{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "b05aa740", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " const force = true;\n", " const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", " const reloading = false;\n", " const Bokeh = root.Bokeh;\n", "\n", " // Set a timeout for this load but only if we are not already initializing\n", " if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks;\n", 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versions are fully initialized.\n", " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", " // If the timeout and bokeh was not successfully loaded we reset\n", " // everything and try loading again\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_is_initializing = false;\n", " root._bokeh_onload_callbacks = undefined;\n", " root._bokeh_is_loading = 0\n", " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", " load_or_wait();\n", " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", " setTimeout(load_or_wait, 100);\n", " } else {\n", " root._bokeh_is_initializing = true\n", " root._bokeh_onload_callbacks = []\n", " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", " if (!reloading && 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!root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n // There is nothing to load\n run_callbacks();\n return null;\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error(e) {\n const src_el = e.srcElement\n console.error(\"failed to load \" + (src_el.href || src_el.src));\n }\n\n const skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n const existing_stylesheets = []\n const links = document.getElementsByTagName('link')\n for (let i = 0; i < links.length; i++) {\n const link = links[i]\n if (link.href != null) {\n existing_stylesheets.push(link.href)\n }\n }\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const escaped = encodeURI(url)\n if (existing_stylesheets.indexOf(escaped) !== -1) {\n on_load()\n continue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } var existing_scripts = []\n const scripts = document.getElementsByTagName('script')\n for (let i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n existing_scripts.push(script.src)\n }\n }\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (let i = 0; i < js_modules.length; i++) {\n const url = js_modules[i];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n const url = js_exports[name];\n const escaped = encodeURI(url)\n if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n if (!window.requirejs) {\n on_load();\n }\n continue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.6.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.6.0.min.js\", \"https://cdn.holoviz.org/panel/1.5.3/dist/panel.min.js\"];\n const js_modules = [];\n const js_exports = {};\n const css_urls = [];\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (let i = 0; i < inline_js.length; i++) {\n try {\n inline_js[i].call(root, root.Bokeh);\n } catch(e) {\n if (!reloading) {\n throw e;\n }\n }\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n var NewBokeh = root.Bokeh;\n if (Bokeh.versions === undefined) {\n Bokeh.versions = new Map();\n }\n if (NewBokeh.version !== Bokeh.version) {\n Bokeh.versions.set(NewBokeh.version, NewBokeh)\n }\n root.Bokeh = Bokeh;\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n // If the timeout and bokeh was not successfully loaded we reset\n // everything and try loading again\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n root._bokeh_is_loading = 0\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n if (root.Bokeh) {\n root.Bokeh = undefined;\n }\n 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));" }, 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comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if 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py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", " if (!reloading && !bokeh_loaded) {\n", " if (root.Bokeh) {\n", " root.Bokeh = undefined;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }\n", " // Give older versions of the autoload script a head-start to ensure\n", " // they initialize before we start loading newer version.