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{
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{
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"id": "c61a1a84-ca37-4bfb-8935-a21312c11410",
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},
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{
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" }\n",
"\n",
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" }\n",
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" element.type = \"module\";\n",
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" }\n",
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" on_load()\n",
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" };\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/1.5.4/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.5.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.5.2.min.js\", \"https://cdn.holoviz.org/panel/1.5.4/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",
" },\n",
"function(Bokeh) {} // ensure no trailing comma for IE\n",
" ];\n",
"\n",
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" if ((root.Bokeh !== undefined) || (force === true)) {\n",
" for (let i = 0; i < inline_js.length; i++) {\n",
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" }\n",
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" }\n",
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" }\n",
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" root._bokeh_timeout = Date.now() + 5000;\n",
" root._bokeh_is_initializing = false;\n",
" root._bokeh_onload_callbacks = undefined;\n",
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" console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
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" }\n",
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" console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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" });\n",
" }\n",
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" setTimeout(load_or_wait, 100)\n",
"}(window));"
],
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},
"metadata": {},
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"\n",
"\n",
"var JS_MIME_TYPE = 'application/javascript';\n",
"var HTML_MIME_TYPE = 'text/html';\n",
"var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
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"\n",
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" var div = document.createElement(\"div\");\n",
" var script = document.createElement(\"script\");\n",
" node.appendChild(div);\n",
" node.appendChild(script);\n",
"}\n",
"\n",
"/**\n",
" * Handle when a new output is added\n",
" */\n",
"function handle_add_output(event, handle) {\n",
" var output_area = handle.output_area;\n",
" var output = handle.output;\n",
" if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
" return\n",
" }\n",
" var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
" var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
" if (id !== undefined) {\n",
" var nchildren = toinsert.length;\n",
" var html_node = toinsert[nchildren-1].children[0];\n",
" html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
" var scripts = [];\n",
" var nodelist = html_node.querySelectorAll(\"script\");\n",
" for (var i in nodelist) {\n",
" if (nodelist.hasOwnProperty(i)) {\n",
" scripts.push(nodelist[i])\n",
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"\n",
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" 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",
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" output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
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" 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/1.5.4/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",
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" try {\n",
" inline_js[i].call(root, root.Bokeh);\n",
" } catch(e) {\n",
" if (!reloading) {\n",
" throw e;\n",
" }\n",
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" 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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" }\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",
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" // 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",
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" if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
" // If the timeout and bokeh was not successfully loaded we reset\n",
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" 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 = '3.5.2'.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 con
},
"metadata": {},
"output_type": "display_data"
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{
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"\n",
"\n",
"var JS_MIME_TYPE = 'application/javascript';\n",
"var HTML_MIME_TYPE = 'text/html';\n",
"var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
"var CLASS_NAME = 'output';\n",
"\n",
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" * Render data to the DOM node\n",
" */\n",
"function render(props, node) {\n",
" var div = document.createElement(\"div\");\n",
" var script = document.createElement(\"script\");\n",
" node.appendChild(div);\n",
" node.appendChild(script);\n",
"}\n",
"\n",
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" * 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",
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" 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",
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" }\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",
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" window.PyViz.plot_index[id] = Bokeh.index[id];\n",
" } else {\n",
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" }\n",
" } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
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" 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",
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" output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
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" window.Bokeh.documents.splice(i, 1);\n",
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"function handle_update_output(event, handle) {\n",
" handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
" handle_add_output(event, handle)\n",
"}\n",
"\n",
