|U{Sz{wC0@~1g|p~57%=VUpkEo*8OTXA}ajis|dRGV+DCYgr7F29Zj
zh;IYnU@yn!3=EQ7&S#7@me9AtGY2<UmowmzA`c;62NJe0v@R>nn~imW5DmS*8x8ER
zbHw+@U5Jcr3T{qD@lGfxP~B_JD8Wf;#>wu^7{cM+=u0Upmz%nP8PBsr==1El`7vn7
z>I^J@Y>b)wOI&$@r>6i&_@{l3bcW>PqBlql;=Y*r26;(+(o+Mnv3a7muC9OtVd3bC
zra<bs!;U&xdXl-DdHRxtB0RBgQ*!E<=?X`jY{j@D8+d*nIHNgh*^kV)ly7v5Si1}U
zH8wK!viV;6Ug@iM2l?{td??6i2=oaU`=7wh>pr_K4$OyLAKp7aQ{x|->uC7WH!(ig
zTF)?jNUL3EC2w+ea{RvE|7~^#$ud^;;=a<HUEJf3DFN-p!Nq+OK14ZqiXCO|kWkKP
zAAv5vhnKGfF8)C=1I#D8Xyxo5Z6|JYhydDiTPN!8w%{i$&OK+*-jvx;=l&X%Q45SK
zZb$PZXP2HbV;*>M>P$`r(-POP5`Wv|YwDVu15aLgIl^jc5)&9@VOlX-90XWy#4Ef-
zo%DN*CfP@MNC>%jTr*q=Vtq$3#C|uhDqy~Pusg@+I11jPVTX7eX!crm(Qj#Rv|8i`
zXg4Q7VfjY&Jdz5(Ws%j+>s7S1GV*s3u%Sb8AcrN2tEiJCaJxD>g#p|YZlCX3^0;U^
zxN%;2b}+0|OpI6NVYfOu=<Wbr5zg;&?CRCBx&(;^iHs6__|*S}8-7<`N!AcuM4X<l
z5_v(w4viZC;5g;}c()YY#m`R}Ag=jaEa9rLW)uF63J4@29w^ZZ0SZ>yVdZP*U-{K9
z(c*JDsEJFq?i;NKNZRZ2n(zLhpnW7SE5Li-gP-Lnr*&E&-<Pd251mHFZ42SPoQYw-
zTzAGAF*Ce78O{c_T@Wk9W=3EBo^J`~#s3ot`(JH77l?!|z;2(PqSOc8U#(hdNt1SJ
z3VMs=IMO_dl`vuBA;^<7=sAvg`zhgcYw4k>x?#0NuFk#EKSXYmkir}t|E+;EF|2eC
z;d!u}Q>_->g|=3^+Aq8v0s%6dv&Y0qH51)|*(jSgzXoT+IClXg;{p$C`$NoNGN|ak
zx<RQfRk*hlw^(gKq6Eatu2N24*Powz&Q2~kO(zu_YgBrlGj#Kv<Ive?)g$P`;Lj;t
z_6W<9_oATN&#&><<i9t!YWAM0u9BHL^9`AC^>4aIgD+J`>+;s(nf@yU%(eMGVDNPV
z)_n{pTg*`2-~J*f?{~XlIR(3mg{)Ex-I$P$P6fsAj4Zjwd<T5DM?c}^e;Ja{yxpD^
z_F{O$U&>BH<FdQ+ruGk|Q0F!J55J}ms_ajSd)OuEA)m2S2(3lnDGKR=$aijVWyn};
zBh6VH4E_Awv?Zd=S~1H7$$p)0ZwJQ8+fPJUKNtW;Sggwgwc(1_C{8@B$N9y)YlQX%
zovRQZcg>(c6t<98ilMn9BAe>$>Pn}~_)m?_g_*x8B+c8%vX?`Tq(rj6E7BJ9$BFfM
zrKuZHx(t+pgL13YXp-6?=<|_Bz8V{Pl;h3kNF#u^&|v>+U)FP$tlO_+{eI2g?^D1(
z&yY-&{e)ZkixbM}UEvX9$G^d8EA!cR2Q?LNg1n#?V2;+`B6+(|%4pc_Oah#Vz%Kw|
z2*l0^%Z*pp{6rgOWtE?rb#+q%zE;QmmSv-pBdi@#L`0j+QSR`i%8sKiy!4n!jekHF
zp7i{?x{8N?=a=67?-X~Ez67M$<=^_OlEc`3jE0H-soCQmN#GuzCf9*;dQ@bY$JjeH
zi{t+Qeq20pNJZZ&`855s?GZkM**urG7$$(i$sg0difvwDu`XK*92Mp+xxNi7><Hkt
z3?W&5TF04cH@9Yj6N)+lKFhbuZ<O$T<S4eI_ZEfTaO`drX8ehQm0oG)P@un3_N5-m
zB#j99XD<MKc~}Xh7?0fi_;=tTEeS<hh83!ZJHEYPNAuh}a{hICyui{jyAbuCT7Y42
z365HKI_^M?F@l-)K)EMW-4Y7ON%YSVa_WyNu>@fhKJQn9Sk{&WQ8@#cCWrK(sIYvg
zLbFX`*0v)dwBeZrZbNGf_aD^^w1xSem~sq9?j#De@Yb5MM^~~Kve6J>1a%t_oqQzw
zW}Kt%D}AEPce_Q`)hq_}5TVQbu{PP=W;e={V+kT{5voA~^l>1NYiqQ$oX^c<BSv?l
zVHfz9`fudN9`v`dl9`jj;QD9Y&_zFy8`3wR7b>Cw%%GIgMfiOvAz4>Hq{0c3UDlLx
zc7iV$JlYjy)k_!tGi$~Xh8Kes9{jVm3I7JlEf&PNdl7opmn8tf{INW&tQ>0grk}P)
zprHN2_aG)K(-B;u)yLKsf|%#77@|MJ!j6$wkcV1YL0hJtRFh&T7)Yl*VCy_5*K;Ax
zHBwc~V2fQ!-CZ5be?c3(8dImJv#n?fEd9Fty7bV>AS13basmrwP^8ri+8MGC2qdtZ
zGl&_4D|_<h;z$|XDMS%|#imKNV%S^iS+Dud&Lfl)6fD_cnjt$(zh+pk5zh6h!GE2^
z@qcNC-ryd?^{?0I&xwO$!O0Es6pN3^+sbbQ!fXG_lU`Gkt91-DMX&oaq`@r8E4=^9
zwT;2TL0x?I>$Se_XR`&VHiE&iDwULNTKJ)+DeJOEE8ltC)Uru>QT$1`&pgOuZK?HI
zN{`<u{`P_Z*?1D$J<6`&b%ru|_Wm;XEShIG!(FZhtzzF1y<moVk}+7c=q=LI{NlWh
zr@3KVvgUXBQ~uwWT%ubMq})4A^ZyIS3=68TmfcT8gND7qpMYrl!ySS^XJEz|o+9Xl
z<YV>7k4_6vwI-se8Uvh_oo+p0=5;=>n+{kfn_D7X0x+!VmC)HgM}L<UC<p{4C!IC#
z4wVcm8wqbAzyD@jp?1@xMc$8+V|k#}o<<^sNVxN`*LpwL8v81>r;PKq<4j8eq2ueT
z6x5cZ<|15d%B8?OR~RXaCA>VK`hyUH1@qd>mIqpcJJ<Ft$13fME_WV~I9&~2GM@<$
zj49pjIp8uiF*3R~yp&t74Qw1xG?vfoCOIcUXM&+AO}5WDB?wNm1iL8RA%1$4Ew!ZT
zr*36!)==*!5m-+%T#oJbc%=PpUlL^cfuU*cn9+|0kRx6dAiV1ds#NvTxi!eM0>Cui
zjodBjAkIYxh>JpGEPh&EKEwV&2Ex`ml}Zw7OrxX3z3E}T08RlBc^anat5;qjK+Z4W
zi+wB24(etGdRXQz^K^mJ#tyycxBCJ%8Jd~>VL+u2%i*2TscHT5axaKqKxuKW#`-ON
zm|HCR(mgAF+5<^LHO0jN*3PFx)LY8w9k;ug!QdFm;`p?@;%S6C9*o`79;Pm|-K`t_
zKlJ#~U<q^I19JUO<Z(y*_HRG2VNI^f)GXUI+HAA5gDJ%-vUB48PK&TyHV_0RI$M)n
z>Eem$T)BE(G90;jeC#v`TCBBLZB}B3{4e?|oOy9VyQp9oC*3&=??Q$u8<Ts~9Q1#3
zd;fn~@Js)GN8(Aoam556Eqr_hd#D@5uaGx|PMZqF!W?BPx5?=l!9`qPs}Fupy0b)d
z(Po8#fsl(`GFGAk&`SbFWF!Hb2iB`O%n4K?4BE8&7F2;MQgUTQ70y$<YD*~=Pf5#B
zmFX0Y!_$3q#5(EnU6}Mii>xT=i$lYFnwvL6KMY_%#Q-LPoje;=(B&+#jSDs-kO;+`
z$qgR^O7dd+Iu<aePod*qZMys9Tv10wkM!6b@tTP$O9ToX;M_-F89nxv-p<g3A^j8O
zPyfxT=!^8|k}ZALZA0Ej{wICal8P2sDV#kT9Uznhww_6lQHBJwdG}s}`6a{M)qZ&w
z>q+x-FY+k<E=%R;1UbzHMSZzXN%EWm=5}QdEqrB7%!tnBDv}ixZR$7AAp2`0Nl9wQ
z#N!U-Zc+JkJ?d51ekG+EW!FrAbDX#M@=(FqZl;QibXxhtdmq2U8!c4MV@YNPt*iM~
zTG)EgwpJGY96bVIGySCs1Tva_4lZs;z~I&_4eC<ni%)P<yKCp!pTm_+D1d-3G-%~&
z?kJD;`h_Uj)U<&ZV8qOka;(72Ue+fq>SticexwNb-{6f3JYF3T*d2;(kDGo#8fL<)
z&MtwXp{W&6q7D=Zb{NwYBxRT%#O~-j(K}-jTjJaf+IaHT2h^sq1gHf?doH$H1>(?v
zGn~GBCjRjZ0lz&+S#&*N1A_cM4Z!~j6*V6*zHf^=i>WiM2S<UOiUWir?gH9=LQBhn
zI#_>+Boql7Ena7$kR-4M^3=O~k<9x31l<Y+oG~Vw2d<>T>8M^#X8kz(nUF0dn|IdD
z#|3s%B2HZ!7}E)qmasNbQXvSMFN_nYn*r-+=R$$eSoeE(wf7W*aZz`gzu$G@w0;CH
z@d*7;+AMgi|Hx1CL_UE^F>#_<rx72k8OlW%420Y@>e`CFn)X0vl)f4kPCb-+I59)^
zvt6-aU=ONS%&pUE9|pO-CLMa(fGHwIBVVJz(vdMMb=&~Fl2m?REI8DoZ2kb_zVJIO
z#nT&83uncyc+BlSSpG;(PeiG)5#MhV9oBT-x)1OZC1e}niTxk6F@p#muyb&W&fAVS
zuC=L1RD9Z>4DSOyK)pc79gLIa0GR~eg^v?+5=jW<000r*Ce9)l^;R3{<}6^4`K8*n
zSoc^32&%dO9MqTS(aizo&WNbf*#-4|=^SX|ASU)>G3Dwaw#%wzQM9|_cUb!eV4qT9
zw07GKlR=3%31HmHri5+}V!kO0hg<myT#qt*clQ0x!Jj^m$i)q1;_KSLmvfkB+A~lP
zws&wn0gy-^-O@C{jFFxFzVmyDAh^0eXTWTe5k=%TR{0qB5&V8pex{cnQsrlV(`B+t
zn_XiI!alD~bTh7*j3fzF&GVc53crxo9H2lhH`zd;0W;Ly`#Kmh2i)^%^~BOS*yz7H
zl+2#o8JfJvdm`eo+@ivYYR0E><b3xZZL|0VmY5kw__Ye#i1kdKkn@j0FjJ~51wopm
z-oZGJES@C7u?AqVeV*s8QDj_sDuWf?eX9pJ^8PW-QQI7K;D?zv<7KBuK$O@~ROKWh
zux?)ngclnMOg~US`=zJ^bFAUCK%{+!s!@gli#P`h;FZ7m7cY>1@nY-Cwh68<KhfcI
zWwcgFD+G1%5yBk23uYfnb@>{ylg7{GS$uFTwfP@6rupx&r`_FQYWKyzLg36e&VqTY
zK4x{NTm=vYPgeO}eqVKa<+`r!MT<NjA|wvsPVJ_yCB+6^ny?1e9Iy3i``y7-g;`s3
zG++#1VqNu$)rs%TIcls#MR=E@eYjk!Ymfxw#LObaKV0IYwFzQV4;ReA_Cc{(A#g8(
z>|qlhKck*x&h)uH84%ccfkH>6Mx>=|K%sj$GbtUB@F0WYQ>^nwxIbaDj<HbE9bmt!
z`Wc*uS=(2+_#7U1W<P+@^gw|#|4Q`oJ?TWDLOpZ!<NhvAT~P12^BR3ZaIccP6o;5a
zc6gzmAS;`$3E(LVJ*K^bIf&NThq^1}pzZ-bp>|(!&>i+-^9F%LK!{0UTh}tBbfHz^
z;?L@um=-5d@LBA=s0T>Qb#I6kMdXZBgRT|eF1d%}#8XsoHMZl&Ox}<qp1EwXX8ZWG
z!?U*B+~)l9%s|F|iv+tIB}LvEqx^MdXbyRV^*d9A6|BWkrBysK@sY&sr?A~uB7U}V
z3<cNc(@Wk#Q8YD)f}AH=O)8DkvV^YohU&y;L*TFmDYI3Cy9-@%U`gFos-@QlPQN@s
zp^ul%vs47d=cawvtOLCHDJx2=r2w)@;hxGCql)z@YYHO?@+5xz@twpZ;K@7Yo-Xc7
zvn;BLF)wp?{vBN6fB5lV(Y?Qb@j1#3F5wZd8x02GH2PB1J`NM0U6Z<FccP?a(1f5U
zVxWOBn@=&|MCLRvgU*T9k<sKcf%FGnU)s*Wtp4;DbB~Q4w*$XuiU=~`QXpotoGnqC
zP*_r)?mePRx$@=~*eO+XjwTGYCXCwQ7iL{VQ_I?Y@Zjhh^N4}!+TgYUI!2|(T=}-Q
z9y;nDJn$8XYp5jXk_n#;qMZUKQq)`B4Dh!64r_w0O$gB%arou;UTpy%u!RX?fN)#$
zaJ{3mYrbcR`>J@10(8EYX{t5w)oT0c*I@QSEMWC(1Xl5I)D+>-mBuHlldAQdXg=GU
z%klUL937CtQ%JilX?K$lg&`l+vY%~uwdkxQ((IR5!#RrusUvEI*2-DG6%$<n@{F-W
z3A%xgk({xgs7>F;iPb<CVq@r~qs7n}zvuU+W0#k-I{~CmXG}4DT<oPP0W+A&ZE4Xk
ze<u{?X4hp+r-m*i>RmiQI{7>DQNW5VKvf)iUcYYmI(-B}6P=qcR+cu#6xXYDAN-<c
zVckGLB1zrh)&N^)v8{97!7?Tqy0Ed!DI8su!PEl1hHZ!qWnG%Lekla*HQN4MVu~d~
zJ6|1Q^5=H_2v`h=EViv9B85IL&G<Od0x%{Jxn-qjJ)xF7s4Z`?k+i!JLqv41S|1q_
zq#^PtM5+RZPdcwND9TmXsLOQh;B3Ee(fE`YCcup%qHyJ+lO3v5lSl@z?4l9cTRl77
zhLq-vUZw0hVVz2~8#uXRXaz})D3dQaXMTU04+_j-t%{?1rlvp>7jXOeU%l6DwvqgM
zUup`B3L0aMZH~=GZRg9N&=6C|ygq*BINLy9A=M|>Mcmp|v`FiOH@+Pz%9>=fil}iM
zC`-0TvDh^1^k1wglH{MuNPvW&{>Vq7nD|z~Rs*7b9n4%c?-T3Fn2WM|%U(s`NfTnI
zNWI&XwT5;MB=umG|3waC5!mkp1pR-Pa&iM2T9kX4NM)myenfeRUtrhEZ(E<ZOB~lo
zo4-mFm?}P~0<oB051!&Ai4g`o`i%;Ot(pOd-)8%MciQ)cfUzSyT>`$NP$(7v793~Y
zusPkKzcK@kE7Vht(Z+_sT<q_KH6x;FcHbC7d_0IY%hO?~nL_B7-an$*7c5;eDrSo2
zo`r4B`n?V2VZm`<nJ2nU`_M)=R3ScqJPgBV4pOP+$^~dr%WK6F>?{2*C#ck@@DO*H
zXpZS4?y{dzs=OIGFzIbB*Y5?QXHrR<22yz98kbGEyM1Da!8I&t>?PeJOG>ZNpwK|a
zG<4u+A+;uHT!=!d&O8mlc$=e03(qKsBeXHlM$J)_DdGYbWcGJ~9hf9B&S`G##-@9P
zq_s)f?l@Thbtti;oEY$tmixTa<ou`N0TT#9?||uDCJr{@)N=-G*pQDi{5*Fdi{v`j
z^ZtiM>CP)f@}=>&!)~Yk$wn!9E&}=;nG_v9W`9{A45<2>x!b8NuW`Ngxxo&O6o`TX
z70;x_S4u9LzM*wkZBS@JL+{ma^iYDx7tT&?X{;kFkRKHg%oH#g`^fd1*Du_JSw{j+
zk5Hk`N^9u$%2;F{VmJ~GlqEv4Uot;2lvd!O;D<Q8(6TY0I@+I&2$wH?*ZiARpNlv~
zPas^$K@WXZ#(@dm1InFHKx@qn@m)`!w^tX{bCxSU@q0sjJAnQ2DL%iH%4mk1LHCag
zaM(Oh*R#6LNKxn%n=Wf#YgS2myJkvrgDe2xCVUsO2w2h?{4FGW;HK`2!&JJ}2;*e*
zS*=_h$hYT(zrarHR4hDXjd370DRc%ORHB#}H^3~W?r5)D-h?lx=9@K<Sa?>0ms(10
zp?(#XZyihONm{?=&%B9Y>~<{v=G2b|93IEq%+woptL1*<BFNz<BtMi}{K9N>m77lp
zSG6Fdc(NMw4p#aj?CM7Z(nS+!ey6nEw^DBvPvR*?&2d>nI1W9Gx6eNP79RxMwo3XS
z7jT24WA1@KKo#x45bD4lp5KGE1O1*4QVuqRe43McpD|}5kUu(O*`rs+ac`)IQ#*`%
zt(v=f16HF916Y6k{|AHsNp*s%z+3gNo{}Jd`SkY+0Uz^Un8-i#>GHyx?K^%y*BtdB
zYO_g=df3;l?PuulpKXLc^Nw3{JBz&#uOqGwG>LfuenDv1?_nV)VU~sB3(>PM)(Qv=
z!V-fO(iBf*qU^@b2=wo{pmLBZO}2=HSxJ(HaQDI&VaCr$_w=uZqHx@XL7W?!a`zR1
z?epja%H+WhWvkj>k2T9)D-`-4Xtk+CTHG6KW|ziL2mrA@lgw`AHiCx+)4g+_$7&z+
z%QO6{;qxF=;UW(*TIZwi?!*>r5N`gr4Vz=C%qgMs(eAL%({Dk>gI`5cwU!ab+|L$R
zwg91O={qB!pp-^hW2Y}lON+>MDRc5=FEABNwG@2p=;t6pBf!7qe#zd6<I}J~Hr~&A
zGV~2_?!ob!{ckpWA7ZVo_N_saTEh))Fc~Kh8s_-M)n;|q5k;g3`(8!+Epk-8cp`c4
z{wz;F29)oyXgM+KgT-4-7~Jz`{`&?B1V-v`q=jSfm3zhfTxYqMpEShkXthLQnMKSw
zBo_5Y5}dHpeQZK4g>$KL3v)P-1ZAv}zw9~%CWevjpea(A_@J_65JOp{cJjUT{SU`~
z1;oD4Fm{DouN)shIY4$}fIC65wVry58dB+({evWw02>Tph?MV0cuOef4Zd9LWpcH2
z!+_$(D;G5O+JNwcBW3&&zrDSp-WLLwJZUdMYb^GOf{n1K{~~Ej#j)(y{5s1PC>Vzm
zaTfEh!h>|R$`WiS0!*VYmSm|u^|*=79z&mYNZ9vAulN3ZJFB!?U+1*|l_aZlRwlxW
zY^<;haF+wG?(%PeC!jbbhc-ZPVq@2|erxVP5tD(-<id5165hMvT2ck9F7y6X&pQ8d
z1+65jq+I=YCzjS-m<j>qm!~s`EZzWJcn$1R5U)W$6}4Pm8r&3y^7JYGL7qJEo9N@!
z3!3CGoaET<9@<h5&C|z@At*o`p%~9dcmOI1y{jD#v#nl}Kcr$kgx@)%aC>xB=bQoT
z!kiW3VI>V@VyXT>O*RbTS@rv1FodP1iKQQRY_I!x!k<0_M&F)`zp9#FXVF+41^Rtn
z`VP`Ve?{5fmr>|Yb3nn`{j+!-1+;qrOkMZhR6r(5!{n-?#+D0Pq>3eeFKgqUMeRJ6
zU|%_lFgGkMnBlKnXO@owE$B<QJM5s>F>ClQ4B4l^E>9{2MDd)zi35hF`SY=DjBsea
zPg4`X|BIDq4^of4%624{C9fDuMN6_M$R;*7*0R`us2ZUa^v#KcxT1LV6JsAfm?$K`
zvIy#)Oli{+ER3cAcWaq&R*3xkE}UUdzSOF;OCBTZAQYLp3xVI~7DZ^!a@bRl3n=u7
zU-VG%egwY_`4rH8YyH--E%pLSvd@i0R!4lx5mDI3vD~kX63D))eEMeGtKY-Me|)1E
z&!|;fEtYH@5EUQ%DbS)pWfd#2Sx{6@YHnCmuQ7bATuzi|zj>#VUe>cXwb#~prp_py
zZI83><jv#=kREIN`9RSTF$_CI%CCg^Rj<MiKsW7D-}axvn@SLbDZ(tLwL{F4SJnoT
z(%yEEUK0T?c%N3vRT(`MyDO^0L2K;E7Vab<7)$2wfVmzJh&+)KYsz*K*8Q`BgjWW8
zscgrv$_u-mpz;a4x}SS{;P<y^ZhhU-c-t@yne<dJ{{x;r>=|2dniP`xSZ-;95LUH)
z+?Th7@A`6$3-DYfS<QsHA#Y=s^(f5IUME-9;bVAgImP)in-nEx(UwrG#2bmQxUeK^
z<&sDTk(OkzrDnyAHC5OT4zIqNg2oNwyWNQaWN@!72K?z>!?Uf=Sa${z9e|iibmL~k
zdkA^S|0#NKc}-APw7L3`FlQ0=dMh7|4@Q?kCn%m!tmUS}I68$8tBxvA^jaob_<CU$
z35$SX9uP<%sKcbN(O|891(y@YA_#m;ijJO&o?){lTGVm7@owI|M7EqPq7CzN$hVCv
zqs+M0F5||)=9d;6lTs5Vs3$3p04m(HQ$niVu59zdBKLLIGX5Sssj)?B>zaR$rAc&&
z{ZA#t4Q+C2u`Q=6peq(u{>@%H1H}-8pmrBKlAs3~a9bVSR;eYPOW4O=scACHSh%|{
zFH}uUF4m0T9-3-pjswn_!{6|<6mT{`{Uj4inI2+rhv>5TN<#2VbG~)ZWMk_$b-#KG
zU2dCGhncsz?~)ucd8m_bx~v!{daT*CGf*0|l86%zz__L#ISMeV_Xo#cGW=f1fE0gB
z_?T^j=Zyj^5-_whc@QR$>F8*V>+*4oEuFhhENT+w@|(Pa5;aFuM^W@O>i+b~=;E?m
z{TQNlze>=iZ{2VWVN796u1R*F@{4m^pv%*hOgr~+Hi#~d4Io2eMgjO}%9>Zg4!8dO
zU_ff@!*C9mr6x@Uj>eiQN|nHH1NUcr?g4B+uC(gmrdNCqBFcUDe$j`n7w_5e;?^Xf
zWN_{VcBnhoIF19TAAIozQlS5+1OF44TdCD1OYscD6Aem#a0E0pX_0WJRQJU9{GO7B
z>cInD01{?xm5?7Ib0&WrP^e#Cgp=;MU>9T4<TZ)c+{;(b-!#m5p67BVu4dee^**do
z>__*s6ofnx45*r<9)Z}8+*)r{wG=FcM2#r_5VBKe1WIC@4O(;Hz`zEjYFe0iWTH0Q
zvQn=Ks8`PoKh=l69A;P8Hpr8_Y!y~r?as3A7xDguI=hjvK+nN=0!&*Sl^DE>N^8IA
z%6bqt5wSZlcgk7xt1m!}KpPAhV*<!CDjo5~ww}=w<{Oo6Br8+Uu=Jc}V+?j}Qs=Zi
z<!^{TZ?60?K{wD2(b>UoB?to-o^zQ9u}hy8rSeVt4oE`GSOp2YudH+LeYonrWuq*)
zO3ofLH4jdr-8yO9($az<9Z0Y3sb?%tEj747?ww3yWnF&u|K!^n;)7hKwE#@<{u@xz
zcBEbbWI^LE)f?u1dUFKQ#Gtd`za7P~uimC*>A00b;A}f}*xWR0erEZYHf7x4Th#c;
z(xyNeb+NI8KIy7)$tKRB3>xUF4%?I<=Rl?4f??oU<FFnXB4n$L1Qr8y6V@2qKZ~*i
zyxhWQX*OEB{Bd|{fRqz@c^t`BoM@z(3D=!Ju>JWYqPb7(s_sW?L6YnnhZ{9B37T$q
z-kere`}*Kc+I>CjatwY$*t6<I$Wq<I00?<cKcsMUv{Y8o4b-6VV+Ue6<t_4CZAqBL
zEJ)pb0_royw`M{L15dNY6|QbZyrohUMEiKxZ$p<%OGI{A>lIqIiny%V`@nf_&yi%h
z%+?t(53HE~hp?{<s&i?&-MG8EySuw5Xt0e2cL)|B!QDMTg1fs*aEIXT9^3-K<vyJA
zz2CLEb*pw&s`86@re}Js)vKp5I56W0eO7^QO=UaxV%LsdA3uM#f7}_Oi}GSm5Cik;
zX2mq>@hG%q3|oT)b0WZ4kW2W4?6um&a_Ly3LiPj*<9Z;B*-$?=-wFC<@#@_Im43%P
zI2Ctm?fezO_B6n;&ITk<3>T8LlkY2f)@c*_f%>_8z<a}kS8#=XHmy1$1GE>+7H$r^
zc@oAGY7IcF9%*63@*>W<W?VCeoK6N^ib$-$8TFxmAA$}#ppNSLUXfj4(j=yL^%fxL
zD2U%PkTu0rXsq%!SA@XxFFam88yB37ny>XzA^QGO*XE`|f9oJOv+#IJbfaCdVQl;U
z@d+l>hP(lRLydC45Mh9W;?_|GLD82MllES|?lP0ejQmbgJS3pOkTaKAQU@GJDuTe^
zgPic`e5|uDt+^?6uzgM#xC}~~I|*xHs8Q<@DSj!3lv1p;&v{M!^{4E^upyoQEmE9z
z3(Bs@1}w;DC0f9zX2-cTrl#a+CtW40w?EUU)%;rjIj!)<J(kCSFZ)=KTQk%;Rh0*&
z&{}eZ&uVj0GroaPqktonKlOgj7y)8<?KqORbPgBLCl0aNKldx@*8wop<v$7qjIDRT
z$UOpq{{NDSmwVtsLDXyOur&;J4pepyP&A-p+euz&>^}bMJN1h}TJkVYx+R=<vOH#h
zSN2yFEIYw16}gkAWMfphrf03%knGp!jgood$3R|RWPMjeeKW{ZUu@c$yT)o>D=ZIO
zDvX-tyb7I3-rBm~Czp<6`-g^;g|_V^<v$7$H0O+d;A&`=g;<6-aG&JNzbh;GjkfD8
zlH#sh&V8I`q0`R8@C<BAP3oun#i2Kd!K(_mK%5Bn=)PO!!6@kTCg~mD4ZF0v2Zfz9
zC=J@^?hum2lIJhH-G;+ad41R8J6^sGSeLB0u(E&WM0{z%j6u_c)vY1Zsc0=-)QgJ>
zVewNy|B~#yqx$&fZ<9j8TBTSRaz|#sQLrDNmM%n4aTA|AoI5_l-Re-?D=00-D2F}6
z9$}4d+XS(Qw#>a&ej^3#7TD+<%ZmU};sm2^KUqjX%gbf+_uwF#biWeXf~J7@n!dn!
z6MXMo)Sv%*BJfW~UbbSS7CZ6;?@8zU(6>$(DEQ?hb(FS5aW+8STTwm_y#`d-Fna*#
z+yL!2DN}$O_+Q~8N)@iC5q(>xoDI1&hT_?7z{?NjjG(D$D%r|Uu25V(rrkmRBP`)^
z#LIuY0FeVY=m`bl31pCeq`u*kId56B*QmowZyU9=FW@y$teAPZA4t%cd2T7TBy<Q~
z`$_W39q8W1KK!MeD@$ZNhPHpYP+;Fec*}2SD`fRB)Rl#DdOO)ln~NfezK=XsWyA~R
z8b1yP)_2k}dTN=W<KaeY(P45PJTuRKopzXW;~RFdTk$cvae*I;^b~gIlIMH~z9GGX
zuZaeXSe2vnDhL_Y_2To$)MtgoHWhFRS|vrL>hhnqVQT(TZX<AT1;eLFBwK@vPXuz=
zK-jead*a66u7gj#d5xxi@BhsJREsoJhru3i%APm#S*mfF=xMkajVrJFDBAN>$!o8e
zo(D|lU|Uq<rHwN)nUxO#LIwp5bfYOXATJ}*<OzB~FA~*jLY{v;g0U~td-r$Is%@~9
zpv-fy3#Y(!v)N+rHiJ3>5}5v=!uYZ5eaD}_-zFA**xo+g2Yz~2BNU7YtishX+i%9X
z)c@6Obe?G21-SB-Wx5HiNB?nA0;<aGZ%M>|&{>>Awr4mr3=I&$>3e8Fac-VieT_2%
zcy~dsrcF)8MEgikfcd)&jE}#Ng7$n#!G&aJ>3=FTgw!j(%U^!LEg2uU^@+v|I8uo<
zYwvhLbHlkwY3_(iVS3WWpJHo^fDty9d-3DAafvwJxMMXoe<w~}?WyJx)}v%WOYTet
z$^Wy1GJ%o}Vk`0*c7*ksKFUm4mRx$ZB=sD+Ij66EvW)&Z1tY&oc2b2W{C5N{gwZm0
zN+nY15e*QGZpok;0=ERRXV*=ygN@zKcwR2inA}R|NP&d;wq{vi&3gZMFPi7;ZB79P
z<K2pwz<PT$#vlxJ450)Trx9Zy^86x0Q<F&~b`&JPm!nkr+!4H$kfbi6e1TD+g9L7f
z-;fjgr8+#cl;^y4t4@0jo<}3lNMc!Qbvov0UVD%iExAD~Mb#d<hu`-E(buylUXp(}
z7ssNfYQ$YieAKv{9gXEb$se?cU2X(i{Xo825A_2k3XnU&1vJi8Q1ZILQWEcZxSPv&
zz3imQ5nxV+?P8%3FPP~xawQ7gZV@0+D8qaz*<?YP#QF9?#>`tyIzZGjE6AKsyX>@D
zBQ~g0seJd{9bMX-4It=TGXMgGu>oY9RpaJ=D?0OXkeDjngcMK*%{4(NZpgA|`n(qb
z@R{b>o&^&8B{sM4#Rkk#HbaH>YYZy5(V0QmVs1v+!G&qSZbF3jYOH=lS+b#v)N)up
zulRlVIo4G(Y~yj220@rMgz7s!YpAA<j)byS+&GS^V{y0l3Np@+a7Z!(UobF~>eq$S
zf&NZ6=VM^w+KU~fEM+uPnvJg^Q`Y`#d&|0Gd+v$WkIy^J7Bu|F*G^;4A$OR}ww9^o
zIe}be)yWXUr62umA@96u!od7~GlQ~SbI?C-_W~zABTw)kkAp8A7ob&S1j#5H|HS%&
zYwAv9fxS-c)uZqpdDI^2O9W{9N1&DM4%P3RTkm%C*!KJ5jVmA^36@M+=<reG`@WGP
z;XbId_YR->le1%i+X_d{(0P_B+f9=$va(8I1$~r4?}_2g6bp_v68A0Jlquq7L_J~v
zypZpdK&R}+EH5sap9QGhqeeh`he3Pu^YTLXvd6Q~uM6?E(pf!%qVOTHe)yqh1YzLY
z64u*WjLL~Hoxs{pZLU{ayy`I>56>FEyhkl2LF{r_1@0@a{j@)FFeH$+-DohvitM_i
zQ3X<*W(S1`S8+==g4$hlABDRQO{wON<d;fvSdpQ?ZLgHZIc?rB=Y@!ODQtGnk00N9
zWqj4DWn)ZGyFR6|czhCw{BN{7&Ea|_F7I7C0Qu7Wc#qGM2-MHMn#B2wp-UHvdxy5?
zC(4PRmWJ4s+0s|s#~~>OL}vuy6NsSK&MD(o1li4)`X8B1xpdQyXGCc+*;>`o3ovUQ
z>mN)wF`F2llMwB-tcBbNk3wo;3<*Ogdc*sh8zD-7bDXNT6zE?R@D+FTrk~&FlD3Fo
ztC(V$CgUzz`>RdT59-gS&9p#gxH63Cw=O+KYAbYtigfgO(a&0(43$qJT+CP@#=y9(
z`o{@-eA*7Mt{neCFYp!MUNV;u5XWQ(jfsd?)?)u6ut)Aev-i3Cf(QR6ihgsgKHvi~
z=j59=$gOl9V1k49=b1;-mGBgOI<@mcSk;23-)n-H-_U9O?^p=n!ei<g5^{#FC;7j!
z)MvWL`4GFr%e5T1q`1>yE)LKl$yad-B2aez)NL2)jO*E>#`)rbTcuxlx$EvCfbWre
z0lAXhsEZxc#)Xq$iq9T3wjh@WZ-(ptixL_X!VMinX9P2-h8>y)Yu}LQ3k-yom+rHQ
zjI;O6s(+WHv)63#dD#Ll##T2O#&DFBzjC-%+=FgXiYSGExHn)20%#4^#dYnq6EYhe
zMs!&F*lpQ=ktt@$h$Tkks%7LBz@u(^S*W078&Z_JCIb5~WrIpq_4|Z51MrmOPrOCR
zkvRx0jitGT0#E~E(JMaavHcEocpY0LpV5fD{z4HG@t->D>iRqN7xCi1l+!qnwp$yS
zCfQ}`i>u?Nrb6dmuRafdAUDVJJAwRT2d%h^|LeeYEw18*)`#<~_m@?*ppGl&S=mdg
zvxaLNT_huRIY|VTrYcU7X`XnCAVWJ=Esr4YNG(VYE8R&b$?|6}7DLN4uj(=^uUKe4
z5xp~p;}nBqJc8AW+IN%yUM0TUrzqZ@TI7L&P!gd^f=#769jF9NXy7X+5rgsL6btlA
zY<Dn+xku+LiCyl_c}CgMznrO3q#1u-{J;(3*&u6^D4F|N#6aaJKAdrb%<Ea!E;DDd
zTMRHQxrF}*DcG&1Mm|I7Ha__jAu{9|9nc`*^iR$A_L>|jd1n&~+&Xm&Os8j)Fk=Z5
z#hAfzQdH@SAIFIs$E|MhzRT0iz~rp!a>HHh2>i*8EFIGup-hD>;IZ4{48`_FfZCGm
zK&3N?JiV(GW<;BD(Tmy@h|c(fHTVRG#@x7&6Sef#gwD<gFa<%b%Ay~#XBZ02C8m(Y
zm3rw5jmy_yLG1rw&8h{)dRdVb2*KcFkq<n&d(E`!C7de+nCKYD(9IbOKbBL7$g<OD
zk)bA)U|WqpeUB+j+Z3GjJ)x)2gQ5B+)fm!?1no&-eroy@yR$}Qb31{Q1~iKoHxUYO
zo*UvM8gl-hl4BnRL^xwTJb^2*tKxSiutmGb_W`zWRc=JyRafES9gl-e6rBRP*<Z>-
zjWro^%5JY4rJOpmifaMP$Pn>6M%zW`@Mjf%v#wYG%?BFH$uI0$$X3S9)cj4$DD?hG
zuaKOaC^O`1{p@@1xH3(<IFUVKU?%2C7qB^S*F=;OhVg2WSjZFEB;Aaci_LZD0gGzC
z3Ncc3|7XVnCC9X^o<Vl@z#TwPU=J{*zqoA8`oyJYcYGYmk3#%QWRM4<>(dB|*uVs$
zmFsMvrOpL2`KgBAVF<(;Gn{^O56c+)mP@u@jp0xs5pBJ<|3>4M;*f0%cfpE9!}2kJ
zg>!*y1LX&J8q9<=ZN=a&-qn59cBoRXp5F-&{W?M(1R9K{vE$sK=NP}vJA{4{LC!*X
z0^b?2c$wzTx+)a}SeVgk*tcdXnj2)<4fPa<*e%az{4tw4P>{bgg=0+OSu0n@MUf{i
z+<vhc(h}0C$E;n`8=G+?pPpQ#fZMrXhDtW?s$+)EtRUUDljGi%<JR!;It~f1D34M!
zl#pf3oy|5WNyHul^|D_e*fqH8<xv&e8I#bL2SSlUPD&1T$<pTB%jG!ki5eHS+<G3G
zqTGQ=eij5YakJZWeAQ1hMV7~fi?_`AK^tLn><gD^n^#1vnn^wafj&Z!t3|hZ)aNv*
zWg`Vt6m={m_>}C@JUbmmZQdpO&skMVn1?stt{ZbsW_X7C8ZP$PbeM~$L;TgEB9U|-
z&R1v6)0bUw)Yk_DY@it7^C!K@^D53DI6*?wCMd4e;ujovGt<eF2<5Vyzq)1bh8I?A
z55Or6N()F4M?ZY*0BrI`3HzLfaD5S97W@2%bAuDqP?i`IW$1Q%ps5{HhLYnOY*cs=
zmtZH<%G^DYA)3^^2V9t;(-H|*9tepeYry!XHO}%hc$#Lrl$>8+^p*y2^yRkj(0uh6
zcT@bIIQc=COyX!!#*ltp1;zw~%ndJ@1hBv?3Q9H&DbBYy8E|KDp%h}K-D64+nr=ev
zN-cWW+|I#gFC(oYkLAd8R_NuN!V4GQ56*Jiw^qMx+&@O7+bm{4fBgYe7K#AKEeJF^
z|Jx@9cwO3C;(CIMLNRK;&}KE4sZImt!l2oHoii<*hHbQI5L6SlAf%0USKjj?z}Jrm
z-`R@pLM9rS(ox9Dq6OJCB+4*RtXzY$1$D0V*&|%&@`i>8&nkXf*B*e>G#>{8Mm6=2
zp<_NXOV?2y;9*=xekWw;^feUVZLR&`WG*(wUI<Ik8O0A`7w?`_Jr`pA!|VmA-E>q=
z{O3@qw_(I6l<M4lgJbMN8A2NB(sYIZ><Bz@dFpGSW9Zi*o-386grTNN!C%?>zzY>}
zZ!0S?pGz$#kY80&n<E4|tp;IeP&qZBKPP2SWuIQyi17mXVi*nLp5k2;TFV9Z_r;Rd
zL>XJD#^JlO$-s9*H$6X`)1r<hp@7Xp)KkCjV@HOhM9X9fwLT^0IVl{enew3a6N*i!
