From ff553f1ffda8f0668855f91c1488ccf012222287 Mon Sep 17 00:00:00 2001 From: Victor Phan Date: Sat, 7 Mar 2026 17:28:57 +0700 Subject: [PATCH] del uneccessary file --- 01.train_DecisionTree_PlanetaryComputer.ipynb | 4757 --------------- train_files/01.train_DecisionTree_ODC.ipynb | 1947 ------ train_files/01.train_ODC_CNN.ipynb | 3430 ----------- train_files/01.train_ODC_DecisionTree.ipynb | 887 --- train_files/01.train_ODC_MobileNet.ipynb | 768 --- train_files/01.train_ODC_RandomForest.ipynb | 353 -- train_files/01.train_ODC_SVM.ipynb | 372 -- train_files/01.train_ODC_SwinUNet.ipynb | 5395 ----------------- train_files/01.train_ODC_XGBoost.ipynb | 5370 ---------------- ...01.train_ODC_local_with_Mic_supplyer.ipynb | 2095 ------- train_files/crediential.txt | 6 - train_files/test_cognito_odc.ipynb | 565 -- train_files/test_planetary_computer.ipynb | 155 - 13 files changed, 26100 deletions(-) delete mode 100644 01.train_DecisionTree_PlanetaryComputer.ipynb delete mode 100644 train_files/01.train_DecisionTree_ODC.ipynb delete mode 100644 train_files/01.train_ODC_CNN.ipynb delete mode 100644 train_files/01.train_ODC_DecisionTree.ipynb delete mode 100644 train_files/01.train_ODC_MobileNet.ipynb delete mode 100644 train_files/01.train_ODC_RandomForest.ipynb delete mode 100644 train_files/01.train_ODC_SVM.ipynb delete mode 100644 train_files/01.train_ODC_SwinUNet.ipynb delete mode 100644 train_files/01.train_ODC_XGBoost.ipynb delete mode 100644 train_files/01.train_ODC_local_with_Mic_supplyer.ipynb delete mode 100644 train_files/crediential.txt delete mode 100644 train_files/test_cognito_odc.ipynb delete mode 100644 train_files/test_planetary_computer.ipynb diff --git a/01.train_DecisionTree_PlanetaryComputer.ipynb b/01.train_DecisionTree_PlanetaryComputer.ipynb deleted file mode 100644 index 8df7487..0000000 --- a/01.train_DecisionTree_PlanetaryComputer.ipynb +++ /dev/null @@ -1,4757 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b1dab7f7", - "metadata": {}, - "source": [ - "# 🌍 Decision Tree Land Classification - Planetary Computer\n", - "\n", - "## 📌 Notebook này chạy trên:\n", - "- ✅ **Server ODC/JupyterHub** với Dask Gateway\n", - "- ✅ Load dữ liệu từ **Microsoft Planetary Computer** (không cần ODC database)\n", - "\n", - "## 🎯 Nguồn dữ liệu:\n", - "**Microsoft Planetary Computer STAC API**\n", - "- Sentinel-2 L2A (optical)\n", - "- Sentinel-1 RTC (SAR)\n", - "\n", - "## 🚀 Infrastructure:\n", - "- **Dask Gateway**: Adaptive scaling (1-10 workers)\n", - "- **Datacube**: Initialized nhưng không dùng để load data\n", - "- **S3 Access**: Configured với requester_pays\n", - "\n", - "## 🔄 Workflow:\n", - "1. Load data từ Planetary Computer (STAC API)\n", - "2. Preprocessing (cloud mask, NDVI, resampling)\n", - "3. Train Decision Tree model\n", - "4. Evaluate & save model\n", - "\n", - "---\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "df9820b8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - " const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = false;\n", - " const Bokeh = root.Bokeh;\n", - "\n", - " // Set a timeout for this load but only if we are not already initializing\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\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", - " 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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", - " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", - " const js_modules = [];\n", - " const js_exports = {};\n", - " const css_urls = [];\n", - " const 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 (let i = 0; i < inline_js.length; i++) {\n", - " try {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " } catch(e) {\n", - " if (!reloading) {\n", - " throw e;\n", - " }\n", - " }\n", - " }\n", - " // Cache old bokeh versions\n", - " if (Bokeh != undefined && 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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", - " // If the timeout and bokeh was not successfully loaded we reset\n", - " // everything and try loading again\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " root._bokeh_is_loading = 0\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", - " const bokeh_loaded = 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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" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================================================================\n", - "📦 Data Loading Module (No ODC Database Required)\n", - "======================================================================\n", - "\n", - "💡 Usage:\n", - " from load_data_no_odc import load_and_process_s2, load_and_process_s1\n", - "\n", - " bbox = (lon_min, lat_min, lon_max, lat_max)\n", - " date_range = ('2022-09-01', '2023-10-01')\n", - "\n", - " data_s2 = load_and_process_s2(bbox, date_range)\n", - " data_s1 = load_and_process_s1(bbox, date_range)\n", - "\n", - " # Load training data\n", - " train = load_train_data('train/data.shp', label_mapping)\n", - " X, y = extract_features_at_points(train, data_s2, data_s1)\n", - " X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y)\n", - "======================================================================\n", - "\n", - "======================================================================\n", - "📦 Data Loading Module (No ODC Database Required)\n", - "======================================================================\n", - "\n", - "💡 Usage:\n", - " from load_data_no_odc import load_and_process_s2, load_and_process_s1\n", - "\n", - " bbox = (lon_min, lat_min, lon_max, lat_max)\n", - " date_range = ('2022-09-01', '2023-10-01')\n", - "\n", - " data_s2 = load_and_process_s2(bbox, date_range)\n", - " data_s1 = load_and_process_s1(bbox, date_range)\n", - "\n", - " # Load training data\n", - " train = load_train_data('train/data.shp', label_mapping)\n", - " X, y = extract_features_at_points(train, data_s2, data_s1)\n", - " X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y)\n", - "======================================================================\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = false;\n", - " const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = true;\n", - " const Bokeh = root.Bokeh;\n", - "\n", - " // Set a timeout for this load but only if we are not already initializing\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) 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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/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = false;\n", - " const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = true;\n", - " const Bokeh = root.Bokeh;\n", - "\n", - " // Set a timeout for this load but only if we are not already initializing\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\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, 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on_error(e) {\n", - " const src_el = e.srcElement\n", - " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", - " }\n", - "\n", - " const 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", - " const existing_stylesheets = []\n", - " const links = document.getElementsByTagName('link')\n", - " for (let i = 0; i < links.length; i++) {\n", - " const link = links[i]\n", - " if (link.href != null) {\n", - " existing_stylesheets.push(link.href)\n", - " }\n", - " }\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const escaped = encodeURI(url)\n", - " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", - " on_load()\n", - " continue;\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", - " const scripts = document.getElementsByTagName('script')\n", - " for (let i = 0; i < scripts.length; i++) {\n", - " var script = scripts[i]\n", - " if (script.src != null) {\n", - " existing_scripts.push(script.src)\n", - " }\n", - " }\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const escaped = encodeURI(url)\n", - " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", - " if (!window.requirejs) {\n", - " on_load();\n", - " }\n", - " continue;\n", - " }\n", - " const 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 (let i = 0; i < js_modules.length; i++) {\n", - " const url = js_modules[i];\n", - " const escaped = encodeURI(url)\n", - " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", - " if (!window.requirejs) {\n", - " on_load();\n", - " }\n", - " continue;\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", - " const url = 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NewBokeh)\n", - " }\n", - " root.Bokeh = Bokeh;\n", - " }\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", - " // If the timeout and bokeh was not successfully loaded we reset\n", - " // everything and try loading again\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " root._bokeh_is_loading = 0\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", - " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", - " if (!reloading && !bokeh_loaded) {\n", - " if (root.Bokeh) {\n", - " root.Bokeh = undefined;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " }\n", - " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_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 const force = false;\n const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\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 // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\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(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const 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 const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\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 const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const 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 (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\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 const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n 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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" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ All modules loaded successfully!\n", - "📡 Data source: Microsoft Planetary Computer\n", - "🚀 Infrastructure: Dask Gateway + ODC\n", - "CPU times: user 19.1 s, sys: 3.38 s, total: 22.4 s\n", - "Wall time: 13.9 s\n" - ] - } - ], - "source": [ - "%%time\n", - "%matplotlib inline\n", - "\n", - "import sys\n", - "import os\n", - "sys.path.insert(0, '/home/jovyan/remote-sensing')\n", - "\n", - "# Import ODC modules for Dask Gateway and Datacube\n", - "import datacube\n", - "from easi_tools import notebook_utils\n", - "\n", - "# Import module load dữ liệu không cần ODC database\n", - "import importlib\n", - "import load_data_no_odc\n", - "importlib.reload(load_data_no_odc)\n", - "\n", - "%matplotlib inline\n", - "\n", - "import importlib\n", - "import new_import_ODC \n", - "\n", - "importlib.reload(new_import_ODC)\n", - "\n", - "from new_import_ODC import *\n", - "\n", - "\n", - "from load_data_no_odc import (\n", - " load_and_process_s2,\n", - " load_and_process_s1,\n", - " load_sentinel2_stac,\n", - " load_sentinel1_stac,\n", - " mask_clean_s2,\n", - " calculate_ndvi,\n", - " fill_nan_temporal,\n", - " resample_monthly\n", - ")\n", - "\n", - "# Standard imports\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set_style('whitegrid')\n", - "\n", - "# ML imports\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", - "import joblib\n", - "import json\n", - "from datetime import datetime\n", - "\n", - "print(\"✅ All modules loaded successfully!\")\n", - "print(\"📡 Data source: Microsoft Planetary Computer\")\n", - "print(\"🚀 Infrastructure: Dask Gateway + ODC\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "53397b2f", - "metadata": {}, - "source": [ - "## 🚀 Step 1: Initialize Dask Gateway + Datacube\n", - "\n", - "Khởi tạo Dask Gateway với adaptive scaling và cấu hình S3 access\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3c4d6779", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🚀 Step 1: Dask Gateway + Datacube Initialization\n", - "======================================================================\n" - ] - }, - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new 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= 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", - " const scripts = document.getElementsByTagName('script')\n", - " for (let i = 0; i < scripts.length; i++) {\n", - " var script = scripts[i]\n", - " if (script.src != null) {\n", - " existing_scripts.push(script.src)\n", - " }\n", - " }\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const escaped = encodeURI(url)\n", - " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", - " if (!window.requirejs) {\n", - " on_load();\n", - " }\n", - " continue;\n", - " }\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error;\n", - " element.async = false;\n", - " element.src 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return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\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(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const 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 const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if 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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 // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n 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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", - " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", - " const js_modules = [];\n", - " const js_exports = {};\n", - " const css_urls = [];\n", - " const 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 (let i = 0; i < inline_js.length; i++) {\n", - " try {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " } catch(e) {\n", - " if (!reloading) {\n", - " throw e;\n", - " }\n", - " }\n", - " }\n", - " // Cache old bokeh versions\n", - " if (Bokeh != undefined && 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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", - " // If the timeout and bokeh was not successfully loaded we reset\n", - " // everything and try loading again\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " root._bokeh_is_loading = 0\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", - " const bokeh_loaded = 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"output_type": "stream", - "text": [ - "An existing cluster was found. Connecting to: easihub.1ae64254f06742f4ab6a565436953b3a\n", - "\n", - "✅ Dask Gateway + Datacube + S3 ready!\n", - " Dask dashboard: https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.1ae64254f06742f4ab6a565436953b3a/status\n", - " Workers: Adaptive scaling (1-10)\n", - "======================================================================\n" - ] - } - ], - "source": [ - "\n", - "print(\"🚀 Step 1: Dask Gateway + Datacube Initialization\")\n", - "print(\"=\" * 70)\n", - "\n", - "\n", - "import importlib\n", - "import new_import_ODC \n", - "\n", - "importlib.reload(new_import_ODC)\n", - "\n", - "from new_import_ODC import *\n", - "\n", - "# Cấu hình Dask Gateway\n", - "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n", - "\n", - "# Khai báo Datacube\n", - "dc = datacube.Datacube()\n", - "\n", - "# Cấu hình truy cập dịch vụ S3\n", - "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n", - "\n", - "print(f\"\\n✅ Dask Gateway + Datacube + S3 ready!\")\n", - "print(f\" Dask dashboard: {client.dashboard_link}\")\n", - "print(f\" Workers: Adaptive scaling (1-10)\")\n", - "print(\"=\" * 70)\n" - ] - }, - { - "cell_type": "markdown", - "id": "28bc5035", - "metadata": {}, - "source": [ - "## 📍 Step 2: Define Area of Interest (AOI)\n", - "\n", - "Định nghĩa vùng nghiên cứu và khoảng thời gian" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "15a5291c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📍 Area of Interest:\n", - " Bbox: (105.5, 9.2, 106.4, 10.0)\n", - " Lon range: 105.5 to 106.4\n", - " Lat range: 9.2 to 10.0\n", - " Date range: 2022-09-01 to 2023-10-01\n", - "======================================================================\n" - ] - } - ], - "source": [ - "# Cấu hình vùng và thời gian\n", - "date_range = (\"2022-09-01\", \"2023-10-01\")\n", - "\n", - "# Bounding box: (lon_min, lat_min, lon_max, lat_max)\n", - "bbox = (105.5, 9.2, 106.4, 10.0) # Khu vực Mekong Delta\n", - "\n", - "print(\"📍 Area of Interest:\")\n", - "print(f\" Bbox: {bbox}\")\n", - "print(f\" Lon range: {bbox[0]} to {bbox[2]}\")\n", - "print(f\" Lat range: {bbox[1]} to {bbox[3]}\")\n", - "print(f\" Date range: {date_range[0]} to {date_range[1]}\")\n", - "print(\"=\" * 70)" - ] - }, - { - "cell_type": "markdown", - "id": "422e498e", - "metadata": {}, - "source": [ - "## 📥 Step 3: Load Sentinel-2 Data\n", - "\n", - "Load dữ liệu Sentinel-2 L2A từ Planetary Computer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "611cd1c2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📥 Loading Sentinel-2 data from Planetary Computer...\n", - "----------------------------------------------------------------------\n", - "🔍 Searching Sentinel-2 data...\n", - " Bbox: (105.5, 9.2, 106.4, 10.0)\n", - " Date: 2022-09-01 to 2023-10-01\n", - " Bands: ['B02', 'B03', 'B04', 'B08', 'B8A', 'B11', 'B12', 'SCL']\n", - "✅ Found 777 Sentinel-2 scenes\n", - "📥 Loading data...\n", - "✅ Loaded Sentinel-2 data: FrozenMappingWarningOnValuesAccess({'y': 8874, 'x': 9902, 'time': 154})\n", - " Shape: {'y': 8874, 'x': 9902, 'time': 154}\n", - " Bands: ['B02', 'B03', 'B04', 'B08', 'B8A', 'B11', 'B12', 'SCL']\n", - "🧹 Applying cloud mask...\n", - "✅ Cloud mask applied\n", - "📊 Calculating NDVI using B08 and B04...\n", - "✅ NDVI calculated\n", - "🔧 Filling NaN values using temporal interpolation...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jovyan/remote-sensing/load_data_no_odc.py:82: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", - " print(f\" Shape: {dict(data.dims)}\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ NaN values filled\n", - "📅 Resampling to monthly using mean...\n", - "✅ Resampled to monthly: FrozenMappingWarningOnValuesAccess({'time': 13, 'y': 8874, 'x': 9902})\n", - "\n", - "✅ Sentinel-2 monthly data loaded!\n", - " Dimensions: {'time': 13, 'y': 8874, 'x': 9902}\n", - " Variables: ['B02', 'B03', 'B04', 'B08', 'B8A', 'B11', 'B12', 'NDVI']\n", - " Time steps: 13\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":14: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", - "/env/lib/python3.12/site-packages/distributed/client.py:3357: UserWarning: Sending large graph of size 25.57 MiB.\n", - "This may cause some slowdown.\n", - "Consider scattering data ahead of time and using futures.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "print(\"📥 Loading Sentinel-2 data from Planetary Computer...\")\n", - "print(\"-\" * 70)\n", - "\n", - "# Load và xử lý Sentinel-2: cloud mask + NDVI + monthly resampling\n", - "data_sen2_monthly = load_and_process_s2(\n", - " bbox=bbox,\n", - " date_range=date_range,\n", - " apply_cloud_mask=True,\n", - " calculate_indices=True\n", - ")\n", - "\n", - "if data_sen2_monthly is not None:\n", - " print(f\"\\n✅ Sentinel-2 monthly data loaded!\")\n", - " print(f\" Dimensions: {dict(data_sen2_monthly.dims)}\")\n", - " print(f\" Variables: {list(data_sen2_monthly.data_vars)}\")\n", - " print(f\" Time steps: {len(data_sen2_monthly.time)}\")\n", - " \n", - " # Compute to load into memory\n", - " data_sen2_monthly = data_sen2_monthly.compute()\n", - " print(f\" ✓ Data computed and loaded into memory\")\n", - "else:\n", - " print(\"❌ Failed to load Sentinel-2 data\")" - ] - }, - { - "cell_type": "markdown", - "id": "79ef9736", - "metadata": {}, - "source": [ - "## 📥 Step 4: Load Sentinel-1 Data\n", - "\n", - "Load dữ liệu Sentinel-1 RTC (SAR) từ Planetary Computer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bda3dc0", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "\n", - "print(\"📥 Loading Sentinel-1 data from Planetary Computer...\")\n", - "print(\"-\" * 70)\n", - "\n", - "# Load và xử lý Sentinel-1: monthly resampling\n", - "data_sen1_monthly = load_and_process_s1(\n", - " bbox=bbox,\n", - " date_range=date_range\n", - ")\n", - "\n", - "if data_sen1_monthly is not None:\n", - " print(f\"\\n✅ Sentinel-1 monthly data loaded!\")\n", - " print(f\" Dimensions: {dict(data_sen1_monthly.dims)}\")\n", - " print(f\" Variables: {list(data_sen1_monthly.data_vars)}\")\n", - " print(f\" Time steps: {len(data_sen1_monthly.time)}\")\n", - " \n", - " # Compute to load into memory\n", - " data_sen1_monthly = data_sen1_monthly.compute()\n", - " print(f\" ✓ Data computed and loaded into memory\")\n", - "else:\n", - " print(\"❌ Failed to load Sentinel-1 data\")" - ] - }, - { - "cell_type": "markdown", - "id": "de80d48d", - "metadata": {}, - "source": [ - "## 🎯 Step 5: Load Training Data\n", - "\n", - "Load dữ liệu mẫu huấn luyện (training samples)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "70925d09", - "metadata": {}, - "outputs": [], - "source": [ - "from load_data_no_odc import load_train_data\n", - "\n", - "# Ánh xạ nhãn lớp đất\n", - "label_mapping = {\n", - " \"Lua tom\": 0,\n", - " \"Lua\": 1,\n", - " \"CHN\": 2,\n", - " \"CLN\": 3,\n", - " \"TS\": 4,\n", - " \"Song\": 5,\n", - " \"Dat xay dung\": 6,\n", - " \"Rung\": 7,\n", - "}\n", - "\n", - "print(\"🎯 Label mapping:\")\n", - "for label, idx in label_mapping.items():\n", - " print(f\" {idx}: {label}\")\n", - "\n", - "# Load training data from AWS shapefile\n", - "train_path = \"train/ST_training_data_updated_1130points_new.shp\"\n", - "\n", - "print(f\"\\n📂 Loading training data from: {train_path}\")\n", - "train_data = load_train_data(train_path, label_mapping)\n", - "\n", - "if train_data is not None:\n", - " print(f\"\\n✅ Training data loaded successfully!\")\n", - " print(f\" Total points: {len(train_data)}\")\n", - " print(f\" CRS: {train_data.crs}\")\n", - "else:\n", - " print(\"❌ Failed to load training data\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "fed9a8f1", - "metadata": {}, - "source": [ - "## 🔧 Step 6: Extract Features\n", - "\n", - "Trích xuất features từ Sentinel-1 và Sentinel-2 tại các điểm mẫu" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62c41cd0", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "from load_data_no_odc import extract_features_at_points\n", - "\n", - "print(\"🔧 Extracting features from satellite data...\")\n", - "print(\"-\" * 70)\n", - "\n", - "if train_data is not None and \\\n", - " data_sen2_monthly is not None and \\\n", - " data_sen1_monthly is not None:\n", - " \n", - " # Extract features at training point locations\n", - " X, y = extract_features_at_points(\n", - " train_data, \n", - " data_sen2_monthly, \n", - " data_sen1_monthly\n", - " )\n", - " \n", - " print(f\"\\n✅ Feature extraction complete!\")\n", - " print(f\" Total samples: {len(X)}\")\n", - " print(f\" Feature dimension: {X.shape[1]}\")\n", - " print(f\" Classes: {sorted(set(y.tolist()))}\")\n", - " print(f\" Class distribution:\")\n", - " \n", - " import pandas as pd\n", - " class_counts = pd.Series(y).value_counts().sort_index()\n", - " for class_id, count in class_counts.items():\n", - " class_name = [k for k, v in label_mapping.items() if v == class_id][0]\n", - " print(f\" {class_id} ({class_name}): {count} samples ({count/len(y)*100:.1f}%)\")\n", - " \n", - "else:\n", - " print(\"❌ Missing data: Please check training data or satellite data\")\n", - " print(f\" train_data: {'✓' if train_data is not None else '✗'}\")\n", - " print(f\" data_sen2_monthly: {'✓' if data_sen2_monthly is not None else '✗'}\")\n", - " print(f\" data_sen1_monthly: {'✓' if data_sen1_monthly is not None else '✗'}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "f7cd0ca2", - "metadata": {}, - "source": [ - "## 📊 Step 7: Split Data\n", - "\n", - "Chia dữ liệu thành train/val/test sets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "523e1249", - "metadata": {}, - "outputs": [], - "source": [ - "from load_data_no_odc import split_train_data\n", - "\n", - "if 'X' in locals() and 'y' in locals():\n", - " # Split data into train/val/test sets\n", - " X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n", - " X, y, \n", - " test_size=0.2, \n", - " val_size=0.1, \n", - " random_state=42\n", - " )\n", - " \n", - " # Combine train + val for final model training\n", - " X_fit = np.concatenate([X_train, X_val], axis=0)\n", - " y_fit = np.concatenate([y_train, y_val], axis=0)\n", - " \n", - " print(f\"\\n✅ Final training set:\")\n", - " print(f\" X_fit shape: {X_fit.shape}\")\n", - " print(f\" y_fit shape: {y_fit.shape}\")\n", - " print(f\"\\n X_test shape: {X_test.shape}\")\n", - " print(f\" y_test shape: {y_test.shape}\")\n", - " \n", - "else:\n", - " print(\"❌ Features not extracted yet. Please run Step 6 first.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "a469500c", - "metadata": {}, - "source": [ - "## 🌲 Step 8: Train Decision Tree Model\n", - "\n", - "Huấn luyện mô hình Decision Tree" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "445c2d92", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "\n", - "if 'X_fit' in locals() and 'y_fit' in locals():\n", - " \n", - " print(\"🚀 Training Decision Tree model...\")\n", - " print(\"-\" * 70)\n", - " \n", - " # Train Decision Tree with tuned hyperparameters\n", - " model = DecisionTreeClassifier(\n", - " max_depth=30,\n", - " min_samples_leaf=2,\n", - " min_samples_split=5,\n", - " class_weight=\"balanced\",\n", - " random_state=42,\n", - " )\n", - " \n", - " model.fit(X_fit, y_fit)\n", - " \n", - " # Evaluate on validation set\n", - " val_acc = model.score(X_val, y_val)\n", - " \n", - " print(f\"\\n✅ Training complete!\")\n", - " print(f\" Model: Decision Tree\")\n", - " print(f\" Tree depth: {model.get_depth()}\")\n", - " print(f\" Number of leaves: {model.get_n_leaves()}\")\n", - " print(f\" Training samples: {len(X_fit)}\")\n", - " print(f\" Validation accuracy: {val_acc:.4f} ({val_acc*100:.2f}%)\")\n", - " \n", - "else:\n", - " print(\"❌ Data not ready. Please run Step 7 first.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "7c1e681e", - "metadata": {}, - "source": [ - "## 📈 Step 9: Hyperparameter Analysis\n", - "\n", - "Phân tích độ sâu tối ưu (max_depth) cho Decision Tree" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d13274e4", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "import matplotlib.pyplot as plt\n", - "\n", - "if 'X_fit' in locals() and 'X_val' in locals():\n", - " \n", - " DEPTH_RANGE = list(range(1, 51))\n", - " \n", - " train_accs = []\n", - " val_accs = []\n", - " \n", - " print(\"🔍 Analyzing optimal max_depth for Decision Tree...\")\n", - " print(\"-\" * 70)\n", - " print(\"Testing depths from 1 to 50...\")\n", - " \n", - " for d in DEPTH_RANGE:\n", - " m = DecisionTreeClassifier(\n", - " min_samples_leaf=2,\n", - " min_samples_split=5,\n", - " class_weight=\"balanced\",\n", - " random_state=42,\n", - " max_depth=d,\n", - " )\n", - " m.fit(X_fit, y_fit)\n", - " train_accs.append(m.score(X_fit, y_fit))\n", - " val_accs.append(m.score(X_val, y_val))\n", - " \n", - " if d % 10 == 0:\n", - " print(f\" Depth {d:2d}: train={train_accs[-1]:.4f}, val={val_accs[-1]:.4f}\")\n", - " \n", - " train_accs = np.array(train_accs)\n", - " val_accs = np.array(val_accs)\n", - " \n", - " best_depth = DEPTH_RANGE[np.argmax(val_accs)]\n", - " best_val_acc = np.max(val_accs)\n", - " \n", - " print(f\"\\n✅ Optimal max_depth: {best_depth}\")\n", - " print(f\" Best validation accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)\")\n", - " \n", - " # Plot convergence analysis\n", - " fig, ax = plt.subplots(1, 1, figsize=(12, 5))\n", - " ax.plot(DEPTH_RANGE, train_accs, 'b-o', markersize=3, label='Train Accuracy')\n", - " ax.plot(DEPTH_RANGE, val_accs, 'g-o', markersize=3, label='Validation Accuracy')\n", - " ax.axvline(x=best_depth, color='red', linestyle='--', linewidth=2, \n", - " label=f'Best Depth = {best_depth}')\n", - " ax.set_xlabel('max_depth', fontsize=12)\n", - " ax.set_ylabel('Accuracy', fontsize=12)\n", - " ax.set_title('Decision Tree: Training vs Validation Accuracy', fontsize=14, fontweight='bold')\n", - " ax.legend(fontsize=10)\n", - " ax.grid(True, alpha=0.3)\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - "else:\n", - " print(\"❌ Data not ready. Please run Step 7 first.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "b551cdb4", - "metadata": {}, - "source": [ - "## 📊 Step 10: Evaluate Model\n", - "\n", - "Đánh giá mô hình trên tập test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc3d6b9a", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n", - "import seaborn as sns\n", - "\n", - "if 'model' in locals() and 'X_test' in locals():\n", - " \n", - " print(\"📊 Evaluating model on test set...\")\n", - " print(\"-\" * 70)\n", - " \n", - " # Make predictions\n", - " y_pred = model.predict(X_test)\n", - " acc = accuracy_score(y_test, y_pred)\n", - " \n", - " print(f\"\\n🎯 Test Accuracy: {acc:.4f} ({acc*100:.2f}%)\\n\")\n", - " print(\"📋 Classification Report:\")\n", - " print(classification_report(y_test, y_pred, digits=4))\n", - " \n", - " # Confusion Matrix\n", - " class_names = list(label_mapping.keys())\n", - " cm = confusion_matrix(y_test, y_pred)\n", - " \n", - " plt.figure(figsize=(10, 8))\n", - " sns.heatmap(cm, annot=True, fmt='d', cmap='Greens',\n", - " xticklabels=class_names, yticklabels=class_names,\n", - " cbar_kws={'label': 'Count'})\n", - " plt.xlabel('Predicted Label', fontsize=12)\n", - " plt.ylabel('True Label', fontsize=12)\n", - " plt.title('Confusion Matrix — Decision Tree (Test Set)', fontsize=14, fontweight='bold')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - "else:\n", - " print(\"❌ Model not trained yet. Please run Step 8 first.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "44d226fc", - "metadata": {}, - "source": [ - "## 💾 Step 11: Save Model\n", - "\n", - "Lưu mô hình và metadata" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "044eefc4", - "metadata": {}, - "outputs": [], - "source": [ - "import joblib\n", - "import json\n", - "from datetime import datetime\n", - "\n", - "if 'model' in locals() and 'acc' in locals():\n", - " \n", - " print(\"💾 Saving model and metadata...\")\n", - " print(\"-\" * 70)\n", - " \n", - " # Save model\n", - " model_path = \"model_decision_tree_planetary_computer.joblib\"\n", - " joblib.dump(model, model_path)\n", - " print(f\"✅ Model saved → {model_path}\")\n", - " \n", - " # Create metadata\n", - " info = {\n", - " \"model_type\": \"DecisionTree\",\n", - " \"data_source\": \"Microsoft Planetary Computer\",\n", - " \"training_data\": train_path,\n", - " \"max_depth\": int(model.get_depth()),\n", - " \"n_leaves\": int(model.get_n_leaves()),\n", - " \"n_features\": int(X_fit.shape[1]),\n", - " \"label_mapping\": label_mapping,\n", - " \"test_accuracy\": float(acc),\n", - " \"train_samples\": int(len(X_fit)),\n", - " \"val_samples\": int(len(X_val)),\n", - " \"test_samples\": int(len(X_test)),\n", - " \"bbox\": bbox,\n", - " \"date_range\": list(date_range),\n", - " \"saved_at\": datetime.now().isoformat(),\n", - " \"hyperparameters\": {\n", - " \"max_depth\": 30,\n", - " \"min_samples_leaf\": 2,\n", - " \"min_samples_split\": 5,\n", - " \"class_weight\": \"balanced\",\n", - " \"random_state\": 42\n", - " }\n", - " }\n", - " \n", - " # Save metadata\n", - " info_path = \"model_decision_tree_planetary_computer_info.json\"\n", - " with open(info_path, \"w\") as f:\n", - " json.dump(info, f, indent=2, ensure_ascii=False)\n", - " \n", - " print(f\"✅ Metadata saved → {info_path}\")\n", - " print(\"\\n📋 Model Info:\")\n", - " print(json.dumps(info, indent=2, ensure_ascii=False))\n", - " \n", - "else:\n", - " print(\"❌ Model not trained yet or evaluation not complete.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "18c95152", - "metadata": {}, - "source": [ - "## 🧹 Step 12: Cleanup\n", - "\n", - "Đóng Dask cluster" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20445068", - "metadata": {}, - "outputs": [], - "source": [ - "# Close Dask Gateway cluster\n", - "try:\n", - " client.close()\n", - " cluster.close()\n", - " print(\"✅ Dask Gateway cluster closed.\")\n", - "except Exception as e:\n", - " print(f\"⚠ Error closing cluster: {e}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "046bf0f9", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## ✅ Summary\n", - "\n", - "### Notebook này:\n", - "- ✅ Chạy trên **Server ODC/JupyterHub** với **Dask Gateway**\n", - "- ✅ Load dữ liệu từ **Microsoft Planetary Computer** qua STAC API\n", - "- ✅ Không cần **ODC Database** để load data (dùng Planetary Computer)\n", - "- ✅ Sử dụng infrastructure của ODC (Dask Gateway, S3 access)\n", - "- ✅ Tương thích 100% với dữ liệu ODC\n", - "\n", - "### Ưu điểm của approach này:\n", - "1. **Scalability**: Dask Gateway adaptive scaling (1-10 workers)\n", - "2. **Public data**: Planetary Computer không cần VPN/private network\n", - "3. **Consistent**: Cùng infrastructure với ODC training pipeline\n", - "4. **Flexible**: Có thể train trên Planetary Computer, predict trên ODC hoặc ngược lại\n", - "\n", - "### Workflow train/predict:\n", - "1. **Train trên server ODC**: Chạy notebook này với Dask Gateway\n", - "2. **Save model**: Export `.joblib` file \n", - "3. **Predict qua API**: api_server.py tự động dùng Planetary Computer\n", - "\n", - "### Architecture:\n", - "```\n", - "┌─────────────────────────────────────┐\n", - "│ TRAINING (ODC Infrastructure) │\n", - "│ ✅ Dask Gateway (1-10 workers) │\n", - "│ ✅ Planetary Computer STAC API │\n", - "│ ✅ S3 access configured │\n", - "│ → Model: .joblib │\n", - "└─────────────────────────────────────┘\n", - " ↓\n", - "┌─────────────────────────────────────┐\n", - "│ PREDICTION (API Server) │\n", - "│ ✅ Planetary Computer (public) │\n", - "│ ✅ Same preprocessing pipeline │\n", - "│ → GeoTIFF output │\n", - "└─────────────────────────────────────┘\n", - "```\n", - "\n", - "---\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "983478ae-8c91-456d-b4e4-aa5c92286e72", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "135c5a1d-f97a-423a-9dc4-46bda170c86e", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/train_files/01.train_DecisionTree_ODC.ipynb b/train_files/01.train_DecisionTree_ODC.ipynb deleted file mode 100644 index 9b634f9..0000000 --- a/train_files/01.train_DecisionTree_ODC.ipynb +++ /dev/null @@ -1,1947 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "bb54fa69", - "metadata": {}, - "source": [ - "# 🌍 Decision Tree Land Classification - ODC Database\n", - "\n", - "## 📌 Notebook này chạy trên:\n", - "- ✅ **Server ODC/JupyterHub** với Dask Gateway\n", - "- ✅ Load dữ liệu từ **ODC Database** (access nhanh trên server)\n", - "\n", - "## 🎯 Nguồn dữ liệu:\n", - "**ODC Database**\n", - "- Sentinel-2 L2A (optical) từ ODC\n", - "- Sentinel-1 RTC (SAR) từ ODC\n", - "\n", - "## 🚀 Infrastructure:\n", - "- **Dask Gateway**: Adaptive scaling (1-10 workers)\n", - "- **Datacube**: Load data từ ODC database\n", - "- **S3 Access**: Configured với requester_pays\n", - "\n", - "## 🔄 Workflow:\n", - "1. Load data từ ODC Database (nhanh trên server)\n", - "2. Preprocessing (cloud mask, NDVI, resampling)\n", - "3. Train Decision Tree model\n", - "4. Save model → dùng cho prediction trên Planetary Computer\n", - "\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "10e7e875", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\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 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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 // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\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(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n 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'/home/jovyan/remote-sensing')\n", - "\n", - "# Import ODC modules\n", - "import datacube\n", - "from easi_tools import notebook_utils\n", - "from datacube.utils.rio import configure_s3_access\n", - "\n", - "# Import ODC data loading functions\n", - "import importlib\n", - "import new_import_ODC\n", - "importlib.reload(new_import_ODC)\n", - "from new_import_ODC import *\n", - "\n", - "# Standard imports\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set_style('whitegrid')\n", - "\n", - "# ML imports\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", - "import joblib\n", - "import json\n", - "from datetime import datetime\n", - "\n", - "print(\"✅ All modules loaded successfully!\")\n", - "print(\"📡 Data source: ODC Database\")\n", - "print(\"🚀 Infrastructure: Dask Gateway + ODC\")" - ] - }, - { - "cell_type": "markdown", - "id": "0ad19235", - "metadata": {}, - "source": [ - "## 🚀 Step 1: Initialize Dask Gateway + Datacube\n", - "\n", - "Khởi tạo Dask Gateway với adaptive scaling và kết nối tới ODC Database" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5d894cbd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🚀 Step 1: Dask Gateway + Datacube Initialization\n", - "======================================================================\n", - "Starting new cluster\n", - "\n", - "✅ Dask Gateway + Datacube + S3 ready!\n", - " Dask dashboard: https://hub.asia.easi-eo.solutions/services/dask-gateway/clusters/easihub.9d312633e3034586b37988a836a53a1d/status\n", - " Workers: Adaptive scaling (1-10)\n", - " ODC products available: 31\n", - "======================================================================\n", - "CPU times: user 1.3 s, sys: 33.3 ms, total: 1.33 s\n", - "Wall time: 3min 33s\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "print(\"🚀 Step 1: Dask Gateway + Datacube Initialization\")\n", - "print(\"=\" * 70)\n", - "\n", - "# Cấu hình Dask Gateway\n", - "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n", - "\n", - "# Khai báo Datacube - kết nối tới ODC Database\n", - "dc = datacube.Datacube()\n", - "\n", - "# Cấu hình truy cập dịch vụ S3\n", - "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n", - "\n", - "print(f\"\\n✅ Dask Gateway + Datacube + S3 ready!\")\n", - "print(f\" Dask dashboard: {client.dashboard_link}\")\n", - "print(f\" Workers: Adaptive scaling (1-10)\")\n", - "print(f\" ODC products available: {len(dc.list_products())}\")\n", - "print(\"=\" * 70)" - ] - }, - { - "cell_type": "markdown", - "id": "d96f19e5", - "metadata": {}, - "source": [ - "## 📍 Step 2: Define Area of Interest (AOI)\n", - "\n", - "Định nghĩa vùng nghiên cứu và khoảng thời gian" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "98046ea8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📍 Area of Interest:\n", - " Longitude range: 105.5 to 106.4\n", - " Latitude range: 9.2 to 10.0\n", - " Date range: 2022-09-01 to 2023-10-01\n", - "======================================================================\n" - ] - } - ], - "source": [ - "# Cấu hình vùng và thời gian\n", - "date_range = (\"2022-09-01\", \"2023-10-01\")\n", - "\n", - "# Coordinates: (lon_min, lon_max), (lat_min, lat_max)\n", - "longitude_range = (105.5, 106.4) # Khu vực Mekong Delta\n", - "latitude_range = (9.2, 10.0)\n", - "\n", - "print(\"📍 Area of Interest:\")\n", - "print(f\" Longitude range: {longitude_range[0]} to {longitude_range[1]}\")\n", - "print(f\" Latitude range: {latitude_range[0]} to {latitude_range[1]}\")\n", - "print(f\" Date range: {date_range[0]} to {date_range[1]}\")\n", - "print(\"=\" * 70)" - ] - }, - { - "cell_type": "markdown", - "id": "79d37cff", - "metadata": {}, - "source": [ - "## 📥 Step 3: Load Sentinel-2 Data from ODC\n", - "\n", - "Load dữ liệu Sentinel-2 L2A từ ODC Database" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "3e8b535d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📥 Loading Sentinel-2 data from ODC Database...\n", - "----------------------------------------------------------------------\n", - "Most common native CRS: EPSG:32648\n", - "No datasets require offset correction\n", - "The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n", - "\n", - "✅ Sentinel-2 data loaded from ODC!\n", - " Dimensions: {'time': 151, 'y': 8874, 'x': 9902}\n", - " Variables: ['red', 'nir', 'scl']\n", - " Time steps: 151\n", - "\n", - "🔧 Applying cloud mask...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":9: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " bits \\\n", - "qa [0, 1, 2, 3, 4, 5, 6, 7] \n", - "\n", - " values \\\n", - "qa {'0': 'no data', '1': 'saturated or defective'... \n", - "\n", - " description \n", - "qa Sen2Cor Scene Classification " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'0': 'no data',\n", - " '1': 'saturated or defective',\n", - " '2': 'dark area pixels',\n", - " '3': 'cloud shadows',\n", - " '4': 'vegetation',\n", - " '5': 'bare soils',\n", - " '6': 'water',\n", - " '7': 'unclassified',\n", - " '8': 'cloud medium probability',\n", - " '9': 'cloud high probability',\n", - " '10': 'thin cirrus',\n", - " '11': 'snow or ice'}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📊 Calculating NDVI...\n", - "📅 Resampling to monthly averages...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/env/lib/python3.12/site-packages/xarray/groupers.py:392: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " self.index_grouper = pd.Grouper(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "✅ Sentinel-2 monthly data ready!\n", - " ✓ Cloud masked\n", - " ✓ NDVI calculated\n", - " ✓ Monthly resampled\n", - " ✓ Loaded into memory\n", - "CPU times: user 11.5 s, sys: 6.48 s, total: 18 s\n", - "Wall time: 4min 40s\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "print(\"📥 Loading Sentinel-2 data from ODC Database...\")\n", - "print(\"-\" * 70)\n", - "\n", - "# Load Sentinel-2 từ ODC\n", - "data_sen2 = load_data(dc, date_range, longitude_range, latitude_range)\n", - "\n", - "if data_sen2 is not None:\n", - " print(f\"\\n✅ Sentinel-2 data loaded from ODC!\")\n", - " print(f\" Dimensions: {dict(data_sen2.dims)}\")\n", - " print(f\" Variables: {list(data_sen2.data_vars)}\")\n", - " print(f\" Time steps: {len(data_sen2.time)}\")\n", - " \n", - " # Apply cloud masking\n", - " print(\"\\n🔧 Applying cloud mask...\")\n", - " data_sen2_clean = mask_clean(data_sen2)\n", - " \n", - " # Calculate NDVI manually\n", - " print(\"📊 Calculating NDVI...\")\n", - " ndvi = (data_sen2_clean.nir - data_sen2_clean.red) / (data_sen2_clean.nir + data_sen2_clean.red)\n", - " \n", - " # Resample to monthly\n", - " print(\"📅 Resampling to monthly averages...\")\n", - " data_sen2_monthly = calculate_average(ndvi, time_pattern='1M')\n", - " \n", - " # Compute to load into memory\n", - " data_sen2_monthly = data_sen2_monthly.compute()\n", - " print(f\"\\n✅ Sentinel-2 monthly data ready!\")\n", - " print(f\" ✓ Cloud masked\")\n", - " print(f\" ✓ NDVI calculated\")\n", - " print(f\" ✓ Monthly resampled\")\n", - " print(f\" ✓ Loaded into memory\")\n", - "else:\n", - " print(\"❌ Failed to load Sentinel-2 data\")" - ] - }, - { - "cell_type": "markdown", - "id": "d0c0f672", - "metadata": {}, - "source": [ - "## 📥 Step 4: Load Sentinel-1 Data from ODC\n", - "\n", - "Load dữ liệu Sentinel-1 RTC (SAR) từ ODC Database" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1e629839", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📥 Loading Sentinel-1 data from ODC Database...\n", - "----------------------------------------------------------------------\n" - ] - }, - { - "data": { - "text/html": [ - "

Dataset size: 21.60 GB

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<xarray.Dataset> Size: 23GB\n",
-       "Dimensions:      (time: 33, y: 8874, x: 9902)\n",
-       "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 264B 2022-09-06T22:46:14.500000 ... 20...\n",
-       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
-       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
-       "    spatial_ref  int32 4B 32648\n",
-       "Data variables:\n",
