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255000b8f23fcc8c50deaefb3156ccf657985ddb
CSIROBoeingPhase5-Vietnam/new_train.ipynb
T
MinhKha 255000b8f2 test train new model
2024-09-09 13:37:31 +00:00

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{
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"id": "db6b50b8-f9c5-4ee9-b93f-ed38a89156ba",
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{
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"\n",
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" console.debug(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
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"\tcontinue;\n",
" }\n",
" const element = document.createElement(\"link\");\n",
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" 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",
" } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
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" element.async = false;\n",
" element.src = url;\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" for (var i = 0; i < js_modules.length; i++) {\n",
" var url = js_modules[i];\n",
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"\t on_load();\n",
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" }\n",
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" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" element.type = \"module\";\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" for (const name in js_exports) {\n",
" var url = js_exports[name];\n",
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"\t on_load();\n",
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" element.onerror = on_error;\n",
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" 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",
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" `\n",
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" }\n",
" if (!js_urls.length && !js_modules.length) {\n",
" on_load()\n",
" }\n",
" };\n",
"\n",
" function inject_raw_css(css) {\n",
" const element = document.createElement(\"style\");\n",
" element.appendChild(document.createTextNode(css));\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n",
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"function(Bokeh) {} // ensure no trailing comma for IE\n",
" ];\n",
"\n",
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" }\n",
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" if (Bokeh != undefined && !reloading) {\n",
"\tvar NewBokeh = root.Bokeh;\n",
"\tif (Bokeh.versions === undefined) {\n",
"\t Bokeh.versions = new Map();\n",
"\t}\n",
"\tif (NewBokeh.version !== Bokeh.version) {\n",
"\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
"\t}\n",
"\troot.Bokeh = Bokeh;\n",
" }} else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
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" 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",
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" // before older versions are fully initialized.\n",
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" root._bokeh_is_initializing = false;\n",
" root._bokeh_onload_callbacks = undefined;\n",
" console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
" load_or_wait();\n",
" } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
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" } else {\n",
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" bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
" root._bokeh_is_initializing = true\n",
" root._bokeh_onload_callbacks = []\n",
" if (!reloading && (!bokeh_loaded || is_dev)) {\n",
"\troot.Bokeh = undefined;\n",
" }\n",
" load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
"\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
"\trun_inline_js();\n",
" });\n",
" }\n",
" }\n",
" // Give older versions of the autoload script a head-start to ensure\n",
" // they initialize before we start loading newer version.\n",
" setTimeout(load_or_wait, 100)\n",
"}(window));"
],
"application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = false;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; 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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": [
"<style>*[data-root-id],\n",
"*[data-root-id] > * {\n",
" box-sizing: border-box;\n",
" font-family: var(--jp-ui-font-family);\n",
" font-size: var(--jp-ui-font-size1);\n",
" color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
"}\n",
"\n",
"/* Override VSCode background color */\n",
".cell-output-ipywidget-background:has(\n",
" > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
" ),\n",
".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
" background-color: transparent !important;\n",
"}\n",
"</style>"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
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"(function(root) {\n",
" function now() {\n",
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" }\n",
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" var reloading = true;\n",
" var Bokeh = root.Bokeh;\n",
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" root._bokeh_onload_callbacks.forEach(function(callback) {\n",
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" }\n",
" console.debug(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
" function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
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"\n",
" root._bokeh_onload_callbacks.push(callback);\n",
"\n",
" if (root._bokeh_is_loading > 0) {\n",
" console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
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" console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" }\n",
"\n",
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" 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",
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" console.error(\"failed to load \" + url);\n",
" }\n",
"\n",
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" require([\"jspanel\"], function(jsPanel) {\n",
"\twindow.jsPanel = jsPanel\n",
"\ton_load()\n",
" })\n",
" require([\"jspanel-modal\"], function() {\n",
"\ton_load()\n",
" })\n",
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"\ton_load()\n",
" })\n",
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"\ton_load()\n",
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" })\n",
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"\ton_load()\n",
" })\n",
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"\ton_load()\n",
" })\n",
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"\twindow.GridStack = GridStack\n",
"\ton_load()\n",
" })\n",
" require([\"notyf\"], function() {\n",
"\ton_load()\n",
" })\n",
" root._bokeh_is_loading = css_urls.length + 9;\n",
" } else {\n",
" root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
" }\n",
"\n",
" var existing_stylesheets = []\n",
" var links = document.getElementsByTagName('link')\n",
" for (var i = 0; i < links.length; i++) {\n",
" var link = links[i]\n",
" if (link.href != null) {\n",
"\texisting_stylesheets.push(link.href)\n",
" }\n",
" }\n",
" for (var i = 0; i < css_urls.length; i++) {\n",
" var url = css_urls[i];\n",
" if (existing_stylesheets.indexOf(url) !== -1) {\n",
"\ton_load()\n",
"\tcontinue;\n",
" }\n",
" const element = document.createElement(\"link\");\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.rel = \"stylesheet\";\n",
" element.type = \"text/css\";\n",
" element.href = url;\n",
" console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
" document.body.appendChild(element);\n",
" } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n",
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" for (var i = 0; i < urls.length; i++) {\n",
" skip.push(urls[i])\n",
" }\n",
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" for (var i = 0; i < urls.length; i++) {\n",
" skip.push(urls[i])\n",
" }\n",
" } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n",
" var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n",
" for (var i = 0; i < urls.length; i++) {\n",
" skip.push(urls[i])\n",
" }\n",
" } var existing_scripts = []\n",
" var scripts = document.getElementsByTagName('script')\n",
" for (var i = 0; i < scripts.length; i++) {\n",
" var script = scripts[i]\n",
" if (script.src != null) {\n",
"\texisting_scripts.push(script.src)\n",
" }\n",
" }\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
"\tif (!window.requirejs) {\n",
"\t on_load();\n",
"\t}\n",
"\tcontinue;\n",
" }\n",
" var element = document.createElement('script');\n",
" element.onload = on_load;\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" for (var i = 0; i < js_modules.length; i++) {\n",
" var url = js_modules[i];\n",
" if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
"\tif (!window.requirejs) {\n",
"\t on_load();\n",
"\t}\n",
"\tcontinue;\n",
" }\n",
" var element = document.createElement('script');\n",
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" element.onerror = on_error;\n",
" element.async = false;\n",
" element.src = url;\n",
" element.type = \"module\";\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.head.appendChild(element);\n",