z)K2EjJ1;}u6@c}L-{4x!ChZ*Rt*wXh9xBZm^j9ch?j<g9KBsRyy4E(8?%3*68Tjq0
z8DefU=M_^#Ij+<V1DI=GPqn7Ur~gs#^U^4wf&mW50UYKO&{OGBD<9qmkHcB?fxxvi
zM5z-5-SKa?Sdw8xUZP~Xm?Bl5)!P8P^aogu5uMp6D`*L3cM1Oc5Xbka!`8Rkiyov1
zXsrY&T6B>>8|>Ui?U;z05wod;X~R5^+V|M*fv}n8=y6~7Z<q=H?8J7s61(Fr=o5HM
zMmVI->4%w*8tVx8fB(2^oLmpp3wsB+v;IPsNx&EsyrwSY_QV`IClvsqu^ey>=>tAE
zSow2dm&D9CK&pLfH6{iM0k&piT|`aumrcITKRt=48a<w^@F@C(D79esuaCjzvBrsi
zEpTc#kst^V=UexR|7$HE_#Zk(LP&NS5{CG&l!L}HMHmsp%)hIG>>wvhI2-kbVSymi
z;Q9sdJ+x4}LW!iDz;~Emg!zPmLzx)Li(KShyiCZE4gW|W2E{(LaQ900)sA={+Ckgs
zmV`b1w)uOD+)GZ6FvQ?ll+;-2b}Uo!3=E69-WUP`Dr4WGsBBKlnFi1u_KDZv2>@3J
z2HihRPAicscWL_r03jcgh8pi6zoE?PlAK!Y{6W=+0`Y#JyavSk403;$i2s8hHuCRy
z0Sp;2q|vcFyg;rp8AL;wp;)79<z{poK-`Ldw8H4%LsAZQ_o+}+SG{HxrU9=67+L3m
z{4GbH7u{$h5sCHwxk$+DvE$-TZx1QkskYZ7MwKp!BiS<WWhU9Sx89A+58hF7T;!q!
z?1r*&LMP*qKdhZc3kY9f*V@L$>*xhoAM81f4N7TUuN^gsBG2F4f=%5YjP;^p0+vN;
zN7(0#SauzKdD3#0^MFDGj~t&QY=V2GkHW@ciLNI>mpjlmy7!mom$zrDP#Pr(#l^Eb
zg_8y*@H~MZ7xwB;C!!}yBz~9Q*tD@^a2{DJD*?M#fF;L&YZhbDP2-`4Af$!P%!LRk
zkFeHS&wV6OCXbGyKbFKJ3Q#UHm<i+f-xSxl3b$G-8nd>h)S}^BZ)(ccM=n<lbt*Mz
zZ<B^|!qH3gK;tlfbm%EG4tz+qhwk@houb@5&cY!v8Ioq((1?d9`6*-|8W;$tCd;Dt
zT-5r;4M(?&W+N<ZY+f$Tln3uBgM9CMw2B{VP1DGs099i9+rRpI`yR7H`6#~_?#XzJ
z{0g8y#~XS-e)GT$CV76p!0qK@36Zs-(-nTW^WFQ1fjyv&Pjw0utLPuoB2=RH#!nVv
z?OlXo1K_@j|Nm*)n~YW}vP5ro)#yeWaDy4RmBS^hF^|T?8?m&=V5R@5bH0u^c5;YX
zk}8otpD#{9G^C{=JlDqc<a>S!_U)c&MF^J0*K(b&k^AYX5qyACQQN1@>c1M|26-i5
z2?1pm12}?jc8?8-$kGPDV(;|QR`k{cSSgzr2QLuuJoeL5sg+y&lO|dBP63_A&ueHl
zQ9l2>OC7TxAy9cFG!KyC6vrbAq}UF`{}rl@o8dWdb8+^cQ3`m-oc$H5YZB3^^57&J
zmjQt#&i*U2Y6kOHR{Z{!L^U%rlv}MUZk<{4($a2jrEl0cfDSvy4KAF+>(&kbD`EaT
z3C4i!l`?(ktawk(8zF9o-5V^-n}SDCq^0Mp8mpsq8yuXpS>9dqRY(c}AGk)Jx=Ql0
z$tr$dT$Wb6__)N_`Jun*L!S}4{#qb8I1Fht$ig2olrH(6YX}W9=3hp=Fd!;UCP~1h
zvmpamh_1yOdX|;#)-;hBCgBM^jx4wDE)Gd0Fg&9a05H5}X;J5>BE>;=Z!b!8oxR=h
z#5weP;w`+wT&_@G%MddO8?YoiC1PSJ2GLsDQ5Jw0EbO=@zCPp{qE3n100&z)lT8a9
z(NDxavZiFZS$X)6t=oK3Yanz2jz=E{uorayZPIUl*uv7k@18MO#<8Zgnz|O24C7)9
zL<Qt>uxZghwW%KSFhKbMyFDGpt#8ly*xkJ@dDd<6pFNFVrMYXFlh9+IMZ(dGQ!=Pw
z>e=Y5*?lfs`x7+SZrW&S*&Oq?nS=<dW<4x=D)6~lMd@7hRQXdB@zvxSSIt&3kAo>C
zaX^uG5Q`4Wv|R?v2h4scXbE&UC!C|ok<y>GWJe&xVk#uycjrrqO<szb<oltx{MRQR
z-wifIN<C*(`Ed%RNAY`E<9j;B2)lj@H4y$8j#K$ZMxIF2+lT@&JUN++RpujUY#A1T
zG31#bWBf@TsN@FU8emlGBsWUtPOB`KivgFTl8o?l)vn&H2az%WOC3HcQ`+t`#Bg+y
zGTWi}9SsI73TTB-Az}|^E<0immM4fW)x0#Bj62djW!nvS!?DqoBIxa1Q}~MCoJa7s
zX|*v;qyrZt;o_cJ(#42@Qa%gO;^eNx6h)igTl_d&2#$Zj_so)Olz!HUVIB<3L35kQ
zaP)9;CM5!SlvAUtKwh@VyGV5Lg{1pJl5VU|9pw%U)NqGDttj(?3M|yt>Kilcl(3hW
z`{#ozcB+|?QX5aXXRe>%_a*wcQVY8;nSoiiqwJesP_dOHu#d_1q2Xw0=%a|7D#>u=
zk$W7hLS!6G5MR0x`n2%kP=!3DzZqM3NRx`jA|*tI4mri?->a?nvoUS>l`^KQHOR?a
z3FXJDHx6bM>287_x8O$Ics)fph3XcsGqB)=$a@HL3ZyM8aRc$#o*DdnKianOIfRB)
z&%@jmoHf_IBS%k1gMu>s%{fpV#qt%M=o+3=!`1}C*$BdUC!5C6fD_gRj&8pOf}ST*
zH(oEUg|v$gw)7TyHKE8(Xsx>XXHxecEm-3B`&WyIW4XjqDbkH5bqCX0un6~lOCJke
zTyBu_R;{(LCuX6-ikL_Jj0rA?;ehd<VPBkjVm!1WMN?eXRd8jf`eDhrI+WS^FRKH>
zUJ{Gmqw><h+a(N1IU}HZ;QA;8?P7NEez4SCY%Fxt3SAShTc@=wfA(UH`s=!O>jX)y
z7d;6ZRxN7<ml9vaTbZig&T#DE!WwX{1hdjW+atOe^P!X9UmTbTGf(?ap+wCfru8Gd
z5RU}j0b$8Cx1{RJw>W&LkR+(I844!aOp+XqgB)~p)GCz8;V*XFzY>ND<<5Vn$vh8I
zT*sVQ8_c7W%`zY1;zwP#zN1_jx0uQu;_b{l8?l&L+y0ST3df2>FF58FVact;XYVz`
zj*K6IN>S9^kB{!6)0ay;@H0aUZ{C{Jsq$4G8;d(p8Qx9?FLf7_yu|~a7^!oAGrGKC
z1i`AKBS`h(3lkeI8ea<rcfk+0x;aBC2Oi0PHgpfz(CzNX6ho+sK;4ygC#E26<?AkW
z%fhTl(d}9;S>tr%Q3P^jd`RP0=E0~QZ7gy4(HfrW0HmHS1?1=$9&@r6aW;&38+3GL
zM^{vcZb+4VBYeTib?tXsCvEo+--#f$4$>tB1!5~^nF*KuI{Noz8rK<`A*DQ1Ww=Up
zFqb06a(mlZn_&tS&E5}vWk?)TpwufU9FrY|bW<ms!4CR^q1-62pPO0;)GnG`2&%sY
zlgh#{ld!&n8w9;L*HI)O7_s~^$*<yK83B>kiBd0)M|!UPQub%56%JLjD`Ti)x?rZ}
zV9QouB_-uJmlvX6ph}AU+ah>gp*w*W+_25td-mJM;^}0*(4cAAR%wmHUo|+-MN9gC
zyNZI0Q0K3r#Jtk4^~lZJ@x~^O7*&!r$8q@p^(}r*()Z-El<^JhP3Gj^ICJn@dbG*E
zm45EYgynVE-g<t-Cnf4#yu>nEdd{kCUd?<UA~B)ng$u9S+;WZsBVnezcF^aaS_oKZ
z;LOEvDo>a)h1(qysq#tZul8m`4R$|1+=kf+rO8B6uJrk}%`!ja?+zvoKFbdQ<1c6I
zZE@Fha3`5B?JRY~or3Cq0KsAw4-j84OtL0xX3LjsHIc0?U1(-0Q}Rvwk%to#HSTSF
zG3@2F`DaoKuteXcsfvU`Kf0bbJPkqJa~*Ihy7svyBTO?;qMb>B**fdhrTTE#f96U>
zJFj>UJ(hm+looMg`8wIoj2BkP01>0K0(>VZ%5scl$?k?5MnAE#hP5;o4E=Oe%mqUK
z$zU_CLn<~SVx9=erFTCcWX|!91v|hS8~n)$V{ab^tfSv|aezM%Ll$~q<JCvAkuPs-
zxwB+!S`=w%dDj}rd$|Z5OBc!?ru4H~;D}!&R%wU<eK*wa37wIKaxVjf!>?AO<61Pt
zyV_>(RTX<_JZr>qGx;4FDtRr=C}iqVy^d?@Dei=as94Vz)jzgc@3#r;2`<<Lt<;0X
z(=OWlY31a$UPf^7Lu4lxQdfu9itEGh#pDqYb%PF2jP5SQW&G_ebNrfn<L4FVTW>63
z%=ZPOS=so$JD50iPdM09R?Ok7q)^szrKB~gL*}4LgQ@DHhfaQ}05_untYi5yZ>__&
z9N+xq;79xMJG309Re=_Bh;3-doHltg7HVvyaJ$M2o>y=0K^+$kR}!X3w3^yGbg4X3
zd@(0t(od%T%h+S&mI6}Oi)cR-VGE7LhZ(c2fPejS4_n7MQM!a&5Z8aGU+l7n(#i2F
zLJu8&<XZYMnEWm2=g@E9dCY9`TAF>oUE=9_v~QoVlYMh@hqUZ)Vf?lXF$NzBKGYpJ
zR)HU`fXjAazpU*Ow#?d0{g=2}zd&yL<sACVsbmPUGXGI)$0=HTE~re{2SLY)1K?xz
zCF+mLBx4ha1Ge%*QTQ(rkb24ry#=!5YP#yO0%#-cW_xKLlMOAgxwW4ya178lP(S#n
zhBth(;nO>HR(Dq<pUt^CH|tT`;XG=dUpI`}*~ld6%9e&y7lLS`Vq?xDoKz|+uQ$rw
zh~8tdG#dq8F8_UbUm6k|=3k!4gkY^~X8IRnUORc~^wQM%!Rh+u@5+O~)Tu0QhTY}S
zO>AMQswPfqDNe`l&IvQK@0tUf(kajquBQm)RkBke2YA-P0zF&9#;87cPQ~0<S=2pr
zd;LYZkz)*B>;PwWg19h3-#O(N48#V$^9zE9muI421SQenG(M`G(30O%i#UrhOFe<L
zhka5iubu&J*UUk&hZfRI{Y8HdVWzOaG^W#7K3Mu*;)CiW1pO40%ym~S_U<oekM|H6
z$E%hOx@;SKnsO&hg3Hb*{Mr+6X;c9#*mKE11c}*?pdvu0p&Lg}Y9YS8p6pRF@8;{n
zB&}~t(!)JA9Z!*$+UzD2g=G2gZhQG&9YUTmvW;oHU7Q9TK05J(OEVrP`6bZJUPqNE
zFdE=+Fr<TvY#_y&5?E<Fw8xD9^=15F{x>7;*_!|E<@O2J$L`C)LYoc?a{fstW0oYv
z9&RPGU1Y@>`o~tXZ{^4|Oo7JWFjhY4Cg_k7Uz-$F<|k)~el|P};rWT}VGL6B=iP7B
zOW0S7iZeZ0!l%W;|DY5mXC%%AsJj92znOk|jN0E_dwYQ38r36kWHz7|K_6`#wlLFs
zT-Ct^Wgl+q)G!+rc?|3-dRR4>==jZWC?UD0@LpV-nktPge8A@e?kR%j6Lvxt%6I!4
z@41<)wbogNe_u~$2{ANl=4qK)SrcX2hwGhwI$Oa!?cA6p!Gh-{w^R#Kb|mO(YvA?p
z<cQH$hIVxDi=u8!+>54{9FFZ$_Nm+T1Fp`>HPoFaGmsC1@Al)}cRUCGk}plE09?b1
z{aXi)AqaL;uGcW)Qi3Y9?%_oFF8eZ3X#EYr(k*_5!OImG)bntSmF{6jsBW3LiZzSc
z)mU~+jy=euK8)@6Gp_N<wv1f~%2^OWAR~yp^_?=W(CP-KqwgraOASUUQtxDeFs~+G
zHGzU4V{CK?koT?+*1YP9U-^SrpY1aL;{{ls66us{HBL&E83lj^Jmp4w;n?#x7=98>
zF300;!g1voVz7Q4J)r_`81jrc;toa^BuX@HPL6)PL+EB>GK;%R3bFMvM68-nP=iH%
zzzblQIkj`+;S_S&>k^_uC)|zmzD95L2d=85&Hne=YJ&|o0~P4w?OwS`WrEAV)8fH%
z>?S41!XVDu7l^~~s$Q(2STKzl$--HX)0XQ;1^u*xi6&VLr)Y70V&ty`PiJ2cMmeB5
z^;OZRtM{C76=wp>3E5qNIpAH~C}0?f3Y?)sYzOZLgtN5{9n3}Q3AeKKGv<=#YES(-
z{z4sF{kpZ{ceteqFum32(s=p_MaFn5U^E7F8yv_%LBanEGT^`*+aROfkXVrjrSZC_
zz!1w`mXp@zl7RJR-at@Rl#d&M8#G_j4@k??4U{%HaM_iR7BgEh?awJ3Sp_+LxJUha
zGm+rqV+Nr#L($0?_#kiRKG9@&)NM^r*8KA3@8qZnDd5fW27FCEM_&OA-puXsVbzo>
z%IK7L+o=v<oPM6BuFYQ0DQ~_Y4EQx!de3H=&~Q-XhDers)$^Kt!MBD+bt5iU60Py&
z9e&#aM+-cI8Fu$aq+e?<kaVPJ1J_GMn`qa|4@*zq3x8Sy8(I7r>%O5VE?#}p1A?^)
zddmF#>RK&2SzGeT@hhpGf|=fiUn>RW9w{sdKkE9zM%<8A_?kJysnzm{o0&{+Zv#9j
zQBw+&&P`o88UpWPfF_1uzjC*dh1bDU(<ZOGkOG?Qc&W2`bm<Jcfky|}XVVz%#iH>?
z#mz=I0CO1tn0o*pzw~1w0FSL5b@SHN{x(l#;c{n#f38rIM1(UUq%D#N=lmOz01UVz
zBIxhuyFe+>pY3HfhY-gXjqF|U@fAX|V=EdE9kGSY1MflG8HO<aldiOo%r6(mq+T_J
zJ1Iy<Za@4pB`A{*n*A(7LvPP1>TCR`!m;>yzhH`YRIpp>6!4Bs4hZ_q$J)AK8mQ;k
zGJXi>*LN6hjEtTXP0t*p-NlCgBPH$wpM)9zN7)ZCy?BvXH=jOJtq?U_s~b#6Js%XO
zP7I(SlpvEYgE1sdW4V8N-iKUD=B!m166W;wwLG2o`Sdl2?7_&$EsJI>_Ghti?umi>
zy4v15wE<Jw(CVomw?J`qjFNOMQGz|olKS0xXr>-}(%B+Rf|5Zy*39L=3?QmqY)7VZ
z%FHjgB0Dn$gONK~GEQS`PH)R6TaP5qBV{Rq8fy@3I%Jnr(-=#23vlsp4(d&$B6cXJ
z_jt_-;a-JDZH5pDluv=TyR?G7JXr5CHBd)u$Zky22cpFU3mT8Um4mN4G?EZHxhMP9
zv5im2f97h@FJLG+9V)>^enwQaL(MFa6KnuuX(7I^81@Yk1b=~Rg^Lb`^-AQNj}goP
zBLb47N%hleGktzZJrW^HLi_}keEM;yg3|?IbbtLX8h7)wdB=P;q4hz{QG^IF!?DYG
za<M1ifkuleIe^%u)k;AclfC644j?r}0<_=-yQ{ORVU4IsN*Zj-IZC8vCw*d|IL*El
zyDCn+NL6_l5<I;1#A4amx3%X2^_HlDR>61tTL<0M#K>y`4#<+CwrX13-;;wCd0#cP
zxUEvvT8n)};ma+7f__W3YfhK!V&Q>F9z9{eL4e+$fW4CzL|jT(;0OB?Ig@DrauI|S
zU%^E<E7O<2>9A_hcbJ%me|CPjC<ufGEQpi|p)WBG<j3%&?N=C{J<q7cYl^P#-~lCJ
zRgc8T(vv@AP68ocBf`#rEAlYkZCE@qHepf&p=tomTXG&;(f)eN_UGk|`_)%m^^ZQT
zBlu534`F<tv}UFH7=BXK*j-q@Gu8s%!6#EofjTmt5Fkq*Zkr6dv3NMWI5^f1y<Hn~
zRrIHaZg8^Mu`o8w7CJy`n4S=MM)KWwn?k~mr-kiXH+;7e;3sV)M7`^b`)`ij<8P*6
zgPuYuH4H)ZQyuBsSR0o7p2t2nvCV|#wa~Wsw$P8yGlK=^8+UCVw--9U;=1E%VZ0x?
z+juR~7OZ70+OIhc%8dvhv_HNLGeTQG4L&&b-@K8$hBZQ@M93xA$X|SDC3C6(3&+i%
zvHMH%?R#fUE4D4!_)4zY1Sv}e@kq*r{~GTD@M=5kCR{_K=Z+S9Rr-N{=<ylWjq-<*
z{A>)X@9=%1a*BytLihT>gt_&Rti^NOh02M+YEnQ{Q3eJs#FP@L@<8cs*~(5CfpIw_
z=3P^eFcwkCLMo{UVgvAM4-OkPj*1BdylN_UB0rNr8N#Zd?hl5x4fnh4oKc=Jmv4{v
z*c{f}ilznT9-2$8cxH`l*{Y~~HVLOmPahoM#MT!^N`<$9E2PhF`3Vu$0kTSnl(+Ij
z-tkwyg10%&c9|1<t&$^T`ppm>FdueB@1V}i_WjT4M9oc|UYgsg4Fag@)D{j}5{p94
zDges^<-U5jw30`MkHeEKU_(e~vO|-EGNCr$ktwH89}Ktqr4P4SPJc!TX<r4Vi-Vg#
zWB_SKLO&}uDToi!-_g-6*0%JkkD|+dvqc*P<O)UpfDBkfk^$>HU-2hb(KqPEaK9@R
zK2wDjMGYvB%*ydkpZ&ZQ61@3{lW|j|!9SFGbTKg4Fk!?5kHYY|Y^vx}U&+5JKqGE#
z9$?49i|K3YGv$#-XpgBdHBXfaEYn*62U5c8$~hY8lwqvz<LYvK-Jxy#5`oD&wJY&{
z)*(8o2+YEA8R(^a-tE)4jH8pFyuK}pNl|{b8ss10gxk8!*iMkduh82nIUM~ql7V0@
z?@dIqQW-oVj0O0hK+{y*WZqo(L84DjHgG~PjgJU?$T9GCYZ0G8#`bhFcxfN=FOv|~
zr&xL0bZjCDcgA}dAq^3ZxA>*~dx0~^VrbwEJg;#ejwTEDHBOD&PCCJU)YR0fi>BJB
zc;Fp-3@3N5<TcD6(+p{Os`|Lx=J~u&{&hz}dhesNADKz%5p0Es?R(D#XhO5rmZf#e
zgkoBux3#GFp#e%sEMeyMy9l^^SfTi}?1^;vF7t?v?{=zU8a^2t01?w{j<+Rhj&lg7
z?Kk`>xvwFpTpN4gD>W(~9u|uZoWG-n5k4WE+#@^owXrqTvMQ^t5P@6C0Umex5T%w`
zJiMJjSk7^noMhZjpYMebGu-iY<sY`ftWzBoNa1slqM|SBgG{zE1Ypj8F?f_`^`ED^
zG(A*JvgaCAHA8@2j!t9WJ2cMF$&pZ$pZ2l*;TK3H+lwyXgddz{88zL}A*39PV{U*Y
z@YxpjXeyc+B`3yk6I^hMw7qrX3z+veIfg$7zIx4wX~fVrEe!jY9dSC9VE|8BG)OyQ
zrX1T}+?Wuj)p9vYr$<@qHn%lTIa@}`#ddi(!jvYkh$P2-e%iiL)%nuBTV1qk2n+y$
z>t*@lv3-Y5Dkciu)cjglq17`jkbYrtxh+miC#+Mgfw~Fg9%R#OHmmnh6t1{R>KKth
zaZz#<MVLcF3|6U#O6ta`u+zi*lsTWNO}1($K%k3AY%fZfPWiw!920Nf(H}iFljm}=
z8+s2fKk8xwcNkwY&xkReZ>}tLI~oJgFIq1b`#|GjqW+0_-bx}C@Wd#16PRih5|DXx
z;l;96Lbz7acg6><_Z;LEDQ&uuJvt3)liQDLwOojZUFs@fWqwL(_6(dtS2H0Wftk7(
z0Gs9y>FK__`nvLbU!i)8H`8y1jpB~SxBT1U@&E(;22Fu^8>+HAPN&LWxDKQ{A5|l4
zWtvF+Cmp;J!s-&K_yv~Xaw0&J)?r<4cSZuJ!ffRD2i@ZAejR?FV!?^3&hm@`01HP(
zQH1^z4Es@xVbc@UwG6^<aBPR@qjw{9p;AVASMYuo1W4WiuTYt^_h528eIa=6jGxXJ
zpU_)`G(Cg3yQPF9>gJaCpm}SXAPiOHjGsUYeT#_j{F)P94jYNP@owsatrg8X$$Qbk
zp^~04>`{7byt~sjfB&Cv<VEhp?!hMTJr%kx;=MFAf3!BlPd(efCcGU0c!2g{^LbfH
zJgqDgB&eEQIA{0D<acO1AfG{?wQ9#xWqds4+itc!F%7zhwTm_Z{Wh!+hkEzuid^s}
zKxQILq&8)#!{;hIRyJPU=n?I95+r4%+d#=X;_BQ#6OfRV{CB>9jf_mrFDpGB``C6T
z&-d7OO*iqZ?+mbEe@2;d&AIhXV(aJnSx$(?gfe|;lJ;fyvm6(VDM`cv`F-?xB7lAP
z3b8wA?t5CIFo;dS-6L_es4vXGu*mjIr;_NVA~_iDa_{oF>4)ry74!S((!|g088wYn
z1zVwHm5WO!Ho)<&x^04k%;AZn66#h16v#?wHp5oL&4PawNEfy&tC`Z)K&@}s+yQaw
ze!%i=CVKh4cfwDhAaynq^Z+zi@p|6?(I>n&c*Z;Qojw(MdY9^C=t}_5Yp;aqAFZ1B
z9A-&rDq+U#Apk{b+THuZk%Ly6_O)$*of|UXvCE-Y+1bkr`q_Ks>?pSw7WC2g<xpg$
zN}3ZNO>idIGF0|zF|4@JSJ=BZKi_W?AK;{l5xwnZ_YF|}`>|_tvLf#*a^^BEN8}Vb
zr)_<oo)Z6JS28a}D7FDTng~I>QyG>bLA~SVvrZLjs|lB1<Y_A#AS;*|te`21{PcX$
zoLC|=FAt}87WPd+K+1W%FoY&(aM(f1d0+i2@9OnK9sR|%=%>L(^pG45!O6f5Ke)1-
zW~vyZByvbS&&61efe$U5-Q{xZU20!2Do6{hDTr05h*f%-!toIw&@bFV558$N`fgCC
zJY&3C>Go!Sgzk(5DiRnMt-#MX6&GN5yp#%N-%j=QG7G|s8&!ZZ!3FrXg$N-ON@^kd
z_$YI*&gep@D8z}Xw)r;H`SZ9_sqjbV^3$E)nPb3cAb>J3p#UoQqzS+v9EpK6bPV$8
zoOcs@a3v9YiB`AQP2vJ*y$chCTm#hqffc<f13zwG{Aq#EZ!^g3mpw4#ra>A$1`f@#
zq+$WbfpcXXy(6=7%#E>YHXKr!u^b7Nn<R8&U@to}eu%#wc9#n`SXn%#lqC~F9PN`|
z0gHzY7G;`yF3jC;l1TO1fN$mB22i04`S1sRY^!T=)il#+d#_`ARYQ}3{5Un)m+<Lr
zA8(zV-Vf|%!y&Jcm{$Fz|6TL<uSfQg?0SE)V#;zyTeQ^&*a^;yW*LyUV4l$zt_WAz
z?kh{>MzbuSh_ERj&q_);L5U{}kr?HNi7L;V2pQKIufrV8;ff+F!9DZ2Vs_mQkIzpn
zexTGOY1L{j`w0HHEfNW2G-3$=li{z33k&LQyw4pKc+}|2$O1iB=_BT|81uN>i0fbR
zdzt8B3M)kOtiWKDlr<$cL>eb0m0tIg!w;xS|G-T{MvhH<LcH<oHx6!v0XeuGz9S_+
zf~eKB9fP+Rvc^aXK$!`|-~=ejYKWwME>-R*#3Co5*CgR|gya#5u4ALa)y%0eH=wx<
zSF0CDl+Plh$gtidBv@-vh1o}6U&<`Rr<BTrFeg!<Or@UjNlutnfbSRfe(*Bub>o$Y
zY6t6%tn;<g?@yZ1%<*PIENC~lr6NBfhqMOEw;-9)k$C{t)A6Q{9C#31m)~FhgWGQL
zy>hMs0Z}|OX}O)`42sw3A;apI=8&Xl`01se@Eih-O;NdOPYLk;`+(ieP<rQ&$+EJB
zu5{3?))$x_wLJQm)5276A)^jh%l6YQPB2evTvjqyDYwsA^A}ZX4cDS-3+sCoDmKgW
zkY2P((fYcjV<0|d^w{Ld>j~b_go2ry#Z-*#Y4l1C6+`*w(tn{X^*Lo@cCY)NmW1ZU
zpGgxAODvTJS+T|<N53L{AahnO6)3%?(R(hrwWOSiTe)Ny<7+OYW23OvPvWr_Z<sQ1
z<oMdtA(BUUxLcQ$6=z2FUu6>DJ=XA%A2t(p*`#&2W0}Q3FswgcUwBqKyz7n{r=$^Y
zQ?xE;XA!P%c1diKH+%mA6RscfaLUDWyB9j1Nur`u#BZa!;~u(c$o!L<N;`fVcQH8|
zSs^Aw4w~*fdmulDQJV+lT1zL+W%!&*&!E^zV}jSijJOi$!!2cAE(6(ao_@`!V|kL6
zLHABxU=v$GPV}&aqa*n?_GXOne|>02?>kb83OXy`xA>8CHH9>Sx>Zh+3+CeAE4;iM
zx8fR=os}t2n?#IhFm`{w9iG_l0jsQVBI-BRVfarXyq$E;D#$x~kf!A*uQ>?-IWO8v
zj2^Z2K<Ud*+Tnh}v5Sy5VYaQ-TTdk*{t(!~Ne^Seu;Z(mgbQE{WvU7vcNpXt&Z2X&
z%S+57&V=8eDJ3+^fC3jKk~KTJ8V%SQjlw<CT!9FQ>CcD)5fCVw#~XL3KRZah?z+Xa
zpMR9+Zs(<aG-1c@N}QqVwrF(R(qp5-MKCq9g9KtSdyX2P2VsO$f`HNadP^juGPw-*
zS^@rn8ow~u>VqtOs$_0-%wmS5E;9ok^~EJ~<o<X%eRK#e1EN*J`yru8L|+TEi?){3
zVG0a1ilhh=BM;1uuE~y%k(<IjX|Zu(A%g8uP%iHne-0i$S1x?%H~_C4dJs9yO_<jM
zY6@Ln1@Lh+Dh=5w{3j&<F~tD(t(YcH9LU!p1f!Diu-_X5K)Jt0V>OkD@2FdRE)qBV
z$Q?o@A8Am5qnxzY=$hAjvpH^=VHiMla7reby|FISJN+z#vhT+q{wda+B%{V6G$C*(
z2PIcXP4TOq5!19^*YD%Q;DZFCR9NPjpi0|nMXc1quE{heWi@V;n7uA1V(Rz`hRjoo
z{R0b*+h3!EMkL^GQ@~q!v^LX}lBsw(@|dx_xkDD5LuP+aF%7hhQ@<XH^$xv~S}ot~
zqe*cA*8Qjwbvh#sNcvIe3T<3R*yrsWBA5dD>_L{r@fdX;sQCP-21m%!`KPNy6JFh^
zq<Q*wQG`GVvCPa&@y-pXtU;J~OA1HkC(_4Yoal>7T}Xm2o)sNk<RJs3pN2c&Pid0E
z$m<(t56!uwUypro5hOrV2<+xK7GJAW$33Lk6a3PxCD6`Etj)byCs|1a#{5VVw~Vdz
z@1wU;AD@S#d4zg&A-gVzPNFJCR3US)h}ZS(f_i&whlCbNTj$Nz`cB&6ea=t1>o{9s
zLUP^^s)I=N=g>inyK9zwGJOczP94kz%-R^+re5=Zg!OBAVh2`UpXpTwYJY#lWhDdy
z*(M-n;oPffcGReMrV1-8n>+@piLFvYV%jOj+O%mnVY=-G4=mbTCemfg2BZnzce9rn
zd7$(2(MxdYhZT;%JeOOT_=uor*2RsdPDVYwhUVVks-(f&hj8GRXT%o>>8G!wvBuaJ
z$4PAJL|xx=Oz(%L&!I;YR1>@>k#CNrHUCv*XTp`RxUhJ*y9iRwBJQr6!MbP;N=C3s
zl229q0U(#*McUc&sCZM$uFKH(VIuDUJ#{NuF2hh(U4Vw8*+Cnv>Q5%Cz&zrF%6&^J
zTjI3hQ-6h$kf&F4m<@ChpaTTtz3Bi*l?|_8pq#%?+00KO{GpPkX=rEHiVR2*8HN$|
z!e5~QvSmct32OQ_L8ve!=yUl6o=p*ME9DPaS7I-zBXcxx&Xrjgc$r=JQ#Y5xH=W%Z
zOi$Ts>Lgshh(v%#T4JW$EUI}ZGk*lXMqs;4L<HzG#X2-5YQ=>AVUEt}GUPA?%05Z*
zu;EcLuJhu8X|J7!m_IL?@%6SpXbI`_Y11P*UOtKyCFlz)HwW-@U`kUnP^Au$W7o~y
zX=Bc~;i8<RT*Z?0R*Aj#`5T&cGD|bPdTY`|7u+A?6Q+d>E@(Ru1-PgsG0!LeZlx5e
zz))_nULBehn4cHI#tr5XBCrSfxY=SIJ`9f2>_+242jo%o5O!Yzz|;`Q@aHw7pXpp<
z{Aa9BtB6}@Sta&-qITDRJ>tIM$O$RA?MzXr?!N+JdCDfjH57=mG~Cv`2apA;s4JDO
zfeG-oeLRw;lde}+-{9e-Ho?NY9GiErYS~{PR{#qKxmYU$C+*MjFZr^3D-uF0(gdkJ
zmp%yA{o;!A=nZ(e1_Q=e^ZLX{5jNvWc_ySQ;qgXu!i{^;@^jn%fpMBbE>Lo?v1X+Y
z8vf>lV7=C8@eTTiP?ZKJ^PoUSunZ6R2J$=etMhyPBsGWDxTf!Qhv3I|=j#*Gl(r0W
zm3pUd#IqFwNRq9=qi;e2Y+W#BphU@o#1p%-T`?YkjFjvl>22)45ZqKxe+C5!(0VU>
zT-hGH?uiS+efXXnP=Wu+AqxHUD@5FpvLAK|%ow=$w>YnkI1+NDIp2ttfugd;6PlOF
zl0;s3MW9v3HH+jg06?b4dDUWvdZ=pg5t^%$G-`e<;`$~WBJ)yl5R;KWYE%4rB|l>=
za#C&5<pGBbh<8Q$`{9*ZY__LeH9LJGh%7U<lD&%MlIRR7DjImdv7xuzHDZCRquD##
zM_w4)P$_T3kQr<)wHh0&onvMmMJfc9N|9o&{F^<}2E<M|?KtI^p=XmsY(1-NSrdJE
zp0x_D+tZ5tHU2a^kN_VPBQ=5qZy<HCT`&PkFKZZckGTcZ6v6pI9053>G;rLVx&t-j
zp7(+BTh9I#5p&f~F<(Yi=W>W(W?r~R+pT^*d9!pT(fUZ)5hO|5QF$JYtI{>%kUx-Z
zZ)&o=bldhF<_tfefBu2ggE}EyJHbG9)G(B$;zw&5GNnI|`WRbO@HlKkOKMkWjLS;-
zX<e_&f;yy1^e?m*>22m|7XDv<%Z&4Zzb*XW7BALxYh89X1L`&p*aTG}B5N^#D`$;W
zY}U1yd+|0+UoXG`fa=bOZ2-;_0>qT22K))ah)Hc5uYaq=7HTonl5j7FdpKR!I*wB;
zz){3PP))Nl2|O661IBoB5l>$ty6vMwFcOV59V?t55U?k-nEA55;M$2pf)3)jokPO2
zeols70X^Tx<pml~^S+Ng;|Z*?7M3qx#eDG~bJpVYsDd1T^2#|-UODaO@8DYt9hRE^
zr5S4?JN>+OR{DhNjM)757JZ@ifFO%i#E|%vIl}f|TG#wS9ulO;837;UHodST(RF+l
z0Xqe%qC%f7L$nk536b15BFm?6rgIB0n#OHw;2rfT|9J!q*351I+q)d!mzNh6s%-ym
zhw*Z~V(qwpnlT@5a6q4pP{oFcFQ7OZD&VOgRs>3>y69>A&O{3Nmxt{*zgRxiD=IQ=
zWJSqKuGdv%f0Ud&@4l|dNp1tWDF22?^N@=XK3HwJK*PmQ@O%8^L83Z|%%d!Y=u%(}
zT_I$QumOo*-kVA(S6zInF?AKToPB9}*a~fofzbwpkOSJcf(s#_tZTnv1el8t6{kL~
zrX*rs_tI%va|8*skV?Rl$FQ6cu)Vd!-ZgqigtSlbL}5^#eGO4I+m|(7SZN=(9k&$8
zfI24D8~*14&Rb44+Iu3k!F@qon8u)i9!MEkzBYh2ZsC3bJV5@YTS?N@!Gp`;k(Qh8
zF7jr7vV>%uP9r^$JZ^rO7MybT9uYc4SEvqjO=9DxLVPs-)u0w(vi!)mf6L#$+^xK@
z44fw*a#n6UO+PUAz&V=ANxoObA&@eiG2@<vAf{160nzHDCEDT8t{3O3cJ8z=Y#8nz
z(;YMD>(t`>a9dh8frqvfk68?I_^0ge;4pkAOue(}H=Bvm@I5M*f`Ms>p72BM_QB7*
z=MR_**W6x%nXqHKV(I6V)D%px<5`Fx08G+u1D+BI{E)L$ST)eaRp`0Lm&J>Bl;BqK
z+)HMU7<-+NWgiRySOh!Y-@-iJm}O~eGehlZ7D<>V`TW1{Rme81%f8PmU{qk%mXb4(
zF<XjcAe$cg5K;|Jh@<NXohcTNt6|N7jJ}U`%RoH7vu7pNa%)}sxw@W)BG#hTK5Gi6
zO$BV)O&v01j`~}5L=#)zwhHR*`M=}}gCkuoB_sgDD(FZsCmrh2>E=zcW5}<|*hz$*
z@4C=9*?(d3-RjWOZ@Xt#`&xO${Yn|UX=j!J8zVoUOVe5+@on<UDkU1QX)cj8bg3F1
z<wV8UyJCWiP5U+VCS6pd73#;H?}<EKe?xg)^anPc@g1aGobD?(G1J+1miJ>SeIYT>
zu6BJDd<dn(I1u`})SdC0KEg&O#08e|8CP9swOlJ$ZSsSuRd)^V$+G9_(_0ZVc<MpW
zYhzp%Iv|l>H|qs}PH!|6*dUK%W8|uI_<BnjxP1D;{se+SA#q(L@k}{8h<^hyc+j)^
zO&(`OuvbQ3(04!xN4AC(dp9JAq3(ZXgHBVA>u=h9WoO8qXYE0Q0hvX%bK>^B=U{jH
z2v!#Ek*P5N63A{bLDk7>+&_J<AlC2aNTe~HS8jxO(Yvl|C=|(v7;PV25hEOosFqfJ
zV=Ml`4UHgh5-!We)B=EqE||EZ{DQy1CFtjed*iKp{TwU1#rE6Vzi`@Q%Q`*(kUt?J
zOnBu0;v576xi|8cY<uc9blIcJoFu`YEDBtQ4sQziN7^nGAE<x>NKjBB<C`;}=ZB~x
zOiEy49}nafJ=Q;B2}Vp&4PgY9|7l#U(Cx3&kw`0LqBT2S8RLqD^WLObv9e`ELM2=L
z>qvvdhI#sUX~Ng*OM|yX)gE<(^b?xck^5N|(!Ddy%pkt3m#UL#pgc6gXF@^@87LaI
zvWgTq$|VFTo?Y2%IoIRphP2!DrPt*C#|v<eGL$tDT$9=OdA0dxCk{G%Mlif>ki(uP
zs1v2@qkW)~j(fiC7&i+by~7XA>~7b=ih3jc@1pp_vt)_FH$W4NaRBy3qv*q#4qvnb
zZ`w9}@x;!+<rKwgvkTcr&l@|L{bdWuZV$(*gZVLOOzWqoEU`~3%|-7wFV~RviTQr(
z#L7A8Q8_$HYEFZ7?uem!6E9$N{|8rZ6_8i7G>PKw?(P~~f&?c)g1fuByKCUXT>=Dm
zcMA~Qg1fuB!|a?h|GhKw!UH_fd#&#3s_I?~7G6o@`C2)Nb0J)1htl;6aQvO+Ej?yU
z%eiRH1bNy3hRS37zf`7|>0&|;$1zw`_sf*>;QV74HL)wo<V-h({}W+$%VYu^_fCwQ
z4(}RlchbjH&f%nfKN^tl4h23FimNwoFGf&(?xYfhX4uOiPMIE8yI!nSv<Wlz))I*p
zpCN$2@ZXG|sxbTAV8eY&eo<DQ1nrKt%v=KfhN0(kjMeqFiW%!(d}c;T-&dpMZD*bi
z!%ea#CK+pN@Q8(vY)erp4wctfD9_r*mX&}vHo6C)ReD$d+c9$8F*})2;9^Np=Q5Gd
z`hUwy=ABSMm}F8WqC7VQ*AtEZkGyo2PgfGUa}-;dmkAKpB{v@dxBW7gP|x6CZH8OR
z*$zM|-Zz416A6)l$OGJ+F_C$C02#B3w@5w`1%c3NZnMSq``y#(nM$b56Sq8bD~cK>
z6CPUNr0O$9h8mJZ81#6}&qGgg=1QW~VLhxRyo{hBHis)D0Savb%Sx%w(2YO!e*=_&
z$5rXqAE@_DKK_;Y$##uvRK}SK_3V<d+2Dhu=WM5LU&I?bvz%tUL%7;c-6{(Ix6u#O
zg_E959ykzE_3zwO6>k39i7q_9d=3brEb_1`=RnenZsE!R<oACJt#NhB{eOTVL_kN&
zzPoda8?9`ABg#*PC3)7s%`bphG(0T?z-H-Si|EU5+wr4<CsaZpLVf>xrgI^UFm<Pk
zVQm>knDZ){-g-`tlrSorTsy`#`0<*!IV~Q$Cgq<pUswgtw{Ix4*{#XQlB3I`%#18K
zVznPgHg{GKnYA-zHrae_(3CO7Jj}d^MEoDj!v^XR{@M;@twfgns^#=228!0@<9$>+
zlsB4UC3}I<T_!{_fe5{j|Bq4Cw4tY^O1WPrq!$r@GvZF6^;;zglqfEGhleG4Pp#I9
zP8Py8YA(LrWDpo|-!#^u65}Un{sF-E;w?fx)%(XM<-i1EWIB`DMn#=OvFJs(#7rBO
z;ox}V;$wR_pwIoGBCx!T-55*j%_V?ua}hdg2-H5076KalL}Vo<WU^NC?5+84`P&v|
zYKmJJp(qt4e1~m;8WfS?jMf@DwaI}E_Ww@qBhQ9sRpHAaRP%fjTFPg4WBMtcsCPr}
z+|Cf&h!Qp8fsw6`$GTVdj5Ehz9Nx4#1P)!l;2&2J22|kxI_S|<51(sRsoE4JN6Y(I
zV#71k|5(u0Hv+y`2=~`Y@*)9C)65&W)3T#Cy_H3fzEu+Ye;q+c;mIGO+YM^RhU0_Q
zIaH6xr1APwECa%=pNrbg_y<W`NUMY+$jMKz^`n7o1CvX&y*i6$iB=iVQ@i=pVesby
zZCd(V^T6>+c7Sa>IKm1^EVzgfvA`>O9XHPbD^D5;_^BO#zncK_hSBw*t>1TB+lSj$
zeJCj<!37o(sviH9_LJW=(XehkJo=m!<zFmG34-#Gdt|FJ4Fv^CW7}rqGg_Yc(+DdW
zaTQ7;n4#&^sXL&1^CRf+I8p^Wu(EwPXcqmCglK|na$4^@_5T?dOjR&JAWv|t{zeX8
zz`n5R<TwuD_ya7!l;n(tS=5+l4%n!y_{GpAaW}^mmO@=qqaN^bZSuk}^#z#XJ8*B)
zJGBxa^#xV^Q^F++xrGQrek%&-X!adTmY4>u+37%lfClAi>=r5~hroVR=(EV@`Z=pS
z21};Z7dXnni5}*=*)=_74uzz#`$_Y^L*%S!SlVoFfqpHks>VOsQ>K<Nlp>{zQ@1?i
zTSr41DX_Igq9?J%o9Riz8<V2CTkTIap03~3q{(;@*%7|+VgWkJGfjZ|3E<?};0+zl
z+25Z4`T>AQ-&V5bz`>*Xg?X9ngTh(w8^}I=@dr4xe;?W`8+CL>M=UFt$D2>@+Tl}o
zdwYhUG^(1U<9>wJHolk0m0Jk`+iZA`DD8&K9P#YsfvV1wjXMT>(TBvLLygr0nGImb
zmi3&u=frX6@eqx-sZQaKBNJ7#fsAx{xTU57HgS4InAgEYIUzX^o{B1ak%^{3MXvwW
zdjUZ%Ke{!`b~YwNQXw5{PeO}=Tc|;>s4&y?p)(x<iNJh<cJ#D*S?Mm4$JcPmL4diq
zZ%t;u`lu`^ylDLii#6I4F<jH&6qrLghFBvh04XzfWup*4i#?cJ*v3Zx54vsGoR7eV
zOneF9`khDaarGA)jCF2#ztj)@AM~EN#|Nr>1G*r)JR&VdqWluZf}N18fhVK#5j6@D
z`us>9K8$a0!R5V*y@&I2ce-KPCuB@4xxj<gzdIWwqefE_S)P^oD_Tiwp+7iswW(Uo
zr2;11b*YZSA_<!h(Byu7;$IBkDG>9HLIPflCcEC`GxJU=gCNvqTg}!FQ*%fH=IGW*
zSUqATdM*YOKJLx6SW?9V;w35Q`xqkl?NSj_lKVPCsmGX^8pqSQftW>q&*tZpfQqS!