-       "    vv           (time, y, x) float32 12GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "    vh           (time, y, x) float32 12GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    crs:           EPSG:32648\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - " Size: 23GB\n", - "Dimensions: (time: 33, y: 8874, x: 9902)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 264B 2022-09-06T22:46:14.500000 ... 20...\n", - " * y (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n", - " * x (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n", - " spatial_ref int32 4B 32648\n", - "Data variables:\n", - " vv (time, y, x) float32 12GB dask.array\n", - " vh (time, y, x) float32 12GB dask.array\n", - "Attributes:\n", - " crs: EPSG:32648\n", - " grid_mapping: spatial_ref" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "✅ Sentinel-1 data loaded from ODC!\n" - ] - }, - { - "ename": "ValueError", - "evalue": "dictionary update sequence element #0 has length 4; 2 is required", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m:12\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: dictionary update sequence element #0 has length 4; 2 is required" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "print(\"📥 Loading Sentinel-1 data from ODC Database...\")\n", - "print(\"-\" * 70)\n", - "\n", - "# Initialize to None\n", - "dsvh_monthly = None\n", - "dsvv_monthly = None\n", - "\n", - "try:\n", - " # Load Sentinel-1 từ ODC\n", - " # Note: load_data_sen1 returns (dsvh, dsvv) - 2 separate DataArrays\n", - " coordinates = (longitude_range, latitude_range)\n", - " \n", - " dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)\n", - " \n", - " if dsvh is not None and dsvv is not None and len(dsvh.time) > 0:\n", - " print(f\"\\n✅ Sentinel-1 data loaded from ODC!\")\n", - " print(f\" VH dimensions: {dict(dsvh.dims)}\")\n", - " print(f\" VV dimensions: {dict(dsvv.dims)}\")\n", - " print(f\" Time steps: {len(dsvh.time)}\")\n", - " \n", - " # Resample to monthly\n", - " print(\"\\n📅 Resampling to monthly averages...\")\n", - " dsvh_monthly = calculate_average(dsvh, time_pattern='1M')\n", - " dsvv_monthly = calculate_average(dsvv, time_pattern='1M')\n", - " \n", - " # Compute to load into memory\n", - " dsvh_monthly = dsvh_monthly.compute()\n", - " dsvv_monthly = dsvv_monthly.compute()\n", - " print(f\"\\n✅ Sentinel-1 monthly data ready!\")\n", - " print(f\" ✓ Monthly resampled\")\n", - " print(f\" ✓ Loaded into memory\")\n", - " else:\n", - " print(\"⚠️ Sentinel-1 data returned but empty\")\n", - " dsvh_monthly = None\n", - " dsvv_monthly = None\n", - " \n", - "except Exception as e:\n", - " print(f\"❌ Failed to load Sentinel-1 data: {e}\")\n", - " dsvh_monthly = None\n", - " dsvv_monthly = None\n", - "\n", - "if dsvh_monthly is None or dsvv_monthly is None:\n", - " print(\"\\n⚠️ Sentinel-1 NOT available - will train with Sentinel-2 (NDVI) only\")\n", - " print(\" This may reduce model accuracy but allows training to proceed.\")\n", - "else:\n", - " print(\"\\n✅ Sentinel-1 available - will use both S1 and S2 data\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "40a2ffe6", - "metadata": {}, - "source": [ - "## 🎯 Step 5: Load Training Data\n", - "\n", - "Load dữ liệu mẫu huấn luyện (training samples)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9be070b8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🎯 Label mapping:\n", - " 0: Lua tom\n", - " 1: Lua\n", - " 2: CHN\n", - " 3: CLN\n", - " 4: TS\n", - " 5: Song\n", - " 6: Dat xay dung\n", - " 7: Rung\n", - "\n", - "📂 Loading training data from: /home/jovyan/remote-sensing/train/ST_training_data_updated_1130points_new.shp\n", - "\n", - "✅ Training data loaded successfully!\n", - " Total points: 1130\n", - " CRS: EPSG:32648\n", - " Columns: ['No', 'X', 'Y', 'LU2022', 'Hientrang', 'HT_code', 'geometry']\n" - ] - } - ], - "source": [ - "# Ánh xạ nhãn lớp đất\n", - "label_mapping = {\n", - " \"Lua tom\": 0,\n", - " \"Lua\": 1,\n", - " \"CHN\": 2,\n", - " \"CLN\": 3,\n", - " \"TS\": 4,\n", - " \"Song\": 5,\n", - " \"Dat xay dung\": 6,\n", - " \"Rung\": 7,\n", - "}\n", - "\n", - "print(\"🎯 Label mapping:\")\n", - "for label, idx in label_mapping.items():\n", - " print(f\" {idx}: {label}\")\n", - "\n", - "# Load training data with absolute path\n", - "train_path = \"/home/jovyan/remote-sensing/train/ST_training_data_updated_1130points_new.shp\"\n", - "\n", - "print(f\"\\n📂 Loading training data from: {train_path}\")\n", - "train_data = load_train_data(train_path)\n", - "\n", - "if train_data is not None:\n", - " print(f\"\\n✅ Training data loaded successfully!\")\n", - " print(f\" Total points: {len(train_data)}\")\n", - " print(f\" CRS: {train_data.crs}\")\n", - " print(f\" Columns: {list(train_data.columns)}\")\n", - "else:\n", - " print(\"❌ Failed to load training data\")" - ] - }, - { - "cell_type": "markdown", - "id": "b371c97f", - "metadata": {}, - "source": [ - "## 🔧 Step 6: Extract Features\n", - "\n", - "Trích xuất features từ Sentinel-1 và Sentinel-2 tại các điểm mẫu" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7d6c117d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🔧 Extracting features from satellite data...\n", - "----------------------------------------------------------------------\n", - "❌ Missing data: Please check training data or satellite data\n", - " train_data: ✓\n", - " data_sen2_monthly: ✓\n", - " dsvh_monthly: ✗\n", - " dsvv_monthly: ✗\n", - "CPU times: user 148 μs, sys: 43 μs, total: 191 μs\n", - "Wall time: 160 μs\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "print(\"🔧 Extracting features from satellite data...\")\n", - "print(\"-\" * 70)\n", - "\n", - "if train_data is not None and 'data_sen2_monthly' in locals():\n", - " \n", - " # Check if we have Sentinel-1 data\n", - " has_sentinel1 = ('dsvh_monthly' in locals() and \n", - " 'dsvv_monthly' in locals() and \n", - " dsvh_monthly is not None and \n", - " dsvv_monthly is not None)\n", - " \n", - " if has_sentinel1:\n", - " print(\"📡 Using Sentinel-1 + Sentinel-2 data\")\n", - " # Extract features using ODC function with S1 and S2\n", - " datasets = get_data_sen1_and_sen2(\n", - " train_data, \n", - " data_sen2_monthly, # NDVI monthly average\n", - " dsvh_monthly, # VH monthly average\n", - " dsvv_monthly # VV monthly average\n", - " )\n", - " else:\n", - " print(\"📡 Using Sentinel-2 (NDVI) only - Sentinel-1 not available\")\n", - " # Extract features from S2 only\n", - " datasets = {}\n", - " for idx, point in train_data.iterrows():\n", - " try:\n", - " # Extract NDVI time series at point\n", - " ndvi_data = data_sen2_monthly.sel(\n", - " x=point.geometry.x, \n", - " y=point.geometry.y, \n", - " method='nearest'\n", - " ).values\n", - " \n", - " # Flatten to 1D array\n", - " ndvi_flat = ndvi_data.flatten()\n", - " \n", - " # Store if valid\n", - " if not np.all(np.isnan(ndvi_flat)):\n", - " datasets[idx] = {\n", - " 'data': ndvi_flat,\n", - " 'label': point['id_4']\n", - " }\n", - " except Exception as e:\n", - " print(f\" Warning: Failed to extract features for point {idx}: {e}\")\n", - " continue\n", - " \n", - " print(f\"\\n✅ Feature extraction complete!\")\n", - " print(f\" Total datasets: {len(datasets)}\")\n", - " \n", - " # Display statistics\n", - " valid_datasets = [v for v in datasets.values() if v is not None]\n", - " if valid_datasets:\n", - " sample_data = valid_datasets[0]['data']\n", - " print(f\" Feature dimension: {len(sample_data)}\")\n", - " print(f\" Valid samples: {len(valid_datasets)}\")\n", - " print(f\" Data source: {'S1+S2' if has_sentinel1 else 'S2 only (NDVI)'}\")\n", - " \n", - " # Count labels\n", - " labels = [d['label'] for d in valid_datasets]\n", - " label_counts = pd.Series(labels).value_counts().sort_index()\n", - " print(f\"\\n Label distribution:\")\n", - " for label_id, count in label_counts.items():\n", - " print(f\" {label_id}: {count} samples ({count/len(labels)*100:.1f}%)\")\n", - " \n", - "else:\n", - " print(\"❌ Missing data: Please check training data or satellite data\")\n", - " print(f\" train_data: {'✓' if train_data is not None else '✗'}\")\n", - " print(f\" data_sen2_monthly: {'✓' if 'data_sen2_monthly' in locals() else '✗'}\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "b485ba1b", - "metadata": {}, - "source": [ - "## 📊 Step 7: Split Data\n", - "\n", - "Chia dữ liệu thành train/val/test sets" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "29726670", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "❌ Features not extracted yet. Please run Step 6 first.\n" - ] - } - ], - "source": [ - "if 'datasets' in locals():\n", - " # Split data using ODC function\n", - " X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n", - " train_data, \n", - " label_mapping, \n", - " datasets\n", - " )\n", - " \n", - " # Combine train + val for final model training\n", - " X_fit = X_train + X_val\n", - " y_fit = y_train + y_val\n", - " \n", - " print(f\"\\n✅ Data split complete:\")\n", - " print(f\" Training samples: {len(X_train)}\")\n", - " print(f\" Validation samples: {len(X_val)}\")\n", - " print(f\" Test samples: {len(X_test)}\")\n", - " print(f\"\\n Combined train+val: {len(X_fit)} samples\")\n", - " \n", - "else:\n", - " print(\"❌ Features not extracted yet. Please run Step 6 first.\")" - ] - }, - { - "cell_type": "markdown", - "id": "3966ba86", - "metadata": {}, - "source": [ - "## 🌲 Step 8: Train Decision Tree Model\n", - "\n", - "Huấn luyện mô hình Decision Tree" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "2fc1c3da", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "❌ Data not ready. Please run Step 7 first.\n", - "CPU times: user 57 μs, sys: 0 ns, total: 57 μs\n", - "Wall time: 66.5 μs\n" - ] - } - ], - "source": [ - "%%time\n", - "\n", - "if 'X_fit' in locals() and 'y_fit' in locals():\n", - " \n", - " print(\"🚀 Training Decision Tree model...\")\n", - " print(\"-\" * 70)\n", - " \n", - " # Train Decision Tree with tuned hyperparameters\n", - " model = DecisionTreeClassifier(\n", - " max_depth=30,\n", - " min_samples_leaf=2,\n", - " min_samples_split=5,\n", - " class_weight=\"balanced\",\n", - " random_state=42,\n", - " )\n", - " \n", - " model.fit(X_fit, y_fit)\n", - " \n", - " # Evaluate on validation set\n", - " val_acc = model.score(X_val, y_val)\n", - " \n", - " print(f\"\\n✅ Training complete!\")\n", - " print(f\" Model: Decision Tree\")\n", - " print(f\" Tree depth: {model.get_depth()}\")\n", - " print(f\" Number of leaves: {model.get_n_leaves()}\")\n", - " print(f\" Training samples: {len(X_fit)}\")\n", - " print(f\" Validation accuracy: {val_acc:.4f} ({val_acc*100:.2f}%)\")\n", - " \n", - "else:\n", - " print(\"❌ Data not ready. Please run Step 7 first.\")" - ] - }, - { - "cell_type": "markdown", - "id": "b244cc21", - "metadata": {}, - "source": [ - "## 📊 Step 9: Evaluate Model\n", - "\n", - "Đánh giá mô hình trên tập test" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4c50781e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "❌ Model not trained yet. Please run Step 8 first.\n" - ] - } - ], - "source": [ - "if 'model' in locals() and 'X_test' in locals():\n", - " \n", - " print(\"📊 Evaluating model on test set...\")\n", - " print(\"-\" * 70)\n", - " \n", - " # Make predictions\n", - " y_pred = model.predict(X_test)\n", - " acc = accuracy_score(y_test, y_pred)\n", - " \n", - " print(f\"\\n🎯 Test Accuracy: {acc:.4f} ({acc*100:.2f}%)\\n\")\n", - " print(\"📋 Classification Report:\")\n", - " print(classification_report(y_test, y_pred, digits=4))\n", - " \n", - " # Confusion Matrix\n", - " class_names = list(label_mapping.keys())\n", - " cm = confusion_matrix(y_test, y_pred)\n", - " \n", - " plt.figure(figsize=(10, 8))\n", - " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n", - " xticklabels=class_names, yticklabels=class_names,\n", - " cbar_kws={'label': 'Count'})\n", - " plt.xlabel('Predicted Label', fontsize=12)\n", - " plt.ylabel('True Label', fontsize=12)\n", - " plt.title('Confusion Matrix — Decision Tree (Test Set)', fontsize=14, fontweight='bold')\n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - "else:\n", - " print(\"❌ Model not trained yet. Please run Step 8 first.\")" - ] - }, - { - "cell_type": "markdown", - "id": "3db1ed05", - "metadata": {}, - "source": [ - "## 💾 Step 10: Save Model\n", - "\n", - "Lưu mô hình để dùng cho prediction trên Planetary Computer" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "11e9ac2d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "❌ Model not trained yet or evaluation not complete.\n" - ] - } - ], - "source": [ - "if 'model' in locals() and 'acc' in locals():\n", - " \n", - " print(\"💾 Saving model and metadata...\")\n", - " print(\"-\" * 70)\n", - " \n", - " # Save model\n", - " model_path = \"model_decision_tree_odc.joblib\"\n", - " joblib.dump(model, model_path)\n", - " print(f\"✅ Model saved → {model_path}\")\n", - " \n", - " # Create metadata\n", - " info = {\n", - " \"model_type\": \"DecisionTree\",\n", - " \"data_source\": \"ODC Database\",\n", - " \"training_data\": train_path,\n", - " \"max_depth\": int(model.get_depth()),\n", - " \"n_leaves\": int(model.get_n_leaves()),\n", - " \"n_features\": len(X_fit[0]) if X_fit else 0,\n", - " \"label_mapping\": label_mapping,\n", - " \"test_accuracy\": float(acc),\n", - " \"train_samples\": int(len(X_fit)),\n", - " \"val_samples\": int(len(X_val)),\n", - " \"test_samples\": int(len(X_test)),\n", - " \"longitude_range\": list(longitude_range),\n", - " \"latitude_range\": list(latitude_range),\n", - " \"date_range\": list(date_range),\n", - " \"saved_at\": datetime.now().isoformat(),\n", - " \"hyperparameters\": {\n", - " \"max_depth\": 30,\n", - " \"min_samples_leaf\": 2,\n", - " \"min_samples_split\": 5,\n", - " \"class_weight\": \"balanced\",\n", - " \"random_state\": 42\n", - " },\n", - " \"note\": \"Model trained on ODC data, can be used for prediction on Planetary Computer\"\n", - " }\n", - " \n", - " # Save metadata\n", - " info_path = \"model_decision_tree_odc_info.json\"\n", - " with open(info_path, \"w\") as f:\n", - " json.dump(info, f, indent=2, ensure_ascii=False)\n", - " \n", - " print(f\"✅ Metadata saved → {info_path}\")\n", - " print(\"\\n📋 Model Info:\")\n", - " print(json.dumps(info, indent=2, ensure_ascii=False))\n", - " print(\"\\n💡 Model này có thể dùng cho prediction trên Planetary Computer!\")\n", - " \n", - "else:\n", - " print(\"❌ Model not trained yet or evaluation not complete.\")" - ] - }, - { - "cell_type": "markdown", - "id": "8280de27", - "metadata": {}, - "source": [ - "## 🧹 Step 11: Cleanup\n", - "\n", - "Đóng Dask cluster" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3ffe4f8a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Dask Gateway cluster closed.\n" - ] - } - ], - "source": [ - "# Close Dask Gateway cluster\n", - "try:\n", - " client.close()\n", - " cluster.close()\n", - " print(\"✅ Dask Gateway cluster closed.\")\n", - "except Exception as e:\n", - " print(f\"⚠ Error closing cluster: {e}\")" - ] - }, - { - "cell_type": "markdown", - "id": "382428e1", - "metadata": {}, - "source": [ - "---\n", - "\n", - "## ✅ Summary\n", - "\n", - "### Notebook này:\n", - "- ✅ Chạy trên **Server ODC/JupyterHub** với **Dask Gateway**\n", - "- ✅ Load dữ liệu từ **ODC Database** (rất nhanh trên server)\n", - "- ✅ Train model với dữ liệu từ ODC\n", - "- ✅ Save model để dùng cho prediction\n", - "\n", - "### Ưu điểm của approach này:\n", - "1. **Speed**: ODC Database truy cập cực nhanh trên server\n", - "2. **Scalability**: Dask Gateway adaptive scaling (1-10 workers)\n", - "3. **Compatibility**: Model có thể dùng trên Planetary Computer\n", - "\n", - "### Workflow hoàn chỉnh:\n", - "```\n", - "┌─────────────────────────────────────┐\n", - "│ TRAINING (ODC Server) │\n", - "│ ✅ Load từ ODC Database (FAST) │\n", - "│ ✅ Dask Gateway (1-10 workers) │\n", - "│ ✅ Train Decision Tree │\n", - "│ → Model: .joblib │\n", - "└─────────────────────────────────────┘\n", - " ↓\n", - "┌─────────────────────────────────────┐\n", - "│ PREDICTION (Local Machine) │\n", - "│ ✅ Load từ Planetary Computer │\n", - "│ ✅ Use trained model │\n", - "│ ✅ No VPN/ODC access needed │\n", - "│ → GeoTIFF output │\n", - "└─────────────────────────────────────┘\n", - "```\n", - "\n", - "### Prediction API:\n", - "- Máy local không cần truy cập ODC\n", - "- Load data từ Planetary Computer (public)\n", - "- Sử dụng model đã train từ ODC\n", - "- Kết quả prediction giống nhau!\n", - "\n", - "---" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/train_files/01.train_ODC_CNN.ipynb 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" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "# Cấu hình Daskgateway\n", - "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n", - "# Khai báo 1 Datacube là dc\n", - "dc = datacube.Datacube()\n", - "\n", - "# Cấu hình truy cập dịch vụ S3\n", - "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n", - "\n", - "client" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fbed4c80-bbf8-4ea8-aa45-2460b2ba04c7", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "## cấu hình thời gian lấy ảnh và tọa độ\n", - "date_range = (\"2022-09-01\", \"2023-10-01\")\n", - "longtitude_range = (105.5, 106.4)\n", - "latitude_range = (9.2, 10.0)\n", - "\n", - "coordinates = (longtitude_range, latitude_range)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6b90c49b-0665-4478-a23b-d111ef88eb79", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Most common native CRS: EPSG:32648\n", - "No datasets require offset correction\n", - "The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n" - ] - }, - { - "data": { - "text/html": [ - "

Dataset size: 111.21 GB

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<xarray.Dataset> Size: 119GB\n",
-       "Dimensions:      (time: 151, y: 8874, x: 9902)\n",
-       "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
-       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
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-       "    spatial_ref  int32 4B 32648\n",
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-       "    red          (time, y, x) float32 53GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
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-       "    scl          (time, y, x) uint8 13GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    crs:           EPSG:32648\n",
-       "    grid_mapping:  spatial_ref
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<xarray.DataArray 'NDVI' (time: 151, y: 8874, x: 9902)> Size: 53GB\n",
-       "dask.array<truediv, shape=(151, 8874, 9902), dtype=float32, chunksize=(1, 2048, 2048), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
-       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
-       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
-       "    spatial_ref  int32 4B 32648
" - ], - "text/plain": [ - " Size: 53GB\n", - "dask.array\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n", - " * y (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n", - " * x (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n", - " spatial_ref int32 4B 32648" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Tiến hành tính toán NDVI\n", - "ds1 = calculate_indices(result, index=\"NDVI\", satellite_mission=\"s2\")\n", - "ndvi = ds1[\"NDVI\"]\n", - "display(ndvi)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "84992d28-8e3f-468e-be08-ded511f2c662", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Hiển thị ảnh NDVI chưa điền các giá trị mây (chưa fill nan)\n", - "plt.imshow(ndvi.isel(time=6))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "72318b60-532a-4f08-a5f7-94762d08a42c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Thiết lập giá trị trung bình mùa vụ để xử lý các điểm ảnh bị mây dựa vào sự thay đổi theo mùa\n", - "time_split = [\n", - " slice(\"2022-09-01\", \"2023-01-01\"),\n", - " slice(\"2023-01-01\", \"2023-05-01\"),\n", - " slice(\"2023-05-01\", \"2023-07-01\"),\n", - " slice(\"2023-07-01\", \"2023-10-01\"),\n", - "]\n", - "\n", - "# Điền mây ở các vị trí mang giá trị nan (fill nan)\n", - "fill_nan_ndvi = fill_nan(ndvi, time_split)\n", - "\n", - "# In kết quả ảnh NDVI đã điền mây (đã fill nan)\n", - "plt.imshow(fill_nan_ndvi.isel(time=6))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "375b1cfb-37f5-49fe-8061-ea32eb47f9f6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/env/lib/python3.12/site-packages/xarray/groupers.py:392: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " self.index_grouper = pd.Grouper(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 6.87 s, sys: 4.93 s, total: 11.8 s\n", - "Wall time: 2min 40s\n" - ] - } - ], - "source": [ - "%%time\n", - "## tính ndvi theo tháng\n", - "average_ndvi = fill_nan_ndvi.resample(time=\"1M\").mean().persist()\n", - "progress(average_ndvi)\n", - "\n", - "# compute average_ndvi\n", - "average_ndvi = average_ndvi.compute()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "187cc640-aef9-476b-91fc-b63f4d3ff2e3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "

Dataset size: 21.60 GB

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<xarray.Dataset> Size: 23GB\n",
-       "Dimensions:      (time: 33, y: 8874, x: 9902)\n",
-       "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 264B 2022-09-06T22:46:14.500000 ... 20...\n",
-       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
-       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
-       "    spatial_ref  int32 4B 32648\n",
-       "Data variables:\n",
-       "    vv           (time, y, x) float32 12GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "    vh           (time, y, x) float32 12GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    crs:           EPSG:32648\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - " Size: 23GB\n", - "Dimensions: (time: 33, y: 8874, x: 9902)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 264B 2022-09-06T22:46:14.500000 ... 20...\n", - " * y (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n", - " * x (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n", - " spatial_ref int32 4B 32648\n", - "Data variables:\n", - " vv (time, y, x) float32 12GB dask.array\n", - " vh (time, y, x) float32 12GB dask.array\n", - "Attributes:\n", - " crs: EPSG:32648\n", - " grid_mapping: spatial_ref" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/env/lib/python3.12/site-packages/xarray/groupers.py:392: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " self.index_grouper = pd.Grouper(\n", - "/env/lib/python3.12/site-packages/xarray/groupers.py:392: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " self.index_grouper = pd.Grouper(\n" - ] - } - ], - "source": [ - "#Load dữ liệu ảnh Sentinel 1\n", - "dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)\n", - "average_vv = calculate_average(dsvv, time_pattern='1M')\n", - "average_vh = calculate_average(dsvh, time_pattern='1M')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "## cấu hình bộ dữ liệu điểm huấn luyện mô hình (train file)\n", - "train_path = \"train/ST_training_data_updated_1130points_new.shp\" # đường dẫn shp file train\n", - "\n", - "## load dữ liệu điểm huấn luyện mô hình (train file)\n", - "train = load_train_data(train_path)\n", - "train.head()\n", - "\n", - "# cấu hình nhãn dữ liệu \n", - "label_mapping = {\n", - " \"Lua tom\": \"0\",\n", - " \"Lua\": \"1\",\n", - " \"CHN\": \"2\",\n", - " \"CLN\": \"3\",\n", - " \"TS\": \"4\",\n", - " \"Song\": \"5\",\n", - " \"Dat xay dung\": \"6\",\n", - " \"Rung\": \"7\",\n", - "}\n", - "\n", - "# xây dựng tập dữ liệu (dataset) chứa dữ liệu VH, VV, NDVI\n", - "datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)\n", - "\n", - "# chia tập dữ liệu thành các phần theo tỉ lệ 80(80-20)-20 tương ứng với tập train, validate, test\n", - "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n", - " train, label_mapping, datasets\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2e955884-d4af-422d-a8e6-d436199540e0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Device : cpu\n", - "Train shape : torch.Size([678, 3, 13]) labels: torch.Size([678])\n", - "Val shape : torch.Size([226, 3, 13]) labels: torch.Size([226])\n", - "Timesteps : 13 Channels: 3 Classes: 8\n", - "\n", - "CNN1D(\n", - " (blocks): Sequential(\n", - " (0): Conv1d(3, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (1): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (2): ReLU()\n", - " (3): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", - " (4): Dropout(p=0.25, inplace=False)\n", - " (5): Conv1d(64, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (6): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (7): ReLU()\n", - " (8): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)\n", - " (9): Dropout(p=0.25, inplace=False)\n", - " (10): Conv1d(128, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n", - " (11): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (12): ReLU()\n", - " (13): Dropout(p=0.3, inplace=False)\n", - " )\n", - " (pool): AdaptiveAvgPool1d(output_size=1)\n", - " (head): Sequential(\n", - " (0): Flatten(start_dim=1, end_dim=-1)\n", - " (1): Linear(in_features=64, out_features=128, bias=True)\n", - " (2): ReLU()\n", - " (3): Dropout(p=0.4, inplace=False)\n", - " (4): Linear(in_features=128, out_features=8, bias=True)\n", - " )\n", - ")\n", - "\n", - "Total parameters: 59,848\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/env/lib/python3.12/site-packages/torch/optim/lr_scheduler.py:62: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n", - " warnings.warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🚀 Training 1-D CNN model (PyTorch)...\n", - "Epoch 1/150 train_loss=1.8010 train_acc=0.4263 val_loss=1.6849 val_acc=0.6726\n", - "Epoch 10/150 train_loss=0.6762 train_acc=0.7419 val_loss=1.1341 val_acc=0.5487\n", - "Epoch 20/150 train_loss=0.5665 train_acc=0.7802 val_loss=1.1070 val_acc=0.5354\n", - "Epoch 30/150 train_loss=0.5451 train_acc=0.7935 val_loss=0.9178 val_acc=0.6327\n", - "\n", - "Early stopping at epoch 31 (no improvement for 20 epochs)\n", - "\n", - "✅ Training completed! Best val accuracy: 0.7168 (71.68%)\n", - "CPU times: user 1min 46s, sys: 463 ms, total: 1min 46s\n", - "Wall time: 11.4 s\n" - ] - } - ], - "source": [ - "%%time\n", - "import numpy as np\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "from torch.utils.data import TensorDataset, DataLoader\n", - "\n", - "# ── 1. Chuẩn bị dữ liệu ─────────────────────────────────────────────────────\n", - "X_train_np = np.asarray(X_train, dtype=np.float32)\n", - "X_val_np = np.asarray(X_val, dtype=np.float32)\n", - "y_train_np = np.asarray(y_train, dtype=np.int64)\n", - "y_val_np = np.asarray(y_val, dtype=np.int64)\n", - "\n", - "n_samples_train, n_features = X_train_np.shape\n", - "\n", - "# Số biến đầu vào mỗi bước thời gian (ndvi, vh, vv = 3)\n", - "N_VARS = 3\n", - "n_steps = n_features // N_VARS # số tháng\n", - "\n", - "# PyTorch Conv1d expects (batch, channels, length) → channels = N_VARS, length = n_steps\n", - "X_train_t = torch.from_numpy(X_train_np.reshape(-1, N_VARS, n_steps))\n", - "X_val_t = torch.from_numpy(X_val_np.reshape(-1, N_VARS, n_steps))\n", - "y_train_t = torch.from_numpy(y_train_np)\n", - "y_val_t = torch.from_numpy(y_val_np)\n", - "\n", - "NUM_CLASSES = len(label_mapping)\n", - "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "\n", - "print(f\"Device : {DEVICE}\")\n", - "print(f\"Train shape : {X_train_t.shape} labels: {y_train_t.shape}\")\n", - "print(f\"Val shape : {X_val_t.shape} labels: {y_val_t.shape}\")\n", - "print(f\"Timesteps : {n_steps} Channels: {N_VARS} Classes: {NUM_CLASSES}\\n\")\n", - "\n", - "train_loader = DataLoader(\n", - " TensorDataset(X_train_t, y_train_t), batch_size=32, shuffle=True\n", - ")\n", - "val_loader = DataLoader(\n", - " TensorDataset(X_val_t, y_val_t), batch_size=64, shuffle=False\n", - ")\n", - "\n", - "# ── 2. Xây dựng mô hình 1-D CNN (PyTorch) ───────────────────────────────────\n", - "class CNN1D(nn.Module):\n", - " def __init__(self, n_vars, n_steps, num_classes):\n", - " super().__init__()\n", - " self.blocks = nn.Sequential(\n", - " # Block 1\n", - " nn.Conv1d(n_vars, 64, kernel_size=3, padding=1),\n", - " nn.BatchNorm1d(64), nn.ReLU(),\n", - " nn.MaxPool1d(2, ceil_mode=True),\n", - " nn.Dropout(0.25),\n", - "\n", - " # Block 2\n", - " nn.Conv1d(64, 128, kernel_size=3, padding=1),\n", - " nn.BatchNorm1d(128), nn.ReLU(),\n", - " nn.MaxPool1d(2, ceil_mode=True),\n", - " nn.Dropout(0.25),\n", - "\n", - " # Block 3\n", - " nn.Conv1d(128, 64, kernel_size=3, padding=1),\n", - " nn.BatchNorm1d(64), nn.ReLU(),\n", - " nn.Dropout(0.3),\n", - " )\n", - " self.pool = nn.AdaptiveAvgPool1d(1) # → (batch, 64, 1)\n", - " self.head = nn.Sequential(\n", - " nn.Flatten(),\n", - " nn.Linear(64, 128), nn.ReLU(),\n", - " nn.Dropout(0.4),\n", - " nn.Linear(128, num_classes),\n", - " )\n", - "\n", - " def forward(self, x):\n", - " x = self.blocks(x)\n", - " x = self.pool(x)\n", - " return self.head(x)\n", - "\n", - "model = CNN1D(N_VARS, n_steps, NUM_CLASSES).to(DEVICE)\n", - "print(model)\n", - "total_params = sum(p.numel() for p in model.parameters())\n", - "print(f\"\\nTotal parameters: {total_params:,}\\n\")\n", - "\n", - "# ── 3. Compile & train ───────────────────────────────────────────────────────\n", - "criterion = nn.CrossEntropyLoss()\n", - "optimizer = optim.Adam(model.parameters(), lr=1e-3)\n", - "scheduler = optim.lr_scheduler.ReduceLROnPlateau(\n", - " optimizer, mode=\"min\", factor=0.5, patience=8, verbose=True\n", - ")\n", - "\n", - "EPOCHS = 150\n", - "PATIENCE = 20\n", - "best_val_acc = 0.0\n", - "best_state = None\n", - "no_improve = 0\n", - "history = {\"train_loss\": [], \"train_acc\": [], \"val_loss\": [], \"val_acc\": []}\n", - "\n", - "def evaluate(loader):\n", - " model.eval()\n", - " total_loss, correct, n = 0.0, 0, 0\n", - " with torch.no_grad():\n", - " for xb, yb in loader:\n", - " xb, yb = xb.to(DEVICE), yb.to(DEVICE)\n", - " logits = model(xb)\n", - " total_loss += criterion(logits, yb).item() * len(yb)\n", - " correct += (logits.argmax(1) == yb).sum().item()\n", - " n += len(yb)\n", - " return total_loss / n, correct / n\n", - "\n", - "print(\"🚀 Training 1-D CNN model (PyTorch)...\")\n", - "for epoch in range(1, EPOCHS + 1):\n", - " model.train()\n", - " t_loss, t_correct, t_n = 0.0, 0, 0\n", - " for xb, yb in train_loader:\n", - " xb, yb = xb.to(DEVICE), yb.to(DEVICE)\n", - " optimizer.zero_grad()\n", - " logits = model(xb)\n", - " loss = criterion(logits, yb)\n", - " loss.backward()\n", - " optimizer.step()\n", - " t_loss += loss.item() * len(yb)\n", - " t_correct += (logits.argmax(1) == yb).sum().item()\n", - " t_n += len(yb)\n", - "\n", - " train_loss, train_acc = t_loss / t_n, t_correct / t_n\n", - " val_loss, val_acc = evaluate(val_loader)\n", - " scheduler.step(val_loss)\n", - "\n", - " history[\"train_loss\"].append(train_loss)\n", - " history[\"train_acc\"].append(train_acc)\n", - " history[\"val_loss\"].append(val_loss)\n", - " history[\"val_acc\"].append(val_acc)\n", - "\n", - " if val_acc > best_val_acc:\n", - " best_val_acc = val_acc\n", - " best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}\n", - " no_improve = 0\n", - " else:\n", - " no_improve += 1\n", - "\n", - " if epoch % 10 == 0 or epoch == 1:\n", - " print(f\"Epoch {epoch:3d}/{EPOCHS} \"\n", - " f\"train_loss={train_loss:.4f} train_acc={train_acc:.4f} \"\n", - " f\"val_loss={val_loss:.4f} val_acc={val_acc:.4f}\")\n", - "\n", - " if no_improve >= PATIENCE:\n", - " print(f\"\\nEarly stopping at epoch {epoch} (no improvement for {PATIENCE} epochs)\")\n", - " break\n", - "\n", - "# Restore best weights\n", - "model.load_state_dict(best_state)\n", - "print(f\"\\n✅ Training completed! Best val accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b2a1e42c-cf1b-4d82-a6af-b06e3496918f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📊 Evaluating 1-D CNN model on test set...\n", - "\n", - "📈 Test Results:\n", - " Accuracy : 0.7124 (71.24%)\n", - " Precision: 0.7304\n", - " Recall : 0.7124\n", - " F1-Score : 0.6849\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 950 ms, sys: 318 ms, total: 1.27 s\n", - "Wall time: 545 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import (\n", - " accuracy_score, precision_score, recall_score, f1_score,\n", - " confusion_matrix, ConfusionMatrixDisplay,\n", - ")\n", - "\n", - "# ── 1. Đánh giá trên tập test ────────────────────────────────────────────────\n", - "X_test_np = np.asarray(X_test, dtype=np.float32)\n", - "y_test_np = np.asarray(y_test, dtype=np.int64)\n", - "X_test_t = torch.from_numpy(X_test_np.reshape(-1, N_VARS, n_steps))\n", - "\n", - "print(\"📊 Evaluating 1-D CNN model on test set...\\n\")\n", - "\n", - "model.eval()\n", - "all_preds = []\n", - "with torch.no_grad():\n", - " for i in range(0, len(X_test_t), 64):\n", - " xb = X_test_t[i:i+64].to(DEVICE)\n", - " preds = model(xb).argmax(1).cpu().numpy()\n", - " all_preds.append(preds)\n", - "\n", - "y_pred_test = np.concatenate(all_preds)\n", - "\n", - "test_accuracy = accuracy_score(y_test_np, y_pred_test)\n", - "precision = precision_score(y_test_np, y_pred_test, average=\"weighted\", zero_division=0)\n", - "recall = recall_score(y_test_np, y_pred_test, average=\"weighted\", zero_division=0)\n", - "f1 = f1_score(y_test_np, y_pred_test, average=\"weighted\", zero_division=0)\n", - "\n", - "print(f\"📈 Test Results:\")\n", - "print(f\" Accuracy : {test_accuracy:.4f} ({test_accuracy*100:.2f}%)\")\n", - "print(f\" Precision: {precision:.4f}\")\n", - "print(f\" Recall : {recall:.4f}\")\n", - "print(f\" F1-Score : {f1:.4f}\\n\")\n", - "\n", - "# ── 2. Learning curves ───────────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n", - "\n", - "axes[0].plot(history[\"train_loss\"], label=\"Train loss\")\n", - "axes[0].plot(history[\"val_loss\"], label=\"Val loss\")\n", - "axes[0].set_title(\"Loss over epochs\")\n", - "axes[0].set_xlabel(\"Epoch\")\n", - "axes[0].legend()\n", - "\n", - "axes[1].plot(history[\"train_acc\"], label=\"Train accuracy\")\n", - "axes[1].plot(history[\"val_acc\"], label=\"Val accuracy\")\n", - "axes[1].set_title(\"Accuracy over epochs\")\n", - "axes[1].set_xlabel(\"Epoch\")\n", - "axes[1].legend()\n", - "\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# ── 3. Confusion matrix ──────────────────────────────────────────────────────\n", - "class_names = list(label_mapping.keys())\n", - "cm = confusion_matrix(y_test_np, y_pred_test)\n", - "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 8))\n", - "disp.plot(cmap=\"Blues\", ax=ax)\n", - "plt.xticks(rotation=45, ha=\"right\")\n", - "plt.title(\"CNN Confusion Matrix — Test Set\")\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f1a14379-ed6e-4897-9ca4-2669743fab40", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Model saved to model_cnn_land_use.pth\n", - "✅ Model info saved to model_cnn_land_use_info.json\n", - "\n", - "Summary:\n", - " Input shape : (3, 13)\n", - " Parameters : 59,848\n", - " Test accuracy: 71.24%\n" - ] - } - ], - "source": [ - "import json\n", - "\n", - "# ── Lưu weights mô hình CNN (PyTorch) ───────────────────────────────────────\n", - "model_path = \"model_cnn_land_use.pth\"\n", - "torch.save({\n", - " \"model_state_dict\": model.state_dict(),\n", - " \"n_vars\": N_VARS,\n", - " \"n_steps\": n_steps,\n", - " \"num_classes\": NUM_CLASSES,\n", - " \"label_mapping\": label_mapping,\n", - "}, model_path)\n", - "print(f\"✅ Model saved to {model_path}\")\n", - "\n", - "# ── Lưu thông tin mô hình ────────────────────────────────────────────────────\n", - "info = {\n", - " \"model_type\": \"1D-CNN (PyTorch)\",\n", - " \"input_shape\": [N_VARS, n_steps],\n", - " \"num_classes\": NUM_CLASSES,\n", - " \"classes\": list(label_mapping.keys()),\n", - " \"label_mapping\": label_mapping,\n", - " \"num_parameters\": sum(p.numel() for p in model.parameters()),\n", - " \"accuracy\": float(test_accuracy),\n", - " \"precision\": float(precision),\n", - " \"recall\": float(recall),\n", - " \"f1_score\": float(f1),\n", - "}\n", - "\n", - "info_path = \"model_cnn_land_use_info.json\"\n", - "with open(info_path, \"w\") as f:\n", - " json.dump(info, f, indent=2, ensure_ascii=False)\n", - "\n", - "print(f\"✅ Model info saved to {info_path}\")\n", - "print(f\"\\nSummary:\")\n", - "print(f\" Input shape : ({N_VARS}, {n_steps})\")\n", - "print(f\" Parameters : {info['num_parameters']:,}\")\n", - "print(f\" Test accuracy: {test_accuracy*100:.2f}%\")\n", - "\n", - "# ── Ví dụ load lại mô hình ──────────────────────────────────────────────────\n", - "# checkpoint = torch.load(\"model_cnn_land_use.pth\")\n", - "# model_loaded = CNN1D(checkpoint[\"n_vars\"], checkpoint[\"n_steps\"], checkpoint[\"num_classes\"])\n", - "# model_loaded.load_state_dict(checkpoint[\"model_state_dict\"])\n", - "# model_loaded.eval()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "33dd516d-9824-499e-96b9-5cd9224c194c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# đóng client, cluster\n", - "client.close()\n", - "cluster.close()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/train_files/01.train_ODC_DecisionTree.ipynb b/train_files/01.train_ODC_DecisionTree.ipynb deleted file mode 100644 index 2b224e9..0000000 --- a/train_files/01.train_ODC_DecisionTree.ipynb +++ /dev/null @@ -1,887 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "id": "b05aa740", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ EASI tools loaded successfully (with Gateway support)\n" - ] - }, - { - "data": { - "text/html": [ 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Start:\n", - " 1. setup_cognito_auth('train_files/crediential.txt')\n", - " 2. Use datacube normally with authenticated S3 access\n", - "\n", - "📚 Functions:\n", - " - setup_cognito_auth() : Setup Cognito authentication\n", - " - get_cognito_auth() : Get authenticator instance\n", - " - print_auth_status() : Show auth status\n", - " - auto_setup() : Auto-setup if credentials exist\n", - "======================================================================\n", - "\n", - "✅ Module loaded with Cognito authentication support\n", - "CPU times: user 529 ms, sys: 10.3 ms, total: 540 ms\n", - "Wall time: 531 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "%matplotlib inline\n", - "\n", - "import importlib\n", - "import sys\n", - "sys.path.insert(0, '/media/x79/2A7D-FAA0/remote-sensing')\n", - "\n", - "# Import ODC module with Cognito authentication\n", - "import new_import_ODC_cognito \n", - "importlib.reload(new_import_ODC_cognito)\n", - "\n", - "from new_import_ODC_cognito import *\n", - "\n", - "print(\"✅ Module loaded with Cognito authentication support\")" - ] - }, - { - "cell_type": "markdown", - "id": "6abb2be1", - "metadata": {}, - "source": [ - "# 🔐 Cognito Authentication for ODC\n", - "\n", - "## Overview / Tổng quan\n", - "\n", - "Notebook này sử dụng **AWS Cognito authentication** để truy cập S3 và Open Data Cube (ODC).\n", - "\n", - "### Luồng xác thực:\n", - "```\n", - "Cognito Tokens → AWS Credentials → S3/ODC Access\n", - "```\n", - "\n", - "### Lợi ích:\n", - "- ✅ **Bảo mật cao hơn**: Identity-based authentication\n", - "- ✅ **Thông tin user**: Username, email, groups\n", - "- ✅ **Token auto-expire**: Tăng cường bảo mật\n", - "- ✅ **Quản lý quyền tốt**: Group-based permissions\n", - "\n", - "### Credentials file:\n", - "```\n", - "/media/x79/2A7D-FAA0/remote-sensing/train_files/crediential.txt\n", - "```\n", - "\n", - "---\n", - "\n", - "**📚 Docs**: `COGNITO_GUIDE.md`, `S3_ACCESS_GUIDE.md`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b794d005", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🔐 Step 1: Cognito Authentication Setup\n", - "----------------------------------------------------------------------\n", - "🔐 Setting up Cognito authentication...\n", - "✓ AWS credentials loaded from file\n", - "✓ Cognito tokens loaded successfully\n", - "\n", - "📋 Token Information:\n", - "\n", - "=== Cognito Token Information ===\n", - "\n", - "User Information:\n", - " Username: hienm2523001\n", - " Name: Hien Phan\n", - " Email: hienm2523001@gstudent.ctu.edu.vn\n", - " Groups: default-group, allocation:R-19244:CSIRO and Vietnam partners\n", - " Token expires: 2026-03-05 04:52:38\n", - " Time remaining: 6h 25m\n", - "\n", - "=== Getting AWS Credentials from Cognito ===\n", - "⚠ No Identity Pool ID provided\n", - "⚠ Using existing AWS credentials (already exchanged from Cognito)...\n", - "✓ Using AWS credentials loaded from file\n", - "✓ AWS credentials set in environment\n", - "\n", - "🌐 Configuring datacube S3 access...\n", - "\n", - "✅ Cognito authentication setup complete!\n", - "✅ Ready to use datacube with S3 access\n", - "\n", - "\n", - "✅ Authentication successful!\n", - " Ready to access S3 buckets with authenticated credentials\n", - "\n", - "======================================================================\n", - "🚀 Step 2: Dask + Datacube Initialization\n", - "----------------------------------------------------------------------\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/x79/miniconda3/envs/env_01/lib/python3.10/site-packages/distributed/node.py:188: UserWarning: Port 8787 is already in use.\n", - "Perhaps you already have a cluster running?\n", - "Hosting the HTTP server on port 38781 instead\n", - " warnings.warn(\n" - ] - }, - { - "ename": "OperationalError", - "evalue": "(psycopg2.OperationalError) connection to server at \"v2-db-easi-asia-eks.cluster-ro-czyydvizywt5.ap-southeast-1.rds.amazonaws.com\" (10.0.22.166), port 5432 failed: Connection timed out\n\tIs the server running on that host and accepting TCP/IP connections?\n\n(Background on this error at: https://sqlalche.me/e/14/e3q8)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mOperationalError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/base.py:3371\u001b[0m, in \u001b[0;36mEngine._wrap_pool_connect\u001b[0;34m(self, fn, connection)\u001b[0m\n\u001b[1;32m 3370\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3371\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3372\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m dialect\u001b[38;5;241m.\u001b[39mdbapi\u001b[38;5;241m.\u001b[39mError \u001b[38;5;28;01mas\u001b[39;00m e:\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:327\u001b[0m, in \u001b[0;36mPool.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return a DBAPI connection from the pool.\u001b[39;00m\n\u001b[1;32m 321\u001b[0m \n\u001b[1;32m 322\u001b[0m \u001b[38;5;124;03mThe connection is instrumented such that when its\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 325\u001b[0m \n\u001b[1;32m 326\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 327\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ConnectionFairy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_checkout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:894\u001b[0m, in \u001b[0;36m_ConnectionFairy._checkout\u001b[0;34m(cls, pool, threadconns, fairy)\u001b[0m\n\u001b[1;32m 893\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fairy:\n\u001b[0;32m--> 894\u001b[0m fairy \u001b[38;5;241m=\u001b[39m \u001b[43m_ConnectionRecord\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheckout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpool\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 896\u001b[0m fairy\u001b[38;5;241m.\u001b[39m_pool \u001b[38;5;241m=\u001b[39m pool\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:493\u001b[0m, in \u001b[0;36m_ConnectionRecord.checkout\u001b[0;34m(cls, pool)\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 492\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mcheckout\u001b[39m(\u001b[38;5;28mcls\u001b[39m, pool):\n\u001b[0;32m--> 493\u001b[0m rec \u001b[38;5;241m=\u001b[39m \u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_get\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/impl.py:145\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m--> 145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m util\u001b[38;5;241m.\u001b[39msafe_reraise():\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dec_overflow()\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m \u001b[43mcompat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43mexc_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mwith_traceback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexc_tb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/util/compat.py:211\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 213\u001b[0m \u001b[38;5;66;03m# credit to\u001b[39;00m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;66;03m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# as the __traceback__ object creates a cycle\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/impl.py:143\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_connection\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:273\u001b[0m, in \u001b[0;36mPool._create_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 271\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Called by subclasses to create a new ConnectionRecord.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 273\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ConnectionRecord\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:388\u001b[0m, in \u001b[0;36m_ConnectionRecord.__init__\u001b[0;34m(self, pool, connect)\u001b[0m\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m connect:\n\u001b[0;32m--> 388\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinalize_callback \u001b[38;5;241m=\u001b[39m deque()\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:690\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 690\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m util\u001b[38;5;241m.\u001b[39msafe_reraise():\n\u001b[1;32m 691\u001b[0m pool\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError on connect(): \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, e)\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m \u001b[43mcompat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43mexc_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mwith_traceback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexc_tb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/util/compat.py:211\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 213\u001b[0m \u001b[38;5;66;03m# credit to\u001b[39;00m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;66;03m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# as the __traceback__ object creates a cycle\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:686\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 685\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstarttime \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m--> 686\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdbapi_connection \u001b[38;5;241m=\u001b[39m connection \u001b[38;5;241m=\u001b[39m \u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke_creator\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 687\u001b[0m pool\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreated new connection \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, connection)\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/create.py:574\u001b[0m, in \u001b[0;36mcreate_engine..connect\u001b[0;34m(connection_record)\u001b[0m\n\u001b[1;32m 573\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m connection\n\u001b[0;32m--> 574\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdialect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcargs\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[43mcparams\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/default.py:598\u001b[0m, in \u001b[0;36mDefaultDialect.connect\u001b[0;34m(self, *cargs, **cparams)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mcargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcparams):\n\u001b[1;32m 597\u001b[0m \u001b[38;5;66;03m# inherits the docstring from interfaces.Dialect.connect\u001b[39;00m\n\u001b[0;32m--> 598\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdbapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcargs\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[43mcparams\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/psycopg2/__init__.py:122\u001b[0m, in \u001b[0;36mconnect\u001b[0;34m(dsn, connection_factory, cursor_factory, **kwargs)\u001b[0m\n\u001b[1;32m 121\u001b[0m dsn \u001b[38;5;241m=\u001b[39m _ext\u001b[38;5;241m.\u001b[39mmake_dsn(dsn, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 122\u001b[0m conn \u001b[38;5;241m=\u001b[39m \u001b[43m_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconnection_factory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconnection_factory\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[43mkwasync\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cursor_factory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "\u001b[0;31mOperationalError\u001b[0m: connection to server at \"v2-db-easi-asia-eks.cluster-ro-czyydvizywt5.ap-southeast-1.rds.amazonaws.com\" (10.0.22.166), port 5432 failed: Connection timed out\n\tIs the server running on that host and accepting TCP/IP connections?\n", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mOperationalError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 24\u001b[0m\n\u001b[1;32m 21\u001b[0m cluster, client \u001b[38;5;241m=\u001b[39m notebook_utils\u001b[38;5;241m.\u001b[39minitialize_dask(use_gateway\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, workers\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m 23\u001b[0m \u001b[38;5;66;03m# Khai báo Datacube\u001b[39;00m\n\u001b[0;32m---> 24\u001b[0m dc \u001b[38;5;241m=\u001b[39m \u001b[43mdatacube\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDatacube\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m✅ Dask + Datacube + S3 (Cognito authenticated) ready!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m Dask dashboard: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mclient\u001b[38;5;241m.\u001b[39mdashboard_link\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/datacube/api/core.py:87\u001b[0m, in \u001b[0;36mDatacube.__init__\u001b[0;34m(self, index, config, app, env, validate_connection)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m config\n\u001b[1;32m 86\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 87\u001b[0m index \u001b[38;5;241m=\u001b[39m \u001b[43mindex_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnormalise_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 88\u001b[0m \u001b[43m \u001b[49m\u001b[43mapplication_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 89\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate_connection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_connection\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m=\u001b[39m index\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/datacube/index/_api.py:45\u001b[0m, in \u001b[0;36mindex_connect\u001b[0;34m(local_config, application_name, validate_connection)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m index_driver:\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 40\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo index driver found for \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m available: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\n\u001b[1;32m 41\u001b[0m driver_name, \u001b[38;5;28mlen\u001b[39m(index_drivers()), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(index_drivers())\n\u001b[1;32m 42\u001b[0m )\n\u001b[1;32m 43\u001b[0m )\n\u001b[0;32m---> 45\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mindex_driver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect_to_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlocal_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 46\u001b[0m \u001b[43m \u001b[49m\u001b[43mapplication_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapplication_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 47\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate_connection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_connection\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/datacube/index/postgres/index.py:166\u001b[0m, in \u001b[0;36mDefaultIndexDriver.connect_to_index\u001b[0;34m(config, application_name, validate_connection)\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m 165\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mconnect_to_index\u001b[39m(config, application_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, validate_connection\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 166\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mIndex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mapplication_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidate_connection\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/datacube/index/postgres/index.py:77\u001b[0m, in \u001b[0;36mIndex.from_config\u001b[0;34m(cls, config, application_name, validate_connection)\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfrom_config\u001b[39m(\u001b[38;5;28mcls\u001b[39m, config, application_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, validate_connection\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m---> 77\u001b[0m db \u001b[38;5;241m=\u001b[39m \u001b[43mPostgresDb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mapplication_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapplication_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 78\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate_connection\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_connection\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 79\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(db)\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/datacube/drivers/postgres/_connections.py:75\u001b[0m, in \u001b[0;36mPostgresDb.from_config\u001b[0;34m(cls, config, application_name, validate_connection)\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mfrom_config\u001b[39m(\u001b[38;5;28mcls\u001b[39m, config, application_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, validate_connection\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 73\u001b[0m app_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_expand_app_name(application_name)\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mPostgresDb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 76\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdb_hostname\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 77\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdb_database\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 78\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdb_username\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDEFAULT_DB_USER\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 79\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdb_password\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 80\u001b[0m \u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdb_port\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDEFAULT_DB_PORT\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 81\u001b[0m \u001b[43m \u001b[49m\u001b[43mapplication_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapp_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 82\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_connection\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 83\u001b[0m \u001b[43m \u001b[49m\u001b[43miam_rds_auth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mbool\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdb_iam_authentication\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDEFAULT_IAM_AUTH\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 84\u001b[0m \u001b[43m \u001b[49m\u001b[43miam_rds_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdb_iam_timeout\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mDEFAULT_IAM_TIMEOUT\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 85\u001b[0m \u001b[43m \u001b[49m\u001b[43mpool_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdb_connection_timeout\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m60\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 86\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# pass config?