\n", " setTimeout(load_or_wait, 100)\n", "}(window));" ], "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n // Don't load bokeh if it is still initializing\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n } else if (js_urls.length === 0 && js_modules.length 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on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " } var existing_scripts = []\n", " const scripts = document.getElementsByTagName('script')\n", " for (let i = 0; i < scripts.length; i++) {\n", " var script = scripts[i]\n", " if (script.src != null) {\n", " existing_scripts.push(script.src)\n", " }\n", " }\n", " for (let i = 0; i < js_urls.length; i++) {\n", " const url = js_urls[i];\n", " const escaped = encodeURI(url)\n", " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", " if (!window.requirejs) {\n", " on_load();\n", " }\n", " continue;\n", " }\n", " const element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (let i = 0; i < js_modules.length; i++) {\n", " const url = js_modules[i];\n", " const escaped = encodeURI(url)\n", " if (skip.indexOf(escaped) !== -1 || existing_scripts.indexOf(escaped) !== -1) {\n", " if (!window.requirejs) {\n", " on_load();\n", " }\n", " continue;\n", " }\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " for (const name in js_exports) {\n", " const url = js_exports[name];\n", " const escaped = encodeURI(url)\n", " if (skip.indexOf(escaped) >= 0 || root[name] != null) {\n", " if (!window.requirejs) {\n", " on_load();\n", " }\n", " continue;\n", " }\n", " var element = document.createElement('script');\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.type = \"module\";\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " element.textContent = `\n", " import ${name} from \"${url}\"\n", " window.${name} = ${name}\n", " window._bokeh_on_load()\n", " `\n", " document.head.appendChild(element);\n", " }\n", " if (!js_urls.length && !js_modules.length) {\n", " on_load()\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " const js_urls = [\"https://cdn.holoviz.org/panel/1.5.3/dist/bundled/reactiveesm/es-module-shims@^1.10.0/dist/es-module-shims.min.js\"];\n", " const js_modules = [];\n", " const js_exports = {};\n", " const css_urls = [];\n", " const inline_js = [ function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", "function(Bokeh) {} // ensure no trailing comma for IE\n", " ];\n", "\n", " function run_inline_js() {\n", " if ((root.Bokeh !== undefined) || (force === true)) {\n", " for (let i = 0; i < inline_js.length; i++) {\n", " try {\n", " inline_js[i].call(root, root.Bokeh);\n", " } catch(e) {\n", " if (!reloading) {\n", " throw e;\n", " }\n", " }\n", " }\n", " // Cache old bokeh versions\n", " if (Bokeh != undefined && !reloading) {\n", " var NewBokeh = root.Bokeh;\n", " if (Bokeh.versions === undefined) {\n", " Bokeh.versions = new Map();\n", " }\n", " if (NewBokeh.version !== Bokeh.version) {\n", " Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", " }\n", " root.Bokeh = Bokeh;\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " }\n", " root._bokeh_is_initializing = false\n", " }\n", "\n", " function load_or_wait() {\n", " // Implement a backoff loop that tries to ensure we do not load multiple\n", " // versions of Bokeh and its dependencies at the same time.\n", " // In recent versions we use the root._bokeh_is_initializing flag\n", " // to determine whether there is an ongoing attempt to initialize\n", " // bokeh, however for backward compatibility we also try to ensure\n", " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", " // before older versions are fully initialized.\n", " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", " // If the timeout and bokeh was not successfully loaded we reset\n", " // everything and try loading again\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_is_initializing = false;\n", " root._bokeh_onload_callbacks = undefined;\n", " root._bokeh_is_loading = 0\n", " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", " load_or_wait();\n", " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", " setTimeout(load_or_wait, 100);\n", " } else {\n", " root._bokeh_is_initializing = true\n", " root._bokeh_onload_callbacks = []\n", " const bokeh_loaded = root.Bokeh != null && (root.Bokeh.version === py_version || (root.Bokeh.versions !== undefined && root.Bokeh.versions.has(py_version)));\n", " if (!reloading && !bokeh_loaded) {\n", " if (root.Bokeh) {\n", " root.Bokeh = undefined;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }\n", " // Give older versions of the autoload script a head-start to ensure\n", " // they initialize before we start loading newer version.\n", " setTimeout(load_or_wait, 100)\n", "}(window));" ], "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = false;\n const py_version = '3.6.