diff --git a/.github/workflows/static.yml b/.github/workflows/static.yml new file mode 100644 index 0000000..f2c9e97 --- /dev/null +++ b/.github/workflows/static.yml @@ -0,0 +1,43 @@ +# Simple workflow for deploying static content to GitHub Pages +name: Deploy static content to Pages + +on: + # Runs on pushes targeting the default branch + push: + branches: ["main"] + + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: + +# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages +permissions: + contents: read + pages: write + id-token: write + +# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. +# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. +concurrency: + group: "pages" + cancel-in-progress: false + +jobs: + # Single deploy job since we're just deploying + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Setup Pages + uses: actions/configure-pages@v5 + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + # Upload entire repository + path: '.' + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/ARSCL_plot_test.ipynb b/ARSCL_plot_test.ipynb new file mode 100644 index 0000000..0a9de23 --- /dev/null +++ b/ARSCL_plot_test.ipynb @@ -0,0 +1,8488 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "34112503-8eeb-4876-8454-7e020882346d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "## You are using the Python ARM Radar Toolkit (Py-ART), an open source\n", + "## library for working with weather radar data. Py-ART is partly\n", + "## supported by the U.S. Department of Energy as part of the Atmospheric\n", + "## Radiation Measurement (ARM) Climate Research Facility, an Office of\n", + "## Science user facility.\n", + "##\n", + "## If you use this software to prepare a publication, please cite:\n", + "##\n", + "## JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR 1: PROJ: proj_create_from_database: Open of /opt/conda/share/proj failed\n" + ] + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.4.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.1.min.js\", \"https://cdn.holoviz.org/panel/1.4.2/dist/panel.min.js\"];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + "\ttry {\n", + " inline_js[i].call(root, root.Bokeh);\n", + "\t} catch(e) {\n", + "\t if (!reloading) {\n", + "\t throw e;\n", + "\t }\n", + "\t}\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.1.min.js\", \"https://cdn.holoviz.org/panel/1.4.2/dist/panel.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1002" + } + }, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.4.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var reloading = true;\n", + " var Bokeh = root.Bokeh;\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + "\ttry {\n", + " inline_js[i].call(root, root.Bokeh);\n", + "\t} catch(e) {\n", + "\t if (!reloading) {\n", + "\t throw e;\n", + "\t }\n", + "\t}\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = true;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# imports\n", + "import act\n", + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import pyart\n", + "from datetime import timedelta\n", + "\n", + "import cmweather\n", + "import pandas as pd\n", + "import glob\n", + "\n", + "from bokeh.models.formatters import DatetimeTickFormatter\n", + "import hvplot.xarray\n", + "import holoviews as hv\n", + "hv.extension(\"bokeh\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "def666e7-d56c-45f4-8241-b4cfc22253d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200305.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200304.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200306.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200307.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200328.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200301.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200302.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200303.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200324.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200326.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200309.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200321.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200320.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200323.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200318.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200331.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200319.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200313.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200329.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200325.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200327.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200308.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200322.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200330.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200311.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200310.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200312.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200315.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200317.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200316.000000.nc\n", + "[DOWNLOADING] anxarsclkazr1kolliasM1.c1.20200314.000000.nc\n", + "\n", + "If you use these data to prepare a publication, please cite:\n", + "\n", + "Johnson, K., Jensen, M., & Giangrande, S. Active Remote Sensing of CLouds\n", + "(ARSCL) product using Ka-band ARM Zenith Radars (ARSCLKAZR1KOLLIAS). Atmospheric\n", + "Radiation Measurement (ARM) User Facility. https://doi.org/10.5439/1228768\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 34GB\n",
+       "Dimensions:                               (time: 669600, height: 596,\n",
+       "                                           layer: 10, radar_mode: 4)\n",
+       "Coordinates:\n",
+       "  * time                                  (time) datetime64[ns] 5MB 2020-03-0...\n",
+       "  * layer                                 (layer) int32 40B 0 1 2 3 4 5 6 7 8 9\n",
+       "  * height                                (height) float32 2kB 160.0 ... 1.80...\n",
+       "  * radar_mode                            (radar_mode) |S2 8B b'hi' ... b'pr'\n",
+       "Data variables: (12/33)\n",
+       "    base_time                             (time) datetime64[ns] 5MB 2020-03-0...\n",
+       "    time_offset                           (time) datetime64[ns] 5MB 2020-03-0...\n",
+       "    reflectivity_best_estimate            (time, height) float32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    qc_reflectivity_best_estimate         (time, height) int32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    reflectivity                          (time, height) float32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    qc_reflectivity                       (time, height) int32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    ...                                    ...\n",
+       "    minimum_detectable_reflectivity_flag  (time, height) float32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    reflectivity_saturation_flag          (time, height) float32 2GB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    instrument_availability_flag          (time) int16 1MB dask.array<chunksize=(900,), meta=np.ndarray>\n",
+       "    lat                                   (time) float32 3MB 69.14 ... 69.14\n",
+       "    lon                                   (time) float32 3MB 15.68 ... 15.68\n",
+       "    alt                                   (time) float32 3MB 2.0 2.0 ... 2.0 2.0\n",
+       "Attributes: (12/22)\n",
+       "    command_line:                     idl -R -n kazrcfrarscl -n kazrcfrarsclc...\n",
+       "    Conventions:                      ARM-1.2\n",
+       "    process_version:                  vap-kazrcfrarscl-1.6-4.el7\n",
+       "    dod_version:                      arsclkazr1kollias-c1-4.0\n",
+       "    site_id:                          anx\n",
+       "    platform_id:                      arsclkazr1kollias\n",
+       "    ...                               ...\n",
+       "    doi:                              10.5439/1228768\n",
+       "    history:                          created by user malynn on machine node1...\n",
+       "    _file_dates:                      ['20200301', '20200302', '20200303', '2...\n",
+       "    _file_times:                      ['000000', '000000', '000000', '000000'...\n",
+       "    _datastream:                      anxarsclkazr1kolliasM1.c1\n",
+       "    _arm_standards_flag:              1
" + ], + "text/plain": [ + " Size: 34GB\n", + "Dimensions: (time: 669600, height: 596,\n", + " layer: 10, radar_mode: 4)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 5MB 2020-03-0...\n", + " * layer (layer) int32 40B 0 1 2 3 4 5 6 7 8 9\n", + " * height (height) float32 2kB 160.0 ... 1.80...\n", + " * radar_mode (radar_mode) |S2 8B b'hi' ... b'pr'\n", + "Data variables: (12/33)\n", + " base_time (time) datetime64[ns] 5MB 2020-03-0...\n", + " time_offset (time) datetime64[ns] 5MB 2020-03-0...\n", + " reflectivity_best_estimate (time, height) float32 2GB dask.array\n", + " qc_reflectivity_best_estimate (time, height) int32 2GB dask.array\n", + " reflectivity (time, height) float32 2GB dask.array\n", + " qc_reflectivity (time, height) int32 2GB dask.array\n", + " ... ...\n", + " minimum_detectable_reflectivity_flag (time, height) float32 2GB dask.array\n", + " reflectivity_saturation_flag (time, height) float32 2GB dask.array\n", + " instrument_availability_flag (time) int16 1MB dask.array\n", + " lat (time) float32 3MB 69.14 ... 69.14\n", + " lon (time) float32 3MB 15.68 ... 15.68\n", + " alt (time) float32 3MB 2.0 2.0 ... 2.0 2.0\n", + "Attributes: (12/22)\n", + " command_line: idl -R -n kazrcfrarscl -n kazrcfrarsclc...\n", + " Conventions: ARM-1.2\n", + " process_version: vap-kazrcfrarscl-1.6-4.el7\n", + " dod_version: arsclkazr1kollias-c1-4.0\n", + " site_id: anx\n", + " platform_id: arsclkazr1kollias\n", + " ... ...\n", + " doi: 10.5439/1228768\n", + " history: created by user malynn on machine node1...\n", + " _file_dates: ['20200301', '20200302', '20200303', '2...\n", + " _file_times: ['000000', '000000', '000000', '000000'...\n", + " _datastream: anxarsclkazr1kolliasM1.c1\n", + " _arm_standards_flag: 1" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set your username and token here!\n", + "username = 'jeissner'\n", + "token = '196301151e10a63'\n", + "\n", + "# COMBLE ARSCL datastream \n", + "datastream = 'anxarsclkazr1kolliasM1.c1'\n", + "\n", + "startdate = '2020-03-01'\n", + "enddate = '2020-03-31'\n", + "\n", + "# Read in data\n", + "result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)\n", + "\n", + "ds_arscl = act.io.read_arm_netcdf(result)\n", + "\n", + "ds_arscl\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0d765d1f-a6ce-41e5-9800-1e568f0d4b28", + "metadata": {}, + "outputs": [], + "source": [ + "%store -r" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "807e5d72-c098-4d08-84bd-de70256edcc5", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "No numeric data to plot.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 12\u001b[0m\n\u001b[1;32m 10\u001b[0m ref \u001b[38;5;241m=\u001b[39m ds2\u001b[38;5;241m.\u001b[39mreflectivity_best_estimate\n\u001b[1;32m 11\u001b[0m ref_lowest_5000m \u001b[38;5;241m=\u001b[39m ref\u001b[38;5;241m.\u001b[39msel(height\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mslice\u001b[39m(\u001b[38;5;241m0.\u001b[39m, \u001b[38;5;241m5000\u001b[39m))\n\u001b[0;32m---> 12\u001b[0m \u001b[43mref_lowest_5000m\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtime\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mheight\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mChaseSpectral\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m40\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m20\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/xarray/plot/accessor.py:48\u001b[0m, in \u001b[0;36mDataArrayPlotAccessor.__call__\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(dataarray_plot\u001b[38;5;241m.\u001b[39mplot, assigned\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__doc__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__annotations__\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m 47\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m---> 48\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdataarray_plot\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_da\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/xarray/plot/dataarray_plot.py:282\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)\u001b[0m\n\u001b[1;32m 279\u001b[0m plotfunc: Callable\n\u001b[1;32m 281\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ndims \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m darray\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 282\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo numeric data to plot.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 283\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ndims \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m):\n\u001b[1;32m 284\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m row \u001b[38;5;129;01mor\u001b[39;00m col:\n", + "\u001b[0;31mTypeError\u001b[0m: No numeric data to plot." + ] + } + ], + "source": [ + "time_s[0]\n", + "vdates = []\n", + "for time in time_s:\n", + " time = str(time)\n", + " dates = time[0:10]\n", + " vdates.append(dates)\n", + "\n", + " ds2 = ds_arscl.sel(time=slice(dates))\n", + " \n", + " ref = ds2.reflectivity_best_estimate\n", + " ref_lowest_5000m = ref.sel(height=slice(0., 5000))\n", + " ref_lowest_5000m.plot(x='time',y='height',\n", + " cmap='ChaseSpectral',\n", + " vmin=-40,\n", + " vmax=20)\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1bef3228-bf6e-4535-88a9-4e294c726606", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 2kB\n",
+       "Dimensions:                               (time: 0, height: 596, layer: 10,\n",
+       "                                           radar_mode: 4)\n",
+       "Coordinates:\n",
+       "  * time                                  (time) datetime64[ns] 0B \n",
+       "  * layer                                 (layer) int32 40B 0 1 2 3 4 5 6 7 8 9\n",
+       "  * height                                (height) float32 2kB 160.0 ... 1.80...\n",
+       "  * radar_mode                            (radar_mode) |S2 8B b'hi' ... b'pr'\n",
+       "Data variables: (12/33)\n",
+       "    base_time                             (time) datetime64[ns] 0B \n",
+       "    time_offset                           (time) datetime64[ns] 0B \n",
+       "    reflectivity_best_estimate            (time, height) float32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>\n",
+       "    qc_reflectivity_best_estimate         (time, height) int32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>\n",
+       "    reflectivity                          (time, height) float32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>\n",
+       "    qc_reflectivity                       (time, height) int32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>\n",
+       "    ...                                    ...\n",
+       "    minimum_detectable_reflectivity_flag  (time, height) float32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>\n",
+       "    reflectivity_saturation_flag          (time, height) float32 0B dask.array<chunksize=(0, 596), meta=np.ndarray>\n",
+       "    instrument_availability_flag          (time) int16 0B dask.array<chunksize=(0,), meta=np.ndarray>\n",
+       "    lat                                   (time) float32 0B \n",
+       "    lon                                   (time) float32 0B \n",
+       "    alt                                   (time) float32 0B \n",
+       "Attributes: (12/22)\n",
+       "    command_line:                     idl -R -n kazrcfrarscl -n kazrcfrarsclc...\n",
+       "    Conventions:                      ARM-1.2\n",
+       "    process_version:                  vap-kazrcfrarscl-1.6-4.el7\n",
+       "    dod_version:                      arsclkazr1kollias-c1-4.0\n",
+       "    site_id:                          anx\n",
+       "    platform_id:                      arsclkazr1kollias\n",
+       "    ...                               ...\n",
+       "    doi:                              10.5439/1228768\n",
+       "    history:                          created by user malynn on machine node1...\n",
+       "    _file_dates:                      ['20200301', '20200302', '20200303', '2...\n",
+       "    _file_times:                      ['000000', '000000', '000000', '000000'...\n",
+       "    _datastream:                      anxarsclkazr1kolliasM1.c1\n",
+       "    _arm_standards_flag:              1
" + ], + "text/plain": [ + " Size: 2kB\n", + "Dimensions: (time: 0, height: 596, layer: 10,\n", + " radar_mode: 4)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 0B \n", + " * layer (layer) int32 40B 0 1 2 3 4 5 6 7 8 9\n", + " * height (height) float32 2kB 160.0 ... 1.80...\n", + " * radar_mode (radar_mode) |S2 8B b'hi' ... b'pr'\n", + "Data variables: (12/33)\n", + " base_time (time) datetime64[ns] 0B \n", + " time_offset (time) datetime64[ns] 0B \n", + " reflectivity_best_estimate (time, height) float32 0B dask.array\n", + " qc_reflectivity_best_estimate (time, height) int32 0B dask.array\n", + " reflectivity (time, height) float32 0B dask.array\n", + " qc_reflectivity (time, height) int32 0B dask.array\n", + " ... ...\n", + " minimum_detectable_reflectivity_flag (time, height) float32 0B dask.array\n", + " reflectivity_saturation_flag (time, height) float32 0B dask.array\n", + " instrument_availability_flag (time) int16 0B dask.array\n", + " lat (time) float32 0B \n", + " lon (time) float32 0B \n", + " alt (time) float32 0B \n", + "Attributes: (12/22)\n", + " command_line: idl -R -n kazrcfrarscl -n kazrcfrarsclc...\n", + " Conventions: ARM-1.2\n", + " process_version: vap-kazrcfrarscl-1.6-4.el7\n", + " dod_version: arsclkazr1kollias-c1-4.0\n", + " site_id: anx\n", + " platform_id: arsclkazr1kollias\n", + " ... ...\n", + " doi: 10.5439/1228768\n", + " history: created by user malynn on machine node1...\n", + " _file_dates: ['20200301', '20200302', '20200303', '2...\n", + " _file_times: ['000000', '000000', '000000', '000000'...\n", + " _datastream: anxarsclkazr1kolliasM1.c1\n", + " _arm_standards_flag: 1" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds2" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b0eed42d-1367-4f6b-b2e6-bafb264af22a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABOoAAANGCAYAAABDe2HEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzde3gU5f3//9cmmwOnRAFJQBGxiofiESqConggHqoUqYJSCYgtUkSKaK1WW9HyEcu3pVYpKMpBRBS0SLE/SkGtKEWqUhAPaKuiAU1EqCYETUKS/f0RJuxhZndmd3Z3Nnk+rmuvkNl77rlndmY2vOd937cvEAgEBAAAAAAAACCtstLdAAAAAAAAAAAE6gAAAAAAAABPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA/IuEDdK6+8ossvv1zdunWTz+fTihUrYq6zbt069enTR/n5+Tr66KP18MMPJ7+hAAAAAAAAgAMZF6jbt2+fTjnlFM2aNctW+e3bt+vSSy/VwIEDtXnzZv3yl7/UpEmT9Oc//znJLQUAAAAAAADs8wUCgUC6GxEvn8+n5557TkOHDrUs84tf/EIrV67Utm3bmpeNHz9eb731ll577bUUtBIAAAAAAACIzZ/uBiTba6+9ppKSkpBlF110kebNm6f9+/crJyfHdL3a2lrV1tY2/97Y2Kj//e9/6tSpk3w+X1LbDAAAAAAAAO8KBALau3evunXrpqws9zqstvhAXUVFhYqKikKWFRUVqb6+Xrt371bXrl1N15s+fbruueeeVDQRAAAAAAAAGWjHjh064ogjXKuvxQfqJEVkwBm9faNlxt1xxx2aMmVK8++VlZU68sgjtWPHDhUUFCSnoQAAAAAAAPC8qqoqde/eXR06dHC13hYfqCsuLlZFRUXIsl27dsnv96tTp06W6+Xl5SkvLy9ieUFBAYE6AAAAAAAAuD48WsbN+upU//79tXbt2pBla9asUd++fS3HpwMAAAAAAABSLeMCddXV1dqyZYu2bNkiSdq+fbu2bNmisrIySU1dVktLS5vLjx8/Xp9++qmmTJmibdu2af78+Zo3b55uvfXWdDQfAAAAAAAAMJVxXV/ffPNNnXfeec2/G+PIjR49WgsXLlR5eXlz0E6SevbsqVWrVunmm2/Wn/70J3Xr1k0PPvigfvjDH6a87QAAAAAAAIAVX8CYWQFRVVVVqbCwUJWVlYxRBwAAAAAA0IolK06UcV1fAQAAAAAAgJaIQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAAD8jYQN3s2bPVs2dP5efnq0+fPnr11Vejln/yySd1yimnqG3bturatauuu+467dmzJ0WtBQAAAAAAAKLLyEDd0qVLNXnyZN15553avHmzBg4cqEsuuURlZWWm5devX6/S0lJdf/31evfdd/XMM8/ojTfe0I9//OMUtxwAAAAAAAAwl5GBupkzZ+r666/Xj3/8Y51wwgl64IEH1L17d82ZM8e0/MaNG3XUUUdp0qRJ6tmzp84++2zdcMMNevPNN1PccgAAAAAAAMBcxgXq6urqtGnTJpWUlIQsLykp0YYNG0zXGTBggHbu3KlVq1YpEAjoiy++0LPPPqvvf//7ltupra1VVVVVyAsAAAAAAABIlowL1O3evVsNDQ0qKioKWV5UVKSKigrTdQYMGKAnn3xSI0aMUG5uroqLi3XIIYfooYcestzO9OnTVVhY2Pzq3r27q/sBAAAAAAAABMu4QJ3B5/OF/B4IBCKWGd577z1NmjRJv/71r7Vp0yatXr1a27dv1/jx4y3rv+OOO1RZWdn82rFjh6vtBwAAAAAAAIL5090Apzp37qzs7OyI7Lldu3ZFZNkZpk+frrPOOks///nPJUknn3yy2rVrp4EDB2ratGnq2rVrxDp5eXnKy8tzfwcAAAAAAAAAExmXUZebm6s+ffpo7dq1IcvXrl2rAQMGmK7zzTffKCsrdFezs7MlNWXiAQAAAAAAAOmWcYE6SZoyZYoee+wxzZ8/X9u2bdPNN9+ssrKy5q6sd9xxh0pLS5vLX3755Vq+fLnmzJmjjz/+WP/85z81adIknXHGGerWrVu6dgMAAAAAAABolnFdXyVpxIgR2rNnj+69916Vl5erd+/eWrVqlXr06CFJKi8vV1lZWXP5MWPGaO/evZo1a5ZuueUWHXLIITr//PP129/+Nl27AAAAAAAAAITwBej7aUtVVZUKCwtVWVmpgoKCdDcHAAAAAAAAaZKsOFFGdn0FAAAAAAAAWhoCdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8IGMDdbNnz1bPnj2Vn5+vPn366NVXX41avra2Vnfeead69OihvLw8fec739H8+fNT1FoAAAAAAAAgOn+6GxCPpUuXavLkyZo9e7bOOussPfLII7rkkkv03nvv6cgjjzRdZ/jw4friiy80b948HXPMMdq1a5fq6+tT3HIAAAAAAADAnC8QCATS3Qin+vXrp9NPP11z5sxpXnbCCSdo6NChmj59ekT51atX6+qrr9bHH3+sjh07xrXNqqoqFRYWqrKyUgUFBXG3HQAAAAAAAJktWXGijOv6WldXp02bNqmkpCRkeUlJiTZs2GC6zsqVK9W3b1/NmDFDhx9+uHr16qVbb71V3377reV2amtrVVVVFfICAAAAAAAAkiXjur7u3r1bDQ0NKioqClleVFSkiooK03U+/vhjrV+/Xvn5+Xruuee0e/duTZgwQf/73/8sx6mbPn267rnnHtfbDwAAAAAAAJjJuIw6g8/nC/k9EAhELDM0NjbK5/PpySef1BlnnKFLL71UM2fO1MKFCy2z6u644w5VVlY2v3bs2OH6PgAAAAAAAACGjMuo69y5s7KzsyOy53bt2hWRZWfo2rWrDj/8cBUWFjYvO+GEExQIBLRz504de+yxEevk5eUpLy/P3cYDAAAAAAAAFjIuoy43N1d9+vTR2rVrQ5avXbtWAwYMMF3nrLPO0ueff67q6urmZf/5z3+UlZWlI444IqntBQAAAAAAAOzIuECdJE2ZMkWPPfaY5s+fr23btunmm29WWVmZxo8fL6mp22ppaWlz+ZEjR6pTp0667rrr9N577+mVV17Rz3/+c40dO1Zt2rRJ124AAAAAAAAAzTKu66skjRgxQnv27NG9996r8vJy9e7dW6tWrVKPHj0kSeXl5SorK2su3759e61du1Y33XST+vbtq06dOmn48OGaNm1aunYBAAAAAAAACOELBAKBdDciE1RVVamwsFCVlZUqKChId3MAAAAAAACQJsmKE2Vk11cAAAAAAACgpSFQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADMjZQN3v2bPXs2VP5+fnq06ePXn31VVvr/fOf/5Tf79epp56a3AYCAAAAAAAADmRkoG7p0qWaPHmy7rzzTm3evFkDBw7UJZdcorKysqjrVVZWqrS0VBdccEGKWgoAAAAAAADY4wsEAoF0N8Kpfv366fTTT9ecOXOal51wwgkaOnSopk+fbrne1VdfrWOPPVbZ2dlasWKFtmzZYnubVVVVKiwsVGVlpQoKChJpPgAAAAAAADJYsuJEGZdRV1dXp02bNqmkpCRkeUlJiTZs2GC53oIFC/TRRx/p7rvvtrWd2tpaVVVVhbwAAAAAAACAZMm4QN3u3bvV0NCgoqKikOVFRUWqqKgwXee///2vbr/9dj355JPy+/22tjN9+nQVFhY2v7p3755w2wEAAAAAAAArGReoM/h8vpDfA4FAxDJJamho0MiRI3XPPfeoV69etuu/4447VFlZ2fzasWNHwm0GAAAAAAAArNhLL/OQzp07Kzs7OyJ7bteuXRFZdpK0d+9evfnmm9q8ebMmTpwoSWpsbFQgEJDf79eaNWt0/vnnR6yXl5envLy85OwEAAAAAAAAECbjMupyc3PVp08frV27NmT52rVrNWDAgIjyBQUFevvtt7Vly5bm1/jx43Xcccdpy5Yt6tevX6qaDgAAAAAAAFjKuIw6SZoyZYpGjRqlvn37qn///po7d67Kyso0fvx4SU3dVj/77DMtWrRIWVlZ6t27d8j6Xbp0UX5+fsRyAAAAAAAAIF0yMlA3YsQI7dmzR/fee6/Ky8vVu3dvrVq1Sj169JAklZeXq6ysLM2tBAAAAAAAAOzzBQKBQLobkQmqqqpUWFioyspKFRQUpLs5AAAAAAAASJNkxYkybow6AAAAAAAAoCUiUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAzI2UDd79mz17NlT+fn56tOnj1599VXLssuXL9fgwYN12GGHqaCgQP3799ff//73FLYWAAAAAAAAiC4jA3VLly7V5MmTdeedd2rz5s0aOHCgLrnkEpWVlZmWf+WVVzR48GCtWrVKmzZt0nnnnafLL79cmzdvTnHLAQAAAAAAAHO+QCAQSHcjnOrXr59OP/10zZkzp3nZCSecoKFDh2r69Om26vjud7+rESNG6Ne//rWt8lVVVSosLFRlZaUKCgriajcAAAAAAAAyX7LiRBmXUVdXV6dNmzappKQkZHlJSYk2bNhgq47Gxkbt3btXHTt2tCxTW1urqqqqkBcAAAAAAACQLBkXqNu9e7caGhpUVFQUsryoqEgVFRW26vj973+vffv2afjw4ZZlpk+frsLCwuZX9+7dE2o3AAAAAAAAEE3GBeoMPp8v5PdAIBCxzMxTTz2lqVOnaunSperSpYtluTvuuEOVlZXNrx07diTcZgAAAAAAAMCKP90NcKpz587Kzs6OyJ7btWtXRJZduKVLl+r666/XM888owsvvDBq2by8POXl5SXcXgAAAAAAAMCOjMuoy83NVZ8+fbR27dqQ5WvXrtWAAQMs13vqqac0ZswYLVmyRN///veT3UwAAAAAAADAkYzLqJOkKVOmaNSoUerbt6/69++vuXPnqqysTOPHj5fU1G31s88+06JFiyQ1BelKS0v1xz/+UWeeeWZzNl6bNm1UWFiYtv0AAAAAAAAADBkZqBsxYoT27Nmje++9V+Xl5erdu7dWrVqlHj16SJLKy8tVVlbWXP6RRx5RfX29brzxRt14443Ny0ePHq2FCxemuvkAAAAAAABABF8gEAikuxGZoKqqSoWFhaqsrFRBQUG6mwMAAAAAAIA0SVacKOPGqAMAAAAAAABaIgJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADwgYwN1s2fPVs+ePZWfn68+ffro1VdfjVp+3bp16tOnj/Lz83X00Ufr4YcfTlFLAQAAAAAAgNgyMlC3dOlSTZ48WXfeeac2b96sgQMH6pJLLlFZWZlp+e3bt+vSSy/VwIEDtXnzZv3yl7/UpEmT9Oc//znFLQcAAAAAAADM+QKBQCDdjXCqX79+Ov300zVnzpzmZSeccIKGDh2q6dOnR5T/xS9+oZUrV2rbtm3Ny8aPH6+33npLr732mq1tVlVVqbCwUJWVlSooKEh8JwAAAAAAAJCRkhUn8rtWU4rU1dVp06ZNuv3220OWl5SUaMOGDabrvPbaayopKQlZdtFFF2nevHnav3+/cnJyItapra1VbW1t8++VlZWSmj4IAAAAAAAAtF5GfMjt/LeMC9Tt3r1bDQ0NKioqClleVFSkiooK03UqKipMy9fX12v37t3q2rVrxDrTp0/XPffcE7G8e/fuCbQeAAAAAAAALcWePXtUWFjoWn0ZF6gz+Hy+kN8DgUDEsljlzZYb7rjjDk2ZMqX596+//lo9evRQWVmZqx8AgMRVVVWpe/fu2rFjB13TAQ/iGgW8i+sT8DauUcC7KisrdeSRR6pjx46u1ptxgbrOnTsrOzs7Intu165dEVlzhuLiYtPyfr9fnTp1Ml0nLy9PeXl5EcsLCwu5QQIeVVBQwPUJeBjXKOBdXJ+At3GNAt6VleXuPK0ZN+trbm6u+vTpo7Vr14YsX7t2rQYMGGC6Tv/+/SPKr1mzRn379jUdnw4AAAAAAABItYwL1EnSlClT9Nhjj2n+/Pnatm2bbr75ZpWVlWn8+PGSmrqtlpaWNpcfP368Pv30U02ZMkXbtm3T/PnzNW/ePN16663p2gUAAAAAAAAgRMZ1fZWkESNGaM+ePbr33ntVXl6u3r17a9WqVerRo4ckqby8XGVlZc3le/bsqVWrVunmm2/Wn/70J3Xr1k0PPvigfvjDH9reZl5enu6++27T7rAA0ovrE/A2rlHAu7g+AW/jGgW8K1nXpy/g9jyyAAAAAAAAABzLyK6vAAAAAAAAQEtDoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEBdkNmzZ6tnz57Kz89Xnz599Oqrr0Ytv27dOvXp00f5+fk6+uij9fDDD6eopUDr4+T6XL58uQYPHqzDDjtMBQUF6t+/v/7+97+nsLVA6+P0O9Twz3/+U36/X6eeempyGwi0Yk6vz9raWt15553q0aOH8vLy9J3vfEfz589PUWuB1sfpNfrkk0/qlFNOUdu2bdW1a1ddd9112rNnT4paC7Qer7zyii6//HJ169ZNPp9PK1asiLmOG3EiAnUHLF26VJMnT9add96pzZs3a+DAgbrkkktUVlZmWn779u269NJLNXDgQG3evFm//OUvNWnSJP35z39OccuBls/p9fnKK69o8ODBWrVqlTZt2qTzzjtPl19+uTZv3pzilgOtg9Nr1FBZWanS0lJdcMEFKWop0PrEc30OHz5cL774oubNm6cPPvhATz31lI4//vgUthpoPZxeo+vXr1dpaamuv/56vfvuu3rmmWf0xhtv6Mc//nGKWw60fPv27dMpp5yiWbNm2SrvVpzIFwgEAvE0uKXp16+fTj/9dM2ZM6d52QknnKChQ4dq+vTpEeV/8YtfaOXKldq2bVvzsvHjx+utt97Sa6+9lpI2A62F0+vTzHe/+12NGDFCv/71r5PVTKDVivcavfrqq3XssccqOztbK1as0JYtW1LQWqB1cXp9rl69WldffbU+/vhjdezYMZVNBVolp9fo7373O82ZM0cfffRR87KHHnpIM2bM0I4dO1LSZqA18vl8eu655zR06FDLMm7Ficiok1RXV6dNmzappKQkZHlJSYk2bNhgus5rr70WUf6iiy7Sm2++qf379yetrUBrE8/1Ga6xsVF79+7lPxxAEsR7jS5YsEAfffSR7r777mQ3EWi14rk+V65cqb59+2rGjBk6/PDD1atXL91666369ttvU9FkoFWJ5xodMGCAdu7cqVWrVikQCOiLL77Qs88+q+9///upaDKAKNyKE/ndblgm2r17txoaGlRUVBSyvKioSBUVFabrVFRUmJavr6/X7t271bVr16S1F2hN4rk+w/3+97/Xvn37NHz48GQ0EWjV4rlG//vf/+r222/Xq6++Kr+fP0WAZInn+vz444+1fv165efn67nnntPu3bs1YcIE/e9//2OcOsBl8VyjAwYM0JNPPqkRI0aopqZG9fX1GjJkiB566KFUNBlAFG7FicioC+Lz+UJ+DwQCEctilTdbDiBxTq9Pw1NPPaWpU6dq6dKl6tKlS7KaB7R6dq/RhoYGjRw5Uvfcc4969eqVquYBrZqT79DGxkb5fD49+eSTOuOMM3TppZdq5syZWrhwIVl1QJI4uUbfe+89TZo0Sb/+9a+1adMmrV69Wtu3b9f48eNT0VQAMbgRJ+IxtqTOnTsrOzs74qnFrl27IqKhhuLiYtPyfr9fnTp1SlpbgdYmnuvTsHTpUl1//fV65plndOGFFyazmUCr5fQa3bt3r958801t3rxZEydOlNQUGAgEAvL7/VqzZo3OP//8lLQdaOni+Q7t2rWrDj/8cBUWFjYvO+GEExQIBLRz504de+yxSW0z0JrEc41Onz5dZ511ln7+859Lkk4++WS1a9dOAwcO1LRp0+jZBaSRW3EiMuok5ebmqk+fPlq7dm3I8rVr12rAgAGm6/Tv3z+i/Jo1a9S3b1/l5OQkra1AaxPP9Sk1ZdKNGTNGS5YsYcwOIImcXqMFBQV6++23tWXLlubX+PHjddxxx2nLli3q169fqpoOtHjxfIeeddZZ+vzzz1VdXd287D//+Y+ysrJ0xBFHJLW9QGsTzzX6zTffKCsr9L/x2dnZkg5m7gBID9fiRAEEAoFA4Omnnw7k5OQE5s2bF3jvvfcCkydPDrRr1y7wySefBAKBQOD2228PjBo1qrn8xx9/HGjbtm3g5ptvDrz33nuBefPmBXJycgLPPvtsunYBaLGcXp9LliwJ+P3+wJ/+9KdAeXl58+vrr79O1y4ALZrTazTc3XffHTjllFNS1FqgdXF6fe7duzdwxBFHBK688srAu+++G1i3bl3g2GOPDfz4xz9O1y4ALZrTa3TBggUBv98fmD17duCjjz4KrF+/PtC3b9/AGWecka5dAFqsvXv3BjZv3hzYvHlzQFJg5syZgc2bNwc+/fTTQCCQvDgRXV8PGDFihPbs2aN7771X5eXl6t27t1atWqUePXpIksrLy1VWVtZcvmfPnlq1apVuvvlm/elPf1K3bt304IMP6oc//GG6dgFosZxen4888ojq6+t144036sYbb2xePnr0aC1cuDDVzQdaPKfXKIDUcXp9tm/fXmvXrtVNN92kvn37qlOnTho+fLimTZuWrl0AWjSn1+iYMWO0d+9ezZo1S7fccosOOeQQnX/++frtb3+brl0AWqw333xT5513XvPvU6ZMkXTw/5XJihP5AgHyYwEAAAAAAIB0Y4w6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACABxCoAwAAAAAAADyAQB0AAAAAAADgAQTqAAAAAAAAAA8gUAcAAAAAAAB4AIE6AAAAAAAAwAMI1AEAAAAAAAAeQKAOAAAAAAAA8AACdQAAAAAAAIAHEKgDAAAAAAAAPIBAHQAAAAAAAOABBOoAAAAAAAAADyBQBwAAAAAAAHgAgToAAAAAAADAAwjUAQAAAAAAAB5AoA4AAAAAAADwAAJ1AAAAAAAAgAcQqAMAAAAAAAA8gEAdAAAAAAAA4AEE6gAAAAAAAAAPIFAHAAAAAAAAeACBOgAAAAAAAMADCNQBAAAAAAAAHkCgDgAAAAAAAPAAAnUAAAAAAACAB6Q1UDd16lT5fL6QV3FxcfP7gUBAU6dOVbdu3dSmTRsNGjRI7777bkgdtbW1uummm9S5c2e1a9dOQ4YM0c6dO0PKfPXVVxo1apQKCwtVWFioUaNG6euvv07FLgIAAABA2k2fPl3f+9731KFDB3Xp0kVDhw7VBx98EFLGzv+/AADJlfaMuu9+97sqLy9vfr399tvN782YMUMzZ87UrFmz9MYbb6i4uFiDBw/W3r17m8tMnjxZzz33nJ5++mmtX79e1dXVuuyyy9TQ0NBcZuTIkdqyZYtWr16t1atXa8uWLRo1alRK9xMAAAAA0mXdunW68cYbtXHjRq1du1b19fUqKSnRvn37msvY+f8XACC5fIFAIJCujU+dOlUrVqzQli1bIt4LBALq1q2bJk+erF/84heSmrLnioqK9Nvf/lY33HCDKisrddhhh+mJJ57QiBEjJEmff/65unfvrlWrVumiiy7Stm3bdOKJJ2rjxo3q16+fJGnjxo3q37+/3n//fR133HEp218AAAAA8IIvv/xSXbp00bp163TOOefY+v8XACD5/OluwH//+19169ZNeXl56tevn+677z4dffTR2r59uyoqKlRSUtJcNi8vT+eee642bNigG264QZs2bdL+/ftDynTr1k29e/fWhg0bdNFFF+m1115TYWFhc5BOks4880wVFhZqw4YNloG62tpa1dbWNv/e2Nio//3vf+rUqZN8Pl8SjgQAAAAAuwKBgPbu3atu3bopKyvtHYUcqampUV1dXcL1BAKBiP+b5OXlKS8vL+a6lZWVkqSOHTtKkq3/fwHpsn//fh2Te6JWv7dSJ5xwQrqbAyRVWgN1/fr106JFi9SrVy998cUXmjZtmgYMGKB3331XFRUVkqSioqKQdYqKivTpp59KkioqKpSbm6tDDz00ooyxfkVFhbp06RKx7S5dujSXMTN9+nTdc889Ce0fAAAAgOTasWOHjjjiiHQ3w7aamhod1qaNql2oq3379qquDq3p7rvv1tSpU6OuFwgENGXKFJ199tnq3bu3JNn6/xeQLr1zT1eZPtTZJ56rPYFd6W4OkFRpDdRdcsklzf8+6aST1L9/f33nO9/R448/rjPPPFOSIp4QmT01Chdexqx8rHruuOMOTZkypfn3yspKHXnkkdqxY4cKCgqi7xjiN26cNHeu+XJJ8h84ZevrpYYGKS9Pqq2VsrMPvu/3N71fX9+0LLy+CRMO1hHMbLvxttdM+HaN9u/b19Tm7Oymn0GZnM3LZs+O3G5Dg9SunfTgg/bbe/31Tds0jpEUevzmzpUmTTrYTqOMcdyNts2bd3Cf9u2TCgubyrZvL82YYb3tYEYdyTJu3MFjZ/xbitxfo6yxH8HHesKEg+dacHuvvz7088rLO7j/lZXSihXS0KHJ30cro0dLjz/ufp3SwWuuXbuDxyY7++Ayv1+qqTl4jkW7Ph59VPrJT5y14/rr7R3XESOaPo/s7OhtMD5jKfT+Ikn5+Qf3o6bm4L4a95ng/TSu5/r6g+fFvHkHr1Xj3+FtCX5farr+gq/pceOa2mEsMz6Hxx9v+rffH3o8fvQj6cknYx+fRBj3EUmaPVvr1knnLrL5uaSDcc7Ec2wmTJC++KLpXKqpaVqWn990/E8+WRo0KPY9OPgz/dGPpE6dDtYlHazb7HoxzuN27Zp+f/DByP0YPbqp3AsvSDNnRm5zwoSD97UpU6Tq6qbtTJlysPxll0n9+ze14/PPD7Yx/Hty3z737y2xGOd5Xt7B/TD2ybh+CgsP7kuw8Gvujjua7tHh13C0z9DYRnV102ffrl3kcRgxQjr8cGnPHneOz4gR0tKlideTTMZ9wLjnGYK/H4L/1gi/vxqfn9T0GRnHOejeYkdVaam6/+Uv6tChgws7lTp1dXWqlnSLpNh5b9ZqJf2+ujri/yd2sukmTpyorVu3av369RHvxfP/LyCZqqqqtF3/0fE6RR/qPb3wwgu68MIL090sIGnS3vU1WLt27XTSSSfpv//9r4YOHSqp6clO165dm8vs2rWr+SlPcXGx6urq9NVXX4Vk1e3atUsDBgxoLvPFF19EbOvLL7+MeFoUzCplvKCggEBdMk2ZIpkd39zcpp9+vzR/vlRaKs2aJU2eLDU2hv6RmJsrZWU1vaTI+oy6wrtIxPO5Pv20/bLh262vb1rW2Nj0u/EfB+P34GVW+5Cb66zdOTlN6/j9B9vR2Nj0n4/6+qa6gttplDGOr3GsjW3m5kp1dQePebT25OSE/p7s6yg3V1q0qOnfRtuk0PMleD8aG6WPPw5tl7Ff9fXSmDHS8uVNy32+g8exsfHg+rm5Tfs5atTB45kOPp/72zY+v+B9NY5N8HEIPodjnZ/xtDMnx946OTkH2xytfPB+GO03gijB10pj48F9Db5WjfeN9bOyDr5nXE/GudC2rfm1HHyuhJfJzT14HIcMCb1Wc3Ii7w/G8XHyEMEp4z4iSQUFuvypkU3Lxo+XlixJzjYTYRwTu+dOMOOazslpOl/r65vr+eLTT1Vk5x68cGFoW4Lv+8Y2jOsnvK42bSKOd8R+5OQ03buC14/2b+McC15uBML27w9tY/j35DffpP6+ZpznZvuUmysdc4y0c6d5u7KyQpd36iR9++3BOozvr1j3CONzb9Pm4HrB6/h8TcviOcfMtGmTvu8Pu4zzxLjnGYK/H8Lvj1Lo32YHlr30ZoHON45z8Llutx0yfyifCdpIyk9gfeMKdfr/k5tuukkrV67UK6+8EpKJWFxcLCn6/7+AdDi5sI/aqr2KdYT2q05XDP6hKhu+yrgu74Bdnjqza2trtW3bNnXt2lU9e/ZUcXGx1q5d2/x+XV2d1q1b1xyE69Onj3JyckLKlJeX65133mku079/f1VWVur1119vLvOvf/1LlZWVzWUAAAAAoCULBAKaOHGili9frpdeekk9e/YMed/O/7+AVPvss8+0Q9t1jE6QTz4drqNUr/3qnd0n3U0Dkiatgbpbb71V69at0/bt2/Wvf/1LV155paqqqjR69Gj5fD5NnjxZ9913n5577jm98847GjNmjNq2bauRI0dKkgoLC3X99dfrlltu0YsvvqjNmzfr2muv1UknndScCnvCCSfo4osv1k9+8hNt3LhRGzdu1E9+8hNddtllzPjqRWecYb9svE+bg7t0WjlwjiVFcPcPox3hPzOF32ZS7rBhkd1fks3usSwtPfjvY45JbJtff93088A+7svQJ/ymBg2KfkyDuzjZ9cknibTInlSdb+HcyiwLbr9xjNO1T63VqlVNP8PuYUWXX55YveHXTLz3//p66cD4UpbbkZq6hrqxPa/58EPLt7586qnQBcaxiOcaainHK50sjvv5Dw8/+H4rO85ZLrycuPHGG7V48WItWbJEHTp0UEVFhSoqKvTtgUxTO///AlLte0f0V2cVqUBNPeiyla2eOk4f6/3mcxdoadL61/7OnTt1zTXXaPfu3TrssMN05plnauPGjerRo4ck6bbbbtO3336rCRMm6KuvvlK/fv20Zs2akHEo/vCHP8jv92v48OH69ttvdcEFF2jhwoXKNsagkvTkk09q0qRJzTMYDRkyRLNmzUrtzgIAAADAAfEE28LXd2LOnDmSpEGDBoUsX7BggcaMGSPJ3v+/gFR5++239YU+0xk6N2R5kQ7XDm1X77an66PAtjS1DkietGbUPf300/r8889VV1enzz77TH/+85914oknNr/v8/k0depUlZeXq6amRuvWrWuelciQn5+vhx56SHv27NE333yj559/Xt27dw8p07FjRy1evFhVVVWqqqrS4sWLdcghh6RiF5EuXs46Mca3Cl9mVs6LjHHf4uXFz8ZsrMGRI0OzH+20OT90pJl2113nQuMOGDIk+vvGoNzJYkyGYiWejMn8OEbmSdYYaPFmtYbvb/D6Y8eGvLVvwYLI9RO9npBafr+752B4xmSw4cPt1zNkSPRrb+xY6Z13QsuYnet2rt9Y7QrOUk6U1X0v1n4ccFggELrAqmzYtRohPBPR7Lr32vdaKrixz+G9HFrjcUyhQCBg+jKCdJK9/38BqTLo5AvUTT3URu1Clvvk0zE6QZ/qQ+3evTtNrQOSx1Nj1AEAAABAa5Dqrq9AJnnhhRdUpa91lI41ff9QdVahOuq0w76X4pYBycf9HZkvkTFNgp/cbt3qTnucbNNp28OzphIdyyWR8XoSkeoxaGLtX11dZDm7x8RqHbf3sX17+2WT/Xm6VX8yz4NU1h0tmzBoWbvw7J54thW+Hau2pIuTLDBECh8DyixTy05GtjFephljVuN4zhW/P75M2GRy8j0aLesieCZes22k8nsy3ddxqrgxdm2GHysCdYC5xsZGXTH4h+qhY5SjXMtyx+gEfa4yfRhlvFIgE3F/BwAAAIAUI1AHmHvyySe1X/t1uI6KWq6dOqhIh2vAseekpmFAinB/BwAAAAAAafftt9/qJ6XjdLSOU7ayY5bvqeP0P+3Sxo0bU9A6IDUI1KHlctod4v77D/472V1cggeujzZhQaq725iJpwtomPmXLXepMQ6d4+Dp2rJlTT+Du55ZnUNWXRxb+4Di4V0yYx2LVHRZsruNaJNCOK0/Whdqs4H2Y3WZNroqmtVn1Z50nYcZ3g0tQrznhZM6Ey3rdCKUaOeInTpilUnHOWDnOP3yl9LOndbvX3ll4ttAbHaOYys61mTUAZF6tz1dOcpTkQ63VT5P+TpCR+vi/pcqEM8wI4AHcX8HAAAAgBTzKbEgnS/1TQaSavfu3SrThzpGJ8jn4Aw/Ut/RN9qnv/zlL462N336dH3ve99Thw4d1KVLFw0dOlQffPBBSJlAIKCpU6eqW7duatOmjQYNGqR3333X0XYApwjUoWVwMimCVfZKOp7g2hkU3Eo8WQuXXWZdVxKfco/967CD20mgHseOOCJ2Gbv77fcfHCh/yZLE2pUMqcpicTObJpnnQSqyUY1tWG0nVrZdtHJW79s5vmvWxC7jJrP7R/ikCJlq7tymnzEmCkmY1XeYjWw52xnL4XWbTQqRyORMXvfmm8mrO10TM3lNvPsffs4Z110r4HPhBbQkpx12hgrUUYeqs6P1/PKrp47TyCuu1f79+22vt27dOt14443auHGj1q5dq/r6epWUlGjfvn3NZWbMmKGZM2dq1qxZeuONN1RcXKzBgwdr7969jtoIOEGgDgAAAAAApM1HH32kz/WpjtEJca3fVd3lk0+9c0+3vc7q1as1ZswYffe739Upp5yiBQsWqKysTJs2bZLUlE33wAMP6M4779SwYcPUu3dvPf744/rmm2+0xIsP7tFiEKhDy+X3S337prsVziR7TK/f/c7ZdqONteWUMQZcqgRnhsR73MLXM8tAidWGVEl1Jocb22vfPvE6okn0enE6lpiRWRacORreBqdtMls/VruefdbZNloDs7EBE+XWfcVOXVE+87Grh8cuf/zxkdswyhjH5oEHYp9fqc62s3sNBo/jGE9dTjLKrcaTNd5zQ0vPzIs1LqcTmzcntn6aZbvwAlqKc465QF3UTe3UIa71s5Slo3WcPtF/VVVVFfKqra21VUdlZaUkqWPHjpKk7du3q6KiQiUlJc1l8vLydO6552rDhg1xtROwg0AdAAAAAKQYk0kAB51zdf+4g3SGduqgQE6jCgsLQ17Tp0+PuW4gENCUKVN09tlnq3fv3pKkiooKSVJRUVFI2aKioub3gGTg/o6W6cDT2sDPfmZ/nVRkCljNjhn+fjC741zZ8e9/OyufjGOS6iyzYcPMlwe3w262jZ2Mx+Bsi1SOs/P++5Kkzz9P3SajsvM5O81QTFY7UlFfr16Ry8aOjV2/WVZerDa09GycTBZr7MTwz3raNOfbMLvnrV9v3hZJOuaYpp9VVVJ1deKZ1Ok4/+x8V/n9TVm88Xyvma0T/r2Rqlm/b7st+dtwIt6/E9wax7JLF3fqSRMCdUCorKxs5fhz4375s3OUk5OjysrKkNcdd9wRc9sTJ07U1q1b9dRTT0W85/OFjggZCAQilgFu4q95AAAAAACQVv6sbOXm5MS9fl1jU3ijoKDA0Xo33XSTVq5cqVdeeUVHBE1GV1xcLKkps65r167Ny3ft2hWRZQe4iQcxaLnq6+ULBKKXCc5wS1UWgNUMjrFmkHSbm+PPed2VV0Yus5uBYfV7tPXTMWvi8cdLs2er2+Thyd++nRkw7ZzLyR6jLhF2j6HdscVOPTX0d6s6Er0WvXIte2nmUDfaElyHWcZaspx8cvS2SJHjf152WeT9/ZBDItc3fn74YeT4a176/CT32nPgP1xx1x18X0vmd2i0+g5kT3uW3bEAzf7+iccLL8S3nkeQUQeEyvZnKzc3N+5XjsMgXyAQ0MSJE7V8+XK99NJL6tmzZ8j7PXv2VHFxsdauXdu8rK6uTuvWrdOAAQNc2WfAjEf+ogcAAACA1iPRYBuBOiAxN954o5YsWaK//OUv6tChQ/O4c4WFhWrTpo18Pp8mT56s++67T8cee6yOPfZY3XfffWrbtq1GutWFHzBBoA4tk1vZMMlgNjudV7MYpOhts/sEPN37tWJF9Oy4YLfeGrksnrGHjjrKWflE+f3Syy+ndpuxxPrck5lRZ/fzmj8/OTOCGsKPwc6dke9FO05Ozzu/v2mcsXRkK6b7Ok8HtzKpgmcNDq/b4XGdr7Eaq5fNtxHMqDd4rEg730PJnp3cybacfG/W1EidO0u7d8e3rXDV1c7KO9FSryWzLLqWuq8A4pKdla3cnNy418/x5Uh19svPmTNHkjRo0KCQ5QsWLNCYMWMkSbfddpu+/fZbTZgwQV999ZX69eunNWvWqEOHxCa+AKIhUAcAAAAAKUZGHRAqOzu1gbpArGGS1DSRxNSpUzV16tS42wU4xf0d3tWa04ntPNlP91NoJzOtrVyZvHbYEet4Bj9FO/30yPWsMlvMxjg0MgTuuy/u5sbFauykVIj3XAweN8tt8WSiJVKH3bJr1kQuszOjZDxtgrsSGUf0/PPtlUv0vl5aqrF/tZjpOnwbxk+3zym3v5uWLXNvTND27d0ZB7K+3nzW6lRcn+n+7g8Xzz47ncm6BWOMOiCUP9uv3Jy8uF85/viDfICXtN5vRgAAAABIE9+BVyLrAy1Jwl1fA/HPGAt4CQ9iAAAAAAAAAA8gUIeWxY3uE8kcWF4y7y4Z/LvdfXCyr9G6ETsZzDm43GWXWb8XbVvpEKttY8emph2pkuzjbOezjmcCDjcla9tOumy98IKzuqyWhW/L7vFPlylTrN+79NLQ3+fPT25bkiGeY3vqqU0/03FNWE0iYSY/37ybrN31vczvb9o/45xL5LvW7PvbSZ2xuDnBjFe1lP1IUJak7ARe/EcOLU2236/c3Ny4XzkJZOMBXsK3JAAAAACkGJNJAKES7vraQNdXtAzc35G5jCyx4CfPXn/SX19v3kY3Br5PNat98Rq3jpWRQWGVnRicQZbOz6emxln54cOdlbfzuds9L3bvdrZtJ+66q+lnOgfKLyuzV84sgykZmbWpUlFhv+zGjclrR6Zw+z5qliVt9xwzmyDBrP5o7NRhl91jY9Wm4Pt1Iu2yc525eS1Gq+vrr93bTjLFczzCM/VbASaTAEJl+7OVk5ubwItAHVoGD/6FDwAAAAAAWpOmjLr4g2059YQ30DLwIAaZw3g668YYcunMQomVAZhpGYLxlPf6foULHj/QzrmTrv3zwvFN99h0hvvvdydT1SwzKRXX6PHHmy+Ptj3j3uhk3Em3xTrm4e974VxxIt7jmmhmmNPtnnmms23ZzRjz4udl93yP1X6n+5bM+0C0tmTa96cdxj4NH+7NcyyJyKgDAJhpXd+GAAAAAOABjFEHhMr2Z8ufF/8YddmMUYcWgvs7Mk+mjI0WLlqbk71Pbo7T5qV6kr0NOxlWbm7Prl/+0vo9O2M1BXM6pp0XDRkSuay6OrFrysnnGZypE895YLJO+S9+EVkueH+sPk8nM8MmwmI/5164zNl6qbhulsVokx0H2rnoypURy2IaN87V/Zx/2XLrN43tTJoU+d7cuaFljJ/t20tHHNH0bze+i1K1r+FmzIj+fiLZiqnO8nIz88+LjMxrr2ZpAkibrOxs5eTmxP3y53BPQcvAmQwAAAAAKUZGHRAq25+tnLwExqjbT0YdWgbu78gMdmdKdePJ7KJFidfhlN2ZHuPN2omVMRA8xo/TsXHizTxKBTvby5Sn+cHHfudOe+Wc1hvNsGHubdNQXR3fenakOlPQ7viADz5oq7quo0Yd/CV8LLFY52wqMo6NbYRl9o17IcYswuFtr6+Pfm55xYH9LX32YPbmoqEOsr3sfib19eYZokHG/tXieNnNDAu/17dvfzDbrqYm8XHy6uulggJ7ZeNlNj7cm29GXyc/3937gtkYf259n2Rir4FYMuW7NsUYow4IlZWdJX+uP+5Xdk52uncBcAXfmgAAAACQYr4Dr0TWBwC0PDyIQWZqSU9mg/fFbIZJNyxZYv1e+DZb2pP8aDMClpbGHjswWHW1vWyTZBzD3btDf7ea/djptq+91nlbvDKukJGdZtaW9u2bfh51VOLbCT6mdvd77FjTOhp+9rPo9Uf7/D75JHRW3WjnYrqvY6vt5+dHlktmZqUh3vM1+DoLy0QrXWEzE9DJ7KqpuK6svmduu+1gxlki5099vTR0aPzrBxm7OkaGpsHOcQs+95xe06kexy6TxqiLdWycfMdK3ts/ACmV5c+SPy8n7ld2LvcQtAycyQAAAACQYtkHXomsD7QkTbO+xh+i8BOoQwvBmQwAAAAAKeZTYt2b6PqKlsbnz1J2AoG6rFzC12gZ6PqKzOB2V4hoXSFTwdif8HbU1NgblN7NrofR6rKzjQsvdL69VIuxzT0+iz91rbokRhvsPVndQmN1iza6RDrd9rPPOm+LnW047YoWj40brd9btqzp54cfxq7HzW6iZp9L0PLspUvtbd+se+u//22+jfD10tn1NbjdJ55o/b4huCuvl5m0c/5lDiaTsFO/IcbxcLzdaMMrBC8zzh2795FUTAIS3jbD/PnO68rPT/yeY8Wt+36XLgfri7atTOeV4RM8gMkkgFBZ/uyEur6SUYeWgjMZAAAAAACklTHra7yycwhvoGXgQQxarlhPpL3yNDfedriVMeOknuBJKb7+uuln+KD5sUSb3MFNRsaFjePb6brrpJEj7Q8qnupz56WXIjNBzAbllzR/6EpHx3fNeJvZOaeeevDf4ftvdj3ZOU6JngepHpjfLDPJaXbh6tXR37fznlVGrp363BJrgoT8/NBzxmDS5iXXrnKvXSk09q/D7B1rY4IGm5oz5iwmARr71wQy2czOGWMyD6fZ1TH2fdcuh22Lxs4kPlaGH5iQwuHnICny+y3Z31+DBsUu45W/XeAKMuoAAGb4tgcAAACAFEs02EagDi1Ntj9LOQlNJsEYdWgZuL8jM0TLzjEby8nL4s2esVtHLA7GQorqkEPsl83KgFvNUUfFzpQIzjhZssS6fDzjJ0WzcGHTdkeODG2HC2NElZz9jfkb4XX/+tehv9vNPoxm9+7YdSRSfzLYzai0Gt8w2JYtsesxywLy8r3O6v4S5Vwd+WwKxjqLV1gW5aIrV2ruhU3jHzoeK87I7Iq1HbeFZz+Gb8s4x5xmC8f4Tu4yeaSDRppbNPTAMa6vl+66y3p7Vm0P3t+vvz6YCe42t8aFdGv8T69p6WPuJYCMOiBUdnaWcnL98b9yCNShZWjd344AAAAAACDtsrOzlZObE/f6/pz41wW8hAcx8K5UP2VN17h1wbPstdQny1demb5t2z2un3wSu47wzymVn9eZZ0a2J1x9vfOkjvHjzZfHkx3iJFtzwgRp507n2wh2IBOoOevGTXbH6kpkxmSzMdzCWX0OTsbISwerDCO3M4RTrPTZIRq3uikDMKGx4qKIu95omWVm16aRSRfP2G3JEjajdunTlx78pVcv6/XsZrmaZfHamQnWzmzsqcioG2ZzXEQvMbsXpGtWag8iow4IlZ3tU15udtyv3ByuCrQMGfZtDwAAAACZz3fglcj6AICWh5AzMoObT5DNnuSm6+mu1VhBVpIx02sqZ51NR5akG2UM4ZkBZscxGedSfX3kGHHt20udO4cu8/s17oXhTW2xOxtvrJlDJ0xIrJ5o5d2Y9XXNGpU+OyR2WTt1mS2zO3NtPFlJ8YzbFK1Nxx/vnXtZrEzAdDDauGxZ/Ou6sf1ks7ud8Iw6sxlNU33PDstuizoj8MigMfCs2nnhhaG/JzpGXSKzz0r27svRsuaMGXpbMrLtgFYtJztb+Tn+uF95fsaoQ8tARh0AAAAApFj2gVci6wMtSU62T20SmBCCQB1aCjLqkBncmlEtCfb4XOp4UF8fOysnfGyhRLeXiFjjICVru8lgNkurVWZJtGOf7OwTo035+U2vaJmR0TIvjNknu3WL3WazDBS3xmJK9Hj5/QdnxE2U3X1KdOw+p2WtrjPj+AWXf+ed0Dree89ZO52K9Rm6MTNwMqxe7W59U6ZEf9/JLNDJPpejvWecT4leCy7eB0cuvjR6gVjbMq4Jg9V9MVY9dvfJre/VTJglNd59DZ+F2OzfrQhj1AGhcrKz1DYnO+5XIkE+wEta57ciAAAAAKSRT4kF2xijDi1NdqBRuY0Nca+fk8C6gJfwIAYAAAAAAADwAAJ18LbSUnvlnHaZcLG7VafHH49vRaMN0bq6WO2XG11f7W4r2rG1exyDuxulsqubsS03Jr4InizCC1106uulxYsjFs+/2MZA+caA7WefHV/XV2P7VuducBe6YcPM19+9O3Y7Y3Hyud52m/V70bpwujX5TLTJRpxc+3a3I0lTp8ZXRyql43pKRrd/Lwtub3h3+ZkzI69jq8/E6YQhbh4nk7rmX7jk4HvhQwEYdu6MWkczt86JeOoxhiKw0xYvnnuJtMmLQ2GkGF1fgVBZDY3Krq+P+5VVT0YdWgYPfuMDAAAAQMuWaLCNQB1aGl9DvbLqauNeP2t/nYutAdKH+zsyj1V2QLo8+6y9ctEmX4iWgRC0j7WPPuqwcR5hlZ2WDlbbXrIkte1IRPv2TVmVQ4ceXHbgPBn7V4sMtuBrJfi8izUg/CGHOGtbcHaO3y9NmyaNHBlZrrq6aT8S4eQ8Wr8+dplEB0cPPp7hdRm/L1rkrG4n2TXhmYF+f9OEIV5idVxSpbQ08ftPqto8ZIjp4vmXLXdel1Um5yefRH7/xMqUi6WmxrV7/LIxqyzfC7nXJTKRkRsZgG5MjhPchngy3DNRS9ufOKQ6o+6VV17R5Zdfrm7dusnn82nFihUh7wcCAU2dOlXdunVTmzZtNGjQIL377rtx7x/gWH2DVLs//lfd/nTvAeAKAnUAAAAA0MLt27dPp5xyimbNmmX6/owZMzRz5kzNmjVLb7zxhoqLizV48GDt3bs3xS1Fa+VraJSvbn/8r/0eSOIAXECgDpktyWPgJL0NxlhAxpheMeQFAu5s16wdyZTpT83Ds5TStT/GdvPzm8ZeMsaaMytjp41+v/V4h0bdZtuwW78k3XWX+fL6+sQz6twSLdMmfEyvROqzEi2T1uzzMZaFZ+eZZQadfbaztiQi2vHxyj0gWhapDYuuXBma0eakriOOiHu7wWxlzdoR773MyT3GCZMxaaNtYu7FNjILg49J8HibyTgf7RxPN7PuvMRpm7y4D2mS6oy6Sy65RNOmTdMwk/FjA4GAHnjgAd15550aNmyYevfurccff1zffPONlmRSrwMAaAEI1AEAAABAivlceElSVVVVyKu21vkYX9u3b1dFRYVKSkqal+Xl5encc8/Vhg0b4txDwJlAQ6MCdfXxv/YzmQRaBgJ1yDzRZmG0k1HglSe5wU/fE5kxLp7tptAXPt/BX9KZjRbO6lhbzTRslq1klgmZ7M8w68Bt++uvQ9sSz0zA0T6PWGMqWo0VGb7/0bJFUzk+mVVmYDA3Zy8Mr2vy5Mh17IxXF2PcSsuyycp6shJ8fVhdK8HlgqXynhAtizSaZU2zKZeuGKaxq4c3zdTp9H4WPANprDaGzwQaJOoYdWHt2e/zRV6D4VmixjrV1e58Fi5e18NmnW/53rgXgj4Dq21OnHjw38bssG4wu+/H+/22LGimbqOe+nrpxBMjy3rpO9RJO7zS5hase/fuKiwsbH5Nnz7dcR0VFRWSpKKiopDlRUVFze8BSdfQINXVx/+i6ytaCL45AQAAACDFsg+8Ellfknbs2KGCgoLm5Xl5eXHX6Qt+wKqmLrHhy4BkCdQHFKiNPysuUNfoYmuA9CGjDt5l5wmsncwSp9sZO9b+uvEwy6SLlm3ihZltox3P4Bk9TTJGim66Kf1jugWLlv0Vqw47WRPJ+LyCtzd+vPny8G3bbUesDBM7+xrtOPv9qnzmGev3E+FWdozZ9efGOHNB7819Z4DzdkXbjlnGnFXGYyrF87lmYrZNeJt/+Uvn64Szef5ZjlFnUn/OVVdFH2cxeNw2g93rymx//v1vb3xnGR577OC/Y927Ez0P7WRq2imzfHnTMTz11Kbfg7MrvXRs4Qq3xqgrKCgIecUTqCsuLpakiOy5Xbt2RWTZAUlTT9dXQCJQBwAAAAApl+rJJKLp2bOniouLtXbt2uZldXV1WrdunQYMcOlhEwDAFgJ1yBzh49B4JQtjuY2Z5yTzMayMfXI6ZpIbT9WtxhhLxMqVkcuMuo3Mu1Rn+yRjP9OZrbRzZ9NYUuHnTfhxNf79+uvR64tn/DiHsxsWXnNN5PtmmTzxsDtmk1vZd8Y2g9k4r8a9YD3mmJMMzfkXL4v6fsTyVGXgGJ9DrDEPvZAR5OT6tdteJ9lyTssZ3xN26ggvF20dO+NJms14Ha3Ohx8+eK2l8tyz2p5bn1+sMvGOfShJ99578N+ZNM6uXVbtDf97zgv3hlagurpaW7Zs0ZYtWyQ1TSCxZcsWlZWVyefzafLkybrvvvv03HPP6Z133tGYMWPUtm1bjQzuPQEkUdNkEg3xv8ioQwuRYd/2AAAAAJD5fEosa8LpyHFvvvmmzjvvvObfp0yZIkkaPXq0Fi5cqNtuu03ffvutJkyYoK+++kr9+vXTmjVr1KFDhwRaCThQ35jQGHWqI1CHloFAHQAAAACkWKLdV52uO2jQIAUCAcv3fT6fpk6dqqlTpybQKiB+gYZGBWrjz7ANEKhDC0HXV7QsqewSMjxKV7ZogrsmGV2EqqvtdQ8xJLKf4fVZdckNL5PINr3YVSdZbUpGvcGf0V13NXWxMuviZrbtadOi1xer66TV/gR384pnn93svm1nUplkfC5OJhVZtsy8nEPNEwnU10ulpdYF03HNxbq32FknWVw63+ZfZjLUgZO67X4u7dvHXrekREuW2NyunWve7YkWUnkOzp/vrLzZ8bDbRdNOnXaVluqLu++OXG517/LSd2ki11S0bvKtrAusl8aoA7wgUN+oxtr6+F9xBOpeeeUVXX755erWrZt8Pp9WrFgR2qZAQFOnTlW3bt3Upk0bDRo0SO+++65LewyY4/4OAAAAAABanX379umUU07RrFmzTN+fMWOGZs6cqVmzZumNN95QcXGxBg8erL1796a4pWhNCNTBu+J56p+pWvoTZOMzszvwf7K2H008AyWn43PbufNgNl34ZBLBmSGx2jZ2bNPPeAdAr6mJzGgL/nng3/ufesp09Zce2Jr4BA92z6WRI7XoSpOJTmJxI5s1GedIrDpTeY0NGmRv++m+x7l0TJozGoPrtDvRg1V77JxnFtf0yKeHhC4YMyayrmgZYW5niBn3pVSeg07aWV3tbJ2hQ623E76PVvezYSbnTJCiK66IXCd8W7NnW7cxE8Q6dq0YGXVAqEBDoxrr6uN/7Xf+3XXJJZdo2rRpGhZ8vzbaEwjogQce0J133qlhw4apd+/eevzxx/XNN99oie20dsA5z9zfp0+f3jzbkMFOmmltba1uuukmde7cWe3atdOQIUO0c+fOkDJfffWVRo0apcLCQhUWFmrUqFH62q0ZBwEAAADAIZ8LL6AlSbTrqzFGXVVVVcirtrY2rvZs375dFRUVKikpaV6Wl5enc889Vxs2bHBlnwEzngjUvfHGG5o7d65OPvnkkOV20kwnT56s5557Tk8//bTWr1+v6upqXXbZZWpoONg/feTIkdqyZYtWr16t1atXa8uWLRo1alTK9g8pFk8WQLqzvKyyK6JlFbo5ZpxZXU7qjzW2Ubqzacw4bVO0bLVYY6W56dlnIzPYjH+bbT9am+I9j+xkux4YMy/HYtDq828/w/l2wzl44FK6IvIpqW3G+H9mn3PweRHvWFKxrvmgZfMvWx7ZnnCpyGA12mRkZgZvN5qWmkkTb2aqifkXHxjPMMb9ZNmP10Qu/N3vIpeFt83q/m91H3P6XZCqLFLj/I51j4tWRyxDhhzcTvhxitVOIzPDWGY1rq2ddq1fH7tMOsT7t0NLvQ/EIduFF9CSNDY0qqGuPv7X/nrt37+/OUHHeE2fPj2u9lRUVEiSioqKQpYXFRU1vwckQ9q/Kaurq/WjH/1Ijz76qKYFDXoenmYqSY8//riKioq0ZMkS3XDDDaqsrNS8efP0xBNP6MILL5QkLV68WN27d9cLL7ygiy66SNu2bdPq1au1ceNG9evXT5L06KOPqn///vrggw903HHHpX6nAQAAAABAs0B9gxrq9se9fuP+euXk5Gj37t0hy/Py8hJql88Xmr8aCAQilgFushWoM+uvHcvDDz+sLl26xCx344036vvf/74uvPDCkEBdrDTTG264QZs2bdL+/ftDynTr1k29e/fWhg0bdNFFF+m1115TYWFhc5BOks4880wVFhZqw4YNloG62trakBTZqqoqR/uPFAgfd8fNJ7TJzowKn6EzP9/ZNt1oX3Abkrm/drIevCzRGW8TFZyFUV/fNHvoyJGR4yFVV4cus9NmO/vmwsx85U89pa7h43i4cc1WV5vPjhm+Hbevl/D6jffj5SAjdezq4bGvqXSds8FtMps52kqq7w0Ojs1XTzyhQxctsi5glt2aYHvG/nWY1LlzZIamnbHsPvxQ6ts3dFl45qmTczbd9+1Ez+PwbNXw+6ab10nYPfg/9y9Xr/BtxHu/zQSx7kvB7zFmXbNEx5nzRNcowIMKCgpcqae4uFhSU2Zd165dm5fv2rUrIssOcJOt+/uKFSuUm5sbkUJq9fr//r//T9XGgL1RPP300/r3v/9tmopqJ820oqJCubm5OvTQQ6OWMQsYdunSJWq66vTp00P2qXv37jH3BwAAAADsYDIJIFRjY6Pq99fH/6pviL0RB3r27Kni4mKtXbu2eVldXZ3WrVunAQMGuLotIJjtR1gPPvigrQw5SXr22WdjltmxY4d+9rOfac2aNcqPMvNfPGmm4WXMyseq54477tCUKVOaf6+qqiJY5xXh2SLhGRxuZNAka4wVo23JnnnPThuCf4/nmL3/vnT88c4yBNKdnWZl3LjIZVazLqY4E2DRZctU+tewsY1qapoyN6JdB0ammdm4Ssa+1NTEzkiLd+ytoO11ve468/djbTsWOxl18TD7rO2M+2Z2Dd1+u3T//e61zck1lK7rzSxzSWoa7yt8uXGchw2Tli9Pftsc3usODR5j0ezemW7hGWNnnhm5f3bvWca1HmvMt1jHL5VZeE63FeN+ts/nUzuLcTVNt1laKi1a1HRcqqtDjm2v2w+c07HGoPTCeZRq6c7UBOBZDQ0Nqq+Pv+trQxz3l+rqan344YfNv2/fvl1btmxRx44ddeSRR2ry5Mm67777dOyxx+rYY4/Vfffdp7Zt22rkyJFxtxOIxdZfB//4xz/UsWNH25X+7W9/0+GHHx61zKZNm7Rr1y716dOneVlDQ4NeeeUVzZo1Sx988IGk6GmmxcXFqqur01dffRWSVbdr167mCHdxcbG++OKLiO1/+eWXUdNV8/LyEu7LDgAAAABm6PoKhGpsbNT+BIL59Q3O133zzTd13nnnNf9uJOuMHj1aCxcu1G233aZvv/1WEyZM0FdffaV+/fppzZo16tChQ9ztBGKxdX8/99xz5XfwxO/ss8+OGeS64IIL9Pbbb2vLli3Nr759++pHP/qRtmzZoqOPPjpmmmmfPn2Uk5MTUqa8vFzvvPNOc5n+/fursrJSr7/+enOZf/3rX6qsrCRdNdPEGscpVplwXniim8xZGe1u3+zfdgRdU462leosn0RnMIy2PAX7UV9vsZ2amshxDoPPp2hjMblx3sU6d2LVn59vnnXlhN1rOJmfU6yx64xsOrOxXrNs/hfLzlhlVusk8z5XXy8FP8SLNsOmF+63bjC7dsKvRSd1mZh/mY3MwiFDNPyxEmfnQiyxZod1Wp+b7GT2WmVkBh8jox6zz9HvV7vBg+21x2J909+j3SvtXhdevH5aYyZgEviUWLdXhrJHS9PQ2KD99fvjf8URqBs0aJACgUDEa+HChZKaeudNnTpV5eXlqqmp0bp169S7d2+X9xwIFfe37K5du7Rr1y41NjaGLD/55JNtrd+hQ4eIE7xdu3bq1KlT8/JYaaaFhYW6/vrrdcstt6hTp07q2LGjbr31Vp100knNs8CecMIJuvjii/WTn/xEjzzyiCRp3Lhxuuyyy5jxFQAAAEBakFEHADDjOFC3adMmjR49Wtu2bVPgwDgePp+vecy3hgb3BnC0k2b6hz/8QX6/X8OHD9e3336rCy64QAsXLlR2dnZzmSeffFKTJk1qnh12yJAhmjVrlmvtBAAAAAAA8WtobIwrK85Q72IsAkgnxw9irrvuOvXq1UsbNmzQxx9/rO3bt4f8TMTLL7+sBx54oPl3O2mm+fn5euihh7Rnzx598803ev755yMmfejYsaMWL16sqqoqVVVVafHixTrkkEMSaisymMtdSGpjTG4SU/gEE3a6k7i1D2aTI1hta+7c+LaRad1jFi1q+mln0O9UDwwe3oUruDuY2SD9B1T6fNZdX2NNjBDvuWasd+mlpm8vunZNU/uXLImvfsndY221n0nscvalMWh9tC7A8U6Ok8wu5sH1Dh1qXsbJJCSpvkfU10tHHpn6bdp8b+xfTbpJh1l05UpJ0rIxq6wLlZY2/Qy+3qO1w+wzM5usyfi32bXr9nl3oK7a5593r85oYk1OE3b8vnriidD3w4cjCH/P5PjPvdjBJCrh6zsZyNys+71XZNrfCS5i1lcgVGOgQfsb9sf9qm+MfyIKwEscfzNu375dy5cv1zHHHJOM9gAAAABAi9faur4ag/Q7cddddzma1BCZrb6xUXUJZNTtJ6MOLYTjQN0FF1ygt956i0CdC/b5fGpnZFQgOjuZX1bmzpXGjXO3PUHy5s1zvpIbExqkgt1sHrtlMvWpefBxMMuSMF7Jyr4Kzmp5/fWDbTDbnkkGTOFVV1mXs8rmMtavrrZ+P3j7xucbvP36eskie7m+XtLXX0vHH29ev112zqn8fGcZXrGYZcAGZ8aafS4rVkQsOsy4/9uZKCf492jbMWtTqgQfl/Dj7bXB8KdOdbc+F/dv/mXLNXZj9O+s0meHSFLThBLD11gXNDt/zM5ZQ3194pO8uDnZzoE68uL9W8nJJDpOJqc58DfJocHtirW+2bbr6zXuheHSuGX2tu0liZzz4eddqu9VHtLaAnUPPPCA+vfvr9zcXFvl169fr4kTJxKoa0WaMurq4l6fjDq0FI7/inrsscc0evRovfPOO+rdu7dycnJC3h8yZIhrjQMAAAAAtAzPPfecunTpYqts8LjkANCaOH4Qs2HDBq1fv1733HOPrrrqKg0dOrT5dcUVVySjjS1Wu2uuSXcTWh6zJ7Ivv5zUTQauv975SuEZDOG89mTZjSfn4U/PM1WK2x4yXlX79tL991tniNk9xkaGaqwME0nzL7bI9EjwOIxdPVzavVtavDj+SoLPS4ux8BxlydgVbYw/q3qsxnILX8dsfattpDtLNdqYjZmSNRyvJLa5vl6R523Y78YYdaaMth11lPk1btX24OzgWGWjcfP7y0m2aSJtiffzDL7vmGVbx6rbLLPMbJ3wTGU77r039PdEs5fdEmtsV6/9/ZNkrW2MugULFqiwsNB2+UceeURFRUVJbBG8pqGhQfX1+xN4ta57CFoux/f3SZMmadSoUSovL1djY2PIy80ZXwEAAACgpWptgbrRo0crLy/PdvmRI0eqXbt2SWwRvKaxsUH799fF/aqvp+srWgbH9/c9e/bo5ptv5ukGkq++/uAMnJK9J95WZYYPDy0TLaPAIZ/dzEg7s0rGGovP7bH2Yu2vG0+l3BwbLJWcPOFPdIZUu4LbVFMTmsERPjZd8DrxZKUcWG45A6XZ8bEzK67h66/dO15W+2hnpksn7NST6DZjZdR5+UmxnUwsJ+dIsvj90ocfJl6PG203uV7HvTDcvGwQY4y6JWPCxqcLrq+iwnwbyZaqz9RGRnBzOUP4vSq8rU6z9Fat0t5EZ343DA/93Gufesq8nJ02LlokbdkSuuyTT+JqluuCxvizbWWUDNIM19oCdcF27NihnTt3Nv/++uuva/LkyZo7d24aW4V0a9xfr4aa2vhftfGPbwd4ieP7+7Bhw/SPf/wjGW0BAAAAALRwI0eObP4/ZUVFhQYPHqzXX39dv/zlL3VveNdttBqBxgY17q+L+xUgow4thONAXa9evXTHHXdozJgx+v3vf68HH3ww5AV4kheyOKIJzoyKNv5VsFTsg5uZGMH7lOoMDyfbGzlSKi21n+lo9d7w2Fkxdsy/bPnBX6qrm/alpibyONbUhI47Z2QyRsv2iza7o9MsPKtxlqzqMdqb6Hkc67MxxDOTZbznabSx2swyQxyM7RYxZqBXMvDScV0nYtas+NaLto9jx8ZXp5lomV5B741cfGloGTtjzFmdb+3bW6+TLm6MQxc+y61VHfGM+Th2rDpcd515/bHGYgverkl78kaNctaWYCtWxBznMKM89li6W5A0rTmj7p133tEZZ5whSVq2bJl69+6tDRs2aMmSJVq4cGF6G4e08QUa5QvUJ/QCWoK4Zn1t37691q1bp3Xr1oW85/P5NGnSJNcaBwAAAAAtkU9N/3+Ke/1AwL3GpNj+/fubx6t74YUXNGRIU5f+448/XuXl5elsGgCkneMHMdu3b7d8ffzxx8loI5AYJ7PeOfHXv8bfHin06b3xJN5OJl0quTmGmBcksj9p2ofgMeL2PfPMwTfy86O3ycioi5ZFcmD9PWb/STjw3tyLl0e+J9kbty/a+Ryr/XbEyl4xtm0nUyba7Mt2M3as1gl+P9rsrzbaNXb18NhtSkXmjFkmkNUxHjkysTHBkuHiixNb32pcxljlXWI566tZxlb4OKFWbTEy6qLxYlaW3c8gVtankzEow8ub3WfCz3mr+mtqmq4RO2W93jvADqssT2Nf3n8/te1B2nz3u9/Vww8/rFdffVVr167VxQfuy59//rk6deqU5tYhXXxZAWVnN8b9yspuTPcuAK7I5IxpAAAAAMhMwV3W431lqN/+9rd65JFHNGjQIF1zzTU65ZRTJEkrV65s7hKL1icrKyC/P/5Xdna69yAxgwYN0qJFi/Ttt9+muylIM1uBuilTpmjfvn22K73jjjv0v//9L+5GATHZGZMq2ZxmZMQz82ZLYDbrazzjhSUikdlt7WQwhGdGDhnS9NPNMaskVUtqCM6qC29XrLHJDnQrCR9DrtMdd5hsrFqSNO7lkQeXvfOOef2xMlaffjqy/vbt3cuoS9f1E892r702ej1Oro3wbMnwbJ5UZ9tYZc3ZWSeTxLOf4eXt7vcwi1mXzeqMtiyY1XkSPK6lVfvs7LOb93c3zo/6+oNZnUbbgvfbLMvdSd3BP4MF12v1XeD3N99rLeuJxu7fFckYlmbZsthlgtsS7VgFL3v99cTalWlaYaCu+sA5P2jQIO3evVu7d+/W/Pnzm98fN26cHn744XQ1D2mWnWCgzp+dud3BJalPnz667bbbVFxcrJ/85CfauHFjupuENLEVqPvjH/+ob775xnalf/rTn/T111/H2yYAAAAAaNlaYaCuc+fOuuSSSzRnzhx98cUXOvTQQ0PeP+qoo9SlS5c0tQ7p5suSsrPjf2VleEbd73//e3322WdatGiRvvzyS51zzjk68cQT9bvf/U5ffPFFupuHFLIVqAsEAurVq5c6duxo6+Uk+w4AAAAA0PJ98MEHuvTSS/XnP/9ZPXv21Pe+9z395je/0datW9PdNMATsrOz9YMf/EArVqzQZ599ppEjR+pXv/qVunfvrqFDh+qll15KdxORArYewyxYsMBxxUVFRY7XAUK42W0rfND5WE8g7XTdcdLtw0xwtxg73RYlae7cxLbpNjvHsqYmvU983dh2+KQIsbrDdu4c2p3JjW1LKgoEpOXLzbuShnfHC+7iZfy0Giz+ww9Dfx8+3Pwa+PjjpveM7UTrHhd8Xo8ZE1nGzsD1sZgN3B6rXKLi6WropM5YbbU7mHw6ur5G68Jn1U051e3Mz5eqq1W3eJlyE60rvMuy3WveTtfUA2XKn3tOXUeNiq8us2Mefq6ZvZfG+/WeZ55RyBDyTrrxRtO+fdPnY/XdbnVvD26HnbY4vQ+FZ0WZfR7hw0fY2Of5ly0PmYhIyerlYvfaTfQaT/VwGank90sJzPqqDJz1tUePHrrpppt00003qbKyUqtWrdJf/vIX/f73v9ehhx6qIUOG6Ac/+IHOPfdcZWf6gGNwLMsn+RP42P2Zd0lYev3117VgwQI99dRT6tKli8aMGaPy8nJdfvnl+ulPf6rf/e536W4iksjWXzijR49OdjsAAAAAoPXIzpayEpjbrzGzZ7gsLCzUNddco2uuuUb19fV66aWX9Pzzz+u6667T3r179dBDD+lHP/pRupuJFMrKlrITeF6UJUl1brUm9Xbt2qUnnnhCCxYs0H//+19dfvnlevrpp3XRRRfJdyCoP3z4cA0dOpRAXQvHrK/IbHYGvLZ6yh3tqXSyM9fCn85Hy9JJdVZMvNt0MsFHOvYpmmRkkMQzMLmV4Hr++tfIZcbvZtlJZudR8LKaGvPPIzwjLxqrz9OqbqP+RMfXcZKVF885Z5XtFX5/iXUtG+WCf0oHsxOjtTPadXX22ebvpUuCWTuLFrnTDDtyV6+Mb0WrDGi3Mn7Crteu553nvI7w7Nrwa8yqrWbncbQJS6JtO87rOmJim0S/K5YsOVhP8P0m1nVqh51s8vDy4du1myHrUEg2ndl2UsEqU9CqPdFuABk4DpttrXCMOit+v18lJSV66KGH9Omnn+rFF19Ur1690t0spFiWL7Ex6rIzPLpxxBFH6LHHHtPo0aO1c+dOPfvss7r44oubg3SSdMYZZ+h73/teGluJVGg5d3cAAAAAQEaoqanR1q1btWvXLjUGZQf6fD5dfvnlaWwZ0sWXlWBGXYZ3fX3xxRc1cODAqGUKCgr0j3/8I0UtQrpkeMzZo8aOTXcLWo5Yx9ILmVnxtsFqfC07T0gXLXIvYyu4juDjnawxAt2uOxFOj5/T8aCSxSqTzM3jGisTItZnGvx7eGaJ8X6ix8pJFpPTY+Pk83SSdRgrO8nJMRk3Lr3XUvg5YnzOZvtw9dXmdQSdG6VPX+pi46Lw+6Vnn3WvvlRnP9sdm9Hq/fBs2fCx0GJleNvZz6Dt7ncy/tZ999kvG2zkyPjWC2d2rzLEOtaJfM+ZZdnffrt1+Xvvjb7PJm2df3GC4+o64fa14JW/GZKhFWfUrV69WkceeaTOPPNMDRkyREOHDg15Aa3R3Xffra9NeihUVVXp/PPPT32DkDYE6gAAAAAg1VpxoG7ixIm66qqrVF5ersbGxpBXQ0NDupuHNMnKat1dX9etW6e6ushB9mpqavTqq6+moUVIlww/lTNcBn+5epIbT1zjHccq0W2YjVNjltGUjGzN4DGXnI7bYzZDXax1M/nJuN3sKqvx4tzYZvv2TcvCs8msxkqzGj8tfHZYu9s3qyvWeuHbKChwPwMxmedVrLqD98Vsf6yyC6NlYkXbZrSxn9wcGzER4VlfRvZPtAzNVNwbEtmGk3E43d52mNJnhzT9w0k2p1He6r5dXW1vLDG718MBOVddFbuN8bIzJl5wmVgZc9HejyVWRp2dMeqC1502zbyM3y+9/77j63zsapMxMUMKJOnvCzd44Z4G1+3atUtTpkxRUVFRupsCD/Fl+ZTtj/+V5U9gFuU02rp1q7Zu3apAIKD33nuv+fetW7dq8+bNmjdvng4//PB0NxMpZOubb9iwYbELHbB8+fK4GwMAAAAArYKRBhSvDM48u/LKK/Xyyy/rO9/5TrqbAg8xMurilakZdaeeeqp8Pp98Pp9pF9c2bdrooYceSkPLkC62AnWFhYXJbkfmGDpUeuml6GXS8HS9RZo2Tbr//oO/G09UlyxJbDyadD2ZDc+eMRtnKFa23bPPSvPnJ6d98bCTvWOWRdASz30jGyP4eCSSnRFuyBBp5cqmrBdje3bGqoqVaRLr84t3H6JlWw4d6m5GXTK6/0SrM3x5+OceXjbarLAjRzqbvdaLwjMKw+8LsbKejOVuzZwaSyL3H6tsRjv3wmXLnGUtORwLzvR3KfIaDj7OscaSNNuesc7HH0tHHx17HbtS8d3sVpZzcD0TJ0oPPBBZzux4xrOPZveQZByrVHwvW2Wat8S/Cezy+xOLSjgZA9JjZs2apauuukqvvvqqTjrpJOXk5IS8P2nSpDS1DOlkzPoar0TWTaft27crEAjo6KOP1uuvv67DDjus+b3c3Fx16dJF2Zm6c4iLrW/6BQsWJLsdAAAAANB6tOJA3ZIlS/T3v/9dbdq00csvvyxf0L74fD4CdWhVevToIUkhsx+jdYsrObS+vl4vvPCCHnnkEe3du1eS9Pnnn6vayPRoydq1M1/+3nupbUdr0KuXu/UNGRK7jPGUurTUfr2JPNkOz8yIlSF17bXxbyvZGEMmVKJjbpWUhP5uZF3l5x8cSypappbVtocPjzzPzLJy+vZt+nes+3q0LCnjvbDMkq+eeMK9MSXtjtFl9/wMbld4ppvVmHLhmTNmn4tx7dppx7Bh5uWCh6HwUgZK+Kyh8WrfvilLy2233eZ+nZKzrNA4Pq8l166KWWbRlSsd1xsx62s00e4xZllkdtZzwu51G8/1Hb6+VRZtrGWzZjnbVniZsG0HfD7zbZ5/vrPvFaffyYl+h/M3ABy66667dO+996qyslKffPKJtm/f3vz6OBnfBcgIvmwp2x//KysDk85Wrlyp/fv3N/872guth+Nv1U8//VQXX3yxysrKVFtbq8GDB6tDhw6aMWOGampq9PDDDyejnQAAAADQcrTijLq6ujqNGDFCWVkZOqgYksKXJWUlEPdPZN10GTp0qCoqKtSlSxcNHTrUspzP52NG5FbE8Z3xZz/7mfr27auvvvpKbdq0aV5+xRVX6MUXX3S1cQAAAADQIgVnVMb7ylCjR4/W0qVL090MeEyWL6Ds7PhfWVmBdO+CY42NjerSpUvzv61eBOlaF8eBuvXr1+uuu+5Sbm5uyPIePXros88+c61hnmX1hThtWmrbAefat4/dpTUd3cnsdrUxW+52ezP4D76owo+TnQkwor1XX9/UhTRa2USPZefOkqS5g5ZY1xu8X7HaHF7e+N1qIoMPP4xer5NjGNYl8tCf/vTg5BuJnsPR1rcz2YbxnllXPavJDcK7wJqdX+HLFi+OrGfZMus2mdUVvA/FxbHXTZZFi6JPgCOZd4ONNoi88R/O3/3OnTYG27kzctmWLfHVZXXNJeG7Y+TiSy3fM7q8lq4YFvlmtC65fv/B89pq0o/w8lYTVhy4R0Vs22xCkWRx0v3YaiKDBASeeCL69uxsO6ztvvPOM98f45q30/21pX6Xt0TZ2YkF6TJ4cPmGhgbNmDFD5557rm666SZNmTIl5IXWKSsrsa6vGXxJSJI++eSTdDcBHuH4m9wqmrtz50516NDBlUYBAAAAAFqmt99+W6eddpok6Z133gl5z5fBXXqBRBx99NEaMGCARo0apauuukodO3ZMd5OQJo4z6gYPHqwHggYQ9vl8qq6u1t13361LL7V++tuquD34cWuW6DEaOza0nmQ8hU5kwGa/vyk7xW49qZiwpSVl6UXbdrSMDxvHYO6FMbKhEt3vA20Y98Jw87pjZcOEZ/5ceaV5hodV1piRAWb1vlmWjdVxmzw5cplb55nRjmjHO55JDqJ1KQrfVzuf9fLlzttgxtjuzJnu1OcGs2Nh95gH35sTnYzCyl13RW5v61b36nf7HmcjG6302SEHyzoVnglo9f1odk+xuh8EMz7HZN/77WbMGu0O3tfwYxDtOEbJBPVdc4399iYyCYSRgRz83uLFzj5/L2TheWkSHK9oxV1f//GPf1i+XnrppXQ3D2niy5KysgMJvTLZm2++qf79+2vatGnq1q2bfvCDH+iZZ55RbW1tupuGFHMcqPvDH/6gdevW6cQTT1RNTY1Gjhypo446Sp999pl++9vfJqONAAAAANCytOJAHWDGmEwi3pcvw7u+nn766fp//+//qaysTH/729/UpUsX3XDDDerSpYvGGgkoaBUcB+q6deumLVu26NZbb9UNN9yg0047Tffff782b97cPAhii5aXF7sMTwyTw43j6rXsLi+dK7NnN/10coyCMzGMzEAvCR6/yOVjbZnp5pZo2S1m4zKFlzeyL998s+nns88eXMfIIInW3rPPjtyGVXus3jdejz1mvb5bxyyReqwyZqzqDM8qSuT8Mhs3sLg49PhYjREWi99vbxy8eJhlIRnnnN8vff11aFkrwccuGffD4HFJ3TjXUv0dEvz529m2nWMY/Dk52R/jmoiVVRc89mRpaXKPmZNs+eC2h383OBlTL3hb+fmhn0+scejmz2/6WVJysK7w42nUaVy7Zm3z+9UQCDQtt5sd6oWATgJZ7C1WKwvUDRs2TFVVVbbL/+hHP9KuXbuS2CJ4jS8rsckksjM8o87g8/l03nnn6dFHH9ULL7ygo48+Wo8//ni6m4UUcnx3/+abb9S2bVuNHTuWqC4AAAAAIKa//OUv+vLLL22VDQQCev755/Wb3/ymdSSDQFLTZBJZ/viDbb4WEqjbsWOHnnrqKS1ZskRvv/22+vfvr1mzZqW7WUghxxl1Xbp00bXXXqu///3vamxsTEabvM1qWuQlS8yXR5NhT8E8Jd5xWZKR9eX0SbBZ9khFxcF/B2cjhD9Jf/nluJqYVtFmevSSZIwzlaz6zDJAwn83smYWLgxZv/app0LrshoXzMjyCK5zyJDYGRFm45VZHYtEZ4Z0MkZVPJmi0bIVjPesxvgyyzoqLY19XtTXN2W32pwhMmLddIiVLWw3+zJ8Pbckc1Z2N7N1w47TkmtXRS2+7Mdr4jsfwmfajbZ+tGxOs3WDM9TcYOd6sTvGnJ3so0S/B+x+HgfKzb94Wei5b3WvMtnP7NNOayprdn6H76tX/t704nd/urWyWV8DgYB69eqlQw89NOarY8eO2rdvX7qbDKTU3Llzde6556pnz556/PHHNXz4cH300Udav369fvrTn6a7eUghx9/cixYt0lNPPaUrrrhCBQUFGjFihK699lp973vfS0b7AAAAAKDlSbT7asB59tDs2bP1//7f/1N5ebm++93v6oEHHtDAgQPjb4MD//jHPxyvc/jhhyehJfCqpskk4l8/kXW94De/+Y2uvvpq/fGPf9Spp56a7uYgjRx/MwwbNkzDhg3T3r179eyzz+qpp57SgAED1LNnT1177bX69a9/nYx2ZhaeGCaHcVyHm4wNlogkjF8WlVm2nJ2Z9CRp0KCkNCllEs2gyoTtW80s6JTf35TB1r595HKzLLjwrBGjzKxZIedX3uWXH2yjnTGVrJaZrWdWn1m2i1FPTU3k/jkRnmUYTbQZRcPbbRwfq3YbM0gGL3NLrPHcom3v66+lQw5xtz1OBN/HzO5pLWF8qnjvIXbu3WEZVyMXXxq1+PDHShK7fsJFy54L/j3a5zV3rnTOOe6NnWXn3Fi06OAM72aWLJEmTYp9v7M763CsTEMHxv51WOhnaCcL0nDMMdHXMTNpkvTgg84aGY1V9r9VOaTd0qVLNXnyZM2ePVtnnXWWHnnkEV1yySV67733dOSRRyZ9++eee27St4HM5ssKKDuBrq+ZPkZdWVmZfD5fupsBD3Dc9dXQoUMHXXfddVqzZo3eeusttWvXTvfcc4+bbQMAAACAlsmlySSqqqpCXrW1taabmzlzpq6//nr9+Mc/1gknnKAHHnhA3bt315w5c1K514Aln0/Kyg7E/crEWV+3bt3aPKTY22+/ra1bt1q+0HrEHairqanRsmXLNHToUJ1++unas2ePbr31Vjfb5k12xoKwmx0F++xmr2TaU1uzccCMmd7sPKG//Xb32+SU1z8TO9t3MpaP3RkqE80mqa9vyrQw246R+eFkjDZjHM38/NDzzm5GXWmp5l+23LpMePngTLr6eu0PfjoY/B+MRM4Pm2Nhzb9suf1smfD6zQRn1FmVs/v5v/9+9G07OT6roo9p5irj8wse9zN8BszgsuHLDE6yIuNtZ7RtJlpX8HvR3g/O+ErVPdHJOIFGGbPrOPx3Y5kxDqZhzRpn27MyZEj861qxypINL2NX8Fh84TPI2l0/eLtOZ501xqaze/821on292k898j5893LnpSkcePMl3tljL1kcClQ1717dxUWFja/pk+fHrGpuro6bdq0SSXGrMMHlJSUaMOGDSnZXSAWX3ZAWf4EXhmYUXfqqadq9+7dzf8+7bTTdOqppza/jN9PO+20NLcUqeT4m2/NmjV68skntWLFCmVnZ+vKK6/U3//+d1KZAQAAAMAul8ao27FjhwoKCpoX5+XlRRTdvXu3GhoaVFRUFLK8qKhIFcGTmgFIqe3bt+uwww5r/jcgxZFRN3ToUH3zzTd6/PHH9cUXXzTPTAIAAAAASK2CgoKQl1mgzhA+/lUgEGBMLHhGlq9pQohEXvGYPXu2evbsqfz8fPXp00evvvqquzsWRY8ePZqvwU8//VSHH364evToEfI6/PDD9emnn6asTUg/x4G6iooKPfPMMxo6dKhycnKS0SZv8/ulkSNjl7Ej3V0CM0GswevtSPZxDu76FU20fTHrwher3YccIvXqZW/bZpx2i4pWj5e7pdhpWzzd71K1z+HdXP1+affuyHMmvN3BXZyCu2Ydf7y96yp8+c6dGrvaZCKX8C6awQPOB01akTNqlPm6iZ57Nox9YaT9rn/Bvv7a3a6TZutZjTdi1t3fzS5mbgv/LIO70UU738LPl2S0K9rvbtTtZGIRu91n3eie67SrZ3j5WN9T4e8//XR6uvjaZXW/cXpNmXVbNas7vEusk4l7YsnPP/j9/+yz9tbx+5u7K5enIigT7W8Mr97H0iE7O7Fur3aG5Dmgc+fOys7Ojsie27VrV0SWXSpMnTqVwAMi+LKUUNdXXxxdX41JVu68805t3rxZAwcO1CWXXKKysrIk7GF05513nv73v/9FLK+srNR5552X8vYgfRwH6goKCvTRRx/prrvu0jXXXKNdu3ZJklavXq13333X9QYCAAAAQIvj0hh1duTm5qpPnz5au3ZtyPK1a9dqwIABbu9ZTM8//7y+853v6IILLtCSJUtUE884iWhxfFmpz6jz0iQrVhmue/bsUbt27VLeHqSP40DdunXrdNJJJ+lf//qXli9fruoDT+e2bt2qu+++2/UGeo6dJ8ROJpM4+eT429IaWGVD2B1EP/h9v18qLXWvbYlw8jQ5uOz8+Qf/PXt2YtsOG+w/quDtmtU3d27s7aWDnaxBtweWdyuL5EAWxpKrV5ouV319U9aSkwxfv1+69trQ36urzTNBwvejuDiyTrN9NbtGrbKo3Pyj3G6mUjTh7bRqX3j2jJvneLTME69le0YTLcMomFl2ktu8cDyc8vu15NokTQ7Svn3zNiyz6qJl2lp9b7h1nOM5B+K5vuMV615jtTzae8ZPuxmQwffgK6+0XseiDV3NMpxTeZ1Y/W3ntSzMVEhhoE6SpkyZoscee0zz58/Xtm3bdPPNN6usrEzjx49P0g5a27Rpk/7973/r5JNP1s0336yuXbvqpz/9qd54442UtwUekiX5/Am8DgTq7M6E7JVJVoYNG6Zhw4bJ5/NpzJgxzb8PGzZMP/jBD3TRRRelJaCO9HEcqLv99ts1bdo0rV27Vrm5uc3LzzvvPL322muuNg4AAAAAkLgRI0bogQce0L333qtTTz1Vr7zyilatWqUePXqkpT0nn3yy/vCHP+izzz7T/Pnz9dlnn+mss87SSSedpD/+8Y+qrKxMS7uQ2fbv3x8yC7LVTMiSdyZZMdoZCATUoUOHkLYXFxdr3LhxWrx4ccrag/Rz/Pjs7bff1pIlSyKWH3bYYdqzZ48rjcp4DrIeKt9+W4XJbU3LceGF0urVTf/2yLho+3w+tQvYGwvhqwULdOh11znPxHH7CbOT8ZSc1Od1dsbIsXte+f3OMmfjkZ8v1dRo5Irh1udMcMZX+NhRVuu8/HJkHdHGbbI6Tvn59j/78GN1YL35Fy/T2Pdvs1eHGSfncDyfV3D2UHC2i99vftysst8SyYozy0qLtt/xjLeYqFjZc3YyW+PIDLHNI98Xklxry6IrV6p0cUnkG1ZZi8HnrpFRZ1bOSrTsWavtuXHM3fzcrDIFnWZyWu1bfb00ceLB34Ov4fDyP/6xeV1WWXXR7umxMvWCHfjO2PvEE+oQPr5uvMc6kb9jErlHjR0bPds/UyR672tsdLzKhAkTNGHChPi3mQSNjY2qq6tTbW2tAoGAOnbsqDlz5uhXv/qVHn30UY0YMSLdTUSKNI1RF/84mlnZPuXk5Gj37t0hy6NNsCKlf5KVBQsWSJKOOuoo/fznP1fbtm1Ttm14k+OMukMOOUTl5eURyzdv3qzDDz/clUYBAAAAQIuW4q6vXrNp0yZNnDhRXbt21c0336zTTjtN27Zt07p16/T+++/r7rvv1qRJk9LdTKSQLzvBrq8HLgm7MyF7bZKV0tJSffbZZxHL//vf/+qTTz5JeXuQPo4DdSNHjtQvfvELVVRUyOfzqbGxUf/85z916623qtQr438lk8sZUIVm44TAXKyxuNKg3VVX2S57aHjmXawsk1T+8ZXsbbk1w6xTdmfkDef0Ok9m5pJZZpWRXVFTEzrelJ0si5dftp/hFEtwlk4c5/DYFUMSOx+SmVHn9zeN32eWURdtbC7j38Gz9To5P2KN62ZW18qVkcvSwTgno82u6SQzy602pfKeM9xkZmS7wo5DtMNS+uwQW3VECM4EDf+cgrNko9WTn9/0fRxrFlOjfqfHP5nnQzwzkdo9Z+vrpTPOiF2f8d4Qi8/Qqg3hn5Wd7YSXObBeh2uusb9tuxK5z8WDiQcy3sknn6wzzzxT27dv17x587Rjxw7df//9OuaYY5rLlJaW6ssvv0xjK5FyWT7JnxX/K9tZFpzXJlkZM2aM6dh4//rXvzRmzJiUtwfp4/ib8v/+7/80ZswYHX744QoEAjrxxBPV0NCgkSNH6q677kpGGwEAAACgZcnOTixw2dDgXltS7KqrrtLYsWOj9sg67LDD1BhH915ksCyffDkJdDmNo9vslClTNGrUKPXt21f9+/fX3Llz0zbJyubNm3XWWWdFLD/zzDM1MXh4BbR4jjPqcnJy9OSTT+o///mPli1bpsWLF+v999/XE088oezsOOZDbs3cmuFu3LjE6/CqVI5xk8Bn8a3TMQzCsxby81PffSE44yeV0jWmXaztJiPrw41je2Bm7ZgzpwZnWNidtdHJmFTxZhNHacuSq1em7vyLllEXbZwjpxlgyZ611Kz+hx92f5uxxMpMsjsuXyrvB6k416JtY9EiR/tb+vSlkQuHD7efoWxnbEspvnETzcaoNMs+dSLWrMBuMO4D8YwbGbxeolnwl4Z9tn6/tGWL9TbDj2msbYV/xtHa7TazYxsrUzg4CzOe7WSyVtz1NRAI6NBDD41Y/u233+ree+9NQ4vgCdkJZtT5HYc3PDXJis/n0969eyOWV1ZWqiGDA/NwzvmZfMB3vvMdXXnllRo+fLiOPfZYN9sEAAAAAC1bKw7U3XPPPao2HkYG+eabb3TPPfekoUVozSZMmKBPPvlEtbW12rRpk84555y0tGPgwIGaPn16SFCuoaFB06dP19lnn52WNiE9bN3dp0yZYrvCmTNnxt2YVqe6WjrkEHfrLC2Nf1wuL7KT+ZPIk22XnjK3cfKEw+mT42RlnBiZBbHqd2P7xvg66cqmM7btZnalnfHgEtlfo83B49AZjM8uP1/7nnpK/qeeUl7wmENBx3rvU0+pQ/BYmHazVoyMjPAsjnhncww/XvX1Gvn0EOnUU53XFVxPtPPKSXtjZX2F1xPPLIaJ/ofKan2r75Fk/geuujr6PST887YaY9Ts/HZTMsfEC/7+sHOsX37Zfr1u3DPN1p89W4o3UyX8Gog1Rli67vdW6utDz7XwcUatjrfdbLbw5UYAwuo7P/z+tHVrU8ZkrM/d749vfLbwWcJLSqQ1a5zX4wa75/bIkdKSJaZvzR+0SGNdbhZSy2pWzbfeeksdO3ZMQ4vgCVlSPN1Xmzkco85rZsyYoXPOOUfHHXecBg4cKEl69dVXVVVVpZdeeinNrUMq2forfvPmzbYqS+UUxgAAAACQsRLNisvArnCHHnqofD6ffD6fevXqFfL/x4aGBlVXV6dlbDB4gy8rS744uq82r59IkM8DTjzxRG3dulWzZs3SW2+9pTZt2qi0tFQTJ04kgN3K2Ppm+Mc//pHsdgAAAABA65HoZBJey1y14YEHHlAgENDYsWN1zz33qLCwsPm93NxcHXXUUerfv38aW4i0MmZ9jVd2Aut6RLdu3XTfffeluxlIs8w/k73I7pdmTY37X7AtqdtrsiSjO5jdWXhiDahstjyZg9N7dWKHZLDbXSme+pwM8h3vdoK7gR9ySPPydlddpbzf/z6yG+iBnx0Cgcj6Eu3ybacrutkEA4bgCSYSPRfsrB/vxD1Gt7Tly0OXm13H0brf2ukmHVyX1TKn+5HM6yz4mJhNiuNmN3O3JesaTVY9Zl00zd5zUqfZ+k4mLKipOdi1M1x+vvlkE7HYGeoimMn9znZ5q+Vm2w6fbCbatR4sVvfU++833/6Bf39lZBmZdb3/+uvIdaMx1g9uk1tdzp1ORhJtvfp6acKE0GUW3ebHrh5ub7te1wrHqBs9erTGjBmjf/zjH/rpT3+q0aNHN7+uueYagnStndH1Nd5Xhnd9lZq6ul577bUaMGCAPvvsM0nSE088ofXr16e5ZUglAnUAAAAAgKSqqqpq/vdpp52mb7/9VlVVVaYvoDX685//rIsuukht2rTRv//9b9XW1kqS9u7dS5ZdK0OgLp2MgeKHDEl3SzJPtKff8WbPuNEep2XjGRAazrn1xNnJ55zoNo0MCGPA/mBHHBF6nm/caC8Txuq6sTvBhN8v7dwZvVx49pjZU//qaut9c8LOMTbKxDv4ulnGjlV2oJ22JToRx4HzIjzJJ63Msuiszoto596Bc2Jfsse7deN+YLavbn/32MmWCz+v7WZXmf3b6aQrsconen3b2Ybh6qvtlYt2fBL9PjY7t4cNsy5/++1R9+/Qq66KrN+YECNWJqFZvW58HmbcnBjL7BqyancGZpKZamUZdYceeqh27dolSTrkkEN06KGHRryM5WiljK6vibwy2LRp0/Twww/r0UcfVU5OTvPyAQMG6N///ncaW4ZUy6y7OwAAAAC0BIkG2zIsUPfSSy81D4j/0ksvMREhImUnOkZdZp9TH3zwgc4555yI5QUFBfraGPYArUJmh5zTxWKq+GZOvjTDxx1xk8W4HhnFLCvD75deftn+OuFPZ+081X3hBVvNS1g6xmc6/nh75ZyME+RWXV4VbdywZG7TbDy5mhqpuLjpZ7SxquxkdAVnAlmVC//5yivR2xxrm8HlgvehtNS6XiuxxpZKdKxHq2MYnOGRrHO7d2/z9hzY3ti/RsnUkVJ7bzE+h+BzxSojM1ZGnd+vdpdckry2SskZyzX8GJiJ9ZnYHWfNCbM6jbHljLqM89kYVy7WPhifo1Wmk5GZFu3+YubKK52VD7Z4cfT3re5xwedsvGOtBe+nk7+7wse+DK7v5Zetr6fZs60zVK2EZ+CZfc7x3jNcvJ6+euYZ1+rKGK0so+7cc8+V/0CbBw0apHPPPdfyhVYqK4Hx6YxXBuvatas+/PDDiOXr16/X0UcfnYYWIV1s3d1Xrlxpu8IhdOMEAAAAAFhYsGCB2rdvr6vCunk/88wz+uabbzR69Og0tQxp1cpnfb3hhhv0s5/9TPPnz5fP59Pnn3+u1157Tbfeeqt+/etfp7t5SCFbgbqhQ4eG/O7z+RQImkkwOG25oaHBnZa1Bk6e4Np1+ulSa+i/vnix9PTTztbx+/XvifN1+sPjmn6Pduw//zz+ttm1ZIk0ZUrytxPuttuksWMjlw8f7t4scF5jN+vP7esxkfrq681nTgzPpDPKRhsfzKpN0cbUMrJlou2D2TGzWmbWvurq+I/RjBlN9VRXxz5v4/1szWbYtBoXLDwzx+z4OWmHcW+I9fmYSca5HIuxzeAMR2MgPb9fXz7xhA677rrI9czaWVzsfruibc/NbdktFy2LNVp27OrV0oUXOm9bcJ3hs7WaZeZaZUTaGTfPyUyswYzx3Jx8D8U7bmise6hTsTL27LbLcOaZkXXHyiAOrzP4PmUn49PMsGHWmX9W7GZ3mzj08stj1xdHvZ6WnZ3YvmRnu9eWFLv//vv18MMPRyzv0qWLxo0bR6AOrdJtt92myspKnXfeeaqpqdE555yjvLw83XrrrZo4cWK6m4cUshVybmxsbH6tWbNGp556qv72t7/p66+/VmVlpVatWqXTTz9dq1evTnZ7AQAAACDztbKur8E+/fRT9ezZM2J5jx49VFZWloYWwROysiR/dvyvDA5eG/7v//5Pu3fv1uuvv66NGzfqyy+/1G9+85t0Nwsp5jg3dPLkyfrjH/+oiy66SAUFBerQoYMuuugizZw5U5MmTXJU15w5c3TyySeroKBABQUF6t+/v/72t781vx8IBDR16lR169ZNbdq00aBBg/Tuu++G1FFbW6ubbrpJnTt3Vrt27TRkyBDtDJuV8KuvvtKoUaNUWFiowsJCjRo1Kj2DMYbP/mVkRCT6JRv0tPHLzZsj62+JLr00+vsW43Cd/juXx+1z8tmNHOnsSbPZU2SH11hC7D65tzPbntWYPpnAbqaakzJ2hGdvGeMA1ddLu3dHbsvp+Eqxxo8y2w+748FZlTET70yLxsyxqRK8f4ccEvqeWTuSMY5dUKbS/AtjjJWaClb7ZfKfx8NGjTJfJ/icOnC+BxYscLmhLrHa33hn/bazvfBtXnyx/fWtBI8hZ7VPsWbatFrXyKoMyv5afvWy+NoXSzzZdMH7FZxhFtbmqNtyOtNz+Lai1R1t/fp66dRTo2/zpZdCl59/vvk27Gbdhm8v2Y46yvw7N5P+ZoBtXbp00datWyOWv/XWW+rUqVMaWgRPyPLJ589K4JXZY9QZ2rZtq759++qMM85Q+5ba4wlROf7m++ijj1RYWBixvLCwUJ988omjuo444gjdf//9OuaYYyRJjz/+uH7wgx9o8+bN+u53v6sZM2Zo5syZWrhwoXr16qVp06Zp8ODB+uCDD9ShQwdJTYHD559/Xk8//bQ6deqkW265RZdddpk2bdqk7AMR9ZEjR2rnzp3NGX/jxo3TqFGj9PzzzzvdfQAAAABIXKJZcRkcxLz66qs1adIkdejQoXmWy3Xr1ulnP/uZrrbzEBgtUysco25YeDJPFMudDkeAjOX4TP7e976nyZMnq7y8vHlZRUWFbrnlFp1xxhmO6rr88st16aWXqlevXurVq5f+7//+T+3bt9fGjRsVCAT0wAMP6M4779SwYcPUu3dvPf744/rmm2+05MCsq5WVlZo3b55+//vf68ILL9Rpp52mxYsX6+2339YLB2bt3LZtm1avXq3HHntM/fv3V//+/fXoo4/qr3/9qz744AOnu+/u+DZJyHgLGQMoHTOKus1pxpmdcun+o8ZqBtFo44UZHnzQvTa4kR0iRZ+BNxPEe50ku9uJVYZHcJadSfZKyPpm3PoPQbRsqliCx7CKN6POyGqze49wut+Jfq7h659+unW9cbRz7Orh1m/azYZ1m9n2grMlYp2TB8Yc9CVz1tdkfC+6cbyT/VlFG4fOToasWV127jF+f0RnAkeitSP4O9NuVlp425Nx3IPHnrVzbZuV8fubxrE13jfKbNkSWi58vx97zHx5eD1mzNpn9wF8IvfaYA8+GLq+2ZisZtvMZK246+u0adPUr18/XXDBBWrTpo3atGmjkpISnX/++brvvvvS3TykS5avqftq3K/MC9QZvf7svNB6OL67z58/X1dccYV69OihI488UpJUVlamXr16acWKFXE3pKGhQc8884z27dun/v37a/v27aqoqFBJSUlzmby8PJ177rnasGGDbrjhBm3atEn79+8PKdOtWzf17t1bGzZs0EUXXaTXXntNhYWF6tevX3OZM888U4WFhdqwYYOOO+440/bU1taqtra2+feqqqq49w0AAAAAQrTiySRyc3O1dOlS/eY3v9Fbb72lNm3a6KSTTlKPHj3S3TQgpa644gpdfPHFys3NTXdT4CGOvxmOOeYYbd26VWvXrtX777+vQCCgE088URdeeGHI7K92vf322+rfv79qamrUvn17PffcczrxxBO1YcMGSVJRUVFI+aKiIn366aeSmjL5cnNzdeihh0aUqaioaC7TpUuXiO126dKluYyZ6dOn65577nG0L9/6fGoTCNj/wk3G2Bv19U0z7Y0dm9FP2WwzywxLZQadUXe8WUHpfCJsNqOoZH/sGqlp3DSzGWTNpHNf7WZbuC3Rc6+mJjLzNjjDINpslsHnpJH1GL5etHGZnByzWPtpNUahnSxSC5W33KLCUaPsZyYb5TZskAYMiHzfLCMx2n5ZZSFZrXPUUdbrOeGl+/rs2U0/jXPI7LP43e8OzuJpdnzC16mvj5yVNFHJzHqNR/A1F2tctOCsWbfaaGesGzvbtLpvtG+f+vu907Za3ZMS2Z7xWRlZaO3b2/s7L3x2VqOu8LrN6rEqZ4zhGbz98Ky3aN8ZVvXb4fReGs2HHzbdO8PHBUWL0qtXLx177LGSFNf/JdHCGJNJxCsDg9dXXHGFKioqdNhhhyk7O1vl5eWm8Qu0LnHlhvp8PpWUlGjSpEn62c9+psGDB8d9Yz3uuOO0ZcsWbdy4UT/96U81evRovffeeyHbChYIBGJuK7yMWflY9dxxxx2qrKxsfu3YscPuLgEAAABAdK2466skLVq0SCeddFJz19eTTz5ZTzzxRLqbhXTKykqw62vmBeoOO+wwbdy4UZK9WAdah7ju7i+++KJefPFF7dq1S42NjSHvzZ8/31Fdubm5zZNJ9O3bV2+88Yb++Mc/6he/+IWkpoy4rl27NpfftWtXc5ZdcXGx6urq9NVXX4Vk1e3atUsDDmRMFBcX64svvojY7pdffhmRrRcsLy9PeXl5jvYFAAAAAGxpxZNJzJw5U7/61a80ceJEnXXWWQoEAvrnP/+p8ePHa/fu3br55pvT3USkQ5YvwYy6zBujbvz48frBD34gn88nn8+n4uJiy7INDQ0pbBnSyfGZfM8996ikpEQvvviidu/era+++irklahAIKDa2lr17NlTxcXFWrt2bfN7dXV1WrduXXMQrk+fPsrJyQkpU15ernfeeae5TP/+/VVZWanXX3+9ucy//vUvVVZWNpdxS5tRo5ytkIwnYfn50oGJNFqUDz+0XzbWIPfRuhmlgluDLicq/PybNi2+9sQKznv9j8hEP3e7kzg4ZXRNDp9wobpaMr7Ao3V9DVZWFlom1nrvvBO7W6tVtzez8tG6a8XZbbzwj3+Mvg2rNvz1r3FtL0S8Xd2D25HIOna7kqWz+2H4OWbV3uCur0a5RI6vHaWl8a8ba3KDWOLpTuqkvvDPwGx7Vt3Fw8ub1W2nq66Xu3eHn1vh7Y1nki+r7/TwuqzujcFtiGfolPDtDx9uPglD8DK7f3+68VlYdRO2ej+46/u//+39vyES1Yoz6h566CHNmTNHv/3tbzVkyBD94Ac/0IwZMzR79mw96Nbkacg8RtfXeF8ZmFE3depUvffee/rLX/6iQCCg+fPna/ny5aYvtB6O7+4PP/ywFi5cqFFOg1ImfvnLX+qSSy5R9+7dtXfvXj399NN6+eWXtXr1avl8Pk2ePFn33Xefjj32WB177LG677771LZtW40cOVJS0wwp119/vW655RZ16tRJHTt21K233qqTTjpJF154oSTphBNO0MUXX6yf/OQneuSRRyRJ48aN02WXXWY5kQQAAAAAIDnKy8tNkyYGDBig8vLyNLQISJ/jjz9exx9/vO6++25dddVVatu2bbqbhDRznFFXV1fnWibaF198oVGjRum4447TBRdcoH/9619avXq1Bg8eLEm67bbbNHnyZE2YMEF9+/bVZ599pjVr1qhDhw7NdfzhD3/Q0KFDNXz4cJ111llq27atnn/+eWUHRdOffPJJnXTSSSopKVFJSUnyxz+w+0TcradhZoMkHwhmtgSVPp80a1bkG/Eeu3RNajBoUNNPs6wTYxIQO9lIbgk/Dr162SuXiVKZRenm52WWBVpTI3XuHJnxFmuQfitmx+C99+LPFDSboMIqoy6RY2VkxjjN4Nm503x58MQWsfY1kclJYmWX2JnYI9YkF6nOsAifGCF8kpBox8vsfhily0fCrr7anXpiZZROmuROncnoEhd8b4h1Lpmxun6Ncy/ZGZHxMM5NI1vLaHtNTeTkOvHWb7B7HwlulxT6d9uyZaH1Rct2NGuz1cQQ8WQMOuEk09rM7Nn273ct4W8TqVVn1B1zzDFaFnyuH7B06dLmySXQCmX5EhyjLvO6vga7++67lZubqxdeeEGPPPKI9u7dK0n6/PPPVe32ZFvwNMd39x//+MdasmSJfvWrXyW88Xnz5kV93+fzaerUqZo6daplmfz8fD300EN66KGHLMt07NhRixcvjreZAAAAAOCu7OzEgm0Z2M3PcM8992jEiBF65ZVXdNZZZ8nn82n9+vV68cUXTQN4aB18WVnyJTBGnS+DrwlJ+vTTT3XxxRerrKxMtbW1Gjx4sDp06KAZM2aopqZGDz/8cLqbiBRxHHKuqanRzJkzde655+qmm27SlClTQl5wwK2nYMnIVvGQwmuuaRozy3Bg8hFJiWW2pELwWALr1zf99MLnYox7lqq2BD/1Dc+2STY7GUrGsrvuavr3okX267fKbHBjrCajnnBmGTFOsq6M9402mpU1nto5/azsnFcHsliWTXzFWd3Bgsfvs/q8gjO8grcdLzcyeGNlzQaXHz7c+fbSwWizMa5i8DKDF+57knTppclpS3idVuOqhp+PVtev28cvuD7j/hFrXLT/n71zD5OiuP7+d2CABRZYdEVAbipRRDCieAF9FRSRICLyU6IbQUAhSBQ1KtFoEI3gJSgoBBQNIiIQomgQDcELajASBCWGKCYoCKgbXbmuuMDszPvHcHZOn6nqy8zszu7s+TzPPDvTXZdT1VXVvV3fOsWvpwxnitO9eyKvVO0dMiR9xZ+JQ0qtOWczn6pyfPBjd5DzUglnU0z6UZ+a1Is8DxPy2qVbNi/Sua9T3suX+7ejuowp6VKLFXX/93//h3/84x8oLCzESy+9hCVLlqCwsBBr1qzBpZdemm3zlGyR9q6vNVtRd+ONN6J79+7YuXMnGjZsWHH80ksvxRtvvJFFy5SqJvDo/tFHH+Hkk08GAGzgL08A3UpYURRFURRFURRF8eTUU0/VVU+Kk7R3fa3ZirpVq1bh3XffRf369R3H27dvjy+//DJLVinZIPCLupUrV1aGHblFkNnAyprZr44+YlKBZmhffx0YPTr+fdcuc9g5c4CRI/2lSXVv8qeVCrbr+PrrwODB7mGAhILAza9XZVGDZ2N946ZS4ArU++5LnJs3L9HmsgGpxaTKIz/fnx+gbCpJTX3K0LaHzB8Y97eXCgUFjrSMeKkROSZlMh+j/fSTVH0peaXttdOoLX42xxBeb24qb6nSCocxe8BSZKznmdpiJn28SLVUkDq3hJ3dZzHC4XhTLZrf35k+jxvEbyTHtNMu/05/8/Lc+3I4DJx3HvDmm/HfY8cmlOOpIlV8bqQwxhmrnD8PBIEr5ni9EXKstj2XebSZ/738Mo684AKgS5fE6oJwGBg40H4/MKkmUxkPMvUs6VdlmZ9fO55JOOn+L1DD6mvPnj2+wzZt2rQSLVGU6kk0GkV5eXnS8e3btzv89Cu5T80a3RVFURRFURRFUXKBWvairqCgwHMFViwWQygUMr6sUGoBoTrpqeJq+NLXCy64ANOmTcPs2bMBxFcslpaW4u6770b//v2zbJ1SlQQe3Xv37u06wL5JM6uKP/LyErP7fhVhfqjhfitc2b492Y8NQbuaee0yyGfPM6E8sql0TL5nTNeluiggpXrIrz+5kSPj7deEVz0XFQELFgSzMwizZ8eVHhyT7zI3stWX+M6m3BZTu5Jh/ai4SLVnQqqO3BQRXspUvz6UgkBqQ69dDN38Q5nC+TnuVRd+w5ry8Nr5M5tjO++rM2cm9ysTbdo4d9r1Guvy8jB6+WBg9BL3cH4xtbFBgzKTtgkvBaZbPzl0bvTrQ5L7ZioKcJvvNZtt8tr4uS+ZFHR+fK7ZKCvz76MuKJEIhj0/EBi21Hw/9ronyPq09Vs6XlDgXAHgVhaXlRBHXnxxPP1f/9q5M2xpaVyR7Pf5QSqEvfwiApl5NjGplfl3fn7KFEBuIJdNdbiScXRlluJJnTRf1NWp2UtfH3nkEZx33nno3LkzysrKUFRUhP/+978oLCzEwoULs22eUoUEfgIi/3TEwYMHsX79emzYsAFXX311puxSFEVRFEVRFEXJXWrZrq/nnntutk1QqjnxXV9T7xPp7BhbHTjqqKOwfv16LFq0COvWrUM0GsU111yDn/3sZ47NJZTcJ3AvmDp1qvH4xIkTUZpJvy+5xrBh5uN8IHr99cwp6gC7L7eahJcyLpWZVjcVT6ozt0H8EnrtApcNKtsOLz9VlY0fH3WppluZ5ZG7BvrN10t1JRWTflSeXL3m1y+aH3VfqvW/ZYv/+qc8hgxJ+LaTBLmOqezS6BZehh0yBFi8OHHOS/mSDV90q1cnFHXSVxe38d57nfc1U1lkO8nPz7ztnFTTD9JW5TXhO4BLX3Dp4NYW/LZNqcqlY/K3TDsvD+jVy92+dMuX4bZd4f9Q3pOCKKwB+7WjdAYPBlq29JeWaRXA4MGJNmMZAw6uXIl6N9xg9rko7wFSqUi2Zop076XExImZey6rKdSypa+Sv/3tb3jiiSfw+eef409/+hOOOuooPPvsszj66KNx9tlnZ9s8JRvUCdVaRd3Bgwdx/PHHY9myZRgxYgRGjBiRbZOULJKxRdxXXXUV5tiWvimKoiiKoiiKoigJ6EVdOp8aygsvvIALL7wQDRs2xAcffID9+/cDAPbu3YvJkydn2TpFqXrq1auH/fv3e/pxVGoHGXtR99577yHPy1eQoiiKoiiKoiiKUqu577778Pjjj+PJJ59EvXr1Ko737NkTH3zwQRYtU7JKqC5CdcNpfGquog4AbrjhBjz44IOI5LqaWPEk8DTM4MGDHb9jsRi+/vprrF27Fr/5zW8yZliNJqjzcSUYmZhBzPTgF8QevkSElhb5WaaYabycngfBr5o2U8u9UsG2zNPv0skgSxkzgduytXDYuQzVhNvEiZ+lyKbjtjS9lj2VlSU7s0+33vy0ozFjgKeeSuTpttEMP+e1mYofx/4m2/yUOxtjgV+8Nv/ws4GCVxlKSyt/qdtLLwEDBwaPZ7t+fpabLlvm/G1qP1X1UM6vhdzEIEjfDIeBTp0yb59fgo4j4TBGrxoGjJ7nTIP3dz9tWB7nLFoE9OkTX15dWuqvz/JNxcger3JFIqj3z38mxjcvaKlyUDcE6bbJoNeorMyxNP3rUAitLr3UnnYuUIuXvn766ac455xzko43bdoUu3LBfY+SEqE6dRCqm3q7DtWpuX0CAP7xj3/gjTfewIoVK9C1a1c0btzYcX7JEvtmW6ecckqgvEKhEJYuXYqjjjoqJVuVyiVwS27WrJnjd506dXD88cfj3nvvRd++fTNmmKIoiqIoiqIoSs5Si1/UtWrVCps2bUKHDh0cx1etWoVjjjkmO0Yp2adOHYTq1vMOZyONl3zVgYKCAvzf//1fSnHXr1+PW265Bfk+fPHGYjE88MADFUvOlepH4Jb89NNPV4YduUGqN0uubOrbF1ixomryzSX8KBps59NReKWiSuLYFHVc/VUZKgv+YFjZ7Ydm8qWqKleorGsExJUFPO2mTYErrkjkK+0gbPa0aQMUFyfCuKk3ZPtIpZxearMgSitT2qbNCzgdOjiPl5U5N5OYMCG+2QHgVLWY7A+itrGFs/UBuXGHxM2Oytioww+m8vL2lErepO6qbGVZOhtf+b2/eKmvTO0205tL2PqWn7Hf1Kb5MbK1pMQetyrU00Hbihx7ZJtNtd1SvKeeAubPt+cpkdfI9NJmyZKEAnTBgvjfsjLgsssqNvMoD4VQNxZLpGnaPMJLiZ2Jfme7T3htiEOUljoUda2uvDL3N5OoZbu+cn7+85/jxhtvxJw5cxAKhfDVV1/hvffew6233ooJEyZk2zwlW4TqpveyrQZvJgGk/67ltttuQ4sWLXyFffjhh9PKS6lcUu4F69atwyeffIJQKITOnTujW7dumbRLURRFURRFURRFyUHGjx+P3bt3o3fv3igrK8M555yDBg0a4NZbb8X111+fbfMUpcaxefNmHHHEEb7Df/zxx2jdunUlWqSkQ+DNJL755hucd955OO200zBu3Dhcf/31OPXUU3H++efj22+/rQwbqxfl5ZlLy6SiKCxML00+O+ul0qgp0AwyUHUqsCCsWWM/Z5pFrg6zw/PnZ8bXXxCC+PKqSkx2DRvm/O3XRplWpst22WWJfCjtTZuc+bu1r7feSoST6UhS8eXH7XCLz/txquMUKUPc7GrRwr/PN5viw3RNgyjg3I4BQFFR5seEQ+mVP/tsZtO11WVeXmI8sSkc3RTCMm5l37syvfFVppRjlXFvMKVp6zte45epjFyhCiTu16kqwhcvDhY+CLbrlKqql8ehNKZNS3znPuFs9UDqN5m/ly3hMNC9e0UadU87LTld/pf6l4/lUBnHrex+sNVFdXiWygR8/Ev1U4OZNGkSSkpKsGbNGqxevRrffvstfvvb32bbLCWLkI+6dD61lfbt2wfaMbZt27aoW4NVublO4Bd1N9xwA/bs2YN///vf2LFjB3bu3IkNGzZgz549GDduXGXYqCiKoiiKoiiKklvU4hd1zzzzDL7//ns0atQI3bt3x+mnn+7Lt5aS49Spi1C4XuqfWvyiDgDOP/981w0nSkpK1AdkDSHwi7rly5dj1qxZOOGEEyqOde7cGb///e/xl7/8JaPG1Vj8zvJVF3VVTaWyHk6CXpNp04LZYlMWmf7K75kiGq3RD3eBISUawVUGmd51MVN92o8SLBIBPvoo2debzQY39aef/KUCzU2d4pUWQWqSoPVGahW/7Zj8M/F/AijunDnm8dhUlzKcl1rEj30yDVIV+fH35qKCqvBZVRn4aR8cr+tL19NNTdeokX/73Einj7r5mfNznSIRf7tku6UVRGHEfbER3E+Z13W0naO/pp0Z3fytBcXrWgVN36T45PUQND8ZvmNH53FTeoMHO+Obdt31q3aMROI+7Gz2yeM8nlcebulVJpn226hUW2699Va0aNECV1xxBZYtW4aI/k+kIBOKutqtEFu5ciWGDBmCu+++23i+vLwcX3zxRRVbpaRC4Bd10WgU9eol78RSr149RKPRjBilKIqiKIqiKIqS09RiRd3XX3+NP/7xj6hbty6uuOIKtGrVCmPHjsXf//73bJumZJNQfNfXlD91am6fkJSl6Apk1qxZePTRR3HppZeiNJ0NtJSsEvhF3XnnnYcbb7wRX331VcWxL7/8EjfffDPOP//8jBpX40hlJtaPD5gg+dJMfSSSeT881Zkgs3CZ9CPmpZrxssvNlqqYWTTN+ldlvh98UPl5AfHdlL1s4XU/b557elU562saI/yqP9ygMnopMAi5YynZ4dcfm6ltcx9OQZF1EEQFRBwaI2PXXGNXcpnGUT9jiKlPpaKirmzfjkOGBAtfVGS3hdtqU2p5lGfBVa/ay9avXwBDXUi17tyunWmHTQAYPTp4PplWVNvUjXKHXd5PyO+glx2mOiHValWMk0HV7G73WK8xJKjqjp7x/Kqj/doCJMZjiseVwlxhZxsf/SrqqhpbfQVVftYkaNfXVD81WD0UDocxYMAAPPfcc/jmm28wbdo0fPHFF+jduzeOPfbYbJunKFkhGo3it7/9LY466ijk5+fj888/BwD85je/wR/+8AdfaVxyySV477338PHHH6NHjx4VaSg1i8Av6mbMmIG9e/eiQ4cOOPbYY9GxY0ccffTR2Lt3L6ZPn14ZNiqKoiiKoiiKouQWtVhRx2nUqBEuvPBC/OQnP8GPfvQjbNmyJdsmKVkiVKdurd5M4r777sPcuXPx0EMPoX79+hXHu3btiqeeesp3OieccALWrFmDtm3b4rTTTsPrr79eGeYqlUjgF3Vt27bFBx98gFdeeQU33XQTxo0bh1dffRXr1q1DmzZtKsPG6oWfmasgN82gfnyCkCM3b1dypYymXfiqomymWfzKyperNSjfBx6oumtoUz1VtYrRxKuvJh8jVYsJrohJl3DYubOyWzibHUHrjfulS/X6u6mzJNxO4Q/qYCiE0HXX2eP5VSa7tSWumpE2X399sDZoSsOvqtFEJpTXpvLxdOm4m6Lw0Lmil4Z4tomDAXY0qzJSUUqmEifV8coUTy6Hcat3m/pV7vrK8bKPrcwIFM8ULp37CL83pXs/4mNMEDWsLAuFobHZpoijeG7XgeJLBWVV4jVeS+UfKX3drkd1UAAqabNv3z4899xz6N+/P1q3bo2pU6di0KBB2LBhQ7ZNU7JFmj7qarLKFADmzZuH2bNn42c/+5ljR9aTTjoJGzduDJRWs2bN8Morr2DUqFHo378/pk6dmmlzlUok0BNJJBJBXl4e1q9fjwsuuAAXXHBBZdmlKIqiKIqiKIqSu6T7grgSJzsnTZqEV155BevXr0f9+vWxy7B5zNatW/GLX/wCb775Jho2bIiioiJMmTLFoQSyceWVV+Lll19Go0aNcPnll+Ott95Cz549K6EkSk0iFKqTliqusn3UVXa/+PLLL9GRNiZiRKNRHDx40DN+SExmhkIhPPDAA+jWrRuuueYavPnmm55pKNWDQC05HA6jffv2KC8vryx7FEVRFEVRFEVRcp9q/KLuwIEDuPzyy9GjRw+jb6zy8nJcdNFFOOKII7Bq1Sp89913uPrqqxGLxXy5QwqFQvjjH/+ICy+8EOFcWSGjpE2oTh3USUMVV6du4AWDgajsfnHiiSfib3/7G9q3b+84/qc//QndunXzjB+LxYzHf/rTn+L444/HoEGDPNNQqgeBW/Jdd92FO+64Azt27KgMe3IDv87ZTeG4Q+B088sF3Bws2xzEe+E3/EknpZ9mEKfTVQ1fipaNNjR4cLDli5nG1n78Li3LZJ3Nn++ej2mpKF86t3hxsk2ZWMKVbhivfiGX/wXd2MCvjdwOvtwvEkG9F17wXqrm1w635dW2upgxI3n5KdWDn6VihGmTBx/M68M2Thk8OKU0KuwpLU1/g51wGPMuW2o9BwD1rrwyRSN92mCif3/387aNGQBg2LD08k51iavbMVP6Ylm4azpuy++pzXstzZ871/28zQ5pQxDCYeDee81jZapjnm3cNbm08LJNhqd7g23jB/pbWOhtq1R9hMPOccPPxjleBAnvN2wuP+MC1dpH3T333IObb74ZXbt2NZ5fsWIFPv74Y8yfPx/dunVDnz598PDDD+PJJ5/Enj17PNNfsGABLrroIn1JpzgIpbn0tbIVdZXdL+6++25cf/31ePDBBxGNRrFkyRKMGjUKkydPxoQJEzzjr1y5Eocddpjx3Mknn4x169bh6aef9kxHyT6BW/Jjjz2GTZs2oXXr1mjfvj0aN27sOP9BVe3iqCiKoiiKoiiKUsuRLwAaNGiABg0aVGqe7733Hrp06YLWrVtXHLvwwguxf/9+rFu3Dr17967U/BXFjWz0CSD9fnHxxRfjj3/8IyZPnoxQKIQJEybglFNOwcsvv+zL7di5557rev7www/HMD6BqFRbAivqBg0ahFtvvRV33HEHioqKcMkllzg+Cuwzrjfd5DxGs5fkBFg6Oq8MO5Q4fhx5n3xy5vMEzLPvbte9smYaudKoqmczw+HU1KOZyLey8VKlmDC1Raof24x5WZm76sVP3n5UL0Hxo5Lk/S/INfn1r535UFp+kIovYsqU5PbI0/ajUJK28Gsm+7uMe9llycdotztTPrZy898Bxv5hzw/0HdYKtSFbW/Iab/n5sjIMW+ShXvO7AcYQsTFFZd0TvdpINpXD3AZCqrFMSkA/7X7LFnO5Skvj/cnPOJAOFH/06GBxpkyJf585M9i1CaoSzcvzVjzZNokgyE+R1zhpG5Nsf3l+QdIFnOOwW7h0Nvvw08dzRIUVRZ20P0B8s79mzZpVfO6///5Kt724uBhHHnmk41jz5s1Rv359FBcXV3r+So4SCiFUt04anxAOHjzo6A9V1SeAzPSLCy+8EG+//TZKS0uxb98+rFq1Cn379vVtw8qVK/Hwww/j3XffBQA88cQTaNeuHY444giMGjUKP/zwg/8CKVkj8F3u7rvvrgw7FEVRFEVRFEVRag2pbAAt4wPAtm3b0LRp04rjNuXQxIkTcc8997im+f7776N79+6+8peO64G4jyzTcUXxi9nLmv+49erVQ0lJieO4m5quOvWLY445Bu+//z4OP/xwx/Fdu3bhlFNOweeff+4a/8knn8R1112HDh064M4778Tdd9+NSZMmYejQoahTpw7mz5+Pww8/HA888ICvsijZo3K9LSpOTjnF+fvQbODeWbMSv1OZIfTrE68m4uaXJejstpeiw6u+TP6bUlFKmPIypVHZCgyT+sdEuu2I0ndTAFUHIpGErzfCj38kWQ6uBEhHqcHrTJ4nP2syL66a8HtNbSoom+8pm72mtPkxqa6QyrBIxFtFIdWKflWDElLgRCJAmzbOc6n+1yTLaGoHpnRN40qHDvbwNii/yuxXPO38fGDkSOdxm4Im6BjipfK8/vpg6UkydW+ka24qK/2dPTu4jzI3vFRKJuWcrE8/zwyybG7+YgneHvyU5aWXvMP4Zc4c/2FXr05WoHrZa+tXboo4qRoPorCORIBx49zj+FFv5+XFx5iysuR2cKhM/wuFgNNP96eiTIUgz6hkYyr30RoKdxec6gcAmjZt6vjYXkpcf/31+OSTT1w/Xbp08WV7y5YtkxRCO3fuxMGDB5MURcnljuCZZ55R5Z2SRAwxRGPRND7x13x++wRQffoFAGzZssW4cef+/fvx5ZdfesZ/9NFHMXXqVGzatAkvvfQSJkyYgN///veYNWsWfv/73+Opp57C888/76ssSnYJfAdu3ry58W1wKBRCXl4eOnbsiOHDh2PEiBEZMVBRFEVRFEVRFEVJj8LCQhSaNh9JgR49emDSpEn4+uuv0apVKwBxR/oNGjTAqaee6ho3HA7juuuuwyeffJIRW5TcoTwWRSSa+gv68hTiVod+sXRpYiOtv/71r2jWrFnF7/LycrzxxhvoQBO5Lnz++ecYODDu1qRfv34IhUI4/fTTK86fccYZ2LZtW9BiKVkg8Iu6CRMmYNKkSfjJT36C008/HbFYDO+//z6WL1+OX/ziF9i8eTOuu+46RCIRjBo1qjJsrv4EnP1rQtso+52FlgT1OVKTeP75uCJB7mpZ3dSCQVR8pusc1N9WJiA7wuGEQsuk4spEXUslJPlX27IloR7KFty2IUPMvvNS6ZuV6VuQdu/jtlrUEp64+RXjSKVOUDWpn/rw8lvo5e/NBtlZUBAv74YNyWnu2uVs824qOKkI9KtsSkUBaKtnL59UfigqwrzLliLJpfCwYcC8eaYYCR57LL57ps0mN4WPjCPVW3J3SsmMGcEUVJmA+5KVXH89sGyZuz88Wcaq8FvHx3jTtXHLn4exqaFtzx5u9cDxuYQIAPDqq9477/rlgQeAli2dx/yO8VQftjLy+varnJd9gMJ1754Yq7xso3FchsvLA/r0Adaude54zfI5cuhQ+33AlC9PJx1MfaC6PdtVAZla+loZbN26FTt27MDWrVtRXl6O9evXAwA6duyI/Px89O3bF507d8bQoUPxu9/9Djt27MCtt96KUaNGOZbh2jjjjDOwfv16tG/fvvIKoSgZprL6xaBBgwDExU9XX32141y9evXQoUMHPPzww572lZWVoWHDhhW/5SYaDRo0QCSX3hXkMIHviKtWrcJ9992HMWPGOI4/8cQTWLFiBV544QWcdNJJeOyxx2rvizpFURRFURRFURQXqvOLugkTJuCZZ56p+N2tWzcAcUf1vXr1Qt26dfHKK69g7NixOOuss9CwYUMUFRVhCm3Y4sHYsWPxy1/+Etu2bcOpp56Kxo0bO86fdNJJmSuMUmOIRaMojyYv/fRLeTSaQWuSqax+ET1k99FHH433338/ZYVfKBTC3r17kZeXV+EXr7S0tGIXXLkbrlJ9Ceyj7q9//Sv69OmTdPz888/HX//6VwBA//79PR0dKojPSspZ2UgEmDw5WDp+fafURHbscP6eOdN/XL9PL+n4EaP4qe6cSeoSqYZxm5XPNNlsJ25quiFDKj9//oTM1X5ByFT9TZgAvPlmsm0mVQYpjritpFL0ssfkL85EOj7PpP8nWz7SXi9FnVSE0K6KfqHrS+0uL8+p8DLVnx9lXKq+8rzCSuWK27WQ7dgnGdn1lbCpxPz6AAuiauKY/PxlGq7yk/3n9NP9qazk8Uxjq/+8PPt14cjxxCu817OH17Xs18983JTuU0/Z8wmKVIum2u7kMXnfDqo8k3Z06GCvS3mt3e5b5AtQ2ioVnibSVZK7tXvbuXTfXCkZY+7cuYjFYkmfXr16VYRp164dli1bhn379uG7777D9OnTXX2BcX76059i8+bNGDduHM466yycfPLJ6NatW8VfpXYSjUVRHo2k/InGKnf8qOx+sXnz5rSW4cZiMRx33HFo3rw5DjvsMJSWlqJbt25o3rw5mjdvjuOPPz7ltJWqJfBTzmGHHYaXX34ZN998s+P4yy+/jMMOOwwA8P3336NJkyaZsVBRFEVRFEVRFCXHKC9P772kwed8jWHz5s3ZNkGphtCLulRJR41XXfj+++/x9ttvY+vWrThw4IDj3Lhx41zjrly5sjJNU6qQwC/qfvOb3+C6667DypUrcfrppyMUCmHNmjV49dVX8fjjjwMAXnvtNZx77rkZN7ZaIO+IgwfHZz0feSR4WnL2lGY4ly0Dfv1r7/hDhiTvUknp5DJu5UvVz19lYdvFUvq/8trtsrKgurIpmTI1q53KNfHyU5UqmWo/mfIxNXBgvP43bYr7wTrvvPjxsrJ4+vn5Th+CdLyszNlu/O7suHo1cPbZid+2tkc+uQibb8VU6sDmy8yrH/B2aho//cDriaeXSloSr/bjVyFLNr76amInTVNaQY67MXJk3OebH9UbhSkqAk4+2T0sQW3WZJ9J3WlrB+mUmbfVSCSuqhqW5J3Pjk0RZPNlaCu/DBOk3aWjbgraT/0oi6WanF8/7nutsqAyzZ7tL7ybPV5jmcm/oCmtSCSh9PWrWLOl89RTCV96NkUy2Uz3S5nfrl1Oe7iKXyqfg9z7TPamooRzW6nwwQfB0qqhVOelr5WN+qZTTMRi5Wm9qIumEbc68OGHH6J///7Yt28fvv/+exx22GEoKSlBo0aN0KJFC88XdTn7DqYWEvgJatSoUejcuTNmzJiBJUuWIBaLoVOnTnj77bfRs2dPAMAtt9yScUMVRVEURVEURVFyhdr8og4Ann32WTz++OPYvHkz3nvvPbRv3x7Tpk3D0UcfjUsuuSTb5ilKlXPzzTfj4osvxqxZs1BQUIDVq1ejXr16uOqqq3DjjTe6xg3if87Phi9KdklpqvOss87CWWedlWlbFEVRFEVRFEVRlBxn1qxZmDBhAm666SZMmjQJ5YdWLRUUFGDatGn6oq6WkvbS11jNXvq6fv16PPHEE6hbty7q1q2L/fv345hjjsFDDz2Eq6++GoNd/PIWFBQgFAr5yqe8Jq+bryUE3kxCYdBuRMXFzuPpbE5QWgrQZh22JU88rCkdN+ftSjD40ijTciyvDQi82kI2rxUtfeHLYaoiT/53wQLvsJkmyPSzX+fpmSAvD1i1Kt7vacmPadmU19Jdnp6Jjz92pmVzeC7if7twoX3Jk/xtWlblFS8oQZda8bB8ybdbfL9t0E8/DtKelywxp++Wrp8NQmxx/W6gIvPYsAFo18655NHP8lmTDfx3kE1FTGn6YdmyYOFt5TIt/ZS4lcfPBgVB4eOGn+XpvAy8/3rFk2mHw/El0dX12cM2JgW5D7vVi225azr14WczBiDeJ0xLuflzoml5K0v324UL3fOoDOTGHjzfGTMqN+9qAr+VpfqpqUyfPh1PPvkk7rzzTtStW7fiePfu3fGvf/0ri5Yp2SSGGMpj5Wl8KnfX18qmXr16FS/bjjzySGzduhUA0KxZs4rvNlauXIk333wTb775JubMmYMWLVpg/PjxePHFF/Hiiy9i/PjxOPLIIzFnzpxKL4eSPtX0aUpRFEVRFEVRFCV3qe2bSZh2d23QoAG+//77LFikVAei0drto65bt25Yu3YtjjvuOPTu3RsTJkxASUkJnn32WXTt2tU1LvdPd++99+KRRx7BlVdeWXFs4MCB6Nq1K2bPno2rr7660sqgZAZV1AWF303vuiv9mVKTA3i/s7vkyLu6zl5XJn5VRVUBt6WoyG6Lm83cwbOf8JmAqyYiEbOCM4gNsuxeeLXzyih/dZx6JmfhQKLMTz0V/yvryE3pYlIm2ZDpcUiVUVKSCPPBBzgiFjM7p3fbHMBmD4/LnfH7UfAQNHbanPlLSE3CVV+mMpAdffoknOKb0gpiq9sxP3gpXjPltD+IGu5Q2L3btpnj881IeDyTglPku2D4imB2VuX9IJW6lhuzEEEc+LvZ44VXHdA14fcD6l8m1Rzl2bGjOR+/Ck0b6WxqkEoefmVJUqnIoTTonFsbpbw6d04+J3Hb6Ml2XTgFBU41HR9LhOLviFgsOb4pzXSvrykPPhbXsmfa2qyoO/roo7F+/fqk43/5y1/QmfqHUuuIxmIoj5an8anZirrJkyejVatWAIDf/va3OPzww3Hdddfhm2++wWy/mycBeO+999C9e/ek4927d8eaNWsyZq9SedSuu6GiKIqiKIqiKIqSVW677Tb84he/QFlZGWKxGNasWYOFCxfi/vvvx1M0YakotQz+cu2II47Aq6++mlI6bdu2xeOPP46HH37YcfyJJ55A27Zt07JRqRpSVtRt2rQJf/3rX/HDDz8AAGKmmbhcZ8MG8/Egfo1Ms66ksPFSUUycGCztXMIwQ+BJZUw7ymvkp87Lypw+nXg8Pz7AMoXJl1g6eafa3tziDRmSWppB86JyLl7snUY6Pu7cwtjUcyZFC/2W6giTasML2YYpHinqAGDKFGcYm6LEhpz2b9nS7KvMjypO/g7S7vLzneG5f1Fp48iRiWODByerXd3sldeBK/iC+nADEn6c3OIWFCT6S5B7EP9LShk/97aSEiASQZNLL02c4+cLC5Pz8bLtUP1Ym0EQX2Ju+VWWWteiiN792996x7f5EDOFE+l7hnULL1WthOwPpotSVgbs2pV8nKvL0qWynmO8FL4SUrZ53UNkmHnzkhW89LdLF3s8r+ss0ywrM9tOY14m6pFU8zaFaCYwXYOaLBnzQW1W1I0YMQJ33303xo8fj3379qGoqAiPP/44Hn30UVxxxRXZNk/JErFYNC1FXbSGbyaRKaZOnYqZM2eiS5cuuPbaa3HttdeiS5cumDlzJqZOnZpt8xQfBL5zf/fdd/jpT3+KN998E6FQCP/9739xzDHH4Nprr0VBQUHSW1tFURRFURRFURTFSbov22ryizoAGDVqFEaNGoWSkhJEo1G0aNEi2yYpWSYaiyGSxvLVdOJWB7777jtMmDABK1euxDfffIOoKM+OHTt8pdO/f3/897//xaxZs/DJJ58gFovhkksuwZgxY1RRV0MI/KLu5ptvRjgcxtatW3HCCSdUHP/pT3+Km2++uXa9qCspsfsw8kNZmVkR4zO97xs0QGPpN0qmlauMHRv/mPCrNMjE0828eXGfXn5m2f1QlU9cUunDyVT98PRSTD8WCiGUrmK3qMifn69MEw4D77wDnHOOPYzcNTKIooaHpfIFUfH5HScoTFDfRCalDgBs2pSsbsvLS95B249NQcLLXUlLSpIVReGwfSdcwqQWcqt36YMpVdwUkx07Bqu/zz+3n+vSxTv+9u1Ap07ONuSl/jFhUBRZm5nJl6cNuftoJsYzL5WYJY9mr7wCLFrkP55tXAbideDWD01xvK4L799eO+5yNm0Cbr/dvx2ZpEULoF+/4PFM9wE/7WnOHOeuquncI2V+pLJftgwYPtx5zpaHn/HEpso2xQvyDJNKP6e0bGOFSVFO9VLT30R5UNtf1BGFXIWt1GrKY0Akmvpzf3kacasDV111FT777DNcc801OPLIIyt2gPXDRx99hC5duqBOnfiiyTZt2mDSpEnW8P/+979x/PHHI5zL7wxqMIGvyooVK/DXv/4Vbdq0cRz/0Y9+hC+++CJjhimKoiiKoiiKoii5Qbdu3Xy/ePjggw8q2RqlOhKLxdJ62VbD39Nh1apVWLVqFX784x8HjtutWzcUFxfjiCOO8BW+R48eWL9+PY455pjAeSmVT+AXdd9//z0aNWqUdLykpAQNGjTIiFHVGvbGef+sWWgwYkTyTKDf6S0ebvToRBobN8a/e6hXGtN2y7kynVbZpDIzy301ZVI54Hc2uypIxWdW0HByythLFfLYYxVKnRDbVrxSkOVfsCCzac+YYVXURaOHHIVKdaNUDklfUSaFW5D27aX+AhLKGq/pfpta0k3BQco2Urn5td/kMytVH3V9+wIdOpj9Qvmxw0uhSSxYkBjfeR5eFBXF4/r1yzduHLBqlXsYzvXXx/3ImVSVfspWWprox5QfV4X7bZuGtj36rSJgtKEfuimAJ08Gfv3r+O8hQ9yvZSrjbRClKmfkyLgaixR16frM89vubPbI4/z60zWVKlP6yBUE4TDQv78zXd6nvVi9Ou7/0c0+Wxlefz3Zd2aqBPH7Z4OPaUByH5T3P36cwu3ZE+/HFIb7s/Vqf7IM/K9M6xDfv/hi4jkyCH7qyTQGBVFv03caS4IoPWsY5eXpPcaX1zB3XIMGDcq2CYpSrenUqVPFHgBBicVi+M1vfmN8V2PiwIEDKeWjVA2Bn0rOOecczJs3D7895Bw5FAohGo3id7/7HXr37p1xAxVFURRFURRFUXKN2rb09e677862CUo1J+6jLnVZXDpxqwMzZ87E7bffjgkTJqBLly6oV6+e43zTpk2tcc855xx8+umnvvPq0aMHGjZsmLKtSuUS+EXd7373O/Tq1Qtr167FgQMHMH78ePz73//Gjh078O6771aGjdWLunUrvjYYNSr1dKRqh9QHthnHU04BpATcdneuzevMuU8WP2EJWxxe51OmxJURJoI8KQUJW8n+WWKzZiHE23FltB03+085xbyD7+rVCSWAl7+wTNgU9Jqkmo+gzlVF5jSlisKmMJGqpaB2murWZLPNDpN/N/ndRFAfeSbbUmmr0kcdVwlFInHVhlR6+hkn/NR9qn3LT/5AYkdGCuc3bX4NlizxbwtgVsZSmzDtBmpKQx6TbWLYsMSutwa+C4Vw+NCh8TGjMrHZTeqo/Hx7mXn4INfGhFufd8uPh7W1cT9KNi8bg4yl6aikHnggWJ865ZS4T0UTfhV1Nr9q/DtXlfJzbs9rso8PGAC89Vb8t833qGklh0kdTDu0lpYmjZ+NY7H4uDFnjnP8sJWRMPnC9PKB5wUvj1QS2tKqzc+6OcbatWvxySefIBQK4YQTTsCpp56abZOULBL3UZdG/Jq9lwQKCgqwe/dunHfeeY7jsVgMoVAI5S4y2rfo3qHkBIHvcp07d8ZHH32EWbNmoW7duvj+++8xePBg/OIXv0CrVq0qw0ZFURRFURRFUZScorYp6jjbt2/HlVdeiXfffRcFh14q79q1Cz179sTChQt1Z8paSjQGRNIQxaUTtzrws5/9DPXr18eCBQsCbyah5BYpTUe1bNkS99xzT6ZtqRnwGTzy50LQ3dLHzkUfdSnCSc8/n5x2aWkiPr/7us0866yif/z6SgpynmaA/eZv+m2LX8mKutB117n7+spU27L5ierY0R4nqNrKjw1uKpUgvp/c8rD5kyPIZxVH+hDiqgw3Xz9yPEoFW9qkDvKrUCWCKnFs303wfiLVcUH8JpFfNe6Py9TevJQcJhWJ2/jip02n4nvPLR1i9Ghg9uzEb66mE3ntf/JJNOBheRwiLy+hRuRtlbCNaabjZINpl8dNm+xlAnA4+dji90iTqkmS6rhqUy55qblNClSJ3zacLlz5F0SJ66YoMx3zc089/XRgzRr/NnjlD5jH2O7dzYrHVFS6pvbO0wqqnJS+/7ZsSW4nXnVvOs/TttmUqfHGhpeyum9fQGxOl+QPMccVdbX5Rd3IkSNx8OBBfPLJJzj++OMBAJ9++ilGjhyJa665BitWrMiyhUo2iMZCOBhN/eVUJFazX2xt2LABH374YUWfUGovdYJGOProo/Gb3/wm0PpnRVEURVEURVEUJQFtJpHqp6ZtJsH529/+hlmzZjleSBx//PGYPn06/va3v2XRMkXJHt27d8e2bduybYZSDQj8ou6GG27A8uXLK3wITJs2DV9//XVl2KYoiqIoiqIoiqLkGO3atcPBgweTjkciERx11FFZsEipDpCPulQ/Nd1H3Q033IAbb7wRc+fOxbp16/DRRx85PkrtIfCLul/+8pd4//33sXHjRgwYMACzZs1Cu3bt0LdvX8xzcfick9iWMbktoevfHwBw0gPCcS9fSpWXBwwe7NSzt2xpzt+NmqyHD0pVlNVt+YUt/+3bk4+Zwro5Ia+s5R1z5gAzZwKXXWZfwpMJUljCOqfPgkQ86g/p2mCyJ9U1J25x5PI2GXbjRgDMPz5fjimXvtJ3GlPkklc/DuI5Y8ea7eWQ8/EgTuu92o9sAx5jp3W3eNMyvUjEusdLki00vtJ3KqNcRivzM/21LUWzLYutbOSyVElJiTm8oY1aN0rq0MH/UjnLEsh5ly1NDmdzvH/eecDJJ7vnwxk/3py/yd5Und3L35lYOuh3GazbeDVkiD19r6WDbkt53fJ3W+btd8m8XPYYFL/1Ll2KZKJ/2pbNynp1Gx9NfZW3Y770m2Mak0xj9q5dziXu6WzgIdN2w6v/EXS/iUTi93lySxA0vxpMOmq6dJfNZpuHHnoIN9xwA9auXYtYLO5YbO3atbjxxhsxZcqULFunZItoLIRINJ1PtkuQHj/96U/xySefYOTIkTjttNNw8skno1u3bhV/ldpDyne/4447Dvfccw/uuecerF69Gtdddx1GjBiBYcOGZdI+RVEURVEURVGUnKM2+6gbPnw49u3bhzPOOAPhQy9kI5EIwuEwRo4ciZEjR1aE3bFjR7bMVKqYaAyIpOGjrryG+6jbvHlztk1QqglpTVOtWbMGCxYswB//+Efs3r0bl112WabsqhnYnJlL5QKnsBAg2SqPu2tXYmaR0uYzrX43K6gtjBwZzPm/dPLsd7bflIbpOKX1+uvOcx06JIe3KSVM5yp7uvSXvwQeeQRYtiyurhs/PpgjbL+Y0nRxvj5y+ZD4zH8q1ykdG1MliJ1uG2hIO0xt3KZG44o8G4sWxa8zR+ZBiot0NtfwqxDix5jabf78eBf3zOPQx/elk0pFIOG03Ku8crMDt/GA/x08OD7uWzZucNiSDialE3eqn5cHFBUBCxYEy/ecc4B33ol/5+oe2zhBaUp1z6Gww14aDAxb4owjN0KhdLt0ST7Xrh3Qq5ezLBSeq7MqY+ywjWPcBi9lpemZwRQuFdtsx93qQY4bvF+UlSUrsLzaP8dPn0r1Gi1YEG8DtnRtx+R1SSV/qayVzxS2vm6rM6nCfvPNxDmutl+82Kmc5HnSOMbzKCtzPlvm55uvp5+y0vdzzolvzCHxW5+8/t2ehTi2dlST31AxavOLumnTpmXbBKUaUh4DDqahiqvpirr27dtn2wSlmhD4CeU///kPnnvuOSxYsABbtmxB79698cADD2Dw4MFo0qRJZdioKIqiKIqiKIqi5AhXX311tk1QlGrB0qVL8ZOf/AT16tXD0qVLXcMOHDiwiqxSsk3gF3WdOnVC9+7d8Ytf/AJXXHEFWpp8p9UW8vPNSjdbnUyeHJ9BvOKKuN8drshbsgSgZcOm2UPTMS9fKzV5ms0Lk28d+m1TbgHJPmWCzKp7+SUDgK++ch7n6hWejp9rY7J59Ghg9mzvuH7xM8NfGXilTe29rCyhFEiFIUOw809/QvOhQ515V6ZKT6Yv8zo0Zgx7fmB8DOHKQt6uuS813hYovEnR6+H76IedO9GQ22Rqiza/bMTIkd5+2Ez1y+2xpX0ovZGvFwEjDX3HMg4Oe2lwwgeVF7KuuU1+y0Np2Pz48TZw003A88+bfS+lgp+2S/3GFq+oyH8/OKSm2x0Kodmll5oVMab8bPhQFM4ZsAQjgbiPT640B+KqOZPtZWXA+vX2fORvk71r1gCnn+5iPJL7HPcf6VWnQa5/kL7lFdfL55ctvNf9MR3Vbc+ewIABqccPim3M4fjpEyZVLo/P1XW2tEzqV87jjyfaPVfUvfRSQlHnNU6bnm/SUQ8SEycCy5fbw7u1W4vKNim+h9Lu44+Bzp3tJtQ0arOiDgDKy8vx4osv4pNPPkEoFMIJJ5yASy65BOFU2quSE5CPulRJJ262GDRoEIqLi9GiRQsMGjTIGi4UCqG8Jm/1rAQi8Ci4ceNGHHfccZVhi6IoiqIoiqIoSq2gvDy9l201+X/2DRs24JJLLkFxcTGOP/54APGVW0cccQSWLl2Krl27ZtlCJRuUx4CDafiZi9RAH3XRaNT4XandBN71lV7SrVu3DvPnz8dzzz2HDz74IOOGVVvk3TSASmL3nXfGv9Audjb1l8nXiF9fHn7OKXGyUUduah6bTW7qrEzYI/OqrHqRiiO3aWQ++042ue1q6EJz0w6WXgoTP6RaT+S30NQG3K717NmJ9kM7+ZnscVHBJOlf3BQxQccfnq8P1ZQxPS+VsK1+3NI2qQ7DYfzw2mtOmyMRuy9QPz5CZV1S2lOmePviorDjxmWmj5O93GdmJJJQMPlR/JD/r3vvBQA0e/RRoF+/eDmHDfPeFdhL0cbz8qPMIntIUcfHB1OaXioi0/EZM7ztsKUjx3CTrzo3NVZQ/Ci8bdfZ1re5epmUXG593+ZDk8K42Ug7CPutB9MO6jbc7CLINu4D0WbPsGFmdbytjVP+bqpWaYctnNtYyO/Xsh/J9ki/i4vN6blBDkMjEWDu3ODxvRSEgHebZXS+a7B7WkqN4dprr8WJJ56I7du344MPPsAHH3yAbdu24aSTTsLo0aOzbZ6SJeKbSaT+KY9luwTpMW/ePOzfvz/p+IEDBzBv3rwsWKRki8BPit988w2uuOIKvPXWWygoKEAsFsPu3bvRu3dvLFq0CEcccURl2KkoiqIoiqIoipIz1Oalr//85z+xdu1aNG/evOJY8+bNMWnSJJx22mlZtEzJJrVx6StnxIgR6NevH1q0aOE4vnfvXowYMQLDyFWWkvMEVtTdcMMN2LNnD/79739jx44d2LlzJzZs2IA9e/Zg3LhxlWFj9YX7IuFY1BfNuJ8sG9zfTTjsT0Uk85MzrZxTTvFOTwlOEJ9zbr66qvqJK5P5ZVLtR8oOasupKv3kE7Cp7imcLT7HTxltSq+ionh/JqWKVF1IdYRU39x1V+J7aWmy6sDH037oiSccv79/8klv312p+N0yhffawZb7+7IprFJtY1xVcsifWsPevRM7INrqjo4vWeLM300V5aaU9GrHfpU0JqRqJRyO744KAP37AwAWRIbEN4HlNvnwCQUAWL067iMTiO9cbPLFlariUZS74iftPGpKw6tOvfLPlIrW4gds75/+5Hre4WvUpPZKVaUYtKxcRSf9gXq1dy+1lyHMDyH2D5TX2MJ3deU7+nrlH+R+7IdwOK4kc+ufpnrzq6gTx/cvXJicri0/roDs1y95HCgrS6gGw2Fgyxa73SboWg4b5qrWdj1uSo/nzY9J36t0zPS7Jr+hYvAFBql+airHH388/ve//yUd/+abb9CxY8csWKQo2ScWiyEUSn7ZuH37djRr1iwLFinZIvCT6vLly/H666/jhBNOqDjWuXNn/P73v0ffvn0zapyiKIqiKIqiKEouUpsVdZMnT8a4ceMwceJEnHnmmQCA1atX495778WDDz6IPXv2VIRt2rRptsxUqpjyGHAwDTdtkRrq4q1bt24IhUIIhUI4//zzwTdUKS8vx+bNm9GvX78sWqhUNYFf1EWjUdSrVy/peL169Wqn80OpgvEKCySrBJYsAQYPTvzmO+h5KRH85Mfp0sU9Tk3B5kcGCOb3htLy86STKcWYH9VMZeTrlnZlPumlkgddE9m/hgwBFi9O357KLK/0j3Uor6VXLMDAa1sAl10WLwdXDdLf0tL4bn95efHvTEnwv2+/xZGUphte6reiogolWX0goSqT8bmPKi8lBU/fD3776DvvAOec4y+9IH3J1r74cTrm12Y5btuUSW52btxo3zU8KIbrVrR8mNMeaZ+JkpLkY9u3O+uD7mluvvz8qnAikfiOyMOWOs9z9Z7cZdZ2D3bLM9NjgEivSUw4ySHb/Y7nQRSV/fv72/WYnjVk2zT1C65w9WsbwRVPwt6Gl1/uTMdvfZCq1Q8m+7zappcdpPTibTA/P9nHnS1PXp8yHH8mBNBg6FD7SglbXjS2k19KOk9+lMl+GteDjOl+2y3lY7LPL/n5NfvNkxKIAYf8pg4ZMqRCQRQ7NHZefPHFFb91p8vaRTQGHExn6WsN3EwCQMVur+vXr8eFF16IfPZ8Xr9+fXTo0AH/93//lyXrlGwQ+A3AeeedhxtvvBELFy5E69atAQBffvklbr75Zpx//vmB0rr//vuxZMkSbNy4EQ0bNkTPnj3x4IMPVuz8A8QH6HvuuQezZ8/Gzp07ccYZZ+D3v/89TjzxxIow+/fvx6233oqFCxfihx9+wPnnn4+ZM2eiDVsqsXPnTowbNw5Ll8Yf/gcOHIjp06ejoKAgaBUoiqIoiqIoiqKkRW3e9XXlypXZNkGphtBmEqlSUxV1d999NwCgQ4cOuOKKK9CgQYMsW6Rkm8Av6mbMmIFLLrkEHTp0QNu2bREKhbB161Z07doV8+fPD5TW22+/jV/84hc47bTTEIlEcOedd6Jv3774+OOP0bhxYwDAQw89hEceeQRz587Fcccdh/vuuw8XXHABPv30UzRp0gQAcNNNN+Hll1/GokWLcPjhh+OWW27BgAEDsG7dOtStWxcAUFRUhO3bt2P58uUAgNGjR2Po0KF4+eWXg1aBoiiKoiiKoihKWtTmpa/nnntutk1QqiHlMeBgGju3Rmr4rq/nnXcevv322wrB0Zo1a7BgwQJ07txZd0OuZQTeTKJt27b44IMP8Morr+Cmm27CuHHj8Oqrr2LdunUOBZsfli9fjuHDh+PEE0/Ej3/8Yzz99NPYunUr1q1bByCupps2bRruvPNODB48GF26dMEzzzyDffv2YcGCBQCA3bt34w9/+AMefvhh9OnTB926dcP8+fPxr3/9C68fWgLwySefYPny5XjqqafQo0cP9OjRA08++SSWLVuGTz/9NGgVJEjF2TctIeBLPhYtSo7rtczBKz/bUoXKXEaZLYIuczUdq6p64def49chf6bxu2y7MrA5KKdjtDS0pCS9JbqyT6VTr0GWdgoGLioC+vQxO8fny5DKypzlPbRk6cg33nDG83q69+G4vN6llyaHI/u4A3JburbNBNyWmZni0TFZnhkzkuNJKJ05c+K/+db1coMjalO0TNO2VNWrXxiWa/rC7d7gN6yf83xJKoB5ly11LtcLkobbmCk3H6BNTmQ8fq39bABg2/RFpmWyt7LwunZuS0UpHNn+8cfeabmRihuBSAQ4NFFpDAM4l+PbNn/xyovS8Xs93OrMC0s9fPfkk+awfvqz37blZ5m1n/zkEnzAOYbZ4tG1chur5POGaYzzwhTOdEy6UPBjgwxD6dAYTZSWmm3PkedZt00i/H5qMrt27cLDDz+Ma6+9FqNGjcLUqVOxe/fubJulZBHyUZfqp6Yq6oiioqIKtWlxcTH69OmDNWvW4Ne//jXuvffeLFunVCWBX9QRF1xwAW644QaMGzcOffr0yYgxNDAfdthhAIDNmzejuLjYsUlFgwYNcO655+Lvf/87AGDdunU4ePCgI0zr1q3RpUuXijDvvfcemjVrhjPOOKMizJlnnolmzZpVhJHs378fe/bscXwURVEURVEURVGU9Fi7di2OPfZYTJ06FTt27EBJSQkeeeQRHHvssfjggw+ybZ6iZIUNGzbg9NNPBwAsXrwYXbt2xd///ncsWLAAc+fOza5xSpWS0ou6N954AwMGDMCxxx6Ljh07YsCAARXqtVSJxWL45S9/ibPPPhtdDm14UFxcDAA48sgjHWGPPPLIinPFxcWoX78+mjdv7hqmRYsWSXm2aNGiIozk/vvvR7NmzSo+bdu29V8Yt1lUvzPScqqM1C0TJpjD+sk/lzCpo/jMbaoz5xw/9ThwYPD6Nimp/KhMKoN0lGqCb5991nzCVEY/Cga/Sho3lizxLpsfVVEQ/LYrN6WaPB4OA2+9lRzXloZJtenXVorH1WZ+6kHaZFPbeeVrO28KY6jL2NVXJ8LwDTF4fBpPpfKHpz9kiPs1sqnw5O8gY46tvrwwhS0oqLBh2PMDnemTus4W16vt8LhS1UPHpCrO71gnVJ9GeHrSYb9ML0g9+ukzvLxS4eQnjUgEuO8+f3kGVc6ZvlM6bvVqanN+NpKwqcr81DlTC7uGSWEMPnzo0OSDfsZMU14ffeQMa2unvC6oTfKwpvGN+qGt3fJ+JOtaXh/btaBwZJMpvZNOgiu0IQW3S8KvpV8lssl2kzLP9kyXI8+6tVlRd/PNN2PgwIHYsmULlixZghdffBGbN2/GgAEDcNNNN2XbPCVLkI+6VD/lNXzp68GDByv8073++usYODD+DNepUyd8/fXX2TRNqWICv6ibMWMG+vXrhyZNmuDGG2/EuHHj0LRpU/Tv3x8z5DKlAFx//fX46KOPsHDhwqRztBMQQTsAuSHDmMK7pXPHHXdg9+7dFZ9t27b5KYaiKIqiKIqiKIontflF3dq1a/GrX/0KYfYSNxwOY/z48Vi7dm0WLVOySXkshIPR9D41mRNPPBGPP/44/va3v+G1115Dv379AABfffUVDj/88Cxbp1QlgV/U3X///Zg6dSoWLlyIcePGYdy4cViwYAGmTp2KyZMnp2TEDTfcgKVLl2LlypUOP3ctW7YEgCTV2zfffFOhsmvZsiUOHDiAnTt3uob53//+l5Tvt99+m6TWIxo0aICmTZs6Pkm4zaiakGo503muQjHN0JaUmO2QuPmIGjbM3c6aQtBZW4qTyaeaVBRXtmsvz3kpUDLBzJnO/OX3ABwR8zGF5Sef/v39q+yC4qWqSDW+xK1teo0beXlOxRexZUtyejbVkhd+lHdciZFqvftVEEtbwuG4os3NfxUvAwsX+sc/7Pnz8dNFRbT3xRfd7beo+azKMZP6y40hQ+z5ekEqnXvvddpIH6588dMfKI0BA5xpUTq8X1K75fFsv+U5luecPgvcy5eu2pBsadfO3cZDS0985+PXLlO7TnWc96uIdLvvmK6/l6LOUN7vuW84qbY0UVgY/yt9Scr8iors5/3C2yvZZ0pX1sXjjyf7DeXKUR7HS2Fny8NW19xe8qvphU1ZR8dNirru3c1pFRU5bXBTqT7ySGrPVzJNqUY05Zfuc0A1g3Z9TfVTk3d9bdq0KbZu3Zp0fNu2bRUbBiq1j2gt91H34IMP4oknnkCvXr1w5ZVX4sc//jEAYOnSpRVLYpXaQeAXdXv27Kl4s8vp27dvYD9usVgM119/PZYsWYI333wTRx99tOP80UcfjZYtW+K1116rOHbgwAG8/fbb6NmzJwDg1FNPRb169Rxhvv76a2zYsKEiTI8ePbB7926sWbOmIsw//vEP7N69uyKMoiiKoiiKoiiKUvn89Kc/xTXXXIM//vGP2LZtG7Zv345Fixbh2muvxZVXXplt85QskfZmEjV86WuvXr1QUlKCkpISzKGN0gCMHj0ajz/+eBYtU6qawC/qBg4ciBdJbcD485//jIsvvjhQWr/4xS8wf/58LFiwAE2aNEFxcTGKi4vxww8/AIgvV73pppswefJkvPjii9iwYQOGDx+ORo0aoejQTGizZs1wzTXX4JZbbsEbb7yBDz/8EFdddRW6du1ascnFCSecgH79+mHUqFFYvXo1Vq9ejVGjRmHAgAE4/vjjg1ZBAjbjV86X0Jp8bMiZUXlOpkk7XxGH4uy4b6arHbUCqQIyqYJMqhaTPxgZxivfIMdt4fyoHarDjHEmbBg2zF6/dJyrcExhpOLpuOOC2eBH/ea3rH5Us15hbG1XrmOJROLlNfngIp9BJtWWDCuPBW3/NpWE13GTMtgrX6nIIluHDUPM5KaAdm7lZZo2LfHdraxyx1IWtgndy7gNXgoxv/UadLwwHfNSmMhwspzyuJ8xkHYMpfq2xaExNshunkKlVVBgsJV+E9yHop+0TWF79fK27ZBvGCt+lae2unaL41fBS3Z4qev8XG+u/gyobG787rtOe/0oat1UYkHGZhlPYvLJ6EcFabo/+e2btvr2+8yxfLldpeem5jPhpuR2W+Uh0+bf01mdYbu2pnL5VSDWUNJR06UiYqxOTJkyBYMHD8awYcPQoUMHtG/fHsOHD8dll12GBx98MNvmKUrWiMViWLduHZ544gns3bsXAFC/fn00atQoy5YpVYmvJ6DHHnus4vsJJ5yASZMm4a233kKPHj0AAKtXr8a7776LW265JVDms2bNAhB/c8x5+umnMXz4cADA+PHj8cMPP2Ds2LHYuXMnzjjjDKxYscIhiZ46dSrC4TCGDBmCH374Aeeffz7mzp2LunXrVoR57rnnMG7cuIrdYQcOHJiWTz1FURRFURRFUZRUiUQA9u9KSvFrKvXr18ejjz6K+++/H5999hlisRg6duyoLyNqOaSoS5WavvT1iy++QL9+/bB161bs378fF1xwAZo0aYKHHnoIZWVlqqqrRfh6UTd16lTH7+bNm+Pjjz/Gxx9/XHGsoKAAc+bMwV133eU785gPn1ahUAgTJ07ExIkTrWHy8vIwffp0TJ8+3RrmsMMOw/z5833bZsWkXIhEUPfhhwFyfLpxo3saXlNgeXnxHfsMO9IeNmYIsHixL5tqJXwGO8hMvKm+Fgg/SW7Kr3AY34ZC3n7a1q41+4OxzWZXlbJOzq5nKt82bYDt293bqElhajpGaTA/lr7w0xcyWWZSwpkUVlS2/PyEYsVNYZmfH293ZWWokBm5KZncflN6JptMedtUqRyv39KHnoTn76aMBRC6/HK7GpGnXSHHMqTJlV75+U7Foik9qWb0UghyNaG0wdYupP2GsieFJbvIx5bf/ku28P7kNc5Q2sXFwOjRiTqzjVm8rKayhMPA4MHxHZkJ0VYHP94XGLwiEV7aEw6bletB8aOS9FJGcnWi2y6mbu0mE2OPTf3kR1FHv/k1Ky111rFM3zROv/56Ij1bf+Hx+cqBYcOAefPi3+ney8NOmBD3u+gHU33adjmW7dmPMtamVLXVqy1NLzXcN984/eideWayDfK3tM12n5G282s5ciQwZ05yXza1Ez/4beNuY1E4DLRoEa+TqnouUqqEWCyGffv2oXXr1uooXwGQ2PU1VWr6i7obb7wR3bt3xz//+U9Hn7j00ktx7bXXZtEyparxdbfbvHlzZduhKIqiKIqiKIpSa6itirri4mKMHz8eS5curVja17RpU1x66aW4//77rZv9KblPXFGX+s6tNd1H3apVq/Duu++ifv36juPt27fHl19+mSWrlGwQ2Eddrcc2O7x+vXmWlSNnn912+pLnaGabjktVnZ/8comZBl99JtwUP5mclQ2HccSIEe5hIpG4mk76xsq0Lalg2mnNxODBwdLdsCH4UySf3Y9EklUdQevKr6IuKCYfP1IhZkKqE2zKE668pXTdlBhS8WnK3+TXy20c4nba4CotHt7tOvGwbuG9FLLUVmz92qSmo7+kqDMpnL3amG1cMdkr6zoT7ddLNW26pqbrGaTdFxYCXbqYFXMyD/ldhrGpNL36Dtlsam+mNsLzstXjSSfZ8/KDmyJUhvMK4xdTe/VSapr6RSTi3AmZj7WkMnO7pvIZY8MGb9tNNpeWmu3nijtb2n7VqKb2wduw7XnJdtx0H7fFpbKGw06/bm5qaoLbvHq1PS83JaBp3PE7vpvuS7Z+ms5zjOmeIKHVCH5tryHUxl1f9+zZg549e2L58uUYMWIEZs6cid///vcYOnQoXn75Zfy///f/UOq2ikXJaaKxECLR1D/labzk82LLli245pprcPTRR6Nhw4Y49thjcffdd+PAgQOOcFu3bsXFF1+Mxo0bo7CwEOPGjUsKYyMajaLc0LG3b9+uuyHXMlK6q27fvh1Lly7F1q1bkxrdI488khHDFEVRFEVRFEVRcpVIBKiThmyiJr6vfPTRR1G3bl38+9//xhFHHOE4d9ddd+Gss87CY489hl//+tdZslBRzGzcuBHRaBRPPPEEOnbsiA0bNmDUqFH4/vvvMWXKFABAeXk5LrroIhxxxBFYtWoVvvvuO1x99dWIxWKubrqICy64ANOmTcPs2bMBxN2AlZaW4u6770b//v0rtXxK9SLwi7o33ngDAwcOxNFHH41PP/0UXbp0wZYtWxCLxXDKKadUho2KoiiKoiiKoihKDeeVV17Br3/966SXdADQokUL3HHHHXjyySf1RV0tJe6jLo2lr5WoqOvXrx/69etX8fuYY47Bp59+ilmzZlW8qFuxYgU+/vhjbNu2Da1btwYAPPzwwxg+fDgmTZqEpk2buuYxdepU9O7dG507d0ZZWRmKiorw3//+F4WFhVi4cGGllU2pfgSew7njjjtwyy23YMOGDcjLy8MLL7yAbdu24dxzz8Xll19eGTZWX2jpg1wG4OZoVzryluekA2CeF89j0SL3pUWS0aPdz9dEvJwjc/w4S0+3XvxOa3otUXJLq7Kund9l0qnk75a2XI60ZEny0lfbUka/8GVHbksoJUOG2G32my+FHTnSvwNxPo7IOHzZn7DjIN/IxLY0MYgTcD7meF0Dt+VapiV/og7+FxIPVbt2mZfcuS0PlnbLfORSMGmvtM9rmbVtqa+pL6ey5NWGrf3x4/n58TZnq0P6vmVLsLx79UrUVX6+/3FW2iE36fBaYumVphte9XX22c7jH33knodsF36W7BHUDmzLcP3YLfGzdNGWdjgcX/rKxxwqn2384OnJDT1My5Hd8LqGlL4sA18+mircRtt9gdvIz8vnM56eTNstb/7bT12l4s7EtlTVzzORqRymZec8Ta/l36Zjprq0LdHv2zfxO+imFtWUdJa9+mk61ZH//Oc/6Nmzp/V8z5498emnn1ahRUp1IprGsle+9HXPnj2Oz/79+yvF3t27d+Owww6r+P3ee++hS5cuFS/pAODCCy/E/v37sW7dOs/0WrdujfXr1+O2227Dz3/+c3Tr1g0PPPAAPvzwQ7Ro0aJSyqBUTwL/5/DJJ59UvM0Nh8P44YcfkJ+fj3vvvReXXHIJrrvuuowbqSiKoiiKoiiKkkvUxqWve/bsQQHfoV1QUFCAPXv2VJ1BSrUiGguhPJp6pyiP1cHBgwfRrFkzx/G7774bEydOTNM6J5999hmmT5+Ohx9+uOJYcXFx0mYozZs3R/369VFcXOwr3YYNG2LEiBEY4eX/XMlpAveCxo0bV7yRbt26NT777LOKcyXcKXFtwTSz6lcN4KbAsDmGl7PAJkzH584NZl9NxVY2riBwU+ekk7YfWra0pzNyZHJ+5NC7uhDUFj+KJB7mpZec523KjXHj/NvgpgCQ4YiiIv9T1SZFlzi29+mnzcorL3UOfShN7pzcSwViU+16sP/+++Nf6CHar0rKpgb2kfeRI0bYFRQ8L/rL1Y55eU5lhVTUURzqS7wObfb5UdnK9pFppZQXbuoUU9s1KepknQi++QaJcwsWALff7uyTbm3Y5gjcS3UD2Deq8FIEma6bKT/bfTQcBsaMsefJ40s5i5vj87Fjg6uaOEHu2UFVgIWFzuNcYSZtln3LpmjibWztWuDvf7fbK8dLvmFRqs8qtvYjlcZXXGEOYzpmU+cSbuOX7F8yLL/P0985cxL58b/cLs6uXeY8S0vN4yOPb+uPl12WHDYcTrR1r3GaHzfVo1tcqWAk5Sev/xx5lq2uirrKdJofi8VQx+XtZCgUQixWw7fuVFImGquD8vK6KX+i5XVQr1497N692/G54447rHlOnDgRoVDI9bN27VpHnK+++gr9+vXD5ZdfjmuvvdZxLiRXiiDe7k3H3WjatCk+//zzQHGU3CHwXe7MM8/Eu+++i86dO+Oiiy7CLbfcgn/9619YsmQJzjzzzMqwUVEURVEURVEURakCKtNpfiwWw3HHHWd9aaEv6ZRM4OULjnP99dfjCjl5I+jQoUPF96+++gq9e/dGjx49KjZ9IFq2bIl//OMfjmM7d+7EwYMHk5R2XmhfqN0EflH3yCOPVGyZPXHiRJSWluKPf/wjOnbsiKlTp2bcwGqHm7rEyy8JneMzi5Re374ADQCm2UI5a+9nCo3HHzTIuww1DVmHdMwURuJH1WHKL4Vze0MhNInF4nlOngyMH2+P7zYrX5n4UZyliuk6UZ433QQ8/zzQvz/w6qvmeNwm7rvOL6RMzM+3x/OjrvJCXkOWZpPLL08+z5Vy0g5T2eX0ubA5qZW7KYG4akGoPRqQzJ5UjEHVPFxR56Z8ICWJTenDf0vlFpXD5CfNTeUDOBV1UqFIFBTEx1q3sks/azZsCpJUwtvs5eG5CpPCm3zyAYlrbFHUtbipKDku5WuqZ54OV9h71Y9JvWXDTUkKxNWWXmnk5ycUSFSegQPt9eSWb2WP2encr7kSzmZ/Xp5ZDcj96dmebWwKUl6P3bsDM2YAJn9U/LnGtAxOqsTc8nWjqAho0wbo1Mmpzp4716koNNVRkGcFXl8bNwJ9+sSP21S+Mo4XbqpNgpR4VJayMrOKjucvmTcv/nfZMrNykq5Lpp4l5T3QdD8uLEz4Lq3KZ6MqoLw8vSKVl2fOFk5lOs1/+umnK8doJSeIZWDpa1AKCwtRyO8HLnz55Zfo3bs3Tj31VDz99NNJ6tAePXpg0qRJ+Prrr9GqVSsA8b7SoEEDnHrqqYFtU2ovge+yxxxzTMX3Ro0aYebMmRk1SFEURVEURVEUJdeJRICAq+GS4gNI8unWoEEDNGjQIA3LkgnqNL93797GdK6++uqM2qXkFrFoCNHyuqnHL0/D6aMHX331FXr16oV27dphypQp+PbbbyvOtWzZEgDQt29fdO7cGUOHDsXvfvc77NixA7feeitGjRoVSOUHAFdddVXgOEruUHktubbgNbvPw5m+E9IHlxduKjKbXx0Zr6ZiUuDk5SVmgYMooILUx+LF7mlZaHLllc4DbkoZSVXtalaZ7cKmSgmHgfPOc4aVqic6xpUEQW01qMZcbQyatpc/Io5NkSLVRFzBYlPrijEj1KSJe98HzIoMmzIrLy/hL8qmjLMpjHidm+LSdzdVmlTQ8XpYsiShFLH1vYEDk1Vm3B6uMpK2kRrMTZ3nR13Ebaf0/LQ1tzB+/DNFIsD27fEwn3/uVO3ItmVC+sciSkud19Vmi9cYbFOuBRm7I5GEr0K6lkGUT/Q3EgFoFzWKb1I9y/xN373yJF9kNgW113076LOGmy3Evffa48t+sWuXc9yxKbRk3w1in2mM8zPuuEG2b9pktiscBq691p4uvza28sjrx/3ymcYhec7PNTaNodIO+dvmk9OWji2M2zMnD+e3H7q1M9v1lm1LcdC2bVs0a9as4nM/+ZvNEOQ0fwzz45kJp/mKIolG0/NRV57GSz4vVqxYgU2bNuHNN99EmzZt0KpVq4oPUbduXbzyyivIy8vDWWedhSFDhmDQoEEVSlQv5s2bV7EfwKxZsyqUfgcOHMA8+l9XqRXoizpFURRFURRFUZQqhs+hpPoBgG3btvlynF9dneYrChGL1UE0Wjf1T6zyXtQNHz4csVjM+OG0a9cOy5Ytw759+/Ddd99h+vTpvhWuI0aMwO7du5OO7927V3eBrWXotFQ68FlS7g/IbVbWLS2ajeTpbtoEDBvmDEsql6C+jyisl7qoJmJTJhGmmehM+DrhShm/9WrK06amqKqZY/LLYJqVT8cHkx/7ubpGzrJLf2ekSEpHASex+VnyiueWB1cWhMPxHTNJ/cPbnUmBYFKbcVt5XG6XVGvZ4gcph9d3t7huZeS2BOmHPBz5kHPrdzalhslvk7xulJ/fNiPzqyzcVDl0Hoj36XAYWL7cWQ7TfcqPkggAzj7bqTaU4y4Pz5VXXLkYiSQryKXS1oatzbipcNzSmTcvfn/t1cs+LtugPGX7s9kRifjzsZlKG/KjluK2+rlPmlR/NB7T9fKr+GZpHPzTn1CP5+c2ngcdd9zCRiLYuXAhmg8YkLCJ96WTTvKflqnevMYxijNvnnPXarLFdk34OCXvldQHR45MVkvz68fbXipqxJISf0redMc/eb1N9zs35WkNJRLJzNLXpk2b+loiV12d5iuKEsf2onv79u1o1qxZFixSskVu3e0URVEURVEURVFqAJl6UecXdZqvVHdiserro64y6datW4Wq9fzzz0eYTUqUl5dj8+bNjg1elNwncEu+9957sW/fvqTjP/zwA+6Vvk5yHdsMqmmW2Tbj7+bnB4jvUmY650ctkOr5msjq1fG/bkoTr+MmBYFfgqi8bL5ZqiOZnCEnaEact3va8ZXXI1ekyXQyoezLhK8bk0rMTREn7ZE2mNSLJuWKyW7Trom2fDluPpPoQwoom5rBtquhm4qDpfXt00+b03a7zqWl7ooek+qK0nRTZ9quhxduKjvyTWZCKqZTwaQ8IoXN2rVmf4Febd+kdtqyJVGvBQX2XTl5mzHhdwy02UnHbP3CpE7yW16bnfKYVNR5qcukgssvlaHedLsvmp5r+LOKVzl5PzNQ7/LLE+e9FFKZuj8uXw6Ew2guliZhwQIgVT9aXgo6IFkBJ9ttJII5g5Ym0nNLw1RP/PchJ+ZJ56mNyv4cDruPPR98kPhuu5fZ+otbG/DbnsnmBQsS8UxlUCoNcprftm3bCqf5xcXFDt9z3Gn+hx9+iDfeeCNlp/mKQsSidRAtr5v6J1p5S18rk0GDBuGSSy5BLBbDhRdeiEsuuaTic8UVV+CJJ57A/Pnzs22mUoUEvsvdc889GDNmDBo1auQ4vm/fPtxzzz2YMGFCxoxTFEVRFEVRFEXJRcrL01PUlZdnzhYOOc3ftGkT2rRp4zhH/rjIaf7YsWNx1llnoWHDhigqKvLtNL+8vBxz587FG2+8gW+++QbRaNRx/s0338xMYZQaBfmoS5VotGYq6u6++24A8aXnV1xxRcZ3bVZqHoFf1NnWTf/zn/90bNmtKIqiKIqiKIqimEl3kUtlLZIZPnw4hg8f7hmOnOanwo033oi5c+fioosuQpcuXXQDCgUAEIuGEEtr6WvNVNQR5513Hr799tuKF+Rr1qzBggUL0LlzZ4wePTrL1ilVie8Xdc2bN69YN33cccc5BtPy8nKUlpY6tuyu1diW6pB0ny/Zsi0jszk0N21aEUT+nwvLXk3LKv7zn/hfk6NmG3JZZZC6GTYsnlcQJ9KEaRlcCk65M4qXY/9hw1JfEiyXzcglxqksBfPb5vv3B9q0iS8Lclu+Y1vuZVrak4ozbttxudTVthTMdJw7CAfiSw39LPVbtgzgD99em6C4XR+TrXypFrfFpY0fQbtYyTGN0qDNH7zqfvBgoGNHd9v5Ej76a2qjpqWflN7MmXEH7n42QQiHE0tE/bafTC3Jti2llsvPg0B1wustEomPh2PHJn7T9aaHygUL4nUm7TIhl1JLpMsIm/sIW33LJai2e0Akgh9efhkNTTZS2rztmMY7nj/VmWxTfAyyXTs/fdtUD0GfD/iydVOfs6UtlS+02QulO3t2vC0sXpyIu2uXv7LJ6+XVfmQa33yTvMyT0igpSWym5JU2t9Xk9iAFRr41LLHxiwnb+EL55eXFy7Bxozkefae2Kcdh21jgRxFle9b1egb2WvpussfUtnPheRbV90VdVbBo0SIsXrwY/fv3z7YpilJtKCoqwujRozF06FAUFxejT58+6NKlC+bPn4/i4mJdvViL8P0EN23aNMRiMYwcORL33HOPY9eR+vXro0OHDujRo0elGKkoiqIoiqIoiqLkBvXr10dHPrmmKKDNJFJfvhqL1mxl5oYNG3D66acDABYvXoyuXbvi3XffxYoVKzBmzBh9UVeL8P2i7uqrrwYAHH300ejZsyfq1atXaUbVGMhZt2023w3TrKJtNt2kGHCb2SUqwwl1dSJVpYDtfLq2pJOGvJY8vaqYLrXNasvvfup83DjgsccS4WVbJsUJ9R+TosuUn1SmpIppxl/auWABUFRkt0t+N6VvUg6Gw3HlG6kqpbrGTY3A7fSjTLApUyZOjG8KwI95xaNrRdfPpu6wYVMn0jmucisqSqjdvNI3HSdb6bxJsSPHW65yle3Mr5LHpgYM0n/9KqHcxht5v6BylpYmq8CCji1SzWLbOMkrbd7npWJcqi9l/dOnpMRd3WSrv6KieB+0xRF5N5RqT1MZTDa4wa+PH0Urb7NeCiST4ojOP/88MGeOe35cEWtT+ZrG56eeSrab9wEq55AhCVUd76tuhMPJ/VrmL8NL/Cq0TBuU2MZlt/wA5z2Otzm3TXbc7ObfaczKyzO3Z9P1kgpgWQcjRybaB1ffmcIOGZK8gZEc82zt0utcXl5ivOLlpXL6GWNqELVZUXfLLbfg0UcfxYwZM3TZq5IgGgLS2bm1hu76Shw8eLDCP93rr7+OgQMHAgA6deqEr7/+OpumKVVM4Dc25557LqLRKP7zn/8YHX+ec845GTNOURRFURRFURQlF0l3M4jK2kyiKli1ahVWrlyJv/zlLzjxxBOTRCBLlizJkmVKVkn3RV0N3UyCOPHEE/H444/joosuwmuvvYbf/va3AOI7MR9++OFZtk6pSgK35NWrV6Njx4444YQTcM4556BXr14Vn969e1eGjdUfP0oWwDztZVOayFlDUpoE8T0jww4c6K0sqwn4nT7MtIKO/Culm57JLumz0OY3prKunR/7pZ8gEyYVGlfBcNzUOPI3KZtI8eKnHrivQj9KqMqGricp6jhSWcAVJDYFYF5esvLI6/pEInHFI/lM82szV0q4KSRs9gLmPP3WvZuKiKt13OwwKUl4ODneBrEBsPtVMyn4/BBUqe113+EKzqBpE+SH1ktlZFKUutlpav9DhnjXmVt79Iu87rJ+SGHklb6X6jNTKiCv6+xWv1L1JtM0qfFMYU2qWn6e9yFK56233NOVxwcPNttlstsrLQ75cjOlLRWVtjxsz3Kmdgy4qy+9lLdc/crDcIWhvBcQpFwk9Z1X2+Nllf5PZTjbeEhx3cYHL1ukkpnH8VI31kD4I1Kqn5pKQUEBLr30Upx77rkoLCxEs2bNHB+ldhKKhRAqr5PGp2arMx988EE88cQT6NWrF6688kr8+Mc/BgAsXbq0YkmsUjsIfLcbM2YMunfvjldeeQWtWrVSqbKiKIqiKIqiKIrim6effjrbJihKtaNXr14oKSnBnj170Lx584rjo0ePRqNGjbJomVLVBFbU/fe//8XkyZNxwgknoKCgQGc/gGCKOq6usKVDygeTXxjTrHkQXznS70dNx+9Mu0m1aLoObnWzaZM5nM0G2+6zJtWCzRdWVV2rylKc8XZsU9Z49RfAuFPdd34mCWi6uaTEOy+T7X7Sl9jKZVLUmHYAlmnb+r1UFkjfUn7x4++Pt0dTeH6tpDqN/+VQOJs/MlP+srx0jej60nETVI82VbJJURdE0cr7sLxmQdRUXooRt3MLFiQfKysz3yP4/chvfjNmOI+7qV7csO08abLdlIdNNRmJxHc19oPbvcOmtpL5u7UnabOM5xXHlj7vM6Z7hozP4dfPhGkHZJ6W7M+2cvByeqmg3O4Dtucc8o3mp62Z0rId91KByXjcdq9nOr4Lrtu9xfSsYoL3DdNurjw+fcrKkn0rE8OHm31HyjGafvP4pKS3Pa9KvMZEt7bA082RZ9narKhTFBOhKFCnPPVPKOqdR3UnFoth3bp1eOKJJ7B3714A8c1X9EVd7SLwf+BnnHEGNm3apLv0KIqiKIqiKIqipEgkAsRiqcevyT7qAOD555/H4sWLsXXrVhw4cMBx7oMPPsiSVUo2CcXiL9xSJZ241YEvvvgC/fr1w9atW7F//35ccMEFaNKkCR566CGUlZXh8ccfz7aJShXhS1H30UcfVXxuuOEG3HLLLZg7dy7WrVvnOPfRRx9Vtr3ZR84gkw8brlTzmhn2OmaaLUzFv5Nf3yo1Hb+KRr/xbXTokJodqSjSgigFMoEf9ZhEKgbJpxRB/nFMfcamxOC+j0x9gSkIDqfdGN95x2kH5SvTcIOmpfv3T7bVKx6Hl8/UHsJh545/bko+LxViKipIv4ouCisVIFLNxNOVOwBKTMo5Uq9R3bj55ZN5Auadt0tL8a1NbWkqh1Qv+1VrmGz0oyDySlfmL9Pxo4B08+OYjuKS2p1NJcvzMO0YSXCfV1JdIxU8EtnuJOvXeysETeOPTPeUU+xjuqn8XteF2p4ca23jBH334zPNZL88F4kk38NM9UfPMtyfIY1bfsYcU/syqRQpD3neZluQug6CV/3yNs/btQmvPpfKLsm2cUDG8xpvTG329tsTx156ya5uzs8H5s3zftZyU7/arqebfbb0cvE5tpby2GOPYcSIEWjRogU+/PBDnH766Tj88MPx+eef4yc/+Um2zVOyRJ0oULc89U+dGq6ou/HGG9G9e3fs3LkTDRs2rDh+6aWX4o033siiZUpV4+tNwMknn4xQKIQYm/IZyRzr07lQKITymj61oyiKoiiKoiiKUsnUZkXdzJkzMXv2bFx55ZV45plnMH78eBxzzDGYMGECduzYkW3zlCxRJwqE02jX6cStDqxatQrvvvsu6tev7zjevn17fPnll1mySskGvhR1mzdvxueff47NmzcbP3Tu888/r2x7sw+/I8qZP+k3Ru6A6aaUkEoZ5itm98KF5llGL9WAJFdmIQ0KiIM2BY3N/4mb75RUbHFTwPnxwSLj81n8qmDevGS7bN9tyDCvv55QSrjN/nspZLiPLaaoq0DuZLhoUXJ6LVt62y/x2xZkmfhvk5KE79Zny1eW01YvNvVBOu3Gy05beQsKEse4osukouRpmVQcpCRyU3BIdSJRVoYjrrzSPgZKP0x+xlNT/hJqpzYVJOUnod0wKayXSsVUl/xazZmTnE44HFfFSIWkyUaZt8yLysj7ZTgMDBvmLINbmvKa21SoQPKO2DJtqbKZMcOskpM2yPjy+Mkn2+PIeIBdlSz7RCp9001dRrilT3VG46Cf+5/bfcuPekuOg6a0tm93nnv1VXP7l23GS4VmwjaW8Dqz9T9Tf+Lf3ZTGpr9u44jfZxN5vd181PHnSm4L3wGXxg0OPReYbOF5m/5y5S2QrJY32cnTdntGypVn2UOUlzurLOinJr+o27p1K3r27AkAaNiwYYUvrqFDh2LhwoXZNE1RskY0GjUKn7Zv344mTZpkwSIlW/j6T7R9+/aVbYeiKIqiKIqiKEqtIRIB6gTe2i9BtAYv82vZsiW+++47tG/fHu3bt8fq1avx4x//GJs3b3as4lJqF6EYULc89etf05e+XnDBBZg2bRpmz54NIL5ysbS0FHfffTf6cxc9Ss4TUD4ELF261Hg8FAohLy8PHTt2xNFHH522YYqiKIqiKIqiKErucd555+Hll1/GKaecgmuuuQY333wznn/+eaxduxaDBw/OtnlKlqgTjSGcxou6dOJWB6ZOnYrevXujc+fOKCsrQ1FREf773/+isLBQlaa1jMAv6gYNGpTkrw5w+qk7++yz8dJLL6F58+YZM7TawCX3QZbvuUn1+bIOkvwzp+rNmjd3Lp+R+Fnek6NLBqg89YYOTRwzLXGxLUeTy15SwRLvu4ULcfiCBckn+LIg2xKsbGNbfiSP+U3DLaxXP5LLnmR6fpfY+ekDftoOT9+03JUv+aHvLVoAAwbEw5SV2Z1282VWXnWeSlsxjR+2ui0piTuf91ryJ4/RslV+TpbF1O/48izbMjWehuna2uwxLT2jMHLzDjke28YOWQ+mJXL8nI0HHgBWr07Og+zq0iU5jlsb4vFpuStfSsyX//pZiubW/k3nqQ5Nm6LYlgfa2iAPV1rq3HiEX0Mqr1ufMC1XNIWh+6+pr8g2zNOx5S/ttI3ztr4tbeHjiyk9rzZrgl8X+d3Ul0z279rlfE7x08ZKShJL5iOR+FJYk22lpfZr63ccNI3VZKtprJBx+RJOCe8LtmWb/C+/nqbxbsgQ56ZIsgz8N+VpW/pqurZky/PP+3t25Zv8yPuVDOtmr8l2GS8vL9ntAi+nbI85QG1W1M2ePRvRQwUYM2YMDjvsMKxatQoXX3wxxowZk2XrlGxRNwqEI6m/bEtHjVcdaN26NdavX49FixZh3bp1iEajuOaaa/Czn/3MsbmEkvsEvsu99tpruPPOOzFp0iScfvrpAIA1a9bgrrvuwm9+8xs0a9YMP//5z3HrrbfiD3/4Q8YNVhRFURRFURRFqenU5hd1derUQR1W+CFDhmDIkCFZtEipDoSisbRettX0F3VA3GfjiBEjMGLEiGybomSRwLeGG2+8EY888gjOP/98NGnSBE2aNMH555+PKVOm4LbbbsNZZ52FadOm4bXXXqsMe6sfttlKAJBqKpNKgGZnuRPycDg+00z06ZOchskGP7bSjORxx/mLUx3xUnrIcERlzb7yGd5f/rLi8OGXX+7MV87ou8n63Wb2s4mf/KW6I8jst6kf2eKZNhLgLFkCLF3qnS+fqQ+CSUFmU1N0756s5gKSHeVLtUx+fnJaPB+bYtCkxgOS0+M2mPCr8pSqFK8NM2x58b8SmwpJ1gPZYioXVz1JBZRJLSXz5Gqal15yhs/L83bobkJuOkTpEWxMMdpqimMLx9VntnB+/0mi8so2yO9plLaXitWtnk3hbcdN19ArPzlGcTWgG6b0vVR4tv4uw7r1Abf+zcPIdhUOA/Pnu6dPNvKy2VT8pnGO8uabFtjg5aA8uKLOdD1NdviFxj7Tc5gtT5vd/LvJRtsGKDZ1MoU75xy7bRKucnMbe6hPSuWeyW5bnZgUbjINwNnnve7jvNwyfdP4lGMqOk5t3kwCAP72t7/hqquuQo8ePSp2tHz22WexatWqLFumKNnhu+++q/i+bds2TJgwAbfddhveeeedLFqlZIPAL+o+++wzNG3aNOl406ZNK3Z9/dGPfoQS/qJJURRFURRFURRFUQC88MILuPDCC9GwYUN8+OGH2L9/PwBg7969mDx5cpatU7JFnRgQLo+l/KkbrZmKun/961/o0KEDWrRogU6dOmH9+vU47bTTMHXqVMyePRvnnXceXuKTxErOE/hF3amnnorbbrsN3377bcWxb7/9FuPHj8dpp50G5Xy+nQAAfbxJREFUAPjvf/+LNm3aZM7KmoacZRw2LP7X5AfFNDtOM6Bes7smfyde0Cxmr17+41Q3pAoxqBLKzceKG1KFYaKgwF++XvnbZr6DljUTyDw/+ihYXJuyxZS+m8rBpOAwEWTG3aas4edsSiUvuwnqn1SG/Hz3euFqLt6euJqG20H4VQHJcrjZLRVYtrzlNSotjfs/suXNy29TZ3gpYnmedG7xYnflnU1N40d5Jdmxw26v/B5UBeLVX7zUJaSgkeU1tTs/dUGUlCRd9z0z5pnrNRwGxo41q25sah46Tvc/nh732eV2bXi7koom6eNOhqc6M91XuNpVltXNJq6mkmo1iZuq0+2ZwRTXVMdnnun8Lf+WlDjzNfVBP/cgv+O99AVJirr//MdcJpkGjY9jx8b/0hI6k402BbYsk03NJa8/j8+PlZXF/fWZoHzcFMdu7ZrDFcHUdk1Qm3a7duncM6VNlJfX8xk/LuvDFM90/8sR0lHT1fQque+++/D444/jySefRL169SqO9+zZEx988EEWLVOySZ3yGOpGoml8auaLuvHjx6Nr1654++230atXLwwYMAD9+/fH7t27sXPnTvz85z/HAw88kG0zlSoksIb8D3/4Ay655BK0adMGbdu2RSgUwtatW3HMMcfgz3/+MwCgtLQUv/nNbzJurKIoiqIoiqIoSi4QiQChUOrxYzXznQQA4NNPP8U5tOSb0bRpU+yyvfBWcp5QLIY6afiZC9VQRd3777+PN998EyeddBJOPvlkzJ49G2PHjq3w43jDDTfgTJpwU2oFgRV1xx9/PD755BP8+c9/xrhx43D99ddj6dKl+Pe//43jDvk9GzRoEIbyXThzCT+KFHlz8fIBJdPlvkfor0114jdNk4KlhrLfz9bUfmel00XOUksfO4D/WWzbbz9KvsrCpDi7777kcLNnA8uXJx+3qby8poFtyjauKuHnhwwBHnkkOa7pt1tefpF9y1QWk+KLx5PjAp1zGy+4us1LQSdtKiryp7ojhA+3b+k/CVO7Jrvo765dwKBB9vJK1Q4fs2zqLJknV7eYFDG2OLJ8JpWZTf3qpvLbtcsZxqRms6Ut8+GsWeN+3q/N8lrw4+FwvH107uxum6FvNR1TZM4/Ekn4WvXTX2zY+ha33WuM9NP/uJ8v07Vys8OWr59zNsWl9HNG3712t7XFBZz9v2fP5HjSV67E1j9NdtqwqeT49zFjkstq2uW4sND5+6237PnKsY/avLTNNEb6eZ4wPVtxpSKvW6kgM/m0k8j2J/2N2mwkBTdPY8IEc1hedp6+16oNm0pRqmdlXjaFKh+fxo6tuue5LFGbFXWtWrXCpk2bko6vWrUKxxxzTBYsUqoDoWgMdcqjaX1qIjt27EDLli0BAPn5+WjcuDEOO+ywivPNmzfH3r17s2WekgVSusuFQiH069cP/fr1y7Q9iqIoiqIoiqIoSg7z85//HDfeeCPmzJmDUCiEr776Cu+99x5uvfVWTLC9UFZynriiLvWXbXVq8FbIISGvlb+V2oWvF3WPPfYYRo8ejby8PDz22GOuYceNG5cRw2octllit7BuM/8SOdPPw/qdVfRSjNQQGgTV+We6rCNHJr5LlZdbXibFifwt1Td+lD6VgZ82xW3ZuBHo1Ml53FQffhQq+flOxYH0r8NVUPQ7Lw9Yu9a9PH4UTUGVP9wXD/cFxJHXlT78mFRmmHwYybozKUTc7M3LA4qLk+PY4PUVDuMIm0qa7C8oSE6f52NSEHJ/fbIMpnqUmMZF2f5MKiHKJy8vXtem3XX99jUKZ1M6lZYGV8XyOpk2LbGD+Jw5wOjRyWlwBcrAgQmlEVclcQWjbHuHyrv7k0/QrHt3c/koPa6CzMtLvoYyrES2A369pM8ymT//bRoHvFRtciwx2eXnWpnuvW5h3cZwW9u3pc2vgcSvGg6I7wArd4HlalSv5wu3csj4tr4kx0W5e7RsuwRdq7w85zg4d25851Q3RZppxYOfe4Kp3/C+YBv7AXdlmVcbsrVv6XvO63rQWBgOA5s2eav2uH9UOQZ4qTrz8hL17GUb/8vjE9u3J2yRfT5HiERq79LX8ePHY/fu3ejduzfKyspwzjnnoEGDBrj11ltx/fXXZ9s8Ralyhg8fjgYNGgAAysrKMGbMGDRu3BgAKjZbUWoPvu52U6dOxc9+9jPk5eVh6tSp1nChUKj2vqhTFEVRFEVRFEXxTRSxWDoKoJqrHgKASZMm4c4778THH3+MaDSKzp07I19Onim1ilA0hlAairpQGv7tssnVV1/t+H3VVVclhRlGG1QqtQJfL+o2b95s/F7rsc2Emnb+NM10S4WDLUxZWUK14DaLLm1zmwnPlRlJW1mCKM9sYYuKEkoWIKFSKS01+8sxYdoh1a8Kw++1ziS2tuRFXh7QoQMQieB/06fjyMcesyvhZLqRCGKhEEKxmHmGnavV+LFwOL47oJuKxKsMXooyr77ClRW2NKX6ghRzXInBw3ElmFQumOrP5dzXoRBaHZpu3//002ggN/mRihSObXyy0aEDsGWLPV2TqkIqovhupSa/SDblhdsYYLOd6tamyguiqvM73vpJT+br1f9k+cjXGi8/tTeeHtlcWlpxz2oWi9kVw5Q2UVBgLpdUUkn7pWIWSFx3r74kj5sURSefbPdVZmsLlC/5GbTtzioVbzalkA2v6++mupLXgZSatvLY2jwxcSLQsWNyvlx5RWnZbDL9tvWbefO87aR6pTZMzz4yPzpGYbxs5edMdnv1N5tqTcaR7d10z5J5mFSUMp7pu6kPmGxyU635bb+mXVmlPba8eX93u5+aymmyPVeeXRUHjRo1Qnep5lZqL9EYkI6fuRrqo+7pp5/OtglKNSPlO96BAwewefNmHHvssQjrjVNRFEVRFEVRFCUA5Yc+6cSvWYzkE0IuzJkzp5ItUaojsVgUsWjq7ToWq3l9QlFMBN71dd++fbjmmmvQqFEjnHjiidi6dSuAuG+6Bx54IOMGVjvq1nX+jkTiPoFMs708jOk7x202+vnnE+dMM4smf1YSqeqobGVWTcRLzfHQQ/G/QepuyhRzPhKvGepsQvXipVo4pD488tNPE+G4zyFbGUtLEeL+z0wqBItKIXb88cm7JNuUW9J2P0o8P32FVAM8LVKH0W+TQkemS7+5+knG4zuVFhR41lWrn/yk4nuDVq3M5XMrl7z2xOzZznCRSLwOpAKQK7BoB1CeN28TXIUnVT28Pmw2usHrxs8Oi6nAbTEpa0zt0+Yqwk15IhVzUn1ouk9wP4q2/EzlOUQsFAJefdU+Fpj6iU3RSG166dLEMd7mLTYkpSk51F5++NnPzGoikw08XapTrqSVOxXLtGS5vfoT/ZV9RNaVm9KJ75Zp6v/0VyrNdu3yVjOZxhnTPdGmOpPpye+mPLntPC1TPNqp1msXUhuDB5sVtDa7+G8/KlfDvQ2DB7urZE1KO9szgtu9zW2HddMY6WfM9LJXwvsVjVM2paDfvKXNtrZRoynPwKdmMXfuXKxcuRK7du3Czp07rR+llhKLIRYtT+NTMxV1iiIJ/B/KHXfcgX/+85946623HLu+9unTB3fffTduv/32jBqoKIqiKIqiKIqSe9Q+Rd2YMWOwaNEifP755xg5ciSuuuoqHHbYYdk2S1EUpVoRWFH30ksvYcaMGTj77LMdWwZ37twZn332WUaNUxRFURRFURRFUXKDmTNn4uuvv8avfvUrvPzyy2jbti2GDBmCv/71r4jV5G1slYxAS1/T+ShKLhD4Rd23336LFi1aJB3//vvvHS/uahV+lk7xJSVyKQUtESDksptBg8x5eS3hAIDRo+12FhV5251r2JYyBlkO7HcpEB33Ws7C/7rlURX4WQ5KyOVYxOOPJ87zdmxymg04N+fwWiIjlt6FLr/c39JUcrxO+Flm5xXeFlaWwbSsiZaJSmjJJy3npQ1MTDbIpXkyTDgMnH124ne/ft5L1gDgkUfs52yQE36CysaXKZkcksuNWfxcf5mHXztN15cvC/OztM22zNZr+W1+vr8lun4xLZ90g5aWmuoa8GzfoREjgEWLnMve1q9PXm4p0zXlRe2Wb/jgtpyUY1v6x+I2HDHCOx53T+Fmv2xjcumvCbflum5xbWOvyTbTsnDb0kc322w2UFqmJcl8ebApT35Ngi5RtJWd2i39petn2+TAhOncxInJbc9PPUps9cqX8vNw8nmPl8ttaSkdHzfO2Rbob+vWyWFNS1+DjkO2pfVebdXvEmVpm9sGRzlJNAOfmkeDBg1w5ZVX4rXXXsPHH3+ME088EWPHjkX79u1R6selj5KzxGJRRKPlqX/S2kVZUaoPgV/UnXbaaXjllVcqftPLuSeffBI9evTInGWKoiiKoiiKoig5S+3zUScJhUIIhUKIxWKIqn+xWk8sFkM0Vp7yRzeTUHKFwC/q7r//ftx555247rrrEIlE8Oijj+KCCy7A3LlzMWnSpMqwsfpim/Vjx78nlaGczeUzjR07+leE+FHEEDbnw7nijNerzrxmpVNFOiInTPW9a1fF16+ffTb+hbcbqaiTs+CmPCobmyKEzpns4mXiM6F+nEi7KQhMNkj8tGk/9SbL5scxvHTY7lcJJMNLhSEQb2ekHLGpbaTSQYbdtAkYO9bdHpvqwaYMNDlMp3ZOyrFIBNi40ZmWHC+pvgsKEuly5+NeTta5OpljU43Rb1M9yTZOfdlL6cXPy3FBKt5M6lk/bcwLU93abJfKVa/8KA1qk5TuvHnx60b52tKdN88+ZkyblkiT//XCa1z00wfpWr35ZuIcpcs3hpGKOt62SP0k7ZL5c3WpzXZ+zpYOP25ThfNwcpMK/tcN2R/kdz722VStftq1rE+/SkwgMWbIzWts+dKGDpzSUuCuu5zHvOqWf7eNLdIeaaOMy6/9oQ2ZcNddwDnn2MtEx2ijMaJTp+SwfGMQef2kLZTuO++4l9PWjkgBLvOylUPC1ddudZZzRJHeS7qa+WJr//79WLhwIS644AIcf/zx+Ne//oUZM2Zg69atyJeb8im1iriiLpLGR1/UKblB4Bd1PXv2xLvvvot9+/bh2GOPxYoVK3DkkUfivffew6mnnloZNiqKoiiKoiiKoig1nLFjx6JVq1Z48MEHMWDAAGzfvh1/+tOf0L9/f9SpE/hfU0VRlJwkpSmqrl274plnnsm0LTUP6XvGQOM//AFYtSo5Hs3Wh8Nx31Hz55tnIKVaiJQAbv6U3GameR65oKoD7MoDOuemOKTwQeqCKxXotw9affqp/zyqk+pR1F/5s8+i7rx5wJIlznBLlyba67x58WMFBfHZce6rxzZDv2CB+bytHkhhIdVDElJeSQWbV/3a1I2yfbnZZ1LemdLnx8rK4vVmUgqSksSP2pJ+33RT4hhXRhG29uvX55WpbJEI0KWLM5xUWkYi8fJIf2H8rxsmf5+2uF6qS5MKxjajb1PV2dK3KXBtabnZZopjSlOG4cpMk3rLqz+YzvO2TT4Xed5eSrnLLrPbzRVApjxlOD9KNZM9L72USIfaMlcHeiko3fq/RPpZk7aYyiW/S6WnfAbhZeXtzqZuMpVP5m3yU+fVP21qLHlM2izz4c87fscE/lfmy9Mg1ZZss273c94+CKkek/VHYfhvnoZ8ngCAY45Jvkayruja8HugCRrPyE4+rtnubY8/7syX6nPx4rhC23ZNeB2UltrvVzZ7TQrNIPeEGkvt2/X18ccfR7t27XD00Ufj7bffxttvv20Mt0Q+Zyq1AtpMIuX4qqhTcgTfd749e/b4Cte0adOUjVEURVEURVEURakd1L4XdcOGDau9GxAqnsQQRTQNP3PRGrocXFEkvl/UFRQUuA6qsVgMoVAI5eU174YRCD/l4zN/y5cnKzOWLgVGjkz8njHDrCqR/lts/mb8KD9MM+U1fYbSzX43ZZabOiQoXqojnu4DDwBz5tgVGEHUZFmk7tCh5hPLliVUbsOGxZWkph1fJaSaeOml5B2QCa5akAoA6QdIxrf57+J22Wwz5cmRCgdTelIRI5Uupvzpe3GxM22pnpAKEVnuSASYMgWYPTsetqTEXhcSU72Z4GokWV5uo/TNSG3FFNamUJO/S0udO+PyMFI5lpdnVna6XVu/yiFpP/8u1YQ8jh+Fn598bcouqkO6R5gUQbZ64G3fljbvc1zlM2+e8x5nUt7Ydr70o3AyqY1MyiRTeUz2mMpG5eJQn3JTtttURlxRZ2sDvFxcJSfrwXRdpDKNwmzYAHTokNzXTeOQl8pJ2uulJjeNc/ycW748fZudXopLbrNJBcbHLZud0l7TPUjmRXAla79+wNq1znLZ0rClJ9v78OFmX3P8N1c2RyLxe4Cb+s4E87Xry1Zb/ZjqkP+l68HDyXtBTX9uVQAAc+fOzbYJSjWGfNSlHD+NuIpSnfB9x1u5cmXF91gshv79++Opp57CUUcdVSmGKYqiKIqiKIqi5C5RpLchhKqHlNwi/qIuDUWd7hys5Ai+PXaee+65FZ9evXqhbt26OPPMMx3Hzz333Mq0tXrwhz84f5tm0fls6eLF5pl8PnNJO33ReTnDyL/zeF5qLqlM4DP00oaaiNvMahC1TNB6IHVM//5xhZwtH5k+hQ1qZ3W4TtzOli2Tz5OqzU0RZ/tN7VSqgmzH+Xk/s+tS0Wayh8JJeN80hbep8Gzp2vz68GOkKAiH40oGGZ4rDmS+/DpRuNmzk+2S6hsTXJkDxOvC1s4jkbittOurVPlS++C/qZ1wlZu0TZaNp0H+j3gc02+TktZL1Ua2SX9OJigv6YOP21BamqgbP+OWG/I6U7lWr3amIVVpJjWmrV/xv/y79K1lU3V52W9SFNnapFTk8Dy9dtSU5fBjJ79Pyrz5jsxeyippg5fizo/ij6tCpeLOpjr74AO7qtikeHa7FwHO8Ylfx1277Konv/Vkak+29mL6bbun2/Inv6hu2MZsGcZEfj7w6qvx76Smc7PH9Nt2znR/MNmxZUviXF5efJWHrY+Zrl8kYt7JnYd3U/3x7273GsK0O7hbfjlBOju+prtsVlEURamu6NY6iqIoiqIoiqIoVY6+qFMUTiwWQzRWntanMhk4cCDatWuHvLw8tGrVCkOHDsVXX33lCLN161ZcfPHFaNy4MQoLCzFu3DgcOHCgUu1Scg99UZcOfn1mSN8yMj7/TjOd+fnO9AsKErPpfGZbpsPzCYcTu29SOFKIzJkTV/vVZKiu5AzsnDkJ9ZptNtzPzCyvOxkfMCvHZH37UVyZzmXLH4uX4gcAHnrIHO/QjP3X99/vPE6763Hlh1TmbN8e/86Vn27+icLhuBqCKwrc7C4uNiuIbAoMIKGmc7uGJr9agF0VYFLqmNIkdaL0qVZWlthtV7Z9026Etjz8qKB42lwNKuuMdqgtKUnYlJ+f6AsUTvrQk4o9N4WS6RoXFiZ+cx9KVA/kD9K0O2dpqVMBxM8tXpxoK9JmaQMpq7jvMlPfpbqRx/k4ZforwwNORROFzcsDPv88EYbUe4RUXJWVofzZZxPtTN4rKF95TUx2yZ0avdRGZMu8ecB//pM8HvN2zXcs5nlIX3G8H3IbbPab2p7E1IcpHo1ZfvoRV4Xt2mX2icbj0nl+DXlZpapPwuuQ+8Xjv8vKEuMy4Ow/8+cnjs+ZY37OMLWNJUucbcJNrUvpmcYEstdUxhUrzLY89pg5PtGhQzzckiWJZyAaR70Ubbx9Ul421Ti1Tdk3/vMfp39aUgOblIz09+9/T86D8p05M3EPpLj5+cA77yTCUjnJJl4W22oMynvePG+lngm6DlQHZWWJtmbKh6cficTHXt5OIxHsffFFd7W+oig5B/moS/UTq+QXdb1798bixYvx6aef4oUXXsBnn32Gy9hO9uXl5bjooovw/fffY9WqVVi0aBFeeOEF3HLLLZVql5J7pPUWQHfsURRFURRFURRFSYXat+urorgRi6W562slv6i7+eabK763b98et99+OwYNGoSDBw+iXr16WLFiBT7++GNs27YNrVu3BgA8/PDDGD58OCZNmoSmTZtWqn1K7uD7Rd3gwYMdv8vKyjBmzBg0btzYcXwJzVIqiqIoiqIoiqIoFqJI72WbOs5XcotMbSaxZ88ex/EGDRqgQYMGadkm2bFjB5577jn07NkT9erVAwC899576NKlS8VLOgC48MILsX//fqxbtw69e/fOqA1K7uJ76WuzZs0cn6uuugqtW7dOOl7riESAyy5LXh5hws2BMB3jy3FMy9rk0h63/ABg3LjkcFW9nLKycSuPafkN1aEfx9du6XqFD1LP6S5TzBR+luWamDmz4mur3/wGX5PaVi5Ns8GXUpnaqfzutlTQdL0LC/05DQ9y3Au39iU/tBxJts3SUuc5Xo9yOZFcKuzXdlObKiryH5/Iz49/pIN/ucyQ50tLmuS4JuvOtMx948bEd750z9ZHDMs/AcTrkS8DlHG80iXcNp4wlc2UDw/jFl6mzTFtdGBIp24s5p6uaYmmV96m/Gx9FwAeeMB5XOYzbZqznZvs4MssTUsyTcjlw0A8H2qLh5ap/vDaa854fGyi5dM8TRNkWyRi3hyG1xPvw25l4WnKY/Rdjrnz5iWOlZbGf/MlkYc4+MMP5v4ql6OaynL77cl5yzLQZkS09JUvFzctr5XHObZnIRl++/bEOVkvtuW3krPPNoeT18CP3aZxxauPybJKtwhuzyHkJsDUxmy43X9N3zl0beUSaxnGZgf73eTyy5PDpXpPrrZEM/BRFIVz8ODBpHcU93PXPGnyq1/9Co0bN8bhhx+OrVu34s9//nPFueLiYhx55JGO8M2bN0f9+vVRXFycMRuU3Mf3f/1PP/10ZdqhKIqiKIqiKIqiKEotJYZYWn7mYrFy1KtXDyUlJY7jbmq6iRMn4p577nFN9/3330f37t0BALfddhuuueYafPHFF7jnnnswbNgwLFu2rMItmMk9WCwWU7dhSiB0M4lMMGOGeQbaDbdZcpr15OoDUg0civv1n/4UzEaeTq7gpWB0i0NIFSNXSNgwzep6qXFSpapnjk35+VVvHnJY/t1vf4tW/+//JdKjzQbot5vqwE0Zx22QqjuTgsRUBjf1pN++IZUstrQpPe4cm8KZ1EO8v5NahStpTXbwPPkGNATfIIBvCCHzNR2TYxDPl6AySkffJmWczQE7LwelY6obEyZllF8FGlcXmdL1yluet6kBeTibikXa79XP3OLz89wWP31ryJDkc7ZNC7hy0mssljab+pptcx5TmnTN5FjgZxw21YUc4w59GpKax5ROcbG57ZigTSRkXlIZZ2rLvF4KCxMbEUiCqLTkBhHkBDsSQT2ptHTrX/z7KafEFZAmu3h82jgIcCqGifx84PnnneUgG845x718trGYh5X3eA/lKWbPTtzH3OKZ2qHtniSvt9v4Je+dtnC2DYzkOC7vKzwd2xjOj5eUuLcvkx1uY5/EpBD0+wxSY9FdXxWFk6nNJJo2ber4uL2ou/766/HJJ5+4frp06VIRvrCwEMcddxwuuOACLFq0CK+++ipWr14NAGjZsmWScm7nzp04ePBgktJOUdzI6ou6d955BxdffDFat26NUCiEl156yXE+Foth4sSJaN26NRo2bIhevXrh3//+tyPM/v37ccMNN6CwsBCNGzfGwIEDsZ0/CCLeOYYOHVohfR06dCh2+XkhoyiKoiiKoiiKUinoizpF4ZCPunQ+QSksLESnTp1cP3mWyZfYoQmu/fv3AwB69OiBDRs24Ouvv64Is2LFCjRo0ACnnnpqCjWi1Fay+qLu+++/x49//GPMmDHDeP6hhx7CI488ghkzZuD9999Hy5YtccEFF2Dv3r0VYW666Sa8+OKLWLRoEVatWoXS0lIMGDAA5eWJTlpUVIT169dj+fLlWL58OdavX4+hQ4emZvRRRzl/h8PAFVfEv9PsIp9lHzLEGVYiZ4DD4YRSgc+E0mz8odnKVrFYcLWVyadKTSYVdSDN1EolQxBfVBR//Xr/cUxpmPKtTj5YvGa2TZSW4vBHH8UPf/tb/LftGknFkl8fdmSXyU5bXnl5Cf9hXn3Qr3rK9psfl+pB3tZMqjxWvu9eftk9f0pXpuGlBDMpn/yooDp3Tg4vVVxuvohMqiyT/0KuvDD1Sds1dlOkcTWy9PVJSieZX14esGCB8zipE21tgMpj89FniivTkAoxiu+GSYEn87MpaEzpL1jgjCOh49zvl0m5x8vhpv6xtUWbitSGrW+b6lT2R7KHxgn662aDVIK52UDKMT8KRN5W+XMAHTOpnkzxy8qS1WMy30O/y3/6U3s5pJKMFMLyupaWJnyBmvyg3Xtvsq2yX1MdFRSYVdJeykypFOTYnn/83rvJb6cpTUpHtjc/ijq/YwPP65FH3PuXjE/n8/Od+ZCvQpkXkDhu6rvS5sWLk/MtLDSPrfK3bfzix6lOpU/FnFHX6Ys6ReHQrq+pfyrPb+OaNWswY8YMrF+/Hl988QVWrlyJoqIiHHvssejRowcAoG/fvujcuTOGDh2KDz/8EG+88QZuvfVWjBo1Snd8VQKR1bvcT37yE/zkJz8xnovFYpg2bRruvPPOih1nn3nmGRx55JFYsGABfv7zn2P37t34wx/+gGeffRZ9+vQBAMyfPx9t27bF66+/jgsvvBCffPIJli9fjtWrV+OMM84AADz55JPo0aMHPv30Uxx//PFVU1hFURRFURRFURRFUWocDRs2xJIlS3D33Xfj+++/R6tWrdCvXz8sWrSoYmlt3bp18corr2Ds2LE466yz0LBhQxQVFWHKlClZtl6paVRbH3WbN29GcXEx+vbtW3GsQYMGOPfcc/H3v/8dALBu3TocPHjQEaZ169bo0qVLRZj33nsPzZo1q3hJBwBnnnkmmjVrVhHGxP79+7Fnzx7HBwBw6IVgBeEw8NZb8e+GHdQAJGYGpT8b20yonE0EEjOzXK1hmz32UstUB7VWJvBSCNp8PaU6oy7T2Lo18d1mQzjsnLG25RkOu+8amS14WySljY1D7fngjTei4ZVXxo9Rm5d9w6SWclMQ2dSPXFFh8xdFqhQ3tVmqM/PcZpNKwKTSk6ox/veQrYdLP1Gy7JGIfbfCIGWxjUHS312nTslpy7Jt356sIOFKCJOfPjebvK6RVBoRbkrZe+91v2bp4Gc3RdtYIdUxbgpfHkYqTQjuu0+qFk35emFqz+R7Uu7KKq+JSe1oUvXZlIfU9qhcXKEp69qv4k62Ma5i5/ZISDUaDid20vQD7Yhsu/fIcnAlHb/efKdVqbYz2bJhgzk/Uc8VOwCb0rL1F54OAHTsmPA/Z7om9JwkyyiViZEIcNVV8WP8eF5eYsdYU/6m8pnOmfBSp1nqreIvKSbpWDpjsOn5EXBea1mXcnyV6diU1Ca1pAmpNjYd57Rs6awT0zhrUiPa0q0uz0OVhirqFIUTi8XSWvYaS2Hpq1+6du2KN998E9999x3KysqwefNmzJo1C0eJFXft2rXDsmXLsG/fPnz33XeYPn26q488RTFRbV/UkRNG6XTxyCOPrDhXXFyM+vXro3nz5q5hWrRokZR+ixYtXLdIvv/++x1bOrdt2zat8iiKoiiKoiiKoiSIAYim8YklJ5khBg4ciHbt2iEvLw+tWrXC0KFD8dVXXznCbN26FRdffDEaN26MwsJCjBs3DgcOHKg0m5TcJ770NZLGR19eK7lBtX1RR8htjP1sbSzDpLJF8h133IHdu3dXfLZt2xY/YVM38HNBZgo5UlVAx0gxIdUIbv5YTPnRro+54teDz+yaVGtumGbQbQo8id9wQeNzZUp1u0Z+1Vqlpah36aXx73l5Th9JNj81vI+Y1AUynNwhUKYfRE3hpoZ0w48ij6t1TIoJrpAwqWilWonSkvabbOAKzWHD3Mti4vHH42l36ZKsdqF+N3Om87rt2hXPU/qjMynqKC2uDJTqIRM2/1zSRnlc+juiY7SDock2Gc+ELZxJvWUrV506/lQ8Mj1bn7EpCqVdpt/ynE39Rn9Nqjb6zvsmHbOp0Hgfl/U0fnyyTbzspmts+i3LBgCTJyena2uH1B/pnPT35Tb+cN9ypmtgGit4/dIxvoM2xbVdHz5+0DHe1yg/U1xpu7Sbq7yIgoKEX0dTm+jQwZmOqd9ReTZtMvdJspfuK26qXNsxk2LNrxqO90n57MDTkvXthuzPXnkDcUWdaTzj8OtG/iSlvTKu33unWz3xe9mWLcnpe+XB/dqZ6tnv87SSEXr37o3Fixfj008/xQsvvIDPPvsMl9Eu0QDKy8tx0UUX4fvvv8eqVauwaNEivPDCC7jllluyaLVS04nFoogdUsal9NEXdUqOUM3eBiRoeWiJQ3FxMVq1alVx/JtvvqlQ2bVs2RIHDhzAzp07Haq6b775Bj179qwI87///S8p/W+//dZ1i+QGDRqoRFVRFEVRFEVRlEoi3eWr8bgVLnoOkYn/Y26++eaK7+3bt8ftt9+OQYMG4eDBg6hXrx5WrFiBjz/+GNu2bUPr1q0BAA8//DCGDx+OSZMmqeN8JSViiKaliqvMzSQUpSqptoq6o48+Gi1btsRrr71WcezAgQN4++23K17CnXrqqahXr54jzNdff40NGzZUhOnRowd2796NNWvWVIT5xz/+gd27d1eECQS/6dFs3sknu8fhs882Hyd85tA0S1hS4q7a4JhmO6ubQisTeO366qfMfDbbNuPuhYyfKn5sqOzrmE765IPJNOPtpiwgpYSMZ1MJee0Qa4KrvPzsOJkKppl/2b+B5Lrg40JZmVP1In1xcV9/bioMm+8qNzUPJxqNX8/Ro/2rPcLhZJ9HhJdizDQe+vFT5KXOoHRsY69UJ2cCk6LIZu+4cQBTJxiVq6Z64Go12l3RloZUXnup7CThMHDffXZbbMo7SpfvUMl951F8UpvJ/m6rP67EMo0TpjK5KRBlOJOSh6B8SVknVYNSPcttsh3z6sP807KlWW3kphZzw01RR9/ljtk8D1b+8oULE8dJ8cbDkrqKp0P28+sJJMoux0pqhxs3uhbLtR5MvoL9jv0mZZdbn/UaV0zhbM9u7PjOvXuTx0qZl8mvsSlvWxlMNkyenKxANEHPq6WlznZsg5/n12fOHGe7CTp21Rgy46Oubdu2Dpc9999/f0at3LFjB5577jn07NkT9erVAxD3A96lS5eKl3QAcOGFF2L//v1Yt25dRvNXFEWpbWT1LldaWopNmzZV/N68eTPWr1+Pww47DO3atcNNN92EyZMn40c/+hF+9KMfYfLkyWjUqBGKiooAAM2aNcM111yDW265BYcffjgOO+ww3HrrrejatWvFLrAnnHAC+vXrh1GjRuGJJ54AAIwePRoDBgzQHV8VRVEURVEURckSmVHUbdu2zaFgy9SqoF/96leYMWMG9u3bhzPPPBPLli2rOFdcXJy0Oql58+aoX7++qx9wRXGDNpNIOX4lbiahKFVJVhV1a9euRbdu3dCtWzcAwC9/+Ut069YNEyZMAACMHz8eN910E8aOHYvu3bvjyy+/xIoVK9CkSZOKNKZOnYpBgwZhyJAhOOuss9CoUSO8/PLLqFu3bkWY5557Dl27dkXfvn3Rt29fnHTSSXj22WertrCKoiiKoiiKoigZpmnTpo6P7UXdxIkTEQqFXD9r166tCH/bbbfhww8/xIoVK1C3bl0MGzYMMbYzfSp+wBXFjfhmEuVpfHTpq5IbZFVR16tXL8dgLwmFQpg4cSImTpxoDZOXl4fp06dj+vTp1jCHHXYY5s+fn46pPMPkYwUFcWe9pjBcmi+X90j40hm53KGsLL6cxOSM22tJgfxdmxzwbtkCdOqUfDydOvC7rI3CEnPmACNHJsLKJYmmpShVDV+GxI/5XUYsl/2ZjtGSGLd0TEvPbEvXZF3ddZe5TKWlyUv18vMTy3P8OP7mdngdN/0uK0tsrMCXMHKbTGUzLc+2LWmScU3H/Cy948v6OLZ6kmPWO+8ktwMTpo1y5LIy+i77DC2Ps6XN0yspSXynZWKFhc5lhAT1AdkubMuv3JZ4cxtsS0dNcWzLGWX/3L49viTSBq/bsjL78mRTvLKyeDsg5/Wm5Yi2cYzqm/PII/HlvtwGPj6YNh0xYWofNpt4Hjan9KZymNos9Vu+RJPaktv9PT/f6STfzVayjbfrvDznRi2y//I2Zhs/eV6mZYaAc/mkaVmsaRw6RF3aRMhkG7Ujky1u9VFa6ty044EH4vdRvkmRadmoqU/Zykz3AArjFpcjN/6gv+Gwdxs2LYn1qg8WpznVNQ9v2pgDiNcVT3PFivhzSJBnQarvtWudfcBWV3zps6lf0XfbElr+m555Zf3MnOnP9hpBZhR1frn++utxxRVXuIbpQJu/ACgsLERhYSGOO+44nHDCCWjbti1Wr16NHj16oGXLlvjHP/7hiLtz504cPHjQ1Q+4orgRi0XTUtTprq9KruDjv29FURRFURRFURQls0QPfdKJ7x968ZYKJK7Yv38/gLgf8EmTJuHrr7+u2PhvxYoVaNCgAU499dSU8lCUGGJp7dwaU0WdkiNU280kqi1ytjgcxt5f/MJfPD8qLK62AfDtk08mZi/ffNM84xtEUWfKM1eJRIC//92/IozH84Kn5zf95cu90zGdD2J7uvhVqNgIh53qHumc3U98mR/FX7zYOQs/ZIjZXpMC16SW4mnz31622cLbnIpLFUFJiVMpw5U2Uk3CnarT+cGDkxVCMl83e2QYSnPFCnN5+XfZHrnNeXnApk2J8vB2YFNe8PZhslkqCG3Xhx+XqiCymfvLkW3AplpzU4TI/Hh5pPLMpAjhx7nazw88bWobQHzzD65iMl1vN8JhYOzY5ONnn52cNyldTApcwKl6kteHFHU2FZCbnanEkch6JtWWaaMX/p0r6NzagSk/StvUZ7mii/4WFyfafWlp/FNQYFcL8rSpDcqNRLyUXqbxjDYJMoWxpSHVUlwpaerjtmtYWupUDpuel0zqNJMqjdKQSk8/93KbUs+mXDSlzZF9w4+SVI61/Jq2bOneL/h4FsROnjeF86PEk+2Oj1EyPzlOSVtefz1ZhRcOA6tXu9tQo8jMZhKZZs2aNZgxYwbWr1+PL774AitXrkRRURGOPfZY9OjRAwDQt29fdO7cGUOHDsWHH36IN954A7feeitGjRqlO74qiqKkib6oUxRFURRFURRFUQAADRs2xJIlS3D++efj+OOPx8iRI9GlSxe8/fbbFf7v6tati1deeQV5eXk466yzMGTIEAwaNAhTpkzJsvVKTYaWvqb60c0klFxBX9QFxaAyaXLBBe4zznxm0zQL379/st+bQxxBPvzoGM0wu2FKa/hw9zg1kdtvj//18i3mpSoxqXmGDfNvh82Xlw3bzLlJgRNUDZhNbMok6aOLqxsO7eBs9PXjRV6eU5lgUz1I5QZ9bD6TJF6qR5vSSypsuNLKpASxqTW4msym4vGLSxl3XnihvzSkD07+2b49YdPEic66N/UTWbfc35ckEnGqYWRbImzjgexfpFZ0U9AAyeoXWX6bryqpQuE2A3FFKH3neZjic0ztjWwqK3P6aPRSXspzkQhwaMd0Hnbnj3+csMmk/opEkn3f8ettUkJxeB1xZaLEpJw0XT+bmpTqn649z5+3L5NSipRlvO/aFKoSmypT+lcz1QFX2dNyNZuqjtvD1X/8mM0u+VvaJhVxMgzB60Aq/KQtVC6Z75IlcV90Uokl4apBbr/tGQww+6gz9VtbfvRXjml8HAxKJOL+HFFUZG9DgFMtzNMEgDZt7P4T3eqJY/O7bIL7YZXt2RZfPrPya7pvX+K+6WV/jSWK9NR0lbPMr2vXrnjzzTfx3XffoaysDJs3b8asWbNw1FFHOcK1a9cOy5Ytw759+/Ddd99h+vTpGdtxVqmdxGIx3UxCUQD1UacoiqIoiqIoilL1VO1mEopS3dHNJBQljirqgiLVB5FI3D8InyV0m32Us4qmmXmaiaRzixcnnwOcO0babKV0+/Z1L1dN5PTT3WfApR8qIHkm2qbgKiuLK15sGJSV1mttimvKTyoquP+yqoQrVaRqxUs9SCo37kuHn5N1RYoR2R/cVD/83KuvOlUy8rwsE1epmNIzYbqOJpUeD8vVgybFJv02qST8KA5M2BR6bgofVrbmcgduN0WXLX/aqdJUDlN/lHA/Xia1jxx/TeorU9/kii+TysNml1Rema4Zrw/yIeY17rvl6QeTuol+H3ecsx9xeyjeggV2e5Ytc6YXDqP5lVcmtz+Z9+zZdkXZ7NlO23md8evH1YJu7dZtvOC/paKX2yDDm8K1aeOMw9V4vI9TOUxpU5q28lD5eXuWqruWLRNqRjn28O88rlsfdBsbCCoP9UmufrP1T1saVL5zzknuwzKuVE7xeuW7MfN4a9aY45js87pP++2Pss34GU8Ik59U2/0rLy/eX12e8w4uXGjPa/v2xHeq2zlzzGH9YHq+HTjQYU+FAi4vL6724+1bhpPXxa1NmsIripJ7xKKIxcpT/kBf1Ck5gt7pFEVRFEVRFEVRqpyq3fVVUao7sVgsPUVdVPuEkhuooi5dTOostxlbkwKAxzOpjngYuduXFyb1S67NRNpmkk0z94D3jK2c6bWF42nblCSmOG5KEa+8KhuT/x7G7t/9zj0+VweY1A8mRZ2bmoDim1RSJhWbm12Ac4dVkzrKq2ym77awUvVDyD4uVVm83rjayKSa4dfHj2KNws+bl0iT8uCqJ79wNVB+fkJFQWnzPKSPOFMabj7qZJ+U6iHCpFYJh53+vfxcP0JePzmWcAUp2SJ9a7kpfKjupO0uChpHGJPvJtnnpGKXpyVtsym1ZX/m9U91JHfcNUHtf+RI57WnPG0qZwDlL7+c+GFTMfF0bGMH7fLK86HdaHk9yf5VUJAIYxrzTQpoyouX0UvRS32bngnIj9quXclqSZ6G7VnClpfbs4rp+ss6t7VXOQbk58ftpj4o8+N1aRuTuW20ozRd4xkz4Bs5ttjGJS/4eGVSVrphu25+4hjC1xs61K6o5kpik2KYh7X5BnWzRf7mYwJdd6/0+LH8fLviVd5Tc+ZZtnru+qooiqJkl1y5yymKoiiKoiiKotQg1EedonBiiKblZ0591Cm5girqgmJSdHBFBR03hfdKj/+2HV+7NvmY1wylV541GS/lmym8nxniIIobFwVIILtk+ib/hZV97bjijNtyiGbSjxmH+kOfPgkVCKUpyyIVNFIZxVU2tnr18sMlkQoUv0pIG17KSFs9cvu4Ak2qm6RCRY4Nspy2Xfa8bCa/YE895Tzutz3z69CmjdOPFVdTMOXKt08/nVD9AMDZZ/vzx2hSbnjZalKWEH7UszItntfkyfF2Toonup5SGeamto5EnDum2u4BMo5b//DTBwCnT0GC+i2/jhRX7h4pr7NNGSUh1QzFp11vPVTidUeNMvdjt7FD1g+pEGVbojJwRaFpV1ZKj+77sj/K/Ej1ZbLFZBup6UiVTPaWlQEbNzrVyjwNPqbRMamOdFMuSyguV/bJ+0M4HFdGujFnjj1fqZDiNtJ30yoC2zjp1mdM6VOcVO/hsp3Jtu/n+U6WUeK2wzD/blOwUttxu9e5tQdT/druL0B8LKPxkPt1NLV1wqQyB4DOneN/ZVlNfUlRlJwhGovqrq+KAlXUKYqiKIqiKIqiZAFV1CmKg1gMsTR81OlmEkquoC/qFEVRFEVRFEVRqpwo0nvZpuohJbdIf+mr9gklN9Clr6nAt6IHgJtuci47MC3LsC2z4w6aeVy5HImnP2eOM22JyTE3P5dLSwbclnP06hX/67YUK5Vlj/La2OrTz/UhpONv7vy9Kq+XXKIl26cbdC3uuy+RTllZYskUkFwmr/KZrh1958u6+NIf0xIg7jCelrQFXf5qCuO2nMfm3JvHcXMqT3VlstlkC18+2amTu938nJdjbtNSR5kGlaWwMHnMk0tBARzx4IPOuKtXO5eR0VItma9p/ORLq/v3d6bL26Fc0umnb5n6uqn8fAmrtNPPeOG2vN2r3dk23uDn+TXh4fmyumHDnNePp8X7suxLZ55ptsVtGSulY7rv0RJiAz88+aR7H5NL5OSmHrJcPKxc6mpCjt28DdmWbpvKsnixMx25+Ud+fnxpLdlFaaxdm7gOPB63gdqiafMGwmtpJv9u2uiE4P2KY1uW7/WsI/sKd6EAJOrBZpNpKW2Qc5Yx8Pvp013PJ9Wb21J3wFkmCs/HpwC2VbRbW13Q0lM3O+mvqa3Ke4TpGsrxi65bXl68HfN7gKnN8frgzz58bCFSXaZcrYlm4KMoiqLkGqqoUxRFURRFURRFURQlq8SiMUTTWPqaTlxFqU6ooi4ocjYxEkHs/POdYWwzoiYljcnBeX5+8swmzYiaZpdtDqltSoxUVGTVFbeyDBsW/+vmeNpNbeGlRJK4Kblsx+R302yzl8ojk3AlHdlDjt79EA7HNwbgdufnJ1RP0hG0VPDZ1AhcEUDpdu9utn3BArNdXD0n1Wp5eVYVjydS3SLtlIoRGY5sN117rkaUeUgFBVfP2By821R+8jv/bVLHEKaxRyqLTPW7fn0iPld1mdR0NnvpN0/Dpl6T10Aq43j9Dx5sPm5Kn9tEarXi4mTn6VwZZFLD0V+uorLh53pIO21jCG0UQGFLStz7gRxLw+G4GpLbQvH9jp8mp/gWexsecUQiH2prNgU7hbPlHw6b7xF8jHjkEWdfystL3LPlRg3U/0zl5m3OjxJIKm2pXfAx1NTH+G9SR44enZynrT3w9kjlpPxatkxWpfG246agNPUbt/ugLQ2C171J0XfZZcnHyD43RZ2FxrZNlKhctrFE1sny5fZ8uSLaC9l/wmFgzBh7WNrcwaaOdlPUSfyokAmp+DSNH/Iamvq1tDeXnmEBJHzUpfNRlNwhhihisfI0PqoyVXKDXLvbKYqiKIqiKIqi1AB0MwlF4cRi0bRUcTHdTELJEVRRF5QGDZwKo3AYoSuvdIYpKUmO5zabzJUehz4HFy6MHzvmGGe8li29bfSjksklvGZ4ucLKy79JKv5P5Kwxx3QtTOFoBtmkFKhKXyxSAUB5+5ntJx9dlA6ViWbppTLP5rOGzrkpHiORRD/j/ccG+cnhCiKuMPAzU29TR3I7pbrDlJ5JUcfT4qoVrhySdpgUErZ8bHB1zrRp5jBSsWVSq3AFBH2neKWl9nGI/r70klNRZ7LfpkaUyjFeJ1zdRe2PXzPeToktW+ztwNZWKV1en7xNFRcn2h6vJ+6PidtmUiARlA5BcVascNaDybZw2Kmakco/G1yNxNVCW7aYw1GeNnbtsiv+XPxiHvz2W+cBWXeUNi+vVKdxuy67LKE2ovOUllSPyf5J36nM27ebFXM0rtuUQqaxh6vladwqK4uPr5QOlZOXjequoCDR70wKMi+/lLZ6lWO26f5mGrep3dmUlHL8kGkC8bLR84/06UdQvUnlGk/LSzXm1m5t9S3D2OqX7JLl4v1KxpH9VdpC94n5853xyAaT30kbXuOe7d61dKnTrtJS53OwX2UdT8PNnrw8s3K+xqKKOkXhxGIxRGPlaXxUUafkBvqiTlEURVEURVEURVEURVGqAfqiLiiRCPDmm/HvfDZVKiUIk/KBjnM1g5itr/fpp/Hjctcr06yjbQZY+reS8WsbJr9U/LubisWUFmFTft1+u12FZSI/P3mXPttMc2XBlR9UroICoF8/f/HJ3pKSZF9cXCEmy2VS9ph2quNMnBj/68cfFakS+O5zXGFk2jXPL3z236v9cLUF/ZbKFZN6afv2RBg+7vC0KAwQ96tlCuNmP9WnPC7HrfPOSw5HKgpZFt7nCgqwNxQy2zB3rlllwXFTR0llD+2MTf2J+07jylXTGMp3zJXnTIop6WeO9yGyZ9euZN99S5YkdiXlilWu+DTB7x28PXFFDV0LroSxpSuVTVI9S2mRnfKeZ1LDUTwbZWWJMYX6H10rqRhk1IvFEuorOY7Y1I5851fZrpYvNyuWeJ2cfHIiLr/WprbupibjYXh+sq3Qbx6HdhPmqlBqUya/lfn5wKZN9t11CduuudxmUu7xevKqcz6+yjikwvRzX+Pti1SFZBNPl6t3Aex89lmzXaa85PXw8rXH0+Bl9FL7AsAppyTbINuMn3s8rwuvchUXm69TkGcJGi9nz3bW9623msNHIvFnACqbVALKdmG7R3E1a06jijpF4cQQQzQWTeujKLlALX5royiKoiiKoiiKki2ihz7pxFeU3CEWiyKWjo863fVVyRFUUReUctb5+UwozSC/8477LLWMK2dFSb1w333xc9IPhx8fQFxtkJcHjB3rzDfXSHVXVJu/FSLdWdyTTvKfr1Q/cVVbVSrqbLP7trJwSL0hFR7kH87kz4fy5KoAKjupJqRalcJecYXZZreyyXLZ6tlPGtx2qdLiyitbujyuVD7Rd0qHfDORSsGmOCOobigdm/38GFep8J0upfrDFJfGMpNiiMKVlTlvONwv1bJliWOyPVB8qd7iYWzqS4oj60D6SJOYlECm9Hk5qS1JP4XkY8zkq4/byPOwqQUpPJWZ7aa8l6uIZH+i77YyuCmkpF+1SAQYODCu1HFTJ9nG5XA4rhB7/nnneCf93dngajN5bcJhZz8kNRipz+j4IfbOmpVo99RGqI8R48c786U6Jd92Np+K3C7+1+R7kaBry1VQlJ9UyPPxkedB4wbt0urWltzunfLakpqSK6r89hM+bv/97/Zw8jcvM0+D+3KVSkcAzWlFAi+LTXkmcTtvu8ZcEe73XjJvXvK1M+VvOm5q96T8JPzcf/wcA8wrM+g51S1tqeYz1Y20k9q/tMekolUUJSeJxaJp+ajTXV+VXEHvdoqiKIqiKIqiKFVOFOktX9WXEkpuEYvF0tr1Naq7vio5girqgmLzJUKzfVOmmI8DybOA5JuHs2sXsGFDskqIwpn8/rj5HCou9r8TX00lyOyq1wx+phg82J6+bUa4rCyxUxr3VyZ9QFU2Un3gt464eqNXr8RxUhKRAsXko0Yqy/g5U3+S/YrUTG5lonSkmsqrTm3l52mS2kYqikx2yri29kBqHVIgkWpP7kJpKseiRd72yzLyHaVtNrkdIz9WUsHDvjcmH3W2NkBqJqniMuVLag2bwgdIKKDy853+x7jvLt52+HWx8dhjyce4spr+ctUPV/zw9MkfHK8vvjOxCT5GsHpsMmJEIoxU6khllSkc/ZZqNbJ9wYLktOS9bdMmbyUP4LyP8etA+PGrKOuc7zZsSt9Q3iYjRiSPSXw3XqJ/f2d/JpWmqZ2axjBeHumPkPvyovR4+iZfZKRuKyhIqJWp7DRGnHxy/DfPz+/9IxxOVhrSs4Str9Hv0tLkfLifQB7e9GxkW5HAbZG+Ain+vHnxvyall2zPpnMyP4ls2273Bq+0ZszwDiPzNN0z6PhbbznjUT126JCIx9W+pp1mbeP72We7x5P50k6zu3bFrz3dc+U90DQu8d/yr5ff2hqL+qhTFEVRksm1u52iKIqiKIqiKEoNIN2XbfqiTsk1YmktX43FYhm0RVGyhyrqgiJ3bCT4DKNJwWZSDRBy91eCqwJMu1/ZZhVpRvKpp9zLkgvMmZO6oi5VdZpNSdChg3vabrPQXCnmRmUrIk3+vIL4AKRZ9LVrnb6cuGKK+16jOIC7WoDPpMt6dNtdEogrxWS/Zb69HGW3IfuxPE6++LiNUqlE4bnqx1QPJnVXWVlc0VJY6FQA2fxd2ZRhbn3FtkO0l6pS+KFzqGe4reEw8NJLiTg8Hinx1qxxz4eXt02bRPvg9cThKkHpO47Xu22nbomtTk1tQiqBTL4QyX7uV02qRkxKJK68kru0yrA2u/l5/pv7KgSc6UvlGdlCx7p0Mat3JNu3O+ucKx7d1IQcU5nIPq7g4fdX7k+Of0wqLm7DmWcmrg1XZnI/fZs2mcdw1p9jf/qT+R5ke5bgbaawMFmlSQo6Hpfi8x16bSpHU9uivxs3mssh2zr9JqWpqSy8XXNMvt34taD7Ed+tW9prOr5+vblc8jtPQ5bHhEyHtzFT2rZ7Sl4eMHx4sDFa5i3r07RjvPzutbrCpqhbvdr5m+65pGCU4fPz42Mv3XtNPjjd8jO1V694iqLkDOn7qNOX10puoHc7RVEURVEURVGUKkcVdYrC0V1fFSWOvqhTFEVRFEVRFEWpcvRFnaJwYrEYomksfY3q0lclR9Clr+lgWjognYXbJP/cWTQ5iudOzgHnEiRasiCXN/BzPB8AuOqqxBJEeS6X8Ls0Uy4DMjkk52H9OJXm6fbp4wxTv75/20zLQE3LP+SytExjWrrjt83QcpxwGLj33vgxcmZuWrLClw7J8vL6l0txTUvj/CxXlOnTcjvT8jO/5QUSyxBLSpzLPGW/lMtn5dJbeb3JITdPUy6b47/5EmDb8kHTMkm+jI6wOQx3WzpHSzGBxMYh3Jb8fHx/ySXJ6UQi8aVqpmV6bs7bf/3r5OVyPF9us9yohafNlxealtLZvst6kGWS47fbUjP+l7cjsk9CDtttS/Vk+7ItfeThpS2m8dFr6SRgXvJ63HHOOPSRy4FTWdoml4jyJZq878jNbCiP0lLnxk58qSVBS1xp2V84HL++cmMIk+2svKHevZ11tmuXs++ZloTz5a75+fE8aUzlZeFlo/GI8ue2uNUvLwN9p3R4PbrB7eKY+oDcHIIvA6e827SJH+PLo4uLk8vE6d7dnI8tvNsYx5k50xmO9xWbyxMbc+cm22aC25OfD5x3nrN+Te3VZoO8l8rv9PeXv7TbQ/3WZjelkZ8fH6MobCSS6Du83nif9UNOPs9GM/BRFEVRco1cucspiqIoiqIoiqIoilJDicViiKaxfDWqPuqUHEEVdUHhM3jLl5tnq6WyhX+XKhOTkoA76ZbpSNWWzcZDs/F7//tf7xnwmo4fJZScXebqA1NYrzRN153P8gNxZ9FBoWtVUJBwyu+lBsgkJlVRkE03uJKMZtFJdUKz/mVlTnUEx+YsXJ4ntQmQ6BO2uuHKJq5c5ba6zezbnNbL31J9KPs3b29udUthuGqGK29tCkCp2JN52cpC5X7rreRz3B5TfK7qkEomUnQVFFSojxr/4Q/OuHQ9Pv7YeZzajszblL/Mm9fp/PmJtkLKIFkOk2ovHAaKipLrwqRo5W2Gq1ukg3lbmWT/drt+lB5t0EJKI9NYxu8ltg2LTHFM6l6+wQnfhIHKZVKT8XL95z/OfA7Zv3vhwuR+Yiu3tJOl48jT1Mf4cf47HE5sOEJlM90vuTqxuDgxfrVsmSgnhbEpHE3f+QY0sl652o4c8tNmLVwNyOuA7KfNbeS4FInE27VNiURjaiSSqBe5oQP/mOrKdv80jeW8vfHjMh6Vjdoh/ys3KzAhN9zg2DbR8YLb7ueZgROJACefbC6/KSy3q6AgWY3N74duaXDkRh7E3/9uvxfw/Gz1xNscbfjD1awSuQGRtEf+JmWeKVyNJYrE8tdUPqqoU3KL2KFdX1P/6NJXJTfIlbucoiiKoiiKoihKDUJ91CkKJxaLpqWo080klFxBFXVBycsDBg8GAOzeuzd5VtU0y8xnjvkMJn3ftcupQgHMW9oDcYUIYfI3RTYeSqvJxRebbckl/Ci+TDO0bvGuuMJ9xlj+jkSA559PHDPN8nMVhJxdJt88FGbmTG9/QpUFV6IEzZv7UKTyUDrSX5RUtBFUb506xf9KBYqs/9Gj/akSuFJVqgZTVSqSbSZ/YHSeK/W4ck8qkUztiqtYZPpSVSHt8qOgorGnrMyp1rWp6GwK1Ly8hMqhrCyRLkEqoA4dzPHlb5six4RN3QUAvXolqz/CYWc74m2T91k65qZU4XbxNEzKOemPjcfl7YOOm1Rq8hypp2wqNKpLXqeyPF59gCuy+PcxY+I+ULlfQL/q20PtoVks5lTXmhR9XvhRSHG76f5IYy4pV6UilJOfnxi3SQVHfg+lKtfLTqnKJBUcjYk0PvI2ypV1NtUjH1e5/aTEJZvJtyFg7o+UnmwXUtnH8zeV0021KqE6kHVIfjq5GhtIPGORukpeLze1dps28b9r1pjt9NPuuGJYjleyLdniy/zkGGAqC7/+PI2yskS5/OTFbeV/S0uBGTOAfv2c8aUiUSqBTXly9S3F56pIXibZJ9zqTY7TOUE6arp0X/IpSvUjFo2ivLw89Y++qFNyBH1RpyiKoiiKoiiKoiiKoijVAH1RF5S6dSu+Njv66MRxPsNJvl3oOMFnfknVwGcYueLIttumX5UBzfZu3548+5iqeqi6EsQHn1Sz2OrzkGrSN3y2esgQp/ph3rzksNIWIFkNZNolryrUdVK15lclQzPofGaflCh0jaRyhMeVM/733msv74YNZn9HNkwKMa6WMikj3CD7ZV/lSgsejr6bVFXcJv6dfFSRUoZs5X9NqluZjpsijqs4beOETV3Hy8TrnytwuEomLw/fnn9+4pzES7lnUnhytQ39NilSZJ1zFRRXJpvaIf++fn2yXTyevEaAc9dDW9l4XlKN66YQk0pO+s53CpVw26iskUh8nPJSylAcAFi1Cnj1VWfdbtzovkO5TINslGO4m4rRtss5YPcxSYpOpjZ3KJPoPL8H83w2bkzEa9kyEVYq9Wz3bNOYR3FIfSoVyDw83z171664vbT7K/e9yBWW0uedrCs3SkudfYQ+ZWXxZwpKy3YPNSlKbWHo2peWAnPmJPc9riYkP7D0jBUOx4/J1QU2m/i5RYuc5bPZaUPGMfVDW18yPd/Z8ra1b6/6lef9+OI71D/23n+/2RaTHaZ86bryj5sSjrcj3ge5/Tz+mDHu9tU4dNdXReHQZhKpfmJR7RNKblAF//UriqIoiqIoiqIoTtRHnaJworFoWstXo9Fy1FEpkpIDaDNOh+7dnb9JoWNTY7j5lKKwfCbRSynG/c5weD4m/y25Rioqs6AKKhmXGDLEfJwrKF56yRmfq374LL9NkVHV/li4QspNdWiLS0qa4uJkRVlBgdO/EG/DpOZxy498B37wgfO4SUlFcNUQP2byLRS0PbjFIRUIz4eUKlI9QN+5AoOUjVxFw8NL1Q8do7RMcPUN4LzWtrowqf34b1JrUJtv2dKpGCY78/NxxGmnJeKZVFNNm7rvvCvLa1LAcUgBJ8dBnh5XWvE2bxhfy7dtS87D1AZol17+3XRNSkqc7YNs4T7+vFQofNdR/p3KKfuzSXHI4dedtzV+DgBWrzaHo+82H6qyn8sxRuZpKjf95fXG2yGpz2zXk6tWySab6o2QCrVIJK4epro2KTcJysemJOXKWSI/P+GjjalSK763aRMvJ9+RlcZXUv1R3UjVH1dASlsonGyTVHclJU5fcSaVoKmMpvstr/eZMxP5EXwcAZx9avToxDOXra0RnTrZFa2pPD+Q7TIN2WfperZo4YzL1YH8GI9jy1OOy9xnognuk5CXVe6EDACzZwORCJqMGmW2jce1IdXKtOs3rxuuLOftmpdTPjvz39Om2fNXFKXGE41GUV4eSf1TRT7q9u/fj5NPPhmhUAjrxYqLrVu34uKLL0bjxo1RWFiIcePG4cCBA1Vil5I7qKJOURRFURRFURSlylFFnaJwYrH4ZhLpxK8KLdL48ePRunVr/POf/3QcLy8vx0UXXYQjjjgCq1atwnfffYerr74asVgM06dPr3S7lNxBFXVB+c9/Et9JOcRmvHe/+GKyDyAT0lcVn1Hks+cE3zVr9Oj4d69ZZL7bJifXFXZeUL3v2mWehfZSdJgwqVSkIk2qRuQ56cMlFUVbJuDqFMC/j7pwON4naFZJ+tlq2TLhQ4kUVVzNZSqrVHvNnevMz/Rd8thjTntMyjG3+DalAu+nXBXA1Qtz5jjTl76wTFBflztAd+jg7v9IpmfaedBtLDDVPR23qVEo3K5difGIxkWudiNbzzwz+Ry3YcAAp8pG2mZrH7YyUB1wRUdpKdCxY/IYa1JgCZVj3VgsufwUV/YZPrZzlYybipMUQlxBkp8PTJjgLBOpNbmaittqu3amvizrz9RG5DhlS4vKK9M15ekWzuSfk+A+/ExKrbw8pxINMO9aSeUoLk740aLzJmU8XUdeB9IPn6kspJSj6yPrjdR/fMdWfu1JTcvLU1AQHw8KC5PbGinreHvkzwp8nJXqLamwAhL20Nidn59on17tiSP7qkm5CTh3oi0pAbZsSd4pnfoIqcnks5C0S/o0lWG8dml1K4/bPZqOyx1Zed1Ruei50S0trqgkm8ln4KZN8b+TJ8f/yv7l1mdtSjtCtvOCAnelPy8T9SfA3N6CPOPwsZnyyQl011dFqWn85S9/wYoVKzBlypSkcytWrMDHH3+M+fPno1u3bujTpw8efvhhPPnkk9izZ08WrFVqKlX8BkBRFEVRFEVRFEWJbwaRzss2dZyv5A6LFv0RABCLpf4iPoYY9u07mPRSrEGDBmjQoEFa9gHA//73P4waNQovvfQSGjVqlHT+vffeQ5cuXdC6deuKYxdeeCH279+PdevWoXfv3mnboNQOVFGnKIqiKIqiKIqiKErWmD//WcRftVlWMPgiHrdZs2aOz/1eO1r7STkWw/DhwzFmzBh0l77qD1FcXIwjjzzScax58+aoX78+iqV/UkVxQV/UBeWFF4Bbb41/HzQoaelUsyuvTCxFOHTMCF+WQr+BxJIVKenny3Jk2jKsXMJlctpeW/Gz9M9rGYZpyWo4DCxeHD/Gl0J6LQ/haeblOZ390xIXP0upMwUvmx/n1pxIJL78hi+1ow85RqflX3IJqFz+Ymvb3Cm/27JNU7nk0iK57BEAlixJjmvb4IK3Ab4cSS7jpWVi1OflMkl+3LQMsawMOPlk51IpuZySjsulm3Schxk5MjkP0/Ijnp9b3yDbpWN7wOlEnzbSoHRMfWPLlvj4aWoTbn3JtKQOAHbsiMfjSxl37Yovd+R20nXjS06DjAdyGZu8jlQ3Mj3bNaelkkDycjUqD40VtHGAbUm3XE4pyyPr2G0ZINWPXBpKmDYXkNBSaZ6/wcavFy5Mjjt2rLNfUH2XlCTqiS9z5nXIlwrTcd52ebn5MuJGjeJ1LN0ZBGknxcVmlxZkG0+Tlu5SXcqNWYDEkljT8vfS0nh83u552Xj58/LwXSiUiE/haDkw1afMw/SdoLbrNmYS8jlI5rdrV3zZKt0/aHknXXu67vIfH5lPx47Jz1C0HFUuO/WzeRN/RpNLeHl8uj6mJaebNjnD841NOHw5PW+/dHzFCmd51q5NzofKtmFDcrqSvLx4ezeVlXBbrsrbMl0fec+VY4ysMzm+UJuictN92u+S2WpPNAMfRckNrrzyykPfUntRF3/BF8PHH3+M3bt3Oz533HGHNd7EiRMRCoVcP2vXrsX06dOxZ88e17QAIMTvrWRbLGY8rig2cuUupyiKoiiKoiiKUoPQzSQUhahTpw7eeOMNnH/++YghhhCCvtiKAgjhhBNOCBTr+uuvxxVXXOEapkOHDrjvvvuwevXqpCW03bt3x89+9jM888wzaNmyJf7xj384zu/cuRMHDx5MUtopihuqqEsF2hre5Ni+ZUunKorDZ/9oZpFUJnxW3qbgARIzyYC7U3maIT755ByadbTgZwZcbJtdMePLFTTceT+F8YKrIIjCwmTn3YD9etE5fm152ja1UGUgFSNSqeMGKWWkvaQCIKfpts0XKDzZQfB6IdUi4H+TCx6WO97n157nLbn2Wnu60mk/V7zQ944dk5V0VE/0myvqpGKFbG/TJlm9IccUU3vkcKUWt1+OOeGwPyUl2c774K5dTnUJjUclJcl9waQq5c7qeT4UxoSf68jVHDw9XvcmJZAfhRi3kztR53nz9i/t4k7y+YYrgNlRvlTucIUYt5m3EZMyJhJJbLbC0onRjK9fZRQ/blLJSPg1MKnzIhG0qphVF/GARDuWadCHb8zA65b3fxqv6LrYHNT36QNs3OhMm7clk/LUVC9A8iYZvG/zvKm9RCKJ60/2k70lJYlyyPGgsDBZtUjpcqV3OIzD3303EY6eG6gdUhn5OEU2ed0b+DlSQMnwJrX/zJlOu0lNt3Ztoi64cjc/37mKgZeX4OflPSbIfUSmwfOjYzT+EZ07J34PHhz/S3XJN4fg7dSWF+/3csMPADjnnOQxtkOHZJWoTNf2nX5LVb88xpGKurKyRH+Um7HwckglvkzTZltOoJtJKArnvPPOO/QtmKqOlsuWlHwbOM/CwkJ06tTJ9ZOXl4fHHnsM//znP7F+/XqsX78er776KgDgj3/8IyZNmgQA6NGjBzZs2ICvv/66Iv0VK1agQYMGOPXUUwPbptRecvwNjqIoiqIoiqIoiqIoNYF//etf6Nq1q29VXfwlXVxNd/jhh1eaXe3atXP8zj80qXXssceizaFJt759+6Jz584YOnQofve732HHjh249dZbMWrUKDRt2rTSbFNyD1XUpYNJtfDII/bZSTlTWFrq7vvDpGjxo/LivmlyXU3nl7vuSj4m1S22WVuJ6Tyv5+Jip28uEyZfTnKG2qSO6tLF3bZMwBUXXn72ODTT3qFD/Df3CVRY6CyL9BfEVWWmWXNTPUpFkVtbLyxM9p9G6hAvRQW/LrY8SHFC6c+cmQj7618nK++4fy2uOADMCoM2bRIqF1v75Lb58RVGcfyo1GxwNQi3m1+bcDjeH6RyiquBKFxBQWJcNKmh3Gyw2W1SMXGfR1xhFQ4nFGZBlDZc0UbpkfqnoCChQJJKRWk71SMpUShNW568vqRKxaBSc22/jBCp2aT6iCtkTeMlz8PLN6pU0EjbFiwwxzP5dONqTLKBK3Z5f+P5k+9AUn1SXfJxr2XLRPujz6ZN8by4SlSqHAneP/z6lczPT/R5spHUf3ws40o3Sp/nR6o8riimD12jl15K2NOpU/xvmzbJYzTlV1jotF+OR9xPp6wHzqJFycpZCb8PcZ98dH+l+4pJdWrLW46z/L7kV6Flu0/Rh+dP/g2lHbyOTG1DQs91/frZ+7ftN2+jMk2TXbLvSt+5ADBnjtlOXq5NmxJ2l5UlxkEaE2228rGlqCi5DO+8Y867xqKKOkWRdOnSBUAI/lV18XA//LCvskzyTd26dfHKK68gLy8PZ511FoYMGYJBgwZhypQp2TZNqWHoWxxFURRFURRFUZQqR33UKYqJL7/cjqOOOspTVUcbSDz77LPIS8WdQRp06NABsVjyy8R27dph2bJlVWqLknuooi4VTH4++MywVGktX56chlSh0KwuzeSTUsKUt9dMtVS3uKlNcgE/ZRoyJPGdVAi8zv2mI8OZ4nAfXTZ1JfcLRXA1Rv/+CYVGVfthoRltv0o6U3wgoUIh1QmfQee+eSiOVFHx9PjsPd8tlYhE7DP83CaTj0d+3kT//vb0OFyVBTj9IlF74761vBRF/DypSUj5Y2pfUuFk8mUkxw+uNpO+NU3tTh5bsiSRBqnAuPqNFD6FhQklkFvaFN5LgcLL7Oc6cpVffn7iQ21TKsc4NmUXweuXq5Dou9ytUSrq+G+6PtLXo7zncMUYV4uZlH1+8KpDqaoypTtwoPO4TSEky2xSEhPDhjnP3XRTIg2pIOT3T+7HjxRpPI4cV+ncrl2JdkLKYCDRTvLz4+lRX5FKLtO4SXmTaoqXl7cLrsrNy0v0F8qXlLdAop/w5w7Kg4+ppDjjPlqlGrJ7d6BOHWfdk/KJwtM9LS8vrhDj/utsY6HbmAYA+/Ylq28lVGauNKTj5AuW38NtSLUikGhbXs9TtvQAcznlLrmkiqTwffs6rxX3m+gGKd3OPDP+W47rPD3Oxo1OWwmTj0v6LhXRsi+71bfsZ7Kf0jXjKnuuYJX3RakOLyuLqzEVRcl5WrdujbiqLlrhf85M/FwRKXAVJUfQF3WKoiiKoiiKoihVThTpLXuNVr3JilJF7Nmz2/U8qeneeOMN1KmjrzWU3EJbdLrQTCjf/S0vD7j3XqBdO/eZZX6OvtPsMc04Enx236Q4smGasc5FVV0QpA8Ufg39zqab/NxwTLt4mpQWPE2ZHm8XQFz1IHe3qwy4OsfUdm3I+iMFCakJ+I5v0tcalbusDD88+aTTFgAYOzZxzORvx8tGNyVqOopF0y6bNoWITT3Br7Utfb4LI09Lpm9SYMprYmqrQFwpQ0hfY251RXbxcPSXrrO83rYxMT8fGDPGqRiTYUy/Xa7/3qefTtTvuHFOZSLFlXXJ83FTalJ8Cs/vARxS8En/VdJu2lGUq0hIFcbDUJ6kdJIqN1O75Mdt+cvr4ldtxMdQwk11w3dOJeT9kI5xZswwt0E5XnKlIak5aXyi3ZOlmo1/SkoSO3SuXx/fPR1I3vW0Uydnndj88vFrKndj5TbzfkOfggKnkq642Hn96ZzsX1TGTp2cu6VK/2AbNwKPP56IR3/LyuL+NUntVFqaeCah8peWJu+obuq3NrzG7fz8+Li0fXuyEpWrFP3co0gFSWUYPz45jGl89Gs31blUn4XDyfVPUBncFK38mkQiwO23m22yKQul4pKwqcs9iD39dLKSVOZHbZnqmtvGlcay3cs0ZBly9tk1moGPouQmTZo0waxZs2BX1cWPJXaKVZTcQV/UKYqiKIqiKIqiVDm6mYSiuHHNNdcc+uZ8UUdqun/9619VbpOiVAX6oi4dpF8PghQQvXrFZwC3bk0c5zOCXFXA/Q6Fw87d5ID4LPqwYe4KLQ5Xc/F0gqqHhgwBGjUKFodz2GGpx/WLV5lOOSX5mKwb00ytTREi43F1DpDwL8h3ejSlx5UAJmWNvL4zZnjvDJcJUlWYUfuVfrq2b4+3Z1L/yDgmJRA/D8R3UZVh/ChZgPiuceQ3TaqPuJolqANakz8uL1Umbws2RYFUtZAig3wU2aA2Q3FMvvxkGRcscKonTWXjmI6bfODxdk8KJK4k4vnxa5GXFx/npC877sPILX8gqf6bxGJOu7j6We5saVIOjhxpzpcwqRh5/6Xr17JlXM0FAP/5T/wv+WniZSEVGPcxKMso/UuZlIW8jk2KNxOyHmzqS3nvM/Ud6atLIpWFPC+KS7uQ8rzIfx/vO1IhWVzsTJvGYlKj8esvfXvRuEWQmo7uy+QfLRyOj22yjk3qIMqD1Hy8PKROkmpCUtFyP3XUfigu1aN8JpD1SeUhdV4kgu9CoUQ6b72ViMfLwuujuNj5nJKfH6+HpUsBrmSwqczd1JjhcLI/QiChyCotjfefSCSurs7Li18X3q+Jpk2T27lBnfX9737net4VOWbSb7mzNcHbEx8buPLTj7qN36sojUceSYwRXLls8kEn7zO2sor6OPjyyxU/QzSe2uBl6tgx0TcpDim3uRLUpLa2Kf6C3qcVRanx/P/27jyqqmqPA/j3SszDjUEmkSFR1ERMCUMq0Qg0U0ifmRnhwzRfgpnPTBOXVE6ZlpVZSiqsHPKVYK0cMWdRUpOXFc+FCEGKkkqizMh+f+A5nHu5IDhwgfv9rIVy99lnn9/5nem6PfscY2NjbN26FbVdc8rOOgFAdesNsUTtDzvqiIiIiIiIWlzrv6OuoqICffr0gUqlQobWcPe8vDwMHz4clpaWcHBwwNSpU1FZWXnfYyLDMmLEiFu/iVt/1t5Nd/78n3qLieh+Y0cdERERERFRi2v9HXUzZ8689QZOrchv3sSwYcNQUlKCw4cP4+uvv8aWLVvw73//+77HRIZFpVIhPT0ddXfV1QBQ6dwvidoLdtTdCeUwE2n4g/br7A8fbvzlAbqGyEnDOHQN7VMOg9M1/EhJuVzlMC9Jc4c2Pvts8+orhYTc+bxNdbv18fauX1d7KIj2UKrGaA991V6+8uHQymFhuobXKR+srBzq0dAQs/v9Mgnt4WRSbE2hfDg7UPe3NBRY2bb2kEPpd2dnmL//fv14GopV1++63LhRNyRHV92mvkhEe/nKXEnDjnTFq1xv5TA7XQ+A1953lMNzpXpSO9rnHeX+I01TDgPUtZ7SfMphtcqhhE0ZlqUcvqc8J0rDnbXXV3tdlcvVFevlyw3nVfsc19DwXDOzuqGKuh74rms4blMoj1XpHC7t89rLcHaurRMXV1umfDi/Mg7lcdTQizh0lWv/rhxWp7xeNZeuxzxoP9xfonwBSkO092ftv6V8/Ppr/XmV2zoioi4X0vpJ+4M0bFQ69s3M6vZHZS4efFDzONP1QoETJzRjltpWDkuWlqVr+L5yuKgyV7qWLX1WDnuX6ubm1g2D15U/5T7o4FD7+6+/1g0llob/WlnBfvTouqHE2i9BkWKRztvOznVlUmzK9VS++Eq7He08KDU0VFRiZVX7sgugLh/SY0Gka6F2u0OG6P7uJdW5lQvLVatqPyuPiTsZVqk81nSto7JMea6QzrG6Hgmhq31pm0u5lto6dkzzOiS1JW1z5WMWlLEp9yHt86dWPMajR2vG1NgQ+r//rsuF9gtUzMzqhr4qz3HSPi2tb0PfsaT1lF4M0m607pdJ7NixA7t378bSpUvrTdu9ezd+//13rF+/Ho888ghCQkKwbNkyJCQkoLi4+L7GRYYnICAAgArSPn+7N8IStXV38I3dMAlRe6ttcXExUFkJFBcDVVVARUXt56oqzfKqqtoZpTKpTk1Nw/NWVNR+wamsrP3p0KG2LlA7TapXXV1bXlGh+bdE2aYyLmmaNH9TSOtxpxfcqqo7n/deLUOarrwVX8pxZWXtNpG+nEpfCJXbWVtFheY2kChzrNy+0jZX7gvS3ypVXTvSPiDtH9K80vzl5fc/n1IcHTrUrqO0fk1ZZmVl3Rd4af1KS2vbUKlqfy8vB8rKar9sS9tDyru0ztIxoNxm2vu39vZpLC/K46ampq5NafsDmnluaN2UamrqtyEtp6H4tI9LZVzSfMp8S5+V5VJ9qVx5LCvPJ9L5QtqXtOso11Par5R5ltpR7osNHRNSXSE09x8p7ps36/YD5boppyvXV3k8KM+pHTrUxqq9L0jbQmpD1/lNmRNpH5N+V6k0t4PyfK29vIb2Del4kZZVXl63LmVlQElJ3bqWldXlVtrXpeUot7OyrKys/jZU7n/S8WNkpHncKKdJ66fcH7S3q3IfBmrXSzpmlNtIe5+QypTrrX1d0t5muq6jUo6kfUL7uJZiu3lT8/yiPJ9LuZPakjqWpFxI5yEpl9K56OZNzViUOaqpqWvT2LgunrKy+vus9v4nxVFeXrtsZdvSNb+0tK4OUPtZarukpC5m6RiTpkkdZtJ+I9W9caOujnI7K9tVbmtpurSfSfkCauMyNq7Lq9RxIp3LdZ1jpGNKur4p90Xt7akdo5K030odq8rjUtmRozxOpW2ovQ2AunVTrqtyeyvPR41Rfkerqak7f3booHlslJVpnleU539pH9a+9mvnQRmblA9lzMrriDRd2Y5UXzsv0j6vzImUP+19VPs4LC1t/Hrb4dY9ANJ+JC1bOhdK+7zyO5K0TOW+pzwXAXXXcOV3uuJiuUNI+p7e9jTjP4YamV+7Y8zU1BSmpqZ31fKlS5cwceJEbN26FRY6nld99OhR9OrVS+OuprCwMFRUVODkyZMYNGjQXS2fSFt29ll06dIFK1euhLW1tb7DIbqvVKLtXtla1Llz59ClSxd9h0FERERERAr5+flwU75wpZUrLy+Hl5cXLirver9DVlZWuKF1F/O8efMQHx9/x20KIfDMM88gKCgIcXFxyM3NhZeXF06dOoU+t16yM2nSJOTm5mL37t0a85qamiIxMRFjx4694+UTERk63lHXRHa33l6al5cHtVqt52gMT3FxMTp37oz8/HzY2NjoOxyDw/zrF/OvX8y/fjH/+sX86xfz3zghBK5fv97mnlVlZmaGnJwc3IsXLwghoJLe5HxLQ3fTxcfH45133mm0vePHjyMtLQ3FxcWYPXt2o3W1l9tQPERE1DzsqGuiDrdu5Ver1fyipEc2NjbMvx4x//rF/OsX869fzL9+Mf/6xfw3rK3+B7qZmRnM7uTZiHchJiYGL7zwQqN1PD09MX/+fBw7dqxeh5+/vz/GjRuHpKQkODs733rAf52ioiJUVVXBycnpnsdORGRI2FFHRERERETUzjk4OMBB+wUwOnzyySeYP3++/PnChQsICwvD5s2b0b9/fwBAYGAgFixYgIKCAri4uACofcGEqakp+vXrd39WgIjIQLCjjoiIiIiIiAAA7u7uGp+tbr3pu0uXLvKzAENDQ9GzZ09ERkbigw8+wNWrVzFjxgxMnDiRd38SEd2lDvoOoK0wNTXFvHnz7voNSnRnmH/9Yv71i/nXL+Zfv5h//WL+9Yv5p9bKyMgI27Ztg5mZGYKCgvD8888jIiICS5cu1XdoRERtHt/6SkRERERERERE1ArwjjoiIiIiIiIiIqJWgB11RERERERERERErQA76oiIiIiIiIiIiFoBdtQRERERERERERG1AgbdUTd+/HioVCpMnjy53rTXXnsNKpUK48ePb/nADNzKlSvh5eUFMzMz9OvXD4cOHZKnSdtM+fPYY4/pMdr2p7H8X7p0CePHj4erqyssLCwwZMgQZGVl6THa9uPgwYMYPnw4XF1doVKpsHXrVo3p8fHx6N69OywtLWFra4uQkBCkp6frJ9h26Hb51z7vSD8ffPCBfgJuZxYtWoRHH30U1tbWcHR0REREBM6cOaNRJzk5GWFhYXBwcIBKpUJGRoZ+gm2HmpJ/Xn/vn6bkn9dfIiIiw2HQHXUA0LlzZ3z99dcoKyuTy8rLy7Fp0ya4u7vfVdtVVVV3G57B2bx5M6ZNm4Y5c+bg1KlTeOKJJzB06FDk5eXJdYYMGYKCggL5Z/v27XqMuH1pLP9CCERERODcuXP47rvvcOrUKXh4eCAkJAQlJSX6Dr3NKykpgZ+fH1asWKFzerdu3bBixQqcPn0ahw8fhqenJ0JDQ/HXX3+1cKTt0+3yrzznFBQUYO3atVCpVBg1alQLR9o+HThwAFOmTMGxY8eQmpqK6upqhIaGapxbSkpKEBQUhMWLF+sx0vapKfkHeP29X26Xf15/iYiIDIwwYFFRUSI8PFz4+vqK9evXy+UbNmwQvr6+Ijw8XERFRQkhhNixY4cICgoSarVa2NnZiWHDhomzZ8/K8+Tk5AgAYvPmzWLgwIHC1NRUrF27tqVXqc0LCAgQkydP1ijr3r27mDVrlhCibpvR/dFY/s+cOSMAiF9//VWeVl1dLezs7ERCQkJLh9quARApKSmN1rl27ZoAIPbs2dMyQRmQpuQ/PDxcDB48uGUCMkCFhYUCgDhw4EC9adL19tSpUy0fmIHQlX9ef1uOdv55/SUiIjIsBn9HHQD885//xLp16+TPa9euRXR0tEadkpISTJ8+HcePH8ePP/6IDh064LnnnkNNTY1GvbfeegtTp05FZmYmwsLCWiT+9qKyshInT55EaGioRnloaCjS0tLkz/v374ejoyO6deuGiRMnorCwsKVDbZdul/+KigoAgJmZmTzNyMgIJiYmOHz4cIvGaugqKyuxevVqqNVq+Pn56Tscg3Pp0iVs27YNEyZM0Hco7da1a9cAAHZ2dnqOxDA1lH9ef1uGdv55/SUiIjIs7KgDEBkZicOHDyM3Nxd//PEHjhw5gpdeekmjzqhRozBy5Eh07doVffr0wZo1a3D69Gn8/vvvGvWmTZuGkSNHwsvLC66uri25Gm3e5cuXcfPmTTg5OWmUOzk54eLFiwCAoUOHYsOGDdi7dy+WLVuG48ePY/DgwfKXWLpzt8t/9+7d4eHhgdmzZ6OoqAiVlZVYvHgxLl68iIKCAj1FbVh++OEHWFlZwczMDB999BFSU1Ph4OCg77AMTlJSEqytrTFy5Eh9h9IuCSEwffp0PP744+jVq5e+wzE4DeWf19+WoSv/vP4SEREZlgf0HUBr4ODggGHDhiEpKQlCCAwbNqzeP36zs7Mxd+5cHDt2DJcvX5bvpMvLy9P4Iuvv79+isbdHKpVK47MQQi4bM2aMXN6rVy/4+/vDw8MD27Zt4z+a75GG8m9sbIwtW7ZgwoQJsLOzg5GREUJCQjB06FA9RWp4Bg0ahIyMDFy+fBkJCQl4/vnnkZ6eDkdHR32HZlDWrl2LcePGadzdQvdOTEwMfvnlF94ppCcN5Z/X35ahK/+8/hIRERkWdtTdEh0djZiYGADAZ599Vm/68OHD0blzZyQkJMDV1RU1NTXo1asXKisrNepZWlq2SLztkYODA4yMjOS75ySFhYX17vKSuLi4wMPDg28+uweakv9+/fohIyMD165dQ2VlJTp27Ij+/fuzg7qFWFpawtvbG97e3njsscfQtWtXrFmzBrNnz9Z3aAbj0KFDOHPmDDZv3qzvUNql2NhYfP/99zh48CDc3Nz0HY7BaU7+ef299xrLP6+/REREhoNDX28ZMmQIKisrUVlZWe/ZcleuXEFmZibi4uLw1FNPoUePHigqKtJTpO2XiYkJ+vXrh9TUVI3y1NRUDBgwQOc8V65cQX5+PlxcXFoixHatOflXq9Xo2LEjsrKycOLECYSHh7dkqHSLEILDzlrYmjVr0K9fPz4b8B4TQiAmJgbJycnYu3cvvLy89B2SQbmT/PP6e+80J/+8/hIREbV/vKPuFiMjI2RmZsq/K9na2sLe3h6rV6+Gi4sL8vLyMGvWLH2E2e5Nnz4dkZGR8Pf3R2BgIFavXo28vDxMnjwZN27cQHx8PEaNGgUXFxfk5ubi7bffhoODA5577jl9h94uNJZ/APjmm2/QsWNHuLu74/Tp03j99dcRERFR7wUU1Hw3btzA2bNn5c85OTnIyMiAnZ0d7O3tsWDBAowYMQIuLi64cuUKVq5ciT///BOjR4/WY9TtR2P5d3d3BwAUFxfjm2++wbJly/QVZrs1ZcoUbNy4Ed999x2sra3lO3vVajXMzc0BAFevXkVeXh4uXLgAADhz5gwAwNnZGc7OzvoJvJ24Xf55/b2/mrL/8/pLRERkQPT1utnWICoqSoSHhzc4PTw8XERFRQkhhEhNTRU9evQQpqamonfv3mL//v0CgEhJSRFCCJGTkyMAiFOnTt33uNu7zz77THh4eAgTExPRt29fceDAASGEEKWlpSI0NFR07NhRGBsbC3d3dxEVFSXy8vL0HHH70lD+hRDi448/Fm5ubnL+4+LiREVFhR6jbT/27dsnANT7iYqKEmVlZeK5554Trq6uwsTERLi4uIgRI0aIn376Sd9htxuN5V+yatUqYW5uLv7++2/9BdpO6co9ALFu3Tq5zrp163TWmTdvnt7ibi9ul39ef++vpuz/vP4SEREZDpUQQtznvkAiIiIiIiIiIiK6DT6jjoiIiIiIiIiIqBVgRx0REREREREREVErwI46IiIiIiIiIiKiVoAddURERERERERERK0AO+qIiIiIiIiIiIhaAXbUERERERERERERtQLsqCMiIiIiIiIiImoF2FFHRERERNTGLFq0CI8++iisra3h6OiIiIgInDlzRqOOEALx8fFwdXWFubk5goOD8dtvv8nTr169itjYWPj4+MDCwgLu7u6YOnUqrl27ptFOUVERIiMjoVaroVarERkZib///rvR+Pbv3w+VSgVbW1uUl5drTPvpp5+gUqmgUqnuLglERETtEDvqiIjakPj4ePTp00ffYegkhMCkSZNgZ2cHlUqFjIwMnWUt5ciRI/D19YWxsTEiIiJabLn3mqenJ5YvX95oHZVKha1btza5zcTERDz44IPNimP8+PFtOo9E7c2BAwcwZcoUHDt2DKmpqaiurkZoaChKSkrkOkuWLMGHH36IFStW4Pjx43B2dsbTTz+N69evAwAuXLiACxcuYOnSpTh9+jQSExOxc+dOTJgwQWNZL774IjIyMrBz507s3LkTGRkZiIyMbFKc1tbWSElJ0Shbu3Yt3N3d7zIDQGVl5V23QURE1OoIIiJqFQA0+hMVFSWuX78uLl++rO9Qddq+fbswNjYWR44cEQUFBaKqqkpn2d3IyckRAMSpU6duWzcgIEC89NJLIj8/XxQVFd3VcvWpsLBQlJSUNFoHgEhJSWlym6WlpeLSpUvNiiMqKkqEh4c3WsfDw0N89NFHzWqXiO6NwsJCAUAcOHBACCFETU2NcHZ2FosXL5brlJeXC7VaLb744osG2/nPf/4jTExM5PP177//LgCIY8eOyXWOHj0qAIj//e9/Dbazb98+AUDExcWJkJAQuby0tFSo1Woxd+5cofynyOXLl8ULL7wgOnXqJMzNzUWvXr3Exo0bNdocOHCgmDJlinjjjTeEvb29ePLJJ5uYHSIioraDd9QREbUSBQUF8s/y5cthY2OjUfbxxx/DysoK9vb2+g5Vp+zsbLi4uGDAgAFwdnbGAw88oLOsJeMZPHgw3NzcdN49JoRAdXV1i8Vzpzp27AgLC4t72qa5uTkcHR3vaZtEpF/ScFU7OzsAQE5ODi5evIjQ0FC5jqmpKQYOHIi0tLRG27GxsZHP10ePHoVarUb//v3lOo899hjUanWj7UgiIyNx6NAh5OXlAQC2bNkCT09P9O3bV6NeeXk5+vXrhx9++AG//vorJk2ahMjISKSnp2vUS0pKwgMPPIAjR45g1apVt10+ERFRW8OOOiKiVsLZ2Vn+UavVUKlU9cq0h75KwxEXLlwIJycnPPjgg3jnnXdQXV2NN998E3Z2dnBzc8PatWs1lnX+/HmMGTMGtra2sLe3R3h4OHJzcxuN78CBAwgICICpqSlcXFwwa9YsuaNr/PjxiI2NRV5eHlQqFTw9PXWWAcC3334LX19fmJubw97eHiEhIRpDtdatW4cePXrAzMwM3bt3x8qVK+VpXl5eAIBHHnkEKpUKwcHB9eLMzc2FSqXClStXEB0dDZVKhcTERPl5Sbt27YK/vz9MTU1x6NAhVFRUYOrUqXB0dISZmRkef/xxHD9+XG5POd8jjzwCc3NzDB48GIWFhdixYwd69OgBGxsbjB07FqWlpQ3mTxpu+sMPP8jPg/rHP/6BkpISJCUlwdPTE7a2toiNjcXNmzfl+bSHvmZlZeHJJ5+EmZkZevbsidTUVJ3rn5ycjEGDBsHCwgJ+fn44evRovViU5s+fD0dHR1hbW+OVV17BrFmzdA6zXrp0KVxcXGBvb48pU6agqqoKABAcHIw//vgDb7zxBp89RdTChBCYPn06Hn/8cfTq1QsAcPHiRQCAk5OTRl0nJyd5mrYrV67gvffew6uvviqXXbx4UWfHvqOjY4PtaNcbOnQoEhMTAdQOe42Ojq5Xr1OnTpgxYwb69OmDhx56CLGxsQgLC8M333yjUc/b2xtLliyBj48PunfvftvlExERtTXsqCMiauP27t2LCxcu4ODBg/jwww8RHx+PZ599Fra2tkhPT8fkyZMxefJk5OfnAwBKS0sxaNAgWFlZ4eDBgzh8+DCsrKwwZMiQBp/3c/78eTzzzDN49NFH8d///heff/451qxZg/nz5wMAPv74Y7z77rtwc3NDQUEBjh8/rrOsoKAAY8eORXR0NDIzM7F//36MHDkSQggAQEJCAubMmYMFCxYgMzMTCxcuxNy5c5GUlASg9gHkALBnzx4UFBQgOTm5XqydO3dGQUEBbGxssHz5chQUFGDMmDHy9JkzZ2LRokXIzMxE7969MXPmTGzZsgVJSUn4+eef4e3tjbCwMFy9elWj3fj4eKxYsQJpaWnIz8/H888/j+XLl2Pjxo3Ytm0bUlNT8emnnza6rUpLS/HJJ5/g66+/xs6dO+X13759O7Zv346vvvoKq1evxrfffqtz/pqaGowcORJGRkY4duwYvvjiC7z11ls6686ZMwczZsxARkYGunXrhrFjxzZ4B+GGDRuwYMECvP/++zh58iTc3d3x+eef16u3b98+ZGdnY9++fUhKSkJiYqL8j+/k5GS4ubnh3Xffle8CJaKWERMTg19++QWbNm2qN02701wIobMjvbi4GMOGDUPPnj0xb968RtvQbufhhx+GlZUVrKysMHTo0Hp1o6OjkZiYiHPnzuHo0aMYN25cvTo3b97EggUL0Lt3b9jb28PKygq7d++W78ST+Pv768gAERFRO6LXgbdERKTTunXrhFqtrlc+b9484efnJ3+OiooSHh4e4ubNm3KZj4+PeOKJJ+TP1dXVwtLSUmzatEkIIcSaNWuEj4+PqKmpketUVFQIc3NzsWvXLp3xvP322/Xm+eyzz4SVlZW87I8++kh4eHhozKdddvLkSQFA5Obm6lxO586d6z2T6L333hOBgYFCiOY9o06tVot169bJn6XnJW3dulUuu3HjhjA2NhYbNmyQyyorK4Wrq6tYsmSJxnx79uyR6yxatEgAENnZ2XLZq6++KsLCwhqMZ926dQKAOHv2rMY8FhYW4vr163JZWFiYePXVV+XPyue+7dq1SxgZGYn8/Hx5+o4dOzSeUSfl6Msvv5Tr/PbbbwKAyMzMlGNR7l/9+/cXU6ZM0Yg3KChI575WXV0tl40ePVqMGTNGZ6xE1DJiYmKEm5ubOHfunEZ5dna2ACB+/vlnjfIRI0aIl19+WaOsuLhYBAYGiqeeekqUlZVpTFuzZo3O65FarRZr164VQgiRm5srsrKyRFZWlvjzzz+FEHXnzqKiIlFdXS1cXFxEcHCwGD16tBBCiJSUFI1n1L3//vvC3t5efPXVVyIjI0NkZWWJYcOGaTwbc+DAgeL1119vVn6IiIjaGt5RR0TUxj388MPo0KHudO7k5ARfX1/5s5GREezt7VFYWAgAOHnyJM6ePQtra2v5Dgg7OzuUl5cjOztb5zIyMzMRGBiocVdFUFAQbty4gT///LPJsfr5+eGpp56Cr68vRo8ejYSEBBQVFQEA/vrrL+Tn52PChAlyXFZWVpg/f36Dcd0J5d0Y2dnZqKqqQlBQkFxmbGyMgIAAZGZmaszXu3dv+XcnJydYWFjgoYce0iiTctwQCwsLdOnSRWMeT09PWFlZNamdzMxMuLu7w83NTS4LDAzUWVcZr4uLCwA02O6ZM2cQEBCgUab9Gajd14yMjDTavd06E9H9IYRATEwMkpOTsXfvXvnRABIvLy84OztrDI+vrKzEgQMHMGDAALmsuLgYoaGhMDExwffffw8zMzONdgIDA3Ht2jX5jmYASE9Px7Vr1+R2PDw84O3tDW9vb3Tq1KlerEZGRoiMjMT+/ft1DnsFgEOHDiE8PBwvvfQS/Pz88NBDDyErK6v5iSEiImrjWu6p3kREdF8YGxtrfFapVDrLampqANQOn+zXrx82bNhQr62OHTvqXIbQMVRK3Bqu2pxnkRkZGSE1NRVpaWnYvXs3Pv30U8yZMwfp6enyCxMSEhI0HlouzXevWFpayr83tA661leZ09vluCHN3VbapHi1699uWVKdxuJraPs21ObtYiWi+2vKlCnYuHEjvvvuO1hbW8vPi1Or1TA3N4dKpcK0adOwcOFCdO3aFV27dsXChQthYWGBF198EQBw/fp1hIaGorS0FOvXr0dxcTGKi4sB1F4PjIyM0KNHDwwZMgQTJ06UX94wadIkPPvss/Dx8WlyvO+99x7efPPNBl+I5O3tjS1btiAtLQ22trb48MMPcfHiRfTo0eNu0kRERNTm8I46IiID07dvX2RlZcHR0VG+A0L6UavVOufp2bMn0tLSNDpv0tLSYG1trfPuicaoVCoEBQXhnXfewalTp2BiYoKUlBQ4OTmhU6dOOHfuXL24pDtFTExMAEDjZQt3w9vbGyYmJjh8+LBcVlVVhRMnTrTKfxz27NkTeXl5uHDhglymfEnEnfLx8dG4WwYATpw40ex2TExM7tm2IaLGff7557h27RqCg4Ph4uIi/2zevFmuM3PmTEybNg2vvfYa/P39cf78eezevRvW1tYAau+wTk9Px+nTp+Ht7a3RjvRcU6D2OZa+vr4IDQ1FaGgoevfuja+++qpZ8ZqYmMDBwaHB/1yYO3cu+vbti7CwMAQHB8PZ2RkRERHNTwwREVEbxzvqiIgMzLhx4/DBBx8gPDxcftlDXl4ekpOT8eabb2oMq5S89tprWL58OWJjYxETE4MzZ85g3rx5mD59usaw29tJT0/Hjz/+iNDQUDg6OiI9PR1//fWX3CkWHx+PqVOnwsbGBkOHDkVFRQVOnDiBoqIiTJ8+HY6OjjA3N8fOnTvh5uYGMzOzBjsXm8LS0hL/+te/5Dfkuru7Y8mSJSgtLcWECRPuuN37JSQkBD4+Pnj55ZexbNkyFBcXY86cOXfdbmxsLCZOnAh/f38MGDAAmzdvxi+//KIxtLcpPD09cfDgQbzwwgswNTWFg4PDXcdGRLrpuutVm0qlQnx8POLj43VODw4OblI7dnZ2WL9+fbPiu13bERERGtPt7OywdevWRtvcv39/s2IgIiJqi3hHHRGRgbGwsMDBgwfh7u6OkSNHokePHoiOjkZZWRlsbGx0ztOpUyds374dP/30E/z8/DB58mRMmDABcXFxzVq2jY0NDh48iGeeeQbdunVDXFwcli1bJr8l8JVXXsGXX36JxMRE+Pr6YuDAgUhMTJTvqHvggQfwySefYNWqVXB1dUV4ePjdJQPA4sWLMWrUKERGRqJv3744e/Ysdu3aBVtb27tu+17r0KEDUlJSUFFRgYCAALzyyitYsGDBXbc7btw4zJ49GzNmzEDfvn2Rk5OD8ePH13tW1e28++67yM3NRZcuXRocRk1ERERERA1Tiab8NxoREREZlKeffhrOzs7NHt5GRERERER3jkNfiYiIDFxpaSm++OILhIWFwcjICJs2bcKePXs03hZJRERERET3H++oIyIiMnBlZWUYPnw4fv75Z1RUVMDHxwdxcXEYOXKkvkMjIiIiIjIo7KgjIiIiIiIiIiJqBfgyCSIiIiIiIiIiolaAHXVEREREREREREStADvqiIiIiIiIiIiIWgF21BEREREREREREbUC7KgjIiIiIiIiIiJqBdhRR0RERERERERE1Aqwo46IiIiIiIiIiKgVYEcdERERERERERFRK/B/5gHjmV3hd6QAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot reflectivity\n", + "variable='reflectivity'\n", + "#variable='reflectivity_best_estimate'\n", + "# Let's filter out test 5 using ACT. Yes, it's that simple!\n", + "ds_arscl.qcfilter.datafilter(variable, rm_tests=[1, 2], del_qc_var=False)\n", + "\n", + "# There are other ways we can filter data out as well. Using the\n", + "# rm_assessments will filter out by all Bad/Suspect tests that are failing\n", + "# ds.qcfilter.datafilter(variable, rm_assessments=['Bad', 'Suspect'], del_qc_var=False)\n", + "\n", + "# Let's check out the attributes of the variable\n", + "# Whenever data are filtered out using the datafilter function\n", + "# a comment will be added to the variable history for provenance purposes\n", + "#print(ds_arscl[variable].attrs)\n", + "\n", + "# And plot it all again!\n", + "# Create a plotting display object with 2 plots\n", + "display = act.plotting.TimeSeriesDisplay(ds_arscl, 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", + "ref = ds_arscl.reflectivity_best_estimate\n", + "vel = ds_arscl.mean_doppler_velocity\n", + "ref_lowest_5000m = ref.sel(height=slice(0., 5000))\n", + "vel_lowest_5000m = vel.sel(height=slice(0., 5000))\n", + "ref_lowest_5000m.plot(x='time',y='height',\n", + " cmap='ChaseSpectral',\n", + " vmin=-40,\n", + " vmax=20)\n", + "vel_lowest_5000m.plot(x='time',y='height',\n", + " cmap='seismic',\n", + " vmin=-40,\n", + " vmax=20)\n", + "\n", + "#ref_lowest_5000m.hvplot(x='time',\n", + "# y='height',\n", + "# cmap='ChaseSpectral',\n", + "# clim=(-40, 20),\n", + "# rasterize=True)\n", + "\n", + "# Plot cloud base height\n", + "#display.plot('cloud_base_best_estimate', subplot_index=(0,))\n", + "\n", + "# Plot velocities \n", + "#display.plot('mean_doppler_velocity', subplot_index=(1,))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "f277e77d-e37b-47c1-b8ab-b691b76a4da4", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ], + "text/plain": [ + ":Layout\n", + " .DynamicMap.I :DynamicMap []\n", + " :Image [time,height] (reflectivity)\n", + " .DynamicMap.II :DynamicMap []\n", + " :Image [time,height] (mean_doppler_velocity)" + ] + }, + "execution_count": 90, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p4578" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "formatter = DatetimeTickFormatter(hours=\"%d %b %Y \\n %H:%M UTC\")\n", + "reflectivity_plot = ds_arscl.reflectivity.sel(height=slice(0, 7000)).hvplot(x='time', y='height', cmap='Spectral_r', xformatter=formatter, clim=(-40, 20), rasterize=True, clabel='Reflectivity (dBZ)')\n", + "velocity_plot = ds_arscl.mean_doppler_velocity.sel(height=slice(0, 7000)).hvplot(x='time', y='height', cmap='seismic', xformatter=formatter, clim=(-5, 5), rasterize=True, clabel='Mean Doppler Velocity (m/s)')\n", + "\n", + "reflectivity_plot + velocity_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "cae20c35-2473-4fc2-9814-e0ff69991413", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[DOWNLOADING] nsaarsclkazr1kolliasC1.c0.20161106.000000.nc\n", + "[DOWNLOADING] nsaarsclkazr1kolliasC1.c0.20161105.000000.nc\n", + "\n", + "If you use these data to prepare a publication, please cite:\n", + "\n", + "Johnson, K., Giangrande, S., & Toto, T. Active Remote Sensing of CLouds (ARSCL)\n", + "product using Ka-band ARM Zenith Radars (ARSCLKAZR1KOLLIAS). Atmospheric\n", + "Radiation Measurement (ARM) User Facility. https://doi.org/10.5439/1393437\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 2GB\n",
+       "Dimensions:                               (time: 43200, height: 596, layer: 10,\n",
+       "                                           radar_mode: 4)\n",
+       "Coordinates:\n",
+       "  * time                                  (time) datetime64[ns] 346kB 2016-11...\n",
+       "  * layer                                 (layer) int32 40B 0 1 2 3 4 5 6 7 8 9\n",
+       "  * height                                (height) float32 2kB 160.0 ... 1.80...\n",
+       "  * radar_mode                            (radar_mode) |S2 8B b'hi' ... b'pr'\n",
+       "Data variables: (12/33)\n",
+       "    base_time                             (time) datetime64[ns] 346kB 2016-11...\n",
+       "    time_offset                           (time) datetime64[ns] 346kB 2016-11...\n",
+       "    reflectivity_best_estimate            (time, height) float32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    qc_reflectivity_best_estimate         (time, height) int32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    reflectivity                          (time, height) float32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    qc_reflectivity                       (time, height) int32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    ...                                    ...\n",
+       "    minimum_detectable_reflectivity_flag  (time, height) float32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    reflectivity_saturation_flag          (time, height) float32 103MB dask.array<chunksize=(901, 596), meta=np.ndarray>\n",
+       "    instrument_availability_flag          (time) int16 86kB dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    lat                                   (time) float32 173kB 71.32 ... 71.32\n",
+       "    lon                                   (time) float32 173kB -156.6 ... -156.6\n",
+       "    alt                                   (time) float32 173kB 8.0 8.0 ... 8.0\n",
+       "Attributes: (12/23)\n",
+       "    command_line:                     idl -R -n kazrarsclc0 -s nsa -f C1 -b 2...\n",
+       "    Conventions:                      ARM-1.2\n",
+       "    process_version:                  vap-kazrarscl-1.0.0-devel\n",
+       "    dod_version:                      arsclkazr1kollias-c0-1.0\n",
+       "    site_id:                          nsa\n",
+       "    platform_id:                      arsclkazr1kollias\n",
+       "    ...                               ...\n",
+       "    doi:                              10.5439/1393437\n",
+       "    history:                          created by user ttoto on machine talc.d...\n",
+       "    _file_dates:                      ['20161105', '20161106']\n",
+       "    _file_times:                      ['000000', '000000']\n",
+       "    _datastream:                      nsaarsclkazr1kolliasC1.c0\n",
+       "    _arm_standards_flag:              1