" }\n",
" for (const name in js_exports) {\n",
" var url = js_exports[name];\n",
" if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
"\tif (!window.requirejs) {\n",
"\t on_load();\n",
"\t}\n",
"\tcontinue;\n",
" }\n",
" var element = document.createElement('script');\n",
" element.onerror = on_error;\n",
" element.async = false;\n",
" element.type = \"module\";\n",
" console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" element.textContent = `\n",
" import ${name} from \"${url}\"\n",
" window.${name} = ${name}\n",
" window._bokeh_on_load()\n",
" `\n",
" document.head.appendChild(element);\n",
" }\n",
" if (!js_urls.length && !js_modules.length) {\n",
" on_load()\n",
" }\n",
" };\n",
"\n",
" function inject_raw_css(css) {\n",
" const element = document.createElement(\"style\");\n",
" element.appendChild(document.createTextNode(css));\n",
" document.body.appendChild(element);\n",
" }\n",
"\n",
" var js_urls = [];\n",
" var js_modules = [];\n",
" var js_exports = {};\n",
" var css_urls = [];\n",
" var inline_js = [ function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
"function(Bokeh) {} // ensure no trailing comma for IE\n",
" ];\n",
"\n",
" function run_inline_js() {\n",
" if ((root.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i].call(root, root.Bokeh);\n",
" }\n",
" // Cache old bokeh versions\n",
" if (Bokeh != undefined && !reloading) {\n",
"\tvar NewBokeh = root.Bokeh;\n",
"\tif (Bokeh.versions === undefined) {\n",
"\t Bokeh.versions = new Map();\n",
"\t}\n",
"\tif (NewBokeh.version !== Bokeh.version) {\n",
"\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
"\t}\n",
"\troot.Bokeh = Bokeh;\n",
" }} else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!root._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" root._bokeh_failed_load = true;\n",
" }\n",
" root._bokeh_is_initializing = false\n",
" }\n",
"\n",
" function load_or_wait() {\n",
" // Implement a backoff loop that tries to ensure we do not load multiple\n",
" // versions of Bokeh and its dependencies at the same time.\n",
" // In recent versions we use the root._bokeh_is_initializing flag\n",
" // to determine whether there is an ongoing attempt to initialize\n",
" // bokeh, however for backward compatibility we also try to ensure\n",
" // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
" // before older versions are fully initialized.\n",
" if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
" root._bokeh_is_initializing = false;\n",
" root._bokeh_onload_callbacks = undefined;\n",
" console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
" load_or_wait();\n",
" } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
" setTimeout(load_or_wait, 100);\n",
" } else {\n",
" Bokeh = root.Bokeh;\n",
" bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
" root._bokeh_is_initializing = true\n",
" root._bokeh_onload_callbacks = []\n",
" if (!reloading && (!bokeh_loaded || is_dev)) {\n",
"\troot.Bokeh = undefined;\n",
" }\n",
" load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
"\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
"\trun_inline_js();\n",
" });\n",
" }\n",
" }\n",
" // Give older versions of the autoload script a head-start to ensure\n",
" // they initialize before we start loading newer version.\n",
" setTimeout(load_or_wait, 100)\n",
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],
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"text": [
"CPU times: user 12.9 s, sys: 4.31 s, total: 17.2 s\n",
"Wall time: 10.8 s\n"
]
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"source": [
"%%time\n",
"%matplotlib inline\n",
"from new_import import *"
]
},
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"cell_type": "code",
"execution_count": 2,
"id": "bfdefd7d-90d2-43bf-aa58-ebb4caef0f85",
"metadata": {
"tags": []
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"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"An existing cluster was found. Connecting to: easihub.9f2f42f40c134601a68634913d891637\n",
"CPU times: user 736 ms, sys: 28.1 ms, total: 764 ms\n",
"Wall time: 979 ms\n"
]
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"<div>\n",
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" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-6a7b92bd-6e7e-11ef-8f01-d24f7674e586</p>\n",
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"text/plain": [
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"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
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],