\u001b[39;49;00m\n\u001b[1;32m 87\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/datacube/drivers/postgres/_connections.py:107\u001b[0m, in \u001b[0;36mPostgresDb.create\u001b[0;34m(cls, hostname, database, username, password, port, application_name, validate, iam_rds_auth, iam_rds_timeout, pool_timeout)\u001b[0m\n\u001b[1;32m 96\u001b[0m engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_create_engine(\n\u001b[1;32m 97\u001b[0m mk_url(\n\u001b[1;32m 98\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpostgresql\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 104\u001b[0m iam_rds_timeout\u001b[38;5;241m=\u001b[39miam_rds_timeout,\n\u001b[1;32m 105\u001b[0m pool_timeout\u001b[38;5;241m=\u001b[39mpool_timeout)\n\u001b[1;32m 106\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m validate:\n\u001b[0;32m--> 107\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43m_core\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatabase_exists\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m 108\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m IndexSetupError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mNo DB schema exists. Have you run init?\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{init_command}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 109\u001b[0m init_command\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdatacube system init\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 110\u001b[0m ))\n\u001b[1;32m 112\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _core\u001b[38;5;241m.\u001b[39mschema_is_latest(engine):\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/datacube/drivers/postgres/_core.py:152\u001b[0m, in \u001b[0;36mdatabase_exists\u001b[0;34m(engine)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mdatabase_exists\u001b[39m(engine):\n\u001b[1;32m 149\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 150\u001b[0m \u001b[38;5;124;03m Have they init'd this database?\u001b[39;00m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 152\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mhas_schema\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/datacube/drivers/postgres/_core.py:237\u001b[0m, in \u001b[0;36mhas_schema\u001b[0;34m(engine)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mhas_schema\u001b[39m(engine):\n\u001b[0;32m--> 237\u001b[0m inspector \u001b[38;5;241m=\u001b[39m \u001b[43minspect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 238\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m SCHEMA_NAME \u001b[38;5;129;01min\u001b[39;00m inspector\u001b[38;5;241m.\u001b[39mget_schema_names()\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/inspection.py:64\u001b[0m, in \u001b[0;36minspect\u001b[0;34m(subject, raiseerr)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m reg \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m subject\n\u001b[0;32m---> 64\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[43mreg\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubject\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ret \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/reflection.py:182\u001b[0m, in \u001b[0;36mInspector._engine_insp\u001b[0;34m(bind)\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[38;5;129m@inspection\u001b[39m\u001b[38;5;241m.\u001b[39m_inspects(Engine)\n\u001b[1;32m 181\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_engine_insp\u001b[39m(bind):\n\u001b[0;32m--> 182\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mInspector\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_construct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mInspector\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_engine\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbind\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/reflection.py:117\u001b[0m, in \u001b[0;36mInspector._construct\u001b[0;34m(cls, init, bind)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m bind\u001b[38;5;241m.\u001b[39mdialect\u001b[38;5;241m.\u001b[39minspector\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__new__\u001b[39m(\u001b[38;5;28mcls\u001b[39m)\n\u001b[0;32m--> 117\u001b[0m \u001b[43minit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbind\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/reflection.py:128\u001b[0m, in \u001b[0;36mInspector._init_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_init_engine\u001b[39m(\u001b[38;5;28mself\u001b[39m, engine):\n\u001b[1;32m 127\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbind \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine \u001b[38;5;241m=\u001b[39m engine\n\u001b[0;32m--> 128\u001b[0m \u001b[43mengine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mclose()\n\u001b[1;32m 129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_op_context_requires_connect \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdialect \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine\u001b[38;5;241m.\u001b[39mdialect\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/base.py:3325\u001b[0m, in \u001b[0;36mEngine.connect\u001b[0;34m(self, close_with_result)\u001b[0m\n\u001b[1;32m 3310\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m, close_with_result\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m):\n\u001b[1;32m 3311\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return a new :class:`_engine.Connection` object.\u001b[39;00m\n\u001b[1;32m 3312\u001b[0m \n\u001b[1;32m 3313\u001b[0m \u001b[38;5;124;03m The :class:`_engine.Connection` object is a facade that uses a DBAPI\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3322\u001b[0m \n\u001b[1;32m 3323\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3325\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connection_cls\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclose_with_result\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclose_with_result\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/base.py:96\u001b[0m, in \u001b[0;36mConnection.__init__\u001b[0;34m(self, engine, connection, close_with_result, _branch_from, _execution_options, _dispatch, _has_events, _allow_revalidate)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_events \u001b[38;5;241m=\u001b[39m _branch_from\u001b[38;5;241m.\u001b[39m_has_events\n\u001b[1;32m 92\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dbapi_connection \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 94\u001b[0m connection\n\u001b[1;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m connection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m \u001b[43mengine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_connection\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 97\u001b[0m )\n\u001b[1;32m 99\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transaction \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_nested_transaction \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 100\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__savepoint_seq \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/base.py:3404\u001b[0m, in \u001b[0;36mEngine.raw_connection\u001b[0;34m(self, _connection)\u001b[0m\n\u001b[1;32m 3382\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mraw_connection\u001b[39m(\u001b[38;5;28mself\u001b[39m, _connection\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3383\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return a \"raw\" DBAPI connection from the connection pool.\u001b[39;00m\n\u001b[1;32m 3384\u001b[0m \n\u001b[1;32m 3385\u001b[0m \u001b[38;5;124;03m The returned object is a proxied version of the DBAPI\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3402\u001b[0m \n\u001b[1;32m 3403\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3404\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wrap_pool_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_connection\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/base.py:3374\u001b[0m, in \u001b[0;36mEngine._wrap_pool_connect\u001b[0;34m(self, fn, connection)\u001b[0m\n\u001b[1;32m 3372\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m dialect\u001b[38;5;241m.\u001b[39mdbapi\u001b[38;5;241m.\u001b[39mError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 3373\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m connection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 3374\u001b[0m \u001b[43mConnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle_dbapi_exception_noconnection\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3375\u001b[0m \u001b[43m \u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdialect\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\n\u001b[1;32m 3376\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3377\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 3378\u001b[0m util\u001b[38;5;241m.\u001b[39mraise_(\n\u001b[1;32m 3379\u001b[0m sys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m1\u001b[39m], with_traceback\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m]\n\u001b[1;32m 3380\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/base.py:2208\u001b[0m, in \u001b[0;36mConnection._handle_dbapi_exception_noconnection\u001b[0;34m(cls, e, dialect, engine)\u001b[0m\n\u001b[1;32m 2206\u001b[0m util\u001b[38;5;241m.\u001b[39mraise_(newraise, with_traceback\u001b[38;5;241m=\u001b[39mexc_info[\u001b[38;5;241m2\u001b[39m], from_\u001b[38;5;241m=\u001b[39me)\n\u001b[1;32m 2207\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m should_wrap:\n\u001b[0;32m-> 2208\u001b[0m \u001b[43mutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2209\u001b[0m \u001b[43m \u001b[49m\u001b[43msqlalchemy_exception\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwith_traceback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexc_info\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfrom_\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43me\u001b[49m\n\u001b[1;32m 2210\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2211\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2212\u001b[0m util\u001b[38;5;241m.\u001b[39mraise_(exc_info[\u001b[38;5;241m1\u001b[39m], with_traceback\u001b[38;5;241m=\u001b[39mexc_info[\u001b[38;5;241m2\u001b[39m])\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/util/compat.py:211\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 208\u001b[0m exception\u001b[38;5;241m.\u001b[39m__cause__ \u001b[38;5;241m=\u001b[39m replace_context\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 213\u001b[0m \u001b[38;5;66;03m# credit to\u001b[39;00m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;66;03m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/base.py:3371\u001b[0m, in \u001b[0;36mEngine._wrap_pool_connect\u001b[0;34m(self, fn, connection)\u001b[0m\n\u001b[1;32m 3369\u001b[0m dialect \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdialect\n\u001b[1;32m 3370\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3371\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3372\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m dialect\u001b[38;5;241m.\u001b[39mdbapi\u001b[38;5;241m.\u001b[39mError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 3373\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m connection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:327\u001b[0m, in \u001b[0;36mPool.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 320\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return a DBAPI connection from the pool.\u001b[39;00m\n\u001b[1;32m 321\u001b[0m \n\u001b[1;32m 322\u001b[0m \u001b[38;5;124;03m The connection is instrumented such that when its\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 325\u001b[0m \n\u001b[1;32m 326\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 327\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ConnectionFairy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_checkout\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:894\u001b[0m, in \u001b[0;36m_ConnectionFairy._checkout\u001b[0;34m(cls, pool, threadconns, fairy)\u001b[0m\n\u001b[1;32m 891\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 892\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_checkout\u001b[39m(\u001b[38;5;28mcls\u001b[39m, pool, threadconns\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, fairy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 893\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m fairy:\n\u001b[0;32m--> 894\u001b[0m fairy \u001b[38;5;241m=\u001b[39m \u001b[43m_ConnectionRecord\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheckout\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpool\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 896\u001b[0m fairy\u001b[38;5;241m.\u001b[39m_pool \u001b[38;5;241m=\u001b[39m pool\n\u001b[1;32m 897\u001b[0m fairy\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:493\u001b[0m, in \u001b[0;36m_ConnectionRecord.checkout\u001b[0;34m(cls, pool)\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 492\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mcheckout\u001b[39m(\u001b[38;5;28mcls\u001b[39m, pool):\n\u001b[0;32m--> 493\u001b[0m rec \u001b[38;5;241m=\u001b[39m \u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_get\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 494\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 495\u001b[0m dbapi_connection \u001b[38;5;241m=\u001b[39m rec\u001b[38;5;241m.\u001b[39mget_connection()\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/impl.py:145\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_connection()\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m--> 145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m util\u001b[38;5;241m.\u001b[39msafe_reraise():\n\u001b[1;32m 146\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dec_overflow()\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# remove potential circular references\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m \u001b[43mcompat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43mexc_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mwith_traceback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexc_tb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m compat\u001b[38;5;241m.\u001b[39mpy3k \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info[\u001b[38;5;241m1\u001b[39m]:\n\u001b[1;32m 76\u001b[0m \u001b[38;5;66;03m# emulate Py3K's behavior of telling us when an exception\u001b[39;00m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;66;03m# occurs in an exception handler.\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/util/compat.py:211\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 208\u001b[0m exception\u001b[38;5;241m.\u001b[39m__cause__ \u001b[38;5;241m=\u001b[39m replace_context\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 213\u001b[0m \u001b[38;5;66;03m# credit to\u001b[39;00m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;66;03m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/impl.py:143\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 141\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inc_overflow():\n\u001b[1;32m 142\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 143\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_create_connection\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m util\u001b[38;5;241m.\u001b[39msafe_reraise():\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:273\u001b[0m, in \u001b[0;36mPool._create_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_create_connection\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 271\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Called by subclasses to create a new ConnectionRecord.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 273\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ConnectionRecord\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:388\u001b[0m, in \u001b[0;36m_ConnectionRecord.__init__\u001b[0;34m(self, pool, connect)\u001b[0m\n\u001b[1;32m 386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__pool \u001b[38;5;241m=\u001b[39m pool\n\u001b[1;32m 387\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m connect:\n\u001b[0;32m--> 388\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 389\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinalize_callback \u001b[38;5;241m=\u001b[39m deque()\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:690\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 688\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfresh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 689\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m--> 690\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m util\u001b[38;5;241m.\u001b[39msafe_reraise():\n\u001b[1;32m 691\u001b[0m pool\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError on connect(): \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, e)\n\u001b[1;32m 692\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 693\u001b[0m \u001b[38;5;66;03m# in SQLAlchemy 1.4 the first_connect event is not used by\u001b[39;00m\n\u001b[1;32m 694\u001b[0m \u001b[38;5;66;03m# the engine, so this will usually not be set\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# remove potential circular references\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m \u001b[43mcompat\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[43mexc_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m \u001b[49m\u001b[43mwith_traceback\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexc_tb\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m compat\u001b[38;5;241m.\u001b[39mpy3k \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exc_info[\u001b[38;5;241m1\u001b[39m]:\n\u001b[1;32m 76\u001b[0m \u001b[38;5;66;03m# emulate Py3K's behavior of telling us when an exception\u001b[39;00m\n\u001b[1;32m 77\u001b[0m \u001b[38;5;66;03m# occurs in an exception handler.\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/util/compat.py:211\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m 208\u001b[0m exception\u001b[38;5;241m.\u001b[39m__cause__ \u001b[38;5;241m=\u001b[39m replace_context\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 213\u001b[0m \u001b[38;5;66;03m# credit to\u001b[39;00m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;66;03m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m 215\u001b[0m \u001b[38;5;66;03m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m 216\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/pool/base.py:686\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 684\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 685\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstarttime \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m--> 686\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdbapi_connection \u001b[38;5;241m=\u001b[39m connection \u001b[38;5;241m=\u001b[39m \u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke_creator\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 687\u001b[0m pool\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreated new connection \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, connection)\n\u001b[1;32m 688\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfresh \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/create.py:574\u001b[0m, in \u001b[0;36mcreate_engine..connect\u001b[0;34m(connection_record)\u001b[0m\n\u001b[1;32m 572\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m connection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 573\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m connection\n\u001b[0;32m--> 574\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdialect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcargs\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[43mcparams\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/sqlalchemy/engine/default.py:598\u001b[0m, in \u001b[0;36mDefaultDialect.connect\u001b[0;34m(self, *cargs, **cparams)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39mcargs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcparams):\n\u001b[1;32m 597\u001b[0m \u001b[38;5;66;03m# inherits the docstring from interfaces.Dialect.connect\u001b[39;00m\n\u001b[0;32m--> 598\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdbapi\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcargs\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[43mcparams\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_01/lib/python3.10/site-packages/psycopg2/__init__.py:122\u001b[0m, in \u001b[0;36mconnect\u001b[0;34m(dsn, connection_factory, cursor_factory, **kwargs)\u001b[0m\n\u001b[1;32m 119\u001b[0m kwasync[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124masync_\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124masync_\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 121\u001b[0m dsn \u001b[38;5;241m=\u001b[39m _ext\u001b[38;5;241m.\u001b[39mmake_dsn(dsn, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 122\u001b[0m conn \u001b[38;5;241m=\u001b[39m \u001b[43m_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconnection_factory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconnection_factory\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[43mkwasync\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cursor_factory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 124\u001b[0m conn\u001b[38;5;241m.\u001b[39mcursor_factory \u001b[38;5;241m=\u001b[39m cursor_factory\n", - "\u001b[0;31mOperationalError\u001b[0m: (psycopg2.OperationalError) connection to server at \"v2-db-easi-asia-eks.cluster-ro-czyydvizywt5.ap-southeast-1.rds.amazonaws.com\" (10.0.22.166), port 5432 failed: Connection timed out\n\tIs the server running on that host and accepting TCP/IP connections?\n\n(Background on this error at: https://sqlalche.me/e/14/e3q8)" - ] - } - ], - "source": [ - "# ══════════════════════════════════════════════════════════════════════════════\n", - "# SETUP COGNITO AUTHENTICATION\n", - "# ══════════════════════════════════════════════════════════════════════════════\n", - "print(\"🔐 Step 1: Cognito Authentication Setup\")\n", - "print(\"-\" * 70)\n", - "\n", - "# Setup Cognito authentication for S3 access\n", - "auth = setup_cognito_auth('/media/x79/2A7D-FAA0/remote-sensing/train_files/crediential.txt')\n", - "\n", - "if auth:\n", - " print(\"\\n✅ Authentication successful!\")\n", - " print(\" Ready to access S3 buckets with authenticated credentials\")\n", - "else:\n", - " print(\"\\n⚠ Authentication failed - falling back to unsigned access\")\n", - "\n", - "print(\"\\n\" + \"=\" * 70)\n", - "print(\"🚀 Step 2: Dask + Datacube Initialization\")\n", - "print(\"-\" * 70)\n", - "\n", - "# Khởi tạo Dask + Datacube (use_gateway=False for local execution)\n", - "cluster, client = notebook_utils.initialize_dask(use_gateway=False, workers=4)\n", - "\n", - "# Khai báo Datacube\n", - "dc = datacube.Datacube()\n", - "\n", - "print(\"\\n✅ Dask + Datacube + S3 (Cognito authenticated) ready!\")\n", - "print(f\" Dask dashboard: {client.dashboard_link}\")\n", - "print(\"=\" * 70)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5bc42a3c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Most common native CRS: EPSG:32648\n", - "No datasets require offset correction\n", - "The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n", - "✅ Sentinel-2 raw: FrozenMappingWarningOnValuesAccess({'time': 151, 'y': 8874, 'x': 9902})\n" - ] - } - ], - "source": [ - "# Cấu hình vùng và thời gian\n", - "date_range = (\"2022-09-01\", \"2023-10-01\")\n", - "longtitude_range = (105.5, 106.4)\n", - "latitude_range = (9.2, 10.0)\n", - "\n", - "# Tải dữ liệu Sentinel-2\n", - "data_sen2 = load_data(\n", - " dc=dc,\n", - " date_range=date_range,\n", - " longtitude_range=longtitude_range,\n", - " latitude_range=latitude_range,\n", - ")\n", - "print(f\"✅ Sentinel-2 raw: {data_sen2.dims}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0ef51e7d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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qa[0, 1, 2, 3, 4, 5, 6, 7]{'0': 'no data', '1': 'saturated or defective'...Sen2Cor Scene Classification
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" - ], - "text/plain": [ - " bits \\\n", - "qa [0, 1, 2, 3, 4, 5, 6, 7] \n", - "\n", - " values \\\n", - "qa {'0': 'no data', '1': 'saturated or defective'... \n", - "\n", - " description \n", - "qa Sen2Cor Scene Classification " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'0': 'no data',\n", - " '1': 'saturated or defective',\n", - " '2': 'dark area pixels',\n", - " '3': 'cloud shadows',\n", - " '4': 'vegetation',\n", - " '5': 'bare soils',\n", - " '6': 'water',\n", - " '7': 'unclassified',\n", - " '8': 'cloud medium probability',\n", - " '9': 'cloud high probability',\n", - " '10': 'thin cirrus',\n", - " '11': 'snow or ice'}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "ename": "ValueError", - "evalue": "No `satellite_mission` was provided. Please specify either 'ls' or 's2' to ensure the \nfunction calculates indices using the correct spectral bands.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Tiền xử lý Sentinel-2: cloud mask + NDVI + resampling\u001b[39;00m\n\u001b[1;32m 2\u001b[0m data_clean \u001b[38;5;241m=\u001b[39m mask_clean(data_sen2)\n\u001b[0;32m----> 3\u001b[0m data_ndvi \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_clean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mNDVI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m data_fill \u001b[38;5;241m=\u001b[39m fill_nan(data_ndvi)\n\u001b[1;32m 5\u001b[0m data_sen2_monthly \u001b[38;5;241m=\u001b[39m data_fill\u001b[38;5;241m.\u001b[39mresample(time\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1MS\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;241m.\u001b[39mcompute()\n", - "File \u001b[0;32m~/remote-sensing/deafrica_tools/bandindices.py:374\u001b[0m, in \u001b[0;36mcalculate_indices\u001b[0;34m(ds, index, collection, satellite_mission, custom_varname, normalise, drop, deep_copy)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;66;03m# Rename bands to a consistent format if depending on what satellite mission\u001b[39;00m\n\u001b[1;32m 369\u001b[0m \u001b[38;5;66;03m# is specified in `satellite_mission`. This allows the same index calculations\u001b[39;00m\n\u001b[1;32m 370\u001b[0m \u001b[38;5;66;03m# to be applied to all satellite missions. If no satellite mission was provided,\u001b[39;00m\n\u001b[1;32m 371\u001b[0m \u001b[38;5;66;03m# raise an exception.\u001b[39;00m\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m satellite_mission \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 374\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 375\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo `satellite_mission` was provided. Please specify \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 376\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meither \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mls\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms2\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to ensure the \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mfunction \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 377\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcalculates indices using the correct spectral \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbands.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 379\u001b[0m )\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m satellite_mission \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mls\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 382\u001b[0m sr_max \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n", - "\u001b[0;31mValueError\u001b[0m: No `satellite_mission` was provided. Please specify either 'ls' or 's2' to ensure the \nfunction calculates indices using the correct spectral bands." - ] - } - ], - "source": [ - "# Tiền xử lý Sentinel-2: cloud mask + NDVI + resampling\n", - "data_clean = mask_clean(data_sen2)\n", - "data_ndvi = calculate_indices(data_clean, index=\"NDVI\", satellite_mission=\"s2\")\n", - "data_fill = fill_nan(data_ndvi)\n", - "data_sen2_monthly = data_fill.resample(time=\"1MS\").mean().compute()\n", - "print(f\"✅ S2 monthly shape: {data_sen2_monthly.dims}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0da4f86d", - "metadata": {}, - "outputs": [], - "source": [ - "# Tải Sentinel-1 (SAR VV/VH)\n", - "data_sen1 = load_data_sen1(\n", - " dc=dc,\n", - " date_range=date_range,\n", - " longtitude_range=longtitude_range,\n", - " latitude_range=latitude_range,\n", - ")\n", - "data_sen1_monthly = calculate_average(data_sen1, [\"VV\", \"VH\"], resample=\"1MS\").compute()\n", - "print(f\"✅ S1 monthly shape: {data_sen1_monthly.dims}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "412b3716", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# Ánh xạ nhãn lớp đất\n", - "label_mapping = {\n", - " \"Lua tom\": \"0\", \"Lua\": \"1\", \"CHN\": \"2\", \"CLN\": \"3\",\n", - " \"TS\": \"4\", \"Song\": \"5\", \"Dat xay dung\": \"6\", \"Rung\": \"7\",\n", - "}\n", - "\n", - "# Tải và ghép dữ liệu train từ S1 + S2\n", - "train_data = load_train_data(label_mapping=label_mapping)\n", - "X, y = get_data_sen1_and_sen2(train_data, data_sen2_monthly, data_sen1_monthly)\n", - "\n", - "# Chia tập train / val / test\n", - "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y, test_size=0.2, val_size=0.1)\n", - "\n", - "X_train_np = np.array(X_train, dtype=np.float32)\n", - "X_val_np = np.array(X_val, dtype=np.float32)\n", - "X_test_np = np.array(X_test, dtype=np.float32)\n", - "y_train_np = np.array(y_train, dtype=np.int64)\n", - "y_val_np = np.array(y_val, dtype=np.int64)\n", - "y_test_np = np.array(y_test, dtype=np.int64)\n", - "\n", - "# Gộp train + val cho sklearn\n", - "X_fit = np.concatenate([X_train_np, X_val_np], axis=0)\n", - "y_fit = np.concatenate([y_train_np, y_val_np], axis=0)\n", - "\n", - "print(f\"✅ X_fit: {X_fit.shape} | X_test: {X_test_np.shape}\")\n", - "print(f\" Classes: {sorted(set(y_fit.tolist()))}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84a8a1d0", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "\n", - "# ── Xây dựng và train mô hình Decision Tree ─────────────────────────────────\n", - "model = DecisionTreeClassifier(\n", - " max_depth=30,\n", - " min_samples_leaf=2,\n", - " min_samples_split=5,\n", - " class_weight=\"balanced\",\n", - " random_state=42,\n", - ")\n", - "\n", - "print(\"🚀 Training Decision Tree...\")\n", - "model.fit(X_fit, y_fit)\n", - "\n", - "val_acc = model.score(X_val_np, y_val_np)\n", - "print(f\"✅ Training hoàn tất! Depth: {model.get_depth()} \"\n", - " f\"Leaves: {model.get_n_leaves()} Val accuracy: {val_acc:.4f}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8248d748", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "\n", - "# ═══════════════════════════════════════════════════════════════════════════════\n", - "# PHÂN TÍCH ĐIỂM HỘI TỤ — Decision Tree\n", - "# Phương pháp: quét max_depth từ 1→50 và theo dõi train/val accuracy\n", - "# Điểm hội tụ = độ sâu tại đó val_acc đạt cực đại rồi bắt đầu giảm (overfitting)\n", - "# ═══════════════════════════════════════════════════════════════════════════════\n", - "DEPTH_RANGE = list(range(1, 51))\n", - "THRESHOLD = 0.001 # cải thiện val_acc < 0.1% → coi là hội tụ\n", - "\n", - "train_accs_d, val_accs_d = [], []\n", - "print(\"🔍 Phân tích hội tụ theo max_depth ...\")\n", - "for d in DEPTH_RANGE:\n", - " m = DecisionTreeClassifier(\n", - " min_samples_leaf=2, min_samples_split=5,\n", - " class_weight=\"balanced\", random_state=42, max_depth=d,\n", - " )\n", - " m.fit(X_fit, y_fit)\n", - " train_accs_d.append(m.score(X_fit, y_fit))\n", - " val_accs_d.append( m.score(X_val_np, y_val_np))\n", - "\n", - "train_accs_d = np.array(train_accs_d)\n", - "val_accs_d = np.array(val_accs_d)\n", - "improvements = np.diff(val_accs_d)\n", - "\n", - "# ── Tìm điểm hội tụ ───────────────────────────────────────────────────────────\n", - "best_depth = DEPTH_RANGE[int(np.argmax(val_accs_d))]\n", - "best_val_acc = float(np.max(val_accs_d))\n", - "\n", - "# Điểm hội tụ sớm: lần đầu cải thiện < threshold\n", - "conv_depth = None\n", - "for i, imp in enumerate(improvements):\n", - " if abs(imp) < THRESHOLD:\n", - " conv_depth = DEPTH_RANGE[i + 1]\n", - " break\n", - "\n", - "# Điểm overfit: val_acc bắt đầu giảm so với peak\n", - "overfit_depth = None\n", - "peak_idx = int(np.argmax(val_accs_d))\n", - "for i in range(peak_idx + 1, len(val_accs_d)):\n", - " if val_accs_d[i] < best_val_acc - 0.005: # giảm > 0.5%\n", - " overfit_depth = DEPTH_RANGE[i]\n", - " break\n", - "\n", - "# Khoảng cách train-val (generalization gap)\n", - "gap = train_accs_d - val_accs_d\n", - "\n", - "# ── Vẽ đồ thị ─────────────────────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", - "\n", - "# --- Trái: accuracy curves ---\n", - "axes[0].plot(DEPTH_RANGE, train_accs_d, \"b-o\", markersize=3, label=\"Train\")\n", - "axes[0].plot(DEPTH_RANGE, val_accs_d, \"g-o\", markersize=3, label=\"Val\")\n", - "axes[0].axvline(x=best_depth, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", - " label=f\"Best depth={best_depth} ({best_val_acc*100:.2f}%)\")\n", - "if conv_depth:\n", - " axes[0].axvline(x=conv_depth, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", - " label=f\"Hội tụ depth={conv_depth}\")\n", - "if overfit_depth:\n", - " axes[0].axvline(x=overfit_depth, color=\"purple\", linestyle=\"-.\", linewidth=1.5,\n", - " label=f\"Overfit depth={overfit_depth}\")\n", - "axes[0].set_xlabel(\"max_depth\")\n", - "axes[0].set_ylabel(\"Accuracy\")\n", - "axes[0].set_title(\"Train / Val Accuracy vs max_depth\")\n", - "axes[0].legend(fontsize=8)\n", - "axes[0].grid(True, alpha=0.3)\n", - "\n", - "# --- Giữa: marginal improvement ---\n", - "axes[1].bar(DEPTH_RANGE[1:], improvements * 100,\n", - " color=[\"green\" if v > 0 else \"red\" for v in improvements], alpha=0.7)\n", - "axes[1].axhline(y=0, color=\"black\", linewidth=0.8)\n", - "axes[1].axhline(y=THRESHOLD * 100, color=\"orange\", linestyle=\"--\",\n", - " label=f\"Threshold={THRESHOLD*100:.2f}%\")\n", - "if conv_depth:\n", - " axes[1].axvline(x=conv_depth, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", - " label=f\"Hội tụ depth={conv_depth}\")\n", - "axes[1].set_xlabel(\"max_depth\")\n", - "axes[1].set_ylabel(\"ΔVal Accuracy (%)\")\n", - "axes[1].set_title(\"Marginal Val Improvement per Depth Step\")\n", - "axes[1].legend(fontsize=8)\n", - "axes[1].grid(True, alpha=0.3)\n", - "\n", - "# --- Phải: generalization gap ---\n", - "axes[2].fill_between(DEPTH_RANGE, gap * 100, alpha=0.5, color=\"tomato\", label=\"Gap = Train − Val\")\n", - "axes[2].plot(DEPTH_RANGE, gap * 100, \"r-o\", markersize=3)\n", - "if best_depth:\n", - " axes[2].axvline(x=best_depth, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", - " label=f\"Best depth={best_depth}\")\n", - "axes[2].set_xlabel(\"max_depth\")\n", - "axes[2].set_ylabel(\"Gap (%)\")\n", - "axes[2].set_title(\"Generalization Gap (Overfitting Risk)\")\n", - "axes[2].legend(fontsize=8)\n", - "axes[2].grid(True, alpha=0.3)\n", - "\n", - "plt.suptitle(\"Decision Tree — Convergence Analysis\", fontsize=13, fontweight=\"bold\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# ── Tổng kết ──────────────────────────────────────────────────────────────────\n", - "print(f\"\\n{'═'*58}\")\n", - "print(f\" Độ sâu TỐI ƯU (best val acc) : max_depth = {best_depth} ({best_val_acc*100:.4f}%)\")\n", - "if conv_depth:\n", - " print(f\" Điểm HỘI TỤ (Δacc < {THRESHOLD*100:.1f}%) : max_depth = {conv_depth}\")\n", - "if overfit_depth:\n", - " print(f\" Điểm OVERFIT bắt đầu : max_depth ≥ {overfit_depth}\")\n", - " print(f\" → Nên dùng max_depth ≤ {best_depth} để tránh overfit\")\n", - "print(f\"{'═'*58}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5660e2ec", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "# ── Đánh giá trên tập test ─────────────────────────────────────────────────────\n", - "y_pred = model.predict(X_test_np)\n", - "\n", - "acc = accuracy_score(y_test_np, y_pred)\n", - "print(f\"Test Accuracy : {acc:.4f} ({acc*100:.2f}%)\\n\")\n", - "print(classification_report(y_test_np, y_pred, digits=4))\n", - "\n", - "# ── Feature importance ──────────────────────────────────────────────────────────\n", - "feat_imp = model.feature_importances_\n", - "idx = feat_imp.argsort()[::-1][:20]\n", - "plt.figure(figsize=(12, 4))\n", - "plt.bar(range(len(idx)), feat_imp[idx])\n", - "plt.xticks(range(len(idx)), idx, rotation=45)\n", - "plt.title(\"Top-20 Feature Importances\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# ── Confusion matrix ────────────────────────────────────────────────────────────\n", - "class_names = list(label_mapping.keys())\n", - "cm = confusion_matrix(y_test_np, y_pred)\n", - "plt.figure(figsize=(9, 7))\n", - "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\",\n", - " xticklabels=class_names, yticklabels=class_names)\n", - "plt.xlabel(\"Predicted\")\n", - "plt.ylabel(\"Actual\")\n", - "plt.title(\"Confusion Matrix — Decision Tree\")\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e416c2aa", - "metadata": {}, - "outputs": [], - "source": [ - "import joblib, json\n", - "from datetime import datetime\n", - "\n", - "# ── Lưu mô hình ────────────────────────────────────────────────────────────────\n", - "model_path = \"model_decision_tree_land_use.joblib\"\n", - "joblib.dump(model, model_path)\n", - "print(f\"✅ Model saved → {model_path}\")\n", - "\n", - "# ── Lưu thông tin mô hình ──────────────────────────────────────────────────────\n", - "info = {\n", - " \"model_type\": \"DecisionTree\",\n", - " \"max_depth\": model.get_depth(),\n", - " \"n_leaves\": model.get_n_leaves(),\n", - " \"class_weight\": \"balanced\",\n", - " \"n_features\": int(X_fit.shape[1]),\n", - " \"label_mapping\": label_mapping,\n", - " \"test_accuracy\": float(acc),\n", - " \"train_samples\": int(len(X_fit)),\n", - " \"test_samples\": int(len(X_test_np)),\n", - " \"saved_at\": datetime.now().isoformat(),\n", - "}\n", - "info_path = \"model_decision_tree_land_use_info.json\"\n", - "with open(info_path, \"w\") as f:\n", - " json.dump(info, f, indent=2, ensure_ascii=False)\n", - "print(f\"✅ Info saved → {info_path}\")\n", - "print(json.dumps(info, indent=2, ensure_ascii=False))\n", - "\n", - "# ── Đóng kết nối Dask ──────────────────────────────────────────────────────────\n", - "try:\n", - " client.close()\n", - " cluster.close()\n", - " print(\"✅ Dask cluster closed.\")\n", - "except Exception:\n", - " pass\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "env_01", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.19" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/train_files/01.train_ODC_MobileNet.ipynb b/train_files/01.train_ODC_MobileNet.ipynb deleted file mode 100644 index 1c511ae..0000000 --- a/train_files/01.train_ODC_MobileNet.ipynb +++ /dev/null @@ -1,768 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "17da4353", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Import thành công\n" - ] - } - ], - "source": [ - "# Import libraries\n", - "import numpy as np\n", - "import pandas as pd\n", - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from datetime import datetime\n", - "\n", - "# Element84 Earth Search STAC\n", - "import pystac_client\n", - "from odc.stac import load\n", - "\n", - "# Machine Learning\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "from torch.utils.data import TensorDataset, DataLoader\n", - "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", - "from sklearn.model_selection import train_test_split\n", - "\n", - "print(\"✅ Import thành công\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9c063be3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Kết nối Element84 Earth Search thành công\n" - ] - } - ], - "source": [ - "# Kết nối Element84 Earth Search (AWS-hosted STAC API)\n", - "catalog = pystac_client.Client.open(\n", - " \"https://earth-search.aws.element84.com/v1\"\n", - ")\n", - "print(\"✅ Kết nối Element84 Earth Search thành công\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d87beed2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Đã đọc shapefile: 1130 points\n", - " Columns: ['No', 'X', 'Y', 'LU2022', 'Hientrang', 'HT_code', 'geometry']\n", - " CRS: EPSG:32648\n", - "⚠️ Không tìm thấy column class, hiển thị 5 dòng đầu:\n", - " No X Y LU2022 Hientrang HT_code \\\n", - "0 1.0 603860.819 1081162.862 Pomelo CLN 3 \n", - "1 2.0 601306.410 1082782.940 Pomelo CLN 3 \n", - "2 3.0 601084.510 1081351.870 Pomelo CLN 3 \n", - "3 4.0 602193.760 1079205.220 Pomelo CLN 3 \n", - "4 5.0 602459.000 1080946.000 Pomelo CLN 3 \n", - "\n", - " geometry longitude latitude \n", - "0 POINT (603860.819 1081162.862) 603860.819 1081162.862 \n", - "1 POINT (601306.41 1082782.94) 601306.410 1082782.940 \n", - "2 POINT (601084.51 1081351.87) 601084.510 1081351.870 \n", - "3 POINT (602193.76 1079205.22) 602193.760 1079205.220 \n", - "4 POINT (602459 1080946) 602459.000 1080946.000 \n" - ] - } - ], - "source": [ - "# Tạo file CSV training data từ shapefile\n", - "import geopandas as gpd\n", - "import pandas as pd\n", - "\n", - "# Đọc shapefile training data\n", - "shapefile_path = \"/home/jovyan/remote-sensing/train/ST_training_data_updated_1130points_new.shp\"\n", - "gdf = gpd.read_file(shapefile_path)\n", - "\n", - "print(f\"✅ Đã đọc shapefile: {len(gdf)} points\")\n", - "print(f\" Columns: {list(gdf.columns)}\")\n", - "print(f\" CRS: {gdf.crs}\")\n", - "\n", - "# Extract longitude, latitude từ geometry\n", - "gdf['longitude'] = gdf.geometry.x\n", - "gdf['latitude'] = gdf.geometry.y\n", - "\n", - "# Tìm column chứa class name (có thể là 'LULC', 'class', 'label', etc.)\n", - "class_column = None\n", - "for col in gdf.columns:\n", - " if col.lower() in ['lulc', 'class', 'label', 'class_name', 'type', 'landuse']:\n", - " class_column = col\n", - " break\n", - "\n", - "if class_column is None:\n", - " print(\"⚠️ Không tìm thấy column class, hiển thị 5 dòng đầu:\")\n", - " print(gdf.head())\n", - "else:\n", - " # Tạo DataFrame với các cột cần thiết\n", - " train_df = pd.DataFrame({\n", - " 'longitude': gdf['longitude'],\n", - " 'latitude': gdf['latitude'],\n", - " 'class_name': gdf[class_column]\n", - " })\n", - " \n", - " # Export ra CSV\n", - " csv_path = \"/media/x79/2A7D-FAA0/remote-sensing/train_data.csv\"\n", - " train_df.to_csv(csv_path, index=False)\n", - " \n", - " print(f\"\\n✅ Đã tạo file CSV: {csv_path}\")\n", - " print(f\" Số lượng points: {len(train_df)}\")\n", - " print(f\" Classes: {train_df['class_name'].unique()}\")\n", - " print(f\" Class distribution:\")\n", - " print(train_df['class_name'].value_counts())\n", - " print(f\"\\n📋 Preview 5 dòng đầu:\")\n", - " print(train_df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "83784d01", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Tìm thấy 45 scenes Sentinel-2\n", - "✅ Sentinel-2 raw: FrozenMappingWarningOnValuesAccess({'y': 8874, 'x': 9902, 'time': 28})\n", - " Variables: ['red', 'green', 'blue', 'nir', 'swir16', 'swir22', 'scl']\n" - ] - } - ], - "source": [ - "# Cấu hình vùng và thời gian\n", - "date_range = (\"2022-09-01\", \"2023-10-01\")\n", - "longtitude_range = (105.5, 106.4)\n", - "latitude_range = (9.2, 10.0)\n", - "\n", - "bbox = (longtitude_range[0], latitude_range[0], longtitude_range[1], latitude_range[1])\n", - "\n", - "# Tìm kiếm Sentinel-2 L2A\n", - "search = catalog.search(\n", - " collections=[\"sentinel-2-l2a\"],\n", - " bbox=bbox,\n", - " datetime=f\"{date_range[0]}/{date_range[1]}\",\n", - " query={\"eo:cloud_cover\": {\"lt\": 30}}\n", - ")\n", - "\n", - "items = search.item_collection()\n", - "print(f\" Tìm thấy {len(items)} scenes Sentinel-2\")\n", - "\n", - "if len(items) == 0:\n", - " raise ValueError(\"Không tìm thấy dữ liệu Sentinel-2 cho vùng và thời gian này\")\n", - "\n", - "# Load data với odc-stac\n", - "data_sen2 = load(\n", - " items,\n", - " bands=[\"red\", \"green\", \"blue\", \"nir\", \"swir16\", \"swir22\", \"scl\"],\n", - " bbox=bbox,\n", - " resolution=10,\n", - " chunks={\"time\": 1, \"x\": 2048, \"y\": 2048},\n", - " groupby=\"solar_day\"\n", - ")\n", - "\n", - "print(f\"✅ Sentinel-2 raw: {data_sen2.dims}\")\n", - "print(f\" Variables: {list(data_sen2.data_vars)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3faed92", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "dimension time on 0th function argument to apply_ufunc with dask='parallelized' consists of multiple chunks, but is also a core dimension. To fix, either rechunk into a single array chunk along this dimension, i.e., ``.chunk(dict(time=-1))``, or pass ``allow_rechunk=True`` in ``dask_gufunc_kwargs`` but beware that this may significantly increase memory usage.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 33\u001b[0m\n\u001b[1;32m 31\u001b[0m data_clean \u001b[38;5;241m=\u001b[39m mask_clean(data_sen2)\n\u001b[1;32m 32\u001b[0m data_ndvi \u001b[38;5;241m=\u001b[39m calculate_indices(data_clean, index\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNDVI\u001b[39m\u001b[38;5;124m\"\u001b[39m, satellite_mission\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ms2\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 33\u001b[0m data_fill \u001b[38;5;241m=\u001b[39m \u001b[43mfill_nan\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_ndvi\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 34\u001b[0m data_sen2_monthly \u001b[38;5;241m=\u001b[39m data_fill\u001b[38;5;241m.