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n const reloading = true;\n const Bokeh = root.Bokeh;\n\n // Set a timeout for this load but only if we are not already initializing\n if (typeof (root._bokeh_timeout) === \"undefined\" || (force || !root._bokeh_is_initializing)) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls 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document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n 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 = 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append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 18.9 s, sys: 3.43 s, total: 22.4 s\n", "Wall time: 13.8 s\n" ] } ], "source": [ "%%time\n", "%matplotlib inline\n", "\n", "import importlib\n", "import new_import_ODC \n", "\n", "importlib.reload(new_import_ODC)\n", "\n", "from new_import_ODC import *" ] }, { "cell_type": "code", "execution_count": 2, "id": "b794d005", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting new cluster\n", "✅ Dask + Datacube + S3 sẵn sàng\n" ] } ], "source": [ "# Khởi tạo Dask + Datacube + S3\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", "configure_s3_access(aws_unsigned=True)\n", "print(\"✅ Dask + Datacube + S3 sẵn sàng\")\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "5bc42a3c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Most common native CRS: EPSG:32648\n", "No datasets require offset correction\n", "The valid_data_mask and scale (no offset) have been applied to the reflectance bands\n", "✅ Sentinel-2 raw: FrozenMappingWarningOnValuesAccess({'time': 151, 'y': 8874, 'x': 9902})\n" ] } ], "source": [ "# Cấu hình vùng và thời gian\n", "date_range = (\"2022-09-01\", \"2023-10-01\")\n", "longtitude_range = (105.5, 106.4)\n", "latitude_range = (9.2, 10.0)\n", "\n", "# Tải dữ liệu Sentinel-2\n", "data_sen2 = load_data(\n", " dc=dc,\n", " date_range=date_range,\n", " longtitude_range=longtitude_range,\n", " latitude_range=latitude_range,\n", ")\n", "print(f\"✅ Sentinel-2 raw: {data_sen2.dims}\")\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "0ef51e7d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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qa[0, 1, 2, 3, 4, 5, 6, 7]{'0': 'no data', '1': 'saturated or defective'...Sen2Cor Scene Classification
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" ], "text/plain": [ " bits \\\n", "qa [0, 1, 2, 3, 4, 5, 6, 7] \n", "\n", " values \\\n", "qa {'0': 'no data', '1': 'saturated or defective'... \n", "\n", " description \n", "qa Sen2Cor Scene Classification " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'0': 'no data',\n", " '1': 'saturated or defective',\n", " '2': 'dark area pixels',\n", " '3': 'cloud shadows',\n", " '4': 'vegetation',\n", " '5': 'bare soils',\n", " '6': 'water',\n", " '7': 'unclassified',\n", " '8': 'cloud medium probability',\n", " '9': 'cloud high probability',\n", " '10': 'thin cirrus',\n", " '11': 'snow or ice'}" ] }, "metadata": {}, "output_type": "display_data" }, { "ename": "ValueError", "evalue": "No `satellite_mission` was provided. Please specify either 'ls' or 's2' to ensure the \nfunction calculates indices using the correct spectral bands.", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[4], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Tiền xử lý Sentinel-2: cloud mask + NDVI + resampling\u001b[39;00m\n\u001b[1;32m 2\u001b[0m data_clean \u001b[38;5;241m=\u001b[39m mask_clean(data_sen2)\n\u001b[0;32m----> 3\u001b[0m data_ndvi \u001b[38;5;241m=\u001b[39m \u001b[43mcalculate_indices\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_clean\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mNDVI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m data_fill \u001b[38;5;241m=\u001b[39m fill_nan(data_ndvi)\n\u001b[1;32m 5\u001b[0m data_sen2_monthly \u001b[38;5;241m=\u001b[39m data_fill\u001b[38;5;241m.\u001b[39mresample(time\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1MS\u001b[39m\u001b[38;5;124m\"\u001b[39m)\u001b[38;5;241m.\u001b[39mmean()\u001b[38;5;241m.\u001b[39mcompute()\n", "File \u001b[0;32m~/remote-sensing/deafrica_tools/bandindices.py:374\u001b[0m, in \u001b[0;36mcalculate_indices\u001b[0;34m(ds, index, collection, satellite_mission, custom_varname, normalise, drop, deep_copy)\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[38;5;66;03m# Rename bands to a consistent format if depending on what satellite mission\u001b[39;00m\n\u001b[1;32m 369\u001b[0m \u001b[38;5;66;03m# is specified in `satellite_mission`. This allows the same index calculations\u001b[39;00m\n\u001b[1;32m 370\u001b[0m \u001b[38;5;66;03m# to be applied to all satellite missions. If no satellite mission was provided,\u001b[39;00m\n\u001b[1;32m 371\u001b[0m \u001b[38;5;66;03m# raise an exception.