"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"
]
},
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"cell_type": "markdown",
"id": "657547be-5136-439b-b194-e5944c905138",
"metadata": {},
"source": [
"LOAD VH, VV"
]
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"cell_type": "code",
"execution_count": 3,
"id": "05c2a58f-d0e5-4b20-9320-de05897cbdf7",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"## cấu hình dữ liệu train và vh vv file\n",
"train_path = \"train/ST_training data_updated_1130points.shp\" # đường dẫn shp file train\n",
"name_vh = \"vh-0922_0923-full_ST.tif\"\n",
"name_vv = \"vv-0922_0923-full_ST.tif\"\n",
"\n",
"\n",
"train = load_train_data(train_path)"
]
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"cell_type": "code",
"execution_count": 4,
"id": "33471ca1-6e5a-403e-b9af-2e7b45c1d892",
"metadata": {
"tags": []
},
"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 220 µs, sys: 66 µs, total: 286 µs\n",
"Wall time: 169 µs\n"
]
}
],
"source": [
"%%time\n",
"## tải về dữ liệu sen1\n",
"import os\n",
"if not os.path.exists(name_vh):\n",
" !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vh-0922_0923-full_ST.tif vh-0922_0923-full_ST.tif\n",
"if not os.path.exists(name_vv):\n",
" !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vv-0922_0923-full_ST.tif vv-0922_0923-full_ST.tif"
]
},
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"cell_type": "code",
"execution_count": 5,
"id": "8e5fd090-d355-45bd-ba1b-b95d0584dd8c",
"metadata": {
"tags": []
},
"outputs": [
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"name": "stderr",
"output_type": "stream",
"text": [
"Warning 1: TIFFReadDirectory:Invalid data type for tag StripByteCounts\n",
"Warning 1: TIFFFetchNormalTag:ASCII value for tag \"GeoASCIIParams\" contains null byte in value; value incorrectly truncated during reading due to implementation limitations\n",
"Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n",
"Warning 1: TIFFReadDirectory:Invalid data type for tag StripByteCounts\n",
"Warning 1: TIFFFetchNormalTag:ASCII value for tag \"GeoASCIIParams\" contains null byte in value; value incorrectly truncated during reading due to implementation limitations\n",
"Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n"
]
}
],
"source": [
"dsvh, dsvv = load_sen1(name_vh, name_vv)"
]
},
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"cell_type": "markdown",
"id": "26f1d256-48cf-40a2-8de4-6ec7ea220bca",
"metadata": {},
"source": [
"LOAD SENTINEL 2\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "a0a8a450-3725-48d8-837e-2f19c2e6db59",
"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) "
]
},
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"cell_type": "code",
"execution_count": 7,
"id": "68e134d5-1b22-4b10-bdcb-d65d178e30d2",
"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"
]
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"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (time: 151, y: 8874, x: 9902)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2022-09-02T03:35:23.960000 ... 2023-09...\n",
" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
" spatial_ref int32 32648\n",
"Data variables:\n",
" blue (time, y, x) float32 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" green (time, y, x) float32 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" red (time, y, x) float32 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" nir (time, y, x) float32 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" scl (time, y, x) uint8 dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
"Attributes:\n",
" crs: EPSG:32648\n",
" grid_mapping: spatial_ref</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9ab25f52-c48b-4ac4-abb1-8fca67eacf55' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ab25f52-c48b-4ac4-abb1-8fca67eacf55' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 151</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-11814131-3551-4044-ae22-5015d2b99c89' class='xr-section-summary-in' type='checkbox' checked><label for='section-11814131-3551-4044-ae22-5015d2b99c89' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-09-02T03:35:23.960000 ... 2...</div><input id='attrs-25abfd55-6116-407e-b271-9d82d58408e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25abfd55-6116-407e-b271-9d82d58408e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-557f99d4-cfa5-4685-b98c-3d40f81198a3' class='xr-var-data-in' type='checkbox'><label for='data-557f99d4-cfa5-4685-b98c-3d40f81198a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-09-02T03:35:23.960000000&#x27;, &#x27;2022-09-04T03:25:18.037000000&#x27;,\n",