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" function append_mime(data, metadata, element) {\n",
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" var toinsert = this.create_output_subarea(\n",
" metadata,\n",
" CLASS_NAME,\n",
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" events.on('delete.Cell', handle_clear_output);\n",
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"\n",
" OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
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},
"metadata": {},
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{
"name": "stdout",
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"text": [
"CPU times: user 13.3 s, sys: 3.12 s, total: 16.4 s\n",
"Wall time: 10.3 s\n"
]
}
],
"source": [
"%%time\n",
"%matplotlib inline\n",
"\n",
"import importlib\n",
"import new_import \n",
"\n",
"importlib.reload(new_import)\n",
"\n",
"\n",
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]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2298b035-c320-4c52-a2e4-6d8084f66b92",
"metadata": {
"tags": []
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{
"name": "stdout",
"output_type": "stream",
"text": [
"An existing cluster was found. Connecting to: easihub.8daaa179e4964dc7b211b4302753bf26\n",
"CPU times: user 689 ms, sys: 57.7 ms, total: 747 ms\n",
"Wall time: 2min 20s\n"
]
},
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"text": [
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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",
"client"
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" display: inline-block;\n",
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".xr-dim-list:before {\n",
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".xr-dim-list:after {\n",
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".xr-dim-list li:not(:last-child):after {\n",
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".xr-var-item {\n",
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".xr-var-item > .xr-var-name span {\n",
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".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25GB\n",
"Dimensions: (time: 35, y: 8874, x: 9902)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 280B 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&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" vh (time, y, x) float32 12GB 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-5108687a-a9df-4b20-a879-6085d985a168' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5108687a-a9df-4b20-a879-6085d985a168' 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>: 35</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-ecbde275-e11e-462f-9d3a-79be1568198c' class='xr-section-summary-in' type='checkbox' checked><label for='section-ecbde275-e11e-462f-9d3a-79be1568198c' 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'>2022-09-06T22:46:14.500000 ... 2...</div><input id='attrs-8eeb9295-5292-449b-9950-1eb9c50cee2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8eeb9295-5292-449b-9950-1eb9c50cee2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bcc5aac-4ba8-4d44-8eca-0a50cb57690e' class='xr-var-data-in' type='checkbox'><label for='data-2bcc5aac-4ba8-4d44-8eca-0a50cb57690e' 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;2022-09-06T22:46:14.500000000&#x27;, &#x27;2022-09-18T22:46:13.500000000&#x27;,\n",
" &#x27;2022-09-30T22:46:14.500000000&#x27;, &#x27;2022-10-12T22:46:14.500000000&#x27;,\n",
" &#x27;2022-10-24T22:46:14.500000000&#x27;, &#x27;2022-11-05T22:46:14.500000000&#x27;,\n",
" &#x27;2022-11-17T22:46:13.500000000&#x27;, &#x27;2022-11-29T22:46:13.500000000&#x27;,\n",
" &#x27;2022-12-11T22:46:13.500000000&#x27;, &#x27;2022-12-23T22:46:12.500000000&#x27;,\n",
" &#x27;2023-01-04T22:46:11.500000000&#x27;, &#x27;2023-01-16T22:46:10.500000000&#x27;,\n",
" &#x27;2023-01-28T22:46:11.500000000&#x27;, &#x27;2023-02-09T22:46:10.500000000&#x27;,\n",
" &#x27;2023-02-21T22:46:09.500000000&#x27;, &#x27;2023-03-05T22:46:10.500000000&#x27;,\n",
" &#x27;2023-03-17T22:46:10.500000000&#x27;, &#x27;2023-03-29T22:46:10.500000000&#x27;,\n",
" &#x27;2023-04-10T22:46:11.500000000&#x27;, &#x27;2023-04-22T22:46:11.500000000&#x27;,\n",
" &#x27;2023-05-04T22:46:11.500000000&#x27;, &#x27;2023-05-17T11:11:32.500000000&#x27;,\n",
" &#x27;2023-05-29T11:11:32.500000000&#x27;, &#x27;2023-06-10T11:11:28.500000000&#x27;,\n",
" &#x27;2023-06-21T22:46:13.500000000&#x27;, &#x27;2023-07-04T11:11:34.500000000&#x27;,\n",
" &#x27;2023-07-16T11:11:35.500000000&#x27;, &#x27;2023-07-28T11:11:36.500000000&#x27;,\n",
" &#x27;2023-08-08T22:46:16.500000000&#x27;, &#x27;2023-08-20T22:46:17.500000000&#x27;,\n",
" &#x27;2023-09-01T22:46:18.500000000&#x27;, &#x27;2023-09-13T22:46:18.500000000&#x27;,\n",
" &#x27;2023-09-25T22:46:19.500000000&#x27;, &#x27;2023-10-07T22:46:19.500000000&#x27;,\n",
" &#x27;2023-10-19T22:46:19.500000000&#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-d9bad05f-42a8-48df-acad-00ccbcd17de9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9bad05f-42a8-48df-acad-00ccbcd17de9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29ef0cd1-0da0-4824-a113-39cb35882489' class='xr-var-data-in' type='checkbox'><label for='data-29ef0cd1-0da0-4824-a113-39cb35882489' 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-f38d4e50-36fc-4633-8239-9b85b0a01acd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f38d4e50-36fc-4633-8239-9b85b0a01acd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df1a8cc8-9736-402e-a11d-a4b61abbbeac' class='xr-var-data-in' type='checkbox'><label for='data-df1a8cc8-9736-402e-a11d-a4b61abbbeac' 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-645b1f19-e2e2-4b27-b6cb-e5d8beb08270' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-645b1f19-e2e2-4b27-b6cb-e5d8beb08270' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b43c879f-1254-4a5e-bfa3-10b14bef7d8f' class='xr-var-data-in' type='checkbox'><label for='data-b43c879f-1254-4a5e-bfa3-10b14bef7d8f' 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
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vh</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-eb6fa9c7-4180-47f2-8a96-b93348c797ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb6fa9c7-4180-47f2-8a96-b93348c797ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1450be41-0d43-4af1-bec5-3a25c8359c6a' class='xr-var-data-in' type='checkbox'><label for='data-1450be41-0d43-4af1-bec5-3a25c8359c6a' 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>intensity</dd><dt><span>nodata :</span></dt><dd>nan</dd><dt><span>crs :</span></dt><dd>EPSG:32648</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></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",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 11.46 GiB </td>\n",