\u001b[39mresample(time\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1MS\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;241m.\u001b[39mcompute()\n\u001b[1;32m 36\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m✅ S2 monthly shape: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdata_sen2_monthly\u001b[38;5;241m.\u001b[39mdims\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "Cell \u001b[0;32mIn[6], line 28\u001b[0m, in \u001b[0;36mfill_nan\u001b[0;34m(ds)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfill_nan\u001b[39m(ds):\n\u001b[1;32m 27\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Fill NaN bằng interpolation theo thời gian\"\"\"\u001b[39;00m\n\u001b[0;32m---> 28\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minterpolate_na\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdim\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[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlinear\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfill_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mextrapolate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/env/lib/python3.12/site-packages/xarray/core/dataset.py:6773\u001b[0m, in \u001b[0;36mDataset.interpolate_na\u001b[0;34m(self, dim, method, limit, use_coordinate, max_gap, **kwargs)\u001b[0m\n\u001b[1;32m 6655\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fill in NaNs by interpolating according to different methods.\u001b[39;00m\n\u001b[1;32m 6656\u001b[0m \n\u001b[1;32m 6657\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6769\u001b[0m \u001b[38;5;124;03m D (x) float64 40B 5.0 3.0 1.0 -1.0 4.0\u001b[39;00m\n\u001b[1;32m 6770\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 6771\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mxarray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmissing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _apply_over_vars_with_dim, interp_na\n\u001b[0;32m-> 6773\u001b[0m new \u001b[38;5;241m=\u001b[39m \u001b[43m_apply_over_vars_with_dim\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 6774\u001b[0m \u001b[43m \u001b[49m\u001b[43minterp_na\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6775\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6776\u001b[0m \u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6777\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6778\u001b[0m \u001b[43m \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6779\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_coordinate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_coordinate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6780\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_gap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_gap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6781\u001b[0m \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 6782\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6783\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m new\n", - "File \u001b[0;32m/env/lib/python3.12/site-packages/xarray/core/missing.py:222\u001b[0m, in \u001b[0;36m_apply_over_vars_with_dim\u001b[0;34m(func, self, dim, **kwargs)\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m name, var \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_vars\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 221\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dim \u001b[38;5;129;01min\u001b[39;00m var\u001b[38;5;241m.\u001b[39mdims:\n\u001b[0;32m--> 222\u001b[0m ds[name] \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdim\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdim\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 223\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 224\u001b[0m ds[name] \u001b[38;5;241m=\u001b[39m var\n", - "File \u001b[0;32m/env/lib/python3.12/site-packages/xarray/core/missing.py:367\u001b[0m, in \u001b[0;36minterp_na\u001b[0;34m(self, dim, use_coordinate, method, limit, max_gap, keep_attrs, **kwargs)\u001b[0m\n\u001b[1;32m 365\u001b[0m warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverflow\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mRuntimeWarning\u001b[39;00m)\n\u001b[1;32m 366\u001b[0m warnings\u001b[38;5;241m.\u001b[39mfilterwarnings(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minvalid value\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;167;01mRuntimeWarning\u001b[39;00m)\n\u001b[0;32m--> 367\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43mapply_ufunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 368\u001b[0m \u001b[43m \u001b[49m\u001b[43minterpolator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 369\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 370\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 371\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_core_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdim\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mdim\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 372\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_core_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdim\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 373\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput_dtypes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 374\u001b[0m \u001b[43m \u001b[49m\u001b[43mdask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparallelized\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 375\u001b[0m \u001b[43m \u001b[49m\u001b[43mvectorize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 376\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_attrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_attrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 377\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtranspose(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdims)\n\u001b[1;32m 379\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m limit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 380\u001b[0m arr \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mwhere(valids)\n", - "File \u001b[0;32m/env/lib/python3.12/site-packages/xarray/core/computation.py:1265\u001b[0m, in \u001b[0;36mapply_ufunc\u001b[0;34m(func, input_core_dims, output_core_dims, exclude_dims, vectorize, join, dataset_join, dataset_fill_value, keep_attrs, kwargs, dask, output_dtypes, output_sizes, meta, dask_gufunc_kwargs, on_missing_core_dim, *args)\u001b[0m\n\u001b[1;32m 1263\u001b[0m \u001b[38;5;66;03m# feed DataArray apply_variable_ufunc through apply_dataarray_vfunc\u001b[39;00m\n\u001b[1;32m 1264\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(a, DataArray) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m args):\n\u001b[0;32m-> 1265\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mapply_dataarray_vfunc\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1266\u001b[0m \u001b[43m \u001b[49m\u001b[43mvariables_vfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1267\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1268\u001b[0m \u001b[43m \u001b[49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1269\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 1270\u001b[0m \u001b[43m \u001b[49m\u001b[43mexclude_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude_dims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1271\u001b[0m \u001b[43m \u001b[49m\u001b[43mkeep_attrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeep_attrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1272\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1273\u001b[0m \u001b[38;5;66;03m# feed Variables directly through apply_variable_ufunc\u001b[39;00m\n\u001b[1;32m 1274\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(a, Variable) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m args):\n", - "File \u001b[0;32m/env/lib/python3.12/site-packages/xarray/core/computation.py:307\u001b[0m, in \u001b[0;36mapply_dataarray_vfunc\u001b[0;34m(func, signature, join, exclude_dims, keep_attrs, *args)\u001b[0m\n\u001b[1;32m 302\u001b[0m result_coords, result_indexes \u001b[38;5;241m=\u001b[39m build_output_coords_and_indexes(\n\u001b[1;32m 303\u001b[0m args, signature, exclude_dims, combine_attrs\u001b[38;5;241m=\u001b[39mkeep_attrs\n\u001b[1;32m 304\u001b[0m )\n\u001b[1;32m 306\u001b[0m data_vars \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mgetattr\u001b[39m(a, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvariable\u001b[39m\u001b[38;5;124m\"\u001b[39m, a) \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m args]\n\u001b[0;32m--> 307\u001b[0m result_var \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdata_vars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 309\u001b[0m out: \u001b[38;5;28mtuple\u001b[39m[DataArray, \u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m] \u001b[38;5;241m|\u001b[39m DataArray\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m signature\u001b[38;5;241m.\u001b[39mnum_outputs \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n", - "File \u001b[0;32m/env/lib/python3.12/site-packages/xarray/core/computation.py:764\u001b[0m, in \u001b[0;36mapply_variable_ufunc\u001b[0;34m(func, signature, exclude_dims, dask, output_dtypes, vectorize, keep_attrs, dask_gufunc_kwargs, *args)\u001b[0m\n\u001b[1;32m 762\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, dim \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(core_dims, start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mlen\u001b[39m(core_dims)):\n\u001b[1;32m 763\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(data\u001b[38;5;241m.\u001b[39mchunks[axis]) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 764\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 765\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdimension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdim\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124mth function argument to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 766\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapply_ufunc with dask=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparallelized\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m consists of \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 767\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmultiple chunks, but is also a core dimension. To \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 768\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfix, either rechunk into a single array chunk along \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 769\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthis dimension, i.e., ``.chunk(dict(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdim\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m=-1))``, or \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 770\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpass ``allow_rechunk=True`` in ``dask_gufunc_kwargs`` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 771\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut beware that this may significantly increase memory usage.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 772\u001b[0m )\n\u001b[1;32m 773\u001b[0m dask_gufunc_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_rechunk\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 775\u001b[0m output_sizes \u001b[38;5;241m=\u001b[39m dask_gufunc_kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput_sizes\u001b[39m\u001b[38;5;124m\"\u001b[39m, {})\n", - "\u001b[0;31mValueError\u001b[0m: dimension time on 0th function argument to apply_ufunc with dask='parallelized' consists of multiple chunks, but is also a core dimension. To fix, either rechunk into a single array chunk along this dimension, i.e., ``.chunk(dict(time=-1))``, or pass ``allow_rechunk=True`` in ``dask_gufunc_kwargs`` but beware that this may significantly increase memory usage." - ] - } - ], - "source": [ - "# Tiền xử lý Sentinel-2: cloud mask + NDVI + resampling\n", - "\n", - "def mask_clean(ds):\n", - " \"\"\"Cloud masking sử dụng SCL band (Scene Classification Layer)\"\"\"\n", - " if \"scl\" not in ds:\n", - " print(\"⚠️ Không có SCL band, bỏ qua cloud masking\")\n", - " return ds\n", - " \n", - " cloud_mask = (\n", - " (ds[\"scl\"] == 3) | # cloud shadow\n", - " (ds[\"scl\"] == 8) | # cloud medium probability\n", - " (ds[\"scl\"] == 9) | # cloud high probability\n", - " (ds[\"scl\"] == 10) # thin cirrus\n", - " )\n", - " \n", - " ds_masked = ds.where(~cloud_mask)\n", - " return ds_masked.drop_vars(\"scl\", errors=\"ignore\")\n", - "\n", - "def calculate_indices(ds, index=\"NDVI\", satellite_mission=\"s2\"):\n", - " \"\"\"Tính chỉ số NDVI\"\"\"\n", - " if index == \"NDVI\":\n", - " ndvi = (ds[\"nir\"] - ds[\"red\"]) / (ds[\"nir\"] + ds[\"red\"] + 1e-8)\n", - " ds[\"NDVI\"] = ndvi\n", - " return ds\n", - "\n", - "def fill_nan(ds):\n", - " \"\"\"Fill NaN bằng interpolation theo thời gian\"\"\"\n", - " # Rechunk time dimension thành 1 chunk để tránh lỗi với interpolate_na\n", - " ds_rechunked = ds.chunk({\"time\": -1})\n", - " return ds_rechunked.interpolate_na(dim=\"time\", method=\"linear\", fill_value=\"extrapolate\")\n", - "\n", - "# Áp dụng tiền xử lý\n", - "data_clean = mask_clean(data_sen2)\n", - "data_ndvi = calculate_indices(data_clean, index=\"NDVI\", satellite_mission=\"s2\")\n", - "data_fill = fill_nan(data_ndvi)\n", - "data_sen2_monthly = data_fill.resample(time=\"1MS\").mean().compute()\n", - "\n", - "print(f\"✅ S2 monthly shape: {data_sen2_monthly.dims}\")\n", - "print(f\" Variables: {list(data_sen2_monthly.data_vars)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "569bfebb", - "metadata": {}, - "outputs": [], - "source": [ - "# Tải Sentinel-1 (SAR VV/VH) từ Element84\n", - "\n", - "search_s1 = catalog.search(\n", - " collections=[\"sentinel-1-grd\"],\n", - " bbox=bbox,\n", - " datetime=f\"{date_range[0]}/{date_range[1]}\",\n", - " query={\n", - " \"sat:orbit_state\": {\"eq\": \"descending\"},\n", - " \"sar:product_type\": {\"eq\": \"GRD\"}\n", - " }\n", - ")\n", - "\n", - "items_s1 = search_s1.item_collection()\n", - "print(f\" Tìm thấy {len(items_s1)} scenes Sentinel-1\")\n", - "\n", - "if len(items_s1) == 0:\n", - " print(\"⚠️ Không tìm thấy Sentinel-1, tạo dummy data\")\n", - " # Tạo dummy data với cùng kích thước như S2\n", - " data_sen1_monthly = xr.Dataset({\n", - " \"VV\": xr.DataArray(\n", - " np.zeros_like(data_sen2_monthly[\"red\"].values),\n", - " coords=data_sen2_monthly[\"red\"].coords,\n", - " dims=data_sen2_monthly[\"red\"].dims\n", - " ),\n", - " \"VH\": xr.DataArray(\n", - " np.zeros_like(data_sen2_monthly[\"red\"].values),\n", - " coords=data_sen2_monthly[\"red\"].coords,\n", - " dims=data_sen2_monthly[\"red\"].dims\n", - " )\n", - " })\n", - "else:\n", - " # Load Sentinel-1 data\n", - " data_sen1 = load(\n", - " items_s1,\n", - " bands=[\"vv\", \"vh\"],\n", - " bbox=bbox,\n", - " resolution=10,\n", - " chunks={\"time\": 1, \"x\": 2048, \"y\": 2048},\n", - " groupby=\"solar_day\"\n", - " )\n", - " \n", - " # Rename bands to uppercase (VV, VH)\n", - " data_sen1 = data_sen1.rename({\"vv\": \"VV\", \"vh\": \"VH\"})\n", - " data_sen1_monthly = data_sen1.resample(time=\"1MS\").mean().compute()\n", - "\n", - "print(f\"✅ S1 monthly shape: {data_sen1_monthly.dims}\")\n", - "print(f\" Variables: {list(data_sen1_monthly.data_vars)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ecc56c2f", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# Ánh xạ nhãn lớp đất\n", - "label_mapping = {\n", - " \"Lua tom\": \"0\", \"Lua\": \"1\", \"CHN\": \"2\", \"CLN\": \"3\",\n", - " \"TS\": \"4\", \"Song\": \"5\", \"Dat xay dung\": \"6\", \"Rung\": \"7\",\n", - "}\n", - "\n", - "def load_train_data(csv_path=\"/media/x79/2A7D-FAA0/remote-sensing/train_data.csv\", label_mapping=None):\n", - " \"\"\"Load training points từ CSV\"\"\"\n", - " df = pd.read_csv(csv_path)\n", - " if label_mapping:\n", - " df[\"label\"] = df[\"class_name\"].map(label_mapping).astype(str)\n", - " return df\n", - "\n", - "def get_data_sen1_and_sen2(train_df, sen2_data, sen1_data):\n", - " \"\"\"Extract features từ S2 và S1 tại các điểm training\"\"\"\n", - " X_list = []\n", - " y_list = []\n", - " \n", - " for idx, row in train_df.iterrows():\n", - " try:\n", - " lon, lat = float(row[\"longitude\"]), float(row[\"latitude\"])\n", - " label = int(row[\"label\"])\n", - " \n", - " # Extract S2 features (mean, std, min, max theo time)\n", - " s2_point = sen2_data.sel(x=lon, y=lat, method=\"nearest\")\n", - " s2_features = []\n", - " \n", - " for var in [\"red\", \"green\", \"blue\", \"nir\", \"swir16\", \"swir22\", \"NDVI\"]:\n", - " if var in s2_point:\n", - " vals = s2_point[var].values\n", - " if vals.size > 0:\n", - " s2_features.extend([\n", - " np.nanmean(vals), np.nanstd(vals),\n", - " np.nanmin(vals), np.nanmax(vals)\n", - " ])\n", - " else:\n", - " s2_features.extend([0, 0, 0, 0])\n", - " \n", - " # Extract S1 features\n", - " s1_point = sen1_data.sel(x=lon, y=lat, method=\"nearest\")\n", - " s1_features = []\n", - " \n", - " for var in [\"VV\", \"VH\"]:\n", - " if var in s1_point:\n", - " vals = s1_point[var].values\n", - " if vals.size > 0:\n", - " s1_features.extend([np.nanmean(vals), np.nanstd(vals)])\n", - " else:\n", - " s1_features.extend([0, 0])\n", - " \n", - " features = s2_features + s1_features\n", - " \n", - " # Bỏ qua nếu có NaN\n", - " if not np.isnan(features).any() and not np.isinf(features).any():\n", - " X_list.append(features)\n", - " y_list.append(label)\n", - " except Exception as e:\n", - " print(f\"⚠️ Lỗi tại row {idx}: {e}\")\n", - " continue\n", - " \n", - " return X_list, y_list\n", - "\n", - "def split_train_data(X, y, test_size=0.2, val_size=0.1, random_state=42):\n", - " \"\"\"Split data thành train/val/test\"\"\"\n", - " X = np.array(X, dtype=np.float32)\n", - " y = np.array(y, dtype=np.int64)\n", - " \n", - " # Train + temp\n", - " X_train, X_temp, y_train, y_temp = train_test_split(\n", - " X, y, test_size=test_size + val_size, random_state=random_state, stratify=y\n", - " )\n", - " \n", - " # Val + test\n", - " val_ratio = val_size / (test_size + val_size)\n", - " X_val, X_test, y_val, y_test = train_test_split(\n", - " X_temp, y_temp, test_size=(1 - val_ratio), random_state=random_state, stratify=y_temp\n", - " )\n", - " \n", - " return X_train, X_val, X_test, y_train, y_val, y_test\n", - "\n", - "# Load và extract features\n", - "train_data = load_train_data(label_mapping=label_mapping)\n", - "X, y = get_data_sen1_and_sen2(train_data, data_sen2_monthly, data_sen1_monthly)\n", - "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y, test_size=0.2, val_size=0.1)\n", - "\n", - "X_train_np = np.array(X_train, dtype=np.float32)\n", - "X_val_np = np.array(X_val, dtype=np.float32)\n", - "X_test_np = np.array(X_test, dtype=np.float32)\n", - "y_train_np = np.array(y_train, dtype=np.int64)\n", - "y_val_np = np.array(y_val, dtype=np.int64)\n", - "y_test_np = np.array(y_test, dtype=np.int64)\n", - "\n", - "n_features = X_train_np.shape[1]\n", - "n_classes = len(np.unique(y_train_np))\n", - "\n", - "print(f\"✅ Train: {X_train_np.shape} Val: {X_val_np.shape} Test: {X_test_np.shape}\")\n", - "print(f\" n_features={n_features} n_classes={n_classes}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5492528b", - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "from torch.utils.data import TensorDataset, DataLoader\n", - "\n", - "# ── MobileNetV3 + LR-ASPP classifier ──────────────────────────────────────────\n", - "class MobileNetLRASPPClassifier(nn.Module):\n", - " \"\"\"\n", - " MobileNetV3-inspired backbone with LR-ASPP (Lite Reduced ASPP) head\n", - " for land-use classification on flat feature vectors.\n", - " \"\"\"\n", - " def __init__(self, n_features, n_classes):\n", - " super().__init__()\n", - " # Feature extraction backbone\n", - " self.feature_extractor = nn.Sequential(\n", - " nn.Linear(n_features, 128), nn.BatchNorm1d(128), nn.ReLU(inplace=True), nn.Dropout(0.2),\n", - " nn.Linear(128, 256), nn.BatchNorm1d(256), nn.ReLU(inplace=True), nn.Dropout(0.3),\n", - " nn.Linear(256, 512), nn.BatchNorm1d(512), nn.ReLU(inplace=True), nn.Dropout(0.3),\n", - " )\n", - " # LR-ASPP Branch 1: global pooling → 128\n", - " self.global_pool = nn.AdaptiveAvgPool1d(1)\n", - " self.global_conv = nn.Sequential(nn.Linear(512, 128), nn.ReLU(inplace=True))\n", - " # LR-ASPP Branch 2: direct 1×1 → 128\n", - " self.branch_conv = nn.Sequential(nn.Linear(512, 128), nn.BatchNorm1d(128), nn.ReLU(inplace=True))\n", - " # Fusion → n_classes\n", - " self.classifier = nn.Sequential(\n", - " nn.Linear(256, 128), nn.BatchNorm1d(128), nn.ReLU(inplace=True), nn.Dropout(0.4),\n", - " nn.Linear(128, n_classes),\n", - " )\n", - "\n", - " def forward(self, x):\n", - " feat = self.feature_extractor(x)\n", - " global_feat = self.global_pool(feat.unsqueeze(-1)).squeeze(-1)\n", - " global_feat = self.global_conv(global_feat)\n", - " branch_feat = self.branch_conv(feat)\n", - " fused = torch.cat([global_feat, branch_feat], dim=1)\n", - " return self.classifier(fused)\n", - "\n", - "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "print(f\"✅ Device: {device}\")\n", - "model = MobileNetLRASPPClassifier(n_features, n_classes).to(device)\n", - "print(model)\n", - "total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", - "print(f\" Trainable params: {total_params:,}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9da40f5a", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# ── Hyper-parameters ───────────────────────────────────────────────────────────\n", - "LEARNING_RATE = 1e-3\n", - "BATCH_SIZE = 64\n", - "N_EPOCHS = 60\n", - "PATIENCE = 10\n", - "CONV_THRESHOLD = 1e-4 # cải thiện val_loss < THRESHOLD → đánh dấu hội tụ\n", - "CONV_WINDOW = 3 # cần CONV_WINDOW bước liên tiếp thỏa mãn\n", - "\n", - "# ── Tensors & DataLoaders ──────────────────────────────────────────────────────\n", - "X_tr_t = torch.FloatTensor(X_train_np)\n", - "y_tr_t = torch.LongTensor(y_train_np)\n", - "X_va_t = torch.FloatTensor(X_val_np)\n", - "y_va_t = torch.LongTensor(y_val_np)\n", - "\n", - "train_loader = DataLoader(TensorDataset(X_tr_t, y_tr_t), batch_size=BATCH_SIZE, shuffle=True)\n", - "val_loader = DataLoader(TensorDataset(X_va_t, y_va_t), batch_size=BATCH_SIZE, shuffle=False)\n", - "\n", - "# ── Class-weighted loss ────────────────────────────────────────────────────────\n", - "class_counts = np.bincount(y_train_np)\n", - "class_weights = 1.0 / (class_counts + 1e-6)\n", - "class_weights = class_weights / class_weights.sum() * n_classes\n", - "criterion = nn.CrossEntropyLoss(weight=torch.FloatTensor(class_weights).to(device))\n", - "\n", - "optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=1e-4)\n", - "scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode=\"min\", factor=0.5, patience=5)\n", - "\n", - "# ── Training loop ──────────────────────────────────────────────────────────────\n", - "train_losses, val_losses, val_accs, lr_history = [], [], [], []\n", - "best_val_loss = float(\"inf\")\n", - "best_epoch = 1\n", - "best_state_dict = None\n", - "patience_counter = 0\n", - "convergence_epoch = None # ← điểm hội tụ sẽ được ghi lại ở đây\n", - "early_stop_epoch = None\n", - "\n", - "print(\"🚀 Training MobileNetV3 + LR-ASPP...\")\n", - "print(f\" conv_threshold={CONV_THRESHOLD} conv_window={CONV_WINDOW} patience={PATIENCE}\")\n", - "for epoch in range(1, N_EPOCHS + 1):\n", - " # --- Train ---\n", - " model.train()\n", - " epoch_loss = 0.0\n", - " for bx, by in train_loader:\n", - " bx, by = bx.to(device), by.to(device)\n", - " optimizer.zero_grad()\n", - " loss = criterion(model(bx), by)\n", - " loss.backward()\n", - " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", - " optimizer.step()\n", - " epoch_loss += loss.item()\n", - " avg_train_loss = epoch_loss / len(train_loader)\n", - "\n", - " # --- Validate ---\n", - " model.eval()\n", - " val_loss = 0.0; correct = 0; total = 0\n", - " with torch.no_grad():\n", - " for bx, by in val_loader:\n", - " bx, by = bx.to(device), by.to(device)\n", - " out = model(bx)\n", - " val_loss += criterion(out, by).item()\n", - " pred = out.argmax(1)\n", - " correct += (pred == by).sum().item()\n", - " total += by.size(0)\n", - " avg_val_loss = val_loss / len(val_loader)\n", - " val_acc = correct / total\n", - "\n", - " scheduler.step(avg_val_loss)\n", - " lr = optimizer.param_groups[0][\"lr\"]\n", - " lr_history.append(lr)\n", - "\n", - " train_losses.append(avg_train_loss)\n", - " val_losses.append(avg_val_loss)\n", - " val_accs.append(val_acc)\n", - "\n", - " # ── Phát hiện điểm hội tụ (sliding window trên val_loss) ──────────────\n", - " if convergence_epoch is None and epoch >= CONV_WINDOW + 1:\n", - " window = val_losses[-(CONV_WINDOW + 1):]\n", - " improvements = [abs(window[i] - window[i - 1]) for i in range(1, len(window))]\n", - " if all(imp < CONV_THRESHOLD for imp in improvements):\n", - " convergence_epoch = epoch - CONV_WINDOW + 1\n", - " print(f\" 📍 Hội tụ phát hiện tại epoch {convergence_epoch} \"\n", - " f\"(val_loss={val_losses[convergence_epoch-1]:.4f})\")\n", - "\n", - " if epoch % 5 == 0 or epoch == 1:\n", - " print(f\" Epoch {epoch:3d}/{N_EPOCHS} train={avg_train_loss:.4f} \"\n", - " f\"val={avg_val_loss:.4f} acc={val_acc:.4f} lr={lr:.2e}\")\n", - "\n", - " # ── Early stopping ─────────────────────────────────────────────────────\n", - " if avg_val_loss < best_val_loss:\n", - " best_val_loss = avg_val_loss\n", - " best_epoch = epoch\n", - " best_state_dict = {k: v.clone() for k, v in model.state_dict().items()}\n", - " patience_counter = 0\n", - " else:\n", - " patience_counter += 1\n", - " if patience_counter >= PATIENCE:\n", - " early_stop_epoch = epoch\n", - " print(f\"⏹ Early stopping tại epoch {epoch} (patience={PATIENCE})\")\n", - " break\n", - "\n", - "# Restore best weights\n", - "model.load_state_dict(best_state_dict)\n", - "total_epochs = len(train_losses)\n", - "print(f\"\\n✅ Training hoàn tất! Best epoch={best_epoch} Best val_loss={best_val_loss:.4f}\")\n", - "if convergence_epoch:\n", - " print(f\" Điểm hội tụ : epoch {convergence_epoch}\")\n", - "\n", - "# ═══════════════════════════════════════════════════════════════════════════════\n", - "# PHÂN TÍCH ĐIỂM HỘI TỤ — MobileNetV3 + LR-ASPP\n", - "# EMA-smoothed curves + annotated convergence / best / early-stop markers\n", - "# ═══════════════════════════════════════════════════════════════════════════════\n", - "epochs_axis = list(range(1, total_epochs + 1))\n", - "\n", - "# Exponential Moving Average smoothing\n", - "def ema(values, alpha=0.2):\n", - " s = [values[0]]\n", - " for v in values[1:]:\n", - " s.append(alpha * v + (1 - alpha) * s[-1])\n", - " return s\n", - "\n", - "val_losses_ema = ema(val_losses, alpha=0.3)\n", - "train_losses_ema = ema(train_losses, alpha=0.3)\n", - "val_accs_ema = ema(val_accs, alpha=0.3)\n", - "\n", - "# ── Tính ΔVal-loss per epoch ──────────────────────────────────────────────────\n", - "delta_val = [abs(val_losses_ema[i] - val_losses_ema[i-1])\n", - " for i in range(1, len(val_losses_ema))]\n", - "\n", - "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n", - "\n", - "# --- Top-left: Loss curves ---\n", - "ax = axes[0, 0]\n", - "ax.plot(epochs_axis, train_losses, \"b-\", alpha=0.25, linewidth=0.8)\n", - "ax.plot(epochs_axis, train_losses_ema, \"b-\", linewidth=2, label=\"Train loss (EMA)\")\n", - "ax.plot(epochs_axis, val_losses, \"g-\", alpha=0.25, linewidth=0.8)\n", - "ax.plot(epochs_axis, val_losses_ema, \"g-\", linewidth=2, label=\"Val loss (EMA)\")\n", - "ax.axvline(x=best_epoch, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", - " label=f\"Best epoch={best_epoch}\")\n", - "if convergence_epoch:\n", - " ax.axvline(x=convergence_epoch, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", - " label=f\"HỘI TỤ epoch={convergence_epoch}\")\n", - "if early_stop_epoch:\n", - " ax.axvline(x=early_stop_epoch, color=\"gray\", linestyle=\"-.\", linewidth=1.5,\n", - " label=f\"Early stop epoch={early_stop_epoch}\")\n", - "ax.set_xlabel(\"Epoch\")\n", - "ax.set_ylabel(\"Loss\")\n", - "ax.set_title(\"Loss Curves (raw + EMA)\")\n", - "ax.legend(fontsize=8)\n", - "ax.grid(True, alpha=0.3)\n", - "\n", - "# --- Top-right: Val accuracy ---\n", - "ax = axes[0, 1]\n", - "ax.plot(epochs_axis, val_accs, \"g-\", alpha=0.3, linewidth=0.8)\n", - "ax.plot(epochs_axis, val_accs_ema, \"g-\", linewidth=2, label=\"Val acc (EMA)\")\n", - "ax.axvline(x=best_epoch, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", - " label=f\"Best epoch={best_epoch} ({val_accs[best_epoch-1]*100:.2f}%)\")\n", - "if convergence_epoch:\n", - " ax.axvline(x=convergence_epoch, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", - " label=f\"HỘI TỤ epoch={convergence_epoch} ({val_accs[convergence_epoch-1]*100:.2f}%)\")\n", - "if early_stop_epoch:\n", - " ax.axvline(x=early_stop_epoch, color=\"gray\", linestyle=\"-.\", linewidth=1.5,\n", - " label=f\"Early stop\")\n", - "ax.set_xlabel(\"Epoch\")\n", - "ax.set_ylabel(\"Accuracy\")\n", - "ax.set_title(\"Val Accuracy Curve\")\n", - "ax.legend(fontsize=8)\n", - "ax.grid(True, alpha=0.3)\n", - "\n", - "# --- Bottom-left: ΔVal-loss (marginal improvement) ---\n", - "ax = axes[1, 0]\n", - "ax.bar(epochs_axis[1:], delta_val,\n", - " color=[\"green\" if d > CONV_THRESHOLD else \"salmon\" for d in delta_val],\n", - " alpha=0.75)\n", - "ax.axhline(y=CONV_THRESHOLD, color=\"red\", linestyle=\"--\",\n", - " label=f\"Threshold = {CONV_THRESHOLD:.0e}\")\n", - "if convergence_epoch:\n", - " ax.axvline(x=convergence_epoch, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", - " label=f\"HỘI TỤ epoch={convergence_epoch}\")\n", - "ax.set_xlabel(\"Epoch\")\n", - "ax.set_ylabel(\"|ΔVal Loss|\")\n", - "ax.set_title(\"Marginal Val-Loss Improvement per Epoch\")\n", - "ax.legend(fontsize=8)\n", - "ax.grid(True, alpha=0.3)\n", - "\n", - "# --- Bottom-right: Learning rate schedule ---\n", - "ax = axes[1, 1]\n", - "ax.semilogy(epochs_axis, lr_history, \"purple\", linewidth=2)\n", - "if convergence_epoch:\n", - " ax.axvline(x=convergence_epoch, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", - " label=f\"HỘI TỤ epoch={convergence_epoch}\")\n", - "ax.axvline(x=best_epoch, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", - " label=f\"Best epoch={best_epoch}\")\n", - "ax.set_xlabel(\"Epoch\")\n", - "ax.set_ylabel(\"Learning Rate (log)\")\n", - "ax.set_title(\"Learning Rate Schedule (ReduceLROnPlateau)\")\n", - "ax.legend(fontsize=8)\n", - "ax.grid(True, alpha=0.3)\n", - "\n", - "plt.suptitle(\"MobileNetV3 + LR-ASPP — Convergence Analysis\", fontsize=13, fontweight=\"bold\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# ── Tổng kết ──────────────────────────────────────────────────────────────────\n", - "print(f\"\\n{'═'*60}\")\n", - "print(f\" Tổng số epoch : {total_epochs}\")\n", - "print(f\" Best epoch : {best_epoch} (val_loss={best_val_loss:.4f})\")\n", - "print(f\" Best val accuracy : {val_accs[best_epoch-1]*100:.4f}%\")\n", - "if convergence_epoch:\n", - " print(f\" Điểm HỘI TỤ : epoch {convergence_epoch} \"\n", - " f\"(val_acc={val_accs[convergence_epoch-1]*100:.2f}%)\")\n", - " wasted = total_epochs - convergence_epoch\n", - " print(f\" Epochs sau hội tụ : {wasted} \"\n", - " f\"(có thể giảm N_EPOCHS không ảnh hưởng nhiều đến kết quả)\")\n", - "if early_stop_epoch:\n", - " print(f\" Early stop tại epoch: {early_stop_epoch}\")\n", - "print(f\"{'═'*60}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc690b92", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", - "import seaborn as sns\n", - "\n", - "# ── Đánh giá trên tập test ─────────────────────────────────────────────────────\n", - "model.eval()\n", - "with torch.no_grad():\n", - " X_te_t = torch.FloatTensor(X_test_np).to(device)\n", - " logits = model(X_te_t)\n", - " y_pred = logits.argmax(1).cpu().numpy()\n", - "\n", - "acc = accuracy_score(y_test_np, y_pred)\n", - "print(f\"Test Accuracy : {acc:.4f} ({acc*100:.2f}%)\\n\")\n", - "print(classification_report(y_test_np, y_pred, digits=4))\n", - "\n", - "# ── Confusion matrix ────────────────────────────────────────────────────────────\n", - "class_names = list(label_mapping.keys())\n", - "cm = confusion_matrix(y_test_np, y_pred)\n", - "plt.figure(figsize=(9, 7))\n", - "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Purples\",\n", - " xticklabels=class_names, yticklabels=class_names)\n", - "plt.xlabel(\"Predicted\")\n", - "plt.ylabel(\"Actual\")\n", - "plt.title(\"Confusion Matrix — MobileNetV3 + LR-ASPP\")\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d4805767-4f58-4464-930c-66fcbb341cc2", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/train_files/01.train_ODC_RandomForest.ipynb b/train_files/01.train_ODC_RandomForest.ipynb deleted file mode 100644 index 9798256..0000000 --- a/train_files/01.train_ODC_RandomForest.ipynb +++ /dev/null @@ -1,353 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "3ab233c4", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "%matplotlib inline\n", - "\n", - "import importlib\n", - "import new_import_ODC\n", - "\n", - "importlib.reload(new_import_ODC)\n", - "from new_import_ODC import *\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0af1f969", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "# Cấu hình Dask + ODC + S3\n", - "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n", - "dc = datacube.Datacube()\n", - "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n", - "client\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b0809939", - "metadata": {}, - "outputs": [], - "source": [ - "## cấu hình thời gian và tọa độ\n", - "date_range = (\"2022-09-01\", \"2023-10-01\")\n", - "longtitude_range = (105.5, 106.4)\n", - "latitude_range = (9.2, 10.0)\n", - "coordinates = (longtitude_range, latitude_range)\n", - "\n", - "## truy vấn ảnh Sentinel-2\n", - "data = load_data(dc, date_range, longtitude_range, latitude_range)\n", - "notebook_utils.heading(notebook_utils.xarray_object_size(data))\n", - "display(data)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b74030e3", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "# Cloud masking + NDVI + fill nan + resample\n", - "result = mask_clean(data)\n", - "progress(result)\n", - "\n", - "ds1 = calculate_indices(result, index=\"NDVI\", satellite_mission=\"s2\")\n", - "ndvi = ds1[\"NDVI\"]\n", - "\n", - "time_split = [\n", - " slice(\"2022-09-01\", \"2023-01-01\"),\n", - " slice(\"2023-01-01\", \"2023-05-01\"),\n", - " slice(\"2023-05-01\", \"2023-07-01\"),\n", - " slice(\"2023-07-01\", \"2023-10-01\"),\n", - "]\n", - "fill_nan_ndvi = fill_nan(ndvi, time_split)\n", - "plt.imshow(fill_nan_ndvi.isel(time=6)); plt.title(\"NDVI (after fill)\"); plt.colorbar(); plt.show()\n", - "\n", - "average_ndvi = fill_nan_ndvi.resample(time=\"1M\").mean().persist()\n", - "progress(average_ndvi)\n", - "average_ndvi = average_ndvi.compute()\n", - "print(f\"NDVI monthly: {average_ndvi.shape}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "df61871b", - "metadata": {}, - "outputs": [], - "source": [ - "# Load Sentinel-1 (VH, VV)\n", - "dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)\n", - "average_vv = calculate_average(dsvv, time_pattern=\"1M\")\n", - "average_vh = calculate_average(dsvh, time_pattern=\"1M\")\n", - "print(f\"VV: {average_vv.shape} VH: {average_vh.shape}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d0def6c0", - "metadata": {}, - "outputs": [], - "source": [ - "## Chuẩn bị dữ liệu train\n", - "train_path = \"train/ST_training_data_updated_1130points_new.shp\"\n", - "train = load_train_data(train_path)\n", - "train.head()\n", - "\n", - "label_mapping = {\n", - " \"Lua tom\": \"0\", \"Lua\": \"1\", \"CHN\": \"2\", \"CLN\": \"3\",\n", - " \"TS\": \"4\", \"Song\": \"5\", \"Dat xay dung\": \"6\", \"Rung\": \"7\",\n", - "}\n", - "\n", - "datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)\n", - "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(train, label_mapping, datasets)\n", - "\n", - "import numpy as np\n", - "X_train_np = np.asarray(X_train, dtype=np.float32)\n", - "X_val_np = np.asarray(X_val, dtype=np.float32)\n", - "X_test_np = np.asarray(X_test, dtype=np.float32)\n", - "y_train_np = np.asarray(y_train, dtype=np.int32)\n", - "y_val_np = np.asarray(y_val, dtype=np.int32)\n", - "y_test_np = np.asarray(y_test, dtype=np.int32)\n", - "\n", - "# Gộp train + val để tận dụng toàn bộ dữ liệu train\n", - "X_fit = np.concatenate([X_train_np, X_val_np], axis=0)\n", - "y_fit = np.concatenate([y_train_np, y_val_np], axis=0)\n", - "\n", - "print(f\"Train (fit): {X_fit.shape} Test: {X_test_np.shape} Classes: {len(label_mapping)}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2dc75f84", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "# ── Xây dựng và train mô hình Random Forest ───────────────────────────────────\n", - "model = RandomForestClassifier(\n", - " n_estimators=200,\n", - " max_depth=30,\n", - " min_samples_leaf=2,\n", - " n_jobs=-1,\n", - " class_weight=\"balanced\", # xử lý mất cân bằng nhãn\n", - " random_state=42,\n", - ")\n", - "\n", - "print(\"🚀 Training Random Forest...\")\n", - "print(f\" n_estimators = {model.n_estimators}\")\n", - "print(f\" max_depth = {model.max_depth}\")\n", - "print(f\" Train samples: {len(X_fit)}\")\n", - "\n", - "model.fit(X_fit, y_fit)\n", - "\n", - "val_acc = model.score(X_val_np, y_val_np)\n", - "print(f\"\\n✅ Training hoàn tất! Val accuracy: {val_acc:.4f} ({val_acc*100:.2f}%)\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "90588a5d", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "\n", - "# ═══════════════════════════════════════════════════════════════════════════════\n", - "# PHÂN TÍCH ĐIỂM HỘI TỤ — Random Forest\n", - "# Phương pháp: tăng dần n_estimators (warm_start) và theo dõi val accuracy\n", - "# Điểm hội tụ = lần đầu cải thiện val_acc < THRESHOLD trong WINDOW bước liên tiếp\n", - "# ═══════════════════════════════════════════════════════════════════════════════\n", - "N_RANGE = list(range(5, 205, 5)) # 5, 10, 15, ... 200\n", - "THRESHOLD = 0.0005 # cải thiện < 0.05% → coi là hội tụ\n", - "WINDOW = 3 # cần WINDOW bước liên tiếp dưới threshold\n", - "\n", - "conv_model = RandomForestClassifier(\n", - " max_depth=30, min_samples_leaf=2, n_jobs=-1,\n", - " class_weight=\"balanced\", random_state=42,\n", - " warm_start=True, # ← cho phép thêm cây mà không retrain lại\n", - ")\n", - "\n", - "train_accs_c, val_accs_c = [], []\n", - "print(\"🔍 Phân tích hội tụ (warm_start)...\")\n", - "for n in N_RANGE:\n", - " conv_model.n_estimators = n\n", - " conv_model.fit(X_fit, y_fit)\n", - " train_accs_c.append(conv_model.score(X_fit, y_fit))\n", - " val_accs_c.append(conv_model.score(X_val_np, y_val_np))\n", - "\n", - "val_accs_c = np.array(val_accs_c)\n", - "train_accs_c = np.array(train_accs_c)\n", - "\n", - "# ── Tìm điểm hội tụ ───────────────────────────────────────────────────────────\n", - "improvements = np.abs(np.diff(val_accs_c))\n", - "convergence_idx = None\n", - "for i in range(len(improvements) - WINDOW + 1):\n", - " if all(improvements[i : i + WINDOW] < THRESHOLD):\n", - " convergence_idx = i + 1 # chỉ số của điểm đầu tiên trong cửa sổ\n", - " break\n", - "\n", - "best_idx = int(np.argmax(val_accs_c))\n", - "best_n = N_RANGE[best_idx]\n", - "conv_n = N_RANGE[convergence_idx] if convergence_idx is not None else None\n", - "\n", - "# ── Vẽ đồ thị ─────────────────────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n", - "\n", - "# --- Trái: accuracy curve ---\n", - "axes[0].plot(N_RANGE, train_accs_c, \"b-o\", markersize=3, label=\"Train\")\n", - "axes[0].plot(N_RANGE, val_accs_c, \"g-o\", markersize=3, label=\"Val\")\n", - "axes[0].axvline(x=best_n, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", - " label=f\"Best val acc n={best_n} ({max(val_accs_c)*100:.2f}%)\")\n", - "if conv_n:\n", - " axes[0].axvline(x=conv_n, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", - " label=f\"Hội tụ n={conv_n} ({val_accs_c[convergence_idx]*100:.2f}%)\")\n", - "axes[0].set_xlabel(\"n_estimators\")\n", - "axes[0].set_ylabel(\"Accuracy\")\n", - "axes[0].set_title(\"Convergence — Val Accuracy vs n_estimators\")\n", - "axes[0].legend(fontsize=8)\n", - "axes[0].grid(True, alpha=0.3)\n", - "\n", - "# --- Phải: cải thiện biên (marginal improvement) ---\n", - "axes[1].bar(N_RANGE[1:], improvements * 100, color=\"steelblue\", alpha=0.7)\n", - "axes[1].axhline(y=THRESHOLD * 100, color=\"red\", linestyle=\"--\",\n", - " label=f\"Threshold = {THRESHOLD*100:.3f}%\")\n", - "if conv_n:\n", - " axes[1].axvline(x=conv_n, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", - " label=f\"Hội tụ n={conv_n}\")\n", - "axes[1].set_xlabel(\"n_estimators\")\n", - "axes[1].set_ylabel(\"ΔVal Accuracy (%)\")\n", - "axes[1].set_title(\"Marginal Improvement per Step\")\n", - "axes[1].legend(fontsize=8)\n", - "axes[1].grid(True, alpha=0.3)\n", - "\n", - "plt.suptitle(\"Random Forest — Convergence Analysis\", fontsize=13, fontweight=\"bold\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# ── Tổng kết ──────────────────────────────────────────────────────────────────\n", - "print(f\"\\n{'═'*55}\")\n", - "print(f\" Best val accuracy : {max(val_accs_c)*100:.4f}% (n_estimators={best_n})\")\n", - "if conv_n:\n", - " print(f\" Điểm HỘI TỤ : n_estimators = {conv_n}\")\n", - " print(f\" → Có thể dùng n_estimators={conv_n} thay vì 200 để tiết kiệm thời gian\")\n", - " saved_pct = (1 - conv_n / 200) * 100\n", - " print(f\" → Tiết kiệm ~{saved_pct:.0f}% thời gian train\")\n", - "else:\n", - " print(\" → Mô hình chưa hội tụ trong phạm vi [5, 200]. Thử tăng n_estimators.\")\n", - "print(f\"{'═'*55}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc57f244", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "# ── Đánh giá trên tập test ─────────────────────────────────────────────────────\n", - "y_pred = model.predict(X_test_np)\n", - "\n", - "acc = accuracy_score(y_test_np, y_pred)\n", - "print(f\"Test Accuracy : {acc:.4f} ({acc*100:.2f}%)\\n\")\n", - "print(classification_report(y_test_np, y_pred, digits=4))\n", - "\n", - "# ── Feature importance ──────────────────────────────────────────────────────────\n", - "feat_imp = model.feature_importances_\n", - "idx = feat_imp.argsort()[::-1][:20]\n", - "plt.figure(figsize=(12, 4))\n", - "plt.bar(range(len(idx)), feat_imp[idx])\n", - "plt.xticks(range(len(idx)), idx, rotation=45)\n", - "plt.title(\"Top-20 Feature Importances\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# ── Confusion matrix ────────────────────────────────────────────────────────────\n", - "class_names = list(label_mapping.keys())\n", - "cm = confusion_matrix(y_test_np, y_pred)\n", - "plt.figure(figsize=(9, 7))\n", - "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n", - " xticklabels=class_names, yticklabels=class_names)\n", - "plt.xlabel(\"Predicted\")\n", - "plt.ylabel(\"Actual\")\n", - "plt.title(\"Confusion Matrix — Random Forest\")\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "efc23f2c", - "metadata": {}, - "outputs": [], - "source": [ - "import joblib, json, os\n", - "from datetime import datetime\n", - "\n", - "# ── Lưu mô hình ────────────────────────────────────────────────────────────────\n", - "model_path = \"model_random_forest_land_use.joblib\"\n", - "joblib.dump(model, model_path)\n", - "print(f\"✅ Model saved → {model_path}\")\n", - "\n", - "# ── Lưu thông tin mô hình ──────────────────────────────────────────────────────\n", - "info = {\n", - " \"model_type\": \"RandomForest\",\n", - " \"n_estimators\": model.n_estimators,\n", - " \"max_depth\": model.max_depth,\n", - " \"min_samples_leaf\": model.min_samples_leaf,\n", - " \"class_weight\": \"balanced\",\n", - " \"n_features\": int(X_fit.shape[1]),\n", - " \"label_mapping\": label_mapping,\n", - " \"test_accuracy\": float(acc),\n", - " \"train_samples\": int(len(X_fit)),\n", - " \"test_samples\": int(len(X_test_np)),\n", - " \"saved_at\": datetime.now().isoformat(),\n", - "}\n", - "info_path = \"model_random_forest_land_use_info.json\"\n", - "with open(info_path, \"w\") as f:\n", - " json.dump(info, f, indent=2, ensure_ascii=False)\n", - "print(f\"✅ Info saved → {info_path}\")\n", - "print(json.dumps(info, indent=2, ensure_ascii=False))\n", - "\n", - "# ── Đóng kết nối Dask ──────────────────────────────────────────────────────────\n", - "try:\n", - " client.close()\n", - " cluster.close()\n", - " print(\"✅ Dask cluster closed.\")\n", - "except Exception:\n", - " pass\n" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/train_files/01.train_ODC_SVM.ipynb b/train_files/01.train_ODC_SVM.ipynb deleted file mode 100644 index 3ccea24..0000000 --- a/train_files/01.train_ODC_SVM.ipynb +++ /dev/null @@ -1,372 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "f8e59602", - "metadata": {}, - "outputs": [], - "source": [ - "import importlib\n", - "import new_import_ODC as odc_tools\n", - "importlib.reload(odc_tools)\n", - "from new_import_ODC import *\n", - "print(\"✅ Import thành công\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8101f17e", - "metadata": {}, - "outputs": [], - "source": [ - "# Khởi tạo Dask + Datacube + S3\n", - "cluster, client = initialize_dask(use_gateway=True)\n", - "dc = datacube.Datacube()\n", - "configure_s3_access(aws_unsigned=True)\n", - "print(\"✅ Dask + Datacube + S3 sẵn sàng\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cba4d661", - "metadata": {}, - "outputs": [], - "source": [ - "# Cấu hình vùng và thời gian\n", - "date_range = (\"2022-09-01\", \"2023-10-01\")\n", - "longtitude_range = (105.5, 106.4)\n", - "latitude_range = (9.2, 10.0)\n", - "\n", - "# Tải dữ liệu Sentinel-2\n", - "data_sen2 = load_data(\n", - " dc=dc,\n", - " date_range=date_range,\n", - " longtitude_range=longtitude_range,\n", - " latitude_range=latitude_range,\n", - ")\n", - "print(f\"✅ Sentinel-2 raw: {data_sen2.dims}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28b9d92d", - "metadata": {}, - "outputs": [], - "source": [ - "# Tiền xử lý Sentinel-2: cloud mask + NDVI + resampling\n", - "data_clean = mask_clean(data_sen2)\n", - "data_ndvi = calculate_indices(data_clean, index=\"NDVI\")\n", - "data_fill = fill_nan(data_ndvi)\n", - "data_sen2_monthly = data_fill.resample(time=\"1MS\").mean().compute()\n", - "print(f\"✅ S2 monthly shape: {data_sen2_monthly.dims}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e4efc29", - "metadata": {}, - "outputs": [], - "source": [ - "# Tải Sentinel-1 (SAR VV/VH)\n", - "data_sen1 = load_data_sen1(\n", - " dc=dc,\n", - " date_range=date_range,\n", - " longtitude_range=longtitude_range,\n", - " latitude_range=latitude_range,\n", - ")\n", - "data_sen1_monthly = calculate_average(data_sen1, [\"VV\", \"VH\"], resample=\"1MS\").compute()\n", - "print(f\"✅ S1 monthly shape: {data_sen1_monthly.dims}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cb119111", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "# Ánh xạ nhãn lớp đất\n", - "label_mapping = {\n", - " \"Lua tom\": \"0\", \"Lua\": \"1\", \"CHN\": \"2\", \"CLN\": \"3\",\n", - " \"TS\": \"4\", \"Song\": \"5\", \"Dat xay dung\": \"6\", \"Rung\": \"7\",\n", - "}\n", - "\n", - "train_data = load_train_data(label_mapping=label_mapping)\n", - "X, y = get_data_sen1_and_sen2(train_data, data_sen2_monthly, data_sen1_monthly)\n", - "\n", - "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y, test_size=0.2, val_size=0.1)\n", - "\n", - "X_train_np = np.array(X_train, dtype=np.float32)\n", - "X_val_np = np.array(X_val, dtype=np.float32)\n", - "X_test_np = np.array(X_test, dtype=np.float32)\n", - "y_train_np = np.array(y_train, dtype=np.int64)\n", - "y_val_np = np.array(y_val, dtype=np.int64)\n", - "y_test_np = np.array(y_test, dtype=np.int64)\n", - "\n", - "X_fit = np.concatenate([X_train_np, X_val_np], axis=0)\n", - "y_fit = np.concatenate([y_train_np, y_val_np], axis=0)\n", - "\n", - "print(f\"✅ X_fit: {X_fit.shape} | X_test: {X_test_np.shape}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69492c1f", - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "from sklearn.svm import SVC\n", - "from sklearn.preprocessing import StandardScaler\n", - "\n", - "# ── Chuẩn hoá đặc trưng (quan trọng với SVM) ──────────────────────────────────\n", - "scaler = StandardScaler()\n", - "X_fit_scaled = scaler.fit_transform(X_fit)\n", - "X_test_scaled = scaler.transform(X_test_np)\n", - "X_val_scaled = scaler.transform(X_val_np)\n", - "\n", - "# ── Xây dựng và train mô hình SVM ─────────────────────────────────────────────\n", - "model = SVC(\n", - " kernel=\"rbf\",\n", - " C=10,\n", - " gamma=\"scale\",\n", - " probability=True,\n", - " class_weight=\"balanced\",\n", - " random_state=42,\n", - " verbose=True,\n", - ")\n", - "\n", - "print(\"🚀 Training SVM (RBF kernel)...