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" &#x27;2022-09-12T03:35:22.247000000&#x27;, &#x27;2022-09-14T03:25:18.529000000&#x27;,\n",
" &#x27;2022-09-17T03:35:13.648000000&#x27;, &#x27;2022-09-19T03:25:24.773000000&#x27;,\n",
" &#x27;2022-09-22T03:35:18.931000000&#x27;, &#x27;2022-09-24T03:25:17.431000000&#x27;,\n",
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" &#x27;2022-12-01T03:35:12.009000000&#x27;, &#x27;2022-12-03T03:25:14.366000000&#x27;,\n",
" &#x27;2022-12-06T03:35:10.293000000&#x27;, &#x27;2022-12-08T03:25:17.072000000&#x27;,\n",
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" &#x27;2023-01-12T03:25:12.066000000&#x27;, &#x27;2023-01-15T03:35:08.140000000&#x27;,\n",
" &#x27;2023-01-17T03:25:12.454000000&#x27;, &#x27;2023-01-20T03:35:06.593000000&#x27;,\n",
" &#x27;2023-01-22T03:25:14.446000000&#x27;, &#x27;2023-01-25T03:35:09.824000000&#x27;,\n",
" &#x27;2023-01-27T03:25:13.987000000&#x27;, &#x27;2023-01-30T03:35:09.963000000&#x27;,\n",
" &#x27;2023-02-01T03:25:13.595000000&#x27;, &#x27;2023-02-04T03:35:09.008000000&#x27;,\n",
" &#x27;2023-02-06T03:25:15.471000000&#x27;, &#x27;2023-02-09T03:35:10.646000000&#x27;,\n",
" &#x27;2023-02-11T03:25:15.100000000&#x27;, &#x27;2023-02-14T03:35:10.240000000&#x27;,\n",
" &#x27;2023-02-16T03:25:28.099000000&#x27;, &#x27;2023-02-24T03:35:12.118000000&#x27;,\n",
" &#x27;2023-02-26T03:25:14.146000000&#x27;, &#x27;2023-03-01T03:35:09.182000000&#x27;,\n",
" &#x27;2023-03-03T03:25:17.841000000&#x27;, &#x27;2023-03-06T03:35:14.717000000&#x27;,\n",
" &#x27;2023-03-08T03:25:12.032000000&#x27;, &#x27;2023-03-11T03:35:07.230000000&#x27;,\n",
" &#x27;2023-03-13T03:25:20.837000000&#x27;, &#x27;2023-03-16T03:35:15.940000000&#x27;,\n",
" &#x27;2023-03-18T03:25:14.467000000&#x27;, &#x27;2023-03-21T03:35:10.279000000&#x27;,\n",
" &#x27;2023-03-23T03:25:18.795000000&#x27;, &#x27;2023-03-26T03:35:14.570000000&#x27;,\n",
" &#x27;2023-03-28T03:25:14.536000000&#x27;, &#x27;2023-03-31T03:35:10.140000000&#x27;,\n",
" &#x27;2023-04-02T03:25:19.945000000&#x27;, &#x27;2023-04-05T03:35:14.925000000&#x27;,\n",
" &#x27;2023-04-07T03:25:16.518000000&#x27;, &#x27;2023-04-10T03:35:12.000000000&#x27;,\n",
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" &#x27;2023-04-22T03:25:18.572000000&#x27;, &#x27;2023-04-25T03:35:13.748000000&#x27;,\n",
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" &#x27;2023-09-19T03:25:24.089000000&#x27;, &#x27;2023-09-22T03:35:15.439000000&#x27;,\n",
" &#x27;2023-09-24T03:25:18.524000000&#x27;, &#x27;2023-09-27T03:35:14.112000000&#x27;,\n",
" &#x27;2023-09-29T03:25:19.046000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.106e+06 1.106e+06 ... 1.017e+06</div><input id='attrs-73fa1afa-9217-4ce1-bc4b-dc9ca2c28a62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73fa1afa-9217-4ce1-bc4b-dc9ca2c28a62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a90e2018-7d8e-4ca0-b2fd-5006673394ea' class='xr-var-data-in' type='checkbox'><label for='data-a90e2018-7d8e-4ca0-b2fd-5006673394ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([1105735., 1105725., 1105715., ..., 1017025., 1017015., 1017005.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.548e+05 5.548e+05 ... 6.538e+05</div><input id='attrs-dc5bc52a-a357-4838-8551-5d40c429977c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc5bc52a-a357-4838-8551-5d40c429977c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d1519ef-361c-49fa-92bc-55a0f4c9767c' class='xr-var-data-in' type='checkbox'><label for='data-7d1519ef-361c-49fa-92bc-55a0f4c9767c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd></dl></div><div class='xr-var-data'><pre>array([554795., 554805., 554815., ..., 653785., 653795., 653805.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>32648</div><input id='attrs-3690d236-fe8c-4811-9475-ee457ed2c4d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3690d236-fe8c-4811-9475-ee457ed2c4d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55a3fec6-b64a-4377-909e-7ac67243822d' class='xr-var-data-in' type='checkbox'><label for='data-55a3fec6-b64a-4377-909e-7ac67243822d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 48N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,105],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32648&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><pre>array(32648, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5e434a66-9989-429b-9579-e88c1bbfde2b' class='xr-section-summary-in' type='checkbox' checked><label for='section-5e434a66-9989-429b-9579-e88c1bbfde2b' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>blue</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;</div><input id='attrs-0baeddb9-6694-4717-b4ce-bd6f10640ef5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0baeddb9-6694-4717-b4ce-bd6f10640ef5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8da38c56-a2f6-4407-a259-420a783569d0' class='xr-var-data-in' type='checkbox'><label for='data-8da38c56-a2f6-4407-a259-420a783569d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