" <td> 16.00 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (35, 8874, 9902) </td>\n",
" <td> (1, 2048, 2048) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 875 chunks in 1 graph layer </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0980a241-bb28-4360-8a8c-ccfd061c212f' class='xr-section-summary-in' type='checkbox' ><label for='section-0980a241-bb28-4360-8a8c-ccfd061c212f' 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><input type='checkbox' disabled/><label></label><input id='index-d3cf712a-ab8e-4bf8-b055-9ab6c6b7fc4e' class='xr-index-data-in' type='checkbox'/><label for='index-d3cf712a-ab8e-4bf8-b055-9ab6c6b7fc4e' 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;2022-09-06 22:46:14.500000&#x27;, &#x27;2022-09-18 22:46:13.500000&#x27;,\n",
" &#x27;2022-09-30 22:46:14.500000&#x27;, &#x27;2022-10-12 22:46:14.500000&#x27;,\n",
" &#x27;2022-10-24 22:46:14.500000&#x27;, &#x27;2022-11-05 22:46:14.500000&#x27;,\n",
" &#x27;2022-11-17 22:46:13.500000&#x27;, &#x27;2022-11-29 22:46:13.500000&#x27;,\n",
" &#x27;2022-12-11 22:46:13.500000&#x27;, &#x27;2022-12-23 22:46:12.500000&#x27;,\n",
" &#x27;2023-01-04 22:46:11.500000&#x27;, &#x27;2023-01-16 22:46:10.500000&#x27;,\n",
" &#x27;2023-01-28 22:46:11.500000&#x27;, &#x27;2023-02-09 22:46:10.500000&#x27;,\n",
" &#x27;2023-02-21 22:46:09.500000&#x27;, &#x27;2023-03-05 22:46:10.500000&#x27;,\n",
" &#x27;2023-03-17 22:46:10.500000&#x27;, &#x27;2023-03-29 22:46:10.500000&#x27;,\n",
" &#x27;2023-04-10 22:46:11.500000&#x27;, &#x27;2023-04-22 22:46:11.500000&#x27;,\n",
" &#x27;2023-05-04 22:46:11.500000&#x27;, &#x27;2023-05-17 11:11:32.500000&#x27;,\n",
" &#x27;2023-05-29 11:11:32.500000&#x27;, &#x27;2023-06-10 11:11:28.500000&#x27;,\n",
" &#x27;2023-06-21 22:46:13.500000&#x27;, &#x27;2023-07-04 11:11:34.500000&#x27;,\n",
" &#x27;2023-07-16 11:11:35.500000&#x27;, &#x27;2023-07-28 11:11:36.500000&#x27;,\n",
" &#x27;2023-08-08 22:46:16.500000&#x27;, &#x27;2023-08-20 22:46:17.500000&#x27;,\n",
" &#x27;2023-09-01 22:46:18.500000&#x27;, &#x27;2023-09-13 22:46:18.500000&#x27;,\n",
" &#x27;2023-09-25 22:46:19.500000&#x27;, &#x27;2023-10-07 22:46:19.500000&#x27;,\n",
" &#x27;2023-10-19 22:46:19.500000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, 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><input type='checkbox' disabled/><label></label><input id='index-e33e5cb7-024a-40df-bea3-dc3fa1fa4062' class='xr-index-data-in' type='checkbox'/><label for='index-e33e5cb7-024a-40df-bea3-dc3fa1fa4062' 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><input type='checkbox' disabled/><label></label><input id='index-8f1a8756-ecaa-43b8-b7ba-5a99fd877265' class='xr-index-data-in' type='checkbox'/><label for='index-8f1a8756-ecaa-43b8-b7ba-5a99fd877265' 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-ed3a5168-c3b3-466e-88b7-bfa06a6ea031' class='xr-section-summary-in' type='checkbox' checked><label for='section-ed3a5168-c3b3-466e-88b7-bfa06a6ea031' 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> Size: 25GB\n",
"Dimensions: (time: 35, y: 8874, x: 9902)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 280B 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",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# tải dữ liệu vh vv từ vệ tinh sentinel-1\n",
"dsvh, dsvv = load_data_sen1(dc, date_range, coordinates)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c4d11ba1-b40a-4969-b911-e70914629dc9",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/env/lib/python3.12/site-packages/xarray/groupers.py:487: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
" self.index_grouper = pd.Grouper(\n"
]
},
{
"data": {
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"model_id": "5f815efdbc5d4348b8c313833ceff90c",
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},
"text/plain": [
"VBox()"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"average_vh = calculate_average(dsvh, time_pattern='1M')\n",
"progress(average_vh)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3849455c-e4ea-4c83-9693-990a8edce2e8",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/env/lib/python3.12/site-packages/xarray/groupers.py:487: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
" self.index_grouper = pd.Grouper(\n"
]
},
{
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"model_id": "c0b35b621a33476cbfe5223796a21fde",
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},
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"VBox()"
]
},
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}
],
"source": [
"average_vv = calculate_average(dsvv, time_pattern='1M')\n",
"progress(average_vv)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "b6a66c55-ae30-4af9-9b8d-8d4f2152cf93",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 9.29 s, sys: 7.41 s, total: 16.7 s\n",
"Wall time: 5min 25s\n"
]
},
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vv&#x27; (time: 14, y: 8874, x: 9902)&gt; Size: 5GB\n",
"array([[[0.06906006, 0.07957305, 0.09409785, ..., 0.23094253,\n",
" 0.23027407, 0.23319586],\n",
" [0.06906006, 0.07957305, 0.09409785, ..., 0.23425393,\n",
" 0.23664413, 0.2474947 ],\n",
" [0.07378212, 0.08650854, 0.10212713, ..., 0.24990971,\n",
" 0.23486657, 0.24579485],\n",
" ...,\n",
" [0.07391845, 0.07370436, 0.07163539, ..., 0.04897952,\n",
" 0.04920945, 0.04933887],\n",
" [0.07343939, 0.07252609, 0.0702525 , ..., 0.04814997,\n",
" 0.0479173 , 0.04799423],\n",
" [0.07355063, 0.07199537, 0.06989169, ..., 0.04814997,\n",
" 0.0479173 , 0.04799423]],\n",
"\n",
" [[0.00899241, 0.00861329, 0.00829276, ..., 0.23408818,\n",
" 0.2404511 , 0.24306983],\n",
" [0.00899241, 0.00861329, 0.00829276, ..., 0.25091022,\n",
" 0.25812387, 0.2747655 ],\n",
" [0.00900787, 0.00860327, 0.00830202, ..., 0.2770343 ,\n",
" 0.27153593, 0.29995093],\n",
"...\n",
" [0.11153916, 0.13004702, 0.12090257, ..., 0.05369304,\n",
" 0.05208092, 0.04936117],\n",
" [0.10719641, 0.11629926, 0.11002686, ..., 0.05484349,\n",
" 0.05366876, 0.05158882],\n",
" [0.10319728, 0.10290676, 0.09967952, ..., 0.05484349,\n",
" 0.05366876, 0.05158882]],\n",
"\n",
" [[0.00969094, 0.01002615, 0.01025252, ..., 0.2466253 ,\n",
" 0.25675553, 0.2808404 ],\n",
" [0.00969094, 0.01002615, 0.01025252, ..., 0.27609208,\n",
" 0.28855708, 0.33027148],\n",
" [0.00952399, 0.00974397, 0.01000167, ..., 0.31602195,\n",