" + ], + "text/plain": [ + " Size: 2GB\n", + "Dimensions: (time: 43200, height: 596, layer: 10,\n", + " radar_mode: 4)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 346kB 2016-11...\n", + " * layer (layer) int32 40B 0 1 2 3 4 5 6 7 8 9\n", + " * height (height) float32 2kB 160.0 ... 1.80...\n", + " * radar_mode (radar_mode) |S2 8B b'hi' ... b'pr'\n", + "Data variables: (12/33)\n", + " base_time (time) datetime64[ns] 346kB 2016-11...\n", + " time_offset (time) datetime64[ns] 346kB 2016-11...\n", + " reflectivity_best_estimate (time, height) float32 103MB dask.array\n", + " qc_reflectivity_best_estimate (time, height) int32 103MB dask.array\n", + " reflectivity (time, height) float32 103MB dask.array\n", + " qc_reflectivity (time, height) int32 103MB dask.array\n", + " ... ...\n", + " minimum_detectable_reflectivity_flag (time, height) float32 103MB dask.array\n", + " reflectivity_saturation_flag (time, height) float32 103MB dask.array\n", + " instrument_availability_flag (time) int16 86kB dask.array\n", + " lat (time) float32 173kB 71.32 ... 71.32\n", + " lon (time) float32 173kB -156.6 ... -156.6\n", + " alt (time) float32 173kB 8.0 8.0 ... 8.0\n", + "Attributes: (12/23)\n", + " command_line: idl -R -n kazrarsclc0 -s nsa -f C1 -b 2...\n", + " Conventions: ARM-1.2\n", + " process_version: vap-kazrarscl-1.0.0-devel\n", + " dod_version: arsclkazr1kollias-c0-1.0\n", + " site_id: nsa\n", + " platform_id: arsclkazr1kollias\n", + " ... ...\n", + " doi: 10.5439/1393437\n", + " history: created by user ttoto on machine talc.d...\n", + " _file_dates: ['20161105', '20161106']\n", + " _file_times: ['000000', '000000']\n", + " _datastream: nsaarsclkazr1kolliasC1.c0\n", + " _arm_standards_flag: 1" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# NSA datastream\n", + "datastream = 'nsaarsclkazr1kolliasC1.c0'\n", + "startdate = '2016-11-05'\n", + "enddate = '2016-11-06'\n", + "\n", + "# Read in data\n", + "result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)\n", + "\n", + "ds_arscl_nsa = act.io.read_arm_netcdf(result)\n", + "\n", + "ds_arscl_nsa" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "f991fba1-0d48-4f35-8b99-005297c90bc1", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ], + "text/plain": [ + ":Layout\n", + " .DynamicMap.I :DynamicMap []\n", + " :Image [time,height] (reflectivity)\n", + " .DynamicMap.II :DynamicMap []\n", + " :Image [time,height] (mean_doppler_velocity)\n", + " .DynamicMap.III :DynamicMap []\n", + " :Image [time,height] (spectral_width)" + ] + }, + "execution_count": 85, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p3408" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "reflectivity_plot = ds_arscl_nsa.reflectivity.sel(height=slice(0, 3000)).hvplot(x='time', y='height', cmap='Spectral_r', xformatter=formatter, clim=(-40, 20), rasterize=True, clabel='Reflectivity (dBZ)')\n", + "velocity_plot = ds_arscl_nsa.mean_doppler_velocity.sel(height=slice(0, 3000)).hvplot(x='time', y='height', cmap='seismic', xformatter=formatter, clim=(-5, 5), rasterize=True, clabel='Mean Doppler Velocity (m/s)')\n", + "specwidth_plot = ds_arscl_nsa.spectral_width.sel(height=slice(0, 3000)).hvplot(x='time', y='height', cmap='seismic', xformatter=formatter, clim=(-1, 1), rasterize=True, clabel='Spectral Width (m/s)')\n", + "\n", + "reflectivity_plot + velocity_plot + specwidth_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "dc856a05-f796-4fd1-b4ea-3cd5acc6b3f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200311.052700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200312.172800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200321.232600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200305.052800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200330.052500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200313.232200.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200325.172400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200314.053000.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200322.053100.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200306.172900.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200305.172300.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200326.232600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200315.112300.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200315.172700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200313.052700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200301.112300.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200321.172900.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200329.172800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200314.173400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200328.232200.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200304.172800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200319.112100.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200311.234900.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200303.232400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200310.172400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200326.112700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200327.232900.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200326.052600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200303.112600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200314.232400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200317.172700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200310.232700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200309.112400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200320.112700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200329.053400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200329.232800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200309.173000.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200307.112700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200301.172900.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200327.052700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200316.054100.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200323.112500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200302.172500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200305.232500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200325.232400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200319.052700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200318.232700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200320.232400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200319.172200.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200318.112500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200304.112400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200308.052800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200309.232800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200330.112500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200305.112700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200318.173100.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200310.053200.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200329.112300.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200320.053000.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200330.172600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200313.112600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200312.052700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200325.053300.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200306.052800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200322.173000.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200322.112400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200308.172900.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200327.112300.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200327.172700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200317.112600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200328.112300.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200324.052800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200304.052900.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200321.112600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200314.112400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200318.052700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200319.232800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200320.054500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200307.232600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200328.172600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200322.232700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200311.112200.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200303.053600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200312.112700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200323.173500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200330.233500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200321.052600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200320.174700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200325.112400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200307.172900.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200326.172400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200328.053500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200315.052900.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200313.172600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200310.112700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200324.112600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200308.232700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200302.052300.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200303.172500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200311.174700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200324.232400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200307.052500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200306.232500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200302.112400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200323.052800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200316.232600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200324.172400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200317.052600.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200317.232500.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200306.112700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200316.172800.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200304.232400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200301.052700.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200301.232400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200315.232400.cdf\n", + "[DOWNLOADING] anxpblhtsonde1mcfarlM1.c1.20200323.232300.cdf\n", + "\n", + "If you use these data to prepare a publication, please cite:\n", + "\n", + "Zhang, D., & Zhang, D. Planetary Boundary Layer Height (PBLHTSONDE1MCFARL).\n", + "Atmospheric Radiation Measurement (ARM) User Facility.\n", + "https://doi.org/10.5439/1991783\n", + "\n", + "[DOWNLOADING] nsapblhtsonde1mcfarlC1.c1.20161105.173000.cdf\n", + "[DOWNLOADING] nsapblhtsonde1mcfarlC1.c1.20161105.053000.cdf\n", + "\n", + "If you use these data to prepare a publication, please cite:\n", + "\n", + "Riihimaki, L., Riihimaki, L., Zhang, D., & Zhang, D. Planetary Boundary Layer\n", + "Height (PBLHTSONDE1MCFARL). Atmospheric Radiation Measurement (ARM) User\n", + "Facility. https://doi.org/10.5439/1991783\n", + "\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Coordinate variable height_ss is neither monotonically increasing nor monotonically decreasing on all datasets", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[93], line 15\u001b[0m\n\u001b[1;32m 12\u001b[0m result \u001b[38;5;241m=\u001b[39m act\u001b[38;5;241m.\u001b[39mdiscovery\u001b[38;5;241m.\u001b[39mdownload_arm_data(username, token, datastream, startdate, enddate)\n\u001b[1;32m 13\u001b[0m result2 \u001b[38;5;241m=\u001b[39m act\u001b[38;5;241m.\u001b[39mdiscovery\u001b[38;5;241m.\u001b[39mdownload_arm_data(username, token, datastream2, startdate2, enddate2)\n\u001b[0;32m---> 15\u001b[0m ds_pbl_comble \u001b[38;5;241m=\u001b[39m \u001b[43mact\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_arm_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m ds_pbl_nsa \u001b[38;5;241m=\u001b[39m act\u001b[38;5;241m.\u001b[39mio\u001b[38;5;241m.\u001b[39mread_arm_netcdf(result2)\n", + "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/act/io/arm.py:172\u001b[0m, in \u001b[0;36mread_arm_netcdf\u001b[0;34m(filenames, concat_dim, return_None, combine, decode_times, use_cftime, use_base_time, combine_attrs, cleanup_qc, keep_variables, **kwargs)\u001b[0m\n\u001b[1;32m 168\u001b[0m ds \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_mfdataset(filenames, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 171\u001b[0m \u001b[38;5;66;03m# When all else fails raise the orginal exception\u001b[39;00m\n\u001b[0;32m--> 172\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# If requested use base_time and time_offset to derive time. Assumes that the units\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[38;5;66;03m# of both are in seconds and that the value is number of seconds since epoch.\u001b[39;00m\n\u001b[1;32m 176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_base_time:\n", + "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/act/io/arm.py:147\u001b[0m, in \u001b[0;36mread_arm_netcdf\u001b[0;34m(filenames, concat_dim, return_None, combine, decode_times, use_cftime, use_base_time, combine_attrs, cleanup_qc, keep_variables, **kwargs)\u001b[0m\n\u001b[1;32m 143\u001b[0m except_tuple \u001b[38;5;241m=\u001b[39m except_tuple \u001b[38;5;241m+\u001b[39m (\u001b[38;5;167;01mFileNotFoundError\u001b[39;00m, \u001b[38;5;167;01mOSError\u001b[39;00m)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 146\u001b[0m \u001b[38;5;66;03m# Read data file with Xarray function\u001b[39;00m\n\u001b[0;32m--> 147\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_mfdataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilenames\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m except_tuple \u001b[38;5;28;01mas\u001b[39;00m exception:\n\u001b[1;32m 150\u001b[0m \u001b[38;5;66;03m# If requested return None for File not found error\u001b[39;00m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(exception)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFileNotFoundError\u001b[39m\u001b[38;5;124m'\u001b[39m:\n", + "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/xarray/backends/api.py:1082\u001b[0m, in \u001b[0;36mopen_mfdataset\u001b[0;34m(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)\u001b[0m\n\u001b[1;32m 1069\u001b[0m combined \u001b[38;5;241m=\u001b[39m _nested_combine(\n\u001b[1;32m 1070\u001b[0m datasets,\n\u001b[1;32m 1071\u001b[0m concat_dims\u001b[38;5;241m=\u001b[39mconcat_dim,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1077\u001b[0m combine_attrs\u001b[38;5;241m=\u001b[39mcombine_attrs,\n\u001b[1;32m 1078\u001b[0m )\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m combine \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mby_coords\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 1080\u001b[0m \u001b[38;5;66;03m# Redo ordering from coordinates, ignoring how they were ordered\u001b[39;00m\n\u001b[1;32m 1081\u001b[0m \u001b[38;5;66;03m# previously\u001b[39;00m\n\u001b[0;32m-> 1082\u001b[0m combined \u001b[38;5;241m=\u001b[39m \u001b[43mcombine_by_coords\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1083\u001b[0m \u001b[43m \u001b[49m\u001b[43mdatasets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1084\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompat\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1085\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_vars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_vars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1086\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1087\u001b[0m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1088\u001b[0m \u001b[43m \u001b[49m\u001b[43mcombine_attrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcombine_attrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1089\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1090\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1091\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1092\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcombine\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is an invalid option for the keyword argument\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1093\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m ``combine``\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1094\u001b[0m )\n", + "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:958\u001b[0m, in \u001b[0;36mcombine_by_coords\u001b[0;34m(data_objects, compat, data_vars, coords, fill_value, join, combine_attrs)\u001b[0m\n\u001b[1;32m 954\u001b[0m grouped_by_vars \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mgroupby(sorted_datasets, key\u001b[38;5;241m=\u001b[39mvars_as_keys)\n\u001b[1;32m 956\u001b[0m \u001b[38;5;66;03m# Perform the multidimensional combine on each group of data variables\u001b[39;00m\n\u001b[1;32m 957\u001b[0m \u001b[38;5;66;03m# before merging back together\u001b[39;00m\n\u001b[0;32m--> 958\u001b[0m concatenated_grouped_by_data_vars \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[1;32m 959\u001b[0m _combine_single_variable_hypercube(\n\u001b[1;32m 960\u001b[0m \u001b[38;5;28mtuple\u001b[39m(datasets_with_same_vars),\n\u001b[1;32m 961\u001b[0m fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[1;32m 962\u001b[0m data_vars\u001b[38;5;241m=\u001b[39mdata_vars,\n\u001b[1;32m 963\u001b[0m coords\u001b[38;5;241m=\u001b[39mcoords,\n\u001b[1;32m 964\u001b[0m compat\u001b[38;5;241m=\u001b[39mcompat,\n\u001b[1;32m 965\u001b[0m join\u001b[38;5;241m=\u001b[39mjoin,\n\u001b[1;32m 966\u001b[0m combine_attrs\u001b[38;5;241m=\u001b[39mcombine_attrs,\n\u001b[1;32m 967\u001b[0m )\n\u001b[1;32m 968\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mvars\u001b[39m, datasets_with_same_vars \u001b[38;5;129;01min\u001b[39;00m grouped_by_vars\n\u001b[1;32m 969\u001b[0m )\n\u001b[1;32m 971\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m merge(\n\u001b[1;32m 972\u001b[0m concatenated_grouped_by_data_vars,\n\u001b[1;32m 973\u001b[0m compat\u001b[38;5;241m=\u001b[39mcompat,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 976\u001b[0m combine_attrs\u001b[38;5;241m=\u001b[39mcombine_attrs,\n\u001b[1;32m 977\u001b[0m )\n", + "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:959\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 954\u001b[0m grouped_by_vars \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mgroupby(sorted_datasets, key\u001b[38;5;241m=\u001b[39mvars_as_keys)\n\u001b[1;32m 956\u001b[0m \u001b[38;5;66;03m# Perform the multidimensional combine on each group of data variables\u001b[39;00m\n\u001b[1;32m 957\u001b[0m \u001b[38;5;66;03m# before merging back together\u001b[39;00m\n\u001b[1;32m 958\u001b[0m concatenated_grouped_by_data_vars \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\n\u001b[0;32m--> 959\u001b[0m \u001b[43m_combine_single_variable_hypercube\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 960\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mtuple\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdatasets_with_same_vars\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 961\u001b[0m \u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfill_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 962\u001b[0m \u001b[43m \u001b[49m\u001b[43mdata_vars\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata_vars\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 963\u001b[0m \u001b[43m \u001b[49m\u001b[43mcoords\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcoords\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 964\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompat\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompat\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 965\u001b[0m \u001b[43m \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 966\u001b[0m \u001b[43m \u001b[49m\u001b[43mcombine_attrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcombine_attrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 967\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 968\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mvars\u001b[39m, datasets_with_same_vars \u001b[38;5;129;01min\u001b[39;00m grouped_by_vars\n\u001b[1;32m 969\u001b[0m )\n\u001b[1;32m 971\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m merge(\n\u001b[1;32m 972\u001b[0m concatenated_grouped_by_data_vars,\n\u001b[1;32m 973\u001b[0m compat\u001b[38;5;241m=\u001b[39mcompat,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 976\u001b[0m combine_attrs\u001b[38;5;241m=\u001b[39mcombine_attrs,\n\u001b[1;32m 977\u001b[0m )\n", + "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:619\u001b[0m, in \u001b[0;36m_combine_single_variable_hypercube\u001b[0;34m(datasets, fill_value, data_vars, coords, compat, join, combine_attrs)\u001b[0m\n\u001b[1;32m 613\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(datasets) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 614\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 615\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAt least one Dataset is required to resolve variable names \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 616\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfor combined hypercube.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 617\u001b[0m )\n\u001b[0;32m--> 619\u001b[0m combined_ids, concat_dims \u001b[38;5;241m=\u001b[39m \u001b[43m_infer_concat_order_from_coords\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdatasets\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 622\u001b[0m \u001b[38;5;66;03m# check that datasets form complete hypercube\u001b[39;00m\n\u001b[1;32m 623\u001b[0m _check_shape_tile_ids(combined_ids)\n", + "File \u001b[0;32m/opt/conda/lib/python3.11/site-packages/xarray/core/combine.py:111\u001b[0m, in \u001b[0;36m_infer_concat_order_from_coords\u001b[0;34m(datasets)\u001b[0m\n\u001b[1;32m 109\u001b[0m ascending \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 111\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 112\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCoordinate variable \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdim\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is neither \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 113\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmonotonically increasing nor \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmonotonically decreasing on all datasets\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 115\u001b[0m )\n\u001b[1;32m 117\u001b[0m \u001b[38;5;66;03m# Assume that any two datasets whose coord along dim starts\u001b[39;00m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;66;03m# with the same value have the same coord values throughout.\u001b[39;00m\n\u001b[1;32m 119\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(index\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m index \u001b[38;5;129;01min\u001b[39;00m indexes):\n", + "\u001b[0;31mValueError\u001b[0m: Coordinate variable height_ss is neither monotonically increasing nor monotonically decreasing on all datasets" + ] + } + ], + "source": [ + "# COMBLE PBL heights\n", + "datastream = 'anxpblhtsonde1mcfarlM1.c1'\n", + "datastream2 = 'nsapblhtsonde1mcfarlC1.c1'\n", + "\n", + "startdate = '2020-03-01'\n", + "enddate = '2020-03-31'\n", + "\n", + "startdate2 = '2016-11-05'\n", + "enddate2 = '2016-11-06'\n", + "\n", + "# Read in data\n", + "result = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)\n", + "result2 = act.discovery.download_arm_data(username, token, datastream2, startdate2, enddate2)\n", + "\n", + "ds_pbl_comble = act.io.read_arm_netcdf(result)\n", + "ds_pbl_nsa = act.io.read_arm_netcdf(result2)" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "3d2f5e7e-050b-4237-ad4d-1b5559b69d6e", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'ds_pbl_nsa' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[94], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mds_pbl_nsa\u001b[49m\n", + "\u001b[0;31mNameError\u001b[0m: name 'ds_pbl_nsa' is not defined" + ] + } + ], + "source": [ + "ds_pbl_nsa" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fe519a9-9262-4732-bf11-6b0faca5d151", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/COMBLE-MIP_test.ipynb b/COMBLE-MIP_test.ipynb new file mode 100644 index 0000000..20d1cc7 --- /dev/null +++ b/COMBLE-MIP_test.ipynb @@ -0,0 +1,1489 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 90, + "id": "78902a72-7cd0-4ba2-a85a-4befa2aca688", + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.4.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n", + " root._bokeh_is_loading = css_urls.length + 0;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.1.min.js\", \"https://cdn.holoviz.org/panel/1.4.2/dist/panel.min.js\"];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + "\ttry {\n", + " inline_js[i].call(root, root.Bokeh);\n", + "\t} catch(e) {\n", + "\t if (!reloading) {\n", + "\t throw e;\n", + "\t }\n", + "\t}\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.1.min.js\", \"https://cdn.holoviz.org/panel/1.4.2/dist/panel.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1016" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# imports\n", + "import act\n", + "import numpy as np\n", + "import xarray as xr\n", + "import xwrf\n", + "import pyart\n", + "from datetime import datetime, timedelta\n", + "import matplotlib.animation as animation\n", + "\n", + "import cmweather\n", + "import pandas as pd\n", + "import glob\n", + "\n", + "from bokeh.models.formatters import DatetimeTickFormatter\n", + "import hvplot.xarray\n", + "import holoviews as hv\n", + "hv.extension(\"bokeh\")\n", + "\n", + "import sys\n", + "sys.path.insert(0, '/data/home/jeissner/arm-summer-school-2024/tutorials/comble/')\n", + "from functions_plotting_tutorial import load_sims" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "id": "5030434c-7837-4bf3-88ea-957b4c22a6c7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# load in raw WRF data\n", + "raw_wrf_output_dir = '/data/project/ARM_Summer_School_2024_Data/comble-mip/output_les/wrf/WRF_Lx25_dx100_FixN_raw/'\n", + "\n", + "raw_wrfstat_files = sorted(glob.glob(raw_wrf_output_dir+'wrfstat*'))\n", + "raw_wrfout_files = sorted(glob.glob(raw_wrf_output_dir+'wrfout*'))\n", + "\n", + "for i in range(0,15):\n", + " \n", + " filename = raw_wrfstat_files[i]\n", + " filename2 = raw_wrfout_files[i]\n", + " ds = xr.open_dataset(filename, decode_times=False).xwrf.postprocess()\n", + " dsout = xr.open_dataset(filename2, decode_times=False).xwrf.postprocess()\n", + "\n", + " #ds.CST_SH.plot(x='Time', color='red')\n", + " #ds.CST_LH.plot(x='Time', color='blue')\n", + " ds.CST_T2.plot(x='Time', color='blue')\n", + " ds.CST_TSAIR.plot(x='Time', color='k')\n", + " #ds.CST_TKE.plot(x='Time', color='k')\n", + " #ds.CST_CLWP.plot(x='Time', color='purple')\n", + " #ds.CST_CLDTOT.plot(x='Time', color='g')\n", + " #ds.CST_CLDLOW.plot(x='Time', color='y')\n", + " #ds.CST_IWP.plot(x='Time', color='m')\n", + " #ds.CST_RWP.plot(x='Time', color='r')\n", + " \n", + "#ds.CST_SH.plot(x='Time')\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "4ee57f3b-e7a3-4f72-a595-507aa91849de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.Dataset> Size: 5GB\n",