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" <tr>\n",
" <td> </td>\n",
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" <th> Chunk </th>\n",
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" \n",
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" <td> (151, 8874, 9902) </td>\n",
" <td> (1, 2048, 2048) </td>\n",
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" <td colspan=\"2\"> 3775 chunks in 8 graph layers </td>\n",
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" <tr>\n",
" <th> Data type </th>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-40c8a20f-af4c-4880-b6ec-ded97644b323' class='xr-section-summary-in' type='checkbox' ><label for='section-40c8a20f-af4c-4880-b6ec-ded97644b323' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b000dcdd-cebb-4599-9964-92b128544dcf' class='xr-index-data-in' type='checkbox'/><label for='index-b000dcdd-cebb-4599-9964-92b128544dcf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-09-02 03:35:23.960000&#x27;, &#x27;2022-09-04 03:25:18.037000&#x27;,\n",
" &#x27;2022-09-07 03:35:13.646000&#x27;, &#x27;2022-09-09 03:25:27.617000&#x27;,\n",
" &#x27;2022-09-12 03:35:22.247000&#x27;, &#x27;2022-09-14 03:25:18.529000&#x27;,\n",
" &#x27;2022-09-17 03:35:13.648000&#x27;, &#x27;2022-09-19 03:25:24.773000&#x27;,\n",
" &#x27;2022-09-22 03:35:18.931000&#x27;, &#x27;2022-09-24 03:25:17.431000&#x27;,\n",
" ...\n",
" &#x27;2023-09-07 03:35:17.134000&#x27;, &#x27;2023-09-09 03:25:20.648000&#x27;,\n",
" &#x27;2023-09-12 03:35:15.326000&#x27;, &#x27;2023-09-14 03:25:35.755000&#x27;,\n",
" &#x27;2023-09-17 03:35:15.269000&#x27;, &#x27;2023-09-19 03:25:24.089000&#x27;,\n",
" &#x27;2023-09-22 03:35:15.439000&#x27;, &#x27;2023-09-24 03:25:18.524000&#x27;,\n",
" &#x27;2023-09-27 03:35:14.112000&#x27;, &#x27;2023-09-29 03:25:19.046000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=151, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-409fb02a-78e2-4626-8521-219351af3122' class='xr-index-data-in' type='checkbox'/><label for='index-409fb02a-78e2-4626-8521-219351af3122' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1105735.0, 1105725.0, 1105715.0, 1105705.0, 1105695.0, 1105685.0,\n",
" 1105675.0, 1105665.0, 1105655.0, 1105645.0,\n",
" ...\n",
" 1017095.0, 1017085.0, 1017075.0, 1017065.0, 1017055.0, 1017045.0,\n",
" 1017035.0, 1017025.0, 1017015.0, 1017005.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=8874))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ede712f2-9004-4bd1-bba9-398fe847ada4' class='xr-index-data-in' type='checkbox'/><label for='index-ede712f2-9004-4bd1-bba9-398fe847ada4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([554795.0, 554805.0, 554815.0, 554825.0, 554835.0, 554845.0, 554855.0,\n",
" 554865.0, 554875.0, 554885.0,\n",
" ...\n",
" 653715.0, 653725.0, 653735.0, 653745.0, 653755.0, 653765.0, 653775.0,\n",
" 653785.0, 653795.0, 653805.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=9902))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c3d266f4-ddf8-4620-8644-e69cc25b004a' class='xr-section-summary-in' type='checkbox' checked><label for='section-c3d266f4-ddf8-4620-8644-e69cc25b004a' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
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" * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
" * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
" spatial_ref int32 32648\n",
"Data variables:\n",
" blue (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
" green (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
" red (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
" nir (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
" scl (time, y, x) uint8 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
"Attributes:\n",
" crs: EPSG:32648\n",
" grid_mapping: spatial_ref"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"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": 8,
"id": "0c1ac39c-7a78-425d-afbc-b04a7988f012",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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" <th>bits</th>\n",