" 0.33647415, 0.37436265],\n",
" ...,\n",
" [0.07525177, 0.07550529, 0.07569483, ..., 0.01599048,\n",
" 0.01579917, 0.01585157],\n",
" [0.07992493, 0.07882614, 0.07751034, ..., 0.01602835,\n",
" 0.01578975, 0.01558014],\n",
" [0.08913893, 0.08342338, 0.07963105, ..., 0.01602835,\n",
" 0.01578975, 0.01558014]]], dtype=float32)\n",
"Coordinates:\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",
" * time (time) datetime64[ns] 112B 2022-09-30 2022-10-31 ... 2023-10-31\n",
"Attributes:\n",
" units: intensity\n",
" nodata: nan\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.DataArray</div><div class='xr-array-name'>'vv'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 14</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b48776f8-1e22-4d1d-871b-394bcc93ff78' class='xr-array-in' type='checkbox' checked><label for='section-b48776f8-1e22-4d1d-871b-394bcc93ff78' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.06906 0.07957 0.0941 0.0941 ... 0.01707 0.01603 0.01579 0.01558</span></div><div class='xr-array-data'><pre>array([[[0.06906006, 0.07957305, 0.09409785, ..., 0.23094253,\n",
" 0.23027407, 0.23319586],\n",
" [0.06906006, 0.07957305, 0.09409785, ..., 0.23425393,\n",
" 0.23664413, 0.2474947 ],\n",
" [0.07378212, 0.08650854, 0.10212713, ..., 0.24990971,\n",
" 0.23486657, 0.24579485],\n",
" ...,\n",
" [0.07391845, 0.07370436, 0.07163539, ..., 0.04897952,\n",
" 0.04920945, 0.04933887],\n",
" [0.07343939, 0.07252609, 0.0702525 , ..., 0.04814997,\n",
" 0.0479173 , 0.04799423],\n",
" [0.07355063, 0.07199537, 0.06989169, ..., 0.04814997,\n",
" 0.0479173 , 0.04799423]],\n",
"\n",
" [[0.00899241, 0.00861329, 0.00829276, ..., 0.23408818,\n",
" 0.2404511 , 0.24306983],\n",
" [0.00899241, 0.00861329, 0.00829276, ..., 0.25091022,\n",
" 0.25812387, 0.2747655 ],\n",
" [0.00900787, 0.00860327, 0.00830202, ..., 0.2770343 ,\n",
" 0.27153593, 0.29995093],\n",
"...\n",
" [0.11153916, 0.13004702, 0.12090257, ..., 0.05369304,\n",
" 0.05208092, 0.04936117],\n",
" [0.10719641, 0.11629926, 0.11002686, ..., 0.05484349,\n",
" 0.05366876, 0.05158882],\n",
" [0.10319728, 0.10290676, 0.09967952, ..., 0.05484349,\n",
" 0.05366876, 0.05158882]],\n",
"\n",
" [[0.00969094, 0.01002615, 0.01025252, ..., 0.2466253 ,\n",
" 0.25675553, 0.2808404 ],\n",
" [0.00969094, 0.01002615, 0.01025252, ..., 0.27609208,\n",
" 0.28855708, 0.33027148],\n",
" [0.00952399, 0.00974397, 0.01000167, ..., 0.31602195,\n",
" 0.33647415, 0.37436265],\n",
" ...,\n",
" [0.07525177, 0.07550529, 0.07569483, ..., 0.01599048,\n",
" 0.01579917, 0.01585157],\n",
" [0.07992493, 0.07882614, 0.07751034, ..., 0.01602835,\n",
" 0.01578975, 0.01558014],\n",
" [0.08913893, 0.08342338, 0.07963105, ..., 0.01602835,\n",
" 0.01578975, 0.01558014]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-5353a8ba-7fcd-4a32-bf65-f40a0a98f7c5' class='xr-section-summary-in' type='checkbox' checked><label for='section-5353a8ba-7fcd-4a32-bf65-f40a0a98f7c5' 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'>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-0f5ee9f0-0e0a-492e-a417-a856fc12121c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f5ee9f0-0e0a-492e-a417-a856fc12121c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8bf31406-98ab-4d97-a7bf-27fa6b01b8e4' class='xr-var-data-in' type='checkbox'><label for='data-8bf31406-98ab-4d97-a7bf-27fa6b01b8e4' 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-8d3b22ba-d810-4392-9ca2-1ae2a1b20b1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d3b22ba-d810-4392-9ca2-1ae2a1b20b1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e1d4682-9b01-4d33-8dd9-926d6d89bcea' class='xr-var-data-in' type='checkbox'><label for='data-6e1d4682-9b01-4d33-8dd9-926d6d89bcea' 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-c0711061-4ed3-4dae-9d65-63671af0d428' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0711061-4ed3-4dae-9d65-63671af0d428' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d4fa270-11be-405d-81aa-2b2c97127c5b' class='xr-var-data-in' type='checkbox'><label for='data-3d4fa270-11be-405d-81aa-2b2c97127c5b' 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_eastin
" &#x27;2022-11-30T00:00:00.000000000&#x27;, &#x27;2022-12-31T00:00:00.000000000&#x27;,\n",
" &#x27;2023-01-31T00:00:00.000000000&#x27;, &#x27;2023-02-28T00:00:00.000000000&#x27;,\n",
" &#x27;2023-03-31T00:00:00.000000000&#x27;, &#x27;2023-04-30T00:00:00.000000000&#x27;,\n",
" &#x27;2023-05-31T00:00:00.000000000&#x27;, &#x27;2023-06-30T00:00:00.000000000&#x27;,\n",
" &#x27;2023-07-31T00:00:00.000000000&#x27;, &#x27;2023-08-31T00:00:00.000000000&#x27;,\n",
" &#x27;2023-09-30T00:00:00.000000000&#x27;, &#x27;2023-10-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2e363a99-e4e9-42ba-b4bd-2b1bc5550436' class='xr-section-summary-in' type='checkbox' ><label for='section-2e363a99-e4e9-42ba-b4bd-2b1bc5550436' 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>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-47d5b014-03aa-4046-8479-fd5279015f0e' class='xr-index-data-in' type='checkbox'/><label for='index-47d5b014-03aa-4046-8479-fd5279015f0e' 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><input type='checkbox' disabled/><label></label><input id='index-7c7deb50-6395-4f6e-b1c8-b60ac4adf4ee' class='xr-index-data-in' type='checkbox'/><label for='index-7c7deb50-6395-4f6e-b1c8-b60ac4adf4ee' 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><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-5c185d55-2552-4abd-bb4b-319e5f94559e' class='xr-index-data-in' type='checkbox'/><label for='index-5c185d55-2552-4abd-bb4b-319e5f94559e' 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;2022-09-30&#x27;, &#x27;2022-10-31&#x27;, &#x27;2022-11-30&#x27;, &#x27;2022-12-31&#x27;,\n",
" &#x27;2023-01-31&#x27;, &#x27;2023-02-28&#x27;, &#x27;2023-03-31&#x27;, &#x27;2023-04-30&#x27;,\n",
" &#x27;2023-05-31&#x27;, &#x27;2023-06-30&#x27;, &#x27;2023-07-31&#x27;, &#x27;2023-08-31&#x27;,\n",
" &#x27;2023-09-30&#x27;, &#x27;2023-10-31&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=&#x27;ME&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-678e79c6-9d51-483a-9572-a912538ff87f' class='xr-section-summary-in' type='checkbox' checked><label for='section-678e79c6-9d51-483a-9572-a912538ff87f' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>intensity</dd><dt><span>nodata :</span></dt><dd>nan</dd><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>"
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"Coordinates:\n",
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" spatial_ref int32 4B 32648\n",