zPrBMGx)CR^H>fZKuOwMxsTnTi#h6B`OFw_KafE1ho{!p2Cy_tHe`12ShMqp#(}VeN
zS(zW7u?H;NRh*W?P@+=g1wTXj`u~5W>Q5{9QO{@X^#~FMcZp_<xC3-;uwTZLK|41r
zrF)e~0KI`rasfR!wTah8)zAniZ#Ks1G(eLZrwotuLq4ErWo5`)ft#mK0TD%|dqbP1
z0$od{R>Xzwu<x(rEIPnnI#1$dOz=1)tkzS0tqzys>^ze{(16hwe1OWoqQX(<xtbnt
zcYueG|AB1i@`?3JNe1}Lh={Rei#Ul5w)u*JKbY7XxHu;LP+gD(MW_o+bVJ3aI?q9m
zGZPIcW;0TG=dwi??$%@LuEpV_mmMcn9ULqrS?t?HlR(xcU9{zA%^y_!ogspskTzU^
z-i0nhgeF^*A3vC`ZF)s``PR1~NQENkaGoD2TQ-?9N?Q4Fk52r~33I|uF=oG67%^JD
z=`+X%e&Sy{$196ANU)^<l!&8h@Yiy!70$lKrW%5C0;vH-@^R>1c|XjcLk2;$G0h#f
z7Ae3tlfhEOx8^(d)gtWQ1*5X}l#fpQ7&n@RQYw8NO5L~!?H<(jishYOmT5;YbUVmt
z)m=f(f)b5i+xh8cpa~P06TC4NO{2898bzKVZCJt@Fl<a&c(9L~;`kxriPmnI7C~b=
zqzSDST5~0to`Kg7RDLNvSw;+bb4P36@|n>^NLDZMHpn|30W!W(z@th;U!z$R>A@Ky
z&Z#mIO?e9H)|t6<?AXj_Zu#3V?j(}FuUP{QKeYEVG+_u>xPY$xdzs^(TQH-;1R~Ek
zZlyA$?@xINWyL><bG{ExkFT~H6P2Qj4--Tp^4yDU5Z(fq6#(lA3+4jQ$Nk)3P0A35
zuN+{Pv#AgW>24<NgcQ@=(kq|;D`g=p^tFF!IKGH0;(SbyG1e|&l_N+O;Fbhr#5hg|
zh4I27ZPbECRds*Oxj;^f!U;{*AKrOC*vy<fq>y|!AK{pZ{r<zi(Gt6@_U#=A6=bK7
zMhb4E3(Qs0YFy91I|C3PIq5k)KsSq??tVpgJ{Ad`Vmo>DBOf22bMKNp9Rxt@UGcFz
z2?T9nz~>)Uu%;+p<rIpAROz+UE^YaFo0a3wlG<>$?vWi~719R-41k440XAAAfFlB4
z)b*=8TW=v=POXs?=Q3BuS{S>k{(Uxj;0;)3KRk>v|Et$aJ<~kj;PV2`Zqmp@HA~Lf
zJsLY$QDXoTO)c#7TG8n>i@2DdYfeW>I!(fj8%?nqQU6Ul;VYlTng5&4v@44<Elx1)
zfsnbSL9boeJQ%Il4IBIh?y>rmDLGIrp^-9B|2_AQRPO5P%`&GP`DWs#D*!{(af*Ze
z-~m)5E?ys*PZ(i7_e|&p7KXXpCLCpEfJei<klOQ!Gsw5V;5Z4FE7OK1bP@RXzgvSB
z|LU-ioDTIF`nK(})tY5Vo;nWER%ui`DyK;nxJd?38N5FLu&@GF<`!Z<;?-Bx=pY~`
z-pJBZAoWw|75XnGYcJ*@EU=0^6yQU>`?+HC9N$EcydcjZf4>7-sde-XpmYRD_jCOg
z!XrNH1=RZ(X^i0?N-5rdaKt$w@{(`8JZxyNR;n7`g-B&bMOjkIwe%ER3gpf`m@=fv
zalRW;67^+2VRyK{$(QyUTrXadsB7EzfCgzmXZY~Uz(y1xg>24h2uGFUk)94b8U*}s
z;~p}a^;`l-ni(6J>$%e}ikwV{aJE$qat1Qg6cJKlQ%Uy?>m(8oIzr}^YR$9*`~C+`
zYACHLBNL<VmOv+23qMMUMg%>qK}q!O|CM=A)%`i;=8#vtfbDm}?SNY~?+k4LSk7&k
zm5XH1;_j_NL4E>YVjnWc@nKIx`OrO&!7zB*ZF(w<r$n)YTDMPtV!EOCg5^$9vAyAY
zp$+#)6(Op(s<#nk+|pg#$4G@nU`|<Oz+qUeQP4fPkmPIC7C2ss$Nig)sXDaBnMgjD
zfOS-h0}0Z;`$c4Y0i748`(@+??{Mw2-M12uq2}Y|76H<z=Rt55SuZv8=T0QBFD&MP
z`A%@RC}Ix80>(AcY{JaR5xn<A+cj6I)&xSsU^ZWZ=*vIN><tP5@04$AT8w7a<h#;}
zV1UH}-ZU#qS13_+(R~WI@Z^O!VGnhQe{)jxseLs-!>m|2isppy2($%NwupK_v(x7t
zUv~`wxI}*+2{|zF5jA1b<^t3UOB&W(`i{`rZ-37l-PM+MIEfi_nc+v_j~k}-r$g@d
z8U3!%56&B$`R1WkuDqAS2Y7?Sk@I@VujX}AHYTvwMA29AegY4(SbhikKExZ(jm1BY
zVEkO2tCCv()ZM`<s;u>Fbbbs3F8Hc=V#)9+sX#8LR-1))ILC@G5&ZshEHG<hTu9mu
zoX5OIYNqiGQ6^a}p_Snh>ELZ^B5gbZzL^m0=Dpp2YDq{R0zNb~(Ki4vUjg3v>O4SF
z{%<E_-8QeXMn?HlH!4oKC)6*r#WLUAr|s5*O}jF!aX*!}<8>BHr~_9-v?&=`F-(TS
zm~$-*01-%#ZDu{6kSy9zW<D9J!0l6WPW+1k(!-sBV2AJfYxBn|J=kV8$lhMq?M(T#
z{ld5W?1T~x1wu?2F#U+nz;?GB@S$AzIuQy?8aWw3iM`!ZFbLS;=J0aW9yrFDj($Um
z^4YsGPQyEvpvb1SYlN(c7fAau`)xV9+AO!D2PW<E(QE7Ke))YExb-Rm-w@#Whv<2b
zu~>qguw`Me#QGNDAn%|LpsxSGLTo9BG7*G`$pF?J_J9#XN@ze}BWxDs(x*+aVfvM}
z$z+jsDP>32<y3uob#lYzQ`_R}KF<89#lh@zaLm*jFr_M?NM6h#-LJg39A#wei#RG{
zt)aapZov_x!4#<SEv(E!T*aCKRlWK0{orAgB)a&XM257=4t)XO5a6ZnuM5WbaWe%5
zx3#<7P!cgIJuaSYUw4G{2mj2!N4rW&!7vKF52wl~a1!f#ZsXCwoFNisZwC%+77ZJ5
z9AwEP$NWMn46-<avdDmz`@F{5OO`Eb;A=`UEZ(x80yO;AGR}Z}@?E?Ug@fWR9ya1d
zpYCr0RflqXZN<;5cMO0`g2FNJ)Ifoz)wkzGI5r_sL$BU_A}jf&JvX7n?L$mszsZBW
zJBP&@W)CkT!df??=xE3ci)x!*oWjw@F6_#}6cVJyjkf%Q3?q^<UYa~p@u%e-aU`dl
z7@DUv36DTRJF<Up!T4P}>gP^iO3YAzB3j7aH$1Lt1@kog8S?SGS^tYY2-jY>J=R_L
zv4eAa39hqK)jRX@;bP-AE~*-#-?-aNkI`i}J;`e=!xkzw$hf~T=9hCZZq|v~3ZewZ
z&@W|=1`om%O%|1rQr?t(d-8@RaV;TY2#J};;M$QyeBg6yAfKVHGSpzDXFpliFG!N{
z!khajp~ErfJD^n`X+Z`6k7{dCx(IX1LhX7|;rgl4R59C|0+K(gFqZ-WCN(c)-={n}
z2Boj~kwB5kG_0t!GxD@VCOpvwJ~>6aw1~X<PX;Ybc`YuFoZwOB-xm)Qc&EKR2581R
zVt9n4zc&p?gL+A%Nv^$XNX6H?c;6gc5rN14Iw$upC#0Gkcg~GYa3sk%<EQm-y#yPx
zt(Pj^92IknuU3_nA1m$su`}#8?Khs_+z(O-wJ_Cb&Cf12x<mKmw9gY_ufn>RF^2gN
z?D4bhU*yUOwlxpT?D*;R$62rj40fjPIRnc~YOgE;cZ>OuM3O!+I6aPop;|A*1nEo!
zSyod&I5E!u3rDec7<`D?rSJc!8SiIH;7DqCX9jrr!lX0+r)Y#2;Bu<z;Pd|R01Q9@
zVkR)P)r0-+pDT<_Cn#k>8kL_?H^HeJZmOw}{FZA{&jcA}3Z!n}b^wMeO&lieZsfEY
z|F9SDO9$7{`6j?^`<|Qo&YTpKdATx~Yq|FqE~JQ^WGwn(?s^`nPn<nHa2LII>VAUO
z>rL<V56{F-?_UC;_V$3iMrNNg#^No<_OkaMhIrCNCRECIx<VJP$1<<I_MH~i%x5&&
z1($)EEMurj*||g>Sl?Z};JNaa&YhPzDgtB~LR{F9VpD6HI2Vqn(cJ(qe_D{nN2Z`a
z)fFejaZ^i5y}E`&a?2c?)IaeMMev^?4Djn#LHrwD``9W_eZAZRj3^fuR|c-p4M8OT
zV)OUGxQ3=1USv$agbElavI~ERuojRp_RIc3$%!eqf-{EX+%Db|J6b^yG;{#X5e7c#
z?8#OMzMyi~SoR4SQjY<0g!2m_OB`t88R;HY=68k)8ssj4v8{xn+%6wrDk`04jr(Fj
zu_MA$ml0W^=6R|-2fU@q<n?N&ko3kq4PfOo0q!7J8EcP+;7nd$E26|fNd~)eLK>mx
zU9+_XEMA;Q7o5>dMJcmNW({w9ua2Qz5b6<8sHRvCm2C=%HB2U7TaiccVOW6-A|0U6
zxUA1LlR~AE5`xZy4OeTJ5}c2z=%Ru)6Glj0f``HUJZN)4sWY6rr!i9ccF)^zP_Uv^
zC~#7tqa{09?XA>1H*r6lA@@irki>RsufTm;Tc&^Sp)bcsfymxA5fnspybbx`n&X69
z{jXa{{UR9&aj9p`%}<JeqoZ=yQ<({@|IDY42&y>~-{G6$)+L);u<H5b;!uQu!a!vb
zKYE1rvu|LRtd0*1tZ(M<A%R=)^A99sqhk{sXGEE}RTiI<f$GgvQva|yWOldjyjij+
zUY7<Ox~e)HNSIgRFhaSbP0p3K%b%f|Vk$KT_7zesSdC@okYD$s&57v&z3V{jsc_g`
z1DS-uAL+f8W!pA5Tzv1csND1SGKdpBHT3x0g*Mvii~C;rS(qVoO%`m?bjL?-$bL_>
zw>^~X6V&T-4eUoljwFle=z!<H`Lsf8ptfcG!_`tjLDpy8kdNyBok}=hXqo@7s~7M>
zi)2}cN94Dd#rk90>AzD@lAk;djH9t+t+TMv?N_2Z+Ok#>tudv*aT>eK)G-0x=8;+Q
zX$CxH$V_E&B?MjuSk%<=On!pPgbuEDIHDNy31IPDWD0w3j;9j=Ku-KXLEb#wNt|+p
z?Dn`&_x9s1w?Yd&C$y#@vc=2z?J*Stn`xP*R<+F?W=MgPxb@K5>li~i<JLBZ)CZ<b
z`@NFD|Kpt*tS2$C;N|&A?Yf6S7_D2$p-8X>uS0i8%R6dapW{F-aX>Qm52MYK`7E00
zR?+Y_ebMy^zb{e);mw>IMGKY59+eZULnS6KGgxELI>It^5$oxArii3DKHs`tE~kqZ
zxM}bYe1GeR0)~~wnei{GX|A?Q>?fV=;s<9yF@}a*KQy9t?TB#zQvJA3pG@!yLm<!(
zEWl0!fS(;wIy-m-3#9!&3BA9+Yh(l|?irP>SPmZ2NGPM(^0Qb~tA?6tqRHXw$cIap
z{*=WgvZd(JUfEs}a%s!kT;GAnW7kU!01Wa=^`KPAFVv}sfOXaqh#MG~P^T*@r0+^q
zdb|+nd$_%Tm^hc{!uB@)57;%0)v|Mkkv*pfS`%qIaJz?hkSEjV_6VzgsEryp*ey(1
zbD-LGw!Uv<WUt<%;GFY{*0`2HboY}Dboa||c#nbCm-pUS#VPO+hDcrE`KEya;oW54
zvkT|w0slGZ)dsv*^lrh7cP!&=WZfSB60!<gDpNcZG@le`_{=QC$-+MGoD;J_f;dt>
zEL{?lJ(J>8QMwv$C>L6mu&|`BeUj!^|5T&Nj20${-f2MJb1;b*n_z-r)$=0mxmZ{}
zm&&bD$C?lLt#Q~7yiE`^SIMh%{VsnFHz7Ji-*c_{t5#j-voL;-gZ+DmUyFJ_bl$&-
zI3T?x43WhzZRont#<{y3nHFU7F~Z8Ne0WHHmgIrff;Bhm`LaT?X5!X5nelT55Nn$k
zv+=pJzhHS#VL`w3f;|VX7+XsKBt;J2=gxzJeKGu7PiUk&muARd2bZgMKEP(LTFv}t
z2p_x5tmg#%;cSSQ5WoKo_G$aZVoWW<174Nqay0V>*n=w6TJ6gd%&2d-NvnQNS%~*o
z;O|HPhBjhL+5x=rOw4wXNo0p3DgI$DzviCa%eUO)9~#h%Oadf7;qCfM1wH`@!bi+e
z-RZ`CsOIH5vMttUc?d9=J0_Z0+pVTd;Mj_CAbCt9%aQX`eMY8@_-U0@+}TVd$!mM^
zeBe|+FHYtH33&R6xL6?n*$3^<>_+(i>vg?1OT+rF(-H{lxO4*VA_};uJb33T%l47;
zA^xQP<w;m5>V?F=X+utw<5XT9UR_O{6BsO0?N8Q!4r%0OHa(h-3NoJ>%|<18Jioyj
zn~>M3Jsp}6A_&1oYFCnD`TA>m6(x)D7GeOh?c8mPrwz@qz6U{Aq=rryos&a5^vaTT
zY0?iV>r8-FS=3AHsl_|IGc@JU@;ZHYf;%+eJ9N(_=|lb4Lh-xb67#va61m(7Hl6ej
zpN^YQe9TL6pF>u_VX3ncd+`|EyrL*Lfad(u^D)BsSWB}Fnb-%q14gG_P|CNM6B#k*
zw>6<HT|aU`;|7VCSL&UEU|^7KLBN0eP`0vW^?Az!yeO}iv+aS#v{`JL(<84b`tHDe
z;k08m7cf-dWY*F^<Y@ytShl1U9V-84YRe6dHTG$<G%+v%cTv2gEgp9<0l$QwkNi-r
zB@4!X7iRm*R~^Wv`Z>2K>jPTI$IG0z=nS_y=l^8Kw)LcGIbey5^XkXsdb`xH=e&DP
z?!)_hXPN!naf+7Gi5b3j9UF{|K@Ge|KpHuX$sc&$1x(SpR1+SWbU|}BH1CPi^MLfe
z{VZgpdMY$XMYAC69;9CGTIeRz!Nocg%{r}zlxL+iUEPPW=Ht#Y(z&&{IoG3v*@QOj
zivyx;J5|BEdF?sXi<gd#gO`{0D(xdPX~(#OHM1N20YVjOJVfs&tLy|GCiVeFcmCyA
zne`gD<8`b7W81%k^U~N1D*QS=Y)F`WpUNln6ic+zO47KmN+t{{ylPJpC4Tt{7Z^sC
zcA=&FM2eRkmuWF&+zp6Yl!*%97?pHK6K7i4O)KLT<F%%3E~Y!OKmA2!ghf6=AZ<6M
z6!v+;_;_kneqAo+VOn*c)M;8i7y(DH<NET3`7N|Qc9tEn5?{aSk(aE@=PM%`mJUh6
zC{Zmn%F>9a*-C*`q*Rh*@3~UhbY+Ty%l7_VsoIb<!^RPQ5z{w|*zw@f=tCKP^D5Zp
zWg|}gI3=Orld4IJn7P=ZW)^U0k7t)46GzQjzTgA3n-+NV4#?EOamfcURH?w52FFTp
zf$JtAlGe)VwYZ{RP7+E>6s0=T)-E-Yj&VaS-PxBtZsMhGf%U#(aVyQULfo(@G0d+*
z&hLw^%+Q_lg_(%*4JWE?tx@pME^hWm-P7&0G(&_q2gQiYeJkJ<n=p0Xa3yQS)itD8
z3ge<?;N;I<C?g@-Vf&=>r<^{Mkd&u)7&_9#cbC~hxuyrEfpX%)zUzACwd$1m9W+?i
zoXPX?rSC0t18*7l0cO12L9IFBSIxOidYP??nU)@WG@Y(r*o!nQ7e9O}wzUtb_&?G*
z7%wv=EktTwlpANKrZ!__=vhqvKF&TLbnE@jApho5Y@S9hA?0i%#O2&{bOZC-k;?O}
zWgg6C<u~2xBgSebvc#Oi-F$R}#LWJrzf;oR>)2aXD%8`B=7g91ty-MR5H3(=!g)B!
z`XGB%V*tg~<DeVMiI%k#gF2P^Cjr_O0&46<d5_0)en;Dqg-P9&a?{@nVjArvg_-$u
z`DN<%&>TN7s&>#YqQ+%-7U=qd1#iWJdAxYkhzp$0)24^Z3Y=|d#EKMLed+4P?#F{e
zg6|Stv=NohJCi@|$L&A)IVMfM9_8-byr2^ia(QBu>L6CfO1V^1%^J&baBCaUxO)~x
z--y2=XnU2PqpRY;zs#p{Sh970jHi0s`RJO_LH&;xAVFLBO-2?~WmE(*sGZD*tKhrC
zV_6O@U4wA*u5O?(XOL{G(VB5iol2L0x)A{o>9T{9x)q_fyWB(;Rgc?(&p386bo>Qc
zafn)AYqd+tvU7Xcl~fytE4@idg70tVv!y9UdM(z683G?Mf0zhWMu_`&yer@ms0yBf
z;Y3d^R#D}D+ypnLFhh=mX(zzm7ppsc2nftW=w*iv%6+}=dG}pI6?|Rk*;g}0C>jq;
z9A<Q?9LGd_iVS^4x^q|LW>#7%n@V9O$jP)tMq|vPaZLe2?$VY~%)?q$36-LH*3jDG
zGSg(kbTZ;pw)vc77qtjQH<xNRk*jLwaJ2u8{3}ImoH`7G!B=pk!f=<o-#6iUo3E`i
zwu7_@^iB38CWHIEp({kpW96(}J>}tgV4K4q&&{vP_8%lwbc*Ml*riFLa@isd9ZCwI
z#_#?rw5lZyt`1EU%ABofOm(Fzpw_ShM027@UsGX`UTPLz8%*M+NbVJJZ;_$rMPfqF
zJD-H;dg$B0;~j*mDK(u;r-9fJ(cqxbgISDRM%Xkb2_bcqOI5*1NGBj3G4$%cHW38W
z?ysmj84@~u+`@KtH0@S2Tqk3!z;k}sQc%w2POV=KE=TF9k0jQ{Am%T#7PB<2c2!ht
zgu+V9)K(zG`tIG3f$rh*Bl3@j>i#xqf~%}qeb4>sQnw_0B@UcwZ2S3RM~-^b#}hY5
z5`X6?*e1zH*ebt54P}$(_P*Q6(4V+52undC(<O0?cqhN7_ZL(-CG^zCiHt&LOeU90
zGS_2^)YY4%ilbAr$+`5~_*l~_#gF!VipP^#)+)}eTdQ1@U2I)?Tg-HM)ETc;yHZb}
zl~EEG%1_V&;a*2k{j*5@KDyb3{A$m3<n{3=*%G&_n$LQQtt0lB+}B=6CSp4V9tRXj
zUKhH%KB?7=w&aUbHz6~7CMXTu_j7@`VyF;oj*xX6zaH9j1yMxaO1Ux(Y9<aA;M#`L
ztDB63)=Lv<{Yr#_O7*5r6l_Mu^l}T?MznV@mo4s~pWvRRi5LYxImQjSm&WlHk1mKE
z)0El{D7=c(*+(zz*wp%bJk(*qxXPIBbAdZ2Ury?KAej?svzcu0ebg3Y)r*Zi??)1Q
z6V~14@Gx2`b4p*N6{~Ac{ZnSzSL9o0MQU?X5L{3I6(e1lo_b6z(F-WnO$ow_+|%HL
zr`qh_xo6j-FBz1R1M+fiaf~(crkHlV&$%vP^;6eklbB)rkQyfgR(1aFg%>))p{lvw
zBB)4(HE1$+{8M6An@#$hq~4L_y4yxjOH)!|;m>pzl+h}_>8d2pUfC2=ldo+og_#Mv
z7|IJ(Vn&5mCk*Y)x0GM4FY?_ArC;2)qUNB(c4ZTcO8c*PHVmAMux6NySI(e~G!t4}
zU(8^31T}h*cc<9CQMcuzQyJrwCY6n9Kpw(dBDt-lDY&A7l1;evw~VewaI?5wU+_<_
z(WmCu86?-SA4QiY6^lEiS+u#8H#`!AdbgzfuLHw~8VZr3x%KBh=bBUL#BVT#20Z_r
zl=?<qw0d0Y3$nQC#^VrpO!hZ=sPgLvt+#IySr2^UpO1Yuex*~(wqMtiy)rmfTDdx5
z)3>E>GBv_lSn#=)RKT9$q<Xprk#h{Nf85jByRhH73mA^?nEla4xNli{vfW&AgUk`i
z-RH4C(MDg;`b_mz=a9WEEnl&>j@Stra-}rNJ~_%&jL7aLmb1GG!>X?O1_@P7-F+dk
z1&1?ncTxbBxt~ihDNy3K*8X*nJ8r5S3-5Ht-*HvoJ~$XUxotW7a^xrfNzWd4@S_;8
znV?FpjvCO_ZIpnW;dCH^9W8rru*Do@chn`EVRF9R)%;UDv+5WvXVZ0J=v24oj5A`r
zF>ta4K839kFCG{@yp5e4swazsE)jTk)#x68g21Bx7jakDZ~~jgxjzNBzrvGAQ}jHV
z&G8sxvWshYc#D}?qERq^;Gl67lVK;QuQ-`~gS)Zq?_^<Bqy6hCR#1`B>yS4c!`Wo-
z*$5}DaRdhbS4%u^enm3X5{CzeABxNln)w73{(}UwP~`ParkbHV9QjmvCHx5$4de3K
zv7}iV-0H}i5wvBVtEV2nGk~KCr09b-Qm;p{%Xhb(uG?!okgvgs>vFl*tC#AOHI31D
z`|3}xE8*M~HEwN0`&Lm`BigUqzngF=qZ?PaR3%kSI92r=3S39Luou3W`+f7<)P*wc
zbbYXFLwUN#JT)uKtYe}aRbMBAv?|ODvc*6f<#9dQZ#1IGmP!#1dv3S8ty)F%v5W0Z
z8#1V9KYH`*qtI@^%VmxqQ>9B{x-NXKLmw<fu$d{Pfb7gO&8=6dE!99N^SR-N7Mw>#
z?+#*s1@g2~nU0^`Z0He08%1rcwXK;D%)yAzMRM8sJV&f}FE%gxfk`wX%Evg<6~kpB
zB0dT#SCk}p?_PaW?Nskgi7xiBP`TSap0_lMbB_}JSfmduQw-`?>@J!^#B{q451XuW
zP&X)lS*zig1DQlt_jIxJ{##A>ftauBd6Uw>K5H)Ds4Zb1(%Ev)hP$oa;yo+}CYT9T
zazP+ofmLJVfI?x<OWj;n`CV3Z6PCn{;Yvzs^E`ME9mQm_7fWj9JUp$rQwMLxsmWok
z&RwFXM};GB<ogOT8t3O5-CWvw;!7dCUmzCDLHs7`dobgn&4lBZ2B=ME73E`Gx#>j$
zUU32!_Gk+@m~hY4mPbVf+J~a8T`takD6X#j*;0r!iJD&dy+uK{<MIlr@W<>|C4W51
z`fa*I2VA7bQh;bC(EYR~(9MG@`}<@fY_H)0-TAO$?7OK<-82v^TNG2YCMqG)Q~Zs2
zErWKjUOK2JY;l=2ew{{5B4J=0_D3rXO!eRk;U8hD)Tag|y+a7EKk<v|SC~C}^|OdN
zQnu-a({_h=1X-B-`@~kDOoE4RH2QNlS&mea>qL9yZ-Q{zuo>q!D3qfUN+~UxqVD`4
znMKW!OP>-Dt#RgKXQ?t6QM<}nfX_?OC;La^B#B6&nN4L5^J(JDf_J=0ac%`1YuelC
zR=Z#B2j*ynndYIgXi%Jj0g7Xup4M_{lX8jJ$Y1rAWL{9#tY8yi?}eN5zn^Z^!d(4B
zfH`~%%MXE|X2OTZTKfBSvV~<WGmn~Z%mdm5aXp=4iz@KZ2<x@AX~yLyQ%#K&P4%{;
zHX4E=^)}C7%f0K{67ooh%(k}>-*1fthOfs^0qHvF5&9w+)g3<u`i!}NzXt0HQTT<5
z{~@!V4WS-%b<{~+;g%@TK;dX*uYY=WKDeqy>dC-f0N`0HKL&s@>L(IZe`ce%`MbwC
z)sDT!HvwoaOUvr;{Yhg$su$Zs+!l+Dl!t4g1p`E9N|JDL;s2%TvZ2snHXc}<2m}{Y
zM!lWTr%x&*wJGR67Xw<PX~tDPWiaJEioW#W?V2KXY2Amo>6Z&$iKYi|#-k_i#7iCg
zdW#13yxj_3QNs7SnW4Yt1c$L}O@_ZCNcPWPk?K00ag?j6fh=<DH&XaJW<6O|OjTC7
zx^1=|8#uUGSx0p9CL2-0t>J?N{h0Ft0%fo8qj1(z#fTleDSPX^m#)i&cY#!qr|W;a
zSq8WqZ#Dlub&B(xwN(r@F|MgVrT(n@<uYTeIz@=QeyFR#0f1f+!7-PnG1)2~iB=^A
z=2U|3$&0g{+@l);0$Pf$o@#5>k+S2Cs%48=Uw4;qP+5G=Gb63*sHO<@w6{mVWp=ES
z?_Zei#nB}(Z)wb1DM@zgqoy|nUbG|*Dk!IEd3~g<vebR_7_VEM0;*MAa_X?|EhV-^
z#|`gG)V5J$40OY^%QW_-!HqJX1FR;Na*U$#oC4K@1by1gTP6fyE(;bVT?WmH<87a{
zFrvB?o}YThpeVi4fOKFpQN-&;Kibp<CZzh?Ts>lOZsE$rW=B+g%4`gjm@@3I4H}Ay
zGF7XoWV|TqnnxE$mzaw;HI?`#xKjIJ@lv(n>@(_I?&+a&#j++K5U?Bgb`WUK%yV8I
zPFwXhQ6aWJ(0I3huegd3zia)AHOI!Q+c1d^8}YW4_A+$>ogs@hgHOc%irEfx@{-#$
ziCHN~gliC{Qd-uvEI(tgjB5QX@;Eh^3ITvrZm40AQn&mIn!|KA*YTKC38U?TRZRu?
z;e%F1!ey9O3)-NA0j?FtrP(vu5}vAHQqoa_CF^qU8zaYZV)-SiyUt*NvJ?XGMgWyT
zs;R0h7h~g|e{Z1x66c^?He|{lp~j&a3+MUxM={Ewbn5e#!3K!jg(!i16=GXhz4id@
zs+yx!&v%P!D%2tt^jg`j>E?nwDrYwM##ZOcgDqbFxn|?7k2Ygx_q+_%yI_mPuf6vk
zz8ynO`5D~mFbrQAf*hRnep`0+aN#DY5eAX{EFtLng6ID!DBo(~RnWuGB`i8UcB1{I
z>L}L3qk>#uFe6+l#<$15kuZ)fO~93908(4~yN^Idy<WC81vd~*s$byZeHL_USX_x4
z1O%q}`PZ|BF%ePff17aNp>Id+Hn$W_^$boB%Cd4<6v`GKU1EqcVLhJ?wNID0#tb@q
z2j@HoC%%jwB!_xce1<U+Vju9rl`l2o$Y!1)odcqH(67<SI8vwU6ee*>;LfD~^*mYj
zU1{<rzw#1ztBT5+0?H+%6^1(2@UL+uUa?eb9E+n9`)Jksa?l@qsqNZUp9W!785>Mn
zAXA`dScz#K;Y;nxXa#O?iel*mP9{SFbVH3?gXgpw?@k2pWpMs{obF<|74^J@T2hqB
zi%vhuW2P=EZ6_<Oyht^IiS1cEZ37oy^7fN*AXpx#E7LZ)G}Q4-yz$9tw*7|VFgE5F
z>&B~TCj}ft8>@8b)GBpLP8_yt-t3LnjA7Gq^ZpNb5)u-RST0l$^u9cbzJ}fEj=g+5
zPv?ZV`fo4Ss>nr~8dmlSSxoio+j3K*vS7N**ZCAhFzym;mCiS)RpYzu<Ul>aGNw9a
zlKx^fR+aXALpOWE?Ze@E;?D~cRM+aWiqk*}Ef_JHp&`DJLsHQczv1CGGqpU699t33
zl)?)B;8EC(%wZRlnuznc=N&c&CGxvODS!CnM0;Two}j(xSL?1G=o*r%pNTo^ijdNW
zJ~|C7q|Lz))nNw#opR22v^9`$fhOWLpM7fBCa?gP<1nh<pu$;kJp0>1av(u|zoI_?
zsJ`@<of~VFeY6Xum8C2kH6wG>8sh~NylVuP;MB^kXJ&4LXue-dCE8zg*+8t7lDzES
z%qay8A=drjh?>y6+ShzcpVZa{MI?mmBW|KfO#1{9Qj~Rm;uouHIfd|msm3e`mm<!o
z+22x}R4>V4(n_F9BjR0BJh24PNGA*dg+8I-9aT6;L;t>xdNcIAN`mU^uR+XtHK?yx
zi#VwH6MQO7NcS9g7hC=5in7+8y*6FqcBSouea4lT_gs2kQqPt>*$I%vEFg#G*ri24
zXnz;n9x~!6uGb#!t~UQj#)}^wZHK!>_)015%7zc_Lp1;ZKZ5yYFA7B8d?#SkW!c-A
zFicX*pn#(j=(}e{JMF7gId;~XIF|-GyZSPiueEhcRc{Zxw_4YP3UFEbQnfMv*co(v
zRk-XAJ`h(j&Bym@@PGB(oPzLC(ProO8~Vy_s&Xn$f5Ve(<A8LO<Ttb@MCPHysEO~U
zF(3KR9;#aU(J5yNDraF4@(U0mSc;s)z}03&lT@3v{`=!6W>nFdWz(|yKenOz?j5+`
z)bk(7mEnV?pWG83N1S$Y@V<IgHJ0~Om;Wm|MFWk_Mg8v1LS30B9ULpRy%4ceX?Zjj
zFy7mVAz~-p4Tj9yt#fKPKs^5vYS106)?24lNk`lmClU*;yXj1P$8AYt(sNXXLIur9
z_E#&oSqI*xM}<+&Llp;A29X3VsvRze;hh(2G-cRYjn~@S|CqdpK~Ew)fcmb-a6N^e
zDdwgsp6)cz6r}aV;i<yvQnPU$ZCkN&A1BvoAk;}Iz-4%I$YPFd<ujVLno6Qg=IIt9
z9<Dj~qvp4tyDBn5#YtMmVai?}Tg9~&-sTvd-?}}b&uh!lnp<8D0Hpaky{m2eIVLLq
z2U&zbfd!sSf2z4%T^LLTt3w}DNx1|Xl!=7IL--{uWl4s;s>z_BGrx%C(@7>0u@0lB
z$EwZhqkT>I;PDP)PpBItLeW|4d{sgtQx!1_<E!iZq&9YXGehMl>+|bfZ#DTa4T+u3
zCSob}Jq)5xhkb@S>W|9DcP|}cy_ulo!QyX7{Z_cm-fqa47hN5vBTGJ^@sUKv_0$aW
z%LtASmll5teFB>Q>UJcb8|CAX?OT%*NFxY31sV<fcA6hF|1c(e^Lfi5;eI>@_OzE!
zA~#MNK;hxo3Y8PQ#^h)_l#gTLX0@%LEFrK?z`G_cK^W6RiYT|<R((!&Eh{aG?4+Ze
zci7a>`{Jw>jXoLvjN>PqUBbVx!N%6+kh#D3dOk~kJ~7Gk!hg^n$j$gByJ3Qi3?=gA
zrn)ke^{ZM+l2=s@#(KWx&273kt=NO79gy23?W!ni!lhuxvYGCII()@bER8k)9quE>
zfH}%KjPY_u)K>Kor?Ju4z@B%*VI|z#%rW`-tACXe<Hq6^hrq!y!*6RXhqFV3vfB`=
zjY5aZYYu0Ils?(2u_5u;;f5`~MS*$F=okt`QY1;M+=B#dM(;$ZRK3<VheUcchqb$_
z)(w2M$MctG4{!Ig7PLjdDHogEO$U(4b+J`s_thT2^c|45u1-m~<a}o|g@``)uGpru
zTz3Ag71`r$PkW~Qj;He1HOa!4XEKQ%jVA^NRMPf0wskHv#M98$z4;G-x)oY`ip?gP
zd|!=BG-;_L<Rlu9ZTma#qNs*Tv<{LX7cfQ8;;35NKy9SJ6+B0!U@WgUd4Q$;^VzXn
zSDIw;qIFI{Afv{u$_%goP!2=BbPuyvaRqeqaW}4_flM{5x0ko-f@<k*ALT1u=V#(=
zZjxtF?-u;+R_iAEDICwTr<>UPNfywnk#3jS7x%O+%xaRBTv8Gg?YuUWYzC?0*?8Gf
z_KfO=Y`V6&_%1b+f2H0wwL1T#fmo`F=sfJ0p~Q^~DN`PIE#F?&+6zyO^4TxMm#mv}
zwv<MHdT#fA9E(MJuk0KWA>R4RwA6<?MAG$TKe_(3KX=PfKh{?(9(EVO(a>{YeOnSn
zVO;wKRjskzOik@-t+D)Qhp6C+s5<nHhH~ZR?NIWM5=@vSCL-OVO5}GMR-Kd*7}ddL
zaYXes?}|NP*f25&9VHJDJ0@z~a=HvZ-7NhZigc=By0WnRBf5;(eZ-$HVu$Z&U0>3^
z@Jf;q^#P6#Skc2kw5YPmD5XiYF|1L6nLO<?f{Uo|!3@(ozdk_$JAqNRcF$*@W#`ID
zFh5<>!J@7`*eVElCWf~kQ7G*zyz>}O`}t!qjep}B?^k8r-`)~%C;SSoGH`TAxm|hO
zBSV6~^?Yxzj3f6(3-)V?Q-KQ^JxoD~$tVBAv{OkD#MRrnlrdCWI9kZsGE})gJMlSs
z&FV%YR;DTvtN~{z9i^Ij)MsBoP6zUFY~s@e3ZhC8JC?khQ4wO=5rlUGqx-=cWBO|S
z;p%fy`Q?xG@C)K*PT~vs!rJSY`~|%{nki%pmWya+e;@Q<e&N$Kh#rHAX+zTpi~=p7
ze&R{J)jsPq<@b1Ke!(&oS2XJgt-0qezBBJZgUV5Hc`v}(2DWGbMPXsCrwx69^X>Y2
zUi_;T+OUa)F50ll+J*`hVu&#!MLG!_(3z-=hnGFYVPk(yi8sMlw9^09q4m@od9?@I
z8_%~B7#=Q#3A8b&ARIsg_y`Ahe<8Sz?zI-yPJuthq>EnJ_f~HhD2GU>_YQ6qH_?YD
zF<S^(Jl2UtFK;pV$B|KEAVdOxEav<b1Y^@=kxIv`!};%EjA5WGX7q$ImQ0UNlP>$$
z?8O3VIFprbzL%vKx+2L-K5q}=@zi!}-2LT)-_a2stIJeeXw+#+M*ATcbbIGC!*(2<
zomo^r)2`xF<AQKf16kb4nTu?erGo<WwSAReLKqVq#KR=o^HgqlnqWM+H1`dDZ*qj2
zJw6Kxo+7F1U*PgE_}sGQh#j37j?Vju*7cB{R+#GT%ZA5Se3eGWB>~=RDWmqAiK8FO
z4tDM(bJ8y$yZP6vNdLH>Wqo$VMf6$C-U~eYWCv$+v!FK#WP%bLjkZ!~r7OMNF1+B{
zXJ23H#lA^^=6JH3<mJxABi{OG-!vL~Yi)*wdS^nwlpFcd%#Y)hn6L@2xb3OA*GNMw
zMk^kvPM!Rqk<AyZ&JjeH|MZ%cJQCDG4M(w^|KP&4mtIN3ViwiGyR2w;TNeh23d+R4
zfZN%c&hhQ3KZ7C4W^phn>sjxbPoxJ;q`AZDB08SM<vHb3+(iBqQR!fR7%^(jU}=#X
z{X)we*+6-1qqs*I$Zn3*Sz6PCHI4mPY=TRAkqJ}wWHYG~TH{vpOTi6#eTU=3$y9)U
z5Xi-E5mL7>hR8n|3CxT;QWs)#w*~FHktMpEc)qOH0Fu{EDF%AC=yz#r&9LJwM?pRw
zMyB0z+ao7<rs*&HqhB&x=WvuOG$Qf?sw!e9UDvTa{s~rxe3K;8Mwu4%Qgus|TBj?7
zJE;33BK|uE%CFVUl8-T!b5A|6ZueJfsS{V@ARA*ct&7{|=Tahn@2V8j=S@lBhXLQ!