" <th>values</th>\n",
" <th>description</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>qa</th>\n",
" <td>[0, 1, 2, 3, 4, 5, 6, 7]</td>\n",
" <td>{'0': 'no data', '1': 'saturated or defective'...</td>\n",
" <td>Sen2Cor Scene Classification</td>\n",
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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 2.06 s, sys: 84.8 ms, total: 2.14 s\n",
"Wall time: 2.14 s\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d9ad365b4b3d46ca9d0d596113c0a9ce",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox()"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%time\n",
"# Tiến hành loại bỏ các vị trí bị mây ảnh hưởng\n",
"result = mask_clean(data)\n",
"progress(result)"
]
},
{
"cell_type": "markdown",
"id": "cd57a554-a804-4d0f-ac69-136e34d089db",
"metadata": {},
"source": [
"CALCULATING THE MEAN VALUE AND FILL TO NAN POINT"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "973ac9b2-14fd-45e4-934c-b201ac51ec9e",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6878e9bc108249bc9c0152ce6b16d463",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox()"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')\n",
"ndvi = ds1[\"NDVI\"]\n",
"average_ndvi = ndvi.resample(time='1M').mean().persist() ## tính mean cho từng tháng -> time = 12\n",
"progress(average_ndvi)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5af8393a-e667-4c93-ae32-ca69ee9d9da7",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(13, 7680, 8687)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dsvh.shape"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5b81db73-d533-483e-bab5-44d2c3eff64c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"average_ndvi = average_ndvi.compute()\n",
"average_ndvi = average_ndvi[:, :dsvh.shape[1], :dsvh.shape[2]]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "504a37be-62cc-4121-b0bf-46639cb82fe6",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.5 s, sys: 1.2 s, total: 4.7 s\n",
"Wall time: 4.69 s\n"
]
}
],
"source": [
"%%time\n",
"filled_ds = average_ndvi.bfill(dim='time')\n",
"filled_ds = filled_ds.ffill(dim='time')\n"
]
},
{
"cell_type": "markdown",
"id": "2b6caa44-5793-4bac-ab67-d1e75d19aab3",
"metadata": {},
"source": [
"FIND NAN POINT AFTER FILLING AND FILLING AGAIN WITH LINEARREGRESSION ALGORITHM"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "0c47e46d-2c74-4b96-91b8-fcd5e46e7699",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of NaNs: 20800\n"
]
}
],
"source": [
"nan_mask = filled_ds.isnull()\n",
"\n",
"# Print the NaN mask\n",
"# print(nan_mask)\n",
"\n",
"# Count the number of NaNs\n",
"num_nans = nan_mask.sum()\n",
"print(f'Number of NaNs: {num_nans.values}')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ca51b1a5-2499-457b-bb99-65a950092270",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"\n",
"mask = ~np.isnan(filled_ds)\n",
"X_train = np.stack([dsvh.values[mask], dsvv.values[mask]], axis=1)\n",
"y_train = filled_ds.values[mask]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c78559a3-a503-4dac-a21d-63b414f932a2",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
],
"text/plain": [
"LinearRegression()"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = LinearRegression()\n",
"model.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "6a987dee-50b3-46d0-8fcd-d8fefe44ecd8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"X_pred = np.stack([dsvh.values[~mask], dsvv.values[~mask]], axis=1)\n",
"filled_ds.values[~mask] = model.predict(X_pred)"
]
},
{
"cell_type": "markdown",
"id": "146aa63d-217f-4c36-be36-e84a1ff3bf52",
"metadata": {},
"source": [
"MATCH LABEL TO DATASET"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "62024ea2-5b65-4aad-b1ea-66adb843f136",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 2.93 s, sys: 0 ns, total: 2.93 s\n",
"Wall time: 2.93 s\n"
]
}
],
"source": [
"%%time\n",
"\n",
"# Takes 1 minute to complete.\n",
"loaded_datasets = {}\n",
"for idx, point in train.iterrows():\n",
" key = f\"point_{idx + 1}\"\n",
" try:\n",
" ndvi_data = filled_ds.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values\n",
" vh_data = dsvh.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values\n",
" vv_data = dsvv.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values\n",
" loaded_datasets[key] = {\n",
" \"data\": np.concatenate((ndvi_data, vh_data, vv_data)),\n",
" \"label\": point.HT_code\n",
" }\n",
" except Exception as e:\n",
" # loaded_datasets[key] = None\n",
" print(e)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "9d80765f-c25c-465e-8c99-582276477561",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"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",