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"Attributes:\n",
" units: intensity\n",
" nodata: nan\n",
" crs: EPSG:32648\n",
" grid_mapping: spatial_ref"
]
},
"execution_count": 7,
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"source": [
"%%time\n",
"average_vh.compute()\n",
"average_vv.compute()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "46f285ce-490c-42e1-9822-8154b37704f6",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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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".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; Size: 129GB\n",
"Dimensions: (time: 163, 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 57GB dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" nir (time, y, x) float32 57GB dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;\n",
" scl (time, y, x) uint8 14GB 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-69d40c4e-3e70-4020-9e2b-a25876c89a4b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-69d40c4e-3e70-4020-9e2b-a25876c89a4b' 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>: 163</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-8f08e3f5-dd54-47aa-82b3-8bfff9caaeb0' class='xr-section-summary-in' type='checkbox' checked><label for='section-8f08e3f5-dd54-47aa-82b3-8bfff9caaeb0' 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-8314bc7a-fa5e-4bdb-a8e1-921a3246b85f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8314bc7a-fa5e-4bdb-a8e1-921a3246b85f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6181221c-560b-4da8-98c0-61ff402dc8ff' class='xr-var-data-in' type='checkbox'><label for='data-6181221c-560b-4da8-98c0-61ff402dc8ff' 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;2023-10-24T03:25:16.597000000&#x27;, &#x27;2023-10-27T03:35:11.799000000&#x27;,\n",
" &#x27;2023-10-29T03:25:18.344000000&#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-4a28a7eb-0d3b-4bbb-a63e-2a4954a12b61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a28a7eb-0d3b-4bbb-a63e-2a4954a12b61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-342f3b28-91d5-4992-9974-749da9a2b5ce' class='xr-var-data-in' type='checkbox'><label for='data-342f3b28-91d5-4992-9974-749da9a2b5ce' 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-8bcf43b8-a3cb-4a6c-a803-157159af317f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bcf43b8-a3cb-4a6c-a803-157159af317f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f84ddf68-5696-4809-a807-f0e2b1d10bd8' class='xr-var-data-in' type='checkbox'><label for='data-f84ddf68-5696-4809-a807-f0e2b1d10bd8' 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-ed173e99-4b20-464a-85ad-908f4f612245' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed173e99-4b20-464a-85ad-908f4f612245' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86b19465-5388-4924-99f8-9b1b62c0b930' class='xr-var-data-in' type='checkbox'><label for='data-86b19465-5388-4924-99f8-9b1b62c0b930' 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
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" <th> Bytes </th>\n",
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" \n",
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" &#x27;2022-09-07 03:35:13.646000&#x27;, &#x27;2022-09-09 03:25:27.617000&#x27;,\n",
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" &#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;2023-10-07 03:35:12.349000&#x27;, &#x27;2023-10-09 03:25:17.684000&#x27;,\n",
" &#x27;2023-10-12 03:35:13.797000&#x27;, &#x27;2023-10-14 03:25:14.753000&#x27;,\n",
" &#x27;2023-10-17 03:35:11.016000&#x27;, &#x27;2023-10-19 03:25:18.719000&#x27;,\n",
" &#x27;2023-10-22 03:35:13.512000&#x27;, &#x27;2023-10-24 03:25:16.597000&#x27;,\n",
" &#x27;2023-10-27 03:35:11.799000&#x27;, &#x27;2023-10-29 03:25:18.344000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=163, 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><input type='checkbox' disabled/><label></label><input id='index-007afc7b-1940-4a05-a7fe-03b03abc1f8c' class='xr-index-data-in' type='checkbox'/><label for='index-007afc7b-1940-4a05-a7fe-03b03abc1f8c' 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><input type='checkbox' disabled/><label></label><input id='index-935f5424-3695-4754-8fb1-ece6dc7a2f93' class='xr-index-data-in' type='checkbox'/><label for='index-935f5424-3695-4754-8fb1-ece6dc7a2f93' 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-df68aace-af00-4c6e-abaa-b96c5db04726' class='xr-section-summary-in' type='checkbox' checked><label for='section-df68aace-af00-4c6e-abaa-b96c5db04726' 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> Size: 129GB\n",
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"Coordinates:\n",
" * time (time) datetime64[ns] 1kB 2022-09-02T03:35:23.960000 ... 202...\n",
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" * 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 57GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
" nir (time, y, x) float32 57GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
" scl (time, y, x) uint8 14GB dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
"Attributes:\n",
" crs: EPSG:32648\n",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_sen2 = load_data_sen2(dc, date_range, coordinates)\n",
"notebook_utils.heading(notebook_utils.xarray_object_size(data_sen2))\n",
"display(data_sen2)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "8cb877b1-a69e-4ee8-b54c-235b5ba83084",
"metadata": {
"tags": []
},
"outputs": [
{
"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 989 ms, sys: 30.2 ms, total: 1.02 s\n",
"Wall time: 1.02 s\n"
]
},
{
"data": {
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"model_id": "2960178e222f4d6eb97d633bd9d9266d",
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"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_cloud(data_sen2)\n",
"progress(result)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "325668db-040c-44d1-8cac-749f1850067d",
"metadata": {
"tags": []
},
"outputs": [
{
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"html[theme=\"dark\"],\n",
"html[data-theme=\"dark\"],\n",
"body[data-theme=\"dark\"],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
" --xr-border-color: #1f1f1f;\n",
" --xr-disabled-color: #515151;\n",
" --xr-background-color: #111111;\n",
" --xr-background-color-row-even: #111111;\n",
" --xr-background-color-row-odd: #313131;\n",
"}\n",
"\n",
".xr-wrap {\n",
" display: block !important;\n",
" min-width: 300px;\n",