\u001b[39;00m\n\u001b[1;32m 372\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m satellite_mission \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 374\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 375\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo `satellite_mission` was provided. Please specify \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 376\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124meither \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mls\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m or \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms2\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m to ensure the \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mfunction \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 377\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcalculates indices using the correct spectral \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 378\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbands.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 379\u001b[0m )\n\u001b[1;32m 381\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m satellite_mission \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mls\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 382\u001b[0m sr_max \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1.0\u001b[39m\n", "\u001b[0;31mValueError\u001b[0m: No `satellite_mission` was provided. Please specify either 'ls' or 's2' to ensure the \nfunction calculates indices using the correct spectral bands." ] } ], "source": [ "# Tiền xử lý Sentinel-2: cloud mask + NDVI + resampling\n", "data_clean = mask_clean(data_sen2)\n", "data_ndvi = calculate_indices(data_clean, index=\"NDVI\")\n", "data_fill = fill_nan(data_ndvi)\n", "data_sen2_monthly = data_fill.resample(time=\"1MS\").mean().compute()\n", "print(f\"✅ S2 monthly shape: {data_sen2_monthly.dims}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0da4f86d", "metadata": {}, "outputs": [], "source": [ "# Tải Sentinel-1 (SAR VV/VH)\n", "data_sen1 = load_data_sen1(\n", " dc=dc,\n", " date_range=date_range,\n", " longtitude_range=longtitude_range,\n", " latitude_range=latitude_range,\n", ")\n", "data_sen1_monthly = calculate_average(data_sen1, [\"VV\", \"VH\"], resample=\"1MS\").compute()\n", "print(f\"✅ S1 monthly shape: {data_sen1_monthly.dims}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "412b3716", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "# Ánh xạ nhãn lớp đất\n", "label_mapping = {\n", " \"Lua tom\": \"0\", \"Lua\": \"1\", \"CHN\": \"2\", \"CLN\": \"3\",\n", " \"TS\": \"4\", \"Song\": \"5\", \"Dat xay dung\": \"6\", \"Rung\": \"7\",\n", "}\n", "\n", "# Tải và ghép dữ liệu train từ S1 + S2\n", "train_data = load_train_data(label_mapping=label_mapping)\n", "X, y = get_data_sen1_and_sen2(train_data, data_sen2_monthly, data_sen1_monthly)\n", "\n", "# Chia tập train / val / test\n", "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(X, y, test_size=0.2, val_size=0.1)\n", "\n", "X_train_np = np.array(X_train, dtype=np.float32)\n", "X_val_np = np.array(X_val, dtype=np.float32)\n", "X_test_np = np.array(X_test, dtype=np.float32)\n", "y_train_np = np.array(y_train, dtype=np.int64)\n", "y_val_np = np.array(y_val, dtype=np.int64)\n", "y_test_np = np.array(y_test, dtype=np.int64)\n", "\n", "# Gộp train + val cho sklearn\n", "X_fit = np.concatenate([X_train_np, X_val_np], axis=0)\n", "y_fit = np.concatenate([y_train_np, y_val_np], axis=0)\n", "\n", "print(f\"✅ X_fit: {X_fit.shape} | X_test: {X_test_np.shape}\")\n", "print(f\" Classes: {sorted(set(y_fit.tolist()))}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "84a8a1d0", "metadata": {}, "outputs": [], "source": [ "%%time\n", "from sklearn.tree import DecisionTreeClassifier\n", "\n", "# ── Xây dựng và train mô hình Decision Tree ─────────────────────────────────\n", "model = DecisionTreeClassifier(\n", " max_depth=30,\n", " min_samples_leaf=2,\n", " min_samples_split=5,\n", " class_weight=\"balanced\",\n", " random_state=42,\n", ")\n", "\n", "print(\"🚀 Training Decision Tree...\")\n", "model.fit(X_fit, y_fit)\n", "\n", "val_acc = model.score(X_val_np, y_val_np)\n", "print(f\"✅ Training hoàn tất! Depth: {model.get_depth()} \"\n", " f\"Leaves: {model.get_n_leaves()} Val accuracy: {val_acc:.4f}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "8248d748", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from sklearn.tree import DecisionTreeClassifier\n", "\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "# PHÂN TÍCH ĐIỂM HỘI TỤ — Decision Tree\n", "# Phương pháp: quét max_depth từ 1→50 và theo dõi train/val accuracy\n", "# Điểm hội tụ = độ sâu tại đó val_acc đạt cực đại rồi bắt đầu giảm (overfitting)\n", "# ═══════════════════════════════════════════════════════════════════════════════\n", "DEPTH_RANGE = list(range(1, 51))\n", "THRESHOLD = 0.001 # cải thiện val_acc < 0.1% → coi là hội tụ\n", "\n", "train_accs_d, val_accs_d = [], []\n", "print(\"🔍 Phân tích hội tụ theo max_depth ...\")\n", "for d in DEPTH_RANGE:\n", " m = DecisionTreeClassifier(\n", " min_