"label_encoder = LabelEncoder()\n",
"\n",
"# Fit and transform the labels\n",
"labels = train.Hientrang.values\n",
"numeric_labels = label_encoder.fit_transform([label_mapping[label] for label in labels])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "dc42088b-3aae-457f-bed8-206692121a37",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"X = []\n",
"x_new = []\n",
"lb_new = []\n",
"for k, v in loaded_datasets.items():\n",
" X.append(v)\n",
"for i in range(len(X)):\n",
" if X[i] is not None:\n",
" x_new.append(X[i][\"data\"])\n",
" lb_new.append(numeric_labels[i])"
]
},
{
"cell_type": "markdown",
"id": "6abe9a1b-55a6-48b0-a4ee-a1b2c7f209cf",
"metadata": {},
"source": [
"BUILDING DATASETS "
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "e4e234b9-88e0-4f72-bf61-02cd6c6a6cfe",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"X_train, X_temp, y_train, y_temp= train_test_split(x_new, lb_new, test_size=0.4, random_state=42)\n",
"X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)"
]
},
{
"cell_type": "markdown",
"id": "717baa23-76b6-45e0-8e7b-42d7a79fe773",
"metadata": {},
"source": [
"TRAIN MODEL"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "131df37a-b1c0-4c16-9086-0ba0104fd803",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best Parameters: {'classifier': RandomForestClassifier(n_jobs=-1, random_state=42), 'classifier__criterion': 'gini', 'classifier__max_depth': 15, 'classifier__n_estimators': 300}\n",
"Accuracy: 86.0 %\n",
"CPU times: user 8.96 s, sys: 4.88 s, total: 13.8 s\n",
"Wall time: 26.8 s\n"
]
}
],
"source": [
"%%time\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.svm import SVC\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"# Define the models\n",
"rf_model = RandomForestClassifier(random_state=42, n_jobs=-1)\n",
"knn_model = KNeighborsClassifier()\n",
"nb_model = GaussianNB()\n",
"svm_model = SVC()\n",
"\n",
"# Create a pipeline\n",
"pipeline = Pipeline([\n",
" ('scaler', StandardScaler()), # Apply scaling\n",
" ('classifier', rf_model) # Placeholder, will be set by param_grid\n",
"])\n",
"\n",
"# Define the parameter grid for each classifier\n",
"param_grid = [\n",
" # RandomForest\n",
" {\n",
" 'classifier': [rf_model],\n",
" 'classifier__n_estimators': [100, 300, 500, 700],\n",
" 'classifier__max_depth': [6, 8, 10, 15],\n",
" 'classifier__criterion': ['gini', 'entropy'],\n",
" },\n",
" # KNeighborsClassifier\n",
" {\n",
" 'classifier': [knn_model],\n",
" 'classifier__n_neighbors': [3, 5, 7, 9],\n",
" 'classifier__weights': ['uniform', 'distance'],\n",
" 'classifier__metric': ['euclidean', 'manhattan']\n",
" },\n",
" # Naive Bayes (GaussianNB doesn't have hyperparameters to tune here)\n",
" {\n",
" 'classifier': [nb_model],\n",
" },\n",
" # SVM\n",
" {\n",
" 'classifier': [svm_model],\n",
" 'classifier__C': [0.1, 1, 10, 100],\n",
" 'classifier__kernel': ['linear', 'rbf'],\n",
" 'classifier__gamma': ['scale', 'auto']\n",
" }\n",
"]\n",
"\n",
"# Use GridSearchCV to find the best classifier and hyperparameters\n",
"grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
"grid_search.fit(X_train, y_train)\n",
"\n",
"# Print out the best parameters and classifier\n",
"best_params = grid_search.best_params_\n",
"print(\"Best Parameters:\", best_params)\n",
"\n",
"# Make predictions on the validation set\n",
"y_pred = grid_search.predict(X_val)\n",
"\n",
"# Evaluate the results\n",
"accuracy = accuracy_score(y_val, y_pred)\n",
"print(f\"Accuracy: {round(accuracy, 2)*100} %\")\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "bd2d4a3d-061d-4238-8fd9-ab4aaf062a2e",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy for test data 86.0 %\n"
]
}
],
"source": [
"## check accuracy score\n",
"\n",
"y_pred_test = grid_search.predict(X_test)\n",
"test_accuracy = accuracy_score(y_test, y_pred_test)\n",
"print(f\"Accuracy for test data {round(test_accuracy, 2)*100} %\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "c5cd8314-c444-4bd4-a608-41e8a925ac51",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"['model_train/model_new2.joblib']"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir_save_model = \"model_train\"\n",
"if not os.path.exists(dir_save_model):\n",
" os.mkdir(dir_save_model)\n",
"joblib.dump(grid_search, os.path.join(dir_save_model, \"model_new2.joblib\"))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "f11593ca-dcb4-4018-8142-2e933812878e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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