" max-width: 700px;\n",
"}\n",
"\n",
".xr-text-repr-fallback {\n",
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
" display: none;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: inline-block;\n",
" opacity: 0;\n",
" height: 0;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:focus + label {\n",
" border: 2px solid var(--xr-font-color0);\n",
"}\n",
"\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",
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"\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",
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"\n",
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" padding-right: 5px;\n",
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"\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",
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".xr-preview,\n",
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".xr-var-name:hover,\n",
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".xr-var-dtype:hover,\n",
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"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
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"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
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"\n",
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".xr-var-name span,\n",
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".xr-index-name div,\n",
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"\n",
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".xr-attrs dt,\n",
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" font-weight: normal;\n",
" grid-column: 1;\n",
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".xr-attrs dt:hover span {\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;NDVI&#x27; (time: 163, y: 8874, x: 9902)&gt; Size: 57GB\n",
"dask.array&lt;truediv, shape=(163, 8874, 9902), dtype=float32, chunksize=(1, 2048, 2048), chunktype=numpy.ndarray&gt;\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</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'NDVI'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 163</li><li><span class='xr-has-index'>y</span>: 8874</li><li><span class='xr-has-index'>x</span>: 9902</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2f1b8870-3328-42c9-be4a-36126339e96a' class='xr-array-in' type='checkbox' checked><label for='section-2f1b8870-3328-42c9-be4a-36126339e96a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 2048, 2048), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
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" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 53.36 GiB </td>\n",
" <td> 16.00 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (163, 8874, 9902) </td>\n",
" <td> (1, 2048, 2048) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 4075 chunks in 7 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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" &#x27;2022-09-27T03:35:12.140000000&#x27;, &#x27;2022-09-29T03:25:23.829000000&#x27;,\n",
" &#x27;2022-10-02T03:35:19.048000000&#x27;, &#x27;2022-10-07T03:35:09.133000000&#x27;,\n",
" &#x27;2022-10-09T03:25:22.746000000&#x27;, &#x27;2022-10-12T03:35:17.151000000&#x27;,\n",
" &#x27;2022-10-14T03:25:15.533000000&#x27;, &#x27;2022-10-17T03:35:11.148000000&#x27;,\n",
" &#x27;2022-10-19T03:25:18.808000000&#x27;, &#x27;2022-10-22T03:35:13.746000000&#x27;,\n",
" &#x27;2022-10-24T03:25:15.878000000&#x27;, &#x27;2022-10-27T03:35:10.923000000&#x27;,\n",
" &#x27;2022-10-29T03:25:20.127000000&#x27;, &#x27;2022-11-01T03:35:15.818000000&#x27;,\n",
" &#x27;2022-11-03T03:25:14.204000000&#x27;, &#x27;2022-11-06T03:35:10.096000000&#x27;,\n",
" &#x27;2022-11-11T03:35:15.334000000&#x27;, &#x27;2022-11-13T03:25:16.056000000&#x27;,\n",
" &#x27;2022-11-16T03:35:11.542000000&#x27;, &#x27;2022-11-18T03:25:18.155000000&#x27;,\n",
" &#x27;2022-11-21T03:35:12.396000000&#x27;, &#x27;2022-11-23T03:25:16.067000000&#x27;,\n",
" &#x27;2022-11-26T03:35:11.063000000&#x27;, &#x27;2022-11-28T03:25:16.297000000&#x27;,\n",
" &#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",
" &#x27;2022-12-11T03:35:12.184000000&#x27;, &#x27;2022-12-16T03:35:10.130000000&#x27;,\n",
" &#x27;2022-12-18T03:25:15.584000000&#x27;, &#x27;2022-12-21T03:35:09.988000000&#x27;,\n",
" &#x27;2022-12-23T03:25:15.763000000&#x27;, &#x27;2022-12-26T03:35:11.128000000&#x27;,\n",
" &#x27;2022-12-28T03:25:15.151000000&#x27;, &#x27;2022-12-31T03:35:10.753000000&#x27;,\n",
" &#x27;2023-01-02T03:25:15.152000000&#x27;, &#x27;2023-01-05T03:35:09.844000000&#x27;,\n",
" &#x27;2023-01-07T03:25:15.343000000&#x27;, &#x27;2023-01-10T03:35:10.149000000&#x27;,\n",
" &#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",
" &#x27;2023-04-15T03:35:12.743000000&#x27;, &#x27;2023-04-20T03:35:10.328000000&#x27;,\n",
" &#x27;2023-04-22T03:25:18.572000000&#x27;, &#x27;2023-04-25T03:35:13.748000000&#x27;,\n",
" &#x27;2023-04-27T03:25:16.567000000&#x27;, &#x27;2023-04-30T03:35:12.151000000&#x27;,\n",
" &#x27;2023-05-02T03:25:18.866000000&#x27;, &#x27;2023-05-05T03:35:14.433000000&#x27;,\n",
" &#x27;2023-05-07T03:25:16.583000000&#x27;, &#x27;2023-05-10T03:35:11.284000000&#x27;,\n",
" &#x27;2023-05-12T03:25:19.200000000&#x27;, &#x27;2023-05-15T03:35:15.683000000&#x27;,\n",
" &#x27;2023-05-17T03:25:17.911000000&#x27;, &#x27;2023-05-20T03:35:14.193000000&#x27;,\n",
" &#x27;2023-05-22T03:25:36.378000000&#x27;, &#x27;2023-05-25T03:35:16.657000000&#x27;,\n",
" &#x27;2023-05-27T03:25:20.078000000&#x27;, &#x27;2023-05-30T03:35:15.642000000&#x27;,\n",
" &#x27;2023-06-01T03:25:20.268000000&#x27;, &#x27;2023-06-04T03:35:16.047000000&#x27;,\n",
" &#x27;2023-06-06T03:25:20.180000000&#x27;, &#x27;2023-06-09T03:35:15.534000000&#x27;,\n",
" &#x27;2023-06-11T03:25:21.573000000&#x27;, &#x27;2023-06-14T03:35:16.859000000&#x27;,\n",
" &#x27;2023-06-16T03:25:20.769000000&#x27;, &#x27;2023-06-19T03:35:15.908000000&#x27;,\n",
" &#x27;2023-06-21T03:25:20.640000000&#x27;, &#x27;2023-06-24T03:35:15.972000000&#x27;,\n",
" &#x27;2023-06-26T03:25:19.927000000&#x27;, &#x27;2023-06-29T03:35:15.752000000&#x27;,\n",
" &#x27;2023-07-01T03:25:21.763000000&#x27;, &#x27;2023-07-04T03:35:17.227000000&#x27;,\n",
" &#x27;2023-07-06T03:25:21.702000000&#x27;, &#x27;2023-07-09T03:35:17.102000000&#x27;,\n",
" &#x27;2023-07-11T03:25:21.515000000&#x27;, &#x27;2023-07-14T03:35:16.582000000&#x27;,\n",
" &#x27;2023-07-16T03:25:21.326000000&#x27;, &#x27;2023-07-19T03:35:16.050000000&#x27;,\n",
" &#x27;2023-07-21T03:25:22.444000000&#x27;, &#x27;2023-07-24T03:35:17.845000000&#x27;,\n",