zg^e~9=Ps(-+9(hhOXmTL=uhh{zwz3q-BV~J5u*ls=QU&9(XH(*nIHCOcvGwVipXCW
z@uws;GR?ajqX9*%+S*x}t%~WJ56{7%0|H`mHVdx+M?7s~q2shUSt4xbGhaFy^?DMP
zUUE`qMP6XEF)n!F@p+=kciEGGHz`&(_EUp@RA3db`pu)l(yplBAE4rJ$KY#OZ5iyW
zXv8j{B{1=DctSBq?dym>?VxvDJhV}L$y)L-tkcl2POfyDC=)u5%ez+B6b`=`*X53#
zP5Ve<nWtHpbDDZ)8z-4TP;|J8L}A5S|8@QC5=c_|Dw;M&R4bjfaGD9cj%G`Xqp@kM
zxVkzF$9uJ&tJ2G@b6|CDs&rA(lFEQ2dL0&NSsnZL05WBstPGsU)dWR@K%egAX-+9k
zxW2FBp(+MZ4*Y%Q3h5>G7uX{p&47EHR}P1rh3tA2FD0f%2|QaRfZk@PCgqwYnA_Fa
zJ%6X}0VQDFCieBaK6OUgesDUxpZzf>`snWP;-9v`mK3r3OV4lEncW?9N?O-AsYH0`
z7A#(6)WHw?MMETy`12}Jy)4-w`8TOoy3E9OWyXnib&H=8n0dxEysuxv9}f_feMC@i
zaJAxcN0#gImth9?S-ju+(-+mCM>$LI_<k5giE@j$xh45{Wf}QpIMD~I(r@urQVQ5{
z%TMg`@+F>_9ClfEFp`j1lYG~>xzxzn(6j65upT!y)v$CtTT-g*OPkK~JXuPRTrL$B
z?`o9RXdhhA(a_dm@LEqAS{~1aEL+y_B45S`XXi>)`BMG8o&=%zGsePt*=)`XZHvyQ
zPO-PWe|?z$cKkd`qWjre48<r_LMgWI#DK1ZVEp)wWE@NHZPrm4k94qh<@Ps3d~3(T
z+8Gy)x<Z5$=beGZP9t-OKY+mU@(8!lntuLbPwjJnlXDmP)G5RAvAQ-G*7Zr)<4vh=
zN2s1-?BL<g;%2$<q<oSMi7(f1x3}OxD#&~&{cHo#nqlDHUuf*JGl%#og8xQmYZa5T
zo=WDVHV4t;F^1#W0e$?eth1rq6c%?Dzs!>g@ZOip!)12PR@;Hb7?8KJaVNNJv!>R5
z%`Px!(j5_%_P)om&*WG21sC<2GlUlH6<A|0*uJ~F5h0c<<?D<njD1i14ob`>E=<eJ
zSCLj?b6Qku-P>8<AT}%a{SO7h1ZpO_*s4VW5Y42!@a<)Dz=jpid98m}{|+km1;d`>
zQ-z7<;V)Ip6dEl!-DozQz^^j9)$Bs&59%$-94ACJiqn=_^2=j;ZXJsq$ge410%r<=
zMJ^h{u<=Y_!Ce@|h$2M8W^`Hm?^T=|%~*A<ql}6I_Frw(?7HB0lJn{778*8TevVK8
z$*5@2gROK3qrDl~b(Q+|n?}|kO(X>H`^93kEBAe;yUP{~`H9((I9<UQiopg=S5Apv
z>}6BwhbL`utm)#f$EMJ2)tuf`(EwiLH7Em!R~p@TdiSSL66#~A$mdolpOuiy?su@_
z_fh|YU6T~ux4VOGknOFrzRtnuAcufwsovuAVD0h8U^I@tpA^p-8Z>yR(GAF#FkQ)t
zqFaZpw8OJDFS6G|)8OOR&;=525)sh3^tjrLBw9ASTdXbC(XnNelPrXtuZ=&noyf@M
zX$7^-4Am8<8I|Z5d{n@3zMriYjxZXKkZer~IihBog#WZuu)5{}@u9FS81^(SB&WG?
zX*jQG$XOxo%wZ_uf1Qt`!WK!0%h;W%gw?~#zFM^+Az_T5t?=}4`6HBqK92#NSUgea
zVNj0C+pPo$^CpNe97hvsN7Yb$*d7$siEiTWbV}Je*&NhJ>OBt7D5==zv$7MJ(QSd>
z#%T+qiET$6zf+A$*A?eQiqtB#M|3l5Y~ISU23a`zs}lq;zf(vZgfxnUMQ!mTjmFYu
zRg&eDxb48bWFT7KJi1iw`JO54k9e7CitD&cTWWI0BF63*urlOkvf^fstH+54YJ4f=
zsmL42d6_ZZqf^;@)!~^6KnRY1c8)%a`gMA|EerDTta^Ns&okVJ!G_)r>NB6qpi6d#
zp5VujB1$($j%Ty<+~PlZpnP`qd$grr%>ExQz%#X*@8f*fi&)f9S0~%~@%-cOcuDn>
z-Vl)q7dZOL?rtHuy<e<jjJ+A!UF?a*_x-%SM$RHP;~hN@#S%V7a|1L(7*^kJLaPx=
zhJ4vZF4{WJ-NtKM+v9ct9=J`}I|pA?Y6Gi>i+CjQD0IbiaPMIhI)_CwYb2RX3e%@d
z+g#cH&T~PGGKes8M?*C{Q<L@ANK`DDngsXP>G{w_H+;Lsn*|SbKqiI$M|ezwJ6AQb
zg5}!(r?V0;v<*#Xh3{0`UdHtF6+z2v5Pvoe|5`rn$!*B$FXG#>>JjMBF|}F?^YfUx
zg1U-Ju|iiPl!{QQ4!&eOG;F4*A7^0eG>dW)b3rXECylmZjjk<-0jTFqGgRgb`llrv
zC1h|G89(xeh7uxt@R3nvXNrKst5N}-H}19yO{=Z0Kut0&7&vY$!ONqK>Dk<$(K5#*
z+{E}3fm`J0y1s@XMXc0lOIdx=MVPMTG@c1im_c-(EHz$1AH1RzXyVBd&*$p{6&h>$
zjP{#aD0`8d@4dW}U9OQ*!aj@}f~g0sAtKT^i`Z(5o2jj=RX4RUb30-jj%RZwg|^C&
z^`A!qTg{-$jAyvm#xp7F!Kjuya#H_76d|_3t+>0fO>ZxLN-Rfv@9*x>Oj>L08@kTB
zg^H)<Gl!R;ZTDsjY_%=zBzLk`f!uFqr1)l(V*H&bU#0dj8})oIJM-jos#k@8%$jXJ
z9VI<M)V7ooX9(9k)pqDKe!_q$h#tQ~{wE0ualO|>17POKtyU2171u(R#*!rw<{qGI
z_Dsx5-qnh<j+WpG_?zX;#4ra{cH?~BII#E2vb}a}+d;F}fzaMQxfbR!RwF7k1<snh
z(J#NC%rwZ!+0fRH)fD`@jyAawdH27hhKX1*wo9OkYaEF$pG99|YAk$pzK@948xVWJ
zKm<Ikl1GODRYswmr}2l=noseDrO89Qn5Tj~lBa5H!r0b$`BPm9l8d!^fwt|RvXsDk
zy2{UiEK;>bc|#O+E&QynFRtm`@0TckADOId?CBCqUGe(oic1-?3-BhU`T;u7?~@Bf
zo<{j@IRa@V2qgMo>!b^<)f<CAaeCaY<)m)E&*|dVwqvL`J4L0mV34w3ilqOl;TimQ
z*zulDV*QjXYT3n5+WqM*2BrJ5U6vDn#+-miIFYQbIRFdgCm%OJXV=aQaj)$s*2=*}
zL-9A#h7irxUc;D8Z$=Ayr7I0=>vuZCUMzLi9&y`Ao}Mg5^g8}8uHG^x&Nk{6#*4eQ
zNO5;}DDGB#fa31%?pB}-?!{qnclW`HyHi|>+nMKm&&ij3$^5z}nM`u;Ys*?|uZt<W
zK3o@sY+qkU$=J=kW5Jl|QJN#xlPpv#+V#qYfq?;0D`6xi$dNART?8EO)YQh=o<}E9
zVVs}Pw92n;)DVEA0hR4D6)At7u%|x^XT1EQ2kZ<9^)vJCwRHmxC_{^t(DJ)YkJVk8
zCClXIgGod}38=p~Cq0%h)AM`FFi8cXjI35Ma|k_P<Hy?SYQxzO;5v`ttO*RcR&IFt
z4CrYX+dnju4`Xu@3wz^B0g2)sYS}k6v`O_i7+OKG730h~HhA;9gMZbeM@r?)E1o#6
zb1PJrw!RcGVw=p1vBpqf#XpV_$Wvj<Sy;?3H!CrHE(Rd|WPVz1BX>jFgig!~PML=}
zT;B;$NL!m}{IOY$u;7rd9dr7vuQuvY`*J8iR{OEP=Ir31#~ZOlu)k8&J4y8MnB_+B
z&ClB`sZ0mt<irIl?@IgF7uE;)fNT4EJ|kaw9@*T6r>q<vsSTB4z1<m6Tt27?h$>yL
zb3uuU53-8ah3a(Z!#I`DU7?5v(L{!kXyA0}f_{4MTE2p7E{r?O7E=9LXei6wi5zbc
z>%$n3{r0Nqv%6ZzVQOA*BP`G^j&05dJIY&wSDb;?Os%V09a-lY^GNcC^<-zLniBsF
zosqIH_van6FWWnH7LU7IZ*Ql)HL$Z<rivp{cvAQG+>Qko&Ig120plf7v#p()V?_G<
za{zD4O)Re>Cf7|H+<qVGwCb4nuZYAPHU#9}rL(E^QgzYH<9aWSPm9!fc_#|kz$_IL
zWn6zgEv4->(y2d4)*CV5&1FLW4%~jZ6f;l>8L6+7{b^=I{^<-a4+?6uO1qrvWQokl
zr{~Z|Sp5X>1^7~_^)}{|Z14|=i%LKc_=;~lMrKUxq2_Cn1DfLBlO@gMASfAT*@TAv
zayQl7))&*IE9v_SQVYnN?J~fEgT5KUK4PKf<8dLKZ#=$?FQ`B^sACJr8$KR#w$Pw2
zDX~n?%qUiEj)HgpK7DZPOq9+;{24E!#;cndeTRB=bVwfVOH#g0!2<)aEmXoOMa;Lm
zjyie1J=s^Op%E!<lNz&~6gVC!?tp(eM{;r0M0sOZ31y?`UC%WBR3@AmqpEC*ld=>2
z&``<dks7)wQW4xV@;rGfx_N;>Px$WN9w|whJe>g{*OLCU4h<4H(wN(M7E))jP}Hk@
z51jUC<x9$;_P3+C4*NVXGf}LQ^hR$1?LY}*Gnh4a?dmC|`jP?0G3=~YhBuK*LNhI7
zK4r@ss~T$1>Gm>>MX~JA=%PRZGK@*Aa_v$q1U$?$;*VXRaxg~v$XY`BGq87amC#R0
zn%0+XYfpBybUrN1p1dDit+uPw&g%^zf#?Qt<HyY*zJN9sIjW13&dup%$N4A@^K)Qt
za2AJt>H&PE%KaoqS<|L=j~%-a>MkOSZ|FH}MXYN`{tog!aZ)xVw%%^7M)+!2dE=z*
zUD*ydvySi}r9i{e;no)^AYt4C8{wsO>$JOpU-!gm*fL(Zp5n8w?Ljf*!0tZc$|z@>
zUta8-wCaSdIj)V_){uSbrAy-vc!n8KG5Rs0n%%NhAD+MEn&8DWZPKFGPy&yqUAVi*
z(rd3Ri46jK1FCe6D@Wx%(#+#cLH*=ke$6V_F_=ZmUcD=c=L!l>B&tl-7SZ-57>cen
zo!I8B5bs16A&L0f##yh7*hcij7qyA0T2l~rW-{TZOOgua?m#eLyG=|By`QZ|Q@qQ?
z0_i(Yl+4-o)mI8m?K;AnI&FvG{?~PY$yY5wkvJP@7ON&#@S#g?flEQTzP<A;4E1NR
z=%=I*f&Og9hfB$HHL6XEEIX^6Fo-uGmutgVeL~eU2y#yUe*h*6&VK_|t0II^2y|5j
z*q)_{$n-TnWtnO~#9M~<Qk=QUzgBS6qE5AW3YNQy^B@8NLHs31ds24H#8}4cpjs<c
zx90IAWJb4Mo`qsw6g#1G^kg>8Wb~QT{w4cwp(FW_Ii0|o2a{NFAvp|=>$Ev@!|`%O
z=-(zt?KG}5;r%rag74&*SqNo_b%S33?&>kxtrY<3W||Ij5Ex5_GEnxu;~$i`OMQcb
zc@F7C$Y<bK<H6t(64_i+;=}!#RY}mR5>La&o%!D@^W5s*aIN)jG=p#?KU%K|bJ%Q0
z3OkE9Ox$pQ<8EWre!B-qqPKhrvBs;s-vNxj7!-?Ls2boC&=%TFa~ELj7`~^a2e_+b
zYs^axb0BO5xMv5xDCl2Lo!@Q-W2z7<u?c8y2Yx(yaA_X4$%V3#sc@=6)~^LJE3O8u
zjE&Ehc%LDob@+o;scV74P)R&SrgLE0L^?F((CXHlBCjX@VZU)$UjRk2IE0lne8A^W
zXfF^UJK2M1I^?+MU6~COSzbxoyF>!*bMs(CQ@I0r06)C&TAPJ3HPWWEpybq(G|3>9
za!h;iu3f_-#yppQ9)vdn^M9#c4I#$R$dS}KyR_`m=~6jdkkQW*y`2f#TfZl>+)^Cv
zT%r_SG#(k+<ypavYf}+yoo@IylO210-pn23-}v0@ts0d*%TEsyrV}wXICyxoASKY{
zrA;1|K8aCRZ@S$)uJN@<x5nuih2lNl3QnHnJsw0d?NcajQg9&1#zJ!*MLc@?YQDO)
zc;1qU2=8V>E($s05OzdUTBy)S%N0rvd)B=ew9>2oUCQF&p1j$9v-W4D$8$g5G^s%U
zme4h-=<DB!P8YYZ*uCmR=1ZRuQrxyD%Y#2wAw9k)=E)hh^Fzy~=eIXQnfhp7+ht|O
z))*|~SS+f2oQrqmCv^g(;~WxjU-(Vk*tL*S>fCRd#~bl}%%Lu}t!Em42i0twte|U@
z6PL0m-_B(ndB6Rk9=7E8#fXnp$s-!0cXxE8(qjq>)gYDP_))lup3g|UZP0<RjD_Ls
z#WbLyftQ=b<yFZjf3VR(*l8*FT*4yR)mDb?WYymJwPJ(7(Jf+wU<qkFhs4$4y2HJ=
z1PWxn)HZR5Wrk#&pBKGz4&en`#lL6KGMC5M+!g;GF4oOH1JKGFSTj^G(=?6Wbb@FT
zy6)km)<CK(=5%H_`|4?Qho$9&ZMN!j&!ISbIj9{!g=#;t7~l6vNqM0+HNzrD@cw&3
z_Wudke7utVFG<>!eIZswTouYCjz)CODlOy9+Xo~OB`DTB>9ouEWgyh)?{4ZD2e5;l
zmH3`k77_y??fwmtsM=x#0FO$1QI<v{VE_}UeJXQ4&%5Vy8o^tF8S6CpomMxE+mzs<
zpNXqX=0U$<Qx_j0^08G+939g77XcO7`jszCS9ani`Mv~DmLnDd4usc|-97~-tuQ#i
zWwr9NJF#TR*LHa^?Ig*%=XT8%oH;H23Z^-nIaxCSoE<4ODqOo9_ty0MEt#3iO2&^#
z0tKoPjY*4}&VXH}>29|$r7u1!UH)Yb*-R5>o7|JLyfT>%9VU9O$451{6PF#K7g0wc
zof(jka@|goPl0G5uLLdj1wrws?@|K^UXx2{$F8n_EKZ5)Y~ZpoC(q2T&_&Z}d{r;U
z3h4R$CSg48KnBk)L_Iu6N4&60V#NhuH_}zIVK;#nYt}4A<H(j}Ph#)1bp}@xS&;$k
zxXEaL;gNO|sqcs+%S+!mmQ1i#*b&y>h4MSeHNJlvz<NJjVWM^6Y3Zg#W*}^r|KTed
zeE!B45uM6gdyAk2?rkETbPHaxu!Y(5e-m*j8IWDv2VI=B3I2|H9bWuuB$}#!?~Z=V
z@=>yNK(KPs<|XEO9i6|7)ICoTp}pFWdQ%w3JXIGxor)-ZhFnK&z<gj5{rd2Z<h=*w
zkrrc`{tK*9<ap+QxeS4U*6dO`K&F<H3&S>}+8v((OMMl;Y8j~MO0*j7{+X1F#ukko
zeCl^Jx=!)(THp85#v>)w28;ZsH8muja51ac=H{^K%Ojb(srDQeFeC@l8truj<1*>}
z3Y6iSLOHmANWMj}s+51MMcW=JMsHL|eFt>={aZ;Mc4!hvI!$Bk`B!)L$1p0NnUypS
z&o-~<*5a<Nea4I2yp4@X@ZXd>Y!1N;my`nogMoZ*^{Jju;5Ig48ejQ``pAY(AYcan
z9;tW9tOvxMNl{e6VH|kJT@eGoo7YnN(K2$tzD54WP{it~0_oWwj`wAm=i$S<;C%9!
zuZEap=R#PJ0Iu1~h_`daN591Uc7GcEwz9{qC9T6F<;Ut(3RaTEwN&iXX;LjX!izaO
zpe`Ljl|_HDBvvP)F9((-6e~@<0@T!5%>ub6c^ey-G0ot!UY(ua$++?dN-=WAx9IW}
z;&62E(5DU_r`pQAsbZlXZjXu!5v5e3<LIkPM<SDoe<+5+dHbTm?!<vygTh(;>wyZ{
z{=F9_$b#!r8Ju$Mp8wPg_bKq);Wf4MOPh}mEn+6SJxGHdM%*EcMmHrrIt^;~OSgze
zL}g&4(bngE!J`{oy?v%pm{*(iKH3nKuutu$({GkBG*38taGpzP$rIvTV5J8L(E$+z
zo(y+lS53!fKlR1Z3A0y$GF7#Ov`+!Zn!M{Td(Z)<DN6-jU9(koZiZGJ>kdUPiH2up
z%i=Z0!H^oVhhz&01KIyTaRKLQ;Q+gsuK%Tq?i!fsuBA(S$W?0r?`N_=)(bRGDv&u6
z)ZG*9n@#wp2w4ga`T-C1k6R{MP);7sBGknnrd;MG2QhZ*=<Kr5Gc9IirZLl(O+|v9
z)p-%lY8A$m9DTx%;_iF&mTcx7U?st^wG7pH)IYg+Z6_e3lXe<da4$H)Kv)0w@MQdJ
zq5Ay6VAFtkF^E5mrB$1X%R`<npE6dHAakp<ke(x8!xp!mG)c0+bSZPKWPVeKRynbd
zfg=my88{`bkGXnSzsQoHlEMl^eAe&SB_1iZTMSD$G9ud<c(~B@=n+~JDOX=8z`qkW
z>ZLxZ<e||S|6LhsUm%N)R{?uyV5wFoJ66}~I%;l>o5`^3HdZ{0W=MlK(bobK30c^+
zKA2!)7hYb#=!G}>8EOD=COv~o3Ia!LB5v%g!pso_n6qL8o?Gst#RfiDB*>sX<~hW>
zY%x0<dfA4&eGIU?KZXqbRiG~vj~DFt4%UT1YMQlYVfLB5yjXY&O|u{z%RED6ga}n?
z63x7wG=iY>21{347tNqI4_f;0ouq<`%Ri0Wz#O=JraqK8twA;c9pv9HhSjXamLsU4
z{gA9d0u|rRFJGg<N!A`Jmx{>!He1Tm7oFML+wv-0&Gf8v53$#-Jx9i?$eU6QZ%*Ol
zR|Yd(_76^ILE1xx|75v)Cy(~qfDUMgzA@L<9*Zo-_H{t#oZ`u@??eZH^NVF$ec)W)
z`xl8s2fuRww(r>Qc`N{Cbvsjo3LM&8g3eKGrlm<zzFVn7Gu|Q;`o24jdkT)l+s;2m
z(>Ec^i9h^(Ko<!1_+7V1g|R{0zGwX78$<n-uO0~5PC|Z9k^_6@Y1`?c|Kk>@&|j+$
zT(^j$xcu`4HcjQTaY(F4ef58F5f1z0BLfEF(Lv|1ZrL3*-nQ}||7o(&od~?}a3uS0
zP7ipTPh0Nx4%%3Y6ma0Gwa)k4@ze!gKe@>l`}C>c!ZYB-X1Lu^SokqWbz3=|UVUvr
z$ME<kL~5JToj>umRw24JdtO2y;J4Z=(QKv4q&bR@1~8z;ol*NeG11aVtoU++FMI<M
za{>no86B>ab5`SJnzH_q>?S@xz`+XE{}vAh2bU$vh`#dtS15=lb}y$74#S!Pa(x;5
zf~zK$#aKo1Q9I!tB~h4@ENqy%W=QyENVZWzv1L5NR3(F9i^9V%@>s4uv1&?z$RHw{
z0gQjz2DX;BxWI@q=m^-62B(f1pHA;KI_C-smv*XTJvVu99^nYP;=UEso(Z=1?CF~B
zILihTl9bV?wkxV@U#eF6i|fV`*|s)M1pNxj$a+Oky@VT)s;~@?s=ND!StIu9VPK5O
z-TER#{q`W-k{LO&Kz_c~aw&ZYX*V+urpeyTo*^OqpdT!_8ZRhdu#F?=yNcFkeXK5(
zEC1_soJqZ8fYm~2bE~wVc?@FX4qdfN*hg)msmNgZ*n)-jENg*s2gWPzceL-8HLC5M
z|I0?C0O=+bSFt%gZjt^9cb|mm69B+d@s8o)z1|9tmwxHF2;?(8_N(gfmk7~^k>dF~
z!K>9uw>iFuCTw?cx$C61Z8iK2KVPQ8fQkszTkVe((>yU7H`K!V>rsW$TOBX$<vH_c
zXvzXyuW6sXpI6I^buV?|fbz+W;|cpPJ7^YiS>2;f@7G0RUUj9U>zD67MdBvLqF;O{
zAir)>%i8Xw@1^`br7Ibng{_8(2^XAQ*GVb9geO_hCAPJOXKa<9#z{c)I5dl8H&Q;K
zdWt(ypP^*_*<pzrUJ6L}t77XkI)h6_#bAE{=ZI~brp^f}?~tEqiE@hAMuC0YoW_C!
zp!l0GC)=Dy<Eu7TSvvlcdd7-kaE2SHQtEZnv4d|@SxoEM1Kf5{v2(9_8#nJn+cnh@
zP4aKoCf8n}l+47=D$FxBp@1T_ZQ0g#C^ICAy?QXL%hcbe4Oj0?S?yNglKM)~Nw4<7
zQAKEtc`v6>FzB6a%kLfaVi8(>)@h@eS#1#g<oHXW+bMPjQ|O1Otg)kxg{98Uhc{yf
zgo6BMg+1nSCzRO&xHx@a-%k5GOO4)aRnJw`M$9VbM_KOFO2=V{#g=)TYSAw1%@J=<
z_2Xbd7CPQ@ng1R*H>yC~JT3K{k$H-!$Cug*Udq)+Kf(2(030LVZuvtizqmm@EENow
z?a`bIht39(>LN%JW~d-fz|wFtgN{XXOj41_zLzuCSOdZ8w8YWlXrAcnSOS8NmlqdA
z9w;OPN@jiE*y;gu7<@x1kr2wSWnR>+z4xI7DG+Ca@=!|@BV(mHilMls+d5~z!j%_J
zBxNglmQ{)aaEOWiAg}o&Jh+#7byZz$d{1YuY*QF=g1>lJx>q6qBxr)pb`RqUTnmr)
zx~<TwrBZ^G`Ls~6;_dY@fe99h2^LI}&<Ojh@Zc=-)I6|`P!63do$k&Y;GyBeTPCP=
z^BByiOVfffqLy~1zX`<b_x&;)TPWs9B}QXp@*6HN%l1{sQO!<d+cEq2#XU7&rAQDv
zzlT=Qh-na_hJ4#Fh6G|ibWXmJgogQ`En(Gp9S@Qf$4YYa!k~CPp&F*DfKz^XO#h<N
zo8&j>O+x<Z==mZy$m9&0Lsn#|kx|nBIjDpIF+{8!@WMifm*Q$V-|4lFYdKPaiMc$p
zCbYk9`c^oKos^%LZ#2+o7bkBjoq5hTX@QIzk35_2IFuM&!9)>#PQWmZ7i;Ot!-_1>
z!J2IwrRT+nxg!|5=3QY)StIS^#XUFB2SV$_J>QEHRs43F^4*<D{Hx7E9u9)}G5RO(
zl#@2@oftUyWoE5u-JaWBuE>%Jp)@u~P~MDn!Ty}MjZITY=2Uz?u!qggp1c{>`Rx4w
z1lAPn&a?USzbf=7q;1~>k|?-ZwFi)&HkXZue>-myW>dvm^a#fTTcCpRKjSrUnkS{0
zC*iZ(O*sQRa5-NyGi@;!paLwO=n!Bbkaqtl9AtufUE788_*dk3(vS2a%SwMYfkH8m
zpfuU#F^ggw9FsJ}QPVju6ht@n_-c%+S@Tl*6cSeG{}?(O5h#2;C28`8_92#UR}}%C
zVcFsX_UpWdJ~RI(X&qVETlnMU1)%<a1S&z$^nYs>SsFsMcQ*!Dr%ApYniqey_<vR9
z*^$X>@n!O?;Mg;p!NX)(X`w%}+%eS<^#4OBga|l|wDF&x6?q3lLVudwj)bQ8)ms8(
zrqQm|KN;{t#<%wEMRzEJap~&*9<tASoTZ-|{^iO`Js#IVTmt<vRqnyf%xLz~U?AV2
z-4WP)7?KKqZP4YujpuLPyfGCg;-K<eOxqS4?lLo^QOlV<00Z6!6yl$p+qZ9IYZMY$
zk5e6es`wyV9&w6|)6w)m2HE<sMPt5~l3>H#E_UT?(u20Ngj)MsC|^$uPXOs^f)1Zp
zffd(OfDkXwR9l^O?(2&2rt66EX16a&T`d8D6X(v7rU&XBdkWaq*ad>BLw!wUym>BH
zK)sj#dx_4wGM;L@!cyH$0g)s}|3%FR2^PidTb}WQ{M*OFcJ#Dc$Oa5b_g4bHyQ3w8
zaMTvuxpkbMtwp?pB<Yh{3_|vr_5ntf$7fi~kS*1oZtl^o+zHNP?x*-XmAV#%gRO3V
z#)pl$p4u)?yT6BRfkI$1#l-8BZfIm?@GQLcw(9|uAU5agKXucyKHukmKyc&7%Z12&
zmsfP21)RKclWy?eZ%@ceV-6_o5SsV>m#mGl%En=2l)>TPM5O9g+owuZ@n{4@yGzF&
zb}>%_g!P44{}<_<c67WVAH%Cpg;B|IJP#|kpKL(BM%&NKCRRQ_UFZV`hMKf#(A0<P
z^Ab@Fn<2F|W}6?9!L))P>p5gF%xgV(lPAAdCiE(A5?E5nlnB$6@bxawtqoL)CXfj@
ztP%b^N7pj6yR~TmT%HmC`sBDOIZ?BubF?5-5)m7(9Ht02AoeP>NjsmvQzul$YTee)
zdPd%!^&D_rq8~jGBmpMQ&F!N@GuS?H8jCC}sde1KcMC2I9D)kkU)#J#^wRccc#m{^
zyQ;XO0tcWorhBTr%bZOPl{UX*+XUqK2)3`6xZ2Df`tHRaRQ9wmkHYE~8a|bbaEm8N
ze%Gbax7mWP1MnnKVd*+_9y>-*MgM~cA?oSDUKC@tBg*qzUj|BfJ*N*{;L*EW!Uq4`
zY?(_Xjp6AD(lf2;I@Kvn{zgJ@Q~A*`c6DlVOBl)zSfWm0g+!0GE3I~ZQR&y9=HG(-
z-x)@tQIN35)<X!h)9@0)I##l>q&!bpXN=<iap5iMC4F~SMko|2ihyhgs0PdY;$U4x
zE^nKxRVo~Cm_6Jy%;{+7HdF#u@)uBNUB=@-9>qy$s}|{}Eo0yQr&?kUnlf#1FF<x0
zPGz&kT~>sn;UYV<Behg33P%%(mWhz?b8G}o-_^wjAiOp`Jl;nQk|;+cO4@zHCZ;!7
z;aBn?dA~zQ%PW-($i<h{T?n+4hZ#X5Dk%L@YS#w<{&v*xoUoTNh%_GAU7DOzI@Qv^
z#mf^762&ZJ6p`G5NO(-&G(B~{#)x88yXG3Gz^M%qAf`+x?rrAxsEoNhO*{T3_-xd;
zBTDn1xbL)9ft2)=^fCq_frp1ERPzwJ>(^UprIYukvV7g0bMu+5{!Yb1PqEUZO{2)g
zPTih_Br6rD+S6YwErj>_2dR^fCi%U=pORrHL!2Xz>!(Xms?$n8H6dRjYid)AwkZ<K
z>`9S*i26E`(k$t2Jq3URY-&zUy7U!1ZR@BM>X}3BiA)6tEwI0YMCbL<{3KWW3SQ4b
zdZ<HcFts=YE22)qx*my$C1;5_^;+0v6McoY{>p=}f>4uer<>fdknFQn(SF~sB?>Xd
zsJH+*l<RW{<Q-j`cAR|AX;IeBxy`+NdGil6A_aKk`H)A>v44xr6xg@0dy6J|fB`KF
z{y#0iS}SEz1^KIRtLxt{|2!jQY(zHok+J8~Fu;aqTyR#4QWt-hm&YonQ)MJ{ipe`&
zerodQ|2AV{?PaDRMs>Ok7Vdo5_;f5RsCc=u8a$mOTUKE$&KwgVXVd;JcFy~Z@DzI~
zGL;wjK;~8CSbMSb)cJ;V!qCy&Ou^iMX{^KJc%SIh>1j2FK6O{G+Ve%h-1~aUHCsfq
z+st#Cz+_4@@hzoq{%l&gpsg}*{$Crm5f|*gcI>>Rn|1Q6bt+xrUx$GWpOQaswHIf+
zbQ}^?A|Ug8+{IgOexzW%Zy#8jbolZK89TdF49cWp2Uc5pt1{P5Rlc92)XU~qmhaZV
z%EgnYh1W7^2{r#T<C0|hef!ODS6^eH(4tGK{Tw8|>~UPpGo`r$WKy5bp}8H`<*3On
zu69gUA@mk8-hZ;AAF!0y6|up$(jTY`?7%^cu(rGSy0R`#1*F1^DWpr=T9=p~;A*k&
zFnjs<D^`$;J!5^+>Un*AU5>F;cev-8R5sFN(MDQ9_Y7(x@{l^HkVXLNgMa8(#8L+p
z!dbgdFZzMDVz7~b@J&G_`Q;qtcHT6C0OT2*@=3up5~g}NPX5OHQ;W(ojvor0)(o8%
z6`dx3JRIF^EW*?AQ%TTEP2%(wI=XVFBUXNAoT1~Vty_>Ak=h*u#xL*NCl<})Gq(?V
zg41MmS+s5OSzu~Xzn@9Xzd-znnX<H(td<-sUSekDxLV!DT)_8_(~gG~*%283!hmmC
zPmcg56Y=m)V^dHqu45(-=(MoBazgeyUU&kK3hjE;K_#f=SqpzZm1tz$4ky${&Jx9S
z3LPf%<fztwoDRa0D!49H7T4NEesAs_{f@Bap=xG*(V@jkJwcH==IZk!yY4&4f{3Aa
z%qe^G$FJ-vCsUu!UH%RCRtC|j4)vn5NtJnx(HScuS8tmqbHH>%dar%`koedk&r0U0
zh(Ih2lIBH|u1j-dxeZ?u;uaa?HdL$VmzJ<&ERqX#=1h5n9zs?%2141)n6JpP;%I^f
zz09fMEuTC`tT4ujGuQ}o$Edn`7~^O(4OLK*$9sgmjfo6-Z4uTaC^VXT^@qp15vDzy
z74RL#b|a$t<FUfSjtuLFL}Ch)?Hp(^<G#vQzH}QgG>Q=}xzpiQe37iCm0H`YQBhnv
z)q2@MKph)7is>a-^z&oE&TG5eoTE$>Wry+m(RS`LPwlWpOj*?}OoMQ$q=R2ctSVE=
zd|@-6c7|CIr9S1N8bm`s9bFYmYja%Bp)HUyj*lsnL@u*mp?VNMaq%NbT;d@98-MF-
zYxz|P{k)?B>e_@#km#nKcf<AkU%Ia118o(IVv}q-xK<XxR|eLe-B&WO`(%_2k4&lk
zyGJck1-AnFdCUNk26a=Jt#t#-%{EweYLE9Psq|zX$omY6=8wSyFgW1IFDXqu)gj2j
zV80-T@o3@yMuXj$VO>VBeMfppe8srCyEq_*z))y>dxkDlqDZm0WX$5!wa~gE<kXvx
z8|Fm2&`hs$mUmQv7Cf_v`=B6M0~OLPUtqRqXH`o$eLISWHmt5IUBa=CGMue2UwhV;
ztTy62hKGh6G4{tT*OgKun@q_#T}w&4S`_o}OU_ywyRfn&+xUtYp~SDKHJWPabNGT<
z(|Ic*JLbdxqQ+4k0Yg&QLCwG+g-K5`(Vr9j6u*x;vX^G|Addk7ZGx{Kx*izI5bf6r
zdGE;ClgsH2ceXTWp31loFNjxP-VlOCEb8dLY2Y6TihH^|S^|ld9cNJtud|v5@J*#M
zF|KH+=sbV%iu0j9b{^fNLPIt8_&T*2lTqpImH>83dx~;&OZzOL*cb(a694SvTnyC^
zx3S9ASY9iYzY@~SF2}Tf8;GAsQvDqau)rSKY`v%!<=5Uevl)8J$?-d>Gr!dK^Yhah
zNo~K{*=t}?6uDboN_kj+V5>DRx4!{Gc6rJ6=6T&5)_Yk@FRW%C4<UOmC_W2E_K7uj
zhu%&^bu>!EvYNuAK;lgt0od#UUXtHi*5pKMlAbb@{-HQ#vF!EA?l{4MIP*g6v$Yj<
z7=#SEG8JAm8+|6>FZT54#U{k^LyzA_5ChlMI+$Gfc^G;25(a-7FwbnxW0j4XeF4W6
zWk{T%{a!9-W!{fZXQ(@#Gtckh&ytze+R_nj93WWm<ALl>9a|&K3uYEsv9KzyHA^t0
z(&GL>gwtZ7td^=L2mjq9N;|{O5lbs1|4fs`y1R%8dqSSWd_}P3p9xB!*>oKsZ5OPV
zxbX{cKfbWqUzmqgXdkuN;bSScD>(D`V&wgDu&g(!hE4rUz)j}WUJ(D5QM`xFrD;)H
z9^Qk&^A!<>Iags5_Ykg$)cM`BJ@F=x%o#U<54j5@yUFOdMk#QHV=uI}QzZJuGG7(H
z(ZPP!+bhrnRM*!6-XCMG-48QQsyfF~+vfIsznA`lN8!tx<bA*t&UyRE(m47rxJ<nv
zSR9P=sLdb0cV105%%|0^t{SK^xHGbnN+8$uhimNcrmfV9zw`ZuDElwAo@ur)<QI^o
z(&yZW<-YyFlQfWuo;He2oP2$umfJ~;Cw|7G0>OtEn?#qS@^;2qZ?#7@lp6KBvxsF^
zr<5lTB!U;4u5PacFoqxrW#E4jiU<a5VADE6<jef)#llo$*rG?ugU6oL>aI$H1>zXY
zrxXU}c3DS|P+fqNWqPDX*i%YTs@Bs$5dtrF1+n6yh;(wjTgD=juum{4ILJTZ=uEo3
z*@3(&e3SsJiDU~aeg3viICWi~fW^@~h=YTsS-5hW!RAS&<yqAAlNGJm#gvOTZNB*@
zfeqI;C`3e3tl7O4lXLc#V+#JYy*9ZN$6V&Ct@dY|?hg|hyZGWx8_45W+DckmUsL3l
zUF1j^H1v4Fv&1x3Mj0BW=Nleg>ILdAbdQe!#ALeKCi=nh9VRexVd|0h{tY$iJPqz2
z)nCuV1I_lF0z6~AGsl@D^f536LCQQvMo$mw*+j%gtlckMOXsWOzME|$p0#f=6mk^R
zXle)}^QY}(Bd$qd5c0C}X1)^>Qrcc$2}{gVmkQA|clBH;zDY32a(v#M7(z77MI`;F
z_8w2-e%iB;zQIG~#a9|Z#59S5H5~`^+ES5*#7KykHYZxvcM6uWzr@i6NCA~frNXM4
zM**{kYKKLb`?bnm+JY;lM@OVD_Zh^3MaY7M$o442HYgu2z9o}#auU&@F6a32uo}3K
zQ`C+gxNfKJ7TzA#<jZr)1vjxK{JhHs*@e0Cy`O3HgrwUWzS9h}68I!?@xnFzz~Dm{
zF||J}U1bRQbOO~mf<yu@&+V6|GH>qDHkPz`K4+(=LbzmkjF9cy+v~Q`$<{>?g!T4y
z4<m2$u*j_@Z&L*K=)dR_VP?Ud=gwY@9FA6t8TY~HIXxs*0QUFYF=FPILhgo+-9A2k
z2@0kKf=yoyFPL<wtO_DK$lmtE>}pEOM&E8Wox6-lCN1q}uQJE?OAk7{O3Xzd_A!uB
zZ;!L{Cz|V4^lD$rdCXL0M9ldD3+2;8jit6QLXcWbQnBAJBcY5UM>TpyS^USdTtu09
zd?O)U5$TZ<Mul|X>D9@eYe8B4C@8_o0L%?FQ7@;mCYu~pC@UK)9~(m*8r)|rFa0FM
ztywBq-Ndd;RaRGAoy2T^5H~XRm$-B~-syRr?xoprx0o0FM{~CYn>FwFD1KAiq1)oq
zWt;Idd{5r=fsC!oTR8W$>Dc~TP8-*j^{GW*N&W5lZJouvWy#F<jQe52spwI_(UC;6
z9{vIgjJHbA`T22(?BCbUpJ@#Sq(0Wki=NbVxX^HHI9Qz#Y}PAq<E~Nu$x||u*6*um
z6ekA<6iw_QtAu^obVg(<=1d|_>u`DNSUUp-#vamm{V{yBVCYeJoJQ}X^v#u^#m|->
zk9=7Ql*H9Vk&<FzYbgg0ue;I2M<&c%uZPfZx+cubbT9yZhbFK319gm@LaYRyWUDhO
zZuVM-mvjdH*ZPRtdIU247IDbY>3bxy6%je@g>)IS)5rv%TisCqDRR9UsHIE>WIJSe
zAx^`br<pWQ#4!r84eb5VFew{xhVFy@ETTyLtfq=iHmP5HNd$kjfR}^17#}5pIMb{<
z=$sHMAM4znwq+6~7Y(kFFX2#o!2Y0un<U^^TLfaHMqOJ~vrj8~=3a1fUB$>{pz9Z+
z(tCNY@gzEC@`{Ds%@TTnrh{7^9|iH>$1J#YK6#0d%-}h&0DboHu%L!rF<NSv1G2Gz
zGUr``74EJaw)Dq>4-WbJhq`KN*PFEY+(Z{0c%aIVftnEKd{QmKu|hnQEME8BXg*ky
zYqA5h0tSIRc9Wj6uJS>1D|5Z?Bw2F91M2~A4oqu1ugBqe4R#|3liRI%J@<?;bjW7N
z)QYxm*>V+3EE$5!Rz#hjl{m^z6d7+_e|1(lX#)=0@8Od26I!?1U*S2$PAHIUT}|x}
zHO^{4_D#YVO~&t;_T)}~Qu%l3&ag>7!zI26z?Ie$XK*DI9oW|6&ap)tQc@`{?X@|F
z5|k=l*kNcz*+9jZ--;@isAU}*EQQdqeMaK^taOEOgz{_hDK-yLvwlfbdD*Rk)QF!)