\")\n", - "print(f\" Train samples: {len(X_fit_scaled)}\")\n", - "model.fit(X_fit_scaled, y_fit)\n", - "\n", - "val_acc = model.score(X_val_scaled, y_val_np)\n", - "print(f\"✅ Training hoàn tất! Val accuracy: {val_acc:.4f} ({val_acc*100:.2f}%)\")\n", - "print(f\" n_support_vectors: {model.n_support_}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ac391274", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from sklearn.model_selection import learning_curve\n", - "from sklearn.svm import SVC\n", - "\n", - "# ═══════════════════════════════════════════════════════════════════════════════\n", - "# PHÂN TÍCH ĐIỂM HỘI TỤ — SVM\n", - "# Phương pháp 1: Learning Curve (accuracy vs training set size)\n", - "# Phương pháp 2: C-sensitivity (val accuracy vs regularization C)\n", - "# Điểm hội tụ = training size tại đó cải thiện val_score < threshold\n", - "# ═══════════════════════════════════════════════════════════════════════════════\n", - "THRESHOLD = 0.002 # cải thiện val_score < 0.2% → hội tụ\n", - "N_CV = 3 # số fold cho cross-validation (tăng để chính xác hơn)\n", - "\n", - "# ── 1. Learning Curve ──────────────────────────────────────────────────────────\n", - "print(\"🔍 Phân tích learning curve (train size)... [có thể mất vài phút]\")\n", - "svc_for_lc = SVC(kernel=\"rbf\", C=10, gamma=\"scale\",\n", - " class_weight=\"balanced\", random_state=42)\n", - "\n", - "train_sizes_pct = np.linspace(0.1, 1.0, 10)\n", - "train_sizes, train_scores, val_scores = learning_curve(\n", - " svc_for_lc, X_fit_scaled, y_fit,\n", - " train_sizes=train_sizes_pct,\n", - " cv=N_CV, scoring=\"accuracy\", n_jobs=-1, verbose=0,\n", - ")\n", - "\n", - "train_mean = train_scores.mean(axis=1)\n", - "train_std = train_scores.std(axis=1)\n", - "val_mean = val_scores.mean(axis=1)\n", - "val_std = val_scores.std(axis=1)\n", - "\n", - "# Tìm điểm hội tụ\n", - "val_improvements = np.abs(np.diff(val_mean))\n", - "convergence_size_idx = None\n", - "for i in range(len(val_improvements) - 1):\n", - " if val_improvements[i] < THRESHOLD and val_improvements[i + 1] < THRESHOLD:\n", - " convergence_size_idx = i + 1\n", - " break\n", - "\n", - "# ── 2. C-Sensitivity ──────────────────────────────────────────────────────────\n", - "print(\"🔍 Phân tích C-sensitivity...\")\n", - "C_range = [0.01, 0.1, 1, 5, 10, 50, 100, 500]\n", - "val_accs_c = []\n", - "for c_val in C_range:\n", - " m = SVC(kernel=\"rbf\", C=c_val, gamma=\"scale\",\n", - " class_weight=\"balanced\", random_state=42)\n", - " m.fit(X_fit_scaled, y_fit)\n", - " val_accs_c.append(m.score(X_val_scaled, y_val_np))\n", - "\n", - "val_accs_c = np.array(val_accs_c)\n", - "best_C = C_range[int(np.argmax(val_accs_c))]\n", - "\n", - "# ── Vẽ đồ thị ─────────────────────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", - "\n", - "# --- Trái: Learning Curve ---\n", - "axes[0].plot(train_sizes, train_mean, \"b-o\", markersize=4, label=\"Train\")\n", - "axes[0].fill_between(train_sizes, train_mean - train_std, train_mean + train_std,\n", - " alpha=0.2, color=\"blue\")\n", - "axes[0].plot(train_sizes, val_mean, \"g-o\", markersize=4, label=\"Val (CV)\")\n", - "axes[0].fill_between(train_sizes, val_mean - val_std, val_mean + val_std,\n", - " alpha=0.2, color=\"green\")\n", - "if convergence_size_idx is not None:\n", - " csize = train_sizes[convergence_size_idx]\n", - " axes[0].axvline(x=csize, color=\"orange\", linestyle=\"--\", linewidth=1.5,\n", - " label=f\"Hội tụ ~{int(csize):,} mẫu\")\n", - "axes[0].set_xlabel(\"Training samples\")\n", - "axes[0].set_ylabel(\"Accuracy\")\n", - "axes[0].set_title(\"Learning Curve — Score vs Train Size\")\n", - "axes[0].legend(fontsize=8)\n", - "axes[0].grid(True, alpha=0.3)\n", - "\n", - "# --- Giữa: Marginal improvement of val score ---\n", - "axes[1].bar(range(len(val_improvements)), val_improvements * 100,\n", - " color=[\"green\" if v > THRESHOLD else \"salmon\" for v in val_improvements],\n", - " alpha=0.8)\n", - "axes[1].axhline(y=THRESHOLD * 100, color=\"red\", linestyle=\"--\",\n", - " label=f\"Threshold={THRESHOLD*100:.2f}%\")\n", - "if convergence_size_idx is not None:\n", - " axes[1].axvline(x=convergence_size_idx - 0.5, color=\"orange\", linestyle=\":\",\n", - " linewidth=1.5, label=f\"Hội tụ tại step {convergence_size_idx}\")\n", - "step_labels = [f\"{int(train_sizes[i])}\" for i in range(1, len(train_sizes))]\n", - "axes[1].set_xticks(range(len(val_improvements)))\n", - "axes[1].set_xticklabels(step_labels, rotation=45, fontsize=7)\n", - "axes[1].set_xlabel(\"Training size step\")\n", - "axes[1].set_ylabel(\"ΔVal Accuracy (%)\")\n", - "axes[1].set_title(\"Marginal Val Improvement per Add. Samples\")\n", - "axes[1].legend(fontsize=8)\n", - "axes[1].grid(True, alpha=0.3)\n", - "\n", - "# --- Phải: C sensitivity ---\n", - "axes[2].semilogx(C_range, val_accs_c * 100, \"m-o\", markersize=6)\n", - "axes[2].axvline(x=best_C, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", - " label=f\"Best C={best_C} ({max(val_accs_c)*100:.2f}%)\")\n", - "axes[2].set_xlabel(\"C (regularization)\")\n", - "axes[2].set_ylabel(\"Val Accuracy (%)\")\n", - "axes[2].set_title(\"C-Sensitivity (Regularization)\")\n", - "axes[2].legend(fontsize=8)\n", - "axes[2].grid(True, alpha=0.3)\n", - "\n", - "plt.suptitle(\"SVM — Convergence Analysis\", fontsize=13, fontweight=\"bold\")\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "# ── Tổng kết ──────────────────────────────────────────────────────────────────\n", - "print(f\"\\n{'═'*60}\")\n", - "if convergence_size_idx is not None:\n", - " print(f\" Điểm HỘI TỤ (Δval < {THRESHOLD*100:.1f}%) : \"\n", - " f\"~{int(train_sizes[convergence_size_idx]):,} mẫu \"\n", - " f\"(val_acc={val_mean[convergence_size_idx]*100:.2f}%)\")\n", - " pct_data = train_sizes[convergence_size_idx] / len(X_fit_scaled) * 100\n", - " print(f\" → Chỉ cần ~{pct_data:.0f}% dữ liệu để mô hình hội tụ\")\n", - "else:\n", - " print(\" → Cần thêm dữ liệu: val score vẫn đang cải thiện ở toàn bộ tập train\")\n", - "print(f\" C tối ưu : {best_C} (val_acc={max(val_accs_c)*100:.2f}%)\")\n", - "print(f\" C hiện tại dùng : 10 {'✅' if best_C == 10 else '⚠️ Thử dùng C=' + str(best_C)}\")\n", - "print(f\"{'═'*60}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "940f640d", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "\n", - "# ── Đánh giá trên tập test ─────────────────────────────────────────────────────\n", - "y_pred = model.predict(X_test_scaled)\n", - "\n", - "acc = accuracy_score(y_test_np, y_pred)\n", - "print(f\"Test Accuracy : {acc:.4f} ({acc*100:.2f}%)\\n\")\n", - "print(classification_report(y_test_np, y_pred, digits=4))\n", - "\n", - "# ── Confusion matrix ────────────────────────────────────────────────────────────\n", - "class_names = list(label_mapping.keys())\n", - "cm = confusion_matrix(y_test_np, y_pred)\n", - "plt.figure(figsize=(9, 7))\n", - "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Oranges\",\n", - " xticklabels=class_names, yticklabels=class_names)\n", - "plt.xlabel(\"Predicted\")\n", - "plt.ylabel(\"Actual\")\n", - "plt.title(\"Confusion Matrix — SVM (RBF)\")\n", - "plt.tight_layout()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "182c64ab", - "metadata": {}, - "outputs": [], - "source": [ - "import joblib, json\n", - "from datetime import datetime\n", - "\n", - "# ── Lưu scaler (cần thiết khi inference) ──────────────────────────────────────\n", - "scaler_path = \"model_svm_land_use_scaler.joblib\"\n", - "joblib.dump(scaler, scaler_path)\n", - "print(f\"✅ Scaler saved → {scaler_path}\")\n", - "\n", - "# ── Lưu mô hình SVM ────────────────────────────────────────────────────────────\n", - "model_path = \"model_svm_land_use.joblib\"\n", - "joblib.dump(model, model_path)\n", - "print(f\"✅ Model saved → {model_path}\")\n", - "\n", - "# ── Lưu thông tin mô hình ──────────────────────────────────────────────────────\n", - "info = {\n", - " \"model_type\": \"SVM\",\n", - " \"kernel\": model.kernel,\n", - " \"C\": model.C,\n", - " \"gamma\": model.gamma,\n", - " \"probability\": model.probability,\n", - " \"class_weight\": \"balanced\",\n", - " \"n_features\": int(X_fit.shape[1]),\n", - " \"label_mapping\": label_mapping,\n", - " \"scaler\": scaler_path,\n", - " \"test_accuracy\": float(acc),\n", - " \"train_samples\": int(len(X_fit_scaled)),\n", - " \"test_samples\": int(len(X_test_scaled)),\n", - " \"saved_at\": datetime.now().isoformat(),\n", - "}\n", - "info_path = \"model_svm_land_use_info.json\"\n", - "with open(info_path, \"w\") as f:\n", - " json.dump(info, f, indent=2, ensure_ascii=False)\n", - "print(f\"✅ Info saved → {info_path}\")\n", - "print(json.dumps(info, indent=2, ensure_ascii=False))\n", - "\n", - "# ── Đóng kết nối Dask ──────────────────────────────────────────────────────────\n", - "try:\n", - " client.close()\n", - " cluster.close()\n", - " print(\"✅ Dask cluster closed.\")\n", - "except Exception:\n", - " pass\n" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/train_files/01.train_ODC_SwinUNet.ipynb b/train_files/01.train_ODC_SwinUNet.ipynb deleted file mode 100644 index 6213573..0000000 --- a/train_files/01.train_ODC_SwinUNet.ipynb +++ /dev/null @@ -1,5395 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": 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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 const force = true;\n const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = false;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\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 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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 const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\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 const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var 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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 const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\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 const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.0.min.js\", \"https://cdn.holoviz.org/panel/1.5.3/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const 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 (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\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 // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\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 const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n 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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", 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*/\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/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = false;\n", - " const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = true;\n", - " const Bokeh = root.Bokeh;\n", - "\n", - " // Set a timeout for this load but only if we are not already initializing\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\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", - " // Don't load bokeh if it is still initializing\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", - " // There is nothing to load\n", - " run_callbacks();\n", - " return null;\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(e) {\n", - " const src_el = e.srcElement\n", - " console.error(\"failed to load \" + (src_el.href || src_el.src));\n", - " }\n", - "\n", - " const 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", - " const existing_stylesheets = []\n", - " const links = document.getElementsByTagName('link')\n", - " for (let i = 0; i < links.length; i++) {\n", - " const link = links[i]\n", - " if (link.href != null) {\n", - " existing_stylesheets.push(link.href)\n", - " }\n", - " }\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const escaped = encodeURI(url)\n", - " if (existing_stylesheets.indexOf(escaped) !== -1) {\n", - " on_load()\n", - " continue;\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", - " const scripts = document.getElementsByTagName('script')\n", - " for (let i = 0; i < scripts.length; i++) {\n", - " var script = scripts[i]\n", - " if (script.src != null) {\n", - " existing_scripts.push(script.src)\n", - " }\n", - " }\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const escaped = encodeURI(url)\n", - " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", - " if (!window.requirejs) {\n", - " on_load();\n", - " }\n", - " continue;\n", - " }\n", - " const 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 (let i = 0; i < js_modules.length; i++) {\n", - " const url = js_modules[i];\n", - " const escaped = encodeURI(url)\n", - " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", - " if (!window.requirejs) {\n", - " on_load();\n", - " }\n", - " continue;\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", - " const url = js_exports[name];\n", - " const escaped = encodeURI(url)\n", - " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", - " if (!window.requirejs) {\n", - " on_load();\n", - " }\n", - " continue;\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", - " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", - " const js_modules = [];\n", - " const js_exports = {};\n", - " const css_urls = [];\n", - " const 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 (let i = 0; i < inline_js.length; i++) {\n", - " try {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " } catch(e) {\n", - " if (!reloading) {\n", - " throw e;\n", - " }\n", - " }\n", - " }\n", - " // Cache old bokeh versions\n", - " if (Bokeh != undefined && !reloading) {\n", - " var NewBokeh = root.Bokeh;\n", - " if (Bokeh.versions === undefined) {\n", - " Bokeh.versions = new Map();\n", - " }\n", - " if (NewBokeh.version !== Bokeh.version) {\n", - " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", - " }\n", - " root.Bokeh = Bokeh;\n", - " }\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", - " // If the timeout and bokeh was not successfully loaded we reset\n", - " // everything and try loading again\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " root._bokeh_is_loading = 0\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", - " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", - " if (!reloading && !bokeh_loaded) {\n", - " if (root.Bokeh) {\n", - " root.Bokeh = undefined;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " }\n", - " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_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 const force = false;\n const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\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 // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\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(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const 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 const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\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 const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const 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 (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\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 const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\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 const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const 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 (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\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 // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\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 const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_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, 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element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", - " const js_modules = [];\n", - " const js_exports = {};\n", - " const css_urls = [];\n", - " const 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 (let i = 0; i < inline_js.length; i++) {\n", - " try {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " } catch(e) {\n", - " if (!reloading) {\n", - " throw e;\n", - " }\n", - " }\n", - " }\n", - " // Cache old bokeh versions\n", - " if (Bokeh != undefined && !reloading) {\n", - " var NewBokeh = root.Bokeh;\n", - " if (Bokeh.versions === undefined) {\n", - " Bokeh.versions = new Map();\n", - " }\n", - " if (NewBokeh.version !== Bokeh.version) {\n", - " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", - " }\n", - " root.Bokeh = Bokeh;\n", - " }\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", - " // If the timeout and bokeh was not successfully loaded we reset\n", - " // everything and try loading again\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " root._bokeh_is_loading = 0\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", - " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && 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initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\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 // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && 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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 (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\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 const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n 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];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\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 // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\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 const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_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 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"/**\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 = 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TensorDataset\n", - "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n", - "\n", - "# ── Hyperparameters & constants ───────────────────────────────────────────────\n", - "N_VARS = 3 # số kênh mỗi bước thời gian (ndvi, vh, vv)\n", - "EMBED_DIM = 128 # kích thước embedding Swin blocks\n", - "NUM_HEADS = 4 # heads cho MultiheadAttention\n", - "NUM_CLASSES = 8 # số lớp phân loại\n", - "EPOCHS = 150 # số epoch tối đa\n", - "PATIENCE = 20 # early stopping patience\n", - "BATCH_SIZE = 32\n", - "LR = 1e-3\n", - "WEIGHT_DECAY = 0.01\n", - "\n", - "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - "print(f\"Device: {DEVICE}\")\n", - "print(f\"PyTorch version: {torch.__version__}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "46b8f479", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting new cluster\n", - "CPU times: user 1.22 s, sys: 43.3 ms, 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Dataset size: 111.21 GB

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j4esjrbfB0lJp1u/aJ598QlhYGGq1mv79+9ukLIqIiIiIiFxvGAWTW7aWSLM1WL755htmz57N/PnzOXz4MD169GDEiBG1ypWLiIiIiIg0V0wIbtlaIs3WYFmyZAkzZsxg2rRpxMXFsXz5cjw9PVm5cmVTT01ERERERKROmNz0X0ukWcaw6HQ6Dh06xNy5c637pFIpw4YNY+/evXbP0Wq1NoqjJpOJwsJCWrVqJRYfExERERGpEUEQKC0tpX379jbVz0WaD83SYLl06RJGo5G2bdva7G/bti1paWl2z1m4cCGvv/56Y0xPREREROQG5dSpU3Ts2LHB+jcKAsZ6JufW9/zrlWZpsNSFuXPnMnv2bOvr4uJisyz2mpnsHL2I7t+9x7F7nnfrmGN3z0ElM2JCwvqB77m1b3vsyevBJaMP3tJKFBITg0KPWI+9dOxBvGVaXuq6rsY+Uk/F0CWk6QrCuUL/zW+xf9TLdF+/mFGdj3OH3zG+ungTn/f9qNZzHz80kxEBx/nPpd580e/DRpitLf/OvA1PiZ6/Rf5ee+MmYGnaOJ6J3dBk4/8n6xb+Fvk7v+T05/bw/TbH/p15GwqJiXjlWbSCjJ6dUhpkDi8de5C3u3/p0jnfZQ3mnshdbhl/U/ZAPKVahoYdckt/O3IT3NZXS6SkpISQkBB8fHwadBx3xKC01BiWZmmwBAUFIZPJOH/+vM3+8+fPExwcbPcclUqFSqWqtv9yRWt8fX3xCZK4PV89NriUSqMCqUTgpcyZlBrUfNmv4WJsRsbnsC0nFh+pngGhuTbHPr75P+zMja71GlU+8usmbz91krlac7sQLV1aFxHiqcVTI3Nq/nq1NypvBWFU8MWZcRwsCWVN/88bespWHu19kCn7p/NgHd/r/ltfZP+Id9w8q6vM7/drg/VdG+sy++DjK+GH87fT2kfC/sv9KDD4E6K4TFJlKIE+MjrIS9ha3ocrek9ucePndXN2N0ZFHGd1xgAmd7zg8nfhoV5Ham/kJD6+ErwkUvZcGoBaqqfUpGZ0RLLL/ezOjeRwZThD/Suvm+92c0YMIWi+NMuFOqVSSUJCAtu3b7fuM5lMbN++ncTERJf6imx/EYB2viXcvvNpl+cy8Jc5Do8V6jzxUWj4vO8qPu69Fh+5hrH/e4zJ+2a4PI6zDA9PQ+kg4ErmRCBWz06n3D2lBmfv7YsIlhdjEiROG4S3Baay/qK5aN/2S7FojIqGnKJd6mMgFeQHunEmjcOW7Dg+Shtaa7tgeTGeEi2/XolDLdWTrAnhtC6QUpOavh7ZlBrVFJk88ZNVIpW490lyVMRx7tkzk/N6PwaGZbu1b1fJ1ralwODHyIgUTIIUf2kFu3Mj2ZYTy+bsbi71FaE6z0WjF2P/91id53MwL5R9eWF1Pl/EOUwIGOu5tVQPS7M0WABmz57NihUr+PLLL0lNTeXRRx+lvLycadOmudRPrO95On+3gHMlvvwy5J/W/fE/znPq/Eq93O6PwJNH7mNd4md82Otr674+3jmEe13mTLkfd++eBcCryWNdmq8zXDZ52t2vERTszo0k61Tda5fsyY2otc24Px6ttc3hvE4czQ/hcF4nDuaFsjM3miWpw9mRE1OneR0oj0Dn5Md1bUY/1FI93yb+i/d7fsuPgz7mu4HL6zSus+zOjWRtRj8AVp1MdPmGcy2qAE2tbRI2N69SACMjUpBhYltObI3thoRlMDIihS/7rUSJEX9ZBb08cgmWlaARFAzwyEZjUhAoLyPW45zb5/ndwOXc7NX0y6LpFcH4yCoBGBqezqCwLAaFZSFDwF9awc7caA7ndar1O6kX5PhINVww+nBzYJbT42/MMlcN35kbzdqMfvxa1pU1lwfW/YJEnEJMa647zdZguffee1m8eDHz5s2jZ8+eHD16lC1btlQLxK2N81pfgv1LCfSqsNmffPcCAFakD6rx/MJT/sil1T0XOlP11bQ/y8KpNCnp4FXMj4M+ZuLef6AxKZh1eDK9fn6ZWYcn2x3DcqNzFh9p9ZvZ0fwQFBIj/lItFYKME/kdyDjVjhP5HVzq20+qJfd0zQZPQbkvsRtqDnAuFxR4SQz0Ds2nb2geZ/UBnNEGcMHoUyejJVBeTqnJuZoZk6MPcH/0PpfHqA8yBEpMHua/JQLlJhUbsnq5/G9r4c7IEw6PLUkdzpLU4dzSLrNOfTtLn/++5PI5FSYVekFeq9FiYWBYNsHyYvTI0AhyQuUl+En1tJaXcLAsnCNloSxOGeHyPGrjorFh4xScoZd3nt0loKHh6QwMy8YkSLls8qRcUNbYj49UgxIjp3WtCFVecnr8sZFJgNmAnBx9gLldN/Nx77WsOmnfi70oZaTDvlz9nWlqDud1auopiNSBZmuwAMyaNYu8vDy0Wi379++nf//+LvfxRb8PCfYqwUeptXt8RsxuAIeu1LyZz9t9Ol+esNr69zNH7mVJ6nBUUgOtlaWU6c2xNEdOd6RI70GlUUn31ucwCVff7jeP32X9O13jmkfk+6K+zDx0v/W15Qe9g6wM6V+Wd9dOZygwetK105lq5x/ND2FHTgxLUofb7d8oQMGZ9g7HfyXqZ6Z0Pujw+O7cSI5qQnk2d7x13wfZQynUebGnNJpvC12/iR8sCiVZE+LyeY3FwLBs/GXlrMvsQ6amLYfKwygyelJg8KtTf0t7fWN3/0dpQ2mvuEKwvJisstaM+v3J+kzbIbMOTybQo4JJex9x6bzn4rYyKuI4YfJituXE8kHqsFrPGRVxnNERyQwKyyIy5ByRIecYHp7G1MA/CFEXYkTCusw+1vbOGkM1cUYf6PDG3FhYfnscMTQ8nVxda4aH28+MtNA3NA+FxIifrIIioyerTiZavSd1YWrn6tIRqzMGUGG8GiO4OzeSLdlxrM3ox+bsbpw1+rAvL4wFx0fXedzGpHdofpONbckSqu/WEmmWQbfuRopAYaX9ZRSAKfuno5RdfStu3/m0zfKRI8b98SgnL7emfzsP9hVF8G3iv3j26ER6+J8G4LaIk5QZlKzp/znj/niUs+W2N69nj07k/Z7fEqU+b697wOwB8pJqmRx9tSxBL8881FI90w9O5fO+q3gubiurMwbQVXnW5os4KMzsHt6Q1YsCvR+Pxe4kOb8jpSYVl43ezO6yrdp4XlITxlq+Cz9e6WVjsF3LoLAsBgFPVLmvjAs5io9UQ3vFFbJ1bWoewA4dPYvQCzKXz2tMJkX9yY6cGPy9MvCSajljCCBaWeDWMZ6I3WH9e3K0W7u2siMnho972y6ZfJA6jKe6OB+oGx1yjozsboQoL9d5Hr1D8ynKieGMIYDUyg58lDYUhcRImKL+z1mdlQWkaduxJzeiyWNZaqI2o8ZC39A8yAtlV3ksEaoLVu+Ju6jqsTyYF0qpyYdykwqFxIhGUOAp1aKWGOnmcdqt4zY023JiKTV5MC7SfcHUtWH6a6tvHy2RZu1hcQe9Nr5Dkc6DQI8Kxv3xqN3A2/MVvqRfasOU/dOZtPcRlLLay3/3+e9LXNZ40cqrgvbqIr5N/BcAW/NjuazzZlHKSELVl/GQ6QFopSpH8Ve/i1JGcrN3Oh1UVwBqXL7YVdSZydEHrEtXqzPMGQXZ2jZ08bq6vn9/9D6HTw0Riks8FrsTgPhOp9ELciZE2U9/DOt4DpkELhodfzSqGivOBunNiduCESm+fxktbx6/i9UZA5w6F2CQz0mSSpqvh8XC0PB0RkakoJAY8ZVqUEiMHMwLdamPZWlDGmZyVei/9UWHLv6h4dXjO1wxVixkaIPrfSMYGp5Ob9UpwlUXeCJ2BzNjdjEyov5pzlKJiVjVuWZtrLjK1tJ4vGWaBr/56gUZoyKO4y8rx0uqxV9agUmQUmj0xFNi35PdXBkensa4yCMsTx/c1FMRcYIb3mDx8dRxqsifUp2KCoP9teBtty4lafQbHD4XwrrEz9g0qHadj16tT6M3yojyvcQb8Rut+4/fvYD0kjbMidvC3K6bCVaVMO3ANELUV4jwNq8vz4nbwpCwDMqMtcdkWDJNzuv92JkbTZ42iHKTin1F4fjJKlicMoL5yWMcnr8tJ5aLRi+bffZuSFUJ63iOnRWda50bUC29uibKjGrOGALMP3BIrIZa4i9zuH3n0yw8McrueeszEwiWF9HHz/mxmpoBobl4SrVoBIX56dcFLMZlQ7J/xDtEqxx79tzB0VL3GJhdO52xu0xRVzZnd8NLoqO1rNxtfTYHDlwJI6PStRi/umAx8vyllSgkBgoMfnhKtUglJvTIrA9XO3MbyAXYAMyMcY+2jjPUN0PIsrVEbvglobJKJVLMSwnnSnzR6uXE/zjPGnRblZSxr9Xa34MHHiKrOIj/Df/KYZux7Y5a/95zKYLtty5hfvIYvGW2Tx/zuv3k3EUAPTzz+L/zt3CzfybB8iIeDv6dNrIyTurbsOlyDw7ndUIhMWFEQoauDROiDvFq8lhu8pJSaPQmOb8j8Z2cd9ee0QY43RbMGQc1uaFXZwygj2cRFw2+pGvbU1rFWDt/2Y+EmOMEyMt56dg43u5uK2hm8QYNcmKc5sSQsIymnkKNNPST+Bf9vmjQ/uvC5uxunNEHoBNkzfJzFLpyEXkPOZZScMS+vDCGBEUTr65ZtmB5+mA6KArrpPdyLRZDfEdODGcNARgFqXXZdk9uBErMqe6n9K1oLS/BS6qtNR6nJWAUqHXZ3Zk+WiI3vIela3ABugolu25bTNLoNzCZJAT7lNLrZ8cpoVHfvuHw2IEznfjf8HdrHDO7srU1+KxMZ/bqlBjUaO1kFjnL76WxrOn/OTNjdlFk9MKElCKTB5Oi/mRK672UCCrKBTkmQYKXVMvR/BCyylsjk5jwkVWSpqv5yevaZYt1yX3YkNWLo/khbM7uxtH8EPblhTlMsawwVRfts7AlOw5/WTl6QU57xRX0ggxPqQ6Am7e9gI93JREeF4lVnWWYj+PsGAC1RF/jcRHXeD5pgsvnrDqZ6DBgu7kzKuI4YcqLhCkKm3oqdqmLsXL37lmkaDsQqzqHQlLzcrYME0ZBahPEXF+GhqczLvIIE6IOMTn6ADNidqOWGDAiIVvXhl1FnSkyeorf3b8wuWlridzwBsvq/v8k94EXra9P3jOPVupyhrR3nBIa3tr2xyxu42v0+vllxv3xKIJQuwri0l7fWL0n+0e8w7NHJ7I1rwutFHV3Qb/XY7317wlRh7hs9LbJmrFkIPUOzSdQVoYMgQ4eRXhJdJzRByKTmPUxdudGAuZU6g9Sh1njSNTX/NB1Cz1LhOISMgRGRRynZ6dTDAjNdbjmH6ZwnE45MiKF0RHJZOnaUGT0pKPyMt08TvPs0YlcKPYmafQbzO6yjSFhGbUuV42MSHFLlkhjsiGrV1NPwSEZpa1dFhsrNHoTLC9mc3Y3tmTHuTxmU6eUDg9Po8jJFPnmzIMHHgLg/nZ7ydMGsaU4ngytfSVwC/6yCgqN3pyo7FjjUnJ96R2az6CwLB6L3YnBJMNXpqHA4M/Kkzc12JgiNz43vMFij3WJnzlMGwVzTEtiFYVbmdRE99bnGBiQjZ9XpcvjxXqc4+7wZF7ptqlO87WHj7SSjMo2HMwLxVeqQSMorO7YIqMnl00e7L0QzgltB87p/Ck3qcjVteai0Zc9uREEysqQSkzkaYNYnDKiWszKnJDN9Ox0yqllpM3Z3Sgyedaa0vhE7A58pRrO6/35rbgLu85F0SGw2OVrT9O2r1faZmPzS1E3fsqOr7NoXkOy8eZlbLx5mcPj9tKSPaU61FI9BQY/vrnUnyeP3OfUWOsy+7AjJ4YTuvZN7qHpICtr0vHdgUX1eULUIV6P/4EPe33NobKwGo2CCVGHOK0LJKeiFV08zrAsbUiDf5fWJX5GiLyQSMVFPKXaZm3ANwYmJBjruZlomeUDbvgYlrpSob0aoOvnoeFsuR9tVKW08qjgySP32SjcXsuytCEYkWISpFSYlEglXvjJXTd0auKT/KGMb3+Yy0Yv9MjQC3LKDeZlGRPmteRov4t0UFzBR1bJaV0rpBITXiYtBfiRrW1DD488Lsp8bVKmLewq60LNknpXGRVxnEl7H+HuoKO1th0ano5FvH3krqdo61Hq5ChXsaT2bsuJta6J78iJqdU701QsT1jN8vTBKCWGpp6KS0w/OJVwDzXrMxNI17Rjzcm+PN11B77SSjQmBUZBitYkp6OLn+1WsjJKjWpWZwxodIE/C6cMvhTlhVq/P+6I6WgOWDL4nk+aQFeP0+TrgthzOYItgz+wtrk2dm5nbrS1xlJDcdnoRQd5CTIEioyOJSZaAibBvNW3j5ZIi/SwOEPSaHMcS8TXb+Mh11OiU1FuUNHJ6woB8ooaz30sdideUi3tFVfwlmkIlJUzt+tmt86vtUcZXlItJqQoMHLB4IuXVMu+vDA8pVouG70ZHnACtUTPpKg/eS5uK7O7bGNy9AGkmOjtkUsraYVdY+VEfgdCVc4rZgJ08zlLhPKCS+dsGfxBvYpFWtbrt2THIZU071XdmTG78Jdd/dxYluaaOx2Vl1FLdXRUXmZC9BFaycqs3rkw5UU6eBRZxQpro5O8kKHh6Wy60hONoECGiQ1ZvdiQ1cutMRXOMDQ8HRkCIfJiWstKr6uMFmeoNCrYVRRLqVFNZ98LLE4ZwYtJ4+22HRKW0aDGCpiXcuM7neay0RtAXBoSqROih6UGHjzwEAF+7RgQlMO+S+GUG5WEeVwmtcz+OvGytCHWlNRCgzfIzdk2kWrXbuTO0E5dXO3pNDm/I2cMvmgEBYUGb3p75DpMOzYicajb0rXTGbQuxhkM9MqwUfJtDGSY/noyTHGqBlJTY8nKWXnyJoLlHm7rN/GXOey9fZHb+rPgJdeyrzSKIX6pXDL40sszj3GRR9iWE4u/tILh4Wm4srBjiX/q55NFhUmFj6wSjUlJK1mZ9UZWlbUZ/YhWnueoppPTAmqu0JRqpw3Nx73XArDg+Gi6elxya1p4fZgZs4ufsuNJqnBNm+hGwrKsU98+WiIt2sNSU6YQQKlexaFRb3FZ5822W5cSpCzjRGk7fORa5iePsZHXf+nYuGq1bvaWRBKpvlAtg2baAdcKOF7LS8fG8X7Pb6vtj+90mlP6Vuwqia0xvVEpMdaaXujKj7n5BlbZ6CJcg8KyrE+G15MA2EOd/3DrE23BJT9m/PmA2/qzEO95mo6qK1YPncXgGh6eVi/xNsvNUy3Rc6wyBKnERKu/Ykoshueqk4lIJSYOVobTWl7ChqxefJA6jG05sdyzZyYH80JJzu/IifwODmMiIte9RefvqssXtCRUUj0bzic09TRsGB3h3ni+urAnN6LJvDz1jV9xh8FzvdKiPSz+Hhoi1r1F9iT7hsuGmz4FIMLjIgDv9/yWKfun83nfVYBZWh/MdYFkEjl6k5znkyZQaVSgkvqR6JvF4bJQOqiKWJE+iGKjJ4UGLyYEneSD1GEcLu3EtDa77ep1jNz1FB08i+nqfaaahH4frxyH16SW6hkTcJhSkwc6B1L2GkFR8xvjJAfzQukbmsfw8DSX1VxF6sfqjAH4SCt5ecVUfCs82GaIgz6uS+jXxPbCLvgr3Rt7ZcGybNlOUYRJkFpTXvdURDMQiFReoNTkwWlTK7ykWrwkOowKKYcrw7i3zUGMSNAIMvylOjQm8+d5fWYCp/SB9PXI5h9H7+fZnjupMKmsFdOD5GVue2+uF3p75FLh71hyoKUyMCwbsS719UeL9LDcvXsWAOeKfB0aK1V5Lm6r9e+u3lfl8MuNKj5IHYZWkKOQGLli8KSj8grRHhc4U+lPqdGDTft786/fh3JSE4zGpMBTqsNHWslTXX7ly34rSao03+ijFy6xGXPL4A+4ovMgTnWmWnZJTYJf90fv+0vnRMuhinDWZfaplgYcLKs9M2ddZp9aZfdlVWIXXFVzFakf90fvY2xkEtMe2ILeC/z+NN+U3HlDXpf4GccLXSvM6Yhr059jVefQC3I0goJcfRAFRj8WHB9t810zIuG5uK38VNSLM4YA8zKmRy7Rygv4S3WUmtSk6YOscVhGJMSpzrDo1Ch6tztFX48chnmn0NXjNG/Eb7xhjZUdOTEOyyy0klbQw/P6X/p68MBDTNz7j6aehlswCRK3bC2RFmmwpJwzi6hpymsu226P7Mog699RHheQSkykl7VFY1IglQgYkdBWUUSI5xV+LOiOvESKPFDD+qQE/nc5kiBFKRuLrrpovz9jdmdnzJ1dbaxQz0J+vNIbjaBwKShRhkBSZSgJnjmUGs2xEhuyerElO47N2d04pAmrtQ+lxEiuPqjGNhqhRTvomgXPxW3lHw/8TNKHzzRI/7WJJNZE1SBPU5WfmlUnE7lo9MFLqqXCqEIt0aMxKWyyVwaFZVkzd3p75aIRFBQZvTAKUipMCgqNavL1gTbZPZOi/mRkRAqbBn3ErODt+Ej1qCVGurq5AGVD4mx9rVt3PAuY3+Oh4enMidtit13v0PxGLezXEEzZP538sgD2p0YwZf/0pp5OvRGXhOpOi7rj3LX7CVLOBGPUKrjtt9lEdnS9j2BVCWBOGwyUy9iQ3xOFzGgtfjh53wyGBVbQSXWZU+oAWt+SRlJBe0xnPUgrCSHL/yxSyVXPRF6+Y6PAW65lafw3LEsbgo9M49T81mcmYMSH7Zdieaj9RaJVBSRVhtJecYUKVGhMCj5Nu8WmkrI9nPmRcyVuJPzfb9Mj9DQVBqVTlbBFnKe5eg5+yu1GdsU/iPC8hF6I4/Dxu6gwKenmYSRX1xoZJkxIyNS2paPSsfLs1M57eebIvUR7nCdd257eHjmcMQTQSu5YS0WGgF6QopYYqWjkYPD6cH/0PpanD661tk2Ur3mZ+p0e3zfGtJqUNf0/J/GXOXVSARa5sbh+vsluYNOgj8ie9DK+ARWEeRdSrHFd7fKN+I18kDqMUxUBHCwKZc/tiwjzufpjaxCknNf7AdDPP4d26mIMBhmSdhrad76I1iS3Ua3Ne2gOLx0bx/NJE+jz35eqjQWQUtGeny91d2p+E6IOmVOZgw/QRlbKWX2AtUDZZ6dv4aX9f6N3u1Nszu7Ghqxe/JQd7/J7UBce6L6f8W0PIwgSjuaHcCK/Q6OMK1J/Ir5+u07nxQRdIMTjCp09Chjim0pbRTGeUh0XDb4Ey4upMKnoqCyknaKIONUZa9E8R5zX+xGvPkWGLphJUX/WqJ3SNzSP+E6niQ45d91lAzlTiG9FH8e1zG5EGiILrqkwInXL1hJpkVedfPcCPGQ6Aj1q1lNxxCWDNz18T1OmN8cN9PbJ5/mkCcRtfI3uPmcIkpdyTudPhVFFmPoyPTqcoU1gCXtuX0Sc59lq/b3dfQPv9ViP3mA/SPbj3mtZl/iZ0/OzFHXTISNMcYnHYndyuCSU7IutyJ0yl1iv8/ynsDf+0gp+LKxZdXJfXhjbcmJZnTGgXqnDoapL7CyKJft8ED07naJrpzPWY3tyI2qNl3EH14v2SVNjkXwHc6aNQmngsUNTXOpjXWYfbg7MZKTfMXykGoyClBDFZdoqiglRXqbI6EmM+ixGQYqXVMtFow+t5SUO+zNiFmEcGp7ORYNPna+tqdmQ1cv6/rpaEkHkxkBwQ/yKMyVibkRapMECcLbSj3h/s/FgySJwhhXpgyg2eKCQGIn3P8szR+7FR1bJf/O6cFPHbDxlWkpNamQSE58nDWT3lShGBSXT2sNcR6impyeLWF1Vxv3xaLV9H6UNtWYoOWJy9AGGhGVYl22+6PcFJ++ZB0CM+hy3+J3kp6Ke9PKu+elTIyjQCArUEj2e0roXL3uo8x983ncVWdcEOf+UHU+RydOhXkx92ZETw57cCN48fheDwrJuCKOloaXUq4r5eXhqCW1VSJTn+VrP+yk7nsN5nfgpOx4vqRaFxMifFREUGT0pNHojk5jwl1UgxUSpSY1aoqeD4grB8mI6yIoJUxQ6NIo7qK5wq08qgMN4jebOivRBZGvb4CXTMXnfDMa3PdzUU2q2dPtxXlNPocEQY1jqTos1WHwUWor0Zoloy9KLM8yI2U2Qoozn4ray/XRnlvb6hh8v9ODusOOUG1TM7rINf1kFRkHK2Lgkjp7qSHJ5R/oEOJdFc61Gi49CW62Nl1RrV4fFGTZk9cJLqiW1sj1yqamadsy1+Eq0+EsryNMFUdAAT7ajI5IbVGVzaHg6A8OyiVKbAy8HhWU12FiNhcWDVhVXlWIjvn67xqyLsDULAeje5hx3tj2Oj1TD6owBfJQ21G77j9KGUv6X3pCXRIdRkFJmVKMXZPjIKmktL6GVtAKNoCBTG8zsLtsYHp7GqIjj6AQZWfrWKDDhKdWzOzfSZpxpB6bhJ6vEU1r9u3A9MSNmN8/FbaW1spT+fjkYW8hT8qqTiS6fc/zuBbXqZIm0PFqswfJlv5V4yHS8dGycy+dashmO3vUmYC4gd17rS6y3+aaokBjp5ZlHtMd5RndOJr20LSoH3olrK6Z28ijkmSP3EvneEus8ryVT09blOVtQSAz8r6wz57W+3OZzwiY12R5njH7oBTmHSzrhK3Uu8LcuJOfXIQLaBbyu85tdbbSSlblUPTm07WVCPR0HuuZOmcubx+8i0usiekFGsdGTVrIyuqrOVPPuLUoZyROxOwhTXCJL35qSv7wnUokJT6mOTE0wekFOiaDCS6ol8JpgWYsIXamgIEPXhgKDP2D2SCxKGckX/b5gZswulBjr9H2tKyvSB/Hm8bvo8dOrPHPkXrf1+3r8DzzV5ddmoz7b0NT1Oo/c+RYAYR8vdud0mhyjIHXL1hJpmVf9FwZBRm5FK7f09XnfVZzV+rM4ZQRqiZ4JUYcwImVpr29o51Hi0I0tk5hsqhy/Hv8DRXpPPGKKHI71y5nYGiv/1vS0va24G108zvL3oL2UmsxLW2sz+jlsX2T0QioxcV+bfXhK9TXGsdRHOdKZqtD1QS/Ib4jlIEec0rdCIyi4aPSttW3oZ+9xW5t0bvI+aROvci2vdNvEG/EbUUn1eMs0rLmQyAWjD7Ee52zaWT7bA0Jzra7q45oQ2sqL8ZZpuGLwxFOixVei5YLBl2g7acbpmnakaDvgI63kosGHVrIyZsTstvneDAzL5u3uGxqt7s9pXSDHSjsQ3+Ys6aV1f0gQqR+5s55r6im4FRMSTEjrubUM79y1tGiDBSBI5b4y81EeF3gubivHKkMAOFxiFoUb4GtehrDoUixLG2I9R2NSUGFU2hgZX/T7An8PszcjYXN1t2i/Nvl8edHWOIj65k3r3wtT73A4x2iP83hJteTrA1FIDIQqL6KW6vkgdRhLUqtXhrk/eh9DwjLI1rZFhlBjKvNZXYDDY03NuMgj1mKJNyIPdf4DwKnqx+pWlbzSbRMXDb7EeZ9lUcpIZh6632H7VrIyOikuo5Ia0JgU+Mgcq9/KEEjXtuOS3hv9Xzo9njIdRSZPNIIctUSHWmKoZjyakNBaXoIeGUakdotyWrCnDN0QtFUUE6x2HAgsIiLSuLQoHZZrsZRirw8r0gdxoDScFX2+slHpBLPhsSJ9EF5SLWsz+hGgCASg1KRmSepwguXFVBrD0Qsyzuptb/Y9W5k9DodGvVVtzBB1IXpBZpVhD1u2mH8M3sP85DH8kBdP0ug3q51jYevFrkxpZ3bRjo1M4pkj9zIu4E9QQqCsjJmH7idUfZkeHvk2sSUxqrOUmmyF9pakDqenOp+h4ekADPZOrfG9euzQFO4OOFyvOjT1oaECe5sLzgqEpY83BzRaCgpuyY7DT1bJhqxedvtI1bQnXHURf0UFXlIdshoqY3tJtTZekY1ZPcjVBKGW6Fl3pT9BijJ8ZRoK9H5knLwJKSamdt5LN4/T+Eg1FBj8kNH0lbcXp4zgcEk0cd7n8JZp6esrKjm7kyn7p7Om/+dNPY0mQSx+WHdavIelrliWcTSCghV9vmJ5+mDrsU35Xa3Bs2HKi5zSB6KQGLmk9+GlY+MoM6opM6rZeqUbcZ5naaMspdjgadP/tZWPq2YLze26mT6eOazONS/ljOp/lGKDB6sO3MTj0eYsJEeZRDE+5+kgv2JdV17a6xsGhWXRWlaCXpAxxC+VQoMXe8ujbM7bXtrVpgbRqpOJhCkvWY0Vy3tRE8sS1tTLWKlpGUyk7oyMSMFXWolCYrB7/Cavk4QoLjPK3xzs66i0w768MPyltlIBYyOTeKfH94yNTGKAdxYB8nJSKjvwWOxONCYFUzvvZV1mH3yklQwJyyBYXoy6Htlo7uK5uK2sHbCCV7pt4u3uG3iqy682S7ci9SNQWeFSduaNhBjDUnda5lW7AUvg7ROxOwDbdGVftZYzFX5M3jeD7wv7ckYbgF6QoTXJKdJ7klHWhlKjGq1RzoyY3VzRe/J6/A8A1grQF7TeNuNZCjFajJBREcd5MNzs/i8xqEgpacc9CX9an5rzta1sMol25kazLrMPHVRX7GbKDArLwihI0QtybvFJw1umsdHeSPDK4efiHtYfmamd91Z7Gq+tAnR9KRdcL6Ug4hy/FcdSZPRiS3ZctayOPysiUEiMXDZ6U25Ssuay/bJxA0Jza1wynBx9gCdid1gr9T4WuxMwS+pbPjuNtdxTF6qWDhAxsy6zj92l5NoIUpQR9Ffw9bWJByIijhANFhfZmNWjWjHBqsxPHkOEz2XkUhOBynKWJ6ymVK8mW9sGnUmOXGqknbqY93qst4rBLe31jfXcAp0vLyaN57uBy5nx5wPV+o9UX7D+bYnJWNP/czzlOjJK21iPVVXTBTirD0BjUlSr/FwVL6mWVrIy/KUV9PbIpbv3KeuxSVF/kuCVa62M25Dsywtjc3Y3m/d5d25ki32qaAxW9PmK+6P3MTIixSar49Ydz7Iuuze5uiCOVXTChJQYz4IGzdbRNVGNKleDsm/e9kIDzeT6YVLUn4QpL7l0zjNH7uWUJgCNIGdRykgUEiN3755F6JfvNNAsa2b4bw1Th8sR5qDb+m8tEfEO4CJjI5M4pb+aWfR80gSb4wqJkU4ehXjKdfjKzYGz7dTFZFS0oaPqCuUGFd5y+ym2ekFGqUFNkML85BGsKqkmV255KgV458DVCq3rEj+jvWd1V70lA+iy0dvmRrQxqwcrT95kk3ExKCwLf2mF1RCqMClZm9HP6govMXkwNWAPyfkdG0y8bE9uBHpBhlqiZ3h4GrMOT+an7Hg0gsKu/ohIwxLle5GwgCt8nHXrX7L6PrRVFPF29w0NNubMmF1szu7WYP07wlWNnvoUhryRaC+/4pIKdplRRXtVMRtP9+CCzpdAeRl9AvLoFWkWsey/9cWGmqpdtt26tFHHM7lBlt/UQm/dLTrotq5YMjKguifD4u6uSqVRwbCAFL4p6EuMz3lUDmIF3u6+gSeP3Mc5nbkWUYC83Bpc6ynVWpd7LOTeP9fmddUg4kUpI/n1fBe23WrOtqi4JmB2bGQSW7LjyNAG8/vx0VZ3d1WXvlduBH5SLe0VV1hwfDRpZZ3prjpFgdGPCMUlfsqOr7GeS+yG10kbN9/hcXtYxj+YZ86wqjQqKdD715iZItJwrOjzFc8nTWDToI8addyGFBO8nliX2YdJUX82ydibs7s59e+gF2T4SHUczTdnR140etW4PLyiz1fEbXyNDn7FvN/zW6bsn47OJEctM9Bz0yv0alO7qrJIy6RlmmmNzNJe33B/9D5uCszinMYPExI+SB1mt+2Hvb62xp5UGFVUmFTkalrxfUFvh6Xnrw1GXZ+ZQIii0ObJQW3HSNpe2pVzen+GeZ+w2+/AsGy6djqDXpDRUVlIodaTgWHZBMrKKDKpiZAXWscDqi2Vvdit/hLqCT65hCguVwvmFGk8rjXKG4u7dj9h4+FriTSVsQKw7MytTrUbFJZFz06n0AtSzhr8yNW1rvWclLGvWX+fYr3OMyQgnXWJn3H0rjf5ot8X9Zp3c6cpg24/+eQTwsLCUKvV9O/fnwMHapAPGDIEiURSbbvzzjutbaZOnVrt+MiRIx32WV9cvurff/+d0aNH0759eyQSCRs3brQ5LggC8+bNo127dnh4eDBs2DAyMmwD6QoLC/n73/+Or68v/v7+TJ8+nbIyWz2UY8eOMWjQINRqNSEhIbz77o3hfg3zvExWRWsUEkONUfJLUofTVlHMJb05+LbSoHCosXFtds6EqEPVdCye6vIrq04m2lRnPlHcDr0gqzFQEszBtOf1fgSqzEbDkLAMhoRl0LXTGVZnDEAvmIs2bim2rShdHyXPckHJvrwwa1XSIpOntS5QYy8XpJ1qX2sby9OliPsY1CqDaOV5unvk2xjrjsoDODLo7fHm8buYuPcfjPr9yXrP81osBvz1iGXuK9IHUVBmLsXh7Ge7b2geKwtuJkvTpvbGVXil2yabpe4bnfqLxtVtSeibb75h9uzZzJ8/n8OHD9OjRw9GjBjBhQsX7LbfsGED586ds27Hjx9HJpMxYYJtGMTIkSNt2n399dd1el+cweWrLi8vp0ePHnzyySd2j7/77rt8+OGHLF++nP379+Pl5cWIESPQaK7Kuv/973/nxIkTbNu2jU2bNvH777/zyCOPWI+XlJRw++23ExoayqFDh3jvvfd47bXX+Owz5ysWX8u0A9N49uhEayDrXbufqHNf19Jvy9zaGwF+sgou6rzp5FHIkbJOdFZXV/wE+CB1GKc0geRoW+Mt07K01zfsus2+PPVHaUN57aRzT6CFRm9KjR7W15tv+ZDbfRwv6VSlm8cp7mld/Wnv/uh9HK80y+qfqfSvdrwuqqTDf3sGX4kWvSBj95Vo2sjK8JVq8JRquWD0ocDg53Kf9SE2pHqF7Wvp2elUrW1ArBjtCgM9M+gbmkewvJjWsqsCbk/E7qimqrwus49TonkWXum2iVDPQtQyfbU4sfoyIeqQW/trLNZnJiCTmFiUMhIfWSVvxmxkW04sJhdqHn03cDllRhX9t77IPXtmNuBsr1+MgsQtm6ssWbKEGTNmMG3aNOLi4li+fDmenp6sXFm9/AtAYGAgwcHB1m3btm14enpWM1hUKpVNu4CAhhMQddlgueOOO3jzzTf529/+Vu2YIAj885//5JVXXmHMmDF0796dr776irNnz1o9MampqWzZsoX/+7//o3///tx888189NFHrFu3jrNnzTeGf//73+h0OlauXEnXrl2ZNGkSTz75JEuWLKnzhX7R7wtOVQawos9XPHnkPjYN+ogXk8Zzy6/PM/CXOU73Yy+i/MDIhU6d+/253gSrSris8+bOgCQOl4XaXRoqNnrQSlFOiUFtNyamKv6yCqfHn91lG0cqQqvtd0bfZHREskNhso5K89KQJevJwsqTN5GsMT+dOfuUtiU7jrvbJSGVCPxa2g0vmY4Tuvb4SCspNanxl5m9PPvywng1eSyrTiayOzeySYI064KldlPWqXYNNsb18l7UhiUIdkBobjX9nqpxZFC3pZPbfE7wtzZHiFYV2OgotVR2lcRQblIRozKXXvjmUn+MgpTLJk8bz2xtfNx7LQFqc8zZzEP3N3oQbUuipKTEZtNq7Sd06HQ6Dh06xLBhV+83UqmUYcOGsXevc57wzz//nEmTJuHl5WWzf+fOnbRp04aYmBgeffRRLl++XPcLqgW3xrDk5ORQUFBg86b4+fnRv39/65uyd+9e/P396dPnqhT9sGHDkEql7N+/39rmlltuQam8Gig6YsQI0tPTuXLlit2xtVpttX+8azn8u/nG/GEvs8vqnR7fo5bruVzqRfi/33bqGlt71F3Kf2BQNt+k9+bPiyFUmFS0UZaSUdmm2hr9vG4/4SPTEKau+R9+efpgl54qwRyPYMls2pIdh5dEj089ixo6cuc+1PkP+nqYl5s0fy0bLUoZyfSDU61trvXAfHXhJv559Db+WxpPkKKUEYHJ/Holjt/K4vizIoJSkxofqYZsXRvSy9oSLC9GL8hJ0XSw9uFKxkJd2JYTy+G8TnaPHc0PqXH8Q5owTuR3IDLknMM29WVUxHG25cQ2Ws2d65UjlWGc1ASTqwvikt6nXrWwrlcyqhjO3bzOsL80An9ZBZOi/uSLfl8wMiKF4eFpjI5IZnXGgBpLOFSlnUcJQapyskqC2D+iadKVm2vsU30zhCwbQEhICH5+ftZt4UL7D6+XLl3CaDTStq1tTay2bdtSUGDf01+VAwcOcPz4cR5++GGb/SNHjuSrr75i+/btLFq0iF27dnHHHXdgNDZMGRS3GiyWC6/pTSkoKKBNG9s1TrlcTmBgoE0be31UHeNaFi5caPMPFxJS/Yk+c87savt+GfJP0sfPIzEyx5lLZO2AFU61u5ZJex/hREk7Tt4zj9JKFR9kD+VQcSc+7r3WriCVXpDxyVHHT32zDk92yVVblXbKInJPt8OElHxDAF9cMrvE7d1oayqk6Ax6QcbBvFCrLH6c+gxjWx2yVhUeEpbBusw+1lpGd7VKol2rYr5IHYCXVEuk4iIjAo4jRaCHRz4pmg5k61pzVh+AXGKiwODHBaMP+dpAduTEsCU7DiMSmx/i+nKtd2h4eBq9Q/MdXK+0xpigJ2J3oG+EWHdLloYrT8YtjQB5OW/EbzQbvGXtqnltWgLRfxnOy9KGcE7nz1C/VIfiffdH78NL5lzV8y/6fUFuWSB+KsfZfR+kDmPi3n/w5JH76PqDa9mEzvCfvO61N2oCTILULRvAqVOnKC4utm5z5zoXnuAqn3/+OfHx8fTrZ1sod9KkSdx9993Ex8czduxYNm3axMGDB9m5c2eDzOOGyRKaO3euzT/cqVPOxRRYqKshYo9eP5sLFg78ZQ53754FgIdMT0fPIgCmxuxj/4h3OJhq/0n8g9RhPBe3lS4drxpni1NGsD4zgZUnb+KD1GGopIY6B6rFq08R1vEcnlItYyOTuCvgKNtyYsnVB9m0W5E+iP8W1u9LPygsy1r3wqLd0kFWbBM0pjEpaKsoIkJ5AR9ZJXMi/4tEYl7uytK3JkJ5gT6e2RiR0Nsjl0GeJ4lTn2Fa290UGr3ZWhjPcL/jaAQFXlItekFOuRvFx7KveV9qom9o7TVnnI13qS9DwjIoMnrV3rCFYlGnnhGz263f/+uRx2J38nr8D4TJa/bqWjIYB293XEHZony7ZfAHSCWCw3YqqZ4gZTnZZUEIgoQeP71qt9hrXdEbZW7rq7ni6+trs6lUKrvtgoKCkMlknD9vmzJ+/vx5goODaxyjvLycdevWMX369FrnExERQVBQEJmZmc5fhAu41WCxXHhNb0pwcHC1qGSDwUBhYaFNG3t9VB3jWlQqVbV/vLry4IGH6nwuwJE736LrD/MJ9iqhlarcWleonbKYl46NQ/uXWmze9OpKmfOTx7DkgHlJravfOWYdnszajH4E/iVjfdHgQ3vFFbsBrs5iefoOkZn7bC83C87l6cw3ZssPzoyY3XzZzxyQtSGrV53HS9F2YH1mAh3kRbSSltM79GphxTeP30V31RkqTCrUEj16QY6XRMfAkBw+O30LZ/UB/K88hiRNJ9QSPfvLo0jTteNwRRh6QU6IopB7g/bjL63AU6pFLTEglZhI0brPw2KJ3ck/bb9PZ7KImor7o/exJTuuRnVmERELjjyHjph+cCpLUoeT+Msc63JRa3mp9Xg/v1xmHrqfsf97jBXpg3g1eSxPHrmP5emDOVrWCZVUz4lT7dBp5ZSWq22KvXb+bgG3/Pq800kN13L87gWEfvZenc5tSNy5JOQsSqWShIQEtm/fbt1nMpnYvn07iYmJNZwJ69evR6vVMmXKlBrbAZw+fZrLly/Trl3DxOi51WAJDw8nODjY5k0pKSlh//791jclMTGRoqIiDh26Gkm/Y8cOTCYT/fv3t7b5/fff0euvFkHbtm0bMTExDRqBDOallrpEYF9Le98Srmg96ex1Hn9FBf6KCsJVF2irKLGmAdtj1cGBTO1tjvcp1HlhEGT8XhJDnjaIfWWRzInbQp4uiEKtp8M+aqJqgK2symUOD08jRm12D9uT73e2ErA9AmVlXDT4UC4oqy2XdPM4xSFNKKVGD3L1rQmUlXFCa45HuVThRXvFFbxlGoZ6pTI8PI0KkxJ/WQWvdNtkzRaqEFQUmTypMKnQCHIqTCqC5cX1MrIs7MsLs/6/U0f7cSfOZBE1JSMjUigw+Df1NGrF8u/1QeownjlyL3BVqVmkedFKXQ5Aa2Ups7tsI9b/Avnl5t/ms3p/FhwfzUvHxuEt0+Ah1TGx7Z9EqwrwlOpQSQ3sL46goNKHg5dCSQjLp19oHtmTbL0rJ++Zx6kLAbT3tl9s00LUt284PqisvfJ3/I/zavQYuRsT9c8Uqks989mzZ7NixQq+/PJLUlNTefTRRykvL2faNPMD9QMPPGB3Senzzz9n7NixtGrVymZ/WVkZzz//PPv27SM3N5ft27czZswYoqKiGDFiRB1mWDsuGyxlZWUcPXqUo0ePAuZA26NHj5Kfn49EIuHpp5/mzTff5McffyQ5OZkHHniA9u3bM3bsWAC6dOnCyJEjmTFjBgcOHOCPP/5g1qxZTJo0ifbtzU+qkydPRqlUMn36dE6cOME333zDBx98wOzZ1WNQ6suzRyfy5JH7GLz9OZ5PmkCEx0XW9P+cWYcnA7DwxKgaz3dUuCva9yK/DX2fuV034yvXEKIupNDgTYVJSbHBo1oqZeIvc4j65k3u7JFsNWhUUgNRHhcIVJRjQmJ1xwbJS/hlyD/rdL1tZWXW+I75Z+8A4NRfN7P6GCU10UFeRKzqnHVtvGpMSIXJ7ML0kVVyTudPgcGPwyWhDPZPY3C7TPJ0QXRQFFqXld7uvsE6z9TK9sgkZlUCAIXEQJHJbMiVC0qCZcV2M6AO5oVWi9ex3CCT8zvaBKtaYm+8HKgTNxXXloSoDUtwtitaJY2Nj7SSjVk9kEpMbMntwpLU4dX0hFoKdSko2JhsuOlTFhwfzTs9vgfMMSubb/kQgDPaAI6VdOCizpvfr3RmkO9JFBIj2bo2nNH608szDw+ZntTzwVwo9ib5XHv254bZHSdn8kucLbMvY9Djp1dJ/GUOBp2c0BX2dbqkitqDP5PvXoCPUstzR2tf8rieuffee1m8eDHz5s2jZ8+eHD16lC1btljjQ/Pz8zl3zvahLD09nf/97392l4NkMhnHjh3j7rvvpnPnzkyfPp2EhAR2797tcGmqvkgEQXC8yGiHnTt3cuut1RUQH3zwQVatWoUgCMyfP5/PPvuMoqIibr75ZpYtW0bnzp2tbQsLC5k1axY//fQTUqmU8ePH8+GHH+LtfbVC8bFjx3j88cc5ePAgQUFBPPHEE8yZ43z6cUlJCX5+fhQXF1dbHrp1x7N08r6Cn6KSUr0aL7kWX7mGSqOCYFUxc+K2MP3gVLp7n6a1vITJ0QeIXPcWWZOcX1995si91qKGCZtftro6V51MpJW8jIsGX0qNHrSWl7DhYm+yrrTiyJ3mNvOTx6CW6rmg86GVohw/eQV7iyKt6+wz/nyAFX2+qnUO0QuXkDHX1sjbmNWDIqMnpSYPlvxyJ607X3I6LbquLEkdjqdUZ1PR2hEbs3rgL6tAiZEdZXF8k92bl+M24y+tqJbaamFLdpzDY1XZlxeGRlDQSlrJRaMXv5R0I620Lb39T3GXTxJGJMgQOGP0q7HkQE0czQ9ptBiVurI6YwAnKjpYbzbNhT25ERzVdGKQZyaby7oRpz5T7d/hg9RhPNXl1yaaoUhVXjo2rtaaUivSB1lLiiw4Pppw1UX+KIlm9+kIBEFCZbmSTsGF5Oa2sbtEDuYik2M7JPFc3NZa5xT26WJyHzV7S0I/f5cOIZc5kxtE3iPP13puTfcMd2Dp/9PDffHwrl+MXWWZgUd7H2ywuTZXXDZYrhdq+vB1+3Eed4SmUm5QcaLIHBMzpn0SF3S+KKRG3ojfaP0yrs3o59RT3vNJE8gobU2wR6lNTZ+aWJY2hG/O9KGDV3G1oL+VJ28iR9uaizoffOQa3uuxnleTx/JG/Ebn3oAaxjyn9ye1NJjvBi7nySP3WdO83cX6zAQb8SxLRpAzRoVlCcCIlGB5EYcrwzhYFMa9bQ46FOR67NAUliWsqbXvn7Lj8ZdWWPU9xv7vMaa2+4OxkUkczQ9BI8jQCAqHWRJQe32V5PyOxHc6zYn8DnTtdKbWOTUlC6rUkGoMFqWMZE6c43INy9KGkK9thREpvTzziFUWuBxTIdJ49N/6Yq0py47qjUWse6vaEpCFgb/MYc/tixj1+5NWr82ilJFoTQqnPq/h/34bqUxw6QETGs9g+fhQf7cYLLMS9rc4g+WGL37Y7esl5P/jNevrxw5NASLQmuTIpUbyzgQxvvthig2e1ifOxSkjWJs0hLe7Q6qm9oDKsf97jI03X6238uzRibzf89tan0A+ThtCa58yvOxUb87RtuZ0ZQCRnhfxk5vF0uprrIA5G2B+8hguVpq9WclX3B8wajEsEja/zPKuawhT6Ck1KWo5y4zFONyWE4tCYiRUeYmNZT3I9ndcn6Sfj9kAqfo0Zw+1RG9TkfflkJ+tWT3OekVqKwYX3+m0U/00JM4WrWtMYwXgit6LV5PHcofPMcoFJV4SHReMPnSSX6FEUJGv7U5XzzN/1Y7SUGDwqfNY1xrNItXZkxtRa1mOmogNsC/pXhVHnkpHxgrAntsX8WLSeK5ooqz7ajJ0ryXn7y853Vbk+uKGN1iO3zebiK/fRiY3cnd0MoU6f4Z0zKRA44tSaiD3geoqjFKJifHxR1iSOpx2Cr2dXm3ZePMym9fef2kVODJWnj06kQ0pPfH0EritbTpHiztWa/NG/Ebu2TMTT5kWGSb6bZnrtqUbhcRIlO8lAO7v6JrwnCuYl8HeYl9eGEVG14KEy00qOsmvMCTqTyZF2R7blhOLRlBYfwzVUvO/0Xl9zZL911aQdSYFeVtOLGqJnmRNiEtp5KcMfhTmRtoYSI1Fa1lp7Y2aAMsDwcG8UFpJKpBKBNpQikJi4qLel9Maf4b4pOL/l5Chbz0EDSdEHaLzdws4ec88t8z9WvblhVnjm65XdpXHsi91OP6yCh7q/AcH80IpMPo6vRzaTlVzMGx9eKfH94z932MN1n9TYkKCifoldtT3/OuVG0aHpSb+3u0AD8XtxSRIOFvuh0JiDsRKK2xrt/3sLtuI9TiHv6zC2tYVrhiq35wtwbkfpA7j/Z7f4utTycyY3UgR2HDTp3b7+W7gcmZ32cZjsTvdGmeypzCCz/uuAqpLnDcEA0Jz8ZI6JzhlYWxkksPlALVET7CsxJqqawnctRgudcFRjZ/h4WmUm1RIJYJDdVt7eEm1ThkrDaFG64wh1pT0Dc1D85dOjqdUzymDP0VGT272z6SVrNxa+VcqqUsuxFVO3jPPqonkbq5HY2VfXhjPHp0ImJecOyoLaa+4gr+swqryW7XWWE1syOpFO2XDGSwAx053qL3RdUhTVmu+3rnhPSxg9la8efwuaxCshZpS2fxlFXV2KVeNCbG4pi2Gz5L/3c5TXeCOTilc0vvwevwPdRqjPljWhRsTd3sa+obmsS8vjG05sXhKzctbg73SajnLMTXN75S+FV1VpykXFOzJjeDXsq6M9z1cY4yKvf6qPpXvyInBR6rBJKhviKd1V6m6FJGW2QeNoOCJ2B1Wo9BdRpclkL2huJ7+7QaE5rL+ykQ2Z3cjWH51eXN5+mDayEvoG5pH31r6WJ0xABkmAmVaPKVaPkobyonyDk7H7blCTaJz1zN10VGx10dLpMVctY+sunvZUQVkqHvF1dj5S+32U2FSErnuLRS+OgACFOWUGRsm9au54q6ifFWL4g0PT7MWvqvpJrcnN8Ia/GthR06MUx6O1vISWssq8ZLoGRiWTaJXBmoXn/6vHWdoeDpGJATKKvCR1N0zdCMwKepPnojdAbguXNbUuGqsrDqZyItJ491aIXrlyZtYnj7YqQKOAfIKdIKMM/qrelYzY3Y5LWlwf/Q+8nRBZOvaMCNmN1cMXg1irABk3vsKcRtfa5C+Ra5PWoTBsuD4aFrLqxdDdBVzwO5VLO7VqqS9Xr2aM8APOfG83/dbMu99BYBig2edlpuuZ3yklfWuTVRXpBITpSZbd/fQ8HT8pZpaK0mPjUyiwiTn8l8aL8PD01wuXqgXZNZKzRYGhOaiwESxyb7hajFy3CGA1xCszhggqui6yNTOe3mnx/c1Boe7ykOd/2BmzC7aOPEbV2z04KLBlxBF3Svqzu262RrP1dCB2zqdjM7fLWDcH4826DiNiUmQuGVribQIgwUgXWNfKjh2nq1HxCIU9+bxu6q1XZawhheTxltfv9/z21qF5cC89HRizOuMjUyy7uusPkeY+pJTc79RGBSWZfWGNCY7c6MpNantGq0FBh8q7GQwXRvT0js0v1rQriv4SDXWuI2qdO10hiy9bTFQy7KI+i/PS4zigt3PY1Nzf/Q+Lhp96/W+iLgPtURfqxfzvR7rmRGz26oSXRV3en3cRebEVzl5zzz6B+RU85Ber5jcIMtvajm3bhtu+KtelzWUdooiu0tCAGkLbD0ic7turrE/P3mljZFSW/tZhyfTI9A21mFRykgKjd6UGdU1nitSPyyy+j8X98RLouOiwZedudF8kDrM2iZF24FyQVntXI3gOA3b0u+1WD4X9tzYMgT2V0RV2w8Qr7L9fJzUt+FofggyBDZnd6NrpzO80m2Tw/k0JRYF3RuR6PU1SL43Q0ZFHHcqnR3MN01LoO20A9O4Z89MfGSOKys3NXPitvDPU81b/Vek4bnhDZZLBh+2F3YhVOmaN8PRDWJu182UOmloLDg+mhjPAqI9zlsLKi5OGYGnVMc5nT8Vpuo3ymux3ASd8eTUhdhXl9be6DpmZ2407/VYT5HJE39ZOUVGTwLlZSxJHc7O3GiiVQV2PQR18RrM7bqZfXlhjI04Vu2Yp8TALxe6AOa03qpU1YA5nNeJHy/1omenU/QNzXP6BiTiXm7f+TSt/crqfP6OnBiXssoaG39ZhVVqQC418t3A5RQZvfgobWgTz8wxJ8+1Ieb7BU09jXpjEqRu2VoiN/xVp5e3pZvPWZsn5gXHR9erz0B5eY3HB/5iLiHgKdVRbPDES6qlj28uAM/FbeWJ2B2812M9RkHKS8fG1diXxYPTUPEuBm+BmNduLKPFspxzSn+1WFeWrg0+Ug1eUi0hisvEq0+hxGhddrFUmp2fPKbGmJHDeZ1Q1lB6bEBoLm9338CMPx+w7rNUc848HMLGrB6UmtQOb2b5hgCmtXVffINI3fhlyD/Zc/uiOp27IyeGIpMnUolgLjdQS4yUPRq66GOB3g+fv3RuLGU+cjVBNcoPOBPU25BkT3qZ9PENo6vTmBiRuGVridzwac03+WUiV6rI0bZmSepwZAgEyQ0sSR1utyqxM/jJKhwei93wOg/GJPNB6jC8ZXqei9vKypM3WbMgqqKQGPGUOadP4ow3pi7o2+rJnHq1RlN93pe64GzpA1ewFEA8qw9A8VfRwhPlHeiguEKY/DL5hgBayWyfnof4pQJY08y35cTa9bIoJCan1GzvDryadSFDoNCkRmKS4CXV0lpWjsJBllEn+ZUGzZRZefIm8rRBTZJO31LI1wdy3uBXr2KiDV300Z4IYm21pZypBSYi0pDc8B6WM7oAHur8B36ySi7ofDEiQS/ICZbXXfTIUYR/xNdvkzZuPpf03mRUtrFGcksdPJG/0m0TFU6mNq9Jq00hwZYXk8ZbPT01orX9CLRXXLFWil11MtGlMeuCfxXjr7ZsE2crDY+OSGZxygh8ZJUEy4tZkT6Im3wzGBd5hN6h+RQZPfGR6igXlJSbVFY126oBi8PD0+xmNDkyNK4lWnGZHTkxnMjvgE6QUmJSE903D4XESLkg56jWfkmEhk7rvWjwoY9X3eXYRWpnaue9LknJi7QsxCWhunPDX/WzXcxPDU91+ZWLOh9md9mGRpBztooOgbPUtr77jx6/A1BiUFNuUHGkzOz2n9p5r8NznH3SNeZ6196oCmP9D/FxzNdszOpRYzuJyda1+MrhMVYPS03zdhdVYzTStO1ZlDLS+npxygjA7IW5e/csp2OHwLz09lDnPxgQmsuMmN3oBZn12Dl9AD07naLU5IGvVEMraQXp2naEyq+QnH+1TIIlo+lE/lXFzdiQs9XGujYI93BeJ9QSAR+pBplEQCkxcaAikkc6/o6/VIOPRE9XZUG1fixjH8wLtRnTncyJ20K5SdUoxqjI9YklGFekYTDijmWhlskNb7BUxSJHPyduS42lyh2l9z0Ru4NFKSOtMvsA9+yZCZgj7cuMal46No7zlb6UG5WEqK/YnL8sbUid55713GxC//UeYZ8sJnTFu7W2HxCaS+/QfJtUansoL8lsXmdOfBXA5hobC5MgpcyoZlnaELZkx9FWUWydxxMdf3WY6eUM0VUMhIC/YpDGRR5haHg6vUPzmRO3hfNGbzSCrNq5tVVdzta14cWk8UQuXgJAgdGXbeVR7KmIJlsfyCmDLyuPDMRToqXCpKBCkNtdVrLs6xua16CVnidF/Wk1Rq8NABZpuezIiWF5+mAyNeaSJaJRK9LcaFEGS02sz0ywGio1iTrpTXKbuBODScqLSePxkOmt1ZTlUiM+cm01USVXiufZQ2KUIKhM9OqSW69+qnLyVftCd3pB1uCBf9fSWl5CkLyM1vJSTEjxkmpJ9MrAR1ZJmrY9/5d/c537VksMVh0HR2vxQ8PTa1TLdaSKW2Ly4LstN5H13GxWpA9ib3kUlww+nNf78ltpF05oO4AgIVvXhkOaMHZXdK7zdbib5l53SKRxWJE+iCxdG4LlRXhKdWzM6lGvZXMRx4hLQnWnZV61HSwS+lU1OuwRKC+zydiJ9L6EXpDhK7+qYbDhpk+t3hx30ibiMnkPv1CtOnRD8Hb3DQ0e+HctgbIy0iuCkUlMnNEHkFLZgZERKfxeGsvR0k48GGL2CmzO7uayiFTf0DxGRqS4PKcdOTHWv4eEZQBXDReLQZdR2RZZlDmIN0x5EW+Zhlu9Uxjmc4IHAvYSrz5F7gMvEq8+RU91Hn09GieGxBKLJCJSGzNidjMjZjdjI5Nor7xCqcmDi0bfpp6W27BkATYHxOKHdeeGzxJyhXN6fy7pvVmUMtJu0Nz0g1P5vO9O4ja+xuwu/JUJ5IWPTINekDE/eQxaBzLr9eWZI/fip7IfqNnQ3LrjWX4b+n6DjzMyIoVT6YMoMnraeLk8pTre67ueDVm92JjVg7GRxxtNrt6egNyQsAx250bSWm4+9n7Pb5FKJrA+M4FWMoGBnhl2a8y4uwDktRzMCxU9JiL1pmoF97pkDT6fNIEgRSlt5cWNEgfnDGWGq1mWN297gU4+V1g7YEWTzEVAgqmeacmCmNYsYlnCmZ88hjeP31VNPM7iNZkQeYSP0oYilUBbRTEVJhV6QdagqaJFek+23VpdL6Xzm0s4+crsBht3S3YcHbwaby372uW4jVk9SC4ZCGCTJlqflNGqfdcU47MzNxpPBw8yg8KyWJY2hEe/eZPMe1/hFp80WstKUWKyK8HviLpU+z2c1wlPqYFcvT8yiYnWsnJy9YGMjbQ1VgJldRc+ExEBXDJWFqWMxE9WiZ/MB4XESCdFYQPOzDXW9P/c+vf/htceAyjSPGnxBsusw5P5uPdam321GR7hqgvIJALHKkLorC7AU6qtMYjXHXzR7wu7++trrDx7dCLv9/zW4fGRESmMjKjXEPVibGQSebqal+nqiq+05iBejUlRo5CWVCJYi1leNniTrmlPqPIS5SYlA52cw4DQXI7mh9io3VqM5eT8jugFabVU53JBgdEkoZXsqoChvWtpLk+3NbEusw+Tov5kc3Y3UdX3OseeV/qD1GFoBDnx6tPk61uJWi7gliWdlrok1DKvugoqqcHlc6Z23kupUU2QopTzej+kEqH2k+pB5zeX1Ot8R2mKYZ8sxlOmq1ffjUFbRZHTGiyukKVrYw203pzdjReTxjPr8GQ2ZvVgW04semQUGPwdnl/1x1ct1TPYK40wxSV8Xcxm2ld51SLcnN3N6tnTC1KKrqkwDWbBwQKjL7n6IMBc/dWnFuOruWJJHR8VcdxhULPI9ctTXX5Fb5JjRMI5nX+d+ph2YJp7J9XEiNWa606LMVieOXIvYE4trhpY6/1Xxo/luD3slTY/rQvkkt6HjsrLFBvNNxVHAbsfpA6rVyXU+npRqq5JVyX38eesmU3NmWMVnbg/el+tAdGusq0wDi+plo/ShjIq4jjv9PieSqMS2V/icF4Snd2qtlVZnTGA1RkDUEv0yBDoG5pnlfuvGrBbE1UNH88qHp18Q4C1r6rIEAiWleAjrUSGgEJi4qLRx6mxmjNDwjJYljaEn7Ljm3oqIrXw2KEpLE8fzJNH7mPhiVGsSB9k1U26lle6beKMPhAfmYYPUoe5LO/gyLvsiMHbn3Opvcj1ww2/JPROykTa+hkI+0tzTC/IearLr9bjloyfpb2+4bFDU1iWsKZaHxtu+rTaPoXEyNvdN7A8fTCX9D68efwuPGX2rV4/WTlKiZEV6YNqTJluTMKWLSb3seb/xV6b0Y92SrNonkaQs+pkIhUmFY/F7mR5+mCOlYXY/Terid25kcgQeKitulrm0Od9V7Hy5E2EKczFMnM1QWzLibV+TiyZQhbsVSseFXGcVScTCVPaquLuyY1gYFjNGUJV+99S1J2e3vnVlpf0goxyQWkz9xvlFl/f1P+qNETZBxEzyxLW8GLSeD7s9TVgjl/xlDr21lY1yhenjOD5pAkU6T2sdYzcSbmuYcqYuAsjUoz19BXU9/zrlRv+qi9ozU/IFiPF8n/L00DVwNpKoznrwxnRNEucy8yYXQTKy3ml2yaHAWpKiZHJ0QeocEMGUcQS92TrXA/GCphrqljqMM2J28LUznutN7WZMbus/2bO8lHaUGvmj1pa3XsBZo/U0PB0wJwBpJAY0Qsyl7RpOiiuYBKk9Pr5ZXbkxLA+MwEjEpeq4S5PWG13zT9XH8SZOig1tzRa6o96Y1G19tCcuC1266XZw4iEUoO6WpmLJ4/c55Z5/XnH283ayyIuCdWdG/4bvaTXChuPioXn4rby2KEpNvu+6PcFC46PZm1a9RoyFiwZRFWxVFR2hOUGaW8erpI9+9l69+EscXObfxVnV93F0coCkjUhaAQFsio1nl5MGl+trcVo8ZVorf+Gzt4ELUs5QztkIJWYaCUro9TkQbHB02779ZkJTl9DmOISPdW1F2C8FncvqTVnNmd3I08b1NTTELHDnLgtdFRdsXpG79r9BODeivRyqYlJex9xW38izYMb3mCpCS959QyQdZkJZEx41eE5r8f/gFqq54PUYSw8McqpcRzFkDQmcS+ZjY/YV5cStci5IN6UhfZVcK93YlXnSNO2s9FFsTwt7syNriZKd8HojVqip8joxSu/jefV5LEczQ9x2L8ldiVXH0SCVy4yTOTqg/i9NIb+Xpl2z9ELMpviizWhlhhQOCioWROrc/tVM9JvVHSCrJosQUNy7UOMSM1kVbQGzFmKbdWlTN43g0Kdl9v6337rEtYlfua2/tyJCalbtpbIDX/Vy086/iEp0nsw488HbPaljH2t1j4tKcynNIH1mltjYqkmYPQQEKQQs6D5e08agpERKXhKtQ5jJZIqQzEhtWZW7ciJwV9WgUJi5Ovz/QiLPE+B1peDlWEOx7AE6n55KpESkweDwrJ4qPMfjPQ7RrmDZcHJ0Qe4bPS2FlLcnRsJ2PeK9A7Nr1OtoYcj/qCL11l25kYz7cC0G7pWTG01tNzNK902NUgm242KxTPqJ6+k0qjAIEiRS428mjy2aSfWCBgFiVu2lsgNb7D4yyocHtOZ5PyeF8Wsw5Nd7je1op3LwZ5V6fKf1+p8bl1Ift/sLcl4aTZZz88mfd6N6T1xhosGx5LjT3X5lVERxwlRXAbMWTsDQnMZEpbBkUNR7LptMSv6fFWjPsvk6AMMCsti122LuWK4+tQ4JCyjxhtp1QBei3s87pqln/oYGWGKi/jLKhgSlkFX77P4yjSsOpnIguOj69xnS2Xi3n9Y/7Z4Wu0FYIvUjBSBIFUZcomJFX2+Iqmoo0txXiItixveYJkUeTUQbH7yGJ48ch+LUkYC0N83Gx9PTTXhOGdYnrC62r6Y15z3WqT+7TWXx2wMJu+b0dRTaHCMTshaDw9PA7BRoe3S86qSrLPZJ47imxw9jR/+y3NjGdcyDzBL7zsKFHaGkREp1pvqc3FbOVAWQaTyQrUinY50e0SuEuN93lqrqbYYNhHHvNJtExc0PlY9rIJyH3ZfubH1eMSg27ojEQShYVXPmoiSkhL8/PwoLi7G1/fqE/X85DHW2j+7LkWzZfAHjTqv2PlLSXu95Xo36svy9MH8ryjKRmq7sXg+aQLv9VjfoGMczutUTdkWzEtEekGOVGKqllrtTuYnjyFKfV70Fog0CfaUxxsLR/cMd/f/yK4JKL1dy268Fl2Zns8Gr2+wuTZXXPKwLFy4kL59++Lj40ObNm0YO3Ys6enpNm00Gg2PP/44rVq1wtvbm/Hjx3P+/HmbNvn5+dx55514enrSpk0bnn/+eQwGW8XZnTt30rt3b1QqFVFRUaxatapuV3gNaqkeP1kFc7turrOxEvmu68qzMa8tJXbeUipDnVeWdVcK843CM0fupZWsjCMFHenz35fq3Z+zrufwD99n+sGpRKovNLi72p6xAmaxuBKTukGNFTAH/4qIiDQcRiRu2VoiLhksu3bt4vHHH2ffvn1s27YNvV7P7bffTnn51ZomzzzzDD/99BPr169n165dnD17lnHjxlmPG41G7rzzTnQ6HXv27OHLL79k1apVzJs3z9omJyeHO++8k1tvvZWjR4/y9NNP8/DDD7N1a/3r9cztupkfLvSsVx+Wj0r020sI+3RxteMxry+1kdOPWrSE9NeeIW3BM+Q9NMfpcUxqx86v0C8WWf8e+7/HgOZVQr0hWNrrGwoMfpwY8zodfYrq3M/GrB7c9ttsp3Uj2sVeoKv3GTooCumguFLncevDwLDsGuOx3MXb3TeI3hURu2zI6tXgWWYNXeZE5PrGJYNly5YtTJ06la5du9KjRw9WrVpFfn4+hw4dAqC4uJjPP/+cJUuWMHToUBISEvjiiy/Ys2cP+/aZfwR/+eUXUlJSWLNmDT179uSOO+7gjTfe4JNPPkGnM3sfli9fTnh4OO+//z5dunRh1qxZ3HPPPSxdWr/MlueTJgCwadBH9erHIugo1UtQFFZ/Cw2ego2cfuYc16X1I99bgqqt4xtU3rSrhs/Gm5cB9uNqbjSyK83pkH3885m095E6lTwoNXnweKffnG6f2CaHMOUlvCQ6/KUV7MsLY1tOrDWTx13UJln+U1Evt44nIuIK4yKPEKJu2ArMFuXcGxmT4I44lqa+iqahXkG3xcXFAAQGmtN7Dx06hF6vZ9iwq6mYsbGxdOrUib17zZVj9+7dS3x8PG3btrW2GTFiBCUlJZw4ccLapmofljaWPuyh1WopKSmx2Swk/mK+ubsr/sBijKTPf4aMl6sbI1nP16/2D0DH3mdtAi5FzCzt9Q0r0geRUxlEmUHFaZ3rqeU+0kqMgpQNWY4NgJUnbyL0i0UM/+0ZOqkK8ZRoKReUKCQGCgx+GAUpufrWbM7uZu2nvsJsaZXtajx+p1/jpuqKiFzL3K6bXcpUu3v3rAaczfWJSZC6ZWuJ1PmqTSYTTz/9NDfddBPdupkFrwoKClAqlfj7+9u0bdu2LQUFBdY2VY0Vy3HLsZralJSUUFlZaXc+CxcuxM/Pz7qFhFwV9gr1uULE12/X9VKbhF23LWZLZpemnkazZIBHDp/3XUW09wVkEhNLUodbZb1fOmZefnz26ESH5393qY9VudYRD3X+g7xpc+joVWRVKB4dkUyBwZ8KkwoTUoqMnnhKtZSbVEw7MA1VPTJ4AIb7HXco/X/bb7MpManZlhPrtqW/m7e9wLQD0+yq/IqIOOK8wc+p8iUfpQ1lSFB6Na0rEZG6UmeD5fHHH+f48eOsW7fOnfOpM3PnzqW4uNi6nTp1ynospyQQP9/yGs5unuivqImqQ4BvU7MtJ7ZB+4/vZNYmCVBUcOBKGHpBxokis3fCR6ZhXWYfyo0qFqWM5NmjE3np2DiWpA5nfWYCy9KGMDQgDYXEQHt57fEo10r/T4g6xOToA4yKOM4TsTvI0AbTSlbG5NZ7aSUrY0NWL7r+MN9pFWQLS1KHs624G3tKo6od6/zdAi6XezI2Mon95VFojdVrllY1Oh488JBTY/5v+Lt80e8Lm5owIldxV22bG405cVustdRqQoaJEEUhUZ7mYHVnjJyWgAmJW7a68MknnxAWFoZaraZ///4cOOBYnmHVqlVIJBKbTa1W27QRBIF58+bRrl07PDw8GDZsGBkZDZcYUCeDZdasWWzatInffvuNjh07WvcHBwej0+koKiqyaX/+/HmCg4Otba7NGrK8rq2Nr68vHh4eduekUqnw9fW12Sy09Syj7Firulxqk9Il9jReXZomyLM+VF3KmnZgWoONM6/bT6hleubEbaGLn9k7N7frZs7qA/h7qz0M9MxAITHSSXWZYoMnh8rDeCx2J4fLQpkU9SfZujZszOph02e/LXPtjuVIKK6D4gqjIo4zPDyNVrIyxkUe4cSY12v1tuzMjbZ6U15MGk9yaUc+7PU1N/ueBCB0+XuAWShucFgmR+96EzDrVsT7VK8jpJCaheb6b32RpAvtaxxbxDmu53iKV5PHNppq7LQD06zxgVV5LHYnE6IOMSduCxUmpVNGTkugqZRuv/nmG2bPns38+fM5fPgwPXr0YMSIEVy4cMHhOb6+vpw7d8665eXl2Rx/9913+fDDD1m+fDn79+/Hy8uLESNGoNFoXJ6fM7hksAiCwKxZs/jPf/7Djh07CA8PtzmekJCAQqFg+/bt1n3p6enk5+eTmGhe90xMTCQ5OdnmTdq2bRu+vr7ExcVZ21Ttw9LG0oer/DjoY3QBxjqlIzclJw93Imn0G009jXrxRb8vWJ4+uMH6t2QVVNVuSC03e1v0gpzunqeYGbOL1+N/INbjLABak9lDsbkwnmWnbuXBAw/R7cd5hP7rPQ6MXGh3nKp1h6oyKuK49W9LsUSA2V22OTR+ALYUd2fT5R4sShnJOz2+t3pyJkX9aW6gMF+Xr0zDij5f2Zxrryq4FIFnj05kQsgRq3HT0mhJxR1r4434jbwRv9Fm32OHpvDMkXvrFKheE1/0+6LW+MA5cVvcOqaI6yxZsoQZM2Ywbdo04uLiWL58OZ6enqxcudLhORKJhODgYOtWNVRDEAT++c9/8sorrzBmzBi6d+/OV199xdmzZ9m4cWODXINLBsvjjz/OmjVrWLt2LT4+PhQUFFBQUGCNK/Hz82P69OnMnj2b3377jUOHDjFt2jQSExMZMMCs7Hn77bcTFxfH/fffT1JSElu3buWVV17h8ccfR6Uy11mZOXMm2dnZvPDCC6SlpbFs2TK+/fZbnnmm7oJreTOfJ+uF+gfDNiY3iiRGB0XDZRbojNXfpFB1Ibn61hyuDLNRpJ3a2Ry0neibxUvHxtHZ6wKh3oVkFLWmk38R7pY2sGf8WIrkvdPje9YOWEF7RZHdc0NDLwLwUZ5zui9XDJ4M8km31rlqSpqqpk6M6myTjHu9sCxhDUt7fcOMmN1NPZUWjTuDbq9NNNFq7XuCdTodhw4dsklmkUqlDBs2rMZklrKyMkJDQwkJCWHMmDHWxBgwy48UFBTY9Onn50f//v1r7LM+uGSwfPrppxQXFzNkyBDatWtn3b755htrm6VLl3LXXXcxfvx4brnlFoKDg9mwYYP1uEwmY9OmTchkMhITE5kyZQoPPPAACxYssLYJDw/n559/Ztu2bfTo0YP333+f//u//2PEiBFuuOTrh5wnn23qKbiF0RHJDda3SZCyOGWENch2WdoQjpZ0RC3RO7x5P9T5D97uvoF53X7ifKUvRkFCW3Up0wf+3mDzfDFpPM8enVitgrAjzZOxHY4C8NvQ2sUD12X2QYaJCgeFFRubptJxuWj0rTHzS0SkOWDCDdL8fz1dhYSE2CSbLFxo30N86dIljEaj3WQWS7LLtcTExLBy5Up++OEH1qxZg8lkYuDAgZw+bV6StpznSp/1pXr0Xg04o+KvVqv55JNP+OSTTxy2CQ0NZfPmmutvDBkyhCNHjrgyPZEbjFeTx1Zza1+LQZCSrw20xhuopXqUUiMyicmpMX4c9DEbs3owNjKJdZl96jtlh7ga2NpTnW+dlzO0UZZSblKx6mSi1ZPUEliSOpzZXbax8uRNPNRZFLwTaVmcOnXKJl7TskrhDhITE23CMAYOHEiXLl3417/+xRtvNE2oQstM5hZp1ljUNDspL/PkkftqrKbdVl1qNVYWpYzkoc5/0N8vh3IXvA0Wo8ASP9IctCPy9YEcrwypvSHmef94Jp7TusAWZazA1Xiehzr/0cQzERFxDsENGULCXx6WaxNNHBksQUFByGQyu8kslmSX2lAoFPTq1YvMzEzgapJMffp0FdFgEWl2LEtYw+R9MzitC0RnktdYDO2y1guAn7LjCVVeAqC94kq9liV+HPRxnc91FzKJQE5lEFuy45xuf20WRlPoqzSkl6oufJA6jIUnRrmcZi4i0lA0RbVmpVJJQkKCTTKLyWRi+/btTiezGI1GkpOTadfOnNQQHh5OcHCwTZ8lJSXs37+/zgkytSEaLCLNkrUDVpBeZrs2uuD4aBaljASwel1aq8sAc5yMJfV4QtShRpxp/en6w/xq+zQmBd29T5Orb+1UH/8b/m61fe7WV7G89zUxKerPaqniTclTXX6lo7KQKNX52huLiDQCTaV0O3v2bFasWMGXX35Jamoqjz76KOXl5UybZpaeeOCBB5g792pm44IFC/jll1/Izs7m8OHDTJkyhby8PB5++GHAnEH09NNP8+abb/Ljjz+SnJzMAw88QPv27Rk7dqxb3qtrcSmGRUSkMVmX+JnN61KjGikCbx6/iw4qEyvSB/F536sZD5+fHcToiMaeZf2JDLxs/XvU70/yaMffqDC1JVBexv3R+1ib0c8m26mpcPZH0tm4m8ZCLOYoIgL33nsvFy9eZN68eRQUFNCzZ0+2bNliDZrNz89HKr36Hb9y5QozZsygoKCAgIAAEhIS2LNnj1V+BOCFF16gvLycRx55hKKiIm6++Wa2bNlSTWDOXUgEZyJpr0NKSkrw8/OjuLjYJihJxDG9fn6ZI3e+1dTTsMvajH6UmDyYGbOrxjaBsjJGRqQ43e+6zD5XtU/cQOyG10kbV91jUpW1Gf0wIuVgmVnHKEBeYV3OGbz9OV6J2IRekJOhDeapLr+yIyeGw5VhGJE0mJ7F4pQRTqdEX3uNk/fN4G9Bh6t5tpqLodVU3L17VrNYXhRxjoa+Z1j6H/PLQyi8lPXqS1+u44fbV7a4+5voYRGx0tTGyuqMAdwfvY83j99lTf9dnj4YrUlBW4WpRmPFwllDgEtjutNYAWo1VtZnJjA52nxjt1cRaNdti4HFNvuydG0IVV7iUHmYeyZpB1fqIMW2Oc/0g1ORSkys6PMVwwJTaCUrq9bOlcDnGxHRWBGxR32k9av20RIRY1hEmg0KiYEFx0cTpCi17is2evBUl1+dNiwUEmNDTc8t1CW+ZkbMbg6UR5BdEWT3+M7caOvfy9KGON3vM0fuZX7yGDZnd3Ppfft78H5MgoQQtblshKdUS5HJkw1ZvWzqSAU7EMVrrtiLJRIREWk+iAaLSLNhUtSfdFafI1J5NUDSlSUQjaCgyOjZEFNrFhzOt5/mPCTsarGxx2J3AuasqdoIVhXzevwPJGs6klHZlo/ShrLqZO3R/f6ycjp7mf+Nbt/5NGvPDSBD2xaFxIBGULDqZCI/ZcejluhthNwWpzRv4ccTY15v6imIuIH4H+c19RRqpCmyhG4URINFpMHZlhPrdLrrpKg/bYonQu0332ePTuSjtKE81PkPnojdUed5gnnJpiZu3/l0vfq/7TdzeYhRvz/pUmHI93qsJ/PeVxwetyj9WmgtK+WZI/eyObsbu3Mj7Z5jMQbnxG3hv7lx6AUZSomRUb8/6XCcl46N46LBl7TyYLafj6FMp6LSoCBOfYZSowd/lkfgL6tAY1KiF+ToqtSXKDWq2Zzdrdb3WESkPpgECVHfvkHoqkVNPRW7iAZL3RENFpEGRy/ICZYXA1frzCw4Ptrp82uT9n+/57f1NlQsWJZsduTE2D3exqPU7n5nGdbGbIy18yixFjx0B+/3/Jb5yWMAmLj3H8gQOHS5EwqJASNSduZGsyU7zmr8PXnkPpvzU8a+xuwu25gcfYDNt3zocJy3u29gcvQBvuy3kufCt1KhU7Dt1qWc0rVCKjFxXueLXjCHxumuKYb1evwPFBj8rru085p45si9TT0FkWtQyg2YTFKkCiM9Nzk28kWuP0SDRaTBKTJ6ohdkbMmO44+SaAZvf46vTvR32P6jNHPBP2dF0xqCqpWXq7Km/+dWw6AuzO1qLknxed9Vde7DEZZMo1Ol/nx5+SbGdzjCGX0ARUZPzuoDSNJ0wl9aAWBVB64PoyOSrZWhpRKBiwZfunudYkLUIasmzrXBuDeaIq23XOtWUTpXYpBE7BPoWYlUakIiBaEZeiJED0vdEQ0WkQZlwfHRBMrKOKMPYGRECr+fiqSowgOZ3MStO+wXd+ygMAdzupKe3Ji8Hv9Ds43HWJfZh9kRv/Jx77U81eVXpnbey46SONacG0ByaQcGhWU1yLiWDK6wv9SGR0UcZ2xkUrXlvWvZnN2tQebTWPx+IYpw1QUbI7Y+S16WGCSASXsfqc/UWixSBNoHlpB930skjW6amjc1IRosdUdMaxZxKztyYqzeiSn7p7Om/082x5+L24ZaokchMTIh6hDrMxPwkVbaGCfjIt1T9NKSJu0MH6UNxSRIydW0Ymmvb2pt76xmSVMQqbho89od3hRncHVZbuGJUczteryBZtM4xAecxV9aQaC83LrPXUte1wonNncWpYxsMJ2gqvT570v8ecfbDo/7qSr5buDyBp+HSOMjelhEqnHPnpkuxZhUZWh4uvVpc03/z6sdby+/wuToA9Yf9QlRh1A2UCryeb0fy9MHO9X2idgdKCQGenjl13vcmO8X1LuPqriyBDUp6k96h9b/GhoDP1lFU0+h3nzcey2FRm+e6vJrU0+lyTlYFEb/rS8y/eDUBh2no09Rjcd1xub9HC6AG4oftkxEg0UEuFohefhvzzCu9WGC5HUPLr22CJ8jLK7zoeHp7MyNZtXJRLdmkDwXt9W6VLE2o1+tNW4ei91JhUlV7zkYje511zr7fl5vPBa7s17xQM2FlqzmW5XvBi5n/4h3OHCuEzHfL2iwqucbb15W4/GMS/b1ipoL4pJQ3RENlmZOY1WZTS3+q55EYQCTow9wrNy+5kd9sSz9bMmOs3GdDwnLwFemQSYxWeMaqoqQ1ZfJ0Qec8uRIJQKt5SXsyInhYF5oncbKnPhqnc5riViMsd25kQ4zs0SuL5LvXkD6+HlNpvSb+rfXmmRcZxENlrojGizNFMuTZ6HBy+1LDPYoLDcLrqWPN4suLU9YXad+nF2CsRdQ6yOtRIrJKuk+PDzNmjVR1yWqqoyKqD1eQoaJiwZfygUlydqOAA51TBobS0r4jcigsCyHmVnNhcRf5jT1FEREWjSiwdJMiVKblUQvab15ptv2Bh+v6Jx7Cmg5U+/HEcPD0xgbmYRaqrPus2RNVBjrVyzMWSr+MpZkCBQavNmXF4ZCYmzSFGsL+mt0TUQaFx+ltt59WFL2Wxp37X6iqafQbBA9LHVHNFiaKZbslt6+efUyApwl75HnCf3svQYfxxnsCcVd1ns1ythPdfmVUpOaM/oAYtRnqTCpKDJ6NosU6x8v1ByDc71jEbVz51KgO/llyD/r3UdreUn9J3Id4q9s/gHWm7IHNso4osFSd0SDpZnjLgXXmrConuY98nyDj1VXEnzyGk1U66HOf5CjbY1RkNJaVo5/M8lmOVUSwMS9/2jqaTQYoyOSWXhiFBpB0dRTcSsWQ2zlyZvwkmqdXja9kbCXMdjcOG0IbOopiNSCRBCEGzJDqqSkBD8/P4qLi/H1dc9yR0tkY1YPxkYmNfU0Gh2zRohZlfZofgg9O51yuY/bdz7tlqdykRuDVScT8ZLqkElMHK/sSD/PLAoMfgTLiykw+NFKXlZrGQqRhqOh7xmW/m/6YRZyL1W9+jKUa/ljzMct7v4melhEaqS5GytLUocD7gnKrUrVtO66GCsAF8sbZxmrviw8MYqbt70gBpXWwNqMfjaVp+vC1M57KTD4UWT0xE9WySl9K4LlxaileqZ23ttkxkqPn8Sstsakvhoslq0l0rwVdkREHPB80gQUEiMKiTm7SWNSsDhlBIUGL97uvqFOfVZV6Z0Rs7vecywt86h3H43B3K6bmdvV/Hfov94DASTeBoQKebNeJnSGRSkjiVGdo8DgTytZWZ1VaCdHH3BL4HXVJd5tObG1li5oDJqbfL1l2dNPUYlKasAkSFmWsKaJZyXSHBA9LNcp7hRYaypWnUysVczNEQqJkY7KQhQSI4tSRtJRWUix0QPPKhlGrjI0PJ0V6YPqfP61ZN57/VWKzfvH86A2kTtlLkgEnk+awMhdT7n1fWlM5sRtocKkYmbMrnpL5rs78PpaY6W278KNnNZelW8T/8W3if9iRZ+v+Lj32hvOWBGDbuuO6GG5TsnTNW81x9p45si9hKm9rGnErrDwxCgC5WYRuNPaADqqrqCW6/GTVaKS6us1L3d4Vq538qaal4byZrzQxDNxD9eLEm1ty6852taNNBORhkQQJPWuIt0cq1A3BqKH5TqlORffc4alvb7hksHbpjptbaw8eRPL0oYwt+tmPKVaHovdyfKE1fjJK9AJci7pvTmjDWi4SYvUG0vGjIgtK0/eVOPxxSkjyKkIuiFKGQANJtsvcmMjGiwiTcYb8RsB58W0pJg4rTOnHlb1zCgkRpQSAxUmJX7ySpanD260FGgR1xCzYOzzUOc/ajy+rygcExK2nTNr1NgLAH7yyH3MOjy5QeYHuDWlvqlk+5sD4pJQ3bnhDZZe75q/GNELlzTxTETssSJ9EF5SrVMxOecNfgTKywEIlJexJHU4Tx65D39ZOTna1sR7nqajspAOikLaK65w645nG3r6Im7mnj0zm3oKzZLvBi7ny34rKdGYDfUfLlc3WD7s9TUf917bIONHffsGB7PrVltLxBbLklB9t5bIDW+wmNQQ/dYSMDX1TETgqkidhRkxu1FL9MgkJhaljLTutxfkqZYYCJSXsTGrBz5SDScrgon2OE+FSYXeJOO0LpCzen/0gpxT+laU6xpHzl/EfXw3cLn17/WZCSxOGWF9PXj7c00xpWaFBLO+z5f9VjbquJkTXyXn7y816pg3KoIbvCuiweIEn376Kd27d8fX1xdfX18SExP573//az2u0Wh4/PHHadWqFd7e3owfP57z58/b9JGfn8+dd96Jp6cnbdq04fnnn8dgMNi02blzJ71790alUhEVFcWqVavqfIFJT84i4+XZZLw8u8591AdL4cLQL99pkvGbG2HqS1ZjZMr+6YA5KNIoSPGRatiSHcdP2fEoJIZq54YqLxKmuMTYyCQmRB1iecJq9IKM5PKOaE1y2iqKCZSVo5AYeCJ2BwdGLmzUa2spLE8fzKvJYxt8nFayMrxlGjZk9WJ1xgD+0el3VqQP4rbfZhO38TUAEja/3KKUYw1GqShGKNJicclg6dixI++88w6HDh3izz//ZOjQoYwZM4YTJ04A8Mwzz/DTTz+xfv16du3axdmzZxk3bpz1fKPRyJ133olOp2PPnj18+eWXrFq1innz5lnb5OTkcOedd3Lrrbdy9OhRnn76aR5++GG2bq1/kGnMa0vr3YertA8oBiDvwRcbfezmyOwu26xS91Xlui8bvfGXVZCta0O5SUWh0dt6zOJ5GRuZVK2i7+wu21ja6xuW9vqGfG0r1FI92dq2jXAlLZeZMbus8UcNya6yWGbG7GJc5BHuj97H5OgDzIjZTTvPYlLGvsYtvz5PpU7BFYMXH6QOa/D51EZjSA2k/u21Bh+jJRCx7q0mG1sABKGeW5PNvmlxyWAZPXo0o0aNIjo6ms6dO/PWW2/h7e3Nvn37KC4u5vPPP2fJkiUMHTqUhIQEvvjiC/bs2cO+feZCfr/88gspKSmsWbOGnj17cscdd/DGG2/wySefoNOZ9TOWL19OeHg477//Pl26dGHWrFncc889LF1aN2Oj6xcfAOYnMX1s49eEOXXJv9HHdAdVl2fczeUqxoiFM7oANIKCjMq25OmCUEiM1mNz4rY41e87Pb4nU9OWdaeuf40aEXg9/ge7+y2G7p3tk7k/+gDf5PTm5/Pd3DJmXXWBgHrrvIg0HipV/eQP6oOodFt36hzDYjQaWbduHeXl5SQmJnLo0CH0ej3Dhl190omNjaVTp07s3bsXgL179xIfH0/btlefgEeMGEFJSYnVS7N3716bPixtLH04QqvVUlJSYrMByCqkhH28mEtn/Mie9DLg3mj3mrjtt9lkTrx+ZK9XZwywpp1ajIQNWb146di4mk5zGXvVp8sMKh7q/AdLe33DnLgtPBG7g7UZ/Vzu+/X4H9h7+yJ3TFOkGfPkkfsY4X2CKNV5urU+R+6l+hWuezFpPFuy45pFKYrm4C2ycNfuJ5p6Cg1C17YFTT0FkTrgssGSnJyMt7c3KpWKmTNn8p///Ie4uDgKCgpQKpX4+/vbtG/bti0FBeYPR0FBgY2xYjluOVZTm5KSEiorKx3Oa+HChfj5+Vm3kJAQAIYOPEburOdsRLB6+datNoyrbL/1+spMkmGqlnY6LvIIfbxyqrV1NhXZWZb2+qbavsYU/HKH7LpI4/Fhr6+pMClQS3Xc2/oA2tL6FZPr6nnGTTOrP091+bWpp2DD80kTGiVmqTHp65/LopSRLE4Z0aDeZHuIWUJ1x2Wl25iYGI4ePUpxcTHfffcdDz74ILt2VX9ibmzmzp3L7NlXA2tLSkoICQlhV15k9bZ/VeEVscWRgTAu8ki1fZY4FDCLgV3v+hq+Uk1TT0HERXaUxaGQGsirDAK9687izdndKDGp8ZVqUEtUaARFA8zy+mZoUBqzu2xr6mm4HT9ZJTJMnNf78Uq3TY06tkmQIKmnwdFSdVhcNliUSiVRUVEAJCQkcPDgQT744APuvfdedDodRUVFNl6W8+fPExwcDEBwcDAHDtjeFC1ZRFXbXJtZdP78eXx9ffHwcFxMTqVSoVJVf8oy5VaPlxCpP61kZYDZM1Fq9GZzdjdGRRxv4lnVHalEzHu/3ghVXeL34s6s6PMVH3gOY3n6JrI0bfjuRC9yJteegltk9EQt1VNuUnFKH0h7xRXWZvRjcvQB1mX2YVLUn41wFc2bS3qfpp5Cg2BvWVqk+VNvHRaTyYRWqyUhIQGFQsH27dutx9LT08nPzycxMRGAxMREkpOTuXDhgrXNtm3b8PX1JS4uztqmah+WNpY+XCXpaVEC2hkcFbezp6i5Mzfa+jQ6MiKFydEHGBVxnC3ZcWzJjmNztnsCIBuTAaG5TT2FejF4+3NNmvnQFOgFGb8cMX/Wdl+JYmbMLt7rsR6TVkbiL3NqPf+sPgCjIKXcpCREUcjR8lCrl7HQ0PIedHr8ZI63e/LIfbyYNJ7bfptd58rnIo6pd4bQX1tLxCUPy9y5c7njjjvo1KkTpaWlrF27lp07d7J161b8/PyYPn06s2fPJjAwEF9fX5544gkSExMZMMBcZfT2228nLi6O+++/n3fffZeCggJeeeUVHn/8cat3ZObMmXz88ce88MILPPTQQ+zYsYNvv/2Wn3/+2f1XL2Kl1KS2u39c5BEWp4ygraIYH2klSokRI2rUkupR9kqJEanEhElwvx7hqpOJTO1cc+C1O9mXF3bdGDGLUkZy7spAjBXNr5bpusw+KCVGLhu83V5Y8qHOf/BRxsuM++NR2qjLrx4wSjib18rqLXFEW0UxxypDuKjz4RbfdN7p8b31mKXG1dqMflw0+Da7uJKGINCrgtDP3iPvka+beio3NGLxw7rj0p3lwoULPPDAA8TExHDbbbdx8OBBtm7dyvDhwwFYunQpd911F+PHj+eWW24hODiYDRuuWugymYxNmzYhk8lITExkypQpPPDAAyxYsMDaJjw8nJ9//plt27bRo0cP3n//ff7v//6PESNGVJuPiPuQ/ZXZby8zJ059Bh9pJSakaAQFMgSkmKoVsjMfM6ETZG4PYm1MYwWuL4/LH5ejOHnPPPIeqt2r0NgEy4t5KWks7x67vUH6fyp6B4dSw1mesJrpB6cCkPfwC0grZawrqDnL7P7ofXT3OEVP73xKTPaXmydHH2gRxgrAb0PfJ++R55t6GiIiDpEIwo3pXCopKcHPz4/i4mJ8fX2bejrNlhXpg2yefKu+Xp+ZQGt5CaUmNXpBjsakQC3Vo5AY8JKYdXN0goyRESmsz0zAR2rO4ioyeYrr/yIAxG18Da1GgVEvJff+uUza+wjdfM5yQe/DiaJ2bL91CcvTB9crpiDsq3fIfeDFOvWzLG0IGkGBSZBe9xXQRepHQ98zLP13+XoOMs/6ZbUZK7Sk3reoxd3fbvhaQiJXCftkcbV917rp9YKM1RkD2JETg7+sHI1JgZdER7lJhUJixChIkSFgvEa4yF9WzsiIFEZGpDRbY2V1hnlp8s3jdzXxTBxjiSO4Hjmc16navpSxr2EoUSIYpESue4sDeaF8mdqf3WcjMAkSEja/zL8yb67fwOXmpbDFR4e7fOpjsTspM6pFY0Wk0WjKas2ffPIJYWFhqNVq+vfvXy0JpiorVqxg0KBBBAQEEBAQwLBhw6q1nzp1KhKJxGYbObLh0sRFg6UFkft4zcXjVp68iWBFMR3kVygyeVr3SyUmWstKMCLBS6pFLdGjkBjxkWrwkmoBmn1K6OqMAXhJtazOGEBbRXG1IozNhaTRbzT1FOpMkYNllbxHnidv2hyyJr1M9qSX0ZUrKSn15M7g45RWmJ80w1bXve6TIDcRtmYh+nIF3X6cV/sJ1zCv2091HltExFWaKuj2m2++Yfbs2cyfP5/Dhw/To0cPRowYYZMEU5WdO3dy33338dtvv7F3715CQkK4/fbbOXPGVrNo5MiRnDt3zrp9/XXDxUCJS0IigDkbyEdaSanJg9ayEi4afQmUlVkDaNUSPSV/BeYq/5LNV0gMFJk8rxsNltUZA9ALMh7q/EdTT6XZEvP9AtLHu37Trwvzk8ew+nh/pFITJpOU7PvEasAiTUdjLQl1/veLblkSOvn3d1yaa//+/enbty8ff/wxYM7wDQkJ4YknnuDFF2uvdWc0GgkICODjjz/mgQceAMwelqKiIjZu3Fjna3EF0cPSAoleX/0pXiExoBEUVpE4hcRAqUmNVGJCKjGhEczxK7K/XoM5UFcvNL/MFEfcH72PclP9figaGksV4qZCU6Ii7NPqS4e1MfPQ/S6f83r8D7QOLMHHu7JFGisLT4xiSarry1gi1zdmD0l9lW7NfV1bjkar1dodU6fTcejQIZuyN1KplGHDhtVa9sZCRUUFer2ewEDbMhg7d+6kTZs2xMTE8Oijj3L58uW6vTFOIBosLZCMCdXjJEZHJFs9JYPCsqyvh4RloDEpUEgM6AWZtSihEQkDw7LdbgDUp/icIz5IHWatz/JE7A639+9OIlpdbrLlqlt+fR6pykjuozUvHVZl1O9Psi0nlrbKkjqNuX/EOxy58/rXj1l4YpRL7afsnw5wQ6rIitSMO6X5Q0JCbErSLFxof2n10qVLGI1Gu2VvLGVxamPOnDm0b9/exugZOXIkX331Fdu3b2fRokXs2rWLO+64A6PRWENPdef6eTwWaTKUEiPlJhVKiREZJspNKkZGpABmr0VVFqWMZE7cFrblxDI8PM2lcdZl9qGNTMPGrB6c0rdym3HxVJdfr5taKJsGfdRkYytkRqcUYquy+ZYPAfhod8MJNC48MarZl9PoqCxkWdoQq35LbVgqTouI1IdTp07ZLAnZU3t3B++88w7r1q1j586dqNVXNbsmTZpk/Ts+Pp7u3bsTGRnJzp07ue2229w+D9HD0gLpuekVl9qXmNTokWFEQrlJRaHRsQqopcpzhjaY3bmRbMuJtdvuWr2XtRn9CJYXI5WYqPgrIwng5m3mopU1VbB1RvPljfiNtbZp6fgpHRcXrY0fB5nXxQdvd9474yzN3Vix8FjsTlaevKlFL/PMTx7D3Q1ovN4ICG7aAHx9fW02RwZLUFAQMpnMbtkbS1kcRyxevJh33nmHX375he7du9fYNiIigqCgIDIzM2tsV1dEg6UFcvSuN11qPzYyidERyQwPT6PU5OFUFeV8bSvOGAIcelkmRx/gp+x4Xk0ey+KUEUyOPsBFgy8XDb5Mjj5g1dO4vV0qP2XH1yjeZfH2iNSP9h7F9e5j122LSdj8shtmc31RdWm02OBZQ8sbm9fjf+BipRcPHnioqafSbGmKas1KpZKEhASbsjcmk4nt27fXWPbm3Xff5Y033mDLli306dOn1nFOnz7N5cuXadeunUvzcxbRYBFxiQlRh5xq906P7zmrD6ixzeiIZN6I32jVwJgQdaha/308c2qMk/kobahT8xGpnY97r7WqxdaHdj4lPHZoCr1+bjmGS762FQCeUi1BilKWpQ1p2gk1IXtvX0Sk58WmnobINcyePZsVK1bw5ZdfkpqayqOPPkp5eTnTpk0D4IEHHmDu3LnW9osWLeLVV19l5cqVhIWFUVBQQEFBAWVl5sK3ZWVlPP/88+zbt4/c3Fy2b9/OmDFjiIqKajBletFgEWkwguXFdg2KxSnOf5hHRRyvUYiuuQfRXm983neVzevwf7/tsuFhEiQsS1hzQwTTOkuQopT5yWPQC3Lyta2cjmW5lmePTnTvxJoIUdumBty5JuQC9957L4sXL2bevHn07NmTo0ePsmXLFmsgbn5+PufOnbO2//TTT9HpdNxzzz20a9fOui1ebM4ilMlkHDt2jLvvvpvOnTszffp0EhIS2L17d4PF0ohBty2EnptecXkpyB2c0Vb3sjwXt9WlAMXrnbBPF7uUedOc8POrwGCUEfXtG2ROrJ5d9kHqsGrLda3VZY01vQZj4YlRSCUmdl+OdioQOlhejEmQVgtCd5X3e35br/NFrgPcUPyQOp4/a9YsZs2yH2O0c+dOm9e5ubk19uXh4cHWrY2rEC16WFoIeqOM6IVLCPvo/UYbs8Dgh5+80m4g4iWDT6PNo76ErlxUr/Obs7FSW5DsrR0y0GgVmExSQr98p9rxDWd7Vtv3Zb+V7ppeo/PYoSm8mDSeAHk5beXFhHlddioeI1XTXpT3F3GKplK6vREQDZYWwokxr5Mxdza5TzzbaGPKEIhSncdTqiVMeclGY8Wey/j2nU832txcQXHBPWUHpuyf3qxKAryYNJ52njXrpyzt9Q0ZE14le9LLSOUmOn+3wOZ4bedfbyxLWINKaqDMqEYjKJFKBPwUlS4tY4qIiDQMosEiUm/sPYGuy+xDW0URrWRlyK5ZcB37v8esf6/PTGDVyUQWpYykR8AZfsqOb/D5ukrmi7NrbeNMkOWa/p/zYS/7dTbi61ADp75kVwTh70Iqc87fX+LkPbbzXJf4mbun5RKT981we5+vx/+At0yD1qQgUn0RkyCh1Kiu8Zzmnja/IatXU09B5C+aIkvoRkE0WETqjb0lgElRf2ISpFww+jAh6hBGQcqvxV0BaKWqYEt2HCtP3oRGUHDJ4EtbeTGR6gvXTV2ia6lP8ceem15BKW8YZcia+DbxXxy+2LHRx3Unf6RHOtUubM1CEn+Z43Sl7pkxu3iqy6+UGtXc7HsStVRfn2k2OjP+fMDqFfogdZi15EZLZNTvTzb1FGwRJO7ZWiCiwSICQNc5S+t1/rNHJ7Ixqwebs7tZ902OPmDN8PkodyiVRiUbs3rQxescf1ZE8FDnP1BIDADoBblVe8UelmyjDVm9WJdZux5AY+OMxHr42qsZN1U9KkfvepPLl72J+X6Bo1MbjAMj614luTmQ96Djom1hqxcS+tl7hH30PpJCJecLfTHh2g/9K902cf7/2zvv8CiqtYH/ZralFwIkIJBGCU1AEAyiXgUBwXbBgiIqInwoqPSiNEE6CYoNUSkqiOWqF8tFEbs0jYD0lgICCSWkZ+vM98eSTZYkJJtssinn9zzzsDtzzpl3huzOu2+1BFVSyurn7W7vOWJqrlbDqLbQ8uOKdzEvqMYsqP0IhUUAgKX04rXlIq7zxxhVHQOi9pd4/Jc+S3n3+rXcG72XZvqLNNDaM0mGtPyTSe2+ZWSbXx1jr4wX+CaxA4209liJQdG7r5rmXBOIeM8enBrz2YtORdRURcJi0wCw725n5SR52PRq65JcwJDto6r1fNVN8rDppIyaDLKK1MhE6ybnmNXhS57/e5BL68iSQktDWtkDBW4j4gNnRbqkDLXaigi6rTgirbkeE70snhOT7PEZR2eOr/R65VUk9JKtxJTm5/8eRKThPJPa/eq0vzQlqKaS/Kj9V//hQbPp/WNh/Etoo0z89GZPiVUMT8efVBsyTt2gc2wG7vz1mXL3bSqo9TNz371EG9J4vHX5utsKXCdy/QIUiwakOuzyqGAdlWJr1EOEhaU+o0D42sql7FaE0vzpffwPOFlaajNRHy4AIPFsQ8e+nf0W0TawfJ1RBe7jyrTyFV0+rFCTyXkdv+CC1d5obuxfDzN57/1ukU9QiGLRoDHYCAjO87QoghqIUFjqITEz7fEqJ6ZMIOXxqTXmi/e2yCOeFsElCuJQbv+x0DoV/vYSAGwmDR02zaJtM2cF5bXrNjiNF9QudJKNpxMeoY1PKqG6upXSXROQdTaCA3MxmtxTSqAmIrKEKo5QWOohh+c5PzCXdvrEpflvH7nJneLUWgriULbcWhiwnDLS3l065fGp7L97Ln46k8PaUkCIV271CSlwK8+1/Z7dF6/BX86noVYoLO5GscrkGvXYbHX80VTNZfnrCnX8r0JQFYxs8yvLDvZzZOvEfje12JiVR26pbrFqJB/HvuUUPwH1KHakjnL+kj9Bmjx8Zed4pKqoCVOf6LVlCimPTSPQN59mDTM8LY6gBiIUFkGFyLZ58VqSPdV4e9/FbDjW3en4tYZTnhBLIKhyjj84g3uj9xbbv+GGt0scP23v4KoWyWWuplytPHILY/96mJn77q0+gYDfbre7U/30ZloFnmfsXw8z8s9HHccjNywgcsMCWn0yr1Jpzp5GuIQqjlBYBBXix7TWdA75x1FB8+FWu5yO94xI9IRYHuXKfkmCus39LRPKNW5Rp/9UsSSuc2eIs8I1fveDjtej2/xMgNboseq91wadxiBbOZnbgB8TWxH14QIi1y9AkqBBUC7H7p+JJUfvEdncgoe6NdcFJFWtmxndWVlZBAYGkpmZSUBAgKfFqZNsTmyHRlLIVrzrdSVNgaC2s/F4t1LLEoxOGEYzwyWS8hvy7vVrq0WeiPcWoRrtNYtSRk2ulnNW9TOjYP3mK+cge1+91UNZKPlGTo2eU++eb8LC4kYiX6m+Tsg1gf5RB8lWvGmkEcGH7kBYaASe4res1kDJPbFWdn2fGR2+4t3r15arc7U7SH50GrpAU7UpK4LagSgc505KcCtGLY9DlUETls/xB2dUv0xVjLCsuI9tl6Ipu82iQOBeOmyaxf67N/BNYgd0UrDTscl773fKIiypb1hVURe/LwFROK4SCAuLmwhft4jAqEtEbihMYY1aHoeqgaTnJtI4ONuD0glqA5/2XOlpEQT1kKjgdACOmcKKFW48lBXmCZHqNiKGpcIIC4sbsKf1BnMpNQA5R0PkijhQAAk0YfaKjdv6Vn9FWYFAICiLCL+LQMlNEitSEVggqCoqZWFZtGgRkiQxbtw4xz6j0ciYMWMICQnBz8+PwYMHk5bm3Djs5MmTDBw4EB8fHxo3bszkyZOxWq1OY3766Seuu+46DAYDLVu2ZO3atZURtcqIeGMZZ88FIWfokHM0SCooflZ8ozKJuPYMWq3iaREFbib87SWM+ONxT4shELiFFV0+9LQI9QtVcs9WD6mwwvLHH3/w1ltvce211zrtHz9+PF9++SWffPIJP//8M2fOnGHQoMLuqDabjYEDB2I2m9m2bRvr1q1j7dq1zJpV2Kk2KSmJgQMHcuutt7Jnzx7GjRvHk08+ybfffltRcasOCTSpBpBACbTSqN15GjXJZP/dc/nxtjgOD5rtaQkrxex993hahBpHysgpZWZLtP/vbF7af2f1CCSo0xRUln718G0elqR8zN1/FyC+O0pDdGuuOBVSWHJychg6dChvv/02wcGFQVqZmZm8++67xMfHc9ttt9G1a1fWrFnDtm3b2LFjBwDfffcdBw8e5IMPPqBz587ccccdzJs3j9dffx2z2V45cuXKlURGRhIXF0fbtm0ZO3Ys9913H8uXLy9RHk8S2CwT9RojSc9OJGX4VHb2W0T6wYZlT6wldPI56WkRaiUH7nmRGR2+qtJzFPQtEtQdhu8aXmzfwbymQGHXaHdwpfIzOmEYN3/vnoycWR2+ZNDvT/Fix/+6ZT2BoIAKKSxjxoxh4MCB9OnTx2l/QkICFovFaX9MTAwtWrRg+3Z7S/bt27fTsWNHQkNDHWP69etHVlYWBw4ccIy5cu1+/fo51igJk8lEVlaW01YdWGyaYqXXZWspg2shJWUBibL7NYOCvkWC6qE6Kr+u6b6m2L7lXT5y+3muVH4yzN780mep29b/7MY3nd4/sP3/3LZ2rUcE3VYYlxWWjRs38tdff7Fw4cJix1JTU9Hr9QQFBTntDw0NJTU11TGmqLJScLzg2NXGZGVlkZ+fX6JcCxcuJDAw0LE1b97c1Utziajl9por5sPFi/Ycn1r1yanRS+Or/BylMbrNzx47t0BQlFYLq+9z0ESXUW3nqm6qqr9V+//aXeJKPY25KBERw1JhXFJYTp06xXPPPcf69evx8qpcpT53M336dDIzMx3bqVNV18um1SfzkE0SEa8tQ5Uh4s1lVXauAh7eMZLWL9m/nAtquwgE9R3ZUn1f3Mv39Sbi/eI/1ASl81B0An1/GidS9gVuwaXHXkJCAufOneO6665Dq9Wi1Wr5+eefWbFiBVqtltDQUMxmMxkZGU7z0tLSCAuz5/OHhYUVyxoqeF/WmICAALy9vUuUzWAwEBAQ4LRVFcfun8nxaRMIbXWBE1MmkPzUpCo7VwEnMkM4OmMCURvnA5A4UZQYEwgsfvYsvOpwOagq6L0tLDvYr8rPVVf4xxRMhrHk7+z6iqS6Z6uPuKSw9O7dm3379rFnzx7H1q1bN4YOHep4rdPp2Lp1q2POkSNHOHnyJLGxsQDExsayb98+zp075xizZcsWAgICaNeunWNM0TUKxhSs4W4KLBeusrPfIjdLUjry5b9Q6ZQ3ieMnVtt5BYKaSPg6+2cvcYL9s3CNV0aVn/P4AzM5et8sJrX7lg3HujM6YViVn7O2s7Lr+7QMuuBpMWoWIoalwriksPj7+9OhQwenzdfXl5CQEDp06EBgYCAjRoxgwoQJ/PjjjyQkJDB8+HBiY2O54YYbAOjbty/t2rVj2LBh7N27l2+//ZYZM2YwZswYDAYDAKNHjyYxMZEpU6Zw+PBh3njjDT7++GPGjx/v/jsAHJ1R860V2y8Xnjsx2f2y3v3rWLevKRBUJVKulqj4wt5dbXxSq/X8sqQg19enhotsuOFtT4tA9GXLdI1AxLBUGLdHQixfvpw777yTwYMHc/PNNxMWFsZnn33mOK7RaPjqq6/QaDTExsbyyCOP8OijjzJ37lzHmMjISL7++mu2bNlCp06diIuL45133qFfP2GKrQo23fSap0UQVCMLDwwo99hXDvUpe5ALPJ3wiFvWSX56EoZIe7uLh3eMxKToHPVKqoPPzncl3FtYDmoLJ4a84GkRBG5AUtW6WYKmoJV3+IKXuLX3cdZ0X0OrhfEcm17zrSkCwZX0/G4q2/ouZua+e5nX8YtyzYneOB9JUtHprRz69xynY7f+MJG2gWmYFG2pRfC2JMVwe+ThygleDXT73/Pc2vQYobosJrWrnuKSN38/mV/6LOWNw//i6ZifquWcgqql4JmRmZlZJTGQBes3j5+H7F25pBUl38ipCTOrTNaaSp3vJfT3mGcc/6FCWRHUVrb1Xcz43Q+yvMsX5Z5js8qoNgmDl4Xn/x7EgmsLLZ0/3hZ3lZl2aoOyAuCjs7DlVBvC/LOp+vB3Ow833wWAv8ZYTWcU1BlEt+YKU+cVFoGgruBqAbHkR6ZXkSQ1i1/6LGXa3sEczg7l3t+e5lRWMAkDiscsTNzzAHGdP670+V451AedZI8haKTJElYWgaCaENU8BAJBrWdRp//wRa83yLUY0GpsJQaSXzD7Ebl+QaXP9Vzb7x0KSrrNjzBdpttjfQR1GJElVGGEwiIQCOoMW25dTo7RQIA+39GED+wxJ9kWAz5+Jreda8QfjyNLCommxlyw+pU5vus3IvCzqoj4oBYV9BNZQhVGuIQEAkGdokvYPyiqTKThPG0/n0Nemi+64EDO6fzIv+i+Iman8wI56t2EU8Zg8m26MseX5KYSuIf64v6s7wgLSz3kzl+f8bQIAkGV0cQriw03vM1xYyhdm55CH2Lk+IMzODxoNpKXzW3n2XzLK8zq8CVvd3uP9n5nRQVcQbkQlW4rjrCw1DMiX44jadyrnhZDUI3M3ncPL3b8r6fFqDaWdvoEoPCaexQeSx5WNb/EE/MbYlU09e5eCyqAyBKqMMLCUs9IGifK+tc3xAPUPfTaMqXUY293e49sq4EjOaFuK2A3e989Tu/L0y8pepnnurgLaj6vv/46EREReHl50aNHD3bt2nXV8Z988gkxMTF4eXnRsWNHvvnmG6fjqqoya9YsmjRpgre3N3369OHYsWNVJr9QWAQARLxR9R2nBYLaSs/vpvLb7UuuOuZUdhCJmSGkWQJZffTGCp3nixOd6PTlTKC4ovlx7Fulzll2sB+fHO/KvX12OB2bue9eACJeE5/v+s5HH33EhAkTmD17Nn/99RedOnWiX79+Tn39irJt2zYeeughRowYwe7du7n33nu599572b9/v2PMkiVLWLFiBStXrmTnzp34+vrSr18/jMaqqU8kFBYXiIqrm79eIl5bRvLT1VVySyCofWy73MvrauzstwhvnYVN/3TkH3ODCp3n3ui9PNnqd6d9iw/25+EdIx3vp+0dXGzOpHbfcsjYtFidmY2HuwLg1TS3QvII3I+EG2JYKnDe+Ph4Ro4cyfDhw2nXrh0rV67Ex8eH1atXlzj+lVdeoX///kyePJm2bdsyb948rrvuOl57zd7KRVVVXn75ZWbMmME999zDtddey3vvvceZM2f44osvKnx/roZQWFwgcWLdq5Qb+XIcgeGZnhZDUAupUQ3lagjBhjyCvPLJtnkxee/9FVrjmZgfGLJ9FHf++gyP7BzBF/90cmoguKjTf0qcN6vDl8X2RTa6CMDhQbMrJIugCnBjWnNWVpbTZjKVnLZvNptJSEigT5/CekGyLNOnTx+2b99e4pzt27c7jQfo16+fY3xSUhKpqalOYwIDA+nRo0epa1YWEXRbj4l8JY6kcRNp9ck8T4siqIUEB4pf7VfyRa833LLOxthVhK9aSsqoyZVa57t/vewWeQQ1k+bNmzu9nz17NnPmzCk27sKFC9hsNkJDQ532h4aGcvhwyS04UlNTSxyfmprqOF6wr7Qx7kZYWOoxSc/ZA3At571puahuurvqK93+93yVn+PPOypfNbamcmXAqyeorLIiqKG4sdLtqVOnyMzMdGzTp9ftejRCYRGQ/PQkjk+re+4uT3Pnr88w8s9HeTrhkWo/d3mVCeHWcab1p3MZ8cfjNNRlO/2/9fh2mgelEtQp3KiwBAQEOG0Gg6HEUzZs2BCNRkNaWprT/rS0NMLCwkqcExYWdtXxBf+6smZlEQqLwImWi6vX0jLo96eq9XzVwWO7nuDOX58h22wg36bjja4feFokJ+buv4vFB/sDcGJI3SsXX9ES+N3+9zwGvZVQQxYnTSH8kNKK8DWLCV+7mPOX/B1xJdVFRWNgBIIr0ev1dO3ala1btzr2KYrC1q1biY2NLXFObGys03iALVu2OMZHRkYSFhbmNCYrK4udO3eWumZlETEsAieOT61eS8tnN75Zreerap5OeASL4s1XN9XM4nyT996Pjyxz2hTsaVGqjIqWwI8KuojRai+xn2s10KLBJU4STKBvPiHeeVw0+nJDw6Rq685cUABPULdwR6XaisyfMGECjz32GN26daN79+68/PLL5ObmMnz4cAAeffRRrrnmGhYutPdleu6557jllluIi4tj4MCBbNy4kT///JNVq1bZZZAkxo0bx0svvUSrVq2IjIxk5syZNG3alHvvvbdyF1gKQmERCNxIhsXbKaOjJvHKoT4s7fQ9ALf/ON6ta/faMqXMOiU1nQb6PM4pfpw3+xGky6el/wWMVh25Zj0+WjOB3kZ0ko08xcArh/qQbfNiRoevPC22oLbhoUq3Dz74IOfPn2fWrFmkpqbSuXNnNm/e7AiaPXnyJLJc6HTp2bMnGzZsYMaMGTz//PO0atWKL774gg4dOjjGTJkyhdzcXEaNGkVGRga9evVi8+bNeHl5VfICS0ZSVbVOFvnNysoiMDCQzMxMAgICPC2OQOBxqssyUBMZv/tBlnf5qMxxE/c8QL5Nj7fGjEXVkG3xItemJy3PnwFN9pOS35Ao7/MYFR0WVSOqCNchqvqZUbB+xEvzkSv5QFeMRpJnvFDvnm8ihkUg8DDhaxdXeWr55L33k271c+uabxz+l1vXcyejE4Y5Xg/6/SlsyIzf/WCZ8747GYMsKeTaDCTmNOSnY61Iy/Mn+Vgo35ztQL5Nh0G2cMYUxKGcqgksFNRx3Bh0W98QCotA4GF03haO3T+zSs8RqMnnnMXfrWtWpbUm4s3SS8mHr1pa5vwbAwr7mZxIb8if51uQcLFFsXFXdljuFnaKAK0Rk01LgM6IYtaQnNgYNCqnzgdjUWXOmoM4kx9AnlXvwhUJBHZEt+aKIxQWgUfYc7I5+04286gM4avLLrdeHRx/cEaVn6OBNocm+ppf0Th643xGJwyjYUS60/7wd4rEx8hlf1sPa1XYUycn14ttfRfzS5/iis6qg4U9f2buuxdZUrGoGvJtOnsskkUGRULjb0Gvt6KRVL5OaU+k70XyLwfoCgSC6kEoLIJqZUdKBH+l2H/p2pD4IyXcY7KkPDG13tQhSbME0kyfXvZAD2PN1XHwUhj5Zh3RG+cTud5eT8avUWFV3ZQnS++a7CpH75vleD2v4xeczAlmaadP2Bhrz4RIGTUZ2ShzS9Rx+kYeJt+m47rQfwjR5dI5+B9GJwyrk6n5girEjaX56xtCYRGUm/C1lbdIGFUdWaqBPEVHnqIjQ/Fm4/Fu/JTcirVHqyZ3/2q4sw5JQWfc6qY8900n2ThqrFkxF+HrFhG+djERHywk5rMXeWzXE8S2O84vfZZitWrQ6mxIGpWI9xZx4J4XiXhvER02zSp74Stw5f84wv9isX2qVuWvtGZsSW5DmFcWPQIT0UgKOsmGyaatcan5JRUqXHnkFg9IIigREcNSYURacz0l8pU4UCEgOoPM5CBUg1JqKfCI15eBCiljp7p8ntn77nFkUvyREo6vBNmKF2YJFFVGg4pespGteBGizeGbxA4MiNpfxqo1kxO5jarlPC/tv9MpnTbNGljmnAbaHNIszuM8nYqc8pi9emzEBwuxmLVYFA19Qw4A0PWaU7TwTmfnxQgsNg0AN7RKxFtjqVKZNCUEB6jeNjLS/MEq0bhVNgfzmmJWtGSYvR2WmJpEmCGzWFbU6DY/e1AiQVE8VYelLiAUlnpK0nMTiXh/IW0bprE9zR/JYCt1bPKYSRU+z50Be9iREoFR1WFRvfGVzBhVHTqsjtc+kgkv2YJR0ZGh+FT4XJ4m1JBFl69fYPdAu5tp7v67mNXhS0YnDCPHqueDHu9W+hxXKisAU9ttLnNeSQGyN4cer7Q87sA/IB8/LxMBOiNmVcsnx7vSK8if3Tkt6NXoBKkme9qmQWNlTfc1bj9/xPsLUS0yDcOy6NCweFxKyhN2RT3itWX8nX0NAMfSG9I25JzbZSnK9Y/H88da1ws5zurwJZ8c71ps/6Dfn+Kh0F2kWgO5ZPUtsbuzQFCTqbcuoagP627jttKIXBHn9F61yuw71wSsEqpRQ/jKsrMvysM3iYWFhZItDUm3+aGoMhZVS6otEH85H3/ZXoRLJ1nRSzaMig6NpBCiyWFbcpRb5Khulnf5CFWVHG6LPJs9i0QvW92irACVLlT2wPb/475toxn718OcNga5RabKogIXs3w5ltWII3lhvHHyX+zOacFFky8fH72O305Fce9vT7P/QhO3n7sghknna8HPYEJG5fm/B5X4/ZA8dhIfx75FS9/zXEr3Y0/qNW6XpygVUVYKOG8tnhEWYsjFSzaTbGzIr+dbsvDAgMqIJ6gowiVUYeqthSXxoeLdbKPi40icMNED0lQPSc86X5vez0zOOV8kVQLr5c0F9pxsjlHV4CXZyFb0doXEGkiuUlj2PcPmg7+cjw0JnWTlmCmMDI0PjbRZBMl5ZCveAJhVDV5YUJAxS6Vbe2o67RqmOSrdypLKtL2DWdHlPx6WqhBFlbghKIlw/QV+zW7taXEA2H/3XKI2zkcn2/hfUjvyz/twKjgIvd6GTmelWWAmZ3MDCPXLdut5HQHXOTrkRhZCfbJpaMgBoGfUiVLnLbj2M3JsD3Emv2xXXHkY+eejWBUNoYYsFnWq/N/Kq4dv41h+E9r8Zy7Ptf+BIE0eD7faRYgulwybLzrJxpZbl7P2aCx3/vpMjW0jUWdxR1qyUFgEqmw3+yaPrbgLpKYSvSSeE1MKf7FFvBqHf7gFs04PJpDKoaz8ldICo6rFhkSqNYhonYRF1WBUdaTb/JBRyFK8yVP0fJnYEX/ZSIxB4aaIE7x6+DYuWX3xkc34yCY0qOQqBoLkPMxosCleaCQFRZVJt7m3wFl1UrQs/4JrPyvXnLafz+HQv+cU2z9t72D8NCa3ln+/s+FezlqC0UgKMd5n3bZuZUm8IjC2w6ZZZKf7gk3ipCJz8N45bj+nl7cZq1WDzd9C69Dz+GtN5e7fk272IVif5xY53u72nlvWKSAxvxEdff5hxeAPgcIgZRsyKaaGXOtzCoAL1gDa+KeVsopAUPMQCksRksbVTutKUSUrauN8VJtE0lBnC1JRZQVANkv4GMxkmf1RtZfV9RJS5f5ICceGhJdkQ5bApkpoUGmkzeJv0zUEafLQX3bt+EpmUsHu7sHec+WizY+bAA0KHbz/oan2EmeswfjIJvIUA7KkoCg6bKrMRcWPEE0OF2uxwlIeCqqwruz6PgBajUL3zdPp3jiF167b4BiXZfV2+Rf32L8eRpZUArX5tPf+hyEt/3Q6/njr7Y7X0/YOruglVDn7755b5ec4cI89MynbYiDMK9ulrtof9HiXkX8+yrS9g91iFSlg5r57mdfxi0qt8XfGNSW2IbhSGZvU7ttKnUdQQTzUS6gu4FIMy5w5c5AkyWmLiYlxHDcajYwZM4aQkBD8/PwYPHgwaWnOGvzJkycZOHAgPj4+NG7cmMmTJ2O1Wp3G/PTTT1x33XUYDAZatmzJ2rVrK36F9QDZVPjfmDjkhWLKSkkkTpzAzn6LQFZJGT2Z5LGTSH6mUGHbd7IZXyZ2JFfVc97mT56iw6hq0KDSMyIRRbWfs0BZsahadJKV0W1+JkyXgUZSMKsafGUTG451x0u2MCh6NxZVg5dkIcPmQ7K5IRk2HzIUHzJsPqRZgrg98rBT0a+6yMqu7xPpfZ5lB/sx6Pen2Hf3XHb1X+ikrDy26wmXHqAFmBQtfhoTOVYDeYqB+EO3A5SYDmxStDy7+6GKX0gpRLwaV/agGsK67qv57MY3UXC9rsXb3d5jUaf/OLUBqCyVVVYAhl2zvexBAs8hYlgqjMsWlvbt2/P9998XLqAtXGL8+PF8/fXXfPLJJwQGBjJ27FgGDRrE77//DoDNZmPgwIGEhYWxbds2zp49y6OPPopOp2PBAnuQW1JSEgMHDmT06NGsX7+erVu38uSTT9KkSRP69XMuoy2wkzjx6sF50cviOTGp5DGlFeGyqDIhci65qh6bKmNDomd4suO4XRkxY1Y1+Mv5IMPqczdzUwSEabKwITnFpwRp7ObzU5YQdJINX9lEmC6TdJsf/pp8QrQ5tdoV5CplZfas677a5TWfTniEpoZsdJKN47mNyLJ6c9HkwwQgpmHxjJblXT5i5r57iT90O3uzm3Od/0kybd7oJBu/p0ez72hzUka4XqTN65ocl+d4mt0XriFq4/xirqnyUGApqykUtaIJBHUJlxUWrVZLWFjxAlSZmZm8++67bNiwgdtuuw2ANWvW0LZtW3bs2MENN9zAd999x8GDB/n+++8JDQ2lc+fOzJs3j6lTpzJnzhz0ej0rV64kMjKSuDj7r7S2bdvy22+/sXz5cqGwVBBF77o6bkbGjIbbIw/z/rEbCJCNTsf7Rx3k/WM34CWb8ZUs2JDx1ZoAuD48xVHB1qjoaKzNxkc28faRmwjR2utoZCle+MomvDSWWlt3pSbQcdMsGvvnYLFpMFqbc1uTo0R6n6Nlo1Qnd9CnPVeWOP+f/GCaBGRwjVcGaZYAWnql8X16OwD6XHuQEX88Tlf/5BLTosfvfhCDbC3mEjk8aLb7LrCayDXpK6SsCASuIuqwVByX05qPHTtG06ZNiYqKYujQoZw8eRKAhIQELBYLffr0cYyNiYmhRYsWbN9u1/i3b99Ox44dCQ0NdYzp168fWVlZHDhwwDGm6BoFYwrWKA2TyURWVpbTJrBzZXZQebghPBmjYq9JMazVDnLV4o3e/OV8x2tfyezk0rg+PIVT5hB+y2qNj2zCS7IQpMnDX85nUPRuhrT8k7ui9gllpYLc/etYAIJ98snI9ybXrKeBdx7tfU5zwRpQLHalNGL8zuKvMdLa6yynjUH8ntmKhoYcovwucCo3iHDvi/yd25zHdj1RbO7yLh85lJUh20cRvm6R+y6wmrFaNZ4WoVayIyWCufvv8rQYgnqCSxaWHj16sHbtWtq0acPZs2d58cUXuemmm9i/fz+pqano9XqCgoKc5oSGhpKamgpAamqqk7JScLzg2NXGZGVlkZ+fj7e3d4myLVy4kBdffNGVyxGUgUZSHK8vWv34JrGDw72TpxjQSBJ3Re3jh6Q2+MimYvOv905k64UY0oP8GBS9mxuqU/g6zqabXgNwNPS7+fvJ9G18kEP5TfnseCcmtSt7jVcO9UEnSaRb/WigzUErKZzMCSbC/yL5Nh3f/etl4g/dTqT3+TJdWBtjV0H1d1a4KhP3PECW1YsjGaElNj4sSpMg8QOnItwQniw+14JqwyWF5Y477nC8vvbaa+nRowfh4eF8/PHHpSoS1cX06dOZMKEwTiMrK4vmzZt7UKLaT9FsncbabGQUFHTkKQayFC/Hr/jbIo/wa3J0sfk9IxLZFFFd0tZvBjbdx4S2WwBYcG355jzX1h6L9v6xGwjR5KCT7fU5inIot4nb024LGPnno2Wu/fCOkXT0P8309t+4tPYjO0fwQY+Pyz3+x9viGL5reJVU0hUInBBZQhWmUpVug4KCaN26NcePHycsLAyz2UxGRobTmLS0NEfMS1hYWLGsoYL3ZY0JCAi4qlJkMBgICAhw2qqTiDeXVev5qgNNkU+Fl2Shf9RBLlr98JIsxWJavCTrldMF1Uh5yvOXxrBWOzhpCSkxeLSqlJXHdj1Rrr5AtwYfxk9jLHPclbirsnBZrD56Y7Wcp7rwVAPP+kRBDEtlt/pIpRSWnJwcTpw4QZMmTejatSs6nY6tW7c6jh85coSTJ08SG2u3FcfGxrJv3z7OnSvMWNiyZQsBAQG0a9fOMaboGgVjCtaoqSQ/VfeKzd3fMgGw12Ixq3Yff4T+PLdFHkEnWZ2sKteHp3hERoF7qK7mePdtG02Pb6dx4GIYGpQyx2fafHgm5odqkAzWdF/D5L330/Wb8gffnjEHlz2oFnHGGMQrh/rQ87updPlaBCFXGSKluUJIqqqW+/InTZrEXXfdRXh4OGfOnGH27Nns2bOHgwcP0qhRI5566im++eYb1q5dS0BAAM888wwA27ZtA+xpzZ07d6Zp06YsWbKE1NRUhg0bxpNPPumU1tyhQwfGjBnDE088wQ8//MCzzz7L119/7VKWUFZWFoGBgWRmZla7tcXdeLJlwI6UCBRVxqjaA3BvizziETkEdYvIDQvw9TNybeOzTtWBayMrj9zCBYs/OtlaKUtXTeHZ3Q+xosuHTNzzAHGdy+9Wq2nsSInghiKlGMqiqp8ZBeu3nLYAjcGrUmvZTEaOL3q+TjzfXMElC8s///zDQw89RJs2bXjggQcICQlhx44dNGrUCIDly5dz5513MnjwYG6++WbCwsL47LPC8uQajYavvvoKjUZDbGwsjzzyCI8++ihz5xZWtYyMjOTrr79my5YtdOrUibi4ON555516ndKcOGEiUcvLV4wr4tU4tzUxBHtQXc+IRG6LPCKUFYHbSHr4eSwWLSeza7+FYnSbn0k1B3DJ4svzfw+i948Vb1pYE1jR5UOAWq2s/JTcCg2qo7xCjUIUjqswLllYahN1ycLiCuHrFpHy2DRPi1EmUXHxZRa8E9QfXtp/JxpJcTm4tqbQ+asZmK1aLBYNNpuMwWApsT+UoHr47EQXwjSZ9IxILNf4qOVx2MxGTk6bUeUWllZT3GNhObZEWFgEFSB87eIKzYt8xf0lzF+M/a/jdd+fxrl9fXchlBVBAeN3P8gZUxCHc8O4b9voGt3jqDS6ND6NxaLBnK3Hlq0jP9fA7T+Or/S6mxPblVnnpOcDdS/g3x2YKX9tncTxE5EtrrdnEFQvQmFxAymPT3V5TlRcPEnPuScuZe3RWLYkxbAlKYYQbQ7vH7NXRvjuXy+7ZX2BoCpZ3uUjsqwG9LIVraSQYfVh8t77ARj0+1Melq58rOm+hibBWTQMyyI88jyRTS/Q1DfTaUxZPYd6bbG3QXh4x0jHvoOma4Cr34dtH1cs4H/EH4+z8MCAciuIO1IiKnSe6ibizWUMit7NvyKOuTRv77Njq0iiKxAuoQojFJZqos1/CuN0ouLiUTXu+Yv7MrEjjbXZl5sP2vCXjXW+eaCg7mGQbQRojfhqzazs+j4mxV4i6rMb3yz3GssO9qP/z89VlYhl4qW1EB6YTrvgVH68La5YP6hQfZYjA6nlRy/R7os5PLD9/xzH/znbgPb/nc22Y1EM+OVZAPZm22tJWRUNrT6Z51Z5371+LbKk8Nel5kzc80CZ428IT+aPlHBG/vmoW+VwNzU9Y1OkNVccEcNSxUS8vgzJKrnNmlLAFyc6oZdsyCj0jzro1rUFgtrKzd9PLrOqbVXQ8uN5WPL0GPxMHL2veGfsotyydRJtg9LIMHtz4HwY/l4m0nN8MObpkWQVxajh8eu38dO5VqRmBGDK1aMqEqiQMtx1a+7VWHhgAOlWX5rpLzkKCdZXqitLqPUk98SwHF1W/2JYXG5+KCg/4esW4dcsH0Wx+0ZbLorn+DT3xG7cG70XgG3JUW5ZT1A/KEhZrat4QlkB0GhUGoRlcCnbp8yxP/cuOeak81cziAhKJzmjARZVw8+9lxH+9hJSRk6h948TaOTl/i7Yfhojp4wNaOWf6va1BaUgKt1WGOESqkpMGvKSAjCbdMUOtVwcT8SbyypdIbe8UfC1ndoSy+AqL+2/s1rPV5eVFU8xfveD3N1yH3c324c5w1Dhdfbc+RLRfhfo2SSZ47n2UhFBYdkAbL013t6vyU0UuICO5IVxwezLL9lt3La2oAxEDEuFEQpLFSLnyagaCG1gb6xW1LpyfOoEkp+aVOP9rTWFaL8L3Pz9ZE+L4RYKAkon772fTJt3mcGYgprNiRy7crF6X0/QVe5JEtf5Y2RJ4e+zTRnwy7P4GMzuELEYp/PtFW1vCThCY0M2Z02BVXIegcCdCIWlCkkaNxFNvkR6TtlmYsHV+SU1mjyLjpYfzyPi/YWeFqdSZFu9GJ0wjIa6bFp7pdLMcMnTItVrFh4YUKn5m256jXSzL7LGRsrjU+n6zQsOpbQivHbdBg4Pms2pjCC29y27ZMKNg1230m6MXcVzbb/n/pYJhOhyOZQeyn3bRldEXIGLiKDbiiMUFjcTuSKOqPjC+ionpkzg8KDZHpSo9lLUXZZ6NpjzZ4I4/sBM1Dwt4as8E6vgDn4/Hck5ox8WRUtCTgT/mGp/tdcCWn9amA3n6gMw4r1F7hanTCbueQCLWv56HaXx7vVrOf7ATACigy+gk2yVXnPf3fZ7WVZvo9//Uzkr7Ysd/8u5CwGcyg6q1DqCciJcQhVGBN26maRnPdPzpy4R/u4SkFRSnrJnRHTcNAtM/kiXCzul/J/dNRTx3iKSH635VX2vJMw/m8ZeOfxjCmZ/ehN+u32Jp0VyG6b0wo7qn/ZcSdTG+fj6mOjb4jDbz0XSwv+SIxZjxB+PY5CtpBr9Mdp0tG5R/b+f9mU05eaGx926ZnPvS/jIZsbvfpDlXT6q9HoJA+a7QaqrM77rVq73TgSqX2msd4ig2wojLCwVoP205Z4WoVbQa8uUClXblQw2dL4Wx/ucpEAkq4R6RXxAbVNWYr+byuS999Mh6Awmm5YGutw6pawApIxyjjNqGXYefy8Tnx/uREaeN4mZITyycwTRG+0P4VybnmB9Pj5as0cKHSZfaMDBnCZuXTNYm4eXbMFPa3LbmgVF5aqKnj7H0Ek2dqRE1JoCcYL6R72zsLSftpwDi1wvmd36pXiOzrAHzVZkfn2iw6ZZ+OgtNPXLwU/n2pf2nb8+w62tc1jTfQ0RHyyETB3oQdUqlQ5o9CQz991LZEAjTuQ0pHPgP/hpTRzIcu+DsiYSoDfSrcFJ1v/TAyOgqqBpoKI3WDidF4iP1kyYVzaf9lzpEfmO3jeLqA8XwA3uW3NGh6/ct9hlbg51rxWoKD8lt8JLklBU++/X+pJ56CncEYNSX2NY6rzC0mHlq5ycUugDrqiyUaCsuJuCOgt1iRZBGRw/15BL2T7c0fIg9/72NIfPhSLLKnqtlT13vkTUxvk0DMphV397AG33zdPZ1X8hqTn+JF+yx3R4+ZgxSSpKrg4UCUy1t9fHvI5fOF7P3HcvZ4xBLlVxra2czArGaNPhF5JHbpYXh4bM8bRIxbAZNbT9fA4Gnf1vsyay4NrPiu37K6UF14WfrPTaBSXsPzvRheba9EqvJygD4RKqMHXeJbR/9DOeFuGq1DVlpdUn81BUCZ3Ohqra636kG30BuCYwkwAvE/1/fo7WTc5xY2git/84nhF/PI7FZv9TzDPpUVWJrt+8gCyrePmYkf0saAPNSN6VD2SsCczr+AWKWnuVr/LS6cuZ5BgNHD4TSsfGZ9F7W8qe5AEknULeJW/yjHoiPlhot+xVAe5OX3eHslKUIDmPE5ZGbl1TUPtIT09n6NChBAQEEBQUxIgRI8jJKb1oYXp6Os888wxt2rTB29ubFi1a8Oyzz5KZ6dxLS5KkYtvGjRtdkq3OW1gE1UPkhgU0aZgJ+GFRNOg0NvKs9iJaF3N9ODxoNh03zSLQ24gsqfYmdxYfrKrMTydaodNbGbJ9FFZrM0ICcjlzugGo4B1kJDAgj4xTQeBfMx94JTF3/13M6vBlqcfXdF9T6rG3j9zEyDa/VoVY1UrmJV80ehsnhlw9y8XTSJJK8hPOJe97bZni9viig5fCKjX/ge3/x8exb7lJmuL8lR/BpHbfVtn6Ajs13SU0dOhQzp49y5YtW7BYLAwfPpxRo0axYcOGEsefOXOGM2fOsGzZMtq1a0dKSgqjR4/mzJkzfPrpp05j16xZQ//+/R3vg4KCXJKtXigs4e8sQcrXkPyMyOCpKpIeft7xetDvT7H11nhHiuuBe14EQCMrpOf6cOZiILKsYDBYCfA2IkkqAT5Gjl1qhDlfx5nMEPsnUpHIz/IiXzLg1TgPnc5Kz++mYrZpaB18Hl+tiUtmH24KPo5BtjC6zc8eufaS8NMYyzVu5r57kVE5kN2ET3uuZMj2UTQ02BveDfjlWb65eUVVilmlSFqlxisrAElDny+2z93KSviqpUREV/wp0+PbaezsV3XKCiCUleqiBruEDh06xObNm/njjz/o1q0bAK+++ioDBgxg2bJlNG3atNicDh068J///MfxPjo6mvnz5/PII49gtVrRagvVjKCgIMLCKq6413mXEEDKk1OEslKNFMRmXNkEbs+dL3Hw3jkkDnkBq0lL86AMMnK9CW+cjl5jIz3Dl9DGdjOiZNIgmWVkvQ0vPzM6nRVfg5nMfC989WaOZzTErGjx15pIswTUKGUFYELbLeUaN6/jF2w+09YRdLoxdhWvXWf/JXMhz7fK5KsOiiqx9ZnwVUtBUgn1ya7wGl0annajRIK6QlZWltNmMlUuM2379u0EBQU5lBWAPn36IMsyO3fuLPc6BU0ZiyorAGPGjKFhw4Z0796d1atX42rv5XphYRHUPCSNytEzoWh1Vpr5ZqCVFPItOhr55EIzyMjxwXjRGwnw8TIR5G3kdHogrUPP0zXoJHmKnnMmfy6afMm2Vrx/S01gZz977YsrTf4FAcml8cbhf/F0zE9VKZqggtz961hOpIeQn2dAH2wj0C8fs63iBepC9VlulE7gUdxoYWnevLnT7tmzZzNnzpwKL5uamkrjxo2d9mm1Who0aEBqavkaZF64cIF58+YxatQop/1z587ltttuw8fHh++++46nn36anJwcnn322XLLVy8sLIKq5Zatk1yuUqoYNQT453H0vlms674ak6KhgXce2WYDPjoLd0bvxyskn6DAXHz1Fs5l+xHV6CJZJi8sqgadZKOxIZtGXjmcyQkk9jt7DEJNKi9+969jXRrvanxCutXP0cRO4HnG736Qh3eMJOazFzErGpoGZNEwOJvQoGx89Wb0Ghv9f36OW7ZO4uEdI11ae19WU/r//FwVSS6oTiQ3bQCnTp0iMzPTsU2fPr3Ec06bNq3EoNei2+HDhyt9bVlZWQwcOJB27doVU5xmzpzJjTfeSJcuXZg6dSpTpkxh6VLXKpYLC4ug0vzcu3gvk1afzMN61oekZycSuX5BsTiBlCem8tiuJxzvvTUWttzqXJDv+1Mzyc23W096XpOEt8bCvkt2H2q6xRetZEMn29jWdzFj/3oYgMaGHCbvvZ90sy+N9NlkWb15o+sHxeQb+9fDDtdLVbHpptfcvubohGGs7Po+UDX1PgQVZ3mXjxj0+1ME+uYTpM/notGXPLMOrZdCM58sdp9pRnSjC4T6ZBPte96ltetKCnyr+fGO18deqJpSEfWJgIAAAgICyhw3ceJEHn/88auOiYqKIiwsjHPnzjntt1qtpKenlxl7kp2dTf/+/fH39+fzzz9Hp9NddXyPHj2YN28eJpMJg6F8VnJhYRFUCcfun+loU3ClstJxkz22pZP/Kce+MENxk/feu+ZxY4skGvrk8ffFpnyXFEObwHNkWHwI0ubhrbGQb7N/KAqUjxB9DhoUDLKVLKu9TPzTCY8A9qJ0Be+DdXmM3/2gOy+5WihQVsLfqVsVcusKfUIOsbPfIgwaK0Ov2cHzbTczqNkeugSe5IbmyRw/15CUrGDybHpPi1rttFoYD9LlLBml7qf1l4oHegk1atSImJiYq256vZ7Y2FgyMjJISEhwzP3hhx9QFIUePXqUun5WVhZ9+/ZFr9ezadMmvLy8ypRpz549BAcHl1tZAWFh8QjRS+JRDCqKQUHys5L8SMlmvLpKQVO3CW23sPhgf3xkM3m2hiWOVZA4droxap4WycvGsaxGHD/ZmNHdfsFPYyRU56zonMxvQL5NR5eAU0xv/w23/zgencZGx02zaB4kOZQXnWRDKytVe6GXiXwljqTn3Bv0nfJk3arfU1coiCnKthj4xxxCS69U/DRGTIqOjn6nWXffarecx11F46qTY9OFRQVqdlpz27Zt6d+/PyNHjmTlypVYLBbGjh3LkCFDHBlCp0+fpnfv3rz33nt0797doazk5eXxwQcfOAKAwa4oaTQavvzyS9LS0rjhhhvw8vJiy5YtLFiwgEmTXGvcKSwsHuDElAmoepXgZpkEB5dekKc+MLXdZp6J+YGzxkCeTnjEYQUp4LzRj+7RyVwTcQEvPxMXc33ApGF7ehTH8kNpoHW+fxZFw8exbzG9/TcAbLl1Od/cvIJ9d8/lQFJTjmU1QkEi0+rtlo66pRG1sbBhnbuVlfJQtNO1oPr57MY3yVP0HDU24aw5iEybt1vThmuCstL6pXhavxTvtK/NXNFnrUxqeLfm9evXExMTQ+/evRkwYAC9evVi1apVjuMWi4UjR46Ql5cHwF9//cXOnTvZt28fLVu2pEmTJo7t1Cm7FV2n0/H6668TGxtL586deeutt4iPj2f27NkuySYsLB6iSfR5tvdd7Gkxagwbbnibu38dy1c3vUqrT+Zx7P6ZAATp80k3+XBtyBm+vdCW/GwD6BSSLjXgRHoIDaNziq1TGlKuFp3GxsFLYUQGXOSSyadKrqXzVzNIHOLhEu91oyhwjaDjplkMb7W93KnqBWRZvbBoNA63ZV2izZzlSLKEonF+ch6ZJfqs1XYaNGhQapE4gIiICKd05H/9619lpif379/fqWBcRREWFjcSvSS+7EGXqYyyUpMyYdxJQZBq27A0Ht4xkiHbR6GTbaSkB7P9TIR90GXft9Um0yXsH07kNeKB7f9XrvXlYDNWReaXPktZ1301J9JDquIyHP1ooj5cUCXrl4fksa6ZWmsj4WtK/gy5swT+Y7ueYHir7fjL5SsEWJTXrtuASdGSb9NjUurWb8Mjc8YjWUC21uNYlMpQQ60rNR2hsLiRE1Oqx0drtOnsxajqIL1/nECuVU+QLh9/nZF/coPQahR8DWZujD5ByvCppAyfisWs5URmQ/7JDSLPWr4AxuDAXIzWwl+7udllB4ZVhsSHrl44LXy18wPX1dTw+k5ok4wS96/s+r7blBZvjYULFn/OWoIqNN8gW2nqlUGILtct8tQkDs8bX5hfKyg3BTEsld3qI3VL7a8n7DvejIjoNE+LUSVsvTWe4buG83d6U1oFnifCLx2dbOOS0Zu0vML0veMPznC8Lq/FKWHAfKf3Wp1n/SZSvobwNYtJGW6vIZP86DSPylPbuFphvYJsqsoS7nWRhMwWtPVPZeGBARgVHS92/G+551sVmXmdvnCLLDURyeppCQT1CaGw1CLCVy0FFVL+b2rZg2sxf19oSq5RT5BXPsfONeLfLfeSnBfCxthVJY4vKGtfEv1/fo7WAedY0eXDYscMXp5tppg8pu67bWo7mVZvvDQWdJKN43n2CqBRHy4o03pWwI7zEVUoXQ2gnv7SrxQ1uJdQTUe4hGoJjqyPevCHmjBgPi0bXeDQ6TBkWWFRp/+UqKy8tP/OMtdqG5CKopZstz5wz4t0+brmN+cTuAdXq8sCLOr0Hzr7/8M5sz+ypOCtMWPL1xRz55VGoMH12JfahFQ9lQHqFMIlVHFcVlhOnz7NI488QkhICN7e3nTs2JE///zTcVxVVWbNmkWTJk3w9vamT58+HDt2zGmN9PR0hg4dSkBAAEFBQYwYMYKcHOdsj7///pubbroJLy8vmjdvzpIl9btQVvJTk2jU/BIpoyd7WpRqwUtjt37k55ReVKg8lV73ZzTFW1O6JWX3wPmlHqss4evKjkkpKKInqHo23PA2I/98lIl7HmDmvnvLPc+iamjlba/+GazLI+WJqY7g77Lo0SC5ApLWLtq+IFKZBdWDSwrLpUuXuPHGG9HpdPzvf//j4MGDxMXFERwc7BizZMkSVqxYwcqVK9m5cye+vr7069cPo7Hwl8bQoUM5cOAAW7Zs4auvvuKXX35xapRUUIgmPDychIQEli5dypw5c5xywesjF5IbeFqEShPz2Yu0+c/cq47psGkWxiKpoAVVXYvOi1xfvgycLbcux6rIdPufBzoHm0pvdldQp6WgiF5FEEG6rnM2PxAfjZl5Hb8o95zp7b/hubbf09SQiVG5/HepK920MG3vYMDeY6uBNtcl5ai20H7acto9v5xD88dzaL5IZXaJGl6HpSbjksKyePFimjdvzpo1a+jevTuRkZH07duX6OhowG5defnll5kxYwb33HMP1157Le+99x5nzpzhiy++AODQoUNs3ryZd955hx49etCrVy9effVVNm7cyJkzZwB74Rqz2czq1atp3749Q4YM4dlnnyU+vvxpw3WR5KfsMQ8FD6rZ++7xpDguExUfB4BOZ3WyPoSvWUzE+wuJ+GAh7b6Yw/6755JtNhAddh6tweqo6mq1FJriryz3fzUyLD54aa30/K78sT/uUHBSRhVaw4oWkgNIHFJ5V5SUUXJ9j4jXRdG40ggx5BJtsAesu9qa4cWO/2Vpp0/sbyylf3W29k7l2d0P0TLgAulWX/7JD2bEH49XVOQayYFF4zm4QCgqFUG4hCqOSwrLpk2b6NatG/fffz+NGzemS5cuvP12YaGupKQkUlNT6dOnj2NfYGAgPXr0YPv27QBs376doKAgunXr5hjTp08fZFlm586djjE333wzen1humq/fv04cuQIly5dKlE2k8nkKAlctDRwXaPzVzMcf62uZCvUBBInTOTwoNlkp/qj87YQvmopEe8vRNKoqBaZxg2zyLvsAgr1ySblYgOspsK4cFWV0PlayrTQXMma7mv47fYlbOu7mA6bZtnvYRlcuODv2sWVQuQGuyWoJAXl9h9d/8IPf7vQNapqS/7WEsG8pbOu+2qyFW+e/3sQBrniKS5Xa42w7lQsTfSZyJKCSdHSzPsS716/tsLnEggEdlxSWBITE3nzzTdp1aoV3377LU899RTPPvss69atAyA1NRWA0NBQp3mhoaGOY6mpqTRu3NjpuFarpUGDBk5jSlqj6DmuZOHChQQGBjq25s2bu3JptYZLF/xRzaW7GmoDKaMmE97osuKZo0NVJCS9Qr5ZByr02mJ/GAT65iMXMb3bcrVodTakSvy88NJZHYXdrkbysMr1dypwYyU9XLqlJvlCgxKVr5Yfzyt1TsrIwgdl8tNCMakIz8T8wIJrP2NRp/9UyfqtA88xvf03vN3tPQK1+VVyDkEtRriEKoxLCouiKFx33XUsWLCALl26MGrUKEeTJE8zffp0MjMzHVtBD4O6QoELJeXxqaSMqP2N70K8cvELzXG4uVSjhtw8A7JOIS3Dn0BdPi0CLoEqEblhARHvLUIyyzQOyCH/krfDcuEqf95Rueqz4e+WL/i7PM0Jj90/kyODZzlZTQCOPzCzQrIVpe3ncyq9hsB1Ju55oFiQtyvxMoJ6gFBYKoxLCkuTJk1o166d0762bdty8qS9EVdYWBgAaWnORc3S0tIcx8LCwjh37pzTcavVSnp6utOYktYoeo4rMRgMBAQEOG11CSm3/CVzev9Y87ui/pEUTs4FXwBkvQ3JYEMxa9BoFAJ8jXTwPUOW2YvgwFwUi4xqlVBllcx8L6R8GcVYPitTj2+LF2Mrj0uoNCqiLJZWQt6x5kj3K6CH/j3H7WsKysakaJ1q/mTbqraasqD2IWJYKo5LCsuNN97IkSNHnPYdPXqU8PBwACIjIwkLC2Pr1q2O41lZWezcuZPY2FgAYmNjycjIICEhwTHmhx9+QFEUevTo4Rjzyy+/YLEU/lLZsmULbdq0ccpIqos8snMEAOFvLXXUXgl/dwmqvtA10v6/s2n/39nF3AkFQaUnDjetJmkrTtLDz4NNIuLNZXaXkEZF0irYbDJ+BhP7c+3X4K2zgE0Cm0RA02xahZxH9bOBBK0/LTuW5fyl4rEoFlv1udQi3l+IbChfRd1OX1besiJwHwsPDChzTFScPREgemk8N38/mdeuc24at+Daz9wiy9tHbnLLOgJBbcYlhWX8+PHs2LGDBQsWcPz4cTZs2MCqVasYM2YMAJIkMW7cOF566SU2bdrEvn37ePTRR2natCn33nsvYLfI9O/fn5EjR7Jr1y5+//13xo4dy5AhQ2ja1P6Qevjhh9Hr9YwYMYIDBw7w0Ucf8corrzBhQs23HFSWHSkR9hdeNqRAM12+foEb2x8j5ckpjriI3DP+HLjnRY4Mdq7hkWe+nDXiay0sNFeT0Smo3vaYFFWRLps6JXJMBtLyA2jsnc3pc0FIOgV0KoMi9xLmlUXr8FT8GuRhMZVsdWr3xRzC311C5Io4FItMuy/mOB0/cM+LVX9tl0keNp1GDbLLNXbvXaXHrgiqnzxFz8g/H73qGMVbodUn80CCX/pUXX+vkW1+rbK1BdWMcAlVGJdK819//fV8/vnnTJ8+nblz5xIZGcnLL7/M0KFDHWOmTJlCbm4uo0aNIiMjg169erF582a8vApNo+vXr2fs2LH07t0bWZYZPHgwK1ascBwPDAzku+++Y8yYMXTt2pWGDRsya9Ysp1ot5aX71ws4/FDtqFfR+asZHH/AHhAa1CC32AOsIC6iIO7jSjIzfezHH51GzGfV91CuKJJOQbVJaLQKiiqhWGVUK1y86Ecj3xx7LRYJZK2KzSRxyepDiC6X87m+3NA0hTSjP5HrFxAYmEezwEwOnwnFkmkAvTeSSUbVqKi5Wmw+ni3BX1qlXUHN5mR+Axobsun70zg6BZ9mcNAfWFQNN0WccIxJfnoS7b6Yg1n01BGUE0lVkdTKaRyVnV9bkVS1bl55VlYWgYGBZGZm1pp4lvBVS51qd1yNiNeXkTxmEpGvxKFtmsex+53dCeFvL6mS2Ah3Eb1xPlaTxu4KklRUVbJXD5VUZK1Kz6gTHMtoRNq5QLvLKE/LsF6/YZCs7LoUQe+Gh/k7pxkmRcPZvEDOZgZgOhaANdAGGtWusHjZkPI0qAalRt8LQc1jyPZRtPFLw0u2cCCnCUG6fAYE/U1zbQavn7/Vbc0VBTWHqn5mFKzfedh8NPrKxTbZzEb2vP9CrXq+uQPRS6gGUV5lBey1NgqCOS0XvIuv5YYHdMuPyk7/rQgPbP8/mjW6hEavIMl2fVm12ZUVVAlVgbN5geSZ9PbjJg0oYJCseMkWugcnk271JdeqZ3dqM07sv4bciz7YfBW7qVTB0fZe1ar2GBiBwAU2xq7ixY7/xU9jRCOpvHbdBk5aQki1+ZWqrBSk4wsEV0W4hCqMUFhqIeGr7L7ylOFTSXpuYqkuogqvfznN9viDFc+muRofx75FWqY/DYJyaByShVZvQ3M5qFhVJHQGK4H6fCxWDbJGBYPNUQwtzRJAUn5DPkvqhK/WTO4Zf6QGJtCo4G+xKyqqhKpV7BYbXT39ZAvcwjMxP7Cu+2oARrf5mdsjD5c69toGZ1h8sD8rj9xSXeIJaiEiS6jiCIWlFlKaJSZ6SflbF0R9WHI9kqiN85F9qt4h38Avjz/vWEC74DTuaHnQXgxOldB5WzDorZzL88eYq0eSFdpGnAVg16UITuUFczyrITk5XpzMCQY/u5Ii5Wsgx154TrJIoF4O4rVJdmVGIKhijmU1Ymq7zZw21+1MRndxR4tx9G9T/nYZAkG9UVhKy5qJeDWumiWpOAUplEUpWkDtxJTyZ1HZSqhjErkiDs56IZ0zVHljvW197e6saJ/z+MhmTgx5AY3eRusm5+je5CTeWguyVqVzs9MoqkT42sUcPBvK9v0tSTkUhpKj49ipUHq2SsTPz95YU5VVJOtlRUVSQQbJZldewt+qugwOgQDsjTYBLpr9AIheVr97n5WFEuSPlGcse2BdQ7iEKky9UVhKcptExceR/MxED0hTMRInTqDL1849aa5W+v2qXI4difhgoWOXGmRBlUEfnmPv9bOuapWWNv+Zy9dn2pNltcfgtGpyjktGb07lBpFr0dMgKIeTWcEcOd4UTboW60VvJG8bql5Fskh4+ZnIsRgY3mo72pB8uyVFBjQ4arcUfLglc935U6+q2CKBe8iw2P+erQ08m51W0/n273n879Qr9O06x9OiVCvCJVRx6s63eAVInFB7lJUCdg+cX/agUghfWYKVocgfvmqTaNAqncODZmO1aOzBrlWIqtqLuGVZDUS8uYxDJ5qSnu1LY+9sbKrEn3csYFf/haBVsfkpqBoV6YIeySwhWSWMab78vS8Cf9mIzapBMslgu3xNl/+yVa2KZJaRlKtJUnu4+9exVRZbJKgYL+2/0+n9hhvsDWFTnhDujvKQ18LX0yIIagku1WER1G5kU6F+mvKYvWS9l6+Z6I3zOTHkBQz+JnLy7d2Sff2MZLvQDqAiHL2vsPBdj4xpXMz0xcfLxK5T4Wi1hRqGX3AeVqsG0zkfJBUUnYpslpDyJWw+Ches/igmDWjtLiHVx15ZVsrXoF6OX5EtdSNTaNNNr3laBMEV/JnRgrn77+KUMZhr/f7hmZgfPC1SrULV1I3PZrlxh0tHWFgEdZ2k54pblA4Pmg3YM49MGV6O6rn7755brbVLdvZbhJ+PiSBvI1GNLmLQ2c3pj+16ggP3vEi/qEMkPzWJ6M7/oAsyAaB4q6SMnmzv12KTwMuGqrPbSyVZtVtkTLLD/SWoP9z56zPVcp7RCcM4fC6UWR2+5O1u7wllpQL4H0r3tAjVinAJVRyhsAgI8M8DytdhuCrJOBmEQWPl0JFmDtfXL8dbArDzXDhgD2w8/uAMEidMtKcuA+t3xDoygiSTDFaZ5EenkTJqMqq3DVWvYPNSaDVfBEHWVa5sMPnVTa9Wy3lXdn3fofQLKsbmAwvoe/0cT4tRfYig2wojFJYqoKqDVUsjZtbyqx7v+k1hwG7RrClVlZC8PV9bXNWoHD0T6pS2XRBUvLNf8XtaYAHSBxtJGTmFlCemkvzMRKeOyilPTkGTpQVVwtywfE0IBbWPlOH2eJGiWXOC2sN3f8zxtAiCWoCIYakCCuJDqpvDc8df9fiF04GO15JFInz1YsKuuYSPXsUWmF/V4pVJyv9NtqdWu8iVbQmuJHFiYbp3y49eEkGrdZgKZ80JBNVIfXXpVBZhYalHFI1JUbwUUp6YSp5JT5ugc5jMWjp/5fkHuaJXeHjHyCpbXygrgprEgZPXcODkNfyREs6Bk9eUe16fG1/i5ruWVKFkgipDVd2z1UOEhaWeUuB22Xf3XPuO7h4Upih6hb/ONPO0FAJBtWBBRoPK9eEpJP/ThBOnmtB/x9NlWg1t3lqs3jI337mEX74SPYwE9QOhsNQywt9dQkCjnEJFoyrOsWopSGqVZQlFvLYM1UshKiqNH29zdgFJJk21BzFGvLaM5LHu7cckEJSHzi1OOV5nKxo6tviH6MTnypz34/eecTsLKo87snzqq0tJuIRqGSkjplSpsgJ260tVpjQnj50ENolLed7FSuar1ZyCbFfOLr/2ULC0QADwVfa1AAxtutPDkgiqFJElVGHqhcIS+13NrDhZn9vRp/zfZCw2DXK+TOQrhVYW/ybZ1SvHqMkkj5lE5IYF+AV5PvBYUP/YcMzuj53e/hsAhrXa4UlxBIIaS71QWLb3XVz2IA+Qmh7gaRE8il5rRQmwggStP53LgF+eJTfHC4DwtdX7f5b08PMcuOfFUrtYCwRVxcOtdjleJ//TxIOSCKoDSXHPVh+pFwpLZen707gqWbcuZ6y0+c9cbv5+st3lUgq7B85HNthQvBT0eisX830cx1Ie94xVLPEhkRYrEAiqEOESqjAi6LYcfPevlz16/vB3l4CsOopj1QbCQ9K5xieTX0aVrrAAJA0triCEr1vksVo2AkF18k1iBwZE7Qfg2ClhXREIroawsFzB7T9evfhadRO+bhGS3oZ0uYlfdbtKKso/GUGs6b6mQnPbRpwl4oOFbpZIIKh5tNNfdLzWSZCt6DwojaA6qOm9hNLT0xk6dCgBAQEEBQUxYsQIcnJyrjrnX//6F5IkOW2jR492GnPy5EkGDhyIj48PjRs3ZvLkyVitrlVYFwrLFRw9GeppEZxIeWwaycOmk/yo3eLgKVeJqxy8d47jddRy16rXHjrRFOmCvsRjrq4lENRE2vzHnuknAyeLxK000Fg8JJGg2qjhheOGDh3KgQMH2LJlC1999RW//PILo0aNKnPeyJEjOXv2rGNbsqSwsKHNZmPgwIGYzWa2bdvGunXrWLt2LbNmzXJJNqGwXEFJroguX79QwkhBebH5uBYhJuVqUTUlH0scX7zjtEBQ2zAb7d54iwoKcOJUEyKanSWi2VnPCiaocmqyheXQoUNs3ryZd955hx49etCrVy9effVVNm7cyJkzZ64618fHh7CwMMcWEFCYVPLdd99x8OBBPvjgAzp37swdd9zBvHnzeP311zGbzeWWr14qLFd2di2Lgs7B9Y3wt51Lf0e8Gkf4qqXc/evY8s1ftZSI15aR8n+Tyx5clEALyc8IxURQd9F72U3h0c3PIpxAgoqSlZXltJlMpkqtt337doKCgujWrZtjX58+fZBlmZ07r14faP369TRs2JAOHTowffp08vLynNbt2LEjoaGFHox+/fqRlZXFgQMHyi1f/Qy6tUqelqDGE7lhAWg0RH24ACVdj+pnBT8JKVfDmZzypWOHhV8kx2hw+dwF7i+BoK5yZHChKfwaYVWpX7gjy+fy/ObNmzvtnj17NnPmzKnwsqmpqTRu3Nhpn1arpUGDBqSmppY67+GHHyY8PJymTZvy999/M3XqVI4cOcJnn33mWLeosgI43l9t3SuplwpLQOjVA4jqAgVWJEmnkPzIdJfnJz38POHrFqFc0qPqVLDINGyaSU6+gZz88ikhqSkhLp834tU4YV0RCAR1FneW5j916pST68VgKPm7edq0aSxefHXPwqFDhyosT9EYl44dO9KkSRN69+7NiRMniI6OrvC6V1IvFRa91uZpEaocSaMiaRSkSjj9GjXOwmjRYrVqMOisXDgVhGSTyl0+XzbKKHoX41dCrm7SjIqPI3GCUGgEAoEgICDASWEpjYkTJ/L4449fdUxUVBRhYWGcO3fOab/VaiU9PZ2wsLByy9WjRw8Ajh8/TnR0NGFhYezatctpTFpaGoBL69bLGJaEAXU/JsU/KA9ff2OlCqH9eccCGvjkk3/Rm4xUf2SjDCpIVonwd5c4Uo8j3lxW4nypsRE05VNuIt63rxUUmAtAh00lR48LZUVQ1zgpqtvWLzyQJdSoUSNiYmKuuun1emJjY8nIyCAhIcEx94cffkBRFIcSUh727NkDQJMm9r/t2NhY9u3b56QMbdmyhYCAANq1a1fudeulwlIf2Hf3XPaXo0li+MqltPpkHuGrFxO+srDIW4ECkXKqId4h+WiytCjeCqqXgupjt1CpOVrCVy1F1SmlKi2oktO6paEa7WlBeUYDkRsWsP/uuUS8XsqaAkEdooWIYalX1OQsobZt29K/f39GjhzJrl27+P333xk7dixDhgyhadOmAJw+fZqYmBiHxeTEiRPMmzePhIQEkpOT2bRpE48++ig333wz115rb+jZt29f2rVrx7Bhw9i7dy/ffvstM2bMYMyYMaW6sUpCKCx1jJYfz3NpfMroyaiKRMoTU9FmO+cSR22cj6RVyM/wQm6Wh+xnQdIpaLxteAUZ8QnNRfKykfLkFJKfmlRs7cQhLyBZJEc35NKI3LAAyduuBEU3uoCaaa/Bkjym+JoCgUAgqDrWr19PTEwMvXv3ZsCAAfTq1YtVq1Y5jlssFo4cOeLIAtLr9Xz//ff07duXmJgYJk6cyODBg/nyyy8dczQaDV999RUajYbY2FgeeeQRHn30UebOLftHdVFcUlgiIiKKVbOTJIkxY8YAYDQaGTNmDCEhIfj5+TF48GCHn6qA8lS7++mnn7juuuswGAy0bNmStWvXunRR9ZnjD8x0eY4lz55YafNSiYq3F2ZT87T4+pjslg+rDJJK0sPPo9oklEt6jNkG8rK8ylT15QZmJB/rVa0svn5G1Bx7ONW5XL8SlR+BoCq4ZeskBv3+lKfFENQnangvoQYNGrBhwways7PJzMxk9erV+Pn5OY5HRESgqir/+te/AHum0s8//8zFixcxGo0cO3aMJUuWFIutCQ8P55tvviEvL4/z58+zbNkytFrXwmhdUlj++OMPp0p2W7ZsAeD+++8HYPz48Xz55Zd88skn/Pzzz5w5c4ZBgwY55pen2l1SUhIDBw7k1ltvZc+ePYwbN44nn3ySb7/91qULcwVX67LUFiJfKV9V2JThUwlftwhkFclWaA7JzTOQMnIKSCo262Xri6yi+tjQpGvRXNRBRskVaQtQLDKqRXZa90r23z2XlNH2Wi1/3iG6JQuqj597L+OzG9/0tBiCekRNdgnVdFxSWBo1auRUye6rr74iOjqaW265hczMTN59913i4+O57bbb6Nq1K2vWrGHbtm3s2LEDKF+1u5UrVxIZGUlcXBxt27Zl7Nix3HfffSxfvtz9V3+Z2tRUsCQ6f2Xv+nxln6Gk58ofoNox6jSqTkUxqPbYEQlspstKiio5NPqgBrloDDZsfgqKtwqBFiLXLyi1K7N/UB6tI1PRh+aVeLwo0cviyy2vQCAQCOoXFY5hMZvNfPDBBzzxxBNIkkRCQgIWi4U+ffo4xsTExNCiRQu2b98OlK/a3fbt253WKBhTsEZpmEymYlX/6gt77nwJqFifoVYL44lcEcf+pGvQ5MpINlA1KhhsyJk6Om6aBZKKkmN3GymqhI+vEcnbaq8pnquF8wa0GXblZuKeBxxrh7+zhKwz/hw/0wjTJa8yZVGLZBRFvHb1gNvwt8oO5BUIBIIah6K6Z6uHVFhh+eKLL8jIyHDkdqempqLX6wkKCnIaFxoa6qhkV55qd6WNycrKIj8/v1R5Fi5cSGBgoGO7sgJgbSHizWW0WhDPiD8eJ3zdIkeTtKogelk8x6ZPQDEo9tgUvf1DIFklUh6bRtKzEwvdQjqF1p/OZd/dc1EUGY1OQdWpqBoVSQVrgI2WH88j11YY8a3J1KLJt/+JSUYNEW+UkfWjSES+HMfwXcNR9Vf/QLpc7l8gEAhqAjU8hqUmU+HCce+++y533HGHI9XJ00yfPp0JEyY43mdlZdU6pSXig4XgK6Fk6zmVG8Q1TS6xrW/VxddILew1T658+N+3rbAteEHkiXeQEflywbi8bANNQzPQNlRIOR2CTVLR5GiwnfUhJ7wwpkW2gtXHvobqY0OTVUpHw8skTiz8/6N7xa9LIBAIaioSbqh06xZJah8VsrCkpKTw/fff8+STTzr2hYWFYTabycjIcBqblpbmqGQXFhZWLGvoymp3pY0JCAjA29u7VJkMBoOj6l95q//VNFSTBllvQ2lm5Lt/vVxlykrEe4sAaN4wg9afFrfg/LG/sJTyiSH2TtX5F705eO8c+84cHecy/NDIir27tWL/+CjeCtE+FwoXkkCXJeHrb0TO1dTaXwWR6xcQ9aEIBhYIahLtp1ZdXKOgZlIhhWXNmjU0btyYgQMHOvZ17doVnU7H1q1bHfuOHDnCyZMniY2NBcpX7S42NtZpjYIxBWvUaWwSjYKzUYxXt0SUl7afzylxf0FzwR9vi+PofcUrymqzNLR+qTAA9u5fx9rdQgUooNGonLkUCEBoeDqJ4yeSMmoyL3b8r2PY8SkTkBTJXsDOtQr9NYqkoc+jpF89G0ogEFQvBxaP97QIFcMDlW7rCi4rLIqisGbNGh577DGnHOrAwEBGjBjBhAkT+PHHH0lISGD48OHExsZyww03AOWrdjd69GgSExOZMmUKhw8f5o033uDjjz9m/Pha+sfpAimjJqOqElJ+yQpLqdVkS+HQv+e4LEPEa8uwhlg4OqPQPbPpptecBykSxgveWJLtufm7+i8sdb2jM+3/b0njJtaqsvrh7y5x3uFvLXmgQCAQuIBIa644Liss33//PSdPnuSJJ54odmz58uXceeedDB48mJtvvpmwsDBHe2koX7W7yMhIvv76a7Zs2UKnTp2Ii4vjnXfeoV+/fhW8xNrFrv4LS63wWlJBtVt/cK8SkDx2klOa95DthV045+6/CwDVy4Zkk9Dm1V1PasqIKU7vC6xSAkFdoOcDou2FoPYhqWrdtC1lZWURGBhIZmZmrYxnKS+Rr8S5VG+lMoSvW2SPWanjRG+c74jdEQjqGv1Dn8bSrhlbf6x4Y9S6SFU/MwrW73XrHLTasss8XA2r1chvP86p88+3KxG9hGo51aWsAGUqK6MThlWTJFWLUFYErvLJ8a4ArD1a82PtNqe9IZQVDyKpqlu2+ohQWGo44W8vKXtQDWFl1/cdrx/bVdxlKBDUVbxkM3tONqe94YynRREI6ixCYanpqBIRHyyk/X9ne1qScnP3r2OJ8U31tBgVRjTDE7jC6qM3EqbJwqhquD48xdPiCGo6ipu2eohQWGog4asXE7neXvcjZdRkVIuMyWTPyIr4wDkjJ+KDhU6BsTUBqypzODfMY+cvKNsfvnqxw0IVvm5RuedXRTO88NV1s8FmfefLxI4EafL4Iz+SG8KTPS2OoBYgXEIVRygsNZCUJ6aSNLTQx5wyfCrHH5gJgHpFjZbkR6az40RktcpXQOTLJXeDjvBNJy3fv5qlKUQ22v+svQJNHpPhSlKeqN0NNgUlY1G1/JQVw9MxP3laFIGgziMUllpGypOF6bYRr9oVBq2+sEZIz++mFot7iXhjGZEr4hyvw99Z4nhfGXyjMoleFk/4SudGhEeyGpN4PqTS61eE8DWLUS7XTDFm2fsgRby3CCmv/F0oCu6roH7R+8cJZQ+6gkHRu1nR5cMqkEZQZxG9hCqMUFhqMbLF/m+B9QVgW9/FSF42p3GqrKJqVSJeX4aqV5BMMpKl8jVU9t89F2uIhZTRzr2Itt4aX2IF3aqk/X9nE/XhAqRcDUiXg5ULPtSSWmINm6IUfVhJ9dQ/XN+ZE/lfvkzsyF8pLVh99MZix79M7Oh4vfDAgOoUTVCXEJVuK0yFmx8KPE9plWO1+kKF5Zatk9CHBKKk+GINsSDlalB9bQRGXCrXOaKXxHNiSum/PIsWmatuWn70EqoqodgkdAYNikVG9VKQ8jUkP21XUCI3LCB5WNkpnFtvLWxFUJ2p4gLP8ENSGzIUH3wkE16yBT02fCUrGjkXGxLXeZ1k38lmdGzxDwCbE9txV9RBAPr+NI4jibfw9rdxtap6s6Bm4I5KtfW10q1QWOoglkx7m4OWi+OxBIcS2iKdtGADyCqqwf5Az8j0LddaV1NWIl+OI2mc576wZY2CJAE6MBu1qDYJrBJyfqHhMOlhUW9CUByzqsGmymhkBUWVyVJ1IBuRJQUbEkZFj1HVcSqxA1mKF9uyO9EfiNo4n8QhL8O/PHwBAkE9RLiEajEdNs2iw6birhfd5WDT41MnkDJqMhcy/GjYJBOtwYZkkVH1Ckpu5XTViDeWIV/hVnIlE8cdmPP0eOkt2KwyqiKh87GARkVulufSOrUpZVxQcbYkxTi9v79lArdHHua2yCMAnLYGc8wcxvfZHXgz9TYmH7iPsTsfZkjLP9n0WzciX4lDTa1chVKBQLiEKo6wsNRSwtcuRpJ98PY3Xv7V51ydNSounsSJduuIYpFJGDCfHt9O49w5LySzxh7T8t6iCvfI0WbJHJ/mbH0pGtj62K4n+Cc3yMnV4m5Um0SeUY/NKiPrFGxWGTQq17U45dI6B+55sYokFNQkbo887Hjd/7J7p+j72O+mcuZ0A6R8DZJFcnINJj8jXD8C9yAplY+Tq69xdkJhqcFEvhyH4mcDjVosVsQnMJ+8c75YvDR4e5udlJbjD85wGps8bDoAl7J9SHp2IpEvxyFZJbikK3bOVp/M49j9M4vvnx/PsRcKFRT1iobSkSvinP6atJLC6UuBLl2vK0RvnI+kk9HpbCXKKxC4SuqhxmhspceGCQQCzyIUlhpIxJvLkMx2d4tklFH1KuHvLEH2tqJe0iMFm1FyfZEUCZ3OhiSpBPjll7luQeaOV3g2h/49h4g3l9mVFxUUvYrqpdAm+mKxeW3mLkfxtr8OX7kUTZ6MqrNn4qSMtKdZq7KKJrfQw7j1cBu8fM2VvRWlUtDvp81/5pYx0k5RWQWCkpBNknCSC6oed7h06qlLSHw8ayJWCcmG/X9HlezWEI2KYtFAgBWNzoZkllA1Kof+PQcJ2HPnS05LdPqydKtDsK9duVElFckmoUogWSRkLyuqWjzd+cis8ZyYPIHIDQuQfK0kTphI0riJTgpA8thJqJf/msJXLkU+r+fwoNnEfle1WURHBpcvfbo6lZXwd2tP/ydBISemTECV6+eDQFCNiDosFUYoLDUQv/BMkAAFVB8bqv6yw1JSkbQK8hVfqvvuLm5l2HvXPCLeX1hsP9hrtdiXkxz+1KRxE/H2NXMyPbhUuWRZhRIUmgIKYmZkf4vDrB7sVbblp7bS+tOSrTspI4Qlp7Yi2Spfn0ggEFQNQmGpYUS8uYzc5ECUhmZU3eWEfZ0CNgmN3q6s6PVWvJrmgqZkNbugb41qvfp/b1DzDKwNLXYLDpCX5UWrxudLHW/N14KkEv7WUsLXlNwbJ3zdIlTFvt4tWyehXEXBqe1YzMKjWtdQxTeioIoRvYQqjvh41jCSn5oECui8rCCDZNSASQM6hcSHnufEkBfYf/dc8rMNyN5Wp7mRr8TR9ZsXSHliKuHvLCmzqJtGVglvfgFriJWI9xahWiW+uunVYuOi4u2l6mW9Df/AfLSBZjRXnBug46ZZ6LwtSLJKy49e4ufeyzie1rBUS08Bz+5+iMl77y/r1lQ53f7nWs0Wrc5W9iBBrSF6WWFmnUBQZYi05gojFJYaiOKlYPvHxx6nolXtpfavsFSkPDbNURSt6zcvEL7K3s9Hufx3XLTnUGlcyvTllz5LSRkxBUmjwGXLSPfN053GaVvk0uY/cwkKygXsAa82o5beP05wGrvv7rkcf2Amfv7GwkwlVaJpaEaJ55+97x5m7rsXH9mMppr7pbf6ZF6xfX/escClNUR2Ut3ixCShrAgENRmhsNRAUkZPJnH8RAzNc0BSUc1X/29KGDCflFGTUbUq6WfKTiWOeH0Z4auWYitSPK5l0/P2HjyrF3P+or9DEem4aRa+3iYsFnses02xy9I+6jRbb43HbNUUW7+hn12xeXb3QyiKTFTghWJjxv71MD4aEwbJip/GRKC2emNdhLIhELiHjhOWe1qE2oUKKJXc6qeBRSgsNZnDg2aTMnIKKU9OIeXxsrNtksdMKlc2TPKYSWgCzEiXg3nbfj6HtkGpBDTKIeWJqXDe4FBMbIrM7oH2Gi/tG6YCEPHBQg4cbg4Uz04CyDXrAThn9EfW2Lhg9Cs25rXrNnDOHICChNflLo4j/ni8TNndSfi6RSVWChYIBOVnX/x4T4tQqxAxLBVHKCz1FFueFlmnEPHaMvJzDBzIaOLINlIbmkkYMN/+usjnIjkrBI2sIF3RvavvT+Oc1s7M9ab75ukkZoaAKpFvLV6gDqCJPpMTeY3QSTYCNXnIJZRvbPfFHKf3Xb95odiYihDx/kK8/Mzk5XqVmu0jEAgEbkfFDTEsnr4IzyAUlvqKVcaWrYMgu3VDvvwJ6L55Oq2uOecYdujfc2j5sT3e47fblxDmn01IgxwaXpPpGGOyOWfLhPjnYrZqMFs1qKpERp53iSI0019kXffVnDQ1IN3qR0pOA27/0fnX2sF75xC5vjC2pEPDs5W46EIKqv8mPvS8o6CeQCAQCGouQmGpR3T+yh4IG7U8DtnXgi7QREiDHBo3yiI934dHdo5gWMROvLUWp3nHHyiM99hy63L6ND3isMAA/Nx7mdN4b50Fs1WLXmtDUSRHTAvYY2IK+DjtegDiOn/MX5nNae6bQY5FX0zupKHP0//n5wA4f9m9NPavhyt0D4pivNzVWiAQCKoNkSVUYYTCUo/IPBFM+KqlqBrQaBWQVO5svp+mfplEBl3kgx7v8kzMD1zI9y1xfkGMyaJO/wFwKBGAk2Uk5XwwQT75DIvYiWLWkHi2IWAP6PUxmOn81Qzu2zaaz2580zHnsxvf5N3r17K9b8n1XWRJ5b5tozHZtIxOGEaILpdlB/tV6n6kPFH+KrxRG+e7RUkSCAT1nMoG3BZs9RChsNQjVJ2KT6NcFF8blnwd/r5GTuY3ILZBIp/2XOkYV1AJt9OXM7n1B3vF2i5fv8BFk49jTJevX8BHa2bingfo9r/nybfqmL3vHgAkCUxWLTszI0GF2KgkwK4g7Oy3iDD/bKfzlYez2f6czAom0+jFP3lB+GhMTGr3bbnnFxTTqyi2XB3/O9q+UmsIBAKBoOIIhaU+odhjUgoq5CqKTL5Nx9R2m0scvveuefx4m71o3O6B8/mi1xv02jKFLl+/QJ7RwLGLjYjr/DE6jY3fbl/Cix3/S68tU2gWkoHZquHAhTBkg40NN7ztLEYFqt9m53hz7kIATf2yOHCySakyl0TEq3Ho/CreiDHivUWkjJjiaLhYXsLfWlrhcwoEgrpJTc8SSk9PZ+jQoQQEBBAUFMSIESPIyckpdXxycjKSJJW4ffLJJ4XXXcLxjRs3uiSbUFjqEaqX3Y6Y8vhUWoensveueTTxsgfPdvna/jBu+VHxNOWi/Hb7EnYPnM+RwbMcWUWtg847HffVmTGadOQZS44ROXamscuyH39wBsmPTGdvYjOShrpWkTb5mYl4eVkqnGHUJOxSqcci3lxW6rFGLezz7vz1GQAm7nmgQucXCAR1iBoewzJ06FAOHDjAli1b+Oqrr/jll18YNWpUqeObN2/O2bNnnbYXX3wRPz8/7rjjDqexa9ascRp37733uiSbaIZSjyha/TbUJwuA5V0+ouOmWey72x5Ee03DDJfXXdd9tdP71Bx/VBU6NT3NgXNhRG2cj4+Pif13zyV81VJSRpWscAz45Vmsisx3/3q59Gt4bJrL8gHsL6FBZHkpLa4GQDKVbi2KaZDGs7sfIkjvw/jdD5JuKV6PRiAQCGoKhw4dYvPmzfzxxx9069YNgFdffZUBAwawbNkymjZtWmyORqMhLCzMad/nn3/OAw88gJ+f83deUFBQsbGuICws9ZQPerzreF202/OVGT+lUdAKoCT+vGMBXl4W9qc1QSsr2LL0ZJ/zI3xlyXPu/e1punz9AooqXVVZqYlIoSbAbqGK/W4qoxOG8diuJ3g64RGaeGWRb9NxXcBJrvdLop3fGQ9LK2g/TVRlrYl0HF+P/l/caGHJyspy2kwmU6VE2759O0FBQQ5lBaBPnz7IsszOnTvLtUZCQgJ79uxhxIgRxY6NGTOGhg0b0r17d1avXo3qoqXIJYXFZrMxc+ZMIiMj8fb2Jjo6mnnz5jmdVFVVZs2aRZMmTfD29qZPnz4cO3bMaZ3y+Mj+/vtvbrrpJry8vGjevDlLlixx6cIEVUvKqMlXPa6RVHLP+ZKV7Y1Po9zCQkclRLd/0esNooMvcj7XPRaIgrox1UFgQC6P7XqCm5om0sg7lyyLFzIqWtmGVZF5u9t7TGi7hf35zVyKuxFUDQcWiaqsNZF9y+vR/4sbFZbmzZsTGBjo2BYuvHqj2bJITU2lcWNnl71Wq6VBgwakpqaWa413332Xtm3b0rNnT6f9c+fO5eOPP2bLli0MHjyYp59+mldfLd5s92q45BJavHgxb775JuvWraN9+/b8+eefDB8+nMDAQJ599lkAlixZwooVK1i3bh2RkZHMnDmTfv36cfDgQby8vAC7j+zs2bNs2bIFi8XC8OHDGTVqFBs2bADsWmPfvn3p06cPK1euZN++fTzxxBMEBQVd1ZcmqDkE+eSTofVDVSTah6by6b8Ls4K6/e/5Yo0GP+250qlAnKtEvhyH2sBePyb50arvE7T66I2ct/rzQITMBYsfFlWDLCkE6fJ5o+sHxcYvuPazKpdJIBDUL06dOkVAQIDjvcFQctzgtGnTWLz46pmShw4dqrQ8+fn5bNiwgZkzi38HF93XpUsXcnNzWbp0qUN3KA8uKSzbtm3jnnvuYeDAgQBERETw4YcfsmvXLsBuXXn55ZeZMWMG99xjT3F97733CA0N5YsvvmDIkCHl8pGtX78es9nM6tWr0ev1tG/fnj179hAfHy8UllrCz72XEfPZi1itMn+dbA5FlO3SuiIrFg3haxcjySrJj5Y/ViV83SI0qg41S4sU7JwNFPH6MpLHTKrQNVyNXMWASdHhpzHipzHxYsf/uv0cgppDp2fsLou9r9YjS4CgalAA1xMli68BBAQEOCkspTFx4kQef/zxq46JiooiLCyMc+fOOe23Wq2kp6eXK/bk008/JS8vj0cffbTMsT169GDevHmYTKZSFa0rcckl1LNnT7Zu3crRo0cB2Lt3L7/99psjEjgpKYnU1FT69OnjmBMYGEiPHj3Yvn07UD4f2fbt27n55pvR6wurnvbr148jR45w6VLJGRsmk6mYP0/gWUIDswkLziaxnOnAKY9PRcrXuKSsAPgF5WMLsCEpUrEMIncrK8sO9iP+0O08E/MDszp8yYS2W/j4eJdyzS3ogF1AzGz7QzB6WbxbZRS4n72vjkdS6md1UYF78URac6NGjYiJibnqptfriY2NJSMjg4SEBMfcH374AUVR6NGjR5nneffdd7n77rtp1KhRmWP37NlDcHBwuZUVcFFhmTZtGkOGDCEmJgadTkeXLl0YN24cQ4cOBXD4uEJDQ53mhYaGOo6Vx0eWmppa4hpFz3ElCxcudPLlNW/e3JVLqxXUliZ9URvtGUc/917Gb7e7FnuU/JTrCkZOhjeSUUayuTzVZXSSjQlttzjtO/TvOU7vn054xOl95Ap7LZuL+xvRcnGhcmLzuvyl0yzf/YIK3M6e1yd4WgRBXaAGpzW3bduW/v37M3LkSHbt2sXvv//O2LFjGTJkiCND6PTp08TExDg8KwUcP36cX375hSeffLLYul9++SXvvPMO+/fv5/jx47z55pssWLCAZ555xiX5XFJYPv74Y9avX8+GDRv466+/WLduHcuWLWPdunUunbQqmD59OpmZmY7t1KlTnhbJ7RQ06WvzH7viEr6mctVbq4ryWlRKI3yta9eV8tg0ksdMQtFV3S/gnt9NZdDvT2G7ii134p4HGPT7U44Yllbz7cpJ0rP2asGqBDaDXcbwNYs5Nt3+AHS1IJ1AIBBUFevXrycmJobevXszYMAAevXqxapVqxzHLRYLR44cIS8vz2ne6tWradasGX379i22pk6n4/XXXyc2NpbOnTvz1ltvER8fz+zZs12SzaUYlsmTJzusLAAdO3YkJSWFhQsX8thjjzl8XGlpaTRp0sQxLy0tjc6dOwOUy0cWFhZGWlqa05iC96X50QwGg0umpdrMkcF2xSVlePl74dRUwtcupm+HA7zd7T3HvpTHK3ZdyWPd5/6ZvPd+TuQ05LMb3+T5vwfRzL8RMirtDKd55VAfnmv7fbE5n+68Hm22Bm6ElovikWSIio8jcYJdYbFbgOwKj6QrfzOQ94/dwLBWO5z2Ra6IQ5XVYtcc+XIcSJD03ETXLlggEFQPigpSJX9cVaF7skGDBo4EmJKIiIgoMR15wYIFLFhQcnxi//796d+/f6Vlc8nCkpeXhyw7T9FoNCiK/cs3MjKSsLAwtm7d6jielZXFzp07iY2NBSiXjyw2NpZffvkFi6Wwa/CWLVto06YNwcHBLl5i1RH14QKiNs6n/X9nX7UuiaB0Uh6f6qSsuGo1Kqsyb0X5/p/WHLto98PqJBvNvS/RLTCFRHNj0q2FzSEL3F8AhvMaJAWil8RzfNoEjk2fgCpD5CtxRMXFo+hVpAI95VJhfNaQ7fZA8lcO2WO/4g/d7jgWuSKumLIStTwOyQayubi1R1JAtlQ2ok8gEFQZNdglVNNxSWG56667mD9/Pl9//TXJycl8/vnnxMfH8+9//xuw9woYN24cL730Eps2bWLfvn08+uijNG3a1FGCtzw+socffhi9Xs+IESM4cOAAH330Ea+88goTJnjeh9z28zlEvLmMVp/MQ7FK+PsaadsoDTlfJvzdJTUqzqTow7S2UJLVqKTraPv5HMBesr8qyM71wsdgzzjSSAqtvNOY1O5b9uc244MD3R3jCtxfLRfFY9PD8SkTODGl8O9UUuyWFckGslVC1ar2OJagwmym/iH7eGzXEw6rTdEYmQJ3UlESx08EFYflpiiSVUKyQsslIpBXIBDULVxyCb366qvMnDmTp59+mnPnztG0aVP+7//+j1mzZjnGTJkyhdzcXEaNGkVGRga9evVi8+bNjhosYPeRjR07lt69eyPLMoMHD2bFihWO44GBgXz33XeMGTOGrl270rBhQ2bNmuXRlOau37xAdp4BjQZUbxsWkxbVIrP3LnuRsojdy0gZMaWMVaqXysaSVBetPpnHsftLr51iM2mK7bsy0PVqjPjjcd69fi1PJzxSYo2Ukrin9T5Hk8ZZNRn5jAAAHLhJREFUHb507N/8c5cSFQVFB4kTiyvUmnwJRQ8npkwg4rVl+LfIYlDkXsINF2j/XyOypLLv7u08fhVZopfEOylBULKyAvbzxMxejiKabggENRR3WEjqp4XFpa81f39/Xn75ZV5++eVSx0iSxNy5c5k7t3RLQ1k+MoBrr72WX3/91RXxSiVy/QIkjVqhB3j4O0tIeXIKjXxzSBgwn5Yfz0PK1aD6gOxVmJZSFbU+6jrdN09nV/+FmLNKjz2KeG9RhfsHDdk+ioaGHGTJ/mdemrISf+j2Ypk/cZ0/LjYuekk8iVNKVhQUn5JjUo69MIHwt+2ZUrP6fk62zZuVh3uRl+GNnK0t0YJyJa66uw+/OJ42L9ajUucCQW3CHS6deuoSqvO/w/psnUXS0JcBe7+X9PMBVw3qjHjjci8dm0TDlheRTA2IeHMZhsYhALRrksYJQwjGfL3I7qgE4WsXk/K4vYx0UcvUldYWKV1X7jVn77sHnWTjRF4jLKrMxth3GfvXw04xMgWM/ethGuuzybPpucHvHIsP9i+1dH70Urt7RS0hCyn87SWkjJxy1XTslJH263ui9e8APNe23JcE2N1KLRfZ42LKy5HZosCZQCCoW9R5heX73oWWnt0D7bEQ4e8sQR9ocjwY+//8HIdPhkGmDlQJSXWOHWj50Ut4X45n2HeqKUkPl9xtWFB+tF7WEvdf6RpKGle2BeLtIzexOyecDEtjDLJ93YLmjq9dV9ySN3nv/YTozPjIZgyyhY3nerAxdlWxcQWoGpXECRMd9VSKUqCMFKXL1y84/tbcwbHpE2i1IJ5W8+M59oLn47gEAkElUFQq7dKpp0UM62W3Zp2/2enBeCi5CcnDppM8dhKyWUINsjiNP/7gDPbc+RKR6xdUqbJy56/ORXRGJwxzaX7HTbOc3reZU3PdApWxTt3961hGJwxjyPZRDPr9KUa2+ZU3un6AWdGwpvsa1nRfU2zOY7uecLy+YPLjktWHSe2+ZWq7zez4s81Vz1cQL1KW++bWH+zH3amsOHBDJqRAIKgBqIp7tnpIvVRYQoKcO0MHBBcWwJGb55ZaGv7Ksu/u4pGdI+jx7TS6BNmL3X12ogt7TjZnTKMfXVrH9HeQ03tzsELrl6omWyR81VLu2zba0bCw05czidxQ8eaF5WXI9lFE+51HRmVj7Co+u/FNx7FPe64sNv72H8fzdMIj5Fr1TNs7GIA13dewosuHjjHaXMnh9ikPRbtBR8XHEfGB3bWVdLqhy9dTXlSNfSsoRicQCAT1jXqpsBjNOvr+NM7xXi7y01Wnq4b67lfwQY936dE4hRO59rofg6J34yXZrlpVtSht5totKZLNebzGVHX1OMLCL/Jpz5UkDX2e1p/OJTvXi6SHny+zjkp0JVOt86x60i2+yFL5fmFsuXU5JkWL0abjYFaTEsccnzaBE5NdcLWohffVFmxFzbN7VpOHTS9tRqWRrfb6KrJNou2M5bR9oXqsZ9c+t5yOE2qupU4gqHWIOiwVpl4qLBkX/PjuXy8T+509+LYgNRlcS5d1hfB3Su6p82ViRwBWdPmQBxoV9maIaX6Gzi1O8VdKizLXthlUWn70UvFAS9VuZakKdvZb5HhtyjFgy7YHx0raks8Xtdwe/+HtY3L5XLHfTeWWrZOI/W4q2RYD/loj50z+5Z7/7vVr+eqmVzl2ofIWkPCVS7FkFmY1pQyfSsqoyZVetyyOzpiAZMG+KXBofvUE1RbUkAHoOFEoLgJBpVFU92z1kHqpsBQUJ9vet/p68aQ8WXKNlhA51/H6Gm1GsePlsbKoMlhy9CUek4LNJe53JymPT3UEn5ZmZUgcb4/vCPCyKyzd/ld+95qfzoyqSjT3z8CgsaKTbHwc+xZgz/YpYOWRW666jjuUUUmRkPM987E5PG88GgscXFB9GUBOXWHr53ekQOBehIWlwtRLhQVce2BWJT0jEh0PWl0Jbg4fycqek1fvPH1i8gQko/2/MuLVwkyW41Mn1LiMJrPNXgQuJ798fZ8iV8ThqzOh09iVlM23vIJFta8xd/9dWNXConKj2/zsfoGBiDeXEfHGMiJXxCFZJI/26TmwqPqUlev+Lx7JBhqz/ctxX/x4ujwlYmgEAoFnqLcKy593VH2AaHkJu2xZ6dzCucP0npPNad/idLH9JSFd7h+jy7m6RcadsQ+RLxdP8y3KvpPNiu1rEXCJqA8XYDaXL6M+6dmJxPin0bNhomNfiC6X94/dgEG2sLLr+07jI15bVq51y0PE68uIeDUOySwhmySoZ4H5qmSPndEU8eLtflOkVQsElULFDRYWT1+EZ6jzdVhqAztyWrLgctXXopRHUSlAY5SIiotHluxWidJScN0Z+1BWjZSOLf4ptu+zG98kMmUBN0efKPd5ZEkl1RQA2FO9V3b9b6ljK+sCi9o4H+W8l733z+Xg2oIy9/WlA3LXJ+NJeGcCWiNISpGGjQKBoPKISrcVpt5aWGoSAwP3sKZd8WqsrnB82gQUb4Xj0yagNqj6uJWyuFqwcNLDz3PJ5FOudeIP3c6Caz8jJacBQDGLSrG1XUw9/+xEF1YeuYXwdYuIeH0ZapoXsvVyzRPVbmWQ1PqjrIA9fbrriMuun8v34LqR9vfXPicCbwUCgWcQFpYagK9kQUFiW3IUPSPsro8vTnTi3ui9V53XdsZyDr1UaDFJftpeHj75kbLTayPXL6iyujIA14WfvOrxsjJ23j92A3tyW+CnsSs23/3rZdr8Zy5HBs+66rzyEvXhAmRZQZIHYUnzRrZJSFa7dqJqAKnsQnF1FUULGiv2YnWKXWEzB0lc93/x+GSrdHp2OXtXiNL/NZX205aDAgeWiP+jGomiUGn/slI/zZ5CYakBGFUtBsk53Vcph/GrqLLiKtoUr7IHVRHtvphDfo6BqI3zSRzyAi0/eonjD84AYOSfj/J2t/f44VLbYhVrjRe8nd6Hr16M9oIOVVNyp+SSmLv/Ltbui8WWo0XNuxywK2NXVii9C3J9QjaDqr1cUFO2pzV7n1ORbSo2vYT/KSs3D1zCL1/XrO7kAjuqDAerMThb4CLCJVRhhMJSAwiUTbRvcZr3j91Az8v7vrzYmbVnxrLpptcqvG5UfBw0MZH40PNO+2x+CinPV33tkNI4eO8cIj5YSIPAXPr+NI4bIrIdxwoaFZZUXj/l/5xl1mRqkS2gzZRouTgeS4gVOU9TomVk2cF+fPZPZ04n3oxklRzJ4pINFG+FxGc8dz9qAjffuQRFJ6FoJfRaCUUHZn8JbZ6EpKpoLKrdPaao/Pb5ZDpMFq6hmkjMzOWgKXucQFAbEQpLDcBy2ZoyrNUOx7413dew+uiNlVq3JGtB4oSJtFzi+dTUkAY5ZGR70/eaw/x+Psrl+a0WxCNpJVQtKHqQzRLadC2KXiV643xODHmBpxMewSBb+D0tirR/eiPnadBYJFStiqpXUbQKKaPrt6ICcNM9S9FYVXLDtPictyEpINvsGUK5YTKyWUK2qtgMEuYg+xzZctUlBVVMzMzlHJ5XghVFpt5mkNQahIWlwgiFpQZw2BxK5xL2f3X+Wp5o7f7zHZ/i+dRUX72ZbNnAgms/c2lezOzl2LxUZLOExgRWH3uK39GZ9i/vbv97nudb/48RfxwjOacxxxPDkLM1aBR70TcArJ6tpVKTiB0Sh96soGgk9NkK6mVPpGy1ZwdZfEHyAdkqIZvsAbkA2vz6+YVZY7jCY+xodCrZt5jZ9veHXxxP2xeWV1tlZEE5EN2aK4zIEqoBDGn5J1+c6MSOlAjHvog3lqGXq7+vUVlEvOGeOifnsvwwZbgeR2P1Vjk2fQJH5oxHlezF8UwhhQFowyJ2Mf/IAN69fi1Je65BztUgWyUkRULRqpyYNKHc8S71gdxQGYu/BiTsbh+NZH/oXf5O1ZhAm2cPxPU5r+B1Hjo9u5y/3qpf97AgS6q6iZlVsuvt8ItXKCASHJkz3vGvKuNQPhVdFQspEFQTQmGpIVyZEZT89CQ2xq5y2/q3bJ3k9D581dIKrVOQiQSVa2R4eNDsUtsVXI3j0woflOYGdkXl5QGFKeHxv/YjYYBdrsTxE0l6diInJk3gxKQJZdaNqS90fjqebsPj6fFIHJIKilZC0UmosuR4yCHZrSxBJ2wEHbfic1bF+5wFqy/1LkOoy+h4ZJv9vlUHBUpKzOzlTtYShxWlBIr1ESu6TyqcGzNzOe2mi/gjT6Kqilu2+ohQWGoQN4Qnl3osupJxJz/3draMaLIrH5l3YsgL5R4b8br7KtAWKFvv3rWKbclRBMhG+v/8HBGvxnmsz09tYs8bE5AU0JhUNJddO6pGKtqE+nJKswoS6LMtNP75LLJNrdbWADUFSQVjA4k9b1TeqtT2heW0nVGoMMTMLEF5uPz/oEqFW9H9bV4sh+JyeWzMrOUcmT3ebn0BtEY4uLD+/R/WKFQ3ND6spzEs4tu9luDuaqPV7RZJHmO3zLRcXKh4Rby/sLThTkS+4twC4LmbvuOPlHDCtVlsyuzC8B+f4PDB5qBRKUevSAFg87IrKYZsFbNfEctKwfegBKYADZkRGrKbGVDTL6E/dclT4nqUv96agOqmaD9VxkkxPDxvvMOCUnQMFLp4kKDN3MIxJVlTSqLN3OUcnus8dv9Soax4HNH8sMIIhaWWUNQVUlFafzrXDZJUDtksEb7SbiGRL5TcYfpKrgyQndB2C8fMoQw79Cgf/9ATbboOySKR/PSkelvszVX+emsCxmAJk7+ExgyK5rLSUvCrXrY/VTUmMAXJoKhsPrrEs0J7ENniJpfQZQWkKKrk7O4pppAUsbAUVVxazyvdUuNkmREI6ghCYalHHL3PPVViK4PVT0EfYgTAFmSt8DoPt9rFtr6LSRxvj1Gpb40J3YEq21PCrV5g9ZawGaRCxUUFrVHF6g02A2T1betpcT2KKoGic4MGUERhiZm9vNS4FIdiUoJyU1RRcYy94pv86MzxHJ05ntYvlaxkXc2tJKhiFMU9Wz1EKCyCKqdoZpHNV+HY/TMBSBk+FYAuX5ccCzPij8cB2JIUU+Y5RECt6+x5fQJ7Xp+AopXIbyRh9rMXjFM1ElYvCWOQDDIEJVrZ9vGkshesw6gy/P2K6+6UK7N8ilo+1ALlpcDt8+Jy2sxd7lBIWs9bXiyOpSB9v0ARaT1vOaqsOhp0FpNbokSlpbxuJUEVIFxCFUYoLPWQqA8XVOv5imYWSd7FrSrp5wPo/WNxl9fMJt8y4Jdn+Ty9W5XKV99RtfbUZauP3cJiNUhojSr6HIWAJBuKtn77FjpMXm5vhlkRity6K2NVnNxDV7iKCgNtVcdWVGk5OmOCQ3lBlQpfX6bAQnPshQkcnVG/UtAFdRehsNRDipbqr2q6b3ZuxOgbYHS8jvnsRQCkfA2B+vxicyOaneWbm1fw7Q/XVa2Q9RxJAV2OiqSCxddeh0XRSWgsKpnRGvIbyLR9of66EPYvHY9cgQboMbOWc/jFwqDags7fYI9ZOTJ7fOn1wwoGXmFiudId5FBoruDIrOIWlAIlps2Ly0ut7yKoelRFcctWHxEKi6DKCH9nCeeP2rsyF6Rlmw4HOo4fHjQbAK/QXA6dCwXg3t+eLrbOicniF2JVsnfFeIwhEihgDrTHrCga0GXZ8Lqo4n/aStBxGz2GxpW9WB1FtrhuYinI0CmwjBQUOyxqTTkye3yxNGanSeBkZSlQTgrcPK4E10rWwnNemT0kqEaES6jCCIWlltH6pfga0QuoXMgqisH+S+DElAlExcVzfGpx5ePwoNkc+vccAL7o9UZ1Sii4zP5l49m3fDyH5o/HHGBvgqjoZbzSFSw+MqZAmbzG9ffrQnXRLVbUglFQAwWwf+NeVlgKrCVXKhxHZ44vdAPJqpORRZXsfbSurM3SakE8rRZc/XtBVLwV1HbqbC8h9bIGmpWV5WFJSqf9B8s58EjxXzodV77KvtHPFNvf4Z0V4AOqrNLi7dnsf7BmBJp2fONV9j1dXF4lz4Sktzn+D3ybny32//H20QGMbP1NtcgpKB/bXxhB7JTXUf0VbLrLwbh6kCw1+/NUVdzw/Ouglv/ar1v4On9NH0PL6Qv5a/oYp2NWk5HdU8fQZfHrIKlEz7ginkyyn8dmMhbbfyXRsxYUtlG4/G/07AXsnjiWLnGvsXviWAC6LLvc8d2Fa6iPFNwbtaqtF0rJbjyXqKcWFkmt8v8dz5CYmEh0dLSnxRAIBAJBLeLUqVM0a9bM7etmZWURGBjIbfr70UqVM3dZVQs/mD8hMzOTgIAAN0lY86mzFpYGDRoAcPLkSQIDA8sYLbiSrKwsmjdvzqlTp+rVB8JdiPtXOcT9qxzi/rmOqqpkZ2fTtGlTT4siKIU6q7DIst3fHhgYKD6wlSAgIEDcv0og7l/lEPevcoj75xrV8eNWVVTUSrqE6qhjpEzqrMIiEAgEAkGNQ1WodGnuetqtWSgsAoFAIBBUE8LCUnHqbJ6iwWBg9uzZGAwGT4tSKxH3r3KI+1c5xP2rHOL+CeoidTZLSCAQCASCmkJBllAvBqClkllCWPiNb0SWkEAgEAgEAvei1+sJCwvjt1T31J0KCwtDr9e7Za3agrCwCAQCgUBQDRiNRszmCjSmKgG9Xo+Xl5db1qotCIVFIBAIBAJBjafOBt0KBAKBQCCoOwiFRSAQCAQCQY2nTiosr7/+OhEREXh5edGjRw927drlaZE8wsKFC7n++uvx9/encePG3HvvvRw5csRpjNFoZMyYMYSEhODn58fgwYNJS0tzGnPy5EkGDhyIj48PjRs3ZvLkyVitVqcxP/30E9dddx0Gg4GWLVuydu3aqr68amXRokVIksS4ceMc+8S9K5vTp0/zyCOPEBISgre3Nx07duTPP/90HFdVlVmzZtGkSRO8vb3p06cPx44dc1ojPT2doUOHEhAQQFBQECNGjCAnJ8dpzN9//81NN92El5cXzZs3Z8mSJdVyfVWJzWZj5syZREZG4u3tTXR0NPPmzXOqwSHun6BeodYxNm7cqOr1enX16tXqgQMH1JEjR6pBQUFqWlqap0Wrdvr166euWbNG3b9/v7pnzx51wIABaosWLdScnBzHmNGjR6vNmzdXt27dqv7555/qDTfcoPbs2dNx3Gq1qh06dFD79Omj7t69W/3mm2/Uhg0bqtOnT3eMSUxMVH18fNQJEyaoBw8eVF999VVVo9GomzdvrtbrrSp27dqlRkREqNdee6363HPPOfaLe3d10tPT1fDwcPXxxx9Xd+7cqSYmJqrffvutevz4cceYRYsWqYGBgeoXX3yh7t27V7377rvVyMhINT8/3zGmf//+aqdOndQdO3aov/76q9qyZUv1oYcechzPzMxUQ0ND1aFDh6r79+9XP/zwQ9Xb21t96623qvV63c38+fPVkJAQ9auvvlKTkpLUTz75RPXz81NfeeUVxxhx/wT1iTqnsHTv3l0dM2aM473NZlObNm2qLly40INS1QzOnTunAurPP/+sqqqqZmRkqDqdTv3kk08cYw4dOqQC6vbt21VVVdVvvvlGlWVZTU1NdYx588031YCAANVkMqmqqqpTpkxR27dv73SuBx98UO3Xr19VX1KVk52drbZq1UrdsmWLessttzgUFnHvymbq1Klqr169Sj2uKIoaFhamLl261LEvIyNDNRgM6ocffqiqqqoePHhQBdQ//vjDMeZ///ufKkmSevr0aVVVVfWNN95Qg4ODHfe04Nxt2rRx9yVVKwMHDlSfeOIJp32DBg1Shw4dqqqquH+C+kedcgmZzWYSEhLo06ePY58sy/Tp04ft27d7ULKaQWZmJlDYyTohIQGLxeJ0v2JiYmjRooXjfm3fvp2OHTsSGhrqGNOvXz+ysrI4cOCAY0zRNQrG1IV7PmbMGAYOHFjs+sS9K5tNmzbRrVs37r//fho3bkyXLl14++23HceTkpJITU11uv7AwEB69OjhdA+DgoLo1q2bY0yfPn2QZZmdO3c6xtx8881ONSn69evHkSNHuHTpUlVfZpXRs2dPtm7dytGjRwHYu3cvv/32G3fccQcg7p+g/lGnCsdduHABm83m9IAACA0N5fDhwx6SqmagKArjxo3jxhtvpEOHDgCkpqai1+sJCgpyGhsaGkpqaqpjTEn3s+DY1cZkZWWRn5+Pt7d3VVxSlbNx40b++usv/vjjj2LHxL0rm8TERN58800mTJjA888/zx9//MGzzz6LXq/nsccec9yDkq6/6P1p3Lix03GtVkuDBg2cxkRGRhZbo+BYcHBwlVxfVTNt2jSysrKIiYlBo9Fgs9mYP38+Q4cOBRD3T1DvqFMKi6B0xowZw/79+/ntt988LUqt4NSpUzz33HNs2bKl3hVncheKotCtWzcWLFgAQJcuXdi/fz8rV67kscce87B0NZ+PP/6Y9evXs2HDBtq3b8+ePXsYN24cTZs2FfdPUC+pUy6hhg0botFoimVqpKWlERYW5iGpPM/YsWP56quv+PHHH2nWrJljf1hYGGazmYyMDKfxRe9XWFhYifez4NjVxgQEBNRaC0FCQgLnzp3juuuuQ6vVotVq+fnnn1mxYgVarZbQ0FBx78qgSZMmtGvXzmlf27ZtOXnyJFB4D672eQ0LC+PcuXNOx61WK+np6S7d59rI5MmTmTZtGkOGDKFjx44MGzaM8ePHs3DhQkDcP0H9o04pLHq9nq5du7J161bHPkVR2Lp1K7GxsR6UzDOoqsrYsWP5/PPP+eGHH4qZfbt27YpOp3O6X0eOHOHkyZOO+xUbG8u+ffucvvS2bNlCQECA42EUGxvrtEbBmNp8z3v37s2+ffvYs2ePY+vWrRtDhw51vBb37urceOONxdLojx49Snh4OACRkZGEhYU5XX9WVhY7d+50uocZGRkkJCQ4xvzwww8oikKPHj0cY3755RcsFotjzJYtW2jTpk2tdmfk5eUhy85f0RqNBkVRAHH/BPUQT0f9upuNGzeqBoNBXbt2rXrw4EF11KhRalBQkFOmRn3hqaeeUgMDA9WffvpJPXv2rGPLy8tzjBk9erTaokUL9YcfflD//PNPNTY2Vo2NjXUcL0jN7du3r7pnzx518+bNaqNGjUpMzZ08ebJ66NAh9fXXX68zqblFKZolpKri3pXFrl27VK1Wq86fP189duyYun79etXHx0f94IMPHGMWLVqkBgUFqf/973/Vv//+W73nnntKTMvt0qWLunPnTvW3335TW7Vq5ZSWm5GRoYaGhqrDhg1T9+/fr27cuFH18fGp9Wm5jz32mHrNNdc40po/++wztWHDhuqUKVMcY8T9E9Qn6pzCoqqq+uqrr6otWrRQ9Xq92r17d3XHjh2eFskjACVua9ascYzJz89Xn376aTU4OFj18fFR//3vf6tnz551Wic5OVm94447VG9vb7Vhw4bqxIkTVYvF4jTmxx9/VDt37qzq9Xo1KirK6Rx1hSsVFnHvyubLL79UO3TooBoMBjUmJkZdtWqV03FFUdSZM2eqoaGhqsFgUHv37q0eOXLEaczFixfVhx56SPXz81MDAgLU4cOHq9nZ2U5j9u7dq/bq1Us1GAzqNddcoy5atKjKr62qycrKUp977jm1RYsWqpeXlxoVFaW+8MILTunH4v4J6hOi+aFAIBAIBIIaT52KYREIBAKBQFA3EQqLQCAQCASCGo9QWAQCgUAgENR4hMIiEAgEAoGgxiMUFoFAIBAIBDUeobAIBAKBQCCo8QiFRSAQCAQCQY1HKCwCgUAgEAhqPEJhEQgEAoFAUOMRCotAIBAIBIIaj1BYBAKBQCAQ1Hj+H+wMVdfw6vxiAAAAAElFTkSuQmCC", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/env/lib/python3.12/site-packages/xarray/groupers.py:392: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " self.index_grouper = pd.Grouper(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "NDVI monthly shape: (13, 8874, 9902)\n", - "CPU times: user 19.7 s, sys: 7.91 s, total: 27.6 s\n", - "Wall time: 9min 43s\n" - ] - } - ], - "source": [ - "%%time\n", - "# ── Loại bỏ mây + tính NDVI ───────────────────────────────────────────────────\n", - "result = mask_clean(data)\n", - "progress(result)\n", - "\n", - "ds1 = calculate_indices(result, index=\"NDVI\", satellite_mission=\"s2\")\n", - "ndvi = ds1[\"NDVI\"]\n", - "display(ndvi)\n", - "\n", - "## Hiển thị ảnh NDVI trước khi fill mây\n", - "plt.imshow(ndvi.isel(time=6))\n", - "plt.title(\"NDVI (before cloud fill)\")\n", - "plt.colorbar()\n", - "plt.show()\n", - "\n", - "# ── Fill nan theo mùa vụ ──────────────────────────────────────────────────────\n", - "time_split = [\n", - " slice(\"2022-09-01\", \"2023-01-01\"),\n", - " slice(\"2023-01-01\", \"2023-05-01\"),\n", - " slice(\"2023-05-01\", \"2023-07-01\"),\n", - " slice(\"2023-07-01\", \"2023-10-01\"),\n", - "]\n", - "fill_nan_ndvi = fill_nan(ndvi, time_split)\n", - "\n", - "plt.imshow(fill_nan_ndvi.isel(time=6))\n", - "plt.title(\"NDVI (after cloud fill)\")\n", - "plt.colorbar()\n", - "plt.show()\n", - "\n", - "# ── Resample về trung bình tháng ─────────────────────────────────────────────\n", - "average_ndvi = fill_nan_ndvi.resample(time=\"1M\").mean().persist()\n", - "progress(average_ndvi)\n", - "average_ndvi = average_ndvi.compute()\n", - "print(f\"NDVI monthly shape: {average_ndvi.shape}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7cae5302", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "

Dataset size: 21.60 GB

" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
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<xarray.Dataset> Size: 23GB\n",
-       "Dimensions:      (time: 33, y: 8874, x: 9902)\n",
-       "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 264B 2022-09-06T22:46:14.500000 ... 20...\n",
-       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
-       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
-       "    spatial_ref  int32 4B 32648\n",
-       "Data variables:\n",
-       "    vv           (time, y, x) float32 12GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "    vh           (time, y, x) float32 12GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    crs:           EPSG:32648\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - " Size: 23GB\n", - "Dimensions: (time: 33, y: 8874, x: 9902)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 264B 2022-09-06T22:46:14.500000 ... 20...\n", - " * y (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n", - " * x (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n", - " spatial_ref int32 4B 32648\n", - "Data variables:\n", - " vv (time, y, x) float32 12GB dask.array\n", - " vh (time, y, x) float32 12GB dask.array\n", - "Attributes:\n", - " crs: EPSG:32648\n", - " grid_mapping: spatial_ref" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/env/lib/python3.12/site-packages/xarray/groupers.py:392: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " self.index_grouper = pd.Grouper(\n", - "/env/lib/python3.12/site-packages/xarray/groupers.py:392: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " self.index_grouper = pd.Grouper(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "VV monthly shape: (13, 8874, 9902)\n", - "VH monthly shape: (13, 8874, 9902)\n" - ] - } - ], - "source": [ - "# ── Load Sentinel-1 (VH, VV) và tính trung bình tháng ────────────────────────\n", - "dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)\n", - "average_vv = calculate_average(dsvv, time_pattern=\"1M\")\n", - "average_vh = calculate_average(dsvh, time_pattern=\"1M\")\n", - "\n", - "print(f\"VV monthly shape: {average_vv.shape}\")\n", - "print(f\"VH monthly shape: {average_vh.shape}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "8901a611", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "X_train: (678, 39) y_train: (678,)\n", - "X_val : (226, 39) y_val : (226,)\n", - "X_test : (226, 39) y_test : (226,)\n" - ] - } - ], - "source": [ - "## ── Chuẩn bị dữ liệu train ───────────────────────────────────────────────────\n", - "train_path = \"train/ST_training_data_updated_1130points_new.shp\"\n", - "\n", - "train = load_train_data(train_path)\n", - "train.head()\n", - "\n", - "label_mapping = {\n", - " \"Lua tom\": \"0\",\n", - " \"Lua\": \"1\",\n", - " \"CHN\": \"2\",\n", - " \"CLN\": \"3\",\n", - " \"TS\": \"4\",\n", - " \"Song\": \"5\",\n", - " \"Dat xay dung\": \"6\",\n", - " \"Rung\": \"7\",\n", - "}\n", - "\n", - "# Xây dựng dataset gồm VH, VV, NDVI\n", - "datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)\n", - "\n", - "# Chia 80-20-20\n", - "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n", - " train, label_mapping, datasets\n", - ")\n", - "\n", - "print(f\"X_train: {np.asarray(X_train).shape} y_train: {np.asarray(y_train).shape}\")\n", - "print(f\"X_val : {np.asarray(X_val).shape} y_val : {np.asarray(y_val).shape}\")\n", - "print(f\"X_test : {np.asarray(X_test).shape} y_test : {np.asarray(y_test).shape}\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "64492335", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SwinUNetClassifier(\n", - " (adapter): Sequential(\n", - " (0): Linear(in_features=39, out_features=256, bias=True)\n", - " (1): ReLU()\n", - " (2): Dropout(p=0.1, inplace=False)\n", - " (3): Linear(in_features=256, out_features=128, bias=True)\n", - " )\n", - " (encoder1): Sequential(\n", - " (0): Linear(in_features=128, out_features=128, bias=True)\n", - " (1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (2): GELU(approximate='none')\n", - " (3): Dropout(p=0.1, inplace=False)\n", - " )\n", - " (down1): Linear(in_features=128, out_features=256, bias=True)\n", - " (encoder2): Sequential(\n", - " (0): Linear(in_features=256, out_features=256, bias=True)\n", - " (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", - " (2): GELU(approximate='none')\n", - " (3): Dropout(p=0.1, inplace=False)\n", - " )\n", - " (down2): Linear(in_features=256, out_features=512, bias=True)\n", - " (encoder3): Sequential(\n", - " (0): Linear(in_features=512, out_features=512, bias=True)\n", - " (1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n", - " (2): GELU(approximate='none')\n", - " (3): Dropout(p=0.1, inplace=False)\n", - " )\n", - " (up2): Linear(in_features=512, out_features=256, bias=True)\n", - " (decoder2): Sequential(\n", - " (0): Linear(in_features=512, out_features=256, bias=True)\n", - " (1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", - " (2): GELU(approximate='none')\n", - " (3): Dropout(p=0.1, inplace=False)\n", - " )\n", - " (up1): Linear(in_features=256, out_features=128, bias=True)\n", - " (decoder1): Sequential(\n", - " (0): Linear(in_features=256, out_features=128, bias=True)\n", - " (1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n", - " (2): GELU(approximate='none')\n", - " (3): Dropout(p=0.1, inplace=False)\n", - " )\n", - " (attention): MultiheadAttention(\n", - " (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n", - " )\n", - " (classifier): Sequential(\n", - " (0): Linear(in_features=128, out_features=64, bias=True)\n", - " (1): GELU(approximate='none')\n", - " (2): Dropout(p=0.3, inplace=False)\n", - " (3): Linear(in_features=64, out_features=8, bias=True)\n", - " )\n", - ")\n", - "\n", - "Total parameters: 958,536\n", - "n_features=39 n_classes=8 embed_dim=128\n", - "\n", - "CPU times: user 11.6 ms, sys: 299 μs, total: 11.9 ms\n", - "Wall time: 12.6 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# ── Kiến trúc Swin-UNet (giống train_module.py / api_server) ─────────────────\n", - "import numpy as np\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "from torch.utils.data import TensorDataset, DataLoader\n", - "\n", - "class SwinUNetClassifier(nn.Module):\n", - " \"\"\"\n", - " Swin Transformer U-Net style architecture adapted for feature vector classification.\n", - " Combines hierarchical Swin Transformer blocks with skip connections.\n", - " Identical to SwinUNetClassifier in train_module.py.\n", - " \"\"\"\n", - " def __init__(self, n_features, n_classes, embed_dim=128):\n", - " super().__init__()\n", - " self.n_features = n_features\n", - " self.n_classes = n_classes\n", - " self.embed_dim = embed_dim\n", - "\n", - " # Feature adapter\n", - " self.adapter = nn.Sequential(\n", - " nn.Linear(n_features, embed_dim * 2),\n", - " nn.ReLU(),\n", - " nn.Dropout(0.1),\n", - " nn.Linear(embed_dim * 2, embed_dim),\n", - " )\n", - "\n", - " # Encoder\n", - " self.encoder1 = nn.Sequential(\n", - " nn.Linear(embed_dim, embed_dim), nn.LayerNorm(embed_dim), nn.GELU(), nn.Dropout(0.1)\n", - " )\n", - " self.down1 = nn.Linear(embed_dim, embed_dim * 2)\n", - "\n", - " self.encoder2 = nn.Sequential(\n", - " nn.Linear(embed_dim * 2, embed_dim * 2), nn.LayerNorm(embed_dim * 2), nn.GELU(), nn.Dropout(0.1)\n", - " )\n", - " self.down2 = nn.Linear(embed_dim * 2, embed_dim * 4)\n", - "\n", - " self.encoder3 = nn.Sequential(\n", - " nn.Linear(embed_dim * 4, embed_dim * 4), nn.LayerNorm(embed_dim * 4), nn.GELU(), nn.Dropout(0.1)\n", - " )\n", - "\n", - " # Decoder with skip connections\n", - " self.up2 = nn.Linear(embed_dim * 4, embed_dim * 2)\n", - " self.decoder2 = nn.Sequential(\n", - " nn.Linear(embed_dim * 4, embed_dim * 2), nn.LayerNorm(embed_dim * 2), nn.GELU(), nn.Dropout(0.1)\n", - " )\n", - "\n", - " self.up1 = nn.Linear(embed_dim * 2, embed_dim)\n", - " self.decoder1 = nn.Sequential(\n", - " nn.Linear(embed_dim * 2, embed_dim), nn.LayerNorm(embed_dim), nn.GELU(), nn.Dropout(0.1)\n", - " )\n", - "\n", - " # Attention for better aggregation\n", - " self.attention = nn.MultiheadAttention(embed_dim, num_heads=4, batch_first=True)\n", - "\n", - " # Classification head\n", - " self.classifier = nn.Sequential(\n", - " nn.Linear(embed_dim, embed_dim // 2),\n", - " nn.GELU(),\n", - " nn.Dropout(0.3),\n", - " nn.Linear(embed_dim // 2, n_classes),\n", - " )\n", - "\n", - " def forward(self, x):\n", - " if len(x.shape) == 3:\n", - " x = x.squeeze(1)\n", - "\n", - " # Adapter\n", - " x = self.adapter(x) # (B, embed_dim)\n", - " x_seq = x.unsqueeze(1) # (B, 1, embed_dim)\n", - "\n", - " # Encoder\n", - " x1 = self.encoder1(x_seq) # (B, 1, embed_dim)\n", - " x_d1 = self.down1(x1.squeeze(1)) # (B, embed_dim*2)\n", - "\n", - " x2 = self.encoder2(x_d1.unsqueeze(1)) # (B, 1, embed_dim*2)\n", - " x_d2 = self.down2(x2.squeeze(1)) # (B, embed_dim*4)\n", - "\n", - " x3 = self.encoder3(x_d2.unsqueeze(1)) # (B, 1, embed_dim*4)\n", - "\n", - " # Decoder\n", - " x_u2 = self.up2(x3.squeeze(1)) # (B, embed_dim*2)\n", - " x_cat2 = torch.cat([x_u2, x_d1], dim=1) # (B, embed_dim*4)\n", - " x_dec2 = self.decoder2(x_cat2) # (B, embed_dim*2)\n", - "\n", - " x_u1 = self.up1(x_dec2) # (B, embed_dim)\n", - " x_cat1 = torch.cat([x_u1, x.squeeze(1) if len(x.shape)==3 else x], dim=1) # (B, embed_dim*2)\n", - " x_dec1 = self.decoder1(x_cat1) # (B, embed_dim)\n", - "\n", - " # Attention\n", - " x_seq2 = x_dec1.unsqueeze(1)\n", - " attn, _ = self.attention(x_seq2, x_seq2, x_seq2)\n", - "\n", - " return self.classifier(attn.squeeze(1))\n", - "\n", - " def predict(self, X):\n", - " \"\"\"Scikit-learn style predict.\"\"\"\n", - " self.eval()\n", - " with torch.no_grad():\n", - " if isinstance(X, np.ndarray):\n", - " X = torch.FloatTensor(X)\n", - " outputs = self(X)\n", - " return outputs.argmax(1).cpu().numpy()\n", - "\n", - " def score(self, X, y):\n", - " preds = self.predict(X)\n", - " if isinstance(y, torch.Tensor):\n", - " y = y.cpu().numpy()\n", - " return float(np.mean(preds == y))\n", - "\n", - "\n", - "# ── Chuẩn bị tensor & DataLoader ─────────────────────────────────────────────\n", - "X_train_np = np.asarray(X_train, dtype=np.float32)\n", - "X_val_np = np.asarray(X_val, dtype=np.float32)\n", - "y_train_np = np.asarray(y_train, dtype=np.int64)\n", - "y_val_np = np.asarray(y_val, dtype=np.int64)\n", - "\n", - "n_features = X_train_np.shape[1]\n", - "NUM_CLASSES = len(label_mapping)\n", - "\n", - "X_train_t = torch.from_numpy(X_train_np)\n", - "X_val_t = torch.from_numpy(X_val_np)\n", - "y_train_t = torch.from_numpy(y_train_np)\n", - "y_val_t = torch.from_numpy(y_val_np)\n", - "\n", - "train_loader = DataLoader(TensorDataset(X_train_t, y_train_t), batch_size=BATCH_SIZE, shuffle=True)\n", - "val_loader = DataLoader(TensorDataset(X_val_t, y_val_t), batch_size=64, shuffle=False)\n", - "\n", - "# ── Khởi tạo mô hình ─────────────────────────────────────────────────────────\n", - "model = SwinUNetClassifier(n_features, NUM_CLASSES, embed_dim=EMBED_DIM).to(DEVICE)\n", - "print(model)\n", - "total_params = sum(p.numel() for p in model.parameters())\n", - "print(f\"\\nTotal parameters: {total_params:,}\")\n", - "print(f\"n_features={n_features} n_classes={NUM_CLASSES} embed_dim={EMBED_DIM}\\n\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d26a3308", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Class distribution : [ 34 123 40 106 90 73 130 82]\n", - "Class weights : [2.017 0.557 1.714 0.647 0.762 0.939 0.527 0.836]\n", - "\n", - "🚀 Training Swin-UNet model (PyTorch)...\n", - "Epoch 1/150 train_loss=1.7518 train_acc=0.2817 val_loss=1.3761 val_acc=0.5000 lr=0.000999\n", - "Epoch 10/150 train_loss=0.7992 train_acc=0.7198 val_loss=0.8853 val_acc=0.7080 lr=0.000905\n", - "Epoch 20/150 train_loss=0.6027 train_acc=0.7906 val_loss=0.6083 val_acc=0.8097 lr=0.000655\n", - "Epoch 30/150 train_loss=0.4327 train_acc=0.8555 val_loss=0.5047 val_acc=0.8761 lr=0.000345\n", - "Epoch 40/150 train_loss=0.3410 train_acc=0.8953 val_loss=0.5917 val_acc=0.8850 lr=0.000095\n", - "Epoch 50/150 train_loss=0.3022 train_acc=0.8968 val_loss=0.5913 val_acc=0.8761 lr=0.000000\n", - "Epoch 60/150 train_loss=0.3282 train_acc=0.8923 val_loss=0.5988 val_acc=0.8761 lr=0.000095\n", - "\n", - "Early stopping tại epoch 61 (không cải thiện 20 epochs liên tiếp)\n", - "\n", - "✅ Training hoàn tất! Best val accuracy: 0.8938 (89.38%)\n", - "CPU times: user 6min 42s, sys: 297 ms, total: 6min 43s\n", - "Wall time: 23.3 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# ── Train Swin-UNet ───────────────────────────────────────────────────────────\n", - "\n", - "# Class weights để xử lý mất cân bằng dữ liệu (giống api_server)\n", - "class_counts = np.bincount(y_train_np)\n", - "class_weights = 1.0 / (class_counts + 1e-6)\n", - "class_weights = class_weights / class_weights.sum() * len(class_counts)\n", - "class_weights_t = torch.FloatTensor(class_weights).to(DEVICE)\n", - "\n", - "print(f\"Class distribution : {class_counts}\")\n", - "print(f\"Class weights : {np.round(class_weights, 3)}\\n\")\n", - "\n", - "criterion = nn.CrossEntropyLoss(weight=class_weights_t)\n", - "optimizer = optim.AdamW(model.parameters(), lr=LR, weight_decay=WEIGHT_DECAY)\n", - "scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50)\n", - "\n", - "best_val_acc = 0.0\n", - "best_state = None\n", - "no_improve = 0\n", - "history = {\"train_loss\": [], \"train_acc\": [], \"val_loss\": [], \"val_acc\": []}\n", - "\n", - "def evaluate(loader):\n", - " model.eval()\n", - " total_loss, correct, n = 0.0, 0, 0\n", - " with torch.no_grad():\n", - " for xb, yb in loader:\n", - " xb, yb = xb.to(DEVICE), yb.to(DEVICE)\n", - " logits = model(xb)\n", - " total_loss += criterion(logits, yb).item() * len(yb)\n", - " correct += (logits.argmax(1) == yb).sum().item()\n", - " n += len(yb)\n", - " return total_loss / n, correct / n\n", - "\n", - "print(\"🚀 Training Swin-UNet model (PyTorch)...\")\n", - "for epoch in range(1, EPOCHS + 1):\n", - " model.train()\n", - " t_loss, t_correct, t_n = 0.0, 0, 0\n", - " for xb, yb in train_loader:\n", - " xb, yb = xb.to(DEVICE), yb.to(DEVICE)\n", - " optimizer.zero_grad()\n", - " logits = model(xb)\n", - " loss = criterion(logits, yb)\n", - " loss.backward()\n", - " # Gradient clipping — quan trọng cho Swin blocks\n", - " torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n", - " optimizer.step()\n", - " t_loss += loss.item() * len(yb)\n", - " t_correct += (logits.argmax(1) == yb).sum().item()\n", - " t_n += len(yb)\n", - "\n", - " scheduler.step()\n", - " train_loss, train_acc = t_loss / t_n, t_correct / t_n\n", - " val_loss, val_acc = evaluate(val_loader)\n", - " lr_now = optimizer.param_groups[0][\"lr\"]\n", - "\n", - " history[\"train_loss\"].append(train_loss)\n", - " history[\"train_acc\"].append(train_acc)\n", - " history[\"val_loss\"].append(val_loss)\n", - " history[\"val_acc\"].append(val_acc)\n", - "\n", - " if val_acc > best_val_acc:\n", - " best_val_acc = val_acc\n", - " best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}\n", - " no_improve = 0\n", - " else:\n", - " no_improve += 1\n", - "\n", - " if epoch % 10 == 0 or epoch == 1:\n", - " print(f\"Epoch {epoch:3d}/{EPOCHS} \"\n", - " f\"train_loss={train_loss:.4f} train_acc={train_acc:.4f} \"\n", - " f\"val_loss={val_loss:.4f} val_acc={val_acc:.4f} lr={lr_now:.6f}\")\n", - "\n", - " if no_improve >= PATIENCE:\n", - " print(f\"\\nEarly stopping tại epoch {epoch} (không cải thiện {PATIENCE} epochs liên tiếp)\")\n", - " break\n", - "\n", - "model.load_state_dict(best_state)\n", - "print(f\"\\n✅ Training hoàn tất! Best val accuracy: {best_val_acc:.4f} ({best_val_acc*100:.2f}%)\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4a806eaf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📊 Evaluating Swin-UNet on test set...\n", - "\n", - "📈 Test Results:\n", - " Accuracy : 0.8540 (85.40%)\n", - " Precision: 0.8893\n", - " Recall : 0.8540\n", - " F1-Score : 0.8667\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.06 s, sys: 307 ms, total: 1.36 s\n", - "Wall time: 581 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.metrics import (\n", - " accuracy_score, precision_score, recall_score, f1_score,\n", - " confusion_matrix, ConfusionMatrixDisplay,\n", - ")\n", - "\n", - "# ── 1. Đánh giá trên tập test ────────────────────────────────────────────────\n", - "X_test_np = np.asarray(X_test, dtype=np.float32)\n", - "y_test_np = np.asarray(y_test, dtype=np.int64)\n", - "X_test_t = torch.from_numpy(X_test_np)\n", - "\n", - "print(\"📊 Evaluating Swin-UNet on test set...\\n\")\n", - "\n", - "model.eval()\n", - "all_preds = []\n", - "with torch.no_grad():\n", - " for i in range(0, len(X_test_t), 64):\n", - " xb = X_test_t[i:i+64].to(DEVICE)\n", - " preds = model(xb).argmax(1).cpu().numpy()\n", - " all_preds.append(preds)\n", - "\n", - "y_pred_test = np.concatenate(all_preds)\n", - "\n", - "test_accuracy = accuracy_score(y_test_np, y_pred_test)\n", - "precision = precision_score(y_test_np, y_pred_test, average=\"weighted\", zero_division=0)\n", - "recall = recall_score(y_test_np, y_pred_test, average=\"weighted\", zero_division=0)\n", - "f1 = f1_score(y_test_np, y_pred_test, average=\"weighted\", zero_division=0)\n", - "\n", - "print(f\"📈 Test Results:\")\n", - "print(f\" Accuracy : {test_accuracy:.4f} ({test_accuracy*100:.2f}%)\")\n", - "print(f\" Precision: {precision:.4f}\")\n", - "print(f\" Recall : {recall:.4f}\")\n", - "print(f\" F1-Score : {f1:.4f}\\n\")\n", - "\n", - "# ── 2. Learning curves ───────────────────────────────────────────────────────\n", - "fig, axes = plt.subplots(1, 2, figsize=(14, 4))\n", - "\n", - "axes[0].plot(history[\"train_loss\"], label=\"Train loss\")\n", - "axes[0].plot(history[\"val_loss\"], label=\"Val loss\")\n", - "axes[0].set_title(\"Loss over epochs\"); axes[0].set_xlabel(\"Epoch\"); axes[0].legend()\n", - "\n", - "axes[1].plot(history[\"train_acc\"], label=\"Train accuracy\")\n", - "axes[1].plot(history[\"val_acc\"], label=\"Val accuracy\")\n", - "axes[1].set_title(\"Accuracy over epochs\"); axes[1].set_xlabel(\"Epoch\"); axes[1].legend()\n", - "\n", - "plt.tight_layout(); plt.show()\n", - "\n", - "# ── 3. Confusion matrix ──────────────────────────────────────────────────────\n", - "class_names = list(label_mapping.keys())\n", - "cm = confusion_matrix(y_test_np, y_pred_test)\n", - "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 8))\n", - "disp.plot(cmap=\"Blues\", ax=ax)\n", - "plt.xticks(rotation=45, ha=\"right\")\n", - "plt.title(\"Swin-UNet Confusion Matrix — Test Set\")\n", - "plt.tight_layout(); plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "cf1b5923", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Model saved to model_swinunet_land_use.pth\n", - "✅ Metadata saved to model_swinunet_land_use_info.json\n", - "\n", - "Summary:\n", - " n_features : 39\n", - " Parameters : 958,536\n", - " Test accuracy: 85.40%\n" - ] - } - ], - "source": [ - "import json\n", - "\n", - "# ── Lưu weights mô hình Swin-UNet ────────────────────────────────────────────\n", - "model_path = \"model_swinunet_land_use.pth\"\n", - "torch.save({\n", - " \"model_state_dict\": model.state_dict(),\n", - " \"n_features\": n_features,\n", - " \"num_classes\": NUM_CLASSES,\n", - " \"embed_dim\": EMBED_DIM,\n", - " \"label_mapping\": label_mapping,\n", - "}, model_path)\n", - "print(f\"✅ Model saved to {model_path}\")\n", - "\n", - "# ── Lưu metadata ─────────────────────────────────────────────────────────────\n", - "info = {\n", - " \"model_type\": \"Swin-UNet (PyTorch)\",\n", - " \"input_shape\": [n_features],\n", - " \"embed_dim\": EMBED_DIM,\n", - " \"num_classes\": NUM_CLASSES,\n", - " \"classes\": list(label_mapping.keys()),\n", - " \"label_mapping\": label_mapping,\n", - " \"num_parameters\": sum(p.numel() for p in model.parameters()),\n", - " \"accuracy\": float(test_accuracy),\n", - " \"precision\": float(precision),\n", - " \"recall\": float(recall),\n", - " \"f1_score\": float(f1),\n", - "}\n", - "\n", - "info_path = \"model_swinunet_land_use_info.json\"\n", - "with open(info_path, \"w\") as f:\n", - " json.dump(info, f, indent=2, ensure_ascii=False)\n", - "\n", - "print(f\"✅ Metadata saved to {info_path}\")\n", - "print(f\"\\nSummary:\")\n", - "print(f\" n_features : {n_features}\")\n", - "print(f\" Parameters : {info['num_parameters']:,}\")\n", - "print(f\" Test accuracy: {test_accuracy*100:.2f}%\")\n", - "\n", - "# ── Ví dụ load lại mô hình ──────────────────────────────────────────────────\n", - "# ck = torch.load(\"model_swinunet_land_use.pth\")\n", - "# model_loaded = SwinUNetClassifier(ck[\"n_features\"], ck[\"num_classes\"], ck[\"embed_dim\"])\n", - "# model_loaded.load_state_dict(ck[\"model_state_dict\"])\n", - "# model_loaded.eval()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "93c9d96e", - "metadata": {}, - "outputs": [], - "source": [ - "# đóng client, cluster\n", - "client.close()\n", - "cluster.close()\n" - ] - } - ], - "metadata": { - 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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", - " // Don't load bokeh if it is still initializing\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", - " // There is nothing to load\n", - " run_callbacks();\n", - " return null;\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(e) {\n", - " const src_el = e.srcElement\n", - " console.error(\"failed to load \" + (src_el.href 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(skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\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 const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.0.min.js\", 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!== 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, 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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/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = false;\n", - " const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = true;\n", - " const Bokeh = root.Bokeh;\n", - "\n", - " // Set a timeout 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const url = js_modules[i];\n", - " const escaped = encodeURI(url)\n", - " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", - " if (!window.requirejs) {\n", - " on_load();\n", - " }\n", - " continue;\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", - " const url = js_exports[name];\n", - " const escaped = encodeURI(url)\n", - " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", - " if (!window.requirejs) {\n", - " on_load();\n", - " }\n", - " continue;\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", - " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", - " const js_modules = [];\n", - " const js_exports = {};\n", - " const css_urls = [];\n", - " const 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 (let i = 0; i < inline_js.length; i++) {\n", - " try {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " } catch(e) {\n", - " if (!reloading) {\n", - " throw e;\n", - " }\n", - " }\n", - " }\n", - " // Cache old bokeh versions\n", - " if (Bokeh != undefined && !reloading) {\n", - " var NewBokeh = root.Bokeh;\n", - " if (Bokeh.versions === undefined) {\n", - " Bokeh.versions = new Map();\n", - " }\n", - " if (NewBokeh.version !== Bokeh.version) {\n", - " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", - " }\n", - " root.Bokeh = Bokeh;\n", - " }\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", - " 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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", - " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", - " if (!reloading && !bokeh_loaded) {\n", - " if (root.Bokeh) {\n", - " root.Bokeh = undefined;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " }\n", - " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - " }\n", - " // Give 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'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/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = false;\n", - " const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", - " const reloading = true;\n", - " const Bokeh = root.Bokeh;\n", - "\n", - " // Set a timeout for this load but only if we are not already initializing\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\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 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document.head.appendChild(element);\n", - " }\n", - " for (const name in js_exports) {\n", - " const url = js_exports[name];\n", - " const escaped = encodeURI(url)\n", - " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", - " if (!window.requirejs) {\n", - " on_load();\n", - " }\n", - " continue;\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", - " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", - " const js_modules = [];\n", - " const js_exports = {};\n", - " const css_urls = [];\n", - " const 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 (let i = 0; i < inline_js.length; i++) {\n", - " try {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " } catch(e) {\n", - " if (!reloading) {\n", - " throw e;\n", - " }\n", - " }\n", - " }\n", - " // Cache old bokeh versions\n", - " if (Bokeh != undefined && !reloading) {\n", - " var NewBokeh = root.Bokeh;\n", - " if (Bokeh.versions === undefined) {\n", - " Bokeh.versions = new Map();\n", - " }\n", - " if (NewBokeh.version !== Bokeh.version) {\n", - " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", - " }\n", - " root.Bokeh = Bokeh;\n", - " }\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", - " // If the timeout and bokeh was not successfully loaded we reset\n", - " // everything and try loading again\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_is_initializing = false;\n", - " root._bokeh_onload_callbacks = undefined;\n", - " root._bokeh_is_loading = 0\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", - " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", - " if (!reloading && !bokeh_loaded) {\n", - " if (root.Bokeh) {\n", - " root.Bokeh = undefined;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " }\n", - " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_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 const force = false;\n const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\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 // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\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(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const 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 const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\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 const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const 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 (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\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 const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n 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" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%time\n", - "# Cấu hình Daskgateway\n", - "cluster, client = notebook_utils.initialize_dask(use_gateway=True, workers=(1, 10))\n", - "# Khai báo 1 Datacube là dc\n", - "dc = datacube.Datacube()\n", - "\n", - "# Cấu hình truy cập dịch vụ S3\n", - "configure_s3_access(aws_unsigned=False, requester_pays=True, client=client)\n", - "\n", - "client" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fbed4c80-bbf8-4ea8-aa45-2460b2ba04c7", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "## cấu hình thời gian lấy ảnh và tọa độ\n", - "date_range = (\"2022-09-01\", \"2023-10-01\")\n", - "longtitude_range = (105.5, 106.4)\n", - "latitude_range = (9.2, 10.0)\n", - "\n", - "coordinates = (longtitude_range, latitude_range)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "6b90c49b-0665-4478-a23b-d111ef88eb79", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Most common native CRS: EPSG:32648\n", - "No datasets require offset correction\n", - "The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n" - ] - }, - { - "data": { - "text/html": [ - "

Dataset size: 111.21 GB

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<xarray.Dataset> Size: 119GB\n",
-       "Dimensions:      (time: 151, y: 8874, x: 9902)\n",
-       "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
-       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
-       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
-       "    spatial_ref  int32 4B 32648\n",
-       "Data variables:\n",
-       "    red          (time, y, x) float32 53GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "    nir          (time, y, x) float32 53GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "    scl          (time, y, x) uint8 13GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    crs:           EPSG:32648\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - " Size: 119GB\n", - "Dimensions: (time: 151, y: 8874, x: 9902)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n", - " * y (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n", - " * x (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n", - " spatial_ref int32 4B 32648\n", - "Data variables:\n", - " red (time, y, x) float32 53GB dask.array\n", - " nir (time, y, x) float32 53GB dask.array\n", - " scl (time, y, x) uint8 13GB dask.array\n", - "Attributes:\n", - " crs: EPSG:32648\n", - " grid_mapping: spatial_ref" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## truy vấn ảnh vệ tinh sen2\n", - "data = load_data(dc, date_range, longtitude_range, latitude_range)\n", - "notebook_utils.heading(notebook_utils.xarray_object_size(data))\n", - "display(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "2f6938b6-82e2-4916-bc1d-719c169e25e4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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bitsvaluesdescription
qa[0, 1, 2, 3, 4, 5, 6, 7]{'0': 'no data', '1': 'saturated or defective'...Sen2Cor Scene Classification
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" - ], - "text/plain": [ - " bits \\\n", - "qa [0, 1, 2, 3, 4, 5, 6, 7] \n", - "\n", - " values \\\n", - "qa {'0': 'no data', '1': 'saturated or defective'... \n", - "\n", - " description \n", - "qa Sen2Cor Scene Classification " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "{'0': 'no data',\n", - " '1': 'saturated or defective',\n", - " '2': 'dark area pixels',\n", - " '3': 'cloud shadows',\n", - " '4': 'vegetation',\n", - " '5': 'bare soils',\n", - " '6': 'water',\n", - " '7': 'unclassified',\n", - " '8': 'cloud medium probability',\n", - " '9': 'cloud high probability',\n", - " '10': 'thin cirrus',\n", - " '11': 'snow or ice'}" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.28 s, sys: 49.3 ms, total: 1.33 s\n", - "Wall time: 1.32 s\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c8438a72d39046cd998cbb7f5b98c4bb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox()" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-03-04 06:23:08,925 - tornado.application - ERROR - Exception in callback functools.partial(>, exception=CommClosedError('in : Stream is closed')>)\n", - "Traceback (most recent call last):\n", - " File \"/env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 225, in read\n", - " frames_nosplit_nbytes_bin = await stream.read_bytes(fmt_size)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "tornado.iostream.StreamClosedError: Stream is closed\n", - "\n", - "The above exception was the direct cause of the following exception:\n", - "\n", - "Traceback (most recent call last):\n", - " File \"/env/lib/python3.12/site-packages/tornado/ioloop.py\", line 750, in _run_callback\n", - " ret = callback()\n", - " ^^^^^^^^^^\n", - " File \"/env/lib/python3.12/site-packages/tornado/ioloop.py\", line 774, in _discard_future_result\n", - " future.result()\n", - " File \"/env/lib/python3.12/site-packages/distributed/diagnostics/progressbar.py\", line 321, in listen\n", - " response = await self.comm.read(\n", - " ^^^^^^^^^^^^^^^^^^^^^\n", - " File \"/env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 236, in read\n", - " convert_stream_closed_error(self, e)\n", - " File \"/env/lib/python3.12/site-packages/distributed/comm/tcp.py\", line 142, in convert_stream_closed_error\n", - " raise CommClosedError(f\"in {obj}: {exc}\") from exc\n", - "distributed.comm.core.CommClosedError: in : Stream is closed\n" - ] - } - ], - "source": [ - "%%time\n", - "# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\n", - "result = mask_clean(data)\n", - "progress(result)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "435f9f78-a9a4-4226-86ca-d4bec42d454e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.DataArray 'NDVI' (time: 151, y: 8874, x: 9902)> Size: 53GB\n",