" &#x27;2023-07-26T03:25:21.503000000&#x27;, &#x27;2023-07-29T03:35:17.083000000&#x27;,\n",
" &#x27;2023-07-31T03:25:36.753000000&#x27;, &#x27;2023-08-03T03:35:16.494000000&#x27;,\n",
" &#x27;2023-08-05T03:25:21.570000000&#x27;, &#x27;2023-08-08T03:35:16.313000000&#x27;,\n",
" &#x27;2023-08-10T03:25:22.024000000&#x27;, &#x27;2023-08-13T03:35:17.448000000&#x27;,\n",
" &#x27;2023-08-15T03:25:22.075000000&#x27;, &#x27;2023-08-18T03:35:17.812000000&#x27;,\n",
" &#x27;2023-08-20T03:25:21.568000000&#x27;, &#x27;2023-08-23T03:35:16.224000000&#x27;,\n",
" &#x27;2023-08-25T03:25:22.592000000&#x27;, &#x27;2023-08-28T03:35:17.571000000&#x27;,\n",
" &#x27;2023-08-30T03:25:21.212000000&#x27;, &#x27;2023-09-02T03:35:16.627000000&#x27;,\n",
" &#x27;2023-09-04T03:25:21.856000000&#x27;, &#x27;2023-09-07T03:35:17.134000000&#x27;,\n",
" &#x27;2023-09-09T03:25:20.648000000&#x27;, &#x27;2023-09-12T03:35:15.326000000&#x27;,\n",
" &#x27;2023-09-14T03:25:35.755000000&#x27;, &#x27;2023-09-17T03:35:15.269000000&#x27;,\n",
" &#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;, &#x27;2023-10-02T03:35:13.287000000&#x27;,\n",
" &#x27;2023-10-04T03:25:18.057000000&#x27;, &#x27;2023-10-07T03:35:12.349000000&#x27;,\n",
" &#x27;2023-10-09T03:25:17.684000000&#x27;, &#x27;2023-10-12T03:35:13.797000000&#x27;,\n",
" &#x27;2023-10-14T03:25:14.753000000&#x27;, &#x27;2023-10-17T03:35:11.016000000&#x27;,\n",
" &#x27;2023-10-19T03:25:18.719000000&#x27;, &#x27;2023-10-22T03:35:13.512000000&#x27;,\n",
" &#x27;2023-10-24T03:25:16.597000000&#x27;, &#x27;2023-10-27T03:35:11.799000000&#x27;,\n",
" &#x27;2023-10-29T03:25:18.344000000&#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-e2f6b737-acb6-4842-a96a-d8b673b9da9c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2f6b737-acb6-4842-a96a-d8b673b9da9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a37f10b-d1ed-4293-9f07-77a65e1cd829' class='xr-var-data-in' type='checkbox'><label for='data-8a37f10b-d1ed-4293-9f07-77a65e1cd829' 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-622c00b2-9e2a-403c-ad07-7027fe9f5a48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-622c00b2-9e2a-403c-ad07-7027fe9f5a48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ef18852-7922-4be6-ab99-88514a5154e4' class='xr-var-data-in' type='checkbox'><label for='data-6ef18852-7922-4be6-ab99-88514a5154e4' 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-3f2acd57-58f3-46b3-a171-54cdb7186339' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f2acd57-58f3-46b3-a171-54cdb7186339' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df61f93a-dc85-4528-bb4b-7ef3c2de7d52' class='xr-var-data-in' type='checkbox'><label for='data-df61f93a-dc85-4528-bb4b-7ef3c2de7d52' 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
" &#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;2023-10-07 03:35:12.349000&#x27;, &#x27;2023-10-09 03:25:17.684000&#x27;,\n",
" &#x27;2023-10-12 03:35:13.797000&#x27;, &#x27;2023-10-14 03:25:14.753000&#x27;,\n",
" &#x27;2023-10-17 03:35:11.016000&#x27;, &#x27;2023-10-19 03:25:18.719000&#x27;,\n",
" &#x27;2023-10-22 03:35:13.512000&#x27;, &#x27;2023-10-24 03:25:16.597000&#x27;,\n",
" &#x27;2023-10-27 03:35:11.799000&#x27;, &#x27;2023-10-29 03:25:18.344000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=163, 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><input type='checkbox' disabled/><label></label><input id='index-4dc8cc07-23cc-4a99-b1c4-879dcd80893c' class='xr-index-data-in' type='checkbox'/><label for='index-4dc8cc07-23cc-4a99-b1c4-879dcd80893c' 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><input type='checkbox' disabled/><label></label><input id='index-3654ab77-3d69-427c-8ab0-a0789888090b' class='xr-index-data-in' type='checkbox'/><label for='index-3654ab77-3d69-427c-8ab0-a0789888090b' 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-78201825-dc86-4a02-826e-0ddef596b798' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-78201825-dc86-4a02-826e-0ddef596b798' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'NDVI' (time: 163, y: 8874, x: 9902)> Size: 57GB\n",
"dask.array<truediv, shape=(163, 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"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# calculate NDVI\n",
"dsNDVI = calculate_indices(result, index='NDVI', satellite_mission='s2')\n",
"ndvi = dsNDVI[\"NDVI\"]\n",
"display(ndvi)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "f94969d8-d04f-4495-af63-6323d5c730ef",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/env/lib/python3.12/site-packages/xarray/groupers.py:487: 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 519 ms, sys: 0 ns, total: 519 ms\n",
"Wall time: 517 ms\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d7d2c6867f514f5db51338521e055088",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox()"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-01-18 10:49:45,083 - tornado.application - ERROR - Exception in callback functools.partial(<bound method IOLoop._discard_future_result of <tornado.platform.asyncio.AsyncIOMainLoop object at 0x7fd6b10ef350>>, <Task finished name='Task-1789' coro=<MultiProgressBar.listen() done, defined at /env/lib/python3.12/site-packages/distributed/diagnostics/progressbar.py:281> exception=CommClosedError('in <TLS (closed) local=tls://10.0.74.241:51368 remote=gateway://traefik-dask-gateway.easihub:80/easihub.8daaa179e4964dc7b211b4302753bf26>: 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 <TLS (closed) local=tls://10.0.74.241:51368 remote=gateway://traefik-dask-gateway.easihub:80/easihub.8daaa179e4964dc7b211b4302753bf26>: Stream is closed\n"
]
}
],
"source": [
"%%time\n",
"# calculate average NDVI\n",
"average_ndvi = calculate_average(ndvi,time_pattern=\"1M\")\n",
"progress(average_ndvi)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "06bd6bda-aa8b-44a0-9c8a-d17ccf5b86c3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"average_ndvi = average_ndvi.compute()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "af4fe58b-358a-42f8-b9a4-f3547d2c230b",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fd52bd0ad80>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(average_ndvi.isel(time=0))"
]
},
{
"cell_type": "markdown",
"id": "edab3c39-1d85-4ef5-831c-f64070c31355",
"metadata": {},
"source": [
"#####################################################################################"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d2e3f543-116b-4e48-b0ff-226636bc52af",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "6dcb4ca1-c544-4ed1-868c-bc755fd5216d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 15,