z%CV7%3$LpP+Bg-B7V4r|{>*~~kR2$9i5P(d2TAr9r#UV6X*DV-4AnZ{-Y54p5`{Zi
zV6U#*`n_L@#hxEK(b6IPLus(3*6KypNF{u#ktRBg=7m-HM)A#T1H6wbPvTkPT$Oe%
z5R!27^!tMsGL|Pr_gR@LFUF!H%o`Rq>EH+f``<dqllMMp=j~sDM8>+5G|ce#v#Y6o
zFdyzQFYuy4-Z(e^)}p*4y)2KPaPVg(6!gT;+^!3x>qHhtfSp)cS3Cm)OQf-ot(jiS
z0iAM*W2OXCPA(3;>*=xiE@J;TxT8-9&e_jHZMy0i#Fmk?zzrHAlp<SX&z=`OkvEl#
z!pgnfU%XR?HE&OzAHm;Cx+9Q9MDCn49Won&Ph1~ePhL%nIKQoy(as&QFynHg+#L-u
zOQUP^!ZPIKRnWKIwPAl$;U4}p=nmLiD{%E?m$(?jgxbY6S)52gxcnGH#OMK<l<@_k
zFJo!4NzW;M)MGf#@*jr%CO2FQT@=`A;-&HK<lLY6>U)OP>et$L81Zue#j=!NEuDbe
z(!ljDzYV=bG4VzQ=Z$zY?njoCkmLEt?RkGqW*LvdX-Q7h+z>0FFQffj7D&O(|FQ6L
zePra*70G|+au{+hA$CK(E^Bf4RgA+;2nY4OLm;kn&ytPV3TOXsI~mRm{m|SNt#QEh
zO!M%hAC#r@x;*9K6-QdT6QR|wFRmnv4iD1oy<PDOEGaZYhl@JRg$<UF)V%q<5cKA0
zL-XNm>#p0Y9jUK%;G>^VwttnOG~k-hKD199owOa#E!DJ{Jrx+hseK^*G75xxaDxN7
z)r+r8X;p?TjaUlH9!#|lHi6Be@^@!N`Nqu{lz7qxxRg3kP+?VgxMo*yt8LeUN?d+u
z6By(u{X>g0_;oYm*4&%4qzpT=`Af_&^vm$kjfopasCkXyadp}OM7_zJFm_vK1O3=Z
zdrdyHT}F9Zi?tsbGiEGvAZ7XY_RTYt$VW15ex8O%O<JdL@4-Z9t$0Qhotp!<+6~&4
zB9<8CYeniy09Xww_$_(Lz8V@PVr+gynjTGY!LLSF*_^4{vs+9(ZJCJK2<PL>4S%VT
z9<BJNsW_GYyB>WMjEx4|v~w+|RI?2!nPX!c^RAP@OtHqD*UlMNVmDV9R8+N+<h`3#
zl(RxX8~UYu{Ph~;o87b};xGADV6;6xTO3of?x1^hy_7O0CUA7ZR}fo)tKbvR;rBle
zX`rU0q`XE7V(y5Y1_0W4Z0^kG@qSEE*rDlz|LKqEhxxfLWcb(ppHi$#E8@D<MVTn{
zE=k}t<+65HqOjg&@4i8|1qN*8yLkO6!?sliPCSJS2<HT6?&=wB7D%Pp#Bc}yIIS3J
zQhBfkWmMv08B9u<17Rb^^9x)oNtBQ?HM43^m(oyft{0sh;<QSt{Jd-3$2waU>IHT7
zr#-R@Opyo-z)4=Iwo!i<%*`O}*N2yP`*^&EOzbeMFnNMv=BsF_t)Q7mJ#{Zh^#Thl
zS>9b?fHAYID@Ff;aE@e%K+616`}ldkc#g0z31DMhjtQIUoCdRrFPT&(p)twMq15%j
zq8E><ko>;_+g{=nV216BXYe&`cXFGrCPzR<OvBn%@9A*k=w{Fv0FDQEja|7|$rso1
zuo>(ceQa3A04D_7v%E)uG&GWJ<Ixu6GZbE7@QFQ5+>oFcXy!OFtQ-oM-SHW-&?q1M
z;B!Hz)$VT~mee$?r&4zU)YBq3yEJhY{TW6K23cggkqTN)ruu?i{-hG!Nuta1_~I}u
z4Vj^1;FlTKf_mMAbBUO*cYlq6%lhYwq+$|2fg;(Hr7^{#wGBBJBG}Z$b_#TmW-|%K
zKb0u>9UL7*AZfRR&pP%X@HYFcc*LshFO^2L+MWrmArtMx7xrv>M<qm>b$BJj+h(O}
zLC=qx5P1`iz)eeF4z(1|(8N$go1MlKW;!V4uh0Y325>e5P`rHTVv^)`@MEKtpBx!h
zS(48w_*n|~$Xm^4mC~_7!ik`p@JDD+16;a6PnV8<huywQyq&WnN65qf_4|uSL`$os
zz%?4pdTS*5e^e|95`SH2nNZ3KdlkxU(uT!mjT`q}v^&6eNX6r5ck92$y3@bK8k$mo
zXbQLh+MJ<~hV60ghwaqSy*16(pYDvb#g4cz|BDZlIh8R>;|TvRhIF@-eY9my^jnU8
z>kr0fhKHWEYKN9Jm&x`&B3u3r7jL%CgPRHpRJwOw5TGb!i6f3EmG{}R(MqV5b8@y(
zbAAkArYhFYL&YCVLY5aRCETy~ADk$3>7z8<4~e2NV6!w#yF67{5d-p0zA^`XNk>C;
z^DrOp#m2DUz1#dEAuTD=+#;c+t82D+5wy6%*K%VfnIAIqs{qF={gm}u&o6uBOnc=`
z+@!j)V~r53@n?4*`|1}Q$#31GZyu%sMrJlX{tCXZu>ivus;gU0Yh@K&h-1LfhM>N;
zBO)ZSEVIW?b#<=BLo$mBkvHQc?5usL(hLeEd{r&Vj-5=XAO<BxTx{XoNe95P(VB30
zRDuZFn{-nmjv5SXx{UN#Vv828Q&c{fI#>hQ_7Gx}5W1hD!XahjcnS3)+PfcuM!Tmy
zggcwe<jR)z5wYkobl(Y=etAU=Dy`Mgj9+jC?Gz4RM8>@O=m+7TWZvlfg&CIpOrK8y
z&nTGKO>Dot#Qlj;JD=D7YRf2NFkULUnF}*;Y2o*0o7fHZwf*6Ld=Nbq(6d<DnmYzB
zM2{IRkv~bvO;~7p$eoF)BTAP6#lG2~-_-yUZt(L{c^Xb3-Q3J_MB!orY6i95S&{D<
zJ@yl?^(Yzbm*o&<c)5L*8P0pE_?0Eq9HEt7Alm1rRYXbbeVPbc;2rYj<kr&N_y^x@
zhz;}3x$Ow&uWY;;(XA`N;OECG*8Xsy=;Pxflgdr*OE=u>waZ##7;d-s0~qb|>_Ok<
zJH^m4cJmzsI0ECxQ{rf8(#ro}skHySf0NLQnRh}+u5Rh6N@tnwf8(Y-bp%>MqbM9{
zkg^{GWqHz+tdKC>NRx|wcX{;GM_{}U{K#7K5@~ShUY>;=iIPPR<enQ$L(osN(}LWg
zrl@!ue&Om)l1Na$)8>q>CytOU?bDAPDIK2~m<0hi+4Nj01v*&v`*L=u8~#B~Qtaj|
zaogM+MLiYr*+~?|f^LWw#$Srp-`azjPvu>nkqWOcOES+0BVM~#tg@EcMyuVp{|%NK
zEr3{1HPihIfHeD<j6AERvSTYV8vaPiLJoVSyF8lKkaXWxB~2k_pndX4q&9D@#^#`C
zNLFfO+M@3O#V2=*S-MrOUJG~eA_<LD^+<F&GLK~MyM<vD@&v`!OA|gt%;-Q(@#DML
z)60jgJ#+vEAmjRJ>>TS~1Y00Z6(H=!j^Or;5BL{zbu;GcbYjc0C|D3#cQuq_OGLvX
z`XkHV*)WI0+j{P6-~!Drub-2QmUVNCD4D$6)~^#?NMKwN5&!nERb28J%wbJ)frw$E
zi@YD}Wt~0hyW|jfgM5@iZGy@;3xc67XQ<5Ed_PJDrD6&9X6^7<>ki?-Z6@XA4>6D<
zQY06W%Q5^Xw@#aO_kp<6q!yd1Wpc8rfB#}V+NIp+WpUxGkidr96V8ki7|EQP<4~`$
zZMJqf$MGgqXvu-somtiG-X%I*ks;(6GdcQ%;*&@~gGzd?e=R782H=F0&NqTaPBOAQ
z^XqVKvOISh?`ePMd@`M8)#G5>wa4uQn}?Yi1_;_D%AZ+I<kzEDm^_dZZGB;%xf>zt
zk9PLZ)7Vn-vq^^2I*la4E_&(I?(0l>#_P&G=NU4PcH|D(HYZbV7|0Tnk8m(I7uezL
zv|?YMFldy(b)813L}1_3;FmA)&=K?uz}CWEY~~4%f@kfn<>F($YIv{LX=&=)i%=r4
zRKB~34tRe+8DY)rEvZC?OqUqEMh$y~oq5yGYIGUImn3>i{)jM=um`955?q7#N=l>Z
zx>3)AOL<4d?@!9hWZiYKgVirj3EH~34__z;UIVnB{65x(4h+U$B(tVZZ+Sm-Cl35Y
ziKtt8&3yv3(;<L0A%j5Y2Q4MkYpE^HYo)e2-X(P`O!H~o-J!kN!ijBTD$7qnc3CT5
zMw>l9sDP8UdA~BHESyCTNxW`%1x^`~43#ej)7RauM~4ECgr%2>^RZu>pizHi!Eq_l
zCfxsKU>tCHwD)gG=T$#ipAwxc&PZouiC2O>;Lo4v3%*WIlOV04(GR<o{xQ{q?;5To
zBJ5tt>9#x3Fq<Jm;kv=+>d5wY(Kf8J>Hb8s4Ol{-w0~lCJ?&YJvw!d4s2IS~6qD>7
z`c;_i(B;@A(w`}ZkxS0K+;X@?K$7_3;+IKpIz$*e{gfl-@3%<SgZpR1GUsX1A97`%
z_bU=ft9{$KvLb$)uRd(Wzx#<H9Tc>RQ3HE{3Pj2XUvZeMo#;+A#H&F`SQTY1%oGxj
zPiaQbJi<vw4GX*4V*jju0U)`hl~VJddVvD6MKq;D$!azr#6PQ=R>t!pV44BQT(}XO
zN>K>7fS~F%(pis1EzLKRZjFZlILWbSt(A&1UG(KQ(=Cy$l^d-5w{2YM%?g`k{zacr
z7#fF{jyi}IjG0(f*~krWk8Q40Swjpr7IBXbZ9Qr`IznYtX|d4Qh3f9Dh}WF?{T;&6
zl(P$}>6Ix?P`1+;9ns>JQeQmDN0WmX6x^C<|2n8puwd#t^i+hK!dgenAJ~r71#`ic
zMyftI4jBHt{x@MtX{QB$Ka5M-1pM%i_n{!uJj?-SQaRP?9*w!)nyg(nj%{RpYy3oI
zn9V`NB5>w7qbe2LfHGLQf(-^#1Z`r6O0j&G)1Fzw-d920!A1LJg(;3(OXI>v9b2ca
zOPs|8=#Lpq>EokU8D!SADzbiG*o1@7Zla%AH~?;#?%|JUYfj8x1T{oxAWuKu%px||
zSKpOmpm@Uo(v48fbcG$2w(8dwbi<yj?d+1#omE#BeBd$FP74BU8O%0@w30qH26-8H
z455+XIy6PjM73;?Rn*rg?0$|ZW)_4y$he%WPmvUe1C4<J9ajH?=fm%*!(7goYI+dQ
z3{D76NOGFu%d#TPOcyB6oVhhYDZs{Uszy`@u&WcVlPaYM`{4gy<Fo{Gy0^E70-3tL
z^eHYvBm)Xy5#}Wc$ci1W5kId`1zjyG$Vo*e=%O}`0Xgoo!_*nbXN(U!Qk@;r1OW(G
zm@?R#X}|9~4u7tMqnXE97P`&+dCYFML>4FbqYU-WUzZLYI5>>3dcu1@n#q@xzcHw-
z|MgdHTNdwb`8h`oI~CJwO#F75_Mk;30lY65vI{$Ar|4s^dsEQwPxow|O{Yz+mwtXc
zQGhWJeN}nAtf#Bj%0(oByzBDbAIYaTr8G@mcAW%S+0u@NjExNvg5p>!NVD@KDLQ~-
z&C^7KWTh=FbJBuRZWp{0otm`|pvz=%{Q=Rsafkfl*4=$ua!Y1jA^t)a+t$_@Oo>RB
zH4*Q9@l)w@iK~!zb8nwTP7i_h#Wo*f6^S#hB3$z+jJ<e<r8MK^0|w9Evp5D~BG&88
z`>#v1O>J|k=@8Wwg!Z#8;*rvc`-*%_f>c#b41qZ7XzG_UD6Hqrjd2&g^dZh)-<+Oj
z_m#KMATm-wn(tB${E_*C$%S5^G=!cfq$f*+P_PmMwyaz0B^_rdt*<@$9=ASJeer*<
z+l>Sv9&er1Uo6F4VPr7JmX6PaC#uVnkgp;4&)c$a1=6%kxRL-S203o=a%6;;x{lGX
zaHWb1%F)_}TZ@K7GVA}#I`NY4N_O;N`?_?>=+Kx>b;Vx`c>`J8r0`alKOkR3odFLA
zTU_INqA3kUifbD9n&li!yp(E~1xSqo8Cy&^(YICp!%!n_SECaXrz@`hD?eyIhMRc)
zhp@8{3^Sw$HHAZ9G-A8&PctRnN!6@9?V9W1ar($xS&B{uzk8#<1z9Xe;~ZIj%{;DS
z{_&j~r55X0mDsG{%ut~XyI25V`$hcUYIT@r6D2%ul^Zl5@f4OOW;-d<xu;7FwN{SP
zoBYcmhO+1NprnKpWq8=WORB{z{o21Wo}3s7$$y9VZ_~OQPt*W6l%Ylpd-PgH1{@%3
zqUoNidBd;T#au+pxy2USYzFa72EoNSso+DlFq>mdAS{84jf$iM@j?&X?f=sPEH8wc
z3ucpb)g~lCx@ibbI^qFn7tCTk?($<nk1%(=IiouznT1HlUz(e6Fjy}+1Y4`4Vf9n8
ziO1M7IBUU)J8{KMB^-7{pLagimj+XgV*MQRgF1_yhu%nk*+FozEmkSM%b0a~eMmH!
z**i-2hP-D_jWkQi+F+AiAUAL`&6EB*BDQOQ@M?;m9ojm|_VRiDctlsC8D|5^{kp=C
z)s%ixhiE%~OK*E@qed~MnsVJkNI8!u?1Q46RtY`DI<xsUhZpl^4t*x3zE@nny3wpn
z@wK0fM;8@gpp)2k5R=*==X3dK6Qy2|qnq7GSp$~%B+ru!4y+wBh&YLz0NC@Dj%;n1
z8_lkPZ632AXwT5=NKH!3+R9GiSB0od<FbtJv+lQH%!(8#_j*bBBTV7Yl{qknq3#cy
z37D+ysX$RG$9G3gQvM*?s6mucXDWM^$a9xo9tB(cMRbfbhD;l#zLl|3QvLhxfsv7;
z-JSnQMuV0Y9S;jKq=CB+L96x!bRCN&F+9AUhARemmQu|aZg8qnd;(p~E+_*ykd^e{
zOC@SAYO>wU;qk25+@L<1seRRoITLJ5Arp1)8+<Hb1g#xCeU<)>)nJ#+SP1$!!f`go
zgq<v?-7N5<48rd97JRQ_kgzYcsZ(e`j3$MiHLarox=1Q79b2^XmR!w%*~l)@pG`8S
z&E*&z)wvkMy)^Mx)?3+m)uN#wP7IxzEJy~Eu19b@tlsxE7`njay5e|9Hi|3dAo$g1
zA;@BhF!FXYGXXW?%<t{twxJ*v+CH9p!Gmq=zamXqlsk$gQy_}H2ZS}~tQ6lHdY6ka
zwXV7~?OYM<`|x=pUTO~Wnn;RnmUd1(s7Yy&!{>(!_)*!2$l^I}uD1O{LwDQPx?&yL
zP3F6~ZR<@JMLpjUCJLliZZB?Q3L;|hD2XbQ9i3gxk@%&9X=?Tv-<@DV5F3xkuAx5@
z0;`v`1xph>KbJykR&R;R`*~HZ$3qDwq-c$(YiTac=>gLISfPw}cIY0ZkWds_J8=+~
z@)DXWu#a%Bq=w%tMA>AXUV;7)_Hg?E!E>wASO*usPy{secnA@ct);CPEu8Hd)>rDm
z;G%}Y%&&|12dLD2lKY>T_(N<glQwQ1rKX4URbd#mcab>|zp`L15Ub@)7;zpD&dw1P
z4slv}WP0szk(&nn-9AX-ZqqWhA9s4Xop(P#`Y`%WD4?y~1q<ncx&Jq6Mj^mqk_G&d
zo>BW?j)8MO0cF{dJ=m`NSV=C`a(Jo&d)8vCK+~D7jp81;?FVdx{9;loH?Z2v&$D(E
zWyMJfsWevXMUlUl#_Qh#W){z9{;DF+$&OP=%>pln5Yi^0V7pWUWRU8-Jq-%!C2Bg?
zgn}T{3saj#Lxi9nmq-WgZki9omkn1bGPwMtzG^s1T4#$;&9A_=`iJ^~o}dWDSELz_
zq(wyv@*LQXb}QQ$R|m73!qmu%<+%u^m!7-$l0dKIzw1M%sJgNFpn6+d#$+n&*~%Jg
z8RGB}`}mr4XwoJK&ySnX&N0o8oWsoiK7mGJRX;kGgj#ZLvdUuli59ESnAkvnj+<vf
zLNAGcB>adt{O7iFi_j&5z|TKch~>ogwmY&+eQGj?`DR66z+xl}^q)h*7fS5BHJoC#
zl-34eHd>Ib27fjHi5g#0=ieKi8ImnSqJG6|e3(ZwdByF~rxvrFeR&8RND&32bR^9#
zk8dF)r0t-0MkZq$&$+rX*<+9Oc+N}?Vg9p)HmA)#*j{iLF4r>5EET0v;n1nLFD{X9
zXD$dYPf=Z58<b^)WSM@uMnMhO1VLCwJ4Q30utz1<g2!R$(~K;c_CaaV(%2i{sRIr%
zfg(<gPA|(OvONOG78a(EE6`7PX^2B%a962pbvz@Dx+%K+YGMBly?^-Y{~_wHqT*_p
zreQb`2=4Cg?hZi)cXxMp2@Zka!Cis}cXxMpcMI+g-@fi=z5l<~tT~v2eX@JHtE;NJ
z<84T{cf(dQW7Qd-Ge0+)gMPBB5dI6pjYt?m6XO?VgmVoFAcK_pRoU7MWg1<s2CSXZ
z4eaTSz##aaJZNHrlLC~s)&DDPrh^UceAR9(=gvhg18s|#vwtGk|B_D0sVD2Vvl+%G
z>H@-lP_;AXGF=O@cM#|YxcI<caH#goaQC-XOw&3&#9nPf8IysCc4Aoc*CDG|U2wL-
z%!*pkx-(MPgYssrnrq&TOqXqj!3T&K9BVlZeZXY${Eo%KJUV1Sz|95h7%1!7%D@XA
zYjtMiEG3`Lp}lnAXQ~DuXVC-J5y+imi4mm(IUQB$KYGzl&dF3O&ob2pb7fFPOP|ke
z(xb|b8L%_R7Mz}%cc^1VSVxfO(H+<v{H3~4lR1JjkL_!e&Y_E|s*$`eqPtc2in-(<
z=X`k&92mtu+?Nd19T^-ubOblMwyII&pmPSvCTzvZcJBRy_e9OW<X$xA!GXVyKuyJ4
z@>1AuOtbUrLf^?OXT^kZ%F)MMXP4TZeE2dni`fT)DmFBZk|mQQRaPvczNSZ%-DTjG
z2x}GjTU==T>CXFmG4A^;MpPVov3xX|x>&znmP<}P(77)NAizTg<0sl`n{f-q@9}la
zQD@n~kMS}oq~QY`f`)=wc?*2Y*OBKaCOx&MP;83d0b~(xf2`1|#1nuP9hxtxav+}i
z+Ea&Xjt=7OE8v^^kGw9k4|QarK5CqRwksr+7mUo3+0mp-wlhlqR%w!8YhR@W-in@#
zPl2%tDsV+yVIcm2m`zN!y$j<EnoOsXblF|A6%BU&8hmq|3TrT-xU0^;bMse3GqRe~
zteO7@W!Fup;}lT9n*15|ZUAl@ue17audavx<_Yb+dnkFWh)K`bz$I)k(iiK2lhVN(
zpRfYGzp?BVt@P)99yD`HQ1ipMYvS*ES@r#jj#vZ@Xd3|5>_s7STu4SOr)>7*Qubl=
zrYA8PMiq>-l2))8<0cZgN3$85{Mwyn<dcc87H#Yw1Cm>XUc3`?iF5VLC*q54XOsmz
zrOSergppnmO>2$O0*PCv=wA*OgEf8^Z7(xPVm;L5WL+XCDHYPuiwng6KUKY^<%^Z7
z^{73=yt2M}+_tos1~`qP3Tx>cJi&l9%P|uZmTY+%-;)%{<BDygKTW@R%_Y^SJNj_J
zF%qeccCpDNFR$Ppc-WuiF%a(W8%AUrnPBQAnqn&@UDrZt|GsCXDD!L_TOPA`-f13L
zNp{B6kwGvzAHeywPX9H#-*3i<SZ#kuTI;w0*>f)GP`^wH97r8EH;E&PGhj=7>7z>g
zT*5W}_$K6XHU8})s$XOK!0H>H@`mnimhDo`PQQ9KMlN8NEnln%(8`e{S>T6!&+T8*
z&)S}G0sVA@_6VO9r%q|{a~^s#KKV;E>83m(br8ZQnwYA1A5lAv!qC0MX`n_?YE`K+
z)Ba^NZ`C0>ThAFDFPdt^^9QtodaA%F%=g+tmw~0m!W>{q^tWY}UrcB(Owb|pT+cbR
zJmCt%c+yWs^o7w&1Deu__$6NMHCL>Y>B=#2)!+sg8-I7+vBZ|$miIIO19^K^{9nLm
z=!>-eo}w_nDH+QCmaS#g=0HfK^Vy>-LO3Cn$^6eFVL$U9-fW_Z7wlU3GI=s`oH@{x
zA>G?mdfFP{Sbo1+Zuwr#XQQ`FZfg`erXAPE>)g&tH_DLY;9OfdTnDNs;x2+CM5%eW
zmgFI=!lQj)jm8m|E^A{kp!We7ORAVt7GAy5XWd;?25DD{!zq<wN(g!jebY#&MXp4@
z<czA79x6dkKD%feBv-zhRjZG+CjWjs-(V~!Q$e^i$s_DE-A;%rkvQ@=JslmGf45yw
zYROMQINN1K@|}}|{B;BZShpWGyk2L4I#2r_l=A_y56fOf*#aZ`NPZ)yLPGKvPhPB5
zcY7;{CWT&DYG3d5bUI^`o`a95n5hm2hVm=NN!o56ef31ow_bN|rnD0KXICN%14nq8
z^s39OF0RdU`44+!pI2YGR2Z2QCW)|=G@#u%)ZX(s#T9o+#IgF>+YjfnHN-@DHSFwi
zJ6OaeHJ+7BEI`i|<D4yS)<1<V{IeL~a8Ye(4jJ*3CR9|{4I!AY!Afpi7FEDkGoLAH
zM3}OPQ$Vc6lJ%aU5A$r$p4iU#Gj(O@$ePO78me?+9HLT+%A>~i<<q~ct1d6?8jj=o
zNAVc8T?cn7Z1s4ekgCl11qB7M1`o*AUY~Ted|*AzS`cDlj=b#Xr#=o1CgL+0M-GHa
zqsi;MJ^+WVjLt{VZA-Yd#;BCa&LGGvOT&h83NtyUoj`iP6KIA)CB2M!4HvT$Q}}bG
z+tEy60BZ%hBC;E#l19y(nNFt-AGg+5o5Rn8|9m%4&vRthWG7}r_2@4}--2{9Q1(Zi
zG)tbp8?T{pTa28|{JT`<Yz(SH+Fq^)O+F;ssmIm{01FJB0S6smm-49B^<y~q_2Wnz
zP2=VW>Z4F1#%yOlSkprwtb(6_v$iOL*ku!+t6bO+)k6mA(Z}%z-}5zG)$N`^duP$3
zqy-%ROK{HI!c2ch*N1!;{~c*9%0W(p7zYUTMB=czqy1`~zWFT07LXu<<3W*%I?#Nk
zW%wvT{2p#(Mx*lo#_8Un8RR_CR6I+pehz;7L+@<%J4*tV`puHR&!H<nkYMJ(zc{}%
z^e<iN-bw_h)Il59gK^k$4Mi_2_5$4o&OZBGncu)Y3bHL78OwBrWB10vk7sgDJ9U~<
z5ih@cdi^%{ykd;8;Qc}tT~qQ3TFJY!&cInxT$J3x&!)?0>}{SsT!)g+#fnodX3Ea{
zb)NY>keOx8)c>%z6~X|XUutj;T+Mh@gx&YRC5$5E8UkgY^2671k-U=F1P-=VWhF7!
z5?71@P9jz~HdgoRg!F^}5$iXpL;Zt_<X7f{F)C|NyjX9mG&?>q<}3^(JK*XvV5Lsu
zOw*0i9RU==5Bus${6PzI%qw4j8NvRahY8|8_hZ?J_r>eCD~05>3%V-!+l5E_wLeM~
z*%GX@ROnekYPIZRWiBS`_*dKs8M~Kz(Qgu@l90iS`NQrrhw>X0Kf0Ez>oKNU&6XBK
zgr%_fQR8(e!Wa&7J16+tJ`w)R)+}}s>r^D`Fnq*ZM2v6-pc-`E8Uie;eC5xqvh&K4
z5MmjiVQ1n|%E7oa$8iyRn18>Z*ZOEl1w7SI2}xWG02iG<#|c=O00Zw$qlAa#VPJN{
zh9?mr$CI@~HWd<Q`N_SY1|<*(0h)>{)3yx*uKoNk#iOr638;#!)99gxSA*P(m^ayY
z4~`p^75vv-{yk|u*GUG7!EC{MJ|B2O+pT&!EsslEUU3qh2Q?^pxQY~|V3rYLnt$H~
zG8rD%saEg8WGr0oTTvBOXeICqN3R$EX^;O1`dA@o^L)O8?$W$^6FFMBe7B9N>SF78
zNSo}CiKeTjk@~oU24cbmPSzxQIK>%uvIPb}u`W7glQDmLOJu#WW9P}8lOuP2oH;0Z
z*_AraSY6+{B#V4vXP-~wF4geTRY_Fk$gyykqps=;76s9h{<?2DeNnz>9@rPiHILDg
zylR%H)TM5h;B1x%&5x0F_#1bWo{m!ssc+$j=zKaasrRbKB^G`w5@6y*d<lk$5YfFE
zCicDbEpz8-bm>Xe602mI(bWcgVR9zf4|5?qdn?OA#ZjymRd$sX92s3tOxHO^auP++
zEG`UH{H`1pDq}xgEYc!1?5s<Y=%d%`XkjC4Gf(*-K$@EQcFm5aYWVGG9Nl4IMj#m@
z&m1t>E+YLe%3S%!VSE2;u5#IEF4iT>`e=vA3`4*2t0F&x&y`^=38K0`IaP+?AZ|l&
zY{FW%L%wyK*D@sRxXe7m%s`;dQxoCTQv6ZYwMns4p1h%321NQ4D4mujCu@04IB3EQ
zEZ50$ib;&gD74Lm&eAEGVbU3!CgZ3AmLZ#?ljRoFY`x7U{KfFI4ONQmiQVM#QmOWH
z82FW0U9P#L;nsT?BwrqHdCk*_6`yO<>`osyn%-c(CB{<U>;0E4)0ek@xt6?&4C-9s
zj1@MuY_RWak3uIn!95M<cuYWL4hwzihjO4w8o31|3%`$lC2CyU(`q57?sj@PVEV$g
ziGLS2W;7{E!+{OPlE=+H>U39$g{Y|ffQ0@^;Vq?sn;4Qs7BfCY5K@DiRXmm#I|8Mx
z+u78%esu^~+L{}q{oilMg>WlBvTzC4KaoW7UD?*_y0Ws*8|s<>Z63(Usc1O$i_bTQ
zqQ_A=OwOz(*A`ryHx)YRfL66V)CcNxX{68G*udG}d57tAqa2#crvMyO^YtNGwaiqs
zdXDauVpf9{?i(Bg?bVj}A;!<GP0Y!{+Qj7t3cn5Ei&5MldGg4HL@iMWG)f&%PV*yJ
zs2C6V1Ic1~QHs}-+<4qMC0c0z#WSUwI)jRC@0qsskb?q>zddEZ1R4~TO;K%q+gvNS
z`kj6_L<GG^6s@F;3zPDVPOL$zH-4<1Y{x4$QwyG<nN)a&g1#jHl1+s~0zMYTwkLj+
zu|f7v*~e5fm}XGKVZ|HQYZVjx{H^=ee<rLCID&-#U#lh44_LMCsV-YC$GVA_nl`u$
zh}vvJ8niP142p&<r}sBJa(jJbe?JJW9o7lw?n<G%p)%GAi|ES#1=Iu%J%h-B2KSZR
zsXOY*Sfbm)9`z4rQ$bO)VsjEEO^!<?yBIua5Mmlj{Gjv^%;i7LnUY3iVaypmylK7^
zgPMEWrqVMP7C4P}EazF(urQgU(urE-##Iq<3+F{b0iErcx`r`iduiYvfrdZNHHagp
zf%W_t9_{N!@a$W33T-6BybNCR7Rs$6xe=od!srB?yd1Qvm;kTwAL&iq@MH0~uu#t_
z1n=dIjUKemS1fpi@s#4zQf1&&7@6?wigSzFNd41)lR*IZzGywb?V@)@ur2tzh>fHn
zK&U%ibNk2CNl{y0_uP%5)5;3p?SP_g`5~MW$}EGA+(jH6bu4HV{9|AfqR*6@!UjVL
z=0F&dTR9--{kMiyWIGs<zYx83s#1)uA~j(CLSDpIBCjq9Oqq(hoh|4IEEohnGH}jQ
zokEHqAA;>dEL@c;IFbG@>%c|y8@@~-mVO2X-b+bqe!3BiQ_o5xC0uEeVN_>NL2Hqi
zV{ABh6S<fHJCwAq2#iqi-%tkfmXf&tjo!7Ac2tVx==l1zigF&UTPv*KZq?V9nsh4A
z!$}I{iCP|(yyr~_j(l?~@^Ky9NMuUim-#l=g8G~%+^Om>E2akxZqM9{M75Egw@NRB
z+mu?%FSuSwT9t=(Oh036Fz`tvKftXIeHFgwWE}q$HBlEu&(%pFuqlFf4Lr$3Y=nUo
z)Az_s?V|;`IK~|I^K>4fk0bqVetQRw;P-zahr)k3Taowjq<cbs*}{Z)_qFDr(R+K9
zQ?Qw=g~UV~!9Nx$LB}ze0t4Qj)n&bim(IM1Vc(;jQd3wqs8)sUeXNtmV#)Z2YUD-Q
z!kC~;(NejPMTwU4a+QXHE&#J53s*%{O)rZgXEO~OOn+w?DC-8-R%8Dqsnp8uZf2;U
z=B`YJ@(9&Ps#wgZdtU2NR;vpp>U_O<jd|7ei_$a_uYlybe1@Tx2acf$O!UYwmV@CF
zp?LbbK5wlwoD=e;Gj8CYy$bn;kX2=UGxYF)>ej6bYO`+%1-PZB=d$8b)>T)v9Y9^X
z%EnVy@$oaOQ%bYmAc!U>U4RqJqn<|A&Pup7BI7<vhJ_&%2on8$JWZPX>77&W9QHz@
zb5O0fa(-L&lcw|ON4kYL7Bq28@arnxPy3G0?R&nmO)3v#XrSdfBiO*`#f%x7kDIea
zitsrf6eo?4Ty*vVJm4b?#}K@yx)}%FUm-ur@Wkz+wpSQi(1KIZw0diY=PvE`0MS2$
zt!i0Fen++?k=N_>qGiZ}?cm}9-dRT`U`;z^%45O{yh|rSHO%~fVy{_^lY5sLRqlY`
zjMj{tCevIC>2thPrzL9^lX0qB$o5iymX+)k(-v^lP$`}$37?lEfeZFvA^5k$Y;y^N
z@L16TC}X*$&gZ>CxF~{>34L^K-~aeWTuhod?q0I&T+ma@6jHsDhiJN9&nT#S=ZPUj
z?fFPVL_Rc?qh<eAYi_6WvQz(e^#e~A+TY((g##Ytz?6>&WzG-!toEZ$Ca@em`~MoX
zy~#>$ql4KkX2ybr8c&;^Pd?%397qm7aV^u=A@|TWQ91?agckmU>m*28pfS0$rt&`X
znK&A<wE}9Fx@{<Zi#>`}vAXvH2We~l7~+xjenrxxf*z`o9gh-%Ie`z}quvUZj#l^h
z3ZcR21k(x+d%bKh1QF4fA+!^&-Tift*=DYIfnz+u3q+}+0nOcyy1J2!Q&`?eQeplt
zFdJlQsGeq$+fRSHfhf{Lz+<Z3c)zeYxv)J9@9Wx*?i60d-Fy3ha`^hEEg$DkQD23r
z_S+LyRl?}u`*;I}BYsL9nMz85QjbXQ>c8Swq!N(0fXPaGgZ6kbi{ISrVSkBM?y>wx
zo?bk6>^yA<hg9BCy4~IT8+IUYuu%HIv+(;ft_rV^8qNT5_}^$0`RIa{_i5%Yfc+l@
zMBMb9>*eEnpbCzSypOkmMeL=3&|uB23O9udqc({}8^3_^?wlN}L5=H+E=-3<03hUU
zkYgpsHWst_+$SGmWH67{a;H@4xIUG067~Amna&8zF&meRw=|%_L4}1J+0{1g#WUf`
z9?7^MHnsM1DnUq@kKR~ESbq^}E6OxzyDaSZ^5(d{yVS_k=BJw=k;z@mmGavSeR-$o
zn0j)G#8wLIaHt%Xu*=Jb3UKD^>{aZEkweX)!KCJwZ9a+{MT|w(Z1D<e|NhH~;B#Ue
zJu)<8VJC?s^mbK982oz*W8(jet-reCH8jTV6+zdvr^u@jV+tW(=AmI9(bM*{!vKWb
zWHY8P-ek=5EEpLVgsc3$goZIhtcuixp7=-9G98?3?RJfx-VYJ0AK-f&t(FnBKR0V?