+       "Dimensions:         (Time: 6, z: 159, z_stag: 160, y: 256, x: 256, x_stag: 257,\n",
+       "                     y_stag: 257)\n",
+       "Coordinates:\n",
+       "    XTIME           (Time) float32 24B ...\n",
+       "  * Time            (Time) float32 24B 1.02e+03 1.03e+03 ... 1.06e+03 1.07e+03\n",
+       "  * x               (x) float64 2kB -1.275e+04 -1.265e+04 ... 1.275e+04\n",
+       "  * y               (y) float64 2kB -1.275e+04 -1.265e+04 ... 1.275e+04\n",
+       "  * y_stag          (y_stag) float64 2kB -1.28e+04 -1.27e+04 ... 1.28e+04\n",
+       "  * x_stag          (x_stag) float64 2kB -1.28e+04 -1.27e+04 ... 1.28e+04\n",
+       "Dimensions without coordinates: z, z_stag\n",
+       "Data variables: (12/231)\n",
+       "    Times           (Time) |S19 114B b'2020-03-13_15:00:00' ... b'2020-03-13_...\n",
+       "    CST_CLDLOW      (Time) float32 24B ...\n",
+       "    CST_CLDTOT      (Time) float32 24B ...\n",
+       "    CST_CLDTOT2     (Time) float32 24B ...\n",
+       "    CST_CLWP        (Time) float32 24B ...\n",
+       "    CST_RWP         (Time) float32 24B ...\n",
+       "    ...              ...\n",
+       "    CSS_CLDLOW      (Time, y, x) float32 2MB ...\n",
+       "    CSS_OPDC        (Time, y, x) float32 2MB ...\n",
+       "    CSS_OPDR        (Time, y, x) float32 2MB ...\n",
+       "    CSS_OPDI        (Time, y, x) float32 2MB ...\n",
+       "    CSS_OPDS        (Time, y, x) float32 2MB ...\n",
+       "    CSS_OPDG        (Time, y, x) float32 2MB ...\n",
+       "Attributes: (12/85)\n",
+       "    TITLE:                            OUTPUT FROM WRF V4.2.2 MODEL\n",
+       "    START_DATE:                      2020-03-12_22:00:00\n",
+       "    WEST-EAST_GRID_DIMENSION:        257\n",
+       "    SOUTH-NORTH_GRID_DIMENSION:      257\n",
+       "    BOTTOM-TOP_GRID_DIMENSION:       160\n",
+       "    DX:                              100.0\n",
+       "    ...                              ...\n",
+       "    ISLAKE:                          -1\n",
+       "    ISICE:                           24\n",
+       "    ISURBAN:                         1\n",
+       "    ISOILWATER:                      14\n",
+       "    HYBRID_OPT:                      0\n",
+       "    ETAC:                            0.0
" + ], + "text/plain": [ + " Size: 5GB\n", + "Dimensions: (Time: 6, z: 159, z_stag: 160, y: 256, x: 256, x_stag: 257,\n", + " y_stag: 257)\n", + "Coordinates:\n", + " XTIME (Time) float32 24B ...\n", + " * Time (Time) float32 24B 1.02e+03 1.03e+03 ... 1.06e+03 1.07e+03\n", + " * x (x) float64 2kB -1.275e+04 -1.265e+04 ... 1.275e+04\n", + " * y (y) float64 2kB -1.275e+04 -1.265e+04 ... 1.275e+04\n", + " * y_stag (y_stag) float64 2kB -1.28e+04 -1.27e+04 ... 1.28e+04\n", + " * x_stag (x_stag) float64 2kB -1.28e+04 -1.27e+04 ... 1.28e+04\n", + "Dimensions without coordinates: z, z_stag\n", + "Data variables: (12/231)\n", + " Times (Time) |S19 114B b'2020-03-13_15:00:00' ... b'2020-03-13_...\n", + " CST_CLDLOW (Time) float32 24B ...\n", + " CST_CLDTOT (Time) float32 24B ...\n", + " CST_CLDTOT2 (Time) float32 24B ...\n", + " CST_CLWP (Time) float32 24B ...\n", + " CST_RWP (Time) float32 24B ...\n", + " ... ...\n", + " CSS_CLDLOW (Time, y, x) float32 2MB ...\n", + " CSS_OPDC (Time, y, x) float32 2MB ...\n", + " CSS_OPDR (Time, y, x) float32 2MB ...\n", + " CSS_OPDI (Time, y, x) float32 2MB ...\n", + " CSS_OPDS (Time, y, x) float32 2MB ...\n", + " CSS_OPDG (Time, y, x) float32 2MB ...\n", + "Attributes: (12/85)\n", + " TITLE: OUTPUT FROM WRF V4.2.2 MODEL\n", + " START_DATE: 2020-03-12_22:00:00\n", + " WEST-EAST_GRID_DIMENSION: 257\n", + " SOUTH-NORTH_GRID_DIMENSION: 257\n", + " BOTTOM-TOP_GRID_DIMENSION: 160\n", + " DX: 100.0\n", + " ... ...\n", + " ISLAKE: -1\n", + " ISICE: 24\n", + " ISURBAN: 1\n", + " ISOILWATER: 14\n", + " HYBRID_OPT: 0\n", + " ETAC: 0.0" + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#print(dsout['PBLH'])\n", + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "be94d7ce-9be7-49f1-8304-928a23e6fed8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# spatial slabs\n", + "\n", + "ds.CSS_CLWP[1].plot(x='x', y='y')\n", + "plt.show()\n", + "ds.CSS_CLDLOW[1].plot(x='x', y='y')\n", + "plt.show()\n", + "dsout.PBLH.plot(x='x', y='y')\n", + "plt.show()\n", + "dsout.HFX.plot(x='x', y='y')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "2d7ca01d-7121-4d3b-98a0-a4d290765c68", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Size: 4B\n", + "array(0.20895386, dtype=float32)\n", + "Coordinates:\n", + " XTIME float32 4B 1.02e+03\n", + " Time float32 4B 1.02e+03\n", + " Size: 4B\n", + "array(0.2192688, dtype=float32)\n", + "Coordinates:\n", + " XTIME float32 4B 1.03e+03\n", + " Time float32 4B 1.03e+03\n", + " Size: 4B\n", + "array(0.2638855, dtype=float32)\n", + "Coordinates:\n", + " XTIME float32 4B 1.04e+03\n", + " Time float32 4B 1.04e+03\n", + " Size: 4B\n", + "array(0.28927612, dtype=float32)\n", + "Coordinates:\n", + " XTIME float32 4B 1.05e+03\n", + " Time float32 4B 1.05e+03\n", + " Size: 4B\n", + "array(0.29296875, dtype=float32)\n", + "Coordinates:\n", + " XTIME float32 4B 1.06e+03\n", + " Time float32 4B 1.06e+03\n", + " Size: 4B\n", + "array(0.28805542, dtype=float32)\n", + "Coordinates:\n", + " XTIME float32 4B 1.07e+03\n", + " Time float32 4B 1.07e+03\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# profiles \n", + "\n", + "#ds.CSP_V[0].plot(y='CSP_P')\n", + "plt.plot(ds.CSP_TH[0], ds.CSP_P[0]/100.)\n", + "plt.show()\n", + "ds.CSP_CLWC[0].plot(y='z')\n", + "plt.show()\n", + "(ds.CSP_THL[0]-ds.CSP_TH[0]).plot(y='z')\n", + "plt.show()\n", + "\n", + "x = np.abs(ds.CSP_P[0]/100. - 850.).argmin()\n", + "\n", + "for i in range(0,6):\n", + " print(ds.CSP_TH[i][29] - ds.CSP_TH[i][0])\n", + " plt.plot(i, ds.CSP_TH[i][29] - ds.CSP_TH[i][0])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "id": "90162875-d937-4646-9e54-8872149e37ef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# merge all times together and create a (time, height) plots to show evolution of boundary layer TH, \n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "784c9684-a088-4bd0-a4b2-f63878a6aad8", + "metadata": {}, + "outputs": [], + "source": [ + "# what is relationship between surface flux / CAO index / cloud properties\n", + "# spatial correlations -- I guess this would have to be instantaneous which doesn't really make sense\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3a62705-5e32-47ba-963d-c9cbf153983b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_config.yml b/_config.yml index f2f6049..57e058b 100644 --- a/_config.yml +++ b/_config.yml @@ -8,7 +8,7 @@ copyright: "2024" execute: # To execute notebooks via a Binder instead, replace 'cache' with 'binder' - execute_notebooks: cache + execute_notebooks: "off" timeout: 600 allow_errors: False # cells with expected failures must set the `raises-exception` cell tag diff --git a/_toc.yml b/_toc.yml index 995f86b..f49d661 100644 --- a/_toc.yml +++ b/_toc.yml @@ -4,6 +4,9 @@ parts: - caption: Preamble chapters: - file: notebooks/how-to-cite - - caption: Introduction + - caption: CAO Identification chapters: - - file: notebooks/notebook-template + - file: notebooks/CAO_Identification + - caption: Cloud Comparisons + chapters: + - file: notebooks/cloud_comparison.ipynb diff --git a/environment.yml b/environment.yml index c631642..4ace08c 100644 --- a/environment.yml +++ b/environment.yml @@ -5,3 +5,9 @@ dependencies: - jupyter-book - jupyterlab - sphinx-pythia-theme + - act + - numpy + - xarray + - matplotlib.pyplot + - datetime.datetime + - pyart diff --git a/notebooks/CAO_Identification.ipynb b/notebooks/CAO_Identification.ipynb index 965873e..e3097fc 100644 --- a/notebooks/CAO_Identification.ipynb +++ b/notebooks/CAO_Identification.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "7e3c21b0-507e-4f03-b369-2a262b6fb1e6", "metadata": {}, "outputs": [], @@ -10,7 +10,7 @@ "import act\n", "import numpy as np\n", "import xarray as xr\n", - "import matplotlib.pyplot as plt" + "from matplotlib import pyplot as plt, dates" ] }, { @@ -23,3149 +23,36 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "04dbca09-439c-4dab-95ea-c5250bbb3037", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200124.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200125.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200126.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200122.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200123.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200127.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200121.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200120.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200128.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200118.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200119.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200129.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200115.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200114.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200130.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200116.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200204.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200205.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200206.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200207.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200303.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200301.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200304.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200217.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200308.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200306.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200309.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200307.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200316.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200222.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200223.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200318.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200226.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200319.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200320.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200227.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200321.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200323.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200224.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200324.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200325.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200330.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200131.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200117.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200201.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200112.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200113.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200111.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200202.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200203.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200208.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200211.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200210.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200212.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200209.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200213.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200214.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200215.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200218.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200219.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200302.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200216.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200305.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200311.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200228.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200229.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200310.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200312.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200313.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200314.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200221.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200315.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200220.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200317.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200322.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200225.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200326.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200327.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200328.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200329.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191204.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191205.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191206.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191207.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191228.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191202.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191203.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191201.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191229.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200110.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191221.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191220.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191208.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191222.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191223.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191209.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191226.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191227.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191225.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191224.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191218.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200108.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200109.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191219.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191231.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191230.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200101.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191211.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191210.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200102.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191212.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191213.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200103.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191216.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200106.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200107.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191217.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191215.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200105.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20200104.000030.nc\n", - "[DOWNLOADING] anxinterpolatedsondeM1.c1.20191214.000030.nc\n", - "\n", - "If you use these data to prepare a publication, please cite:\n", - "\n", - "Jensen, M., Giangrande, S., Fairless, T., & Zhou, A. Interpolated Sonde\n", - "(INTERPOLATEDSONDE). Atmospheric Radiation Measurement (ARM) User Facility.\n", - "https://doi.org/10.5439/1095316\n", - "\n" - ] - } - ], - "source": [ - "username = 'hseppala'\n", - "#token = \n", - "\n", - "# Set the datastream and start/enddates\n", - "datastream = 'anxinterpolatedsondeM1.c1'\n", - "startdate = '2019-12-01'\n", - "enddate = '2020-03-31'\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 = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)" - ] - }, - { - "cell_type": "markdown", - "id": "7a502bc9-3abd-4210-93b2-f89e7440c95a", - "metadata": {}, - "source": [ - "We will make a dummy assumption that the sea surface temperature is 0$^o$C to start. This will allow us to quickly filter the days. May also download sea surface temperature data to make better calculations." - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "7a5c6b2f-8d08-4fea-bc88-f7bce97942d6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset> Size: 9GB\n",
-       "Dimensions:            (time: 174240, height: 332)\n",
-       "Coordinates:\n",
-       "  * time               (time) datetime64[ns] 1MB 2019-12-01T00:00:30 ... 2020...\n",
-       "  * height             (height) float32 1kB 0.002 0.022 0.042 ... 39.5 40.0 40.5\n",
-       "Data variables: (12/39)\n",
-       "    base_time          (time) datetime64[ns] 1MB 2019-12-01 ... 2020-03-30\n",
-       "    time_offset        (time) datetime64[ns] 1MB 2019-12-01T00:00:30 ... 2020...\n",
-       "    precip             (time) float32 697kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
-       "    qc_precip          (time) int32 697kB dask.array<chunksize=(1440,), meta=np.ndarray>\n",
-       "    temp               (time, height) float32 231MB dask.array<chunksize=(1440, 332), meta=np.ndarray>\n",
-       "    qc_temp            (time, height) int32 231MB dask.array<chunksize=(1440, 332), meta=np.ndarray>\n",
-       "    ...                 ...\n",
-       "    qc_rh_scaled       (time, height) int32 231MB dask.array<chunksize=(1440, 332), meta=np.ndarray>\n",
-       "    aqc_rh_scaled      (time, height) int32 231MB dask.array<chunksize=(1440, 332), meta=np.ndarray>\n",
-       "    vapor_source       (time, height) int32 231MB dask.array<chunksize=(1440, 332), meta=np.ndarray>\n",
-       "    lat                (time) float32 697kB 69.14 69.14 69.14 ... 69.14 69.14\n",
-       "    lon                (time) float32 697kB 15.68 15.68 15.68 ... 15.68 15.68\n",
-       "    alt                (time) float32 697kB 2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0\n",
-       "Attributes: (12/17)\n",
-       "    command_line:          idl -R -n interpolatedsonde -s anx -f M1 -b 201912...\n",
-       "    Conventions:           ARM-1.1\n",
-       "    process_version:       vap-interpolatedsonde-7.0-1.el7\n",
-       "    input_datastreams:     anxgriddedsondeM1.c0 : 3.1 : 20191129.000030-20191...\n",
-       "    dod_version:           interpolatedsonde-c1-4.0\n",
-       "    site_id:               anx\n",
-       "    ...                    ...\n",
-       "    doi:                   10.5439/1095316\n",
-       "    history:               created by user giansiracusa on machine agate at 2...\n",
-       "    _file_dates:           ['20191201', '20191202', '20191203', '20191204', '...\n",
-       "    _file_times:           ['000030', '000030', '000030', '000030', '000030',...\n",
-       "    _datastream:           anxinterpolatedsondeM1.c1\n",
-       "    _arm_standards_flag:   1
" - ], - "text/plain": [ - " Size: 9GB\n", - "Dimensions: (time: 174240, height: 332)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1MB 2019-12-01T00:00:30 ... 2020...\n", - " * height (height) float32 1kB 0.002 0.022 0.042 ... 39.5 40.0 40.5\n", - "Data variables: (12/39)\n", - " base_time (time) datetime64[ns] 1MB 2019-12-01 ... 2020-03-30\n", - " time_offset (time) datetime64[ns] 1MB 2019-12-01T00:00:30 ... 2020...\n", - " precip (time) float32 697kB dask.array\n", - " qc_precip (time) int32 697kB dask.array\n", - " temp (time, height) float32 231MB dask.array\n", - " qc_temp (time, height) int32 231MB dask.array\n", - " ... ...\n", - " qc_rh_scaled (time, height) int32 231MB dask.array\n", - " aqc_rh_scaled (time, height) int32 231MB dask.array\n", - " vapor_source (time, height) int32 231MB dask.array\n", - " lat (time) float32 697kB 69.14 69.14 69.14 ... 69.14 69.14\n", - " lon (time) float32 697kB 15.68 15.68 15.68 ... 15.68 15.68\n", - " alt (time) float32 697kB 2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0\n", - "Attributes: (12/17)\n", - " command_line: idl -R -n interpolatedsonde -s anx -f M1 -b 201912...\n", - " Conventions: ARM-1.1\n", - " process_version: vap-interpolatedsonde-7.0-1.el7\n", - " input_datastreams: anxgriddedsondeM1.c0 : 3.1 : 20191129.000030-20191...\n", - " dod_version: interpolatedsonde-c1-4.0\n", - " site_id: anx\n", - " ... ...\n", - " doi: 10.5439/1095316\n", - " history: created by user giansiracusa on machine agate at 2...\n", - " _file_dates: ['20191201', '20191202', '20191203', '20191204', '...\n", - " _file_times: ['000030', '000030', '000030', '000030', '000030',...\n", - " _datastream: anxinterpolatedsondeM1.c1\n", - " _arm_standards_flag: 1" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "id": "04dbca09-439c-4dab-95ea-c5250bbb3037", + "metadata": {}, + "outputs": [], "source": [ - "ds_sonde = act.io.read_arm_netcdf(result)\n", - "ds_sonde.clean.cleanup()\n", - "ds_sonde" + "username = 'hseppala'\n", + "#token = \n", + "\n", + "# Set the datastream and start/enddates\n", + "datastream = 'anxinterpolatedsondeM1.c1'\n", + "startdate = '2019-12-01'\n", + "enddate = '2020-03-31'\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 = act.discovery.download_arm_data(username, token, datastream, startdate, enddate)" ] }, { "cell_type": "markdown", - "id": "8aeac7b0-ca11-4b06-99d3-f0bda83b1b92", + "id": "7a502bc9-3abd-4210-93b2-f89e7440c95a", "metadata": {}, "source": [ - "## Analysis:\n", - "We need to find the potential temperature at 850hPa." + "We will make a dummy assumption that the sea surface temperature is 0$^o$C to start. This will allow us to quickly filter the days. May also download sea surface temperature data to make better calculations." ] }, { "cell_type": "code", - "execution_count": 152, - "id": "f082c781-b35d-4ba5-8628-4ce8287ab5dc", + "execution_count": 4, + "id": "7a5c6b2f-8d08-4fea-bc88-f7bce97942d6", "metadata": {}, "outputs": [ { @@ -3555,11 +442,11 @@ " _file_dates: ['20191201', '20191202', '20191203', '20191204', '...\n", " _file_times: ['000030', '000030', '000030', '000030', '000030',...\n", " _datastream: anxinterpolatedsondeM1.c1\n", - " _arm_standards_flag: 1