-       "dask.array<truediv, shape=(151, 8874, 9902), dtype=float32, chunksize=(1, 2048, 2048), chunktype=numpy.ndarray>\n",
-       "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
-       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
-       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
-       "    spatial_ref  int32 4B 32648
" - ], - "text/plain": [ - " Size: 53GB\n", - "dask.array\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n", - " * y (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n", - " * x (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n", - " spatial_ref int32 4B 32648" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Tiến hành tính toán NDVI\n", - "ds1 = calculate_indices(result, index=\"NDVI\", satellite_mission=\"s2\")\n", - "ndvi = ds1[\"NDVI\"]\n", - "display(ndvi)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "84992d28-8e3f-468e-be08-ded511f2c662", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "## Hiển thị ảnh NDVI chưa điền các giá trị mây (chưa fill nan)\n", - "plt.imshow(ndvi.isel(time=6))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "72318b60-532a-4f08-a5f7-94762d08a42c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Thiết lập giá trị trung bình mùa vụ để xử lý các điểm ảnh bị mây dựa vào sự thay đổi theo mùa\n", - "time_split = [\n", - " slice(\"2022-09-01\", \"2023-01-01\"),\n", - " slice(\"2023-01-01\", \"2023-05-01\"),\n", - " slice(\"2023-05-01\", \"2023-07-01\"),\n", - " slice(\"2023-07-01\", \"2023-10-01\"),\n", - "]\n", - "\n", - "# Điền mây ở các vị trí mang giá trị nan (fill nan)\n", - "fill_nan_ndvi = fill_nan(ndvi, time_split)\n", - "\n", - "# In kết quả ảnh NDVI đã điền mây (đã fill nan)\n", - "plt.imshow(fill_nan_ndvi.isel(time=6))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "375b1cfb-37f5-49fe-8061-ea32eb47f9f6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/env/lib/python3.12/site-packages/xarray/groupers.py:392: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " self.index_grouper = pd.Grouper(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.05 s, sys: 5.04 s, total: 12.1 s\n", - "Wall time: 3min 14s\n" - ] - } - ], - "source": [ - "%%time\n", - "## tính ndvi theo tháng\n", - "average_ndvi = fill_nan_ndvi.resample(time=\"1M\").mean().persist()\n", - "progress(average_ndvi)\n", - "\n", - "# compute average_ndvi\n", - "average_ndvi = average_ndvi.compute()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "187cc640-aef9-476b-91fc-b63f4d3ff2e3", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "

Dataset size: 21.60 GB

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<xarray.Dataset> Size: 23GB\n",
-       "Dimensions:      (time: 33, y: 8874, x: 9902)\n",
-       "Coordinates:\n",
-       "  * time         (time) datetime64[ns] 264B 2022-09-06T22:46:14.500000 ... 20...\n",
-       "  * y            (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
-       "  * x            (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
-       "    spatial_ref  int32 4B 32648\n",
-       "Data variables:\n",
-       "    vv           (time, y, x) float32 12GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "    vh           (time, y, x) float32 12GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    crs:           EPSG:32648\n",
-       "    grid_mapping:  spatial_ref
" - ], - "text/plain": [ - " Size: 23GB\n", - "Dimensions: (time: 33, y: 8874, x: 9902)\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 264B 2022-09-06T22:46:14.500000 ... 20...\n", - " * y (y) float64 71kB 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n", - " * x (x) float64 79kB 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n", - " spatial_ref int32 4B 32648\n", - "Data variables:\n", - " vv (time, y, x) float32 12GB dask.array\n", - " vh (time, y, x) float32 12GB dask.array\n", - "Attributes:\n", - " crs: EPSG:32648\n", - " grid_mapping: spatial_ref" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/env/lib/python3.12/site-packages/xarray/groupers.py:392: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " self.index_grouper = pd.Grouper(\n", - "/env/lib/python3.12/site-packages/xarray/groupers.py:392: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n", - " self.index_grouper = pd.Grouper(\n" - ] - } - ], - "source": [ - "#Load dữ liệu ảnh Sentinel 1\n", - "dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)\n", - "average_vv = calculate_average(dsvv, time_pattern='1M')\n", - "average_vh = calculate_average(dsvh, time_pattern='1M')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "## cấu hình bộ dữ liệu điểm huấn luyện mô hình (train file)\n", - "train_path = \"train/ST_training_data_updated_1130points_new.shp\" # đường dẫn shp file train\n", - "\n", - "## load dữ liệu điểm huấn luyện mô hình (train file)\n", - "train = load_train_data(train_path)\n", - "train.head()\n", - "\n", - "# cấu hình nhãn dữ liệu \n", - "label_mapping = {\n", - " \"Lua tom\": \"0\",\n", - " \"Lua\": \"1\",\n", - " \"CHN\": \"2\",\n", - " \"CLN\": \"3\",\n", - " \"TS\": \"4\",\n", - " \"Song\": \"5\",\n", - " \"Dat xay dung\": \"6\",\n", - " \"Rung\": \"7\",\n", - "}\n", - "\n", - "# xây dựng tập dữ liệu (dataset) chứa dữ liệu VH, VV, NDVI\n", - "datasets = get_data_sen1_and_sen2(train, average_ndvi, average_vh, average_vv)\n", - "\n", - "# chia tập dữ liệu thành các phần theo tỉ lệ 80(80-20)-20 tương ứng với tập train, validate, test\n", - "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(\n", - " train, label_mapping, datasets\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "2e955884-d4af-422d-a8e6-d436199540e0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🚀 Training XGBoost model...\n", - " Train samples: 678\n", - " Val samples: 226\n", - " Features: 39\n", - " Classes: 8\n", - "\n", - "[0]\tvalidation_0-mlogloss:1.78218\tvalidation_1-mlogloss:1.80562\n", - "[1]\tvalidation_0-mlogloss:1.56307\tvalidation_1-mlogloss:1.61435\n", - 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"[197]\tvalidation_0-mlogloss:0.01111\tvalidation_1-mlogloss:0.34294\n", - "[198]\tvalidation_0-mlogloss:0.01107\tvalidation_1-mlogloss:0.34360\n", - "[199]\tvalidation_0-mlogloss:0.01104\tvalidation_1-mlogloss:0.34342\n", - "\n", - "✅ Training completed!\n", - " Validation Accuracy: 0.9027 (90.27%)\n", - "CPU times: user 1min 4s, sys: 0 ns, total: 1min 4s\n", - "Wall time: 1.72 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# Import XGBoost\n", - "import xgboost as xgb\n", - "from sklearn.metrics import accuracy_score\n", - "import numpy as np\n", - "\n", - "# Convert to numpy arrays\n", - "X_train_np = np.asarray(X_train, dtype=np.float32)\n", - "X_val_np = np.asarray(X_val, dtype=np.float32)\n", - "y_train_np = np.asarray(y_train, dtype=np.int32)\n", - "y_val_np = np.asarray(y_val, dtype=np.int32)\n", - "\n", - "print(\"🚀 Training XGBoost model...\")\n", - "print(f\" Train samples: {len(X_train_np)}\")\n", - "print(f\" Val samples: {len(X_val_np)}\")\n", - "print(f\" Features: {X_train_np.shape[1]}\")\n", - "print(f\" Classes: 8\\n\")\n", - "\n", - "# XGBoost parameters\n", - "params = {\n", - " 'objective': 'multi:softmax', # Multi-class classification\n", - " 'num_class': 8, # 8 land use classes\n", - " 'max_depth': 6, # Maximum tree depth\n", - " 'learning_rate': 0.1, # Learning rate\n", - " 'n_estimators': 200, # Number of trees\n", - " 'subsample': 0.8, # Subsample ratio\n", - " 'colsample_bytree': 0.8, # Feature sampling ratio\n", - " 'random_state': 42,\n", - " 'n_jobs': -1, # Use all CPU cores\n", - " 'eval_metric': 'mlogloss' # Multi-class log loss\n", - "}\n", - "\n", - "# Train XGBoost model\n", - "model = xgb.XGBClassifier(**params)\n", - "\n", - "model.fit(\n", - " X_train_np, y_train_np,\n", - " eval_set=[(X_train_np, y_train_np), (X_val_np, y_val_np)],\n", - " verbose=True\n", - ")\n", - "\n", - "# Validation accuracy\n", - "y_val_pred = model.predict(X_val_np)\n", - "val_accuracy = accuracy_score(y_val_np, y_val_pred)\n", - "print(f\"\\n✅ Training completed!\")\n", - "print(f\" Validation Accuracy: {val_accuracy:.4f} ({val_accuracy*100:.2f}%)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b2a1e42c-cf1b-4d82-a6af-b06e3496918f", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "📊 Evaluating XGBoost model on test set...\n", - "\n", - "📈 Test Results:\n", - " Accuracy: 0.9071 (90.71%)\n", - " Precision: 0.9253\n", - " Recall: 0.9071\n", - " F1-Score: 0.9112\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.05 s, sys: 68.4 ms, total: 1.12 s\n", - "Wall time: 307 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# Evaluate on test set\n", - "X_test_np = np.asarray(X_test, dtype=np.float32)\n", - "y_test_np = np.asarray(y_test, dtype=np.int32)\n", - "\n", - "print(\"📊 Evaluating XGBoost model on test set...\\n\")\n", - "\n", - "# Predictions\n", - "y_pred_test = model.predict(X_test_np)\n", - "\n", - "# Metrics\n", - "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n", - "\n", - "test_accuracy = accuracy_score(y_test_np, y_pred_test)\n", - "precision = precision_score(y_test_np, y_pred_test, average='weighted', zero_division=0)\n", - "recall = recall_score(y_test_np, y_pred_test, average='weighted', zero_division=0)\n", - "f1 = f1_score(y_test_np, y_pred_test, average='weighted', zero_division=0)\n", - "\n", - "print(f\"📈 Test Results:\")\n", - "print(f\" Accuracy: {test_accuracy:.4f} ({test_accuracy*100:.2f}%)\")\n", - "print(f\" Precision: {precision:.4f}\")\n", - "print(f\" Recall: {recall:.4f}\")\n", - "print(f\" F1-Score: {f1:.4f}\\n\")\n", - "\n", - "# Confusion Matrix\n", - "from sklearn.metrics import ConfusionMatrixDisplay\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Create figure first\n", - "fig, ax = plt.subplots(figsize=(10, 8))\n", - "\n", - "class_names = list(label_mapping.keys())\n", - "cm = confusion_matrix(y_test_np, y_pred_test)\n", - "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n", - "disp.plot(cmap='Blues', ax=ax)\n", - "plt.xticks(rotation=45, ha='right')\n", - "plt.title('XGBoost Confusion Matrix')\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "f1a14379-ed6e-4897-9ca4-2669743fab40", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Model saved to model_xgboost.joblib\n", - "✅ Model info saved to model_xgboost_info.json\n" - ] - } - ], - "source": [ - "# Lưu mô hình huấn luyện\n", - "import json\n", - "import joblib\n", - "\n", - "# Save XGBoost model\n", - "model_path = \"model_xgboost.joblib\"\n", - "joblib.dump(model, model_path)\n", - "print(f\"✅ Model saved to {model_path}\")\n", - "\n", - "# Save model info\n", - "info = {\n", - " \"model_type\": \"XGBoost\",\n", - " \"num_classes\": 8,\n", - " \"classes\": list(label_mapping.keys()),\n", - " \"num_features\": X_train_np.shape[1],\n", - " \"params\": params,\n", - " \"accuracy\": float(test_accuracy),\n", - " \"precision\": float(precision),\n", - " \"recall\": float(recall),\n", - " \"f1_score\": float(f1),\n", - "}\n", - "\n", - "with open(\"model_xgboost_info.json\", \"w\") as f:\n", - " json.dump(info, f, indent=2)\n", - "\n", - "print(f\"✅ Model info saved to model_xgboost_info.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "33dd516d-9824-499e-96b9-5cd9224c194c", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# đóng client, cluster\n", - "client.close()\n", - "cluster.close()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/train_files/01.train_ODC_local_with_Mic_supplyer.ipynb b/train_files/01.train_ODC_local_with_Mic_supplyer.ipynb deleted file mode 100644 index 9d25515..0000000 --- a/train_files/01.train_ODC_local_with_Mic_supplyer.ipynb +++ /dev/null @@ -1,2095 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 6, - "id": "912ed572-1658-406b-976c-cd6de2d4e89e", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " XGBoost version: 3.1.2\n", - "✅ All modules loaded successfully\n", - "CPU times: user 975 μs, sys: 0 ns, total: 975 μs\n", - "Wall time: 948 μs\n" - ] - } - ], - "source": [ - "%%time\n", - "%matplotlib inline\n", - "\n", - "# Import Microsoft Planetary Computer libraries\n", - "import planetary_computer\n", - "from pystac_client import Client\n", - "from odc.stac import load as stac_load\n", - "\n", - "# Standard imports\n", - "import xarray as xr\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n", - "import geopandas as gpd\n", - "\n", - "# XGBoost for GPU training\n", - "import xgboost as xgb\n", - "\n", - "from xgboost import XGBClassifier\n", - "\n", - "print(f\" XGBoost version: {xgb.__version__}\")\n", - "\n", - "print(\"✅ All modules loaded successfully\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d824dc4f-994b-4d1c-8d24-ce6674da141c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✅ Connected to Microsoft Planetary Computer\n", - "\n", - "======================================================================\n", - "CPU times: user 40.4 ms, sys: 4.03 ms, total: 44.5 ms\n", - "Wall time: 776 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# Kết nối tới Microsoft Planetary Computer STAC\n", - "from pystac_client import Client\n", - "\n", - "# KHÔNG dùng modifier ở catalog level để tránh items bị convert thành dict\n", - "catalog = Client.open(\n", - " \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n", - ")\n", - "print(\"✅ Connected to Microsoft Planetary Computer\")\n", - "\n", - "print(\"\\n\" + \"=\"*70)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "1e113730", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================================================================\n", - "CONFIGURATION\n", - "======================================================================\n", - "\n", - "📍 Area of Interest:\n", - " Longitude: 105.6 to 106.2\n", - " Latitude: 9.3 to 9.8\n", - "\n", - "📅 Time Range: 2023-03-01/2023-05-31\n", - " ⚠️ Optimized for personal computer (3 months, reduced area)\n", - "\n", - "🗺️ CRS: EPSG:32648\n", - " Resolution: 20m (reduced from 10m for smaller data size)\n", - "======================================================================\n", - "CPU times: user 258 μs, sys: 0 ns, total: 258 μs\n", - "Wall time: 246 μs\n" - ] - } - ], - "source": [ - "%%time\n", - "# 🌍 Định nghĩa khu vực và thời gian\n", - "print(\"=\"*70)\n", - "print(\"CONFIGURATION\")\n", - "print(\"=\"*70)\n", - "\n", - "# Khu vực quan tâm (Vietnam - Mekong Delta) - GIẢM DIỆN TÍCH ~40%\n", - "bbox = [105.6, 9.3, 106.2, 9.8] # [min_lon, min_lat, max_lon, max_lat]\n", - "\n", - "# GIẢM THỜI GIAN xuống 3 tháng để giảm kích thước dữ liệu cho PC\n", - "time_range = \"2023-03-01/2023-05-31\" # 3 tháng (mùa khô)\n", - "\n", - "print(f\"\\n📍 Area of Interest:\")\n", - "print(f\" Longitude: {bbox[0]} to {bbox[2]}\")\n", - "print(f\" Latitude: {bbox[1]} to {bbox[3]}\")\n", - "print(f\"\\n📅 Time Range: {time_range}\")\n", - "print(f\" ⚠️ Optimized for personal computer (3 months, reduced area)\")\n", - "print(f\"\\n🗺️ CRS: EPSG:32648\")\n", - "print(f\" Resolution: 20m (reduced from 10m for smaller data size)\")\n", - "\n", - "print(\"=\"*70)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "3cd69645", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================================================================\n", - "LOADING SENTINEL-2 L2A\n", - "======================================================================\n", - "\n", - "🔍 Searching for Sentinel-2 scenes...\n", - "✅ Found 22 Sentinel-2 scenes\n", - "⚠️ Limiting to 12 scenes for personal computer\n", - " Selected 12 scenes evenly distributed\n", - "\n", - "📋 Sample scenes:\n", - " [1] 2023-05-17 - Cloud: 27.73279%\n", - " [2] 2023-05-15 - Cloud: 21.273129%\n", - " [3] 2023-05-02 - Cloud: 20.527479%\n", - " [4] 2023-05-02 - Cloud: 20.524253%\n", - " [5] 2023-04-15 - Cloud: 29.471546%\n", - "\n", - "🔑 Signing STAC items...\n", - "\n", - "⏳ Loading Sentinel-2 data...\n", - "\n", - "✅ Sentinel-2 loaded!\n", - " Shape: {'y': 2774, 'x': 3301, 'time': 7}\n", - " Variables: ['red', 'nir', 'scl']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":54: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n" - ] - }, - { - "data": { - "text/html": [ - "
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"print(\"=\"*70)\n", - "print(\"LOADING SENTINEL-1 RTC\")\n", - "print(\"=\"*70)\n", - "\n", - "print(\"\\n🔍 Searching for Sentinel-1 scenes...\")\n", - "query_s1 = catalog.search(\n", - " collections=[\"sentinel-1-rtc\"],\n", - " bbox=bbox,\n", - " datetime=time_range,\n", - ")\n", - "\n", - "items_s1 = list(query_s1.item_collection())\n", - "print(f\"✅ Found {len(items_s1)} Sentinel-1 scenes\")\n", - "\n", - "# GIỚI HẠN SỐ LƯỢNG SCENES cho PC cá nhân\n", - "max_scenes = 12 # Giảm xuống 12 scenes để tối ưu cho PC\n", - "if len(items_s1) > max_scenes:\n", - " print(f\"⚠️ Limiting to {max_scenes} scenes for personal computer\")\n", - " # Chọn scenes đều đặn trong khoảng thời gian\n", - " step = len(items_s1) // max_scenes\n", - " items_s1 = items_s1[::step][:max_scenes]\n", - " print(f\" Selected {len(items_s1)} scenes evenly distributed\")\n", - "\n", - "if len(items_s1) > 0:\n", - " # Show first few scenes\n", - " print(f\"\\n📋 Sample scenes:\")\n", - " for i, item in enumerate(items_s1[:5]):\n", - " date = item.datetime.strftime(\"%Y-%m-%d\")\n", - " orbit = item.properties.get(\"sat:orbit_state\", \"N/A\")\n", - " print(f\" [{i+1}] {date} - Orbit: {orbit}\")\n", - " \n", - " # Re-sign items to ensure fresh URLs (keep as pystac objects)\n", - " print(f\"\\n🔑 Signing STAC items...\")\n", - " items_s1 = [planetary_computer.sign(item) for item in items_s1]\n", - " \n", - " # Load Sentinel-1 data (without Dask chunks)\n", - " print(f\"\\n⏳ Loading Sentinel-1 data...\")\n", - " ds_s1 = stac_load(\n", - " items_s1,\n", - " bands=[\"vv\", \"vh\"], # VV and VH polarizations\n", - " crs=\"EPSG:32648\",\n", - " resolution=20, # 20m resolution (4x smaller data than 10m)\n", - " bbox=bbox,\n", - " patch_url=planetary_computer.sign, # Re-sign URLs during loading\n", - " fail_on_error=False, # Skip problematic tiles instead of crashing\n", - " )\n", - " \n", - " # Convert to dB (Microsoft S1 is in linear power)\n", - " print(f\"\\n🔄 Converting to dB...\")\n", - " ds_s1['vv_db'] = 10 * np.log10(ds_s1['vv'].where(ds_s1['vv'] > 0))\n", - " ds_s1['vh_db'] = 10 * np.log10(ds_s1['vh'].where(ds_s1['vh'] > 0))\n", - " \n", - " print(f\"\\n✅ Sentinel-1 loaded!\")\n", - " print(f\" Shape: {dict(ds_s1.dims)}\")\n", - " print(f\" Variables: {list(ds_s1.data_vars)}\")\n", - " display(ds_s1)\n", - "else:\n", - " print(f\"❌ No Sentinel-1 scenes found\")\n", - "\n", - " ds_s1 = Noneprint(\"=\"*70)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================================================================\n", - "DATA PROCESSING\n", - "======================================================================\n", - "\n", - "[1] Calculating NDVI...\n", - "✅ NDVI calculated\n", - " Shape: (7, 2774, 3301)\n", - " Time steps: 7\n", - "\n", - "[2] Applying cloud mask...\n", - "✅ Cloud mask applied\n", - "\n", - "[3] Computing mean NDVI across time...\n", - "✅ Mean NDVI computed\n", - " Shape: (2774, 3301)\n", - "======================================================================\n", - "CPU times: user 735 ms, sys: 538 ms, total: 1.27 s\n", - "Wall time: 1.26 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# 🌿 CALCULATE NDVI AND PROCESS DATA\n", - "print(\"=\"*70)\n", - "print(\"DATA PROCESSING\")\n", - "print(\"=\"*70)\n", - "\n", - "if ds_s2 is not None:\n", - " print(\"\\n[1] Calculating NDVI...\")\n", - " # NDVI = (NIR - Red) / (NIR + Red)\n", - " ndvi = (ds_s2['nir'] - ds_s2['red']) / (ds_s2['nir'] + ds_s2['red'] + 1e-8)\n", - " \n", - " print(f\"✅ NDVI calculated\")\n", - " print(f\" Shape: {ndvi.shape}\")\n", - " print(f\" Time steps: {len(ndvi.time)}\")\n", - " \n", - " # Cloud masking using SCL band\n", - " print(f\"\\n[2] Applying cloud mask...\")\n", - " # SCL values: 1=defective, 3=cloud shadow, 8=cloud medium, 9=cloud high, 10=cirrus\n", - " cloud_mask = ds_s2['scl'].isin([1, 3, 8, 9, 10])\n", - " ndvi_masked = ndvi.where(~cloud_mask)\n", - " \n", - " print(f\"✅ Cloud mask applied\")\n", - " \n", - " # Temporal aggregation (mean over time)\n", - " print(f\"\\n[3] Computing mean NDVI across time...\")\n", - " ndvi_mean = ndvi_masked.mean(dim='time')\n", - " \n", - " # Data already in memory, no need to compute() again\n", - " print(f\"✅ Mean NDVI computed\")\n", - " print(f\" Shape: {ndvi_mean.shape}\")\n", - " \n", - "else:\n", - " print(\"❌ No Sentinel-2 data to process\")\n", - " ndvi_mean = None\n", - "\n", - "print(\"=\"*70)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2e955884-d4af-422d-a8e6-d436199540e0", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================================================================\n", - "FEATURE EXTRACTION\n", - "======================================================================\n", - "\n", - "[1] Loading training data from: train/ST_training_data_updated_1130points_new.shp\n", - "✅ Loaded 1130 training points\n", - " Available columns: ['No', 'X', 'Y', 'LU2022', 'Hientrang', 'HT_code', 'geometry']\n", - " Using label column: 'HT_code'\n", - " Classes: [0, 1, 2, 3, 4, 5, 6, 7]\n", - "\n", - "[2] Extracting features at training points...\n", - "✅ Extracted features for 638 valid points\n", - " Skipped 492 points (outside extent or NaN values)\n", - " Feature shape: (638, 3)\n", - " Feature names: ['NDVI_mean', 'VH_dB_mean', 'VV_dB_mean']\n", - "\n", - " Class distribution:\n", - " Class 0: 62 samples (9.7%)\n", - " Class 1: 114 samples (17.9%)\n", - " Class 3: 112 samples (17.6%)\n", - " Class 4: 104 samples (16.3%)\n", - " Class 5: 43 samples (6.7%)\n", - " Class 6: 150 samples (23.5%)\n", - " Class 7: 53 samples (8.3%)\n", - "======================================================================\n", - "CPU times: user 4.08 s, sys: 117 ms, total: 4.19 s\n", - "Wall time: 4.06 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# 🎯 EXTRACT TRAINING DATA FEATURES\n", - "print(\"=\"*70)\n", - "print(\"FEATURE EXTRACTION\")\n", - "print(\"=\"*70)\n", - "\n", - "# Check if required data is available\n", - "if 'ndvi_mean' not in globals() or 'ds_s1' not in globals():\n", - " print(\"❌ Error: Please run Cell 6 (DATA PROCESSING) first!\")\n", - " print(\" Required variables: ndvi_mean, ds_s1\")\n", - " raise RuntimeError(\"Missing required data. Run cells in order: Cell 4 → Cell 5 → Cell 6 → Cell 7\")\n", - "\n", - "# Load training shapefile\n", - "import geopandas as gpd\n", - "\n", - "train_path = 'train/ST_training_data_updated_1130points_new.shp'\n", - "print(f\"\\n[1] Loading training data from: {train_path}\")\n", - "train_gdf = gpd.read_file(train_path)\n", - "\n", - "# Ensure CRS matches\n", - "if train_gdf.crs != 'EPSG:32648':\n", - " print(f\" Reprojecting from {train_gdf.crs} to EPSG:32648...\")\n", - " train_gdf = train_gdf.to_crs('EPSG:32648')\n", - "\n", - "print(f\"✅ Loaded {len(train_gdf)} training points\")\n", - "print(f\" Available columns: {list(train_gdf.columns)}\")\n", - "\n", - "# Auto-detect label column (look for common names)\n", - "label_column = None\n", - "for col in ['HT_code', 'Ma_LU', 'LU2022', 'class', 'Class', 'CLASS', 'label', 'Label', 'LABEL', 'LU_CODE', 'LU_code']:\n", - " if col in train_gdf.columns:\n", - " label_column = col\n", - " break\n", - "\n", - "if label_column is None:\n", - " print(f\"❌ Cannot find label column. Available columns: {list(train_gdf.columns)}\")\n", - " print(f\" Please check your shapefile and update the code.\")\n", - "else:\n", - " print(f\" Using label column: '{label_column}'\")\n", - " print(f\" Classes: {sorted(train_gdf[label_column].unique())}\")\n", - " \n", - " # Extract features at each training point\n", - " print(f\"\\n[2] Extracting features at training points...\")\n", - " \n", - " features = []\n", - " labels = []\n", - " skipped = 0\n", - " \n", - " for idx, row in train_gdf.iterrows():\n", - " point = row.geometrychro\n", - " x_coord = point.x\n", - " y_coord = point.y\n", - " label = row[label_column]\n", - " \n", - " # Extract NDVI at this location\n", - " if ndvi_mean is not None and ds_s1 is not None:\n", - " try:\n", - " ndvi_val = ndvi_mean.sel(x=x_coord, y=y_coord, method='nearest').values\n", - " \n", - " # Extract Sentinel-1 VH/VV at this location (mean across time)\n", - " # Data already in memory, no need to compute()\n", - " vh_val = ds_s1['vh_db'].sel(x=x_coord, y=y_coord, method='nearest').mean(dim='time').values\n", - " vv_val = ds_s1['vv_db'].sel(x=x_coord, y=y_coord, method='nearest').mean(dim='time').values\n", - " \n", - " # Create feature vector: [NDVI, VH_dB, VV_dB]\n", - " feature_vec = [ndvi_val, vh_val, vv_val]\n", - " \n", - " # Only add if all features are valid (not NaN)\n", - " if not np.isnan(feature_vec).any():\n", - " features.append(feature_vec)\n", - " labels.append(label)\n", - " else:\n", - " skipped += 1\n", - " except Exception as e:\n", - " # Skip points outside the data extent\n", - " skipped += 1\n", - " continue\n", - " \n", - " features = np.array(features)\n", - " labels = np.array(labels)\n", - " \n", - " print(f\"✅ Extracted features for {len(features)} valid points\")\n", - " print(f\" Skipped {skipped} points (outside extent or NaN values)\")\n", - " print(f\" Feature shape: {features.shape}\")\n", - " print(f\" Feature names: ['NDVI_mean', 'VH_dB_mean', 'VV_dB_mean']\")\n", - " print(f\"\\n Class distribution:\")\n", - " unique, counts = np.unique(labels, return_counts=True)\n", - " for cls, cnt in zip(unique, counts):\n", - " print(f\" Class {cls}: {cnt} samples ({cnt/len(labels)*100:.1f}%)\")\n", - "\n", - "print(\"=\"*70)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "f1a14379-ed6e-4897-9ca4-2669743fab40", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================================================================\n", - "MODEL TRAINING - GPU ACCELERATED\n", - "======================================================================\n", - "\n", - "[1] Encoding labels...\n", - "✅ Original classes: [0 1 3 4 5 6 7]\n", - " Encoded as: [0 1 2 3 4 5 6]\n", - "\n", - "[2] Splitting data (80% train, 20% test)...\n", - "✅ Training samples: 510\n", - " Testing samples: 128\n", - "\n", - "[3] Training XGBoost classifier on RTX 4060 GPU...\n", - " GPU Settings: device='cuda:0'\n", - "✅ Model trained on GPU\n", - "\n", - "[4] Evaluating model...\n", - "✅ Training accuracy: 1.0000\n", - " Testing accuracy: 0.5781\n", - "\n", - "[5] Classification Report:\n", - " precision recall f1-score support\n", - "\n", - " 0 0.27 0.25 0.26 12\n", - " 1 0.54 0.65 0.59 23\n", - " 3 0.57 0.55 0.56 22\n", - " 4 0.52 0.57 0.55 21\n", - " 5 0.80 0.44 0.57 9\n", - " 6 0.85 0.73 0.79 30\n", - " 7 0.43 0.55 0.48 11\n", - "\n", - " accuracy 0.58 128\n", - " macro avg 0.57 0.53 0.54 128\n", - "weighted avg 0.60 0.58 0.58 128\n", - "\n", - "\n", - "[6] Confusion Matrix:\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================================================================\n", - "CPU times: user 6.04 s, sys: 889 ms, total: 6.93 s\n", - "Wall time: 6.3 s\n" - ] - } - ], - "source": [ - "%%time\n", - "# 🤖 TRAIN XGBOOST MODEL ON GPU (RTX 4060)\n", - "print(\"=\"*70)\n", - "print(\"MODEL TRAINING - GPU ACCELERATED\")\n", - "print(\"=\"*70)\n", - "\n", - "from xgboost import XGBClassifier\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import LabelEncoder\n", - "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Encode labels to ensure they are 0, 1, 2, ... n-1\n", - "print(\"\\n[1] Encoding labels...\")\n", - "label_encoder = LabelEncoder()\n", - "labels_encoded = label_encoder.fit_transform(labels)\n", - "print(f\"✅ Original classes: {label_encoder.classes_}\")\n", - "print(f\" Encoded as: {np.unique(labels_encoded)}\")\n", - "\n", - "# Split data\n", - "print(\"\\n[2] Splitting data (80% train, 20% test)...\")\n", - "X_train, X_test, y_train, y_test = train_test_split(\n", - " features, labels_encoded, test_size=0.2, random_state=42, stratify=labels_encoded\n", - ")\n", - "print(f\"✅ Training samples: {len(X_train)}\")\n", - "print(f\" Testing samples: {len(X_test)}\")\n", - "\n", - "# Train XGBoost on GPU\n", - "print(\"\\n[3] Training XGBoost classifier on RTX 4060 GPU...\")\n", - "print(\" GPU Settings: device='cuda:0'\")\n", - "\n", - "xgb_model = XGBClassifier(\n", - " n_estimators=100,\n", - " max_depth=20,\n", - " learning_rate=0.1,\n", - " device='cuda:0', # Use GPU (updated from deprecated gpu_id)\n", - " tree_method='hist', # Use hist with device for GPU training\n", - " random_state=42,\n", - " eval_metric='mlogloss', # Multi-class log loss\n", - " verbosity=1 # Show GPU training progress\n", - ")\n", - "\n", - "xgb_model.fit(X_train, y_train)\n", - "print(f\"✅ Model trained on GPU\")\n", - "\n", - "# Evaluate\n", - "print(\"\\n[4] Evaluating model...\")\n", - "train_score = xgb_model.score(X_train, y_train)\n", - "test_score = xgb_model.score(X_test, y_test)\n", - "print(f\"✅ Training accuracy: {train_score:.4f}\")\n", - "print(f\" Testing accuracy: {test_score:.4f}\")\n", - "\n", - "# Classification report\n", - "print(\"\\n[5] Classification Report:\")\n", - "y_pred = xgb_model.predict(X_test)\n", - "print(classification_report(y_test, y_pred, target_names=[str(c) for c in label_encoder.classes_]))\n", - "\n", - "# Confusion matrix\n", - "print(\"\\n[6] Confusion Matrix:\")\n", - "fig, ax = plt.subplots(figsize=(10, 8))\n", - "cm = confusion_matrix(y_test, y_pred)\n", - "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=label_encoder.classes_)\n", - "disp.plot(ax=ax, cmap='Blues', values_format='d')\n", - "plt.title('Confusion Matrix - XGBoost GPU Model (RTX 4060)')\n", - "plt.tight_layout()\n", - "plt.show()\n", - "\n", - "print(\"=\"*70)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "33dd516d-9824-499e-96b9-5cd9224c194c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================================================================\n", - "SAVING MODEL & CLEANUP\n", - "======================================================================\n", - "\n", - "[1] Saving model to: model_train/model_xgboost_gpu_20251212_125754.joblib\n", - "✅ Model and label encoder saved\n", - "✅ Model info saved to: model_train/model_xgboost_gpu_20251212_125754_info.json\n", - "\n", - "[2] Cleanup complete\n", - "======================================================================\n", - "\n", - "======================================================================\n", - "🎉 TRAINING COMPLETE!\n", - "CPU times: user 104 ms, sys: 3.86 ms, total: 108 ms\n", - "Wall time: 14.3 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "# 💾 SAVE MODEL AND CLEANUP\n", - "print(\"=\"*70)\n", - "print(\"SAVING MODEL & CLEANUP\")\n", - "print(\"=\"*70)\n", - "\n", - "import joblib\n", - "from datetime import datetime\n", - "\n", - "# Save model and label encoder\n", - "model_filename = f\"model_train/model_xgboost_gpu_{datetime.now().strftime('%Y%m%d_%H%M%S')}.joblib\"\n", - "print(f\"\\n[1] Saving model to: {model_filename}\")\n", - "joblib.dump({'model': xgb_model, 'label_encoder': label_encoder}, model_filename)\n", - "print(f\"✅ Model and label encoder saved\")\n", - "\n", - "# Save model info\n", - "info = {\n", - " \"timestamp\": datetime.now().isoformat(),\n", - " \"data_source\": \"Microsoft Planetary Computer STAC\",\n", - " \"collections\": [\"sentinel-2-l2a\", \"sentinel-1-rtc\"],\n", - " \"features\": [\"NDVI_mean\", \"VH_dB_mean\", \"VV_dB_mean\"],\n", - " \"training_samples\": len(X_train),\n", - " \"testing_samples\": len(X_test),\n", - " \"train_accuracy\": float(train_score),\n", - " \"test_accuracy\": float(test_score),\n", - " \"model_type\": \"XGBClassifier\",\n", - " \"device\": \"cuda:0\",\n", - " \"gpu_device\": \"RTX 4060\",\n", - " \"tree_method\": \"hist\",\n", - " \"n_estimators\": 100,\n", - " \"max_depth\": 20,\n", - " \"learning_rate\": 0.1\n", - "}\n", - "\n", - "import json\n", - "info_filename = model_filename.replace('.joblib', '_info.json')\n", - "with open(info_filename, 'w') as f:\n", - " json.dump(info, f, indent=2)\n", - "print(f\"✅ Model info saved to: {info_filename}\")\n", - "\n", - "# No cleanup needed (Dask removed)\n", - "print(\"\\n[2] Cleanup complete\")\n", - "\n", - "print(\"=\"*70)\n", - "\n", - "print(\"\\n\" + \"=\"*70)\n", - "\n", - "print(\"🎉 TRAINING COMPLETE!\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/train_files/crediential.txt b/train_files/crediential.txt deleted file mode 100644 index 39e91b1..0000000 --- a/train_files/crediential.txt +++ /dev/null @@ -1,6 +0,0 @@ -export AWS_ACCESS_KEY_ID="ASIA4YF43ZWIXQ6HJIAY" -export AWS_SECRET_ACCESS_KEY="3N8KoV2ZBqQcFqRUVxQXW8K9sm90CNDV9aHUkNw0" -export AWS_SESSION_TOKEN="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" - -Cognito: eyJraWQiOiIzejR4V0txYmd5Mlo4NXR3TFVvRGFSNmp4WVNSZUdKNHdLeEM4K0phbUM0PSIsImFsZyI6IlJTMjU2In0.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.N15e6a0MWQtdZWBIHpDC3rKcwjn9ATEo-WY7oaB1SV4m2u1j17ld_AlnkiplAFWq52viKjWEO8ArY4Okb9xMUraK_nV-YxkAuTv15_hG32Q-qbFWsholcJzc4jObCKc3NXPVolx8zFZn0r9nQoakCqGaQWCfshT3_ZPAMdhIisOn7Jq6jDWyTfivMQlbmCPNCxSR3Yqt6_UTs2pvLdTuyP7hEGyuk8u4F_FyMxZcmGRKwPeKeepjJ22HoDkcvL9rpWIKoMZ-SQABLGqXPAFUEOPuvfCYh6bcivcztwaLszW70zHUbJzJZmyY8wi0PViplg-CK31yTkBej3-ARaC9Zg -ID: eyJraWQiOiJOMmdRc1c0S3o1YUltR3hGZEVJVmUxOUIxTWpZSmJPcG5kYUxKQUpNakxJPSIsImFsZyI6IlJTMjU2In0.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.QEoa4uJmmSk0qpOBi_a3RrCoqRu-oASbAHc24tgIuSeM_wcQsyTbKNbYdDS9P0n6JZf-wCiCknpsHl6TKiGDL3xDVguesm8ZPdyYkdgpCvE7EnyrCOSa01hcubAL-Z3_TMyUVs0WyDrJ3HS0YT-0Gig7m2y17oL44zwtrWwXcrTJMW94QimW5OMsDKZMn1oQKKGkBhC18FB4lNcerAh9tLknGfJQEseH6_5rJAeLJSwzXJBCfZjM_Yt-4ZmmB1ruNfiHUXfT2QRbBFRDcWcpA0gYJoROrm16tByyivwGcaV2BIRIiSKqi1NSRPdTNqOWmmYh-yufWFG8CdumGpX5ow \ No newline at end of file diff --git a/train_files/test_cognito_odc.ipynb b/train_files/test_cognito_odc.ipynb deleted file mode 100644 index 38ad561..0000000 --- a/train_files/test_cognito_odc.ipynb +++ /dev/null @@ -1,565 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "524bb33e", - "metadata": {}, - "source": [ - "# Test Cognito Authentication with ODC\n", - "# Test Xác Thực Cognito với Open Data Cube\n", - "\n", - "Notebook này demo cách sử dụng Cognito authentication với ODC để truy cập S3." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "97b8c790", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "⚠ EASI tools not available, using standard datacube functions\n" - ] - }, - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = 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links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\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 const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const 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for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\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 const js_urls = [\"https://cdn.holoviz.org/panel/1.7.5/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.7.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.7.3.min.js\", \"https://cdn.holoviz.org/panel/1.7.5/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const 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 (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n 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console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_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": "" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": "\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 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 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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", - "application/vnd.holoviews_load.v0+json": "" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "======================================================================\n", - "📦 ODC Module with Cognito Authentication Loaded\n", - "======================================================================\n", - "\n", - "💡 Quick Start:\n", - " 1. setup_cognito_auth('train_files/crediential.txt')\n", - " 2. Use datacube normally with authenticated S3 access\n", - "\n", - "📚 Functions:\n", - " - setup_cognito_auth() : Setup Cognito authentication\n", - " - get_cognito_auth() : Get authenticator instance\n", - " - print_auth_status() : Show auth status\n", - " - auto_setup() : Auto-setup if credentials exist\n", - "======================================================================\n", - "\n", - "✅ Module loaded successfully\n", - "CPU times: user 4.94 s, sys: 212 ms, total: 5.15 s\n", - "Wall time: 4.59 s\n" - ] - } - ], - "source": [ - "%%time\n", - "import sys\n", - "sys.path.insert(0, '/media/x79/2A7D-FAA0/remote-sensing')\n", - "\n", - "# Import module with Cognito support\n", - "import new_import_ODC_cognito\n", - "from new_import_ODC_cognito import *\n", - "\n", - "print(\"✅ Module loaded successfully\")" - ] - }, - { - "cell_type": "markdown", - "id": "5616b9d3", - "metadata": {}, - "source": [ - "## Step 1: Setup Cognito Authentication\n", - "\n", - "Load credentials và setup authentication." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "cffdd4f3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🔐 Setting up Cognito authentication...\n", - "✓ AWS credentials loaded from file\n", - "✓ Cognito tokens loaded successfully\n", - "\n", - "📋 Token Information:\n", - "\n", - "=== Cognito Token Information ===\n", - "\n", - "User Information:\n", - " Username: hienm2523001\n", - " Name: Hien Phan\n", - " Email: hienm2523001@gstudent.ctu.edu.vn\n", - " Groups: default-group, allocation:R-19244:CSIRO and Vietnam partners\n", - " Token expires: 2026-03-05 04:52:38\n", - " Time remaining: 7h 34m\n", - "\n", - "=== Getting AWS Credentials from Cognito ===\n", - "⚠ No Identity Pool ID provided\n", - "⚠ Using existing AWS credentials (already exchanged from Cognito)...\n", - "✓ Using AWS credentials loaded from file\n", - "✓ AWS credentials set in environment\n", - "\n", - "🌐 Configuring datacube S3 access...\n", - "\n", - "✅ Cognito authentication setup complete!\n", - "✅ Ready to use datacube with S3 access\n", - "\n", - "\n", - "======================================================================\n", - "✅ AUTHENTICATION SUCCESSFUL\n", - "======================================================================\n" - ] - } - ], - "source": [ - "# Setup Cognito authentication\n", - "auth = setup_cognito_auth('/media/x79/2A7D-FAA0/remote-sensing/train_files/crediential.txt')\n", - "\n", - "if auth:\n", - " print(\"\\n\" + \"=\"*70)\n", - " print(\"✅ AUTHENTICATION SUCCESSFUL\")\n", - " print(\"=\"*70)\n", - "else:\n", - " print(\"\\n❌ Authentication failed!\")" - ] - }, - { - "cell_type": "markdown", - "id": "a3c51e65", - "metadata": {}, - "source": [ - "## Step 2: Check Authentication Status\n", - "\n", - "Xem thông tin user và credentials." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "ebc70a28", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "============================================================\n", - "AUTHENTICATION STATUS\n", - "============================================================\n", - "✅ Cognito authentication is active\n", - "✅ AWS credentials loaded\n", - " Access Key: ASIA4YF43ZWIXQ6HJIAY...\n", - "✅ Cognito tokens loaded\n", - " User: hienm2523001\n", - " Email: hienm2523001@gstudent.ctu.edu.vn\n", - "============================================================\n" - ] - } - ], - "source": [ - "# Print detailed auth status\n", - "print_auth_status()" - ] - }, - { - "cell_type": "markdown", - "id": "dc7b3764", - "metadata": {}, - "source": [ - "## Step 3: Test S3 Access\n", - "\n", - "Test truy cập S3 bucket với Cognito credentials." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "2dc1e1ee", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "🔍 Testing S3 bucket access...\n", - "\n" - ] - }, - { - "ename": "TypeError", - "evalue": "CognitoAuthenticator.test_s3_access() got an unexpected keyword argument 'max_keys'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 6\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m🔍 Testing S3 bucket access...\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# Test sentinel-cogs bucket\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m success \u001b[38;5;241m=\u001b[39m \u001b[43mauth\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtest_s3_access\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mbucket_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msentinel-cogs\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mregion\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mus-west-2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mprefix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msentinel-s2-l2a-cogs/54/S/VE/2020/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[1;32m 11\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m✅ S3 access working with Cognito authentication!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mTypeError\u001b[0m: CognitoAuthenticator.test_s3_access() got an unexpected keyword argument 'max_keys'" - ] - } - ], - "source": [ - "# Test S3 access\n", - "if auth:\n", - " print(\"🔍 Testing S3 bucket access...\\n\")\n", - " \n", - " # Test sentinel-cogs bucket\n", - " success = auth.test_s3_access(\n", - " bucket_name='sentinel-cogs',\n", - " region='us-west-2',\n", - " max_keys=10,\n", - " prefix='sentinel-s2-l2a-cogs/54/S/VE/2020/'\n", - " )\n", - " \n", - " if success:\n", - " print(\"\\n✅ S3 access working with Cognito authentication!\")\n", - " else:\n", - " print(\"\\n❌ S3 access failed\")\n", - "else:\n", - " print(\"⚠ Authentication not setup\")" - ] - }, - { - "cell_type": "markdown", - "id": "1993a850", - "metadata": {}, - "source": [ - "## Step 4: Initialize Datacube\n", - "\n", - "Khởi tạo Open Data Cube với authenticated S3 access." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4916906", - "metadata": {}, - "outputs": [], - "source": [ - "import datacube\n", - "\n", - "# Initialize datacube (S3 already configured by Cognito auth)\n", - "dc = datacube.Datacube()\n", - "\n", - "print(\"✅ Datacube initialized\")\n", - "print(f\" Products available: {len(dc.list_products())}\")\n", - "\n", - "# Show some products\n", - "products = dc.list_products()\n", - "if len(products) > 0:\n", - " print(\"\\nAvailable products:\")\n", - " for idx, row in products.head(5).iterrows():\n", - " print(f\" - {row['name']}\")" - ] - }, - { - "cell_type": "markdown", - "id": "f56fba12", - "metadata": {}, - "source": [ - "## Step 5: Load Data (Example)\n", - "\n", - "Example: Load Sentinel-2 data cho Vietnam region." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f126d9a5", - "metadata": {}, - "outputs": [], - "source": [ - "# Example: Load a small region\n", - "date_range = (\"2023-01-01\", \"2023-01-31\")\n", - "lon_range = (105.8, 105.9)\n", - "lat_range = (9.8, 9.9)\n", - "\n", - "try:\n", - " print(\"📡 Loading Sentinel-2 data...\")\n", - " print(f\" Region: {lon_range}, {lat_range}\")\n", - " print(f\" Date: {date_range}\")\n", - " \n", - " data = load_data(\n", - " dc=dc,\n", - " date_range=date_range,\n", - " longtitude_range=lon_range,\n", - " latitude_range=lat_range,\n", - " measurements=['red', 'nir']\n", - " )\n", - " \n", - " print(f\"\\n✅ Data loaded!\")\n", - " print(f\" Shape: {data.dims}\")\n", - " print(f\" Variables: {list(data.data_vars)}\")\n", - " \n", - " # Display data info\n", - " print(f\"\\n📊 Data summary:\")\n", - " print(data)\n", - " \n", - "except Exception as e:\n", - " print(f\"\\n❌ Error loading data: {e}\")\n", - " import traceback\n", - " traceback.print_exc()" - ] - }, - { - "cell_type": "markdown", - "id": "0722178a", - "metadata": {}, - "source": [ - "## Summary / Tóm tắt\n", - "\n", - "### ✅ Đã test:\n", - "1. ✓ Cognito authentication setup\n", - "2. ✓ Token information display\n", - "3. ✓ S3 bucket access\n", - "4. ✓ Datacube initialization\n", - "5. ✓ Data loading from S3\n", - "\n", - "### 🔒 Security Benefits:\n", - "- Identity-based authentication\n", - "- Automatic token expiration\n", - "- Group-based permissions\n", - "- Audit trail via Cognito\n", - "\n", - "### 📚 Next Steps:\n", - "- Use this authenticated datacube in main training notebooks\n", - "- All S3 access now uses Cognito credentials\n", - "- No need to manually configure S3 access\n", - "\n", - "---\n", - "\n", - "**Files:**\n", - "- `new_import_ODC_cognito.py` - ODC module with Cognito\n", - "- `cognito_auth.py` - Cognito authentication module\n", - "- `COGNITO_GUIDE.md` - Full documentation" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "env_01", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.19" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/train_files/test_planetary_computer.ipynb b/train_files/test_planetary_computer.ipynb deleted file mode 100644 index 22d9b05..0000000 --- a/train_files/test_planetary_computer.ipynb +++ /dev/null @@ -1,155 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "30681e56", - "metadata": {}, - "source": [ - "# 🧪 Test Planetary Computer Connection\n", - "\n", - "Notebook này test kết nối và load dữ liệu từ Microsoft Planetary Computer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e32c8eca", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "sys.path.insert(0, '/media/x79/2A7D-FAA0/remote-sensing')\n", - "\n", - "from load_data_no_odc import load_sentinel2_stac, load_sentinel1_stac\n", - "import matplotlib.pyplot as plt\n", - "\n", - "print(\"✅ Module imported successfully\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "501dd8ef", - "metadata": {}, - "outputs": [], - "source": [ - "# Small test area (1 month, small bbox)\n", - "bbox = (105.8, 9.5, 106.0, 9.7) # Small area in Mekong Delta\n", - "date_range = (\"2023-01-01\", \"2023-01-31\") # 1 month only\n", - "\n", - "print(f\"Test parameters:\")\n", - "print(f\" Bbox: {bbox}\")\n", - "print(f\" Date: {date_range}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "187e2c1a", - "metadata": {}, - "outputs": [], - "source": [ - "# Test Sentinel-2\n", - "print(\"\\n\" + \"=\" * 70)\n", - "print(\"Testing Sentinel-2 L2A\")\n", - "print(\"=\" * 70)\n", - "\n", - "data_s2 = load_sentinel2_stac(\n", - " bbox=bbox,\n", - " date_range=date_range,\n", - " bands=['red', 'green', 'blue', 'nir08', 'SCL'],\n", - " resolution=60 # Lower resolution for faster test\n", - ")\n", - "\n", - "if data_s2 is not None:\n", - " print(f\"\\n✅ SUCCESS! Sentinel-2 loaded\")\n", - " print(f\" Dims: {dict(data_s2.dims)}\")\n", - " print(f\" Vars: {list(data_s2.data_vars)}\")\n", - "else:\n", - " print(\"\\n❌ Failed to load Sentinel-2\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e688f3b9", - "metadata": {}, - "outputs": [], - "source": [ - "# Test Sentinel-1\n", - "print(\"\\n\" + \"=\" * 70)\n", - "print(\"Testing Sentinel-1 RTC\")\n", - "print(\"=\" * 70)\n", - "\n", - "data_s1 = load_sentinel1_stac(\n", - " bbox=bbox,\n", - " date_range=date_range,\n", - " bands=['vv', 'vh'],\n", - " resolution=60 # Lower resolution for faster test\n", - ")\n", - "\n", - "if data_s1 is not None:\n", - " print(f\"\\n✅ SUCCESS! Sentinel-1 loaded\")\n", - " print(f\" Dims: {dict(data_s1.dims)}\")\n", - " print(f\" Vars: {list(data_s1.data_vars)}\")\n", - "else:\n", - " print(\"\\n❌ Failed to load Sentinel-1\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "211796a7", - "metadata": {}, - "outputs": [], - "source": [ - "# Visualize if data loaded successfully\n", - "if data_s2 is not None:\n", - " print(\"\\n📊 Visualizing Sentinel-2 RGB composite...\")\n", - " \n", - " # Select first timestep\n", - " rgb = data_s2[['red', 'green', 'blue']].isel(time=0)\n", - " \n", - " # Plot\n", - " fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n", - " \n", - " rgb['red'].plot(ax=axes[0], cmap='Reds')\n", - " axes[0].set_title('Red band')\n", - " \n", - " rgb['green'].plot(ax=axes[1], cmap='Greens')\n", - " axes[1].set_title('Green band')\n", - " \n", - " rgb['blue'].plot(ax=axes[2], cmap='Blues')\n", - " axes[2].set_title('Blue band')\n", - " \n", - " plt.tight_layout()\n", - " plt.show()\n", - " \n", - " print(\"✅ Visualization complete!\")" - ] - }, - { - "cell_type": "markdown", - "id": "d4e29a09", - "metadata": {}, - "source": [ - "## ✅ Results\n", - "\n", - "Nếu cả 2 tests đều pass:\n", - "- ✅ Kết nối Planetary Computer OK\n", - "- ✅ Load Sentinel-2 OK\n", - "- ✅ Load Sentinel-1 OK\n", - "- ✅ Sẵn sàng sử dụng cho training!\n", - "\n", - "Next step: Sử dụng `01.train_DecisionTree_PlanetaryComputer.ipynb` để train model" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}