z%v#l3s}B+L=}<b{Z6@9t51zjNnzWl3<0Apg%>K^oeKjYAzpsCBLM~%<v1j4?GWthU
z_R{2|^p&;ms6+nxscT%v>AUius0`f-ADs5=l71@O^K&HVdk;OF9RbVlG99KYn+h5f
zv#pp3V2H%RlXL>FBlv8srX_El4RyA~$N>rH^Z*XBl`p;JIh|nrhv5<h-s4)J$jJWP
zGfR<+o7;^Jn=o*R<?xUK!~vY;wk{KDDg|dR7g;}XqxhJeeRfa}0LU4B@Qu8_A&g-E
zO3W6yM$0rO<%~)$jUpZy50g}fEf)+GletV7jpXx{6L9s`0G--tK3rtXag6;ic`HM~
zdEZ&bk(GtLY<AsjJT8ERKDc}UD%&T&K(vv1HkYc@wx}Jh#~5YXw_i^g4xjj@FeJKu
zSv<+Y@(0gi^E+F13zF9NOvb(o^CKjN6ZjcZHqWqN=RZo;xkWYt+$fdM0XzsSDNx~;
z^)`u`U<hxSOf1zET+)uDh2U2L2bomw-;ABC9<A%_Y5j=gosyMc;RmLpoJ4~~B$JUT
zaFldgM0ONQ`q|72|Co|qvz$ghe`1*h!ToIbbgT?^lG%B-O|61&x)R|W&vlI|<84K`
z8=FJ9ghdsm`lU%G%5B5@7MXKr#7p;q_~{BzLw?PSU+;jyS-k&&^Zx&wkTQUGwjcT`
z@SyQjE~Oc;fmmk=H{%H>z?<+SxV+42nW%teiSh*EiA7GMiaNv_HEHeNx63FQwCI8m
zPmk@K_}2!sVq4U2AkxjS?9B6UQ3aF%R0$|5vNy>H??M(d9Br1ZMbX3o_=u{qcS~q4
z<k3lt<?TDPGh7^hny6w)c?T7(2hKCx;-G$r_*l-}2rB^-Odq!yJhO+hXnlOz^&B4P
z_s<K1*J6Wb$VcPco3d0LS}YYR6VpZJ;nLq`LGB*9@Th?|LJ1+#N2~OeP$zlcEyhj4
z1u5`EzN`EfXC=1TxA`jVK6yj|7n6q(=l=`33?P3M(KvY>V(2kO$(kSIrFYja>f37|
zMPD<o@n%fVug0(o2Y4!ikQqPjPhu->)Krcl&0{4PvWS*xeJg)*-0~s75LN_5e!2U>
znx8GXi3SZrs1uB;@H;t`{czwXFcSS}hv?!WV%BDHEsL4@tXtQMTlp)CCa+<_q5`J&
zr%o;HA-D{SRdYS088E&=8WBbd!_TFEl7~3Oz4}G)wMha;9?OVVCSPSYf5~C@qY@g)
z9?40spz#mqzi^*P=-DIbOh>aDXxnrYVDb|Y2vVfK6c@=82q@F)k(IQ7%XmaHY|%7v
zf&nIe201QPocfQ_4Y{pwfiTANjORJ1nTiq@u(yMo2=mxkDCTO_((JSD*U>~7BPyxK
z9;{MzF`3u7#A~&s6i<vLpsBLsKeQgqju^4@|3oG~NqQ<-8_mh7&^^XyR-H^_@14PP
z=PcLN`?HF(Iy(h!NPX}afgx`qW7N)DJG~eY75z`W?YZk?6?zK6)Ve({sIGhHKjSvu
zd_ymg@o3(n07t;s&wYW%`>#65YlkrvPJ+Io=aX6~@s~%SP6AoRj0ANDt790^?JD@_
z9?61>^@iTGYaXl=dQaV|3>UBuV2-k^@PO|p<G8ubcr3$pYt7qfF8#wuIB`$K1RW<)
zGjfstibh>vUkLF(*lUkF;$hP4bJqK_3>o`2t|CW;V!<UER~qNWp?nLtxAW~Ala74Q
zUQgD7@S30oCrHDn7ZJej+}Q!-p8W-=FeQ?jt;53Wc(R*6Yjk;n861+wM62u`EfEb~
zHpL!~|DPA&d3lWL<@rOwAlFpe)s6jnrfoB%^IOwg(x`ZnpLN9eVPj~zmo;4YFV%hC
zb>Xs|u>31XwrW$+;3R6O`;4eHLIYk4X44<_-%LOJA2#_VYuosjd?vHK&CeGW7TQ{M
zDlnuB83G23!}+k8jSq2q0uW+szy^)>;0DoL$QYyL`ehAWt7Q8*B2B*8Vw3Yn;jMXs
z)kxA?<JfHdP+?lo(^Jz^(`vEEejg{IS}I+)KDn?y;YT6ROY>?RALTvr*xY*U*vc3b
z(Jkz4-e#q2-G}}5dAh^y+;D5|(r9hE1ih0LJt#V;o4gyB*7@MSwaNW>ZUfzS`MJVB
zbc+yZX&|zr<A85Qsk1|X^w#|GR{YV&!QF(vT|jpuT<9HshBeMS#p~RG{@~CXf-oYr
z1}|$yn>q2abT1^JL}GI9vH9c3G}A-A5p7>U6-36v9!mbayD)_i{8=_{>3psB{%ieg
z{C4&D73`_tnY>OU(Ld2cgGgxL{oe#O+_yR=&O8xHjsz>w17E1d+fJLtN4HEq%+GMK
z1NOAz1zDsI+$DK`90_=`AK&6_SUKL@Z+13(?aB;D<>L|?rf=3|d$tL-{5o?Mgfa#1
zrkA+x0>Ht+7sX<0Fp;ahxS@S2*1(Z+Ms9SLqFFJ|8SZqYH&NVh{?gxIu!N&oU2?mS
z;v%v3ZY3jBvW@ys8#kO2|M-6U)Lz8qfag<*eUa9oKBlMgjTHU&xS=;V6E191$oooH
zARz%On26pw<Isz3fFYLKSzb(wqT%*Khi^rlc2vt52xVb+$)PMV@X+{mWo9OJxcFh<
zN3xAzGAB>FrTe(D%^{h0-g;ewcc)}`8Pr^YW_8{^0m9tv265>&b1tS7kT2=>2iZ$|
zD|;4o8*CxzhnSE$GS__!sdPeBe2#u~0ks#h^J)9>`R>=IlNZuI*d8a{n9Q{N_89`~
zj9Bux>mG<bQJ4b4i+fGU6-T9W9*M&`@z0HFkI02ksGy<L?VC(s6s6*(T92SlwjX8>
zO6*<W`+a^SETMmj<8###<<nUm9l`v;99$Sl;U<MDp29st`AHhTpK;2RhCSXP*>9%B
zfrdS=$e@FpiXnxj^2f;~*&8c~!$dl#Wu<@MMtp(iDy0S=-m^(zYFn?)@~x;{#Z{TA
z+BW%NV?9}-uv=L=mydeg^|F|UlX0>LXfcCcqt<S{1ZYsye`=>D9Ch)x{qB>KmyoQR
zo`s@H!>BNHH+JFLrko$Y$8hnxAWShGdK%<NxR7|!g^yZ&la{C%fX=fB))BvZ+q!k!
zSwX;BJma))uHs)<di0`2UH}K@L<+11LPAQC=*RJ?=;Qh;%paT6ye=;zKb6)+eWHhD
zo1-V)#y0FjG4||e%QqC{u@ukfvfG-cQQ^J6XV(LsX_p6Xg!9VNeH^*b#nr5SAIsTA
z?e+3~GtI}Ww0y(UHSn&3$*b;P&qK>}j^|{4QbK7o=rgIak!>Lb(W7i~N?JPqWi0h-
z)VcIo9ESqtk-K9ixm2hkIgt#QJ?ZUOs~$0h@@l(}93-o$Fm;2#-<+p#=qBSgsObP8
z$5J@_$H(cmgcGjh0%IX1k%5fH?@_?vaD=Sbn+ad>=&9v7lCl&eLJPe1Z@Y2XLAc*F
zlc4n=k{(csU)?;LjvezYU4EH1VFY*Dx@AkQF!1(Yg~1YfjCEGuOpZVcWh^~kpN-yr
zzaEAkw2fH~gekYR8aTBpIre!sh^bWx_UITSl6Y5yzJ+Rvx&0JuQgrz1$nS8S-=p+R
z=*QO(f4|I6=D)~DG;b{$yBW!YBsygP`Wb}p5=9wduhC0U9{41r4!~Eh$u}y3Fbf&G
z)T<4L*1dv|&hB=pJo~&HVaQSivrnNHEL7)gFX{R5lvL3Fhy_90!PfoKOML(>v4^im
zS<Zkp+sfi(NH?dDvjdez-ZT)oE)cs%y)k66*V5O2=aN{@FizzcB70{I1cX%5d*uiR
za1yNBtoe#Q-liJ63q?X#uW7?Cl?MB5FMKjfB;%<+^pc;L*l+^Eg98jKBU0gu&`D!=
zpGEC@hmGMJ2Wvnm_QP}3MBP{5EL3DTwGh?llwC?i2sqIqJfdaA*Ln{zJ->m;JM^<J
z^KsmRx|nH$oaV_KOtFx^mn<p2Dm#Gv)uWpBqs~grcV>*{MWy2B`Pn@Ar7#+EVP6xN
zhv3slEXVwv>0qUcoCGQ8BG;ZTbG`%j?N^bTe9}vf+!jI7^IJ5Gu`t%OhCmEet)YZw
zeLnXBp1jmms^U!*uCQGl>MXkcXS#37D|00_W*rE&Fxm~xCSv?MX|E&gVhxM%>pgiM
zR*BDJ)S7<z>EWjJ;Zy_aZ;o&m9kaNdeP}3YHfs6T_DV5XvU-Ln-pub(&uIL9WM30z
z!{JO3bB;d|eu_e0V?%Yl(!UO&6$~o-tiXej2>Nvl+nR+29k6m_<fgKPljrqs`QbGk
zyq`MAUOoLSy*<@W>*D0dMixy(_F7h7aTc7Ilzv6sX1ZukE?<B5@C>%R1sgEbbc_oa
z!cQX@L?>d+IVwoq-^*=s9Od9j@@6O*dE4w1z7U#Cfu2X(sAWdT8s#;PI4k%h@Zu-h
zJ7+qhx|D1;lkm8fab;kq6CIXDo13bOZ4M7qewB=hZTUWYM@4M%q9q^m&SWFFDDSg#
z?b3l2Br*4k@a5wFz3^V`@7<K7zQ?%|p%*QWO52n6W4?PKUiLm?z}TW{+0h(K!sP+h
zkHgA^IM5^#`7=IqBEuC+!z^j_0z})CDZW0Tq}!%^5{{1fcUGEaQmT|B=Rr`FgHnZF
zUyz@Z8{GRk>PriSF%;!L3c9jFXY<^ONLvB1hnOnlg}al&WcjiUi0WTV*ghtu>nVH{
zX&w#;kaDo#!#pH6F^jv3Uky~(@)=MtUoWNGaHvly8|!WyNKg<)GYuF_Z}8?6%`1$x
z5wy_54Ep7<iIiOhO~k1+GLY9vw0sm<UM7*&R2UE{u1%exFqU@Wt}y+omys><f`$lZ
z4hra;Jy}O?%0ii{LCB`zy|=#M$Zp2@U(sPfevhZWp39InCeOc;Ao9U}_J!Y!tWmY-
za6b2Tb^i1-%^p6jl8_dAZVJp=KR{2LLQgfIRh*UEgkNWr&V(5nrqPyALij`a;Ep7C
z^upNCY{42aQY^~bwC^8I@pt{-__aX++&XaxauzJfJAPi7N(4bS8l%FuJo@nA9qv~$
zaH3VV?j;3xhadHp+&<@~q4hhTA7t^}I!*n{IhGh5*^>-G>;5g*&h$OMn}@kh)=lD)
zj&`4GC->N7_yrRvX$PN$8ZFP1BY&2INC$1lYGbfPoZWPV#qVr&g@y0vZLRhy1{KCJ
zJxxoiCNH75e2&1tz)ne#7lG111a-2b?QNgiUw2z5ccNXk!#9EXj3y9+bLxK;CqM9v
z?^HQz`s~?nJ7V-|3tXH`vz2hFP)B4nk+IbfJL62qhSV?|OvX;qwcRc#LB`UfG=*x>
zHg;N!f1Ol6{RRA+$}ktt^5jp3Wrt)NmIK3IoZXAo*}Uc8X~Z65@)4_C-&a5#p!QSp
zQh}6)r{3$`QpkqC^j9+iN*B0mPx0Xa6TeQRbgPZIvn3_#6(K*xOZQ9f%^}XQV`(*h
z{x#R+O!G;T&0U`>M29I66QK>a4D)3FSp?BnsX_XlXyHyuh!7X{6I)Vjk-TUj78Gc@
zzk67<NxXs&`*^0@3@#H+?1|WFc9WWal}hHatn@V3>~9|?V=QyBXD?SsD#fpIaiPm$
z{)$TcB^F|t5cBzJv*Io1$b#k{XU_9Q9sgA>G*&-)v2OUQoPYUELcR-k3+xMAF|=M&
zMqf-X6&&eh`WUJ+Ua#S>TXSpTY0KrO&jobv1;Ja}pX=1RTJrkA*NS|fY2My2jh(6r
zn`X#Z2r&NUwXA^})P5I*FNE2|J1|^#?;l*aT9J!w6Pql%a7J5#3Hh_;@g%g7`1+bO
zf<g&1ExzQvzaH6X@$rXr9ydzRm37u1+mtIz4xkKp$F{ooMO#)I9@8Zu-PXx1WQOyG
z6t6>)4<M%oG1SDCn65Dd;51?t&-vRhKLX*xmy{PN7d@ucUdGkE+5gKCoTk5kXu%Vo
z#$YElL}PWG>M(%vYg=P&Qz7YmYb;}&0INutI#SDyrh0X$;xK7z&rQ{e>v`CbHV)A|
zT47A<qxzTCx0@G9|9$YpKgXyizjjdGqC>LNp3Avh2uen%Felidy>-Ct@E@jn<IfgF
zOQ-C>oZ{pk8yAffCE4fk?~e%#!PB2kF0~Y&XF(S?2MH92#Tq$8u1l$dQ0`*baC+_X
zC0KAu=#CrPxGmZdG17xDDz5WC(1H%;TYY%5B`2+*E7#`nv5FK_s89OyN%BbCCN8p~
zDGg>@VkZ-5$~UsmOFDDJ*z0LFRk{bO(+43rNElGnrNl%|Q4g%=#TwG5Ytn{`+!e!q
z*D2Cmx42lG=FQs69f+0;Zhh?WzPU_xx%;}Z5B%I#6g#}dQs>P@UpnZ%!*8)IyLwP<
z^`6T-?PL73S|-(Vdb%ab$}?SUKl#TZ_>s|cGbCQ`XA2+*{Pz$Q$r0*cqUY$B7ZdT2
zt<)UjbxpmJmrXmeC`5M320|BfKSMfv{ba32u@fD-#d`>xp1PU7HpK}Zh$RUA@1|By
zc4fRV(T21tJ0c_Eh*g;dd>XY+52`VH&x4mhs3e|UBsfhFh+2TcnXFGw(X;)5+m#-d
zJsun4^}Hq5-DpG4*YiGOH=k~=UV{9ask-CKtRM+va`If`T^QE*wN$a10p?nr9yD@W
z77-AEx%JbMhHqx&UaVgF&|+k}x!7U&3i#vuT#cXlMiS7Bq^Vsp6BBRep~_}`gRqrH
zoE6pNOImpDIf3`M{Fi0s-tz3)3xWBhtbT~YBE~XPJSUI&>}4I6gNNzxcmEW8?eh%N
zD^}e|E=PU|#9=6BmWd^!`t=2KL7HhQH%I4ROQv8JWxGqkNAn^{#bH!ktyyMX3`+)x
zAlw6e;YK0H3T5E}ckW-_D&)pNhRQ)FuMa01E^3Rn>|SNfGzQJz1?Uk}BemXYJ=eKG
z{o>&VYm|S+@lvv^BRnLN*a*eun)TrkA2w1WT~Z~@(vj8fld|!gdw;V*xdlKBjThbm
zVbmB?z$oKo)>+`<N76=uqiwdaf0p_A*s)pL6)+J@9<JMD5#)2v*OvKTJ{rXusA*0E
zHSkb`Vh}-X4}~a4s@N#obx>{)VtTey*X=L2tJb83h4oiIPxL_ex6M0_fFOVKsb;|O
zNk}oSZDj=38Y$J_i<o6a9Mn>mj1#m4Sn&M>eZKLbCqKP&+3!UPW$ni0V=aFUN50MA
zRUNn_GadJDU3jFS$7Jm+o`HWR-&8eV)iWDxxN~?gPB_`PcMfzch9BhNb`!)803S$&
z@<`j_(q}g-lAl2`y=+cm7$yJWmB%3=mb$qX+frAdQ@$c0Cq5kQ*eQi5=&_BPz?u1R
z>zL{{c>`SYC%4{qOdPdndVjK@;M_tr9nLvzDcIY|N`++JkV7;hI5Y9uGGQV;mYfqx
zSzbDswPa=B*-LLJoup|VlxCF2IZtCy`k!Q-^dUEb&|l(TGN$-fHTqJ!5XBk-0w$!k
z0V#dC;BHSFFOt@0kKmPQ0?dwB0zR+`2lr_2dZA_6Hbt`~h$ey|hGWWI>qYDJ&&ova
zbVk+5%MZEze`>QU<3DQG{`mUosA4wT$nLe2ca#=Ed|9HxxJZL{8k(jPB?o*2V$Ik3
zwo8S^yx5DY-#$jdB96S!T_d+=*R~4;C1>n>CRHq^j5H#|C_Y138F7Z6xGb(M!!~q&
zF-b`sh{IZUOrxiH&ND%+Q)8)g)8$Oimu9(AAX2%z9CZ_8EFCDwXBX&V$c18rfW~&G
z2GQ8JezhCIxS^a+4XSwRTP&?wKb@E4_tWtPJD-Mo@D|?=<j8|vL3OnT-|{uyzVB4r
zFPTgc96cKI`q$iQN15?hNv-X7)D`o#F0rc}cE>p7?MyC-&XoK~cL}Q;kTMG@yZMcB
zL+>g~vfq$A(58$%V|7*r^Z^>M`6qLpK_Wb@kMa8zSxmgT%rB?huT%;XH6TXTlC*t-
z@S)qREX%GIvXLsgYlBZ)>cGyJOV>J+b0mYdl%@;pJ&6HUe~(?CF=z3{{x@YZyh|j?
z!)6+FbB$$Uik0T?ny3IJc~w2Y436l0|ERdwSdH8j?&IlUFw~%{SjNK1J2`WE#Pc-A
z_HIk*SOy7Ut6UHag`|IB0u2pq*>}|So+T0R8;pPfmtxF}zO>rHaPO-s6D1kzdf`g@
zOo?NyVGX%7@gs{O8l<e;fEMT6c^c=SE+S_Mo}?6LrqLCv`>rk$`!=i2#vTsuOxf-m
z1s2MdUNI9ofK1JZBZw>gkhOU_mUq>^R_k(WSC44EAN}3nD7qebXW48M7;lvH|9U|O
zri2VlqdaCJmpqLQgXE}w?F(0iN&*r`1+i=Oy01^XxOME4Q+(BORf0%~DBI>`d{%~B
zLJOCoB*`VYN>82$aV6}ll@b#Tvw7O|=yTFdzM+uMGB9;#`DLY-j_4C}s=O$d>`Y;f
z<#NxM&}y{P%$R)sr$jhT_<QgeNoh;o&yvBotWCzlPh&B!*JB72sofdd<l@Naupp+3
zjiI>#(j^HINqtd6&q>EL)_0rPuu@jqKuXWIg5?~W7hW|Idf5F^s-^U&hB<09`+PMj
z5mpnYXn($pz47?T4m4V!^f?rXH|Z}fwqbeYS1;p``cX~lP}q6>aMxg!bmr>ZOX5(w
z_{%)<f5*1Uk5whj{35LNzt<#Va}*&{AoSG5L4f71ZmeT)Kuq<Zp+i(T3En`2Tb@AI
zW=S;G4Xb04v9By0)f4K9vHBK>WYTH*_%{w`jer}pTSxq2lal|JMKF}SG;GbsIAR*o
zpumXj!lS!Enm6q8c33tHca?I0nq-Pihf(r*JDBjZ2>~^ZoOT7(zu}Vom5i5WMfiL7
zgIKpH3Z&SS@QrML(N^GDqU5nkVuKSs&&;)_?HG9*`&7tnaY^D0Jy$~%0y)pq9vS*X
zc7l5j0&gHtjr4<*{_B7!0CaOUE$}G##iwY^+2F^I7(Pn0{zN*3Y3w8c!3^dv!Axdm
zeuVM?jW0MFECtA2et02AQw@p1_L%8)g1otp&_?=>VvS>nWbvPu+J-%GsRpuj)u~j@
zbCaE8ayCCbq74N$UYB{DD2|FNph{M~nDRyw<=Tpa<uD=h<hE?2&?P|}(xITj$L}u~
zzsZ}UhCdz^QSb|L(VuP8SxAB<z3-h^bjVex*^qxT3npr6&=eZ2piA=tfY(QB$O^5~
zw}gd$%^|*;eo8K0jFbdJ&MFaOFk{q&FKHAO+_N&+&O5a+*oZHYuH!Ps<~1^Oz8lmO
z-mEL%FtFo`tsTTrH$}WgH^3+=^9SbxLP;N%rQqqPgUB5B`r9j6r{~S9P7~>xnY8$x
zH}@uc%=20y|B?oQ-DR}Ill#4cQB#rgsO9H|Z=}lic#O`VVLU90W{x%OhsXCFsoIE9
zZwrNmoHirUNd%o;elDHu8aAz3rfhPKOcBESka~-~-~##tzLY~xSI74I_XyOXypk6`
za$w<h|8&Icl>5&+7X}fWC*usNFlhZ-H=~2CEHcn^lsIA;)o`N8hTb(EsC|lLwWcVu
zp^IBce)B|7Tn;Z9xJh^FsC{;cdGGgLyk51@8#Nt<UBdI}zgG&A7cm}Oh?<1R=_H(N
zc*H!igYC3~^942ru#ruRrLeUQ%8ztos9ovevqX_ubLWW<Yk$Au2dESpD;*8nwMN-w
zOmmw3Svq~2!?h`S7M(LM!AUTrJ8fzJ<J_P?+W-to22_ATcLa4DFU8o-bPYREyZzlP
z!2)R8#3|!<O}_;zF4GWNnZ4_u0#b7ORpF155;>a6w8)S~0v;QBj~Irdf)Zx4kuwbj
zO4HBx1u;%$EzfK01AzonC}nPCf6}t}2;$Jh>{tECd#MnG^WPo+AQ}$t$f{w$`}q>V
zLBJIS)Zp1^%E2VkJ?XgfHq)5wlaE~Pm@(tVVdF+kro!@fgM3Q%Bsa7++Em~h*s+=7
zCOd~!%}JJb>;X})SM~Akh`%(IZKLm)yRk|KQ-*52fOGbPuyN0rk1=^}E5(5u!{pSG
z_Sp7ET8nO8dZ8FI`&QDPq_&Px1u9{;Dw&uJ+&cPgmB-Mi8D_;GM-gQ?0u3xLE=9gl
zM3eUlgi%jIn2}5V{<YrHPQ_Q6O1Kf94-pHL-WrBZWTael`biFf&qotJPfo$GMasU@
zufW^;e9V+AvfaU<T!HfMAHHlSV7eeW-77))*q?Vf&QqPzDeKCK7owq*B3-)7KGuF&
zi7JUkW6ZI<mA>36+h>RHP>3BVwja9KO<k2F2Q!96_lC!_-VpAp<6d5JpA+AEbgnSD
zCTsD)x_;D=XqihhkJR6N-}!|3GVpcE9Xr~x{WAR=zk%M*=+2#gv<7(~UIad}d44~S
z9<+?ZimZnwF|U{j9-(6R<Ep|m@3DSEy$fI-2i$q?W}maiz>o@%1m^wm)&l|pWaOE7
zVWF4%RrPG30zWtHTDthM53}DzH41FbgDy~$AdPE<Qs02)8Y>CZNlNKN+5x3LF<gUu
zecBY~|1u8Zp1oJ%W%(5!(dc?pKI2fCeh$-a`8Tscw{n4g*SJrMjda=ZA3R^c^R-e@
zlf$V^#@O2I<L>{_><bt!wU$k;D83Cd^wk({aAhOc=nLotj0X{=^l=ek=(LBe8DzHT
z4X^iOG!GPcB#pn37K1;$ft}6bjX#1ni>K>Xob<v%%pioj-8Wj18`VX#jQHG)<j^5h
zSFl$3r}~_A@s~ck<}N!9QTeM~_$?b9)0#8WtnKlj;^?GJRrmm58(M=^wfF0CZK!p!
zm!8_Zpt_ZtUMJ0_BPN4C5lhdGUF6XWC_MHQS;i7sU0CJWmrQ~+5!{EwCj)bLBYuF(
z^>_KHvxO$uTvu{3WZc(2sY5nM__@kR>+g{|72=w4Pu&b?KDSa^kcT*wZT`>ja}J#t
zqKW@_tQ~YR4;R<@hw^A#m5j%!6KcoRBCWsFE+FgiD}g~kEZNe&-c^m|t)=fAM&Rbz
zsLF^j@GFUP95SX=e~VT2hO_B-%DRTUARNBV3hBYvUBg@=IcO0W6YeV1Md&7THfcxW
ze7_tb>U1aiBE<E2Y5q}k?BAcJzv~@GRHw$~#htKESC`*zGd%T_upauItL2W2b>=I>
zX_dHQ+4bBlq_p|eNEQuv6Htqajuz~=prM<=|6}OE-Xg6fq?7x&^fgybEbCl^J)S#?
zYu_H2*nC_{WoI5Pj{(a5DJW5tvAnWS;EAz(xN>Zcp$xCm9*7or^+v0r6?c}y+Wnl1
zms36A9t<xLjS=p1g`IqtTYS$Q3i9G6m#LXoB3D0W|8kJ3H@)4BPS^O=b+I$>_;s@h
zIy9Rk*ihAu6FG5JbSW`p%Mr`dMt#~+6m_m&%$1|4!Aj9#v@SesGef^n87}HH$QmyH
z&`114Pt%m7Kmk_Xp{A48rvH;&y2o>*m-riDYotbrq8=V%CK564z816Ryg!bApq9x6
z&KWwf{MXRQlc?~RIjNc{YFRA>h#1|%XCaBw+|MQ%%uj}kI7L2--q!4)jHW23qgnbU
z^CKnx&sXa`)@IwF-6Bu;9_nkOg@YLBYy-V?8!x?NvBuvBSMNI`t$*E*X7TyxK)ftj
zcF<Hybbw#vACKSZ8;swpPoGwDt*rgTqzmvwjTGlE_{%Jfv`P%hywU}(;0m!<5HLLr
zD5V=hr$JgOWWW!(+L)J-R<Oou`zQoiiU;i1>uA20{vG|VsS<FHPR_upd~zkXZ3S^{
z8`&zi7nO_7LfsAJZ`Sllo=0kEx<c`$Gj6ZWWgKL3h&K`zkJqP6XGfiAZJs6ghxKf#
z{_pe0-qcn)PW?rL;>ja#<LD|(UDdQoc2Z5xBdP|`Jc!@y|7(G!%9f0gksZyPke{@h
zAQ1@^ZnJ$wqd-;oR6Sh9qlu-)t6asj_`Fpv04t(rg6nO}>QA%Z{CMI;>OG{MM1;A?
z6V##zTw$M(3DleC8ke*12A{&FZTf{E(kmS))vRk|Niy-~d4y_2r*pAZ@eoL01oY3;
ztAxMiD1}nlJO!6DAE&*t0IGH=>sKB%F~`OiTDxy6(!ZPyhVC6R)^%c*JlEfD%|UTu
z@hEO;69|6}?a^p(Z0Hb@Kt`gv4z<+4R$df2%5rH{S*Q(n#<8PLH}Zj*S=~!WgWOa(
z#yZ>}Vq%&2txFKt)UBf~*4q6K-c{K#;l<XiE9s!Hf^DvozxHd+{I*L2ZoW?E^Ut8l
zHMSbCOXfdBz)=N4WyZ3<lpGxN%nQ&VPo5p@^U3c{zl@V=5}{Y-5!XwHA5KxiktBS;
zXz{jYln2WjXdV+naY;j`(QURbmX(@jDD4uA=7b1`X1#oALp7~sFVHOxJ#%BtIeDg)
zTfIzj+m4+Cm$2E+D!!d~pzT_do0QoQn~l*xT$-*ei7j=biFMmKS9(F~i;iRJey-A~
zhDh|(DV2lR_xQy#%B!L&>0c>wH%f;esaiGg--r|T`QU=@&l-knue(p}5)AH?5L@v}
z{@%7Vy~x+*R4J>LIZ+BuikR1YJbAyk`H(-;2OTNS0ta1xAeCkrB#2g|`SUc06?&vx
zm)zz4>g+5cfPjF|RR%X=Al`;022gDk(uT_=RRCTa!y^~d`0Y@W{#&KuIk#5*^Py_U
z?~CwC9`7<!Z=c8Z$>|WVq5tOv@R&LkGYe!ef+;ghczsmqN0j{&-Q>Q=js?f9b}cIp
zQc{k4vDwihL(di(a!yoDoNg43kT73N8lW6Tt@(=9+zLABKWMpb5(vc6F>adejQZ}3
zJJr@2!jb}#e`aetG$u6RTzc$8%v$oIw{t38_A>zgV-4FE_rXYKAdp>b=<Rcpo^&1f
zuFk%+&hOsfQ=#RmQ?XXQCb$|N^gdkyQIAFf5=n-*!6<VekwGTT8~v$<v*t#lUhW2C
zI}v0dXBcV8+IsLO<AH)fOHS2gj?)HlbzsoR7wh*jNKuQi9!w3ToVMPJkRw0+z>_;W
za^7O7BiU{)al(A<xCkVv=UbN+u#iiGdOdycnQ6B6y%E7yi!5Vx#tBCzd_K$OOY*EE
zTAFo%nO9jf-AMP|zbunX?xuh;tANjg3OD6Ml;=J+Cf3CE=yZ0oWy;XS=7(mNqY*G*
zT@^Kp>?$QqS>4=Czldn+S~@(6KB!*2Qd29b+1z^1^xKw*rckgkBXP6&M&LJuo^iDP
z*5<=-SWo-+2J?uR{v)QdK6T@Ehm=0Cr`@(3721NfWA(K>Skd(P_^ie8`WarBcA=RU
z&u*8N!5UAgf1hna#L#am6W*u4muSn=OklXzkQXzrsF{AhOB9rrRVy<x=S-AqDSq9m
zco<s|xrBVrJOEG%UBGvMWodSPg1AbshvWC59P@ras-tqV8Y^ZzX3X(pR2X^iWVE<`
z#zXZrv-hpV4fvcWNutQ1Slc@g*)wnTH2p@WLg{V@PN!4kV{D&BHi4wBtgMt62rX}R
z5TF-JA+q*O>DS(W;P+xn!@S~7#`RLO11HdeFHylD+psu@Y|)`8C7;tx9!(VX#TL^e
zdPu9bBPMUP^Ji}l9#rbFiYnieZRn*Om%Z_7xXrsFT6-&uLF3wRRPEUyLin#=8)mI~
z$Zt1oV}bM$@M5aY2Nx|iL0^mpJdzeN>1(OuBdk?rGit4>R-Y*&M1~#-l*vURexA1i
z32DxqQ3_Fqyr9!MgySk?@1U|le>e}Cl;L!tpL6QoF#2&LkC+)5uFl3d%#>bt%Azjo
z->3aiRAaybntEY!Os`EYI)N~U4E5s6*ge?+=q5ZV=5M-Z5snw_PGItqu2LQ{5tV+b
zXro+I>7h9f=E?5vja)7)sc1O$K}I7>pEQb8{kuybeoRoOvokg0Gx)@eWqjs3vQ2Ba
z^_RJ^b?dx5)U0*T&RtFADOkIsb@EIgcm7wFJR^jbA=4<^qi}Qsks5Vaw#vWY;CVLl
zG9m)PJ)+;gOs1kRVt+goBP0#oviQYHsb?902>JE)J3E{%);yfpmW-u6j1ac75@0(0
zYO9nb8bRvnI_;aBub7LyLETiSFnMYzds;&z8%1{=M9CVGKq)Kt0B>MOXmKfaTx0Li
zMDX|UIq>L0_+5k~aJS)h{$Dc`|G#EP1NuZ1N#H-N;5WF3yUvA-!o*ET(^;#S#DPlv
z25=@~<l*Vg#Y?c2Y7{}Lu-N<jD<A2)V8P8w3heml29z+MC!17ykN-?!Ffx;2n1ZPl
zdwa2Ch~ebH6anlQ>-v2Cv9r6`aB_$hGMWj1;v6b#ZW~^=_)mTevg!7SuRhh>`f>7Q
z?|AdNk+JiB`tnPsVavEWKB8d8j;CK%$xzuJEpqL$+cRfkXuoXb8OtrZd5^zR1TuZS
zr8x;|ECjUbpWr$|IdSw6{i3AVR3^)eMdyaAczejUzg!{TH}gnvl#m_Eh?;fIk56`X
zn)Ms%YhO7@Ns<CRe|oZuu@~ggpqBit5?_vyDcRkcNKWU?0>>_jHgf-5lXi7l*pJL&
zV?e86uHA<tOkd@efVroHjy5A$>bvKsZzHL<OANkS0FIR0CCw}&$b?t3H{(qVv+?>D
zyw=9|!qvdoP93aDr%U3IFzTlk;^Wvm6$CP6x8#XQPBm?~S6mA>*NV~b!hdgV4qF@a
z<xpkd;Si0}F4sJ5bnZ+oB8MVoo4ZLTmmWTv+y=v{SNB+-ltwv6Z~*V-^MiNsYdp9=
zWSt%{4STA5Cx6ucl{EFI*xO=x(gJqe0vEBsQWBIbMlMB)s~#Sz7K*AQRxCp5joo7-
zq($6{05<JW;uY|vvg=L0A!@he{;kU0=Uyx@Eus(j|E_j+q7=*%?sRn%kf9LjP`mJN
z=>@{ckXGU7@{qKzHM}}kgz*1X7|OGCpe4|c)H#1&ocSU8^SX!IMF!HHB0owTcZieE
z*Lfpvbu0~biYf62)A3OEe}#_(;y*z<X+Zc#^J2CR_4S!gIG#lQ2IiXLL%*qDi_qZv
z&>MYx{P}`^r(nrd4}p=rq#GgjdHBfN!352Pkovykk3G?veD5J`u?F1LWMF{#pJe+%
z4RPe(KBw`4PTO||Km%$%&b!mfUXUF)?OInPh{-R=qx(73fSdqA+~n@g7;I}JgG(L@
zE{^m$bq(KTy*?mFK)G1MyR0@3Acnq2txj()k|eEBe3Z|hIORGjxF@T&B5=Oswd*=G
zzU4>SaAu~r=#v3ekvD0tREPgc4E?iquvf2#RVtD}3`=fe6smHKLBMrx-6Rh}jiad1
zcWzwO{r{-?s-V1@u30<;cXxM!y9OsfAh^4`JHg%EEx6l*y9Ot?yE_E8v-5uSU!1C4
zySSjB_RN|!y}EnNl;lL&jLWr9z)8U}l2z?`((@j`G)kh7065*4ERV_@S2IA#Imo>1
z4eC2D|HzmlRCz$JvwN72O%TV73yv=T<m(Yfi($o)+Gk`mM#i}@@l4vGp>S-|`16pZ
z8F^($SiX0hJQ}I3DvU4VY}AVAkYSFeX`*P93AeW`(0cB&%T;}&ry%*Qx%*Z(=KNlR
zzlBp=f`Lk3le&&G)`8GjynlQynw#4)qP^5};!rtI3t=Kr=F4nT!jZ-8!^HO+E6@^g
zrVF!${!P348Eq>8L?MCk@z`uhrMKjh(%fC!z5CwHr9)L6X5||6Ad2={*(4J?dmH+I
za8)uj@#FZWxm#Nq(70&SQoF@nJS_5VTWR-OUsE)wlDYI-;$I%;=-T`tI%Wk2{Wx=X
z<R;m3=X{eh#-FP@qiukNGc>2Rscgmb;n>EEy;=A#Th(7=Lj%#o`ac*|aH9EQwbcwD
zsA%ki)@!u(g$umfPk%T>s)lZPA{B(p9A4s9IQ$r?;Jpu4xI-sT8RZZ;AzWHI3uQEo
zY$t9ixvmC5_z{uH{QgG80qdb#^9<!8O_ffF#su-;Wp8=&0z8o|X0mS%?u)Psv#00_
z7X<JQS3}@CG7r0N3&&)70=nv?rT)z2I^)ZK)zBuMWUPK`YUU)Wa-M^pYyx`9Wsx!;
zP^%9*JaU@z<w2qrP8_7iA&343d_hSY{q!0bxZlKZWh8J2&0)uL4xbD&&JPQDVj^vM
z)RK1Vac5m=j;yRkt}VY}K^q#F^p8$+kjEbJwiFEf*|Pl!+R^M|__>zmO*j4YT%>Ly
zaj2&WbofMc`?e(HqY&F^3^RXP|8t`)^*LXQ{sUw-&n?aqv@wK<fh9JY5(6^9KdApR
zDOMM@y2sGY(jCM!N)S!e+LmN=)HBci!qcgADz-Eh*%jxm{{Yp#d3Er7>+11_uE;@A
zB0BQheg&rwDK=5cRx(8lNG`v;3FgzjUY(VvslE`x!sh(Ip0^|jQ*NPBIzG}wHxAdc
z#yw;j-hnyZ`6mA>X`^o?j_SJ1D&(n8Hj;3#X}gYM^E>%?7@CHN7~OulpGDhy8^IWg
zd=M^9_%;RE(fsKg*5bBcgV@OJbo7uvL)%69m~d{=kh^lmp4O-@TtEcGnT&+TX$%XP
zOfW-h(Z=MUZbw$xm18lp!^PLr4Gm%89!ko8Hi~Nge+uV68wIkwS&P?V5hfFFxD^;s
zJpqqfv_({~n5`6@jgIw$HKN0C+$d19@uzSuGS<?T(TIM+j=@oiGs;(SL9VK5AfUw}
zWAa3RWhzV<LvLHD`^LuL9yNmGg@NC?GUGIqlIF%6iDxKvywuyU<%+ccb9kV!!W*TI
zX`G~NJb&3b1i&R9FLXj3OE%0<>RFfulT5!XCN?b#Jtl|SIIJ+s3gg1QF)JjtF$Y?F
z*T_Kgs)7~AjBM*Ic>0uG!=;OIzDOohFSor_YaHO7oMt<26vQ&Ie!Rfnu)ob)Y_JkZ
zU}+NU2jMIxk`RSXne(`?D{jqN63SBbm=G@{oNfA&RfnT?)Puyzm`s;*WP!}{-K4B*
zT-iIoL&Q)W1*eH#D#o9lbD%}jC_vr8Hn<k1a>P_t97YNoTaEn-O)F9nlIj#o`}p+y
zDo@P-OGZ$$IgQLO<2+p_w(V<anc0%xgnl_%4Yz9<*^=@|`W!Y&Xkt-LW+$<!qwg$d
zD}s}~3cf8DA#8tcmy)B=7Z?=wwx23qKk;B&Y%_e4AaD(5UX=YRAR=n+c%m{ugF5WC
zy>A`<Yt5aJ)Qw4c&2a)tCSx==lXVleW<zchnHJOZWjYKbo($Sq5oV3*^hp~I(aJuZ
zv=5j$rB3(Tz+MEbE%~KC(yqIk9T^4&+yk1+nDRXz6v1_v(c#3oEt`nx<=iPvgYM@E
z*hG^8S8OnXLW~{(A#G~2+k{nUw&JQu=sh>}8SU_$7bx7=@ay~k5rz^ZmC#Vy1^|WV
zT+F<<5_y}dtq!TDO}_%%6tL|3<@qXD(?cS-l))YdrSpC|Hv|qCHY3t_g^Sp0)YaYP
zf-#}&nk(g+q&3bk(%_Mey6Q353Jmm=RR4uIBT`fC^z#H2w}Q9If-3uJxe=sA%PO{W
zHqb!i(CF5NY2Rwd7t;nC=sMfxs#TNF@j8o^x^4YR7xD8@MuTuSc?tJnkol^-5fEOV
z6tF#vtN$oUX?1nJ*x{bFYWEEO#{e{m>$j_m#^m-=qRzi22#-0Oet3Si0c<9<RMLi>
zQEb1Yq7VmOb5(HV;$Pr6{ED_bh@jWoN|(LxqshWktCTqko@4$*ijWD(QSyIxOJzj7
zFc>?V4I3jc_!`sek?(*oYnc_KAzTKh#|Ov8K<W17lGk`13Nm2<^eMy|pGLLyLf@*`
zIwf2gnp+VCHv%z{(QJixF{Lnq)hJjzPE6?<)}v~BZiN#B>GJ0i=6GcxA_Veee({g@
zJf>`wN%gFT-^a(xWq0oPQpKSRkK?kO;U+wW<~wx!xQH{XVteh+WxUGfSn~`fVNn+-
zWz{K3gjfvex5W|L5qszNyV#VNgE%+Y&$l@v$kxg2`_Zw>U+&;?DoFos*H(sG>G7o-
z{JxH>S&m4H_+zoxpsj5+#B`G7qjBh~uP3_O$(LXqNv8~_p{L%UB`N;0hVUtR*h2|#
zA20>S3@<U1uY1kfx~jF8+KE-;^>j~QssDM&Fz0Uh-}dGB-}W`Shu+ifAG3{%Ewf<a
zQ00Hg>t#K&_oV$ma-ML09oZ=UJNa=Xoj`5)7gjv5g>~5Q57=2p-WacJB`Pp9G16<t
z@DfmWSpBMMu;$6>Z0l8WcP!ntIxL{2qKg~}2n7<73=^9i<Yw(w70UdX<*_;QXbHAd
zr!7|zx>LodBLK<q?+}=?qxJ0`#3j1fLCv-IT{<iPAktI3@c_KDVk=u-v->JsWJe`I
z>|1{TYP!iu-G)7Zh9n%3ORHh}fZJA9P^<X+Mx#ky1{r#6NLY|E0I$WI_gksTpNi^g
zI02zvy^fRL>e7%j!Z*cWx#jO<+e#O6F`ps`5$(yMzKrmvUW!IU3<~pj69bn<mJw%N
z)rR|5H$vSQIeU^t`;)VoRPF*Q7MsA}KK^j~dZr`}^lD?xAdY^{5j95_H_e6?P8`BM
zM+Pjo^=$q^xz>an62;2Lf)`<L6cr0tvSTyiVH-O1=#G2Wag%D7)4A}J;aQ&SRa@Sk
zqwnMgq;5OV<KBLRjEkdjsyBn2y<Z^^NJ9=dsL<(VI{A5p#$G<*Y1EQOOe9r1T*g=M
z+PdAGuw<-C5oiY2-UHL1RT9bXneT9MzNeb2F}^E<`b)}Bql}y-`B9FKHquZpF$2g!