"id": "1be81b66-9ee6-4b4c-b511-3a57a541be31",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"(1130, 7)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_path = \"input/train_points/1130/ST_1130_points.shp\" \n",
"# train_path = \"train/updated_data/ThuanHoa_DKS_Kappa.shp\"\n",
"ground_points = load_data_geo(train_path)\n",
"ground_points.shape"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "a24a5b13-a620-4154-bf17-6d277c8d6eb8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"fill_nan_model = joblib.load('output/regressors/mean_no_negative/rf_cloud.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "fa9191d7-6784-4e1d-a1dd-69a9dab56228",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"\n",
"def extract_data_with_HTcode(ground_points, average_ndvi, dsvh, dsvv):\n",
" datasets = {}\n",
" for idx, point in ground_points.iterrows():\n",
" \n",
" # Ensure each HT_code has its own dictionary\n",
" if point.HT_code not in datasets:\n",
" datasets[point.HT_code] = {'ndvi': [], 'vh': [], 'vv': []}\n",
" # Get the data for this point\n",
" ndvi_data = average_ndvi.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",
" \n",
" # Append the data to the lists for this HT_code\n",
" datasets[point.HT_code]['ndvi'].append(ndvi_data)\n",
" datasets[point.HT_code]['vh'].append(vh_data)\n",
" datasets[point.HT_code]['vv'].append(vv_data)\n",
" \n",
" return datasets"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "069c9adb-1671-4470-93b2-abda9d855be4",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"data = extract_data_with_HTcode(ground_points, average_ndvi, average_vh, average_vv)"
]
},
{
"cell_type": "markdown",
"id": "d256e153-183c-46a6-a17f-72d963a8869f",
"metadata": {},
"source": [
"*********************************************************************************************************"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "abf0ec42-817b-4806-aae5-fd34b2e10727",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# Function to filter out NaN values for each 'ndvi' array in each HT_code\n",
"def filter_non_nan_ndvi(data):\n",
" filtered_data = {}\n",
" \n",
" for ht_code, attributes in data.items():\n",
" # Initialize lists to store filtered arrays for 'ndvi', 'vh', and 'vv'\n",
" filtered_ndvi = []\n",
" filtered_vh = []\n",
" filtered_vv = []\n",
" \n",
" # Iterate through each set of ndvi, vh, vv arrays\n",
" for ndvi, vh, vv in zip(attributes['ndvi'], attributes['vh'], attributes['vv']):\n",
" # Create masks to filter out NaN values based on 'ndvi' array\n",
" non_nan_mask = ~np.isnan(ndvi)\n",
" \n",
" # Filter the arrays based on the mask\n",
" filtered_ndvi.append(ndvi[non_nan_mask])\n",
" filtered_vh.append(vh[non_nan_mask])\n",
" filtered_vv.append(vv[non_nan_mask])\n",
" \n",
" # Only add if there are non-empty filtered values\n",
" filtered_data[ht_code] = {\n",
" 'ndvi': filtered_ndvi,\n",
" 'vh': filtered_vh,\n",
" 'vv': filtered_vv\n",
" }\n",
" \n",
" return filtered_data\n",
"\n",
"# Example usage\n",
"filtered_datasets = filter_non_nan_ndvi(data)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9bb9522-3aec-42b0-930a-cbc647d50d8f",
"metadata": {},
"outputs": [],
"source": [
"# filtered_datasets"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "547eb119-d70f-4d60-bdd3-07f0b77ec577",
"metadata": {},
"outputs": [],
"source": [
"# 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",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a2d670a-9598-43c9-ab5a-ff71fdddfca1",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb8c339d-8b42-4b18-9099-bd206b825773",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "d932d9cd-cf05-48a6-8a16-4210296ed690",
"metadata": {},
"source": [
"^^^^^^^^^^^^^^^^^^^^^^^^^^^NEW VERSION OF GRPAH^^^^^^^^^^^^^^^^"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "885dbd0b-0595-4c98-b037-e3b2cf7f18dc",
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"def draw(ndvi_values, predicted_values, name):\n",
" for i, (ndvi, predicted) in enumerate(zip(ndvi_values, predicted_values)):\n",
" plt.plot(ndvi, marker='o', linestyle='-', color='blue', markersize=4, linewidth=0.75, label='Actual Values (o)' if i == 0 else \"\")\n",
" plt.plot(predicted, marker='x', linestyle='--', color='red', markersize=4, linewidth=0.75, label='Predicted Values (x)' if i == 0 else \"\")\n",
" # plt.plot(ndvi, marker='o', linestyle='-', color='blue', markersize=4, linewidth=0.75)\n",
" # plt.plot(predicted, marker='x', linestyle='--', color='red', markersize=4, linewidth=0.75)\n",
" \n",
" # Adding labels and title\n",
" plt.xlabel('Months')\n",
" plt.ylabel('NDVI values')\n",
" plt.title(f'Comparison: Actual vs Predicted Values in {name} code over 13 months')\n",
" # Adding grid\n",
" plt.grid(True)\n",
" plt.legend(loc='best')\n",
" # Adding legend to differentiate the lines\n",
" plt.legend()\n",
" # Adjusting the limits of the axes\n",
" plt.xlim(0, max(len(arr) for arr in ndvi_values)) # Adjust x-axis limits based on the longest array\n",
" plt.ylim(-1, 1) # Example: Setting y-axis limits\n",
" # Display the plot\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "981129c6-82a6-431f-b65f-60128ab1ffe8",
"metadata": {},
"outputs": [],
"source": [
"def show_images(ht_code, indices, name):\n",
" all_ndvi_values = []\n",
" all_predicted_ndvi_values = []\n",
"\n",
" # Iterate over the list of vh_values and vv_values\n",
" for index in range(indices):\n",
" # Extract the values for the given index range\n",
" ndvi_values = data[ht_code]['ndvi'][index]\n",
" vh_values = data[ht_code]['vh'][index]\n",
" vv_values = data[ht_code]['vv'][index]\n",
" # Combine vh and vv into input data\n",
" input_data = np.column_stack((vh_values, vv_values))\n",
" # Predict NDVI values\n",
" predicted_ndvi = fill_nan_model.predict(input_data)\n",
" # Collect the values for plotting\n",
" all_ndvi_values.append(ndvi_values)\n",
" all_predicted_ndvi_values.append(predicted_ndvi)\n",
" # Draw the plots for all the data\n",
" draw(all_ndvi_values, all_predicted_ndvi_values, name)\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "b7c4c821-2132-4e19-8e3d-9c6204061b15",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"labels = [\"LUA TOM\", \"LUA\", \"CHN\", \"CLN\", \"TS\", \"DXD\", \"RUNG\"]\n",
"for i in range(0, 7):\n",
" show_images(i, 50, labels[i])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9cf75b83-63ef-423d-ba74-1ce65c11eff8",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}