z0O_KA#ER<9Z+B%jHFcZGm$0T%#VOeSO+6E{U_c=8&9=D0s%q$zBf44a&4iOq4RK3P
zf$>fk_MPmX?yjBQ@hssD4qgNHCzNOu380K1d(7GxABsv2TZ~vmth2KR7y3%>haYc#
zD=nHrU1oi|veGIDs^G$$HHkFx2=q&1+~!7dL#WWsc6Q%Qp=n02@V>h@xZvwpQqczX
z<|Y3s!ZD%DYil<Z%6J(%P3AJ|_RC%zvl7-b)mIT6b?1BwdB5g1rJ#(uqOO}I{?&YM
zPyffeRT`pn3yU@s?Dox`(STu&hC`m?r6kv<T)7&@=>yx`CbX%QZr?16v1PQB7CXHF
zt&qkI_)pr7Olr&9j`@hMNC}wzrmlj46n`2b4~j&87{j2UhWx0*M1)yUWVKe-IH{N!
zpoboF_?^tPf$PMA0tHLo^`nyNLRY4*TucnOf$MJr;dLLu;F5c1nJ}LLA_-O4UM9hh
zK@&t6GzotL#@_{pT*M<OKHIbf6802`S_{DIs}^nJoEXP?c_qp&hBRsUAPsIdVR-^p
zAsUfz-DsL6RHu>%8s!iSzDY?k{H*eUCZT*z`JXjiWeKsVCOFf>1{gcv7ysv7*Xrg-
z5%oqzu}`b$JgNjkj+E81kGcLt8^J{g*EJfM0*)D4yU|la$0`1)5edl6aQcAtFtK`o
zsP3id<vrDr-`#Wna?5>V=Z8YEnsFg-OrX)J&u6PPk}DOC(GD6Hp6BGHUD};inNEa8
zbU(i2UG3!^1<=`6*6gz&e88w=U-!a~?~v|#6n*QYAGh>-fAAA3V6K=;Ql1?u_F>m7
z!NJuS{<RX=@85uRIaL)}RT=XT@TMFDE=KFN6_&GNp*@W^IYfYl%L*o(cp}XjI7|Lr
z#(;OZ9&adRh51TF4wr};+#K^lsXTlO^TA$m3-n$ZwKF-0pB=0J2;KDo2#*89t_M*$
zbFDVF#RNlAQ-)TiN)C8kgI*&;4I-IdOdg9nKfG$5dB9#A*9Ql5X#kR`#IFrepwH?|
zFKySmx+i_^yMoBIR*5WsopE&;6!t6bh0o_;tq^uw+&?QR@i0)0U*pA!8=xr^NmMaw
ztrwZ`?>bJVa)#*|$`-%z;~9F>^a^_yv6=A%Y!P?U$(q-@ON%8=1d)X!48LV6t-8Ez
zrtW^|p;t4xnN4TpVW}29Umhy8%`*kdRuKlumZTBi4U)@-H=d*MnkSFQA}S8u@yAeu
z;v4CslCi;;5_73Cmtu4<*X#EYGAjKmS=#qGY#taRqffIenz3~gw<$6z<136ER+82=
zWP$*t{%$z3ftiJ+qv=8wT_-fWk7txLIl;Cd##UsbYj#T2D#HuSu@!TbC?jo)(TzQ(
z)BagK^7P!$p^d*M0C(SeFn2&nEVN=bl6#vm$#4<uyCda=fFtIj7#)Ri=rK_ps#bH6
z`Jsj1xMCY{Ji1tuX&0A{X#uY^OdS0&Y{BO!XC34%yE)i~os}W*na*A1o8+9`lzw;<
z!G~ArB~X2RUTP^87SuZl)~;nyMoo#DdX|<(NE;R%XsmG7Cz^{GP_)eNs!kvQt1<L{
zp%bj+Qbh>$?$x~ie&L=0aEw)}O<1aw1)65fs^?UrxBS+_{rw+8l%8vGH~5%q?K&hj
z^f<bHTo^BCcrWF>jhMsx>6F<96I<#VBsPu2B(9^vR+3MDM=#8?zbnmv)`fwL6B<GN
z#Ypzs2w=_ls(w}K-Urvb!t!JLj83d+<-W=qmlBh-!wHH9)8y~tiRpZE24(#r!>G-y
z10IG=b497E)4T^rC?6#i+A!kiqcZGF*81DDZ%;*{c3t1N;ps#TL&V(8cxGZY!IaP?
zcZ}bmwc0#q30+@Rs5|?>LcH=StHKpDn)FpBLRi61hR^s56W7VY=M6^`Bs|q>%zRL#
zY4_q2#^~by_Dy9H4O<%Yl3hz-1;S#<T5IXpiJHd%p6*p95BkfW5kEYQcDD%BX4YJj
zw?_NolLORRpu}r_R;93pa2tf3x}th9xYWU76YE6@OEftd2~-i3&$OgsB4TKiVj=@~
z+S)a@W`s(jr8hLPMU>;kyGr}2B~Eop6u*LTrPyzHImi_#Dr--RwYV82zi5w5sc5;9
z*{8P7i;3^QUHVw<b}GvxO=gp|UV29Tg>KAenG=f$ruJR9ZdsI>go|&$JH#GKb~iS_
zR<p^?<4g!2dv|O4@i4U?ChZ5Y1(e*-7&%F=uwg)}1F~xiyHGl(XM`n3muJCrLhDH@
zQ*}PMWp*K#FR4`))}rs2ML5Lnq5`YW<9^PVAV=rG(H_bI-2wXrT(G+v_qSi3y7q*j
zPs?ooWu%65G|Y-akmDoz0pLwmqxmnepc4V#Ddd0cP5P20CjTj@X@^E!Hph)pdH~C-
zlES+|!Lj+BDHhX_pIa|r(B&IgC}sSrBtSjxAmdq?6b|3OOfft)q!~(y5MC&#-&$kW
zOl5M5q<C_QTv?~esFY^b?YHsSbeq=gg)!PT{YdGsRIG9(kSX8JP*g${t$Yu$7X@k6
zZ!Yxxun4s4bYMrBf#6J18r&P13+_ryg+hgjTMe`2IHR)4Tn*g<j0=3jp*Urn#6Qc!
zR2h$x#J5!r#oBSC3WTd@&I~ztQJ}g#B&3eL&)d>;WM7s)2Kz8gWPm$yLgfeP*pdW)
z|08e;g6L%!kS>k9FP>8Flok7|$!S`0hRJGi@T?tv(kATiEo9IfL2FNm$76&T_J^s&
zS6PEN5zRws!rbL{GHJpP#55ZWT?+d^b?1yzDt(Yyo6nkH9Xn*_Owj=(jn0tJaju)w
z300uKXS)dN?)WRyG-sWYLLo{aK3r!Pz*Vs!7KNGbVi^X@N5#>*ElG4Rt%9uNV=<z|
z*61}zCim1yDIE>Ed*v||fkOsxr5$WH_&@cKH2*>w_Kl)%^_r=rQa*G#QC_U33MYsW
z3=hu85(abVpgiWU2|yV3(L3Zm@dI^JP5EN8PBEHNxE*n^y=&e1DTnrYfwMErU60t*
z!?mZ}?)vtZ{xK>sPF6KWs}_FNc{R`q^?$;8vfO`_)Dr<z*Mg1z*WuX?vrQDEnT=NA
z^%1piN~ks}=_0qh<0(E4Hsl(LoSI*F@S^R9T%PH;P>fMsxHl417LaWC#f=xG7Ogpc
z_T3(yn84vm{@RKd5Kl$@t)W=X(*&c4NYeIg4L;Rt^B2SLeGo^u=Q)jfCdDh=7N$s<
z;mvCqZWf{|a#PEQr#b%W5KJ15e3xaLo31VY6uBBFSL2L`-1r~kZi;5?13LVrzoPMC
zAGr*=pfk~*%J&E%ST%>`z&;@A4Id~=eM*^}f@WZ0kNofF({lj978hx*1!b3hHXADo
zH%~EQ$?X=F`rO35(q_P;DiDiEQZBAm$(MDa<GF;i{dk=+{M%<SvBT2t?ONge&cJq~
zVGw)1yahD4_TwWYAqki8NWh__j8!wWn6lL{SX-J}@2#6KzxZyGo#>!~`q@rhd6#&9
zeV|jd<ds%!2c}F#N-(E+<)B#JlB$~x)Z>QrNv&m{s5Q@NvkQt;ym&8-I~h%;-p8nf
zni8{fSWIl3%_Gq%27(Tm?nSH3i4%RDL)3w4w&7UWfN3lS5EfUXRzt+%xy<qGrW-K<
zGmWi9jwsW*%S**}3AVFL=itl#^iBglM8GS-@E`4qDgQkERj;OP!S)Qaw%PGzig=TE
z*(N^6od|=D>UY@6Y@K4QemU#`<saSqL6v)hKt(ZZC|xv6d1<r~vGl0v_X7#SV}{D#
z|G91MFy_DjgZY1&L#H{-8kVkxjE4PSR|X~nb(@>FIBogbA)<lY(HJ+`?c`80DReX}
z1ZPAfW%=FMk#N?z5u}b?7$hgws>^RRhBcQ6%Yd!OlJc`ijSAY~c}6oG%6>owmnH*V
zvZ-oPS}{kw2+TxgwjJAGbmE~iQTzaSP{&)-@soE%NZ=7tpfkW&LKYb0kWM<WZQK`Y
z!f7j7lod+ln`amnjbG8z%EZSB3y=~!C*<z-UY#)c#j)oM<Kqi*h)QgiRwc9m6Y@R7
zjcA4V-Rag;nd)&(JMJCwgEHP+iX2aO6Uu}Y3%Gf&Kkc$_43j>|8NI)eR^@e9UIJfq
z0U(f4Y8y|AMII`eE8Q<N)O1NJ1y~n1(p`WCRODFZxHv*Vm-2Z+^-~z#KX0s1Q*{oE
z=!zOo%lymR4{P-T4nG4|&iTn)hzm6fdbTDF{xCnda!e7VlR6~ojn~N~v}h{22Nu`q
zobbc2{)LOg=A=?X1pe@KAv(1eGY&fgEUddYaJx1ngf6Z(#G`aGG?8H9zQ9Z9a^gu=
z;0!Y8G`>cig8ckbYB9W7#CPpwu-gRG8-82YZ?@GjK!1atJ|Cq_#~k~}>B5D;TBs~y
z57Wufq3X&jJaB$d$f)nUs4ZP5O^X#hDWD%!yI04Bu?Y!|9<A}mP_T3w;hW4B{;l6U
zXWZ3Z%k0?opr?UGROdSO4o5%4Vrne~ayELu^bpLBf1Yc2SsG^%2PeXJ3RDfWI5CIy
zR@b+-c}pJ8ge|k<@SWuNk`i?jw5QuWbgk1VfWk1fKgK9KQx*ti%w3AG#=kR#`CQoJ
zD!m&uN2>Q71vfkv6lQ5=s?cC#&6hwkpKX1-W4QW_W0sQ_&WZz7J|06U-oGo`77SWI
z;Wtf{yY6qyQUaT#j`DDDnCi~f_s8K_fqo(WSDf6B&G#Q;o<+yANyQ4_Tii@jxf$EV
zor(CKU8V3gI{!+hsp@*|214rf<jrO3lod1Q*G(t#Z{=J!v~>UH1)xICBDQ;ZP8?%Z
z&vpcNNVk&14(h_k`inMiNu%`%b&}!Uxk_Uh?WF|!=}2si86G4I`kI#Up>etoQ9aB3
z$s<SBwd`YUU6&#u(EIH-`WwTu`Iag?th)(H6a_G`-~hArcjTY<7H)0Xrj=QMV+?zv
z-fI1&fgm-&7S#?dhhU2NDeF=QZZDy3&}C_dx1;O%v61GsfWm3b<!G*?Ec0j~iU2+z
zY50_oFEhQ460W+(civFgaZx42V%%hvQz31mEBcA0TWoGl8SobGT7UC;)LnCPkLQXG
zb^oYVC#i+541fCOkABE`VOmTKoIg<*c-=MH2Em!K(o~o!TXpbU&NBlAZY(W^5u`GR
z^0)7{1GCkZV@4js`_l?NsRHwV;#+lUPylnag#^)zi+x4kFu<P6%oq?zlN;f83D4)x
z2=mpAI}kE4Ot7$=MY>1)`jWF2py(3293x(xaN6RFsIo9^oa|0B^WfD;5Lu+uX2Yc@
zo%CI@8O8Gyid1osaZfP;kX?2&FcCxDx7fNDpNVWD*RB^{d4NOr9Zh3S@4spPalHP=
z$z3_uU0|FJg&EZ5HMu3Gsvf6&%r<CwF)I(!Vxx$m(xOSmy*f-r)m}&QGAQcXJO2Ee
zexYFnHauB86PhtZ6@nDZbWM!^x0xtrV0wZzR{oAMglH`e=-q+RcKwf1O78#v^ffg#
z%^9S^m2Dcpd!re%GH94;)iBmXijcf$vz<=axdEI1qj|<Q2!z;#xzOWQEs$T+ZCnpn
z1dKQ(jW2N=E9b@aEfF8`aFoLIx}$-^8FJX#P6eq}_q}FK{uLm=xeQy;kp4Yb(_F2F
z`w8Ke87GJ2&=-wkkw6(H!6o^*b=Sl9skHGK?GOEDK+e+O78PWXrd=8NTH*4^rubf4
zd*Nh1%cNlq)Tq?#OlDb_9z$2bV2X_+ACt$K47GrVZ(bilXLY0+9QS8ApJR=xp$fZI
z#x}ULSPD*&altVv=5&ivi4o|Isp8UEzgd!+O7u9UJsXvI(30lj5lA$>1U2n<R3!&;
zA=z(ki$@4#cppI%^1@n8wRTSV0L7J$3rRFR*~D#W&Z*%(oB9(%00lW6-thp5`6;m-
zljJHVEMZX7!L9;6(xD92YQbByY~y!~PVF8oNuQR=($|l(POBvRL!X1ExlXvnp(1=A
z%K2YQNu}^63E>oc5jbAw#^@{>lcx1(4Y*#ePDkm;8!n>MFD6`;KFe9-nmnUt%h_S+
z6kSo2&rwG5VhUL<m1$84VJXs@7?d(+Sj!YIaO~C47;r9wzQT~v5OB?1b<$nkPVT=X
zx@kJV@JgF=9-cmOI|D^tFCKhXCa#>yW=kfkpL$`T`anAy+Ttj%52W<ehus(K7nzrK
zS((yo3~Nl1iZ?NNYXz~(t|b|q&%x`YuiMlx*PmjMxBT2m!X%F`#{Qfn@^Yb*SCBj;
z;uAt*KbCecXc<?eB_L*{p9*~QxA*&F^?ay_;*QlnLz1(^7V#DNoJ3#_7D+urLU@Pt
z`b>bnT*W{Rar>_e0%KQX*BPU|gxhfz(MDh?b3`77^>b(C0l_3&uJUIzO{vLdeyU;n
zt4RM8lf3mf=HDEsjN~N-%PB^_!0)T;qrAO?!&d$b&=i)C-+g`^e%u;LPPt`MfqqK2
zQh$8mLJ|6)a&B!}rHTJ-M4GO3>!c2k4vs`kWZ_GE@1fk`b_FMYkq!<vK2^=A&f25S
zeRnm?-D(B{UVK}u-7eQyZ_-CcE0un+zwv&<OSS`U&am3p%T0Imm?>GajqJ+1xw&lk
z;3I7w0?u6RBOp18FcfOk0zw%c9IS?*?&>)BM2-Txid5f6fpWpvg|M}_g6M9i(Ti-#
z`ncv^x?WrT-Met>mkf`JpEW|dGeH{&(MrTCFM|_KeI(`P5$5uEiTu}^!6%Qx87!x2
zll;3{LDa^PUZy5pWf^w+#;Jkbgy2STgEp~UZGVv!Z7rUGxW;P};vlcY^x(bXzsl(D
zSn)rxAxS^lGNaI8AMh&6dUN#xgu0;;G4tGC6t55$Mn$x?F}=4mz}w1rMtOb&Q#`*V
zLrwf-hNS@0_?@4JWhuHUVW<vsQ^XHE&u@@va%&Rf1Y!*Q>Zebt=fA1wdB%tAxOkTP
zz|FdqOl)C*QMU<Z=Q71og7qw7e9PfsJrUWcy&hJ}F4)Kd@qQw&=rKyCVZBIoU$lW~
zKQ`U|2Ex%+p_8xi(ZxL6U%1?CsEa5a{Nk*zxf@!=L%?FYh0t7anqyfNhVVct*rfd8
zA-_wig<zc6Tbj41o2VSyNuUuEYH?IMzJU)Ni#YT}3j1om*{mJ+{ci5~$L0Mi2^56E
zs%Q`g#5NI`%k$jC$jo6k($?+ykk4)4KwEHA0ZtV_Rte7l2MdwzInmMtt7eZ~Fg=fQ
z#+<^*-)sOHsu9_dX@79rs-lTlS5@tOgD$*+Cq4WbYFiZQWS>EIFfl#CyQ*pYg`og1
z1&h1ES}1uT(POLE>Yd0Ejw3WCkI62SJ6SO*Y-fK)GR1cVmgs?=u8bBe1!j?XWdun{
z0?KTH{`o9Z6^Dsh1dKZ}{*F%w6T%o2N$i-TRG+QxD9b3nR!yjJIS=#&>bHzKjf_}~
zQ`z!ej7=lBB1Q8ZG<jb$mdaWw_;)xOfrk>U)D^ug#6+td=wU8aV^O}|ym%2B?VXP4
zQ-^3@ZZ4VGs2kc_?0<ZaZCs)pYdRP=Ds3F2Kjn0dVbd_@G#zY(cG()p!Lp~e`|c0L
z6hBSTb;27HFuO8VUI)PX@8^x-nuo8te=Yiwd@<T7w0t}8FO!78T0!-74paR6KoWME
z=z((>R8O^%!xYtjyLc0S?S*w05oKyXSl!U1UNj|18RM>|e<Y&bA)D>kV#ws2ORuu&
zn!I;eY#3l@)Vm77eGnv5@MPp?(s2JI;_cS;#-b~TLFzLPEAa2s<o@X}s1BSMnD8_}
zTZ5dW5mjPqdbi@CR6`$jjpm1yw!dG!ffF<@4oOgaBG3Z0-sr)DRF+GxZ39YS^00!y
zlx0*3ecq%@Q{#V^01qh~8ilSX)#bMnB=<l$(fkydN}xZL&H#qEDhSt_a(Kw+%E@``
z*Yuh<j8QvK>@1v=3HiB^mlKA;a`U%<oS$fUgP0?7d&Q|h#V?D69vO#QoF6+>>>nmP
zL<J;$s+dU$k);<RR1rNNPDK^b`$E#$f-X+|)Dk2p7c>L9!X#@(m06W~$LtqEug;&L
z3m*>d%TXZE?PK+7$ij$%3cqf(c<lzLF{ECfwnYRkSn8oPd=CI6iQiK%^1-P`UnvUp
zP4UmwWi?=`rp7MPc5D$AHNh(s_<uNr4OJ+JZyRal(#3Qp_w4N>V9iY+%8;Od_0Zoe
zu`R>`DY+-7&X=ql6;{W7OeiV|!}iH5QRj_7g-|EOW_w{L>hv*h?vrOx4c%9@fy}6^
z5ZjY2jjrES#p!tLKdk7+##8q|pY!jMqR^b3?g++N2|^0Vn=q8XXe=xfONScxiE!}T
zfJ6Iw+%@gK*Ck-*`+RE4P?M*%@u+9{P7CjIp6;K*h?BBmwSHQi^ELjWU(A~!y5>x4
zy#eu&H<W<=PgN<WftkH%ci8ST=Hg~10;J|yN0}O@b6POL8PQ787wZNR=~;x=ca+zJ
zPa{R&wL5~97I5>f3X9NE=3`Vp2*R|Yg<GgeV}O;vv4^SrT-D@UMH>=4yQuV7CHbjM
z&^#R86=S}<mLS&N8P<^rx=H=$nFYkwU0ngo$hf=8<z=U-jX86c0DhSx@|^(@c;jdk
z*|ZkpKRXibn4d;c<TYp^f*l~jZOSw}V*5~0V4r`Rl_QAy%zb-|L&a=n&vfBCZpnJ&
zANG^^;(Avf8Q!JuEp*A~Cm0aJ!LPsO5{1pTJoPaI8-mnl1W4;-q^5Q=rbAadi2dt>
z1q;K%+USJmse^|40%=O2fn?BDMowOWYI!6(G>y;^fc>P@wJ`f_o~|f;rb~s(OOcsO
z%i2}tnqE5pIbvzy(Bd*@s*Hzb<%Bn&_(*fboCd`fKMm!ol{rxTH@qN9`JL)6Bq?k>
zxVvE?3K^tSmS4kpR{6tqObofh9a?}ODkQ1j@C!Q{^K95EJJTwIVN$fn=n&INK<vp`
zV+O3f`*}L};%!5`3JFy`4I($!RrPO}jVZeCqQ3mcX0>Aqy~BS9XC=VdDg7iNbjOFh
zPURLEBa^Tt$ML~_F1B`#{FvB$%U;m|>-l2UH&SFNG#dbwu`A-lPa_YuZUDJ-cnPTk
zQj{6a-_!J)RIZ4+oT{*iZR(>3(91%~7ZDN^5QmsLIm(5IQi*~Ixn!;%bWbFZ(cS2R
z3%$s%9M18voe&TM?|$C6P?xI!C))RZ_p4(6tGmS(DCmxZ=jYM+GDtyu*Z*MeR0ccv
z=PK%<@M=3?_a1yGAh#|agkxOUgc})3i=KPI#utLxQTU~$fYp7$Y`b=ow${Sar1#{|
zyu+I&fXzv~<5i@3AL3O>rXTwUUQTp)+8QyLz#?olLeQ(!uUi{g(F&hg2tm|%VYi|w
zsJ=G|f5SLqu^x0A`fBJ#{ytF@=~YoQ;e@*_*Xh{xB6jPDsyk^&x`O44_)iW#??%9^
zAA2{{!hrd*fc3MI)I!YhmZ%%)afX;?WLe&I{)zBJ%_tMuqnJ**)gGK?z^DT2cZ{I+
z&hC%NPuG+##XtA#oFVw6dP7$KK?@r{FDC(P&_&8GP%eTfeLb_e{_z!+(B;)lwu4Q6
zAa3tj6#ITxJ-_n;M(-GoD-8)d`g)bADd4++V6Ig^B+_fa8djf%90JAXObEw}J(ABt
zEFOxJVD-4EZiKvlMj!u%){13IPb_QX=vP_>HAjC90Kkk3-|nuqhgP*4j9BUIV^HT(
zDW~l-ol^hJ@PhJE#t*PU9-=(lFMTJsMzlZc7%;HcbTXq~&C8&goR%$+bd-}&V1wGx
z%e6NiSDYaHYX!mxeBP;kW}gqeE|5baE#k@k(8s|e_!?+JQ_CJ5FhNd@<X^DJl#~_{
z^NkK^?1VAjqMen}>d~*{4~=Wx7`~^c!fHbBU#&#~lT0rUNWPZQ)1*3c?OC3^^ZCUb
zL)@U;<yGe_&-=sK$EHU{u%6~W)~oeXbZCgLN<9MxekM#JHA{1RrZSvgJa__WTi=+j
z#eVh!i8j1N!dzNLHLtX7m$PP|pBfIu)42&1_JKS#kWWz)wW<JPx@I3``li|lF{GbT
z!3md0fo<9b{_WjglN<ayE%e*bfr>gPSce=Lgjrf_T9e;Mruv0Fux<$ns-r@_&YC5#
zU_<5WL-o;Zkfo@8vau^JkIB5;WCMRdSDq13bP@-qNeJ`=@6=TItsMws0a)XtXkU-w
zA@eklH0k`^v)j+Z1qunXoAl->U$d99UlwGO9g^mOr_Hf0J1gOOB`P4mqA<23em--N
z2HE>WWX~gYSZ`D`U01ZsR)-CB>tDd_!O?AW*Bw2#XC@$T;w;ZPOj?O&$?3oE-rG)W
z#^AYsTUqn%E>{_fyfke`U3hmn`CC?Fb7Bl21j4}dtgMb%ClVCc%z^Hgd$;;hDBQ-q
zP7~;EUR3dM<{oB86b~$jv7aX9(-^@8;^g$v|Mp?941=xfM+2M{0n%!5D6H7~@JDHx
zV1s7eZ=VBwP(oH7lo}Axi#yr^Ga4+S>afh=;*MBhH1NeU1C(?<kk43Y;I?>!a0n9U
z;U|YF@h>rOOI@uF@xandwhGar8^yOBeus=S;K1^^ZIVL-P9e%lS-5IBFcvL`!6FdB
z$Nv!s;3M*voTlb^_ki-1MZX6k5my5?@8pF6LW-44NeLEw70isUJ2$;PIte3Sxi;Un
z>a{YR(%l`o6mt<rZf4pUk+M?>4bRn~3S3x(QJ<%QPBeMU1SjPfM?sN&4iRJF#H&N)
zI!reRa(ksaj~UkAjkofw9ueqx*ss7C^S6|G<y3)AuSlJSowyDodZg%!eM8_6ENptg
zeL{$UulW3HtQF#w2%PMaNlea?D664Oh#4>MDraJ+Ta#Z@8MpoT{_PvG1k~BR6E`+<
zzCLG-TmU&~8YJTag&?Xm11w9)&!(}*K3n4VDM$mcpapy<GCd8h;{AgMwMNF*ALef&
z&{g^-&`(I8v$|1Pdz<v0D>Lmj8Fq^QIOurM17I4P(~SxWVq0ERwFatBV0?6PsY-sg
z>)MwfYizJm1ttYWv$<3?xWEFyF;~zFDH3h#ICu7s{R#tL4qWtve=tB^=1lH4L_?%&
z6x-)Fwq2Q)u@a$XVM`CDo8T-jz>l_&kZP$V+{*f2^h0kb;&{_ffULir9nzVQcDFMx
zu`X>br!D>XVGQ9fO!9ZAr5>tF1cz=JOE8YC+5q|m4*u%T1zEU>mii?1_qoU>VSh`F
zk^-!__}S`*A9Dvp<=^^^>=D#XD?qw985X?0%_Ejb7L@Zh55R)i3vSM^J%l7h;J?F_
zVlmSwu)!OV>%454sJHI$GeQmj*5GuN84}i9HGkJ<V1+b%N6&3O$Tz7&`@>x$^~#Hq
z8E$_;W+uIr%=b_ft_bU*>`MLR4x7<2FV`I`*XsbBAQ!fgP|Jj5Z@92MBqM96z=qWP
zgz=*38VU!#n@H!4k8pdm`pt+dq0w}A_;<C8E;ZNxM*8c&LT2L-J{u-0UEY8cAj+l8
z8N}?|;45^mo?cKQ288Y6^xdfAaq#q(v2D;6&Evmrq4Tw$fcHsede!gQ;1Qe4J1`D#
zkKx{^nXZOlXx~d(etfhT-!=AlOB!6o4&H}r0if6Y{P2@~o``V8y^)0quPCr=gYNn@
zV2BtQ*u$+{Tcct%kOdJg#NuzchYxMH>y+@ou^mS18I2=xDfYN!BtF+&k%+!W2&YcA
z!&|j&W5|~m<=stVL<7}*?-t54e?C>S1~YrgPghw*=7Lh}?FES8ajJOk52~HR&B|b0
zu;luwtpOt(_h+^(Ncq%Ml@ZcwkdwE-Y{OWK&f?NXX8~}P7xz?hzbp7~H2qcK3s~WU
zHP%P@-msUdQ|s$;5t?q>TH6ObqhlOp-Tr08cZNVGl<N}D!#b4!!OuX6<X@*gLRyPx
zsl)MC552sujS3IbSzFK@T8~IW^k<nhh2ZJtYvWD#F;ndp0WCNs%7u?qEHLhZB#wWd
z8o+XFxDK}Bi2d&AD}36W@zY9-PzIV94cqgrS09uuj~1)Vu3RMozhDF%`ukUlHPN-^
zuGwS$cD;pht@^9gZcy7&%@wKA6&h+)R(J;s3TB@44eCmU&%HcUf2|td66qZZ6vaJ+
zLzqLVlGOE~TTvN346xkxYJdaVYGq*#DQe<@gN1_dpXU4F&tj}udF+uPvc&mCTN_3V
z8;S(pR~x&3+Qto>3=1bnd&Gd#0blob=sG!a^@iGSpx&ft7>ltFh!a|)sO0#QcC*Y#
z&`-J(c~3E}0Sfk(uD)}>jr4sS91S70vV<+M1AAik<rVleRu3SvUrRLJg}X7rfu0l`
zf%eb5=Bok4nKZ`}eK=!ZVsx!OUiJ}u8J<L9t8Lv08+Weyg^385ST86O`%R$Xle|_#
z(K{lh>XL6?fNxn6qF5>Ris5HnOF~;YuG7Vcc%+!sa^wM{VD<RrDE<ly@$Q*tQ?bcG
z{_z(ps#59YX)gyiYwG1Hd;d!mYan)Lqnt~n+!)OJo8*N6BK~7AQ0bd-*#W7@fKT|}
z0{L&E`oVnXEY^POEk}O+iuo{SBL{^~u(Fq~GTnscn7;2-@UY@%DyF^T#q9B0FFiQN
zSuc+ejUxyLzlb%sIpTtEWJi5TWjxY?u8aF6{?rG+`5$fpcJ$Rx>UcG8uVAR7z$6%C
zvK`_NkWKX3Hh_?UavUpln?+UYvp3G#=Vfv2$j5?OM*P`^#jBoacShK)gB^suw!EKo
z2ow5KOS+P*=JX7!qKayH<}y`(8wts%@6TKt#yHU&y_*`7F;VWs!NCHk$TPAaI&V7c
z%w%aoT!rxnY~a{ROPIaj-(Qs$ok|^#x+fvQ0)!giD{J_umwvetO{H^P!RP2n<~aJv
z35}gR!qx0NK2b0gt)x<1JhuvrI9yDf2l=7`I9yITOSe4AF6^l~0DWyl!{jw&p=;_)
z(*oF(o{D}3e{i<6T8#=leIFWriive#0<!(KTsEw~@DkSAn~ORWL_Rq^pNZeP#acxw
z7MSvyC8)*4XWCsv%vpag=cx0ZxeD#QXv6Cm^3<+#x;iB&LK(Nevb`aXJu~EWHdD@L
zaVH?CJErv#E7soR)wJbJ-nqB&IvJjJFM2|5o(u)R49_@>3<fnRHgt$pk|0KH)c(gW
zE$Blsa7X>ETGd7Q?BZY1D4;qfo#mwCwvxSJpZc3tJyM1z^RKJLIP%E)o(92wAr)n_
zix*Czi!R}pg<Gm%XB1%St6v<}W@Yp{3M!({n@312Y0+9G&mq8|5eI-6m)HZf?J?$z
zruz7JH<XkMZQvopkrye-bt=(28gn<SSNrmF8qR32Xqk~#E<P85UAa-krW#ilm4c>z
zXvBVJm{n4S{O&Z4xGGPbuT*TmqV46^LGpF`n1g$xEr0$L$8?PQY*Sm&Mw7D*bslVM
zERz=%z_d0bWqDA2K7Idi(PUx&XWzO!BeEU;t=NFu#+cGg1eyp4<}egWW(1wU^#b;N
zTm4dN11S}U!Czoqt;iAhrl2L(s2(WjaHf8RR8)QIHO{qIi&Lt`A_k(FTUbw?Jyj-q
zV6d|-!F`zIf{e6riLH_;rd5Bj<F_Gt*LT18(gWE<c*zq=R&tJ^Z_8(z7LRi+TSBun
ziu+Ph8n(x-Jgm%OV6bb&JqL3On;z+2u-j>NjQYv)cc#xfis811*voS}*8EA`7^h4~
zyKvt$0$CT0*rdk=ZR(g5y#+wU6jbMxVjF0}fWcvii_E~$m<MYLaby&rUW@nsu@)!M
z>VhlR4%s=E<h>zLE}JOMG3m?9l=M3=vNv@LP$!Qp55AmGnY=942?53VkIdvjoQn@)
zQ0~;U2&5IP+VRqr`~US`a9BMQ<?;5pL_AAj&yo)9f#$bCAGP}f{)*W?+(dCFkU4m$
zZZuKNkpnTQfxqgM+{A!knxf<Pu&Rj8V(Ua-|8Cv(vt#C(r7MTm=R|?ah3p?~C`<tG
zT0Wuxj?Dz?+OSyrxVyK>TPJf$G~gkM_eX^ah;Qdz+d_>Ql2s)#Ox?e4T=Jc1wN_n&
zB2yUxo7HuG)h`R0no`*HYrO;aW#Ng<E8Ep>anZQ6lZ=k>k<!hj<#t{F42*uQz%~P9
z`#ujrlHIOI!xHP~Ii}Qj03CULx;B_Twyut_%#1c^Z};U6`P%IVMlkDbYithVlQZuk
zQ&ia))|eowts79lGX@E%H0_XOy$P8{%oF77jy;ZLQ3RxrtS<k$;7C|(EKABld~s7=
zqz*pF_PLdb<dVk}%Dg*@gUm5Uzqrq$Ae<4bKro};-E;3O*VsGA2Dh2UQEFUB$ho1m
z4d~@gsJ|NB!JmRBT$Vn#%M7}f<~RHIisgfZNv!EFHY!ws&%mMrXRqlwQ3vSpnYp!v
z@nS?GJuqOPuKLzb6Lz?XJ|vP?Bdn5?Ac~TNBhRs`EWGYcpHX{zY`g9ZwVUYQW}guV
z6-?0|_3V6qh9(izGp<3QA$nT~OQzW{L*%48xy_;e%er1@inBI}!u}CGRI6CyZMtTr
zk!WL81zmp|M(55rIv%du(=s|k3>8I$5!&GF&OJfP?2v=_!RtFsS9H<{6oLc7J73)~
z-6WN32%=!GT7z(lSm{{p6ux?YVAUpxFQ3VEu)$M)Z;&2wSiXB0E*ZW~#^)o^LLlqC
zt16Pu<Oi_b<h}eW;FVv!%)=c1{RerQnTzjqoyIt0k(^u`_oKk>*6YqS9qO5y9)AVE
z$AbCoc$5(S_6l)8Y?<yI-PVA)*R8iKZajf0T;lnwEvcXS)_g&T1b8UTvM_B^T#(Ph
z-BW~O&qwk8wVpBJm?|#(*U6+h>O|Q`jjuy`5rOu4cxqoxu^JgvhL|P?zF2MhL`x1?
z>eg0pLeC|G38dSs7ebdZNa+#$xvm3wRyKIRsLjpiV$ZcV^QQ^})T0@{1S;~OZT^Ud
zeg7<SQ6MCJ$$_vUJ3pakUKMr@CK&sthc8ThivSG~Kh8v+t}`TBj|M|>LxvLrJ$0*(
z<5pK$ip?j8ERu*ix&~opJqhz?Tcl1EX=UWK9)dxQKwVJ?d4@Uwlc$sca-JF-%zGKa
z9nAE%aI3Mt;15(c2!d$GNQ!A5n{%cHi6quwKTwM%oJEay0?QS*&5pq8+aITz>|Sts
z|J@<Xj&b5McYdCIZ$`-$)-Lv1Q0rY^Y>JGO72#q54@xthzx}3&XC+W+<9=MMZhBz%
zbVE-&z9`KgUNJ-uORN<hG~=c0Z(YOauVQ^Oy2X~qM}!9!Y#+J|+J}^MZSbgJv-Qej
z?kpE+K~%{Ee;-2}C+(iigU<GPhZA*5(NBYg0I}3MYaV7A<;*^Cg-cQ6G{(hu!mFhw
zp7gvc{6Yw#NGSp0L|{x|-BN(PBLnAWbCP&v61Y3^4Ivt*ti)%6{y-B>JK~%ojir57
z1gpEX3+y)(Z5eiph!BOF{3&eFeC3}O>0cPpL-N1Q%H&z#F|6{dg_y3wS?N-Sn>)fq
z*$7|X7apC5s#{uJT&4%2g8R8tz^>b#BYbGngbLxc`+wN?$6BifC#dEz?gLNURQZUh
z$_O;#)1MQ~%oW`G|MLRqEByRgFy}EFc^gUd;IVi|01W+9os}Ewg&D(Z&yGQ!M|7bg
z!iHJFBi@75@a`i+g_4ZcTYAwIwn7L>3il%gIJ<an<GE&RXS8V3=wrh%#Q0?BTOk0x
z{Mz%8<pEMoU{1pC7^oG_6@Va5JlfK{y&-ttx$Slw*0G8{()`>y&dOkQd;Xr;tJ3<@
zZb$+Q?A;iqJF#<_-E<@GN;kfD7;oP`&Szn@?Nz_m*z!1ECFVXI*fP~qHMqA10k*mp
z;h5sTv4*6g*LOJj43_LHTT_o9Jwb9Du0I1ZXmY5T!HD)3t}J74Dr8}RbX|rSw$~9z
zG0{rv@<H_cN~6HOvdO+c|A>;b2AR%X820@D%XuR(z^-<|$%f>b6sjsaw!^L17;<=1
zzF@k~;9dxkY4>A^B~~b%c!2&k&?a`@$l}`tvVqJ3aA3&t^&%|5fNh~)eBSQ7LZppd
z;(ac9>)E@m@9IM2Wr-hr{gjOH5)KxsmK)JG)%r0(Q;sxSpO@sEmV(%t*VkxGQ?$Wl
z@nNbv_`sLye*nf0<OjM{3le|fmS&PZAhdJQc%EP7z--hshJ9U$W8SO}*ZZrAtyjNe
z;6n2_m6X&|`Ie?$f3*Y4?Y=`LqH#llO5>s#KV~<Tr4lQTQc8!AQ^eV(<v*_Q4)v=9
zj}SNN&5H;9q^E<Nhv{P1w{|Dj^EbP`niO#_il%2HMbWIw(^O}uhCBX5elan$==B;7
zws^Bsxt`V5s<%<gLaCNT<Qp?2rhm)DaB|q95z9k~KZ2ipnM#TrY4E!fBevE908j%x
zf?z$uJLOi1KAEO*QS!p*5EI?>tKS<T009vv!(R{KXFjev2zq@^e<h_~#5C20;Sl}m
zis*6hV7HY}h;+Dj8sYg3|1>~PYLYzN<z56nxKAGkMz{A<Ji5q_Mk`{FtGxv3%pQh%
z!_%wvEJD2xvhbk*<8WhkgQdZZBTTAx-%mi;je_I#jkCxey)d#Mz<<~Yb|8Kwi2g^|
zI=9|xhW(G|o7G<D?yVpk6M?5UDNIEZUu#*fWKJf==}Fl9l&z^X1sY$St(BFKRIEZ*
znPDtRvJ}!%y*-rN;z`4Z-nH8ZF?3$5e9Z>pzoybc6v*?{|LI^pGC=U_fnB9$54}Bg
zKbs6doYE67F?{<f320!uObo~>DF?z@nS7V@bco?Y#T3RY2+=zIs0cwIDxMhok2o&t
ztmssGSLwmQq>Gb_G=OXWPf|R8q3ZPJ2(`MgJpS{$q@Zco-KY2`@IegKf04ZJ%FN{&
z`PkH)gIox3@3uxtKbajwaPv?uUi{b>7VE)&c+3>*UNnV!+|aJ0Dp#@S;NGCYQiiXY
z!T|n}O-5mCk%Rm9f$MOv5K5RI_5L-wI*e|s_*?dddZ+NubN~fBIgaVzS~mB0Lz#GY
z1h^I*%+s|jXweqSQs_kkj+r>Z`Het9{&!29?%M@F#({%(K|_yIjbS~+PTjEnEQ0!v
zuX^`bP;#!AOOCyYBmSt1yXv6*mBq>&Z)u6H#*>%`Xc)c?$r-qjvgGatL50$a9GmiD
zL!fv?H#!f_i^>|Z-T?u`>f$Hqgq~>xL;w3~X08qyG@s>6IpFeAwlCbwtohkr0C5yY
zyRH5p$9nz}hs%E=+RTUy30a1l0G)6X7x*kAyQa&0&3%6bfiV8=F2uSLT*$<L7bo8-
z!)u+y;njumI1S9+HGLm438FK}{7@G$VMc?4S44iUliqNjUc|R|>*2^lQ+re1d5fhg
zVA_$bd!2zog)``%{OR+JtGcP*O9Hs_)!WmGs!<{W8aLScp!ze{_Z#RWr_W!kke60C
z&u~wvckm3pj^K0Zc4b&CZ>>f{qJlFMBQ$rk@cIfKx7c@H(UZ5N{juK>ZIi|*$6k<^
z{~dEvzU%dhb5l#0P0Aeg0?Y~u@HObzN^(1@<X_POQ<23v2jpB$tHV--Mw%GdDh>zV
zND*Way4(dd+Ruf>N<YWi?jhn+GZlNGK~Oz}W^81380=#E)*iK-S9~Jp=3nAzvKM&h
zOl9FlMuU8@lum85=~Yt)d;&`pC>Ek%$5+*wzm1v!e!8V&uZ8g}T6k}z`+n>RDk?ch
zF=rbb9u~Tf5t>KkhlA0(5m*m-JCT)_waO0`0j~)MEj<#bl$yU9w~_DF+AhNT9H(O&
zj&cwx9d9G|=gt)2A+ubZ6dcYXO<$7PzEGa3f3MP%n$EWu2jjy*<!v*JeSK3JzQhB=
zS;7o%a+>FG7wukB`7SH~l&+UDlW5DQoPk2~;*0tac6C9kLR~ZWX^kTWXZgL=P@KD+
ztB=`=)w{)`67_M5S8L5R&TN16O5}5&SEv+10o3|KijD@bs@y_@30ZP&o&6>7ed?u+
z3i=9^T2KzBSZwP%*U%(VM0>1b(@ppB7j7B#Hby+@5@$qZ1z9;dFo23@{X>m>`(o!z
z$u4(OmKITuKX<@t%2~~jlM)|^rR>yWP*$#*jSIl}$^>pqwoF`DD)7L@vNK*{bn2+<
ztc9eyUo?hGaFqWG^uvyZYjRMQ`(Cv`n)|O4dk+2`H`E-PlZ@KV(sIk~rID23g_j~?
zX%Z6>MfJh}N2V#sCyDQW#YoF=fovE+70AH>n-Qh{3b)rogJIYGmBCU{Q7yxk9@kRK
zV4}V%qZCH@xs;I_J**_P)@n~gs?V~9aeX>|oDt9z4VJNnU|iV=r)m)-HO$`Ltc(x{
z1892c88Pk*B@LqM<=p2OAa1m)DrbvvOO<e_=<|-kIm6@df%}VlY6tY)v;Br8B?PK{
z79gUaTbY*3pIl)A`JVHCVCXWvK?0V7FXEy0A?x#ZMzkhyml_O2W^^V8o9tq*)P;9$
z;XA0kcOC}DnIFF8OO%k!8`;pnj`}y8kWp65O`TVF<#P2V;Z}{A(V6Xx6?Gmq=mnqb
zAKGL2=VOwH5%fdqX*dV+tb*;<L(VdMlR8*`Eea(vMJzExv(Fr9hyiL0k_bI!{1}6s
z)`KQlJXB;=Ze~xz5-PmNmU<c}+POL`sj{bjK`8Zj*#wF>K`Ia2K<BYl#>4my!hx)3
zyHyeS9)|lH6!+c)Yq2AYZo7tkOEG{Cs7v;$N5K`e?Ne^}@zEW=kj)VlVyY<GNEs5C
z#F*_;b_o8kkFs%I6KH1jRW}4WYVOE%dMml_fYrx4xfvZvJE5YCpMcZx<)VKh`Orm<
z)1Fheywi1^cM3~&pj$$*>iTgWZN~HP8~ygxQp?meZ+2vAZJs6}a~i(hDfW<$jzH)O
z_#_P($rR~ov!y7P2DryZp3ygsmOOwQ@>$mUUWDS<JEQ4gL>^)y43yj_6Y@&!Ywxd$
z(^;2lxF%3OJ|md*Elu2kp+u#BLuL^P0h-E^6s~^;H3(w6c=!1Kwf7cGaWzr5@ZfI2
z2_6Uz!3Ot0f=h7MKyY^p1PKHQnuOpQV6fm0!3pj%NN|^6LBHmC->R?fU$}LtDQe`*
z)amZsyH~HZ_BjQpzj)?T5?PB8!_7LPMFCOO&)g6dR8UOocK&2CUkCzW!rXp3hrtcS
zWF@lmxeXR=Gs~HmJL#;)D<5L68QCt%TEp}FjRc8RjhpF7R>lI9enk`NF-0AzPgE~|
z<K<EMo)v1-0LlF%{)yMyEsqr>!n-elg*6r0-F>XOtuI2UR6o)6!UAdhe((fFPdFz{
zLF{2go@U(3${5TLf1jUQfR(NlcvOMWACYEL%I`IOp12lu3HHGc8u8gg>kcec7&&Nn
zmE3&|M3d&(f2nHY6ZAuJi0HTt3_?a;@Bv@iE`BRL0#^?VeY`3pY-91;ld59fl$ft!
zL>Hg<3oC(R$Jw7gxLL2~v$|^H=*E_so2QcTu;7QUGjEg1KGYO%|Fu`@`uOVF*Hyfp
ze?Xj!z}a$gVtnZQ&wYHZ<qj?DVFJj}f;wBR!cCfp7hYn-Xo;LT^GAnRe{@5)-CxkJ
z%+}*srz(7D{=PFGunDc5I*C(7KAu-X2r`^$f|+V7yxtpBVnU?EeTf`Vf7vp3akQFb
z&)0xjS4Fw+*ba<b%0J6E*|Icvz8x%lv|m;MnZR5=JgXmlb2A?I$kU@dAG1FN@!M$j
z_i9T%CgzKOp<vKs`9{2be>^Yd<_opSylLnHSU5G+R2MZw1HX7PW0aZH^;lm#lA0hq
zzX{V0XisWTcVs5&P%`RZ{C7vbu==~x<1NyK*e1d&A&Oq6+nN#h_Z|z)qUP4zkl2RC
zuH!`*Lr;#eA$FJZ=XV^mwQJ9!JMfga9B1JHK?N<;qx3UXGx}n0vp*~x4d*<J3c5#i
zTI*3ggtK+Lf`6TsHENt1&R5A4Zt8zS2aI!+=3HR6>ZVPC^vE<mrGavB7SkK>aK~-r
z>k(n`un)R7Tuw`m@r7gu0*%S^%Ryz3=>qi0M+J;|?Mv;yADc_kK3UlSG2YwUoJe(_
zyjzzYxx60wo{!OMDdstT<7!u{X>W!F(*??^h0a3(RzXa(k#Ge_zAGCtpEF+a(<s&{
zWu~D!3qqF+F$4VL+VYQi8zibX|IN5@=kuRyyArnS3W+j%9>luYmEHfjS6f)4d3w3p
z9lmCw@J@N8{G>)`BD-$M4#%-(OHRySy8hOY{!7}g(LYUO`u?P2>yuHxSTUp$sbVg0
zhC`c!@cXxs6UH!f)M1uM`cbxk;<R0(WZUijrn^~B^NV)9ynf7*5e?++{z3#u4z1Zt
zU#LT7X2d0!;jh_P*crC;W8X|7Cix>6cicJEJgo4NdGGR~ANowv74Nu`=(8g5i$^S;
z-RQPnk(_QlNO%`4%LxX+`H9R$ob4C4n2!Rj0`c2!?~Nmhw5<+LDVyi#1tyk$el3%&
zVI5l??qJsXEsZfq`t~>>xkJbMJock4DaqgZ`dLHsz0URLs$sy*gc47T@m><rSW#i3
zxOp<sy*YcvnqnnAoL|w*7p-X?Xi(q%A4iPb+CHyP3QbTF#+oRNjO`=|`b*O2oa@6#
zEn-e5%FP|6D%Oe>cSzBx%l;ut2YEvFW1-btAMqj_3As~lZEL$A<K;}__IoG;w;gMP
zD9^NUmyctqo~~OHY(Rf&`E{-?KLfXeY&mNLT6dXmX}XbXU_qvV{Huem&+2#?F+!Op
zxi35|CquJ^UEI`b^e<*hE?^~vX<I%LzrIlbeZ$ld_tBdQsG^l|;@7s9EmstZLVMZF
zn0y=eUth2zReiH-OfRtU_I85(c4KMzkfz8Bfn;n{Hf0~_mslVXkEyCJIBh4LJ>hHd
z>(EzZtT>y#BmhR#6t3p5#Yy5KMYq1Zaqov7`lYs3h#S&8le-uV{AF1dy^flTNQTR;
zo5=Ds)`di0HA&S#LD-zv+i8X}pC3Hq8%w0z8QHBFJ!xT2?(dpfE`Ke)ZoR&+7x>kx
zvS^Eph(Md41^!DuGM1lnM}nx@w-#bV&X*kgbE3OHn{D@5rKrdA9-~Odi+UX*M&`V`
zx(&dW#alPrjs5U+yD`?#kzY0%{s*ozSoOJY-A~~N`<ekc;0b<7Bz}RFj5@zf_{Rie
z0(5@(hk~ca3W#Lk;eF*2bMcZ8R_TH6q*#_YpH+c%GrPM{3My-q+`lMJ0vkDwS92H8
zHv*RiOs^L8H<$aus{fwv<nx`*BDK~AOrM4yFzcZB`rWCBjz_qiOcC|>SRf23&lrS}
zeUB0OMT=X?!1%<sa6YSzD#d}W@NIS-xdG1eB0R#1L8vAF)_fce1klZ3U#WW9!S*k!
zsHD@Voz(s-k$!#=sXC8v<}dPZ?{!Y;wsAa(c6Yf&&^?6mKVZ?odn}s5SCgT&cVPu-
zeJrd*S`j(6eJlr=TkNbYZ)AskST1jJ+i8|iCvi=fv|Jo)mhr1!UuN3nFWwq$bL|P*
zFgeeD|CM)~w0CzZiWg<225s`XRqjUx+oYq{{g+SQ$SdVHDHLTauK#%SuOL5SmJh6n
z+q9+mQNs5-o=-C82^<7U!>zvZzvm#^MdMm*ID@9F0kD#-8?WAHUCXzarH_aSA%gfu
zD@q}(c#2LWR!pN0juL0bNY1`ImCOGeWC4+UCY}<Wxpx;@eMwDffqM~uHn($bw{N}q
z{B5m;uK5cF?pTc}05!OK0b;;4gK0%dS=b=#z-poP-&_t_3Sj6qQ_jJTU2IuG&5~2m
z9J2g)M=p`hc-S$IMrvl*nSA2a@K$P%g`X&Ok^+S&HC{*tEwai7hSyXmjoQxd@pbzb
zv`v4qKh<$$Um^69qdVSf`lO*r^-+nMMelvV<&s=(Rl`QJ34Z&SIBI#m$Oz0{U)S3S
z_q{IuQba^noinZWNe(v3;qp_a>gZ#cUgv+@>}rROMr9*NVsl#WtYUsD?o$10_L?&|
z*aLXa(eYFhY`2UMSx^}9(IKp5{17e=ip!n|EM|(9bFxZy!ku;c@&0a+KV&7nYgn{A
zFA&uizlG^PPYg5GadauvM|{`#apB}pF&O;=3|M%a*N@hyWcjnk6H6=WbOrEJC+77A
z?F81}1-Tq;*@(_KkKMND6&3+f1o!hu@AymIJ%Z4`v{Xwm|31s8F<P^@8yZU&$G-%R
z)}$kSO$!@T_5=T*h5QfD3q$aN(87-!T9IpoY4a>_><E8Il$xt^^~Fo|4;X~hV?W=j
z<3O$?X~HG$O134=u&<yT4D5r(GynK>)K2ctQJO`>BEl>km2>n(GS2j`#)A=l44#Km
zj+pJx6n6a1Z%>6&of{X|6r~h;HxD0lW2|l&fV+3R5FYhA?`-hK_{U%GGxH0CAv=k1
zO8=&0f+PmAG}V7#>AX2)z&xArM@}qFAf?GfhX+iPaQ4u$58+qEc61kT5J34C+xe8&
ztE$$Zjz|NNC>aNhmwIApmBCE-(2!=crssAl$byv1YhJe^_ym+O4;Uq9PlDqG4wYn%
z<tY5}`NPTh8mAUR=+O^Yd^^>ehg94KYri3)0u^gZ<4~vjm*40SjU@Yv*`JYC^Fv#i
zYP5?yDAfe-iSe<p8w>pPYs2g-Yi9EY=VJ3Sw1DVLk5?DFgM-$LjyCiBP-TR3?e1wb
zm7>_pU}#7T7tS{NPk6wv#GdLiZmQmK36=L5ick#ubZy*n7d#Ioax4GC*!l9q#+XFE
zx3=;HF?@~I0*TFzt0h}ayRZudkS3>NA~3+_9qqrE;{QMFgm>_B!TU5ci(_ZI1@h^d
zCn>3K>3sY_^7LYFc!Mt%i5tnh&Z~y$x+QYf=Sn}P>Dox}F$;-K!m~@;Yr+ksX8J~n
zvxu2|XoF7DDmB0t`u>#{UU|?H<WAJnYo!jlVxS~*(O@>EK&egs9S!QZDd!B{^(;ZQ
zwi7*}#=DJp^K;%#X3wzfoY6T+M6mvWss%K9=>s248SLVg>EYkPVYQyw_{d((13Uvn
zNLrXe3R2P6#iLznd=>TR2iN>$kgf>oP;x!75nxEVHP3op05pDYUyLzR3GcFTr%%=>
z5SZefei+pmrc6^gx%-6Cmwx58IIuiHg2~7wokuKU8Ps|y9k7h8$i<(<8pR!j<m|qH
zO|{*fSHJwC_nPkW4vffE>=V#$1C@G%lw7ZL?e69?7kNWR!d+*BPPqG?SBR!V3J8z!
zF0+G}AwyeIAB`XUdMjWvpE7Yajt6eD_KqkN`vekamf~`=yl%?|v#SV{0h7)jSpodf
z6h0{Jq>z1keo21z+v7FC!1L8}3_jaRNR4jr3~BHP4Pp%fR-G|$cix*KhiuD=r-qo7
ziiNah__so|9F{&)$6mz`!ko-FNGJ7+AfpwdPUuhjSQz_Ih{F<$<qtI#`iM2hxne3{
zHtsAnPo{Qvh?t!voL{gDc6GDNIY(;1z9huNGS<c9XQpaUoQA`pR5ckWQ?{%#4(A!v
z<Y0T=X&c$`wIN5wIlysp;!UQ2G|KekXle^evAq=TCrA;xMU*zdbiFyouIjSOVqvo@
zCy@|=)DJvh?w`^G1gKs=-G6&;vTsuCh6FDK!;zH7${4d_#HJTVm2g57@#^J@`?His
zes-S>{V5LCRbW(8Xu){6G6rxQX-N3!*$;B$b)iu<mDU(+7PZMks{E&6FOf_rUN}QX
zR-CM+`6pNWTmMkNx3pZ0jXmNwNvry}ED09HAhm-_jdrUd{V$Ba(tNOAnj7&nt~-WZ
zHWl}MVQ}OJ&JuKjX>{a}P~w?+hTt}Pfy0<MlH>k#f|p`@%d(o7Jd@@K)F$}GPAjqK
z_RX~PJTF3|#iPyX>dKsv7HU5$6c`27dGUpqcSY<pZ1AUAktn2hx7p5022>U1>?TiE
zCa8Si@2t%+4*rVnY_O?PzW@L<6a^XVtX8bbYF+?RS@7O-{M_sON@#wS0Vz0f!sq)e
z>AtNaQ{i{c6>AGvcPl8c`2Ib?ZX}n&e3ZF9Vac||Vgmd0o{q>rv|>*|;4fptINn=d
z3S09r7aIMuU<%3*le11PZOV87Qfpeap>%q0Pc82p779>0l=La;X%(w`CEr4o{bUu#
zms$+?WIWdfsEI*WIC$tmPVrJ^&u8(f6capQ#uh>{GUgy28ZiuQ<csLLFQh!=2f89_
zm@T4#0825P{+P0&pD_z2xL<c%Z@Jc_N1~&-MX@I`Xm;DJvFB>|w%((y!C04Mu`O}8
zyIG_kI42riO5h6V;<At6EgNGz52&eppMgqf2XXvwX*0@y?h)p&z}!d9-fOMC_@U~z
z;|cJ2k(24^g#0i7J7H&R97ow>U-J>>@Laf27Svo1s^RL@V!nH#AR3<A!7su2zJl0&
zq3BxL*J3f>ON8m-Tquz93lwmJANvQ%Uyam6v*2)<3^iO-*~#X{pI<9wmxwz8_E<T#
zzQ@=$oqBIQX&?VQoFN>&Oe{x%jvY4u(bcqzuJ~O6s)A0oi8q>WdXZc-wRgs?&?p*S
z$A=b;d5rCFT)qB|kz@2NOJp5R8ep%(q%yU6<nYT^LfbL%b+a9~YO529QJ5gXImDC!
z%Vi@$pXRO{wmlqAwHfoT|4_P490|aO6^yaP|0+iA2Vs_0>nqq7V*-DeePf(5uUfNu
zmqnb^iRa7%>}gNLvpt2avch>g{%zt_;wHDLusNPU5!)3-Bvfi5?KULKlJgs0{PSSj
zH=RD^6@UxWPj#$O&cFmZi{fK_!28iCE+j#?W4pw%%Bl_x^%DM!KJh{=-Ij(LrqC54
z-Rlh4a=#@R$?7(KwFW`MZ&%erepwc2^+m<fYtfQulay?3aY`w{6}?11j&wHqC$uf$
z=$EMHWhLFjg&HDW<LeurWZWB4g?M$k`dP?99tRFP<v&0&B^ZhE{q~{ss3FqeTnV{8
zR=FeYldtpJvTbjes^ROku4f%7M<aH;D7PnCg+F&BUv4u;wE)Jbe`kv4Ur&SAf=FzI
zf^hQ!T@gYuwK>wM=pPy**|O?uioZxe=~9w3d8e=X#vR7~M%xuV1l8l5#~v~{E-D(g
zA2C#_>O7`2R8#uXvgjBy*-b)*w{LwSKAEz|Tm=6ZmCp6aX}<rP{vTEbeaa?b4%_Gl
zBBSnxN85sV9qN|i^~hF-at9Y+rLg%Sm=h7Wq)uTwfjz!zgwN+A^WBbJ#$rSltb)=2
zSS$GE(-ap7w4uHi)&aNG?I;uc2VdNzS^6Zi)7fvF|HR&O%_64f(oJdM-fmY7*^VWg
zijz$lBOCVQRUJ~f@vd#|)#*SFFEDuWNdYqkVn;II-5*fe8&uX*T4uC-HAZw(J_5yj
zQ1RzzjmR5ex=8A?-kwI1%lYJBM9IuLA0LV{K&X!5e(IJM%lCiI<MU;DT5gmOJxw-u
z`*eH*8_?^xm-<T;E`U9^aAFj`Kx@(w1Oug*%B!o%@jW^f5b^U<V*(8lA4r7-J9m9R
zuJ@4NTECBf7FJ_A@4e%S+B?GZ_-!zc{#Piu^{<lG{9DQQ{L+h)@h>2A?dYb#Vjr=P
zl<;KnggAv#!cC3<>0=2x^C0ewo0cZq3<k~nM%Ul&;yBlv=kp_HE9g-Lu?Ypj-z{5A
zQx8&DGGqhJ|B8_;AgITi&XJI$Y$0fN)rzbBT_z$f{VgQn2D3tB9&!cq<jMF-(b|Ou
z>-(^_AS8X()p~fQ$^59RTGk(ENjUxi`5y3_Mx@;zM26Wu&?6k#+C`vhRE3;TQ0_}=
znerargbD{v+ex|Vg*XSaB%7$tl7Hv8w^p)s61d|mDnD?tqOWiXI9#kxVg2*ly*Mq~
zWNGr_<Nk)?&m#?Y@CEre*Ar>U==YyTCT=W-#mT0tN+~ZfI$l&9S>~xu!WajU@-wpC
zi1m|ase0j7HJ&L}<9c7-?k)RWO`9};oj>-gP~F#?74g42cBT0YUT;$F0B00@;A4Q)
zcOzCI1tL|IEf-f|6G9J)LDB$rxQF~2T)`r)GUVNwee?P*iAW_^<S%Z5a+NKE#(&`q
zs36&`9%s#J$umKK9HJsl<-khUk~A*dh#J5ilc@Tt371k3dgw;19QVnm5f@xy<jL<L
z1PJyplKfD63HwZ-j#%$#4%%dw@$81ePNC|t)H*ADL5hOphVF6WdsjGWD#=GgHjMA*
zR%r#r?NIDv1ZyAHXR<BmdP5E2l#pR(#PGKje(O^H?tfX`-$XcWuLx1oGtuwI&>H+M
zrGAAkg%{Uu{N+{PCWZ73uB0MkJBo}l9m?lBRXTP5i7~GssC(B)8wNKI1T6Zq;RvdZ
zsIvu!&J$JKVoi%arf72YJb}FS(lyG>g*^2+<KTgQQ(wR{iXjrp(s<rz5rs>KM~$WV
zbJPh|>2>h3qI)tl1Og$ik$)+r8L-{$`)~JO88mX_Kf3J)A)wp5{wu0M=Xk79g959o
z+oRR>Fb0qG*l%PXKG{&zjR;gsL1FjaPMmABUbQ}J_4qWeIYnEH2E&^r=kxq5#|msC
z!rA}WRKtZ+Tv<l30+0y<V?BwTg5OHv=qg8)a#1yt3K4Clb(MHBCZjz=RFV6jKyN)(
z`q7#Yf%+G90n5%$gXf5p#y9`oXDB%1y+j6b`+3qIAy9&VNC-Jw?|CNpBgzhjk9&(P
z$$N|?<$a~q0R^P=Denw~FSYIkTaICMIZZ;vZjOS9INrhwe6h1nxv6@|3F^`+L&9D4
z+GHu}iUd|Rs3H+Pk9w8m>5e|;kG_wQS2=woy!_m;)XOohoDsLbYvyme`yi+p*Pqs|
z_y*~kA6OWM+c5mPJ(!N!`QV1~xG=$)D(sjQnJ2T<lp6-!XZx-?Y0i$CgA#jwFnPY?
zyqV5KW2|DF+kY!$#niW<+LYxfOI>@5#=z2I@3;Rf!^EwnMRObePk>+r%8zl?X--W_
z+9VvO<T%d&wz*;mk0?x<sTE>c01vk7zb$B-E+z|)-MC^0m8unsn<=Q(om)qt%)Yc+
z8nI8NEQ>t=9H`*i(8t^r1}hT&sRiwN#Jf2)O&kUJ^5j5YMrv{O;L#q0SG0=`K+yR9
z&m9}A`}_?v#X!NZ3+Q>2c(YV2DNgx<s4vRYUsC|RF%I7Mp8DyY*I%-c7#|lPQJhPQ
zwDvAf$!-GSr(WjggIw>oOb=DfQi)BVNk8VzIY#2GKZlIhBRwxEsgtI^ulVdE6h_v1
zC32M>=>WdyY%~x#w6ee%fg`!-qlRPBI|}t6iO0&p1&!1oL&DsYrE7yvfro8qY6N}m
zZ$fR8Y~?eEWZ%~eyqLGqigAQyu7?6T=c|!ib$J`qI(8LoCWF5#=BN!IgrIM(2E5>E
ze$(W{k{rw<fONE7zxKsH3lB^v^M9QNpy<&Ai=-&S3qf+d!GWcC19SJAf@DzX3&C`&
z?6T%&P~-D<w%sD(?|F(1EtRg(#0Cp>^yKw-KX5(D!rZ5@fnNeqoPH$%>Ge~O<5F}A
zb!3nS8U)%S(S`$vw%C*7t6UR*f4k?%rAgLHLZmWtwUv~a0KWBER`uom<mlCaWGoVQ
zY(K|}1Ld+^N%n%*AC-~oZ)z!>6`(6?+qH-jA+{bIk{m@Ov3D|^bjGdq5|cm4rkRI7
zPY#2;{e%7sQ(*Rv=K^2F8Q!wfTrM`G^e&>p!dpjqaa;ofB#Rv~LjP;hEFP;FS5;$x
zl8&Kys^VCou5Gv&e$Q$WCRxdaV&=LWKHQpp>h|P=Tdy812X7Ea;l<>9*9}S6Rrbjf
zqDE2;=&4cScez)lq-<z1{9Lz2{<{b6H(B#1N?}dnIb7grY?!j>j*-Wcr6m8Wp1ur^
zp-O^(g>|NuO{7R5ukvQbI?P|*KXPFAzlI#cV}azlYx`(w38M2v17Hj1|K!2o2^bqd
zV&y-NJu^aLZJ7~mrLEf8h|SUeP`66jKg02U6w8jopqX^%Odl))j}{LgI|xo^+q?c(
zg|&u1HBEsl`JuWgHG5S`bKz{ymBhALgQxiSk_}t&7-ii3y?VqTjLeBsV5sHp8M*}?
zL_{0qMVS&pF{WE!eql0u=nhs$?fy5vm~5M*!m<uuJ_sMW-D1=mu|!oFbu6+&8maas
zimG<s<`-`;bbk=F3y(C^W<LY{<Nc}^7~=tplc&qrE(z}W8gJO{T-_uY*OYwp)}UO*
zSn(&>hQlXk;%weJ3@rq}UD6h>5#B4qm3BH{o^BJ*K_TNId0#U1d&%wB|4Z4nA+L0M
zt}R_Tn+<;8Um3nMN@SoZAf&0KB3vJ+S5<}r`(w2CKU)%%i5?o0q1AOw>Q_CYf@CBs
zTiSynq?#pG7yz4$2JE3Wo2+7^Aox&&lK)T3W$=uPs>nU}!6hS5hf5hSmDKPx#VU7x
zU)ekSRrXK5dLESX%yg->-5UwmUE8r+gjuq~3r3voE_xoPJILmhF=(pMP0db0Y+5ug
zz)Q=mp}M|%H^~T`Qqr~ABAdHZaFpq_Ft95k7eXrUYkKbF_=+CEX(YC$daiZx#()L4
zO+t4^Ak|7hoA0uSG4J<q%X#r5;!Q)|TaEd_K!QbeGx@(XRVuz&j3ll5AX^dp3w_6(
zf+%6o8whRJ$Hytkwk0=ZbG0#s(9nhkp~Yqw!q3iG8?)kN*dKrF-&bMp5WJ+thh_jI
z7QMTRVh0@M*GdS$<kHyqa3U5+Nxct;$<;SyJJ|8hW~nG)UNfMJMt=>{oCs*nZCatC
zilE30<+RQoBrj4t_(c#UasLWLi==@{HC05nteQ;dXrTn68bOEa8=d&HMnvsX?F2Cy
zb!Tj>*qjc1=&U`(Mov`H_fY14v^cT!)khE>9WIY^3T8I}N{JwEwpbuPwXj{Mf;?<d
zsiyiz{|iSQ@eC*{kkBR*-KnWyurF;dvhrm^xZBO{3l|R*^MUU|G0q<GE==(Wu)SvE
zj$1r-jPn0_>k<V~gU+Jke*JQ2iLcfRm{FRL6i)6uo9xv!G%SX59vdo}!HhMLf`O7^
z_Y2f%;-gFE6P<08=tZ<oW6&r*?k@Nvu<;L#lm>g^B<knfMc91I(&RW~-9$Rkq(_Ys
znj&(g@+?ulE$v&DShJIK#_HPqET~&$XK*W{FM4YIH+dz*^TQmEF)*2)e-^7}bXfFd
z1=j;HN)2hf)^vX}_}iqVdQ_R*2DcsQKjsl$byxRT4CwiXSW`pVtCk{*f+X<D#b43L
zPRsSH)eCGaW8Jo!oa8UlI59aC0P@>Ek3f^9q^2xo^fXr5Df>|@ncubRtki|ICa!TW
z@0_oN`L=1vZUIFAaH(JuNJbM&%gFXR4~v74pV;FjD%=skNpxRErIF`ZN%JFj70aE?
zQma(?=RMZ*dCs!=>Nx_AZkm%Gd5uI<;u5A##YHJI9R7QE-dpe68GzRSS(@vALQxsa
z&HX651FnUbo2ES%e^+Z{^^s|T+F`2{0xB}SHzPF<Z7nw+9oCeN1n&tWs1qnOh~&Sz
zZuhBEvZ;{BuhC1n*(N%0rP)WzG3%o%N7qX(?7f|k@r!odGsD`Rl^{j4LArR!B=sIq
z=p`p6-;!{X%8at@VCBZST$O!{!?zp;jhsm&mfonBu7VT^GCMa2?iFVVfA!JcouzFB
zWSrOVbIeCy`)=-~{Ho??3bS>Iw$O&mA6-%(1?j$r)@h90o?d`UZi(s<!>z-V8P}!)
z-z5)(lN=K7Uby~EIC-u1g`y&fqC%6k+|H4^EjNmT`k-ZbM!ReBM<LD3fgcLI)j+LP
zSE9?8x1o$MTlww=Dfh5)^slT87Bw}BkpVlO5h2EFr{^<g{x&ifp*b2zA~(A}t2BYo
z!hF|>fu*OOoFY>xIFQ*oK}^gns~;yH_4teQ6Z5RmaCh)BRoEzbtn}`xN{9K8YjcpI
zy3^0MbWIE<(nad{-8PCHms=;sJzD*Us*UsY-!hjm@gD4vB0_>w#c!79jAXcep-&d)
zb?PtAjt&`Ie$!TvAD`_r%O|XK$Ye{NIiK3+cvZd}T~9piraD!g!&R1)%vHv0gY3Cq
zNt!&cS2NHo{p;x9Uru&OzPJ$wisuLW{$<f0tyuzEc9-{da!*U_JlZm_9d}0}rm0mJ
z9A?BO472l=?`BK{^{%ZwIsJP6f@aMW=_0N4EF`$bkocdQDw`N*zB&7RQ{^pM%VF~~
z=g#5Xu8E;<mfCEwA49dF{#0jYNSR5RgOE~5;n;~P+xUGpCIsTW;r%@SYuZAP#M|&c
z6yz%QBCHg3^o@dlOTt?Q<-!S@P_^lJiz+`?2f8>o&fq(hbPh#*!JIB0{oAcI#IY#`
zbN@-d*?KKT#S=P!-JMJxRibIE(^^k2($>6owKu3O%Qb`fOB<T!G+n&B_H`<FxZJ6l
zsMHD?{cUeS&x(YF$VvcxDkIK*duaU|eH_s^iH<^S`2r?dz*L@X&HUF|dq{h$Tm@>D
z6=*b+U))7yoGoVh&i&n+HmZjU78`LBwbpA~JPA_Vub5#nQN0#brI9(>ZPM8-%PS<O
zwHiCQcn?OLu-!DL*0J;Q^1VYbr-iqCdp}a_CqAP>AoFzTku2`aMgA^YQqQs`gKPGx
ze`gVlCVox2cd89sF8-|sH~UjXUyu=Cu4@9<oh3;*&|ZD6RhZHPtKc+BbGi<CVSA&(
z)J$VL$tL|)?H%vMv7gV4vDqtAR;@Vm40Nrw2PWyK*GI>6uZ|wft)zmQJ@woW6oaf=
z>(}<MbV$7d;J<P+KVQ(Gq~r}=REcM}@U5@9r_~{vX3~SPozBZseadlVCEuu(63f@u
zudQeWOBop0s=Hj7#hmZV9<8;=JE{L^L1AZS1#jgq{Bx>c(8(u{iG2Mn+*W(GbOD_L
z0%?nS^(^XDTAho{G+s%)hV7O3iOtIqnvuRTEloXwpR0a7xdiDHez&XFXo_r@Un8hd
zgIw53kDopC)Rk7$wK$a{Kk;?3km6<}T3|03y*$7%$x}-T2-=E|)G;S+fmXD_6kzbc
z(>Z0P!Qr`v{r@@pKLWTAoqa>kdHJdN?mq>AriV+kb)WrPE1uc^O^kfEZP{odm*tW<
z@O$-Ibr%6Li}y#n-fZIbt-e@ngTtM3=eY+SMbj{!W*ufg!9DAl!Sy29(n-yccFA41
z)|ed<F=J@@!BK!&Yu3mX6<k%N)`typ!5zX6@pI81KbOODgz=Z_@A3aA`Z1Qh*(-wc
ztesm8(*E9x3_<Vnuqt}*AvHV9`#uAttT^ow4>q-hKNWTJ5t=0kvslAS2+l*pR@RIy
z%l7afl8ceE)z6k~xoXPA)N!k1%n;MIr?VSHg=WZSw<p+bXaSZUu={unF0HpmHvY~=
zL4MVF0*QMZVYbGol{7|r?)tEcT5D1iXIk}2RMYb3j4e|(9U8KYjjZXj0k5r9Umynf
zvQlZW@89-ra>o`zAh^LGXLvQ~#>7xA=}bT`L**t6XwF~#opI;<_k~1y$Zub4H>}gB
z<+y#?#<G9tf<Ych{m7AoO1q@R#7_+yQbtgUFiR+snywt9+RU^Am)qkc0YycNzUBN9
zkzc~)BG}K<I6h_2L`?{iL!4>5atXw|Zn-z2OF)$W(f*)NLw%vdU2Yxa1ILtgpQD(x
zXDBwvj#PMma4Dr)%Kpx|rF1MgM1^yMc&TWAr!kyBZPAwFcga>RTl6;*Rsn+)(v~TR
zcW~P^#M?|^is!m9{W_I(%K0E^%3yWg^nor0SC0pA_|kz4Aw~^`3K5k2u0kN|MWpjB
zTuFqI1-AYAUH(ZDE1u7kbgo*NU^=p}Yz+m3>zEm%Jp;s6fYz^?@K(c=0kKKPgUXJI
z424$)m{WPXC!`sZNFdUWZvsA=A&c5&pLFD-a3rT`k8EtLED=M#A785KLRu^prqYrc
z2hzq<2k%v<KS^eS-mpS72s+K6tVl9{!VG~N6SUP83PQghTgHXC%09Th>*yc<F&}CM
zndJq+l~Dx$&X-<%w&8lNTV(RJBRE6+$s5*r-(MT?<dd^~tI&OrP)&mrn9K*Trw*Qv
zKZ%&K6#$c9JP`{q7=0Sz-8XDC^15-%M%m}*yxmY7eL?n-6{<dk#^bH-eJ?&fYn8A?
z03q<4ZNH5Lw)67$&xi!Ku!ITuti%jMe2DE87Jl%|^E8h7#@+Jb8W;k~@*sBgqN>B-
ziIReLSCBv{1OiF$=QpIcL_Pn#T~U~Pk1JF2v<9sMvO@lN`o-|l?{>9^g^KVJeO|->
z1zBY<9Ks&<YO49UWwC*d<WGf4-RU=1aluJ6Z$$VduLtK>e*WdV^VK5bA}^gS>Fngw
zf#uG-5l&19|NWskMwX7U!gcjnh)GJGfz{6Ih^S`Jsi-%2?<$rj!C;RprK;gTAZLhd
z=B$okU;*^p{{|M5dSZ1^3T@YRTZ6$*UJn-2{Z(#Yp@xHnT5$BSmxhO}3g4^|ZZWis
z+ra-z9*rWtJ0HlgZ7Lq(HOAsgTHg)f7b8G|jd^&!tKHB08<d-oA>c5XgN~<qm+10e
zR=`q8`7bNrGJ}>MoYv-L3g|oADeLnpQ|;juRaO2-o+m0CzYZVRds@ax!Ht6>D-#tS
z>VPp(`#(QT7$?UEb=Bn+IP$(<0$~miz;CtY++W`*FSKvsZNYDj*EQ^n?D#``O>F)D
ze9Eh%fXRm=`W}UKG@6)j>S)dt-G65-Et4OQ#V=dtBf_E?2R-t&<6@~#t1t-nrWoM`
z1R}|{k0v{`*qp1-4G;bOD(rU_kBkez5V5MS9<~Q^o~KhG1&j|{QE?y#YlC@hlQwzP
zRg||kG~MFKmopQ0Em2g}3GVtjuJnKmfrvS^RAnz*Vh(V@itF_?el#C$dw>(?v$v(C
zP`7R0oexmYHXOtI`H&01k^0}Gg@*kdTziam1X-}W!Zg+h-y{T1+QUsGNN3jW4n!Io
zbaq}UslL^q3VMPCfsC@`pEha_0$AcGE8W^a>bsR#@~zdSZLX?0?+r+tHcY<Y1ncCr
zUZwRu<+v{V8gjqcP){#+6HZ<aMhB#`3wH)l2qc&Wv{Jg@;o;@&%6LeknsPz``V^!(
zO@dkg;Iw_ii&jb{vqppYQ3+!gzOf$`2}mi%zMEv(U?B@uHT=T0&6Ejuuj1o+S3I<U
z0ErDj-2MbQMOL1R&*1V@?RAiAbCZ;IANFp)Qj{c!z4lj}3xn6uK~4L4<=+@$K%3`0
zk1!y&CR<phZGs#-TSFx2ChYPWI-NC+F(1*ukXUep+tNHKM%X60cSRm00I)-S+)rgX
z!-Rb&vz&OLIrm`v<CnmIeHW^yDvzN8xq2O)t#3Sq)(9CTphQ6#RR=ipoqV~V-p%9r
z9KQ@QK%>Yb<OYYIz05TR%?RD>^p`sFcb$3%t_HhY=Qg(kb;N+wu5!sk(l5gEq7yP~
zYL9Qt<=ga)XG~(dldydZWaY8|T3K<TG1PY!E;1dA!<d4&QxMxhggF5wQW&q!t4};9
z$W<+;irKz-)#=yh*oV!U9yuoS*af|q8&OOD){Dh6o!*(>-hdW0DjOgMH-fq5E?ctt
zjUhb}<-D*rmqVwV5sf`RQN6$)5j1NNEtTZ#QX<W6W4rJrxQ1Eu<MGNM)^X$;v`sE|
zKUWw_P`sP<8^TOeV6sra(WSfuQFz>d^%teZ@33(*bpY0Sh9zG<->`oNwa0>d1Bhl+
z%aL^k<m*ur#V@{hHtS|6PtCx{N;>@m;Vxe9ZlH6>+2kXXYzt|OHN}yfX~3DO6%DF4
zS!2T6^NM96=EYIH1RVi!GOA5IKlucUD`T(qUH_~8!4~5G-V`U8E3b!1jxjy?tkZaD
zYsa$9c%bX-j|F$Sl3dcqFwr?0@YThJ7bc^`etM|~Rsp_i_r{&o91^u|b1YmJs1QFe
z71{;BR0vIY&Un{1co`r(SFRhW00pC_(rC}+N01}N%C&<7me-3@Wdu_$&?Q$Y4n-a0
z4Wbkv_Xh`T$;cOyYe3qa9zW55`8p_!8^D*q{DCxXhUe8C7+%=d2_Qn6=m5I21A(HW
zB}RR%;N=B6JQ#PN<T~CXij^M{_=tF!@GC%cQKBQ;LVPhhD1tK*C^7{ZcO2xMBB|}c
z58iZ>7Ngo)V@yKMo`4)D_G3EqAZEVz$je6rd};&7k+)_{uXLM>hA&}5>ZK}BO-+iZ
zh~d|QjKy={fZI(hc~VAY#c!bTb7hC95K+m;LyEPk(-ZjgZ@>|fTgr+F>4Pd>UQZ#T
z)W~+=E$|nN_1@9xf1X0NW&i(o|KGD<tpEG^-~rFrHu)2iQ)AKNkju-eyeyM84f+27
D6Sr5r

literal 0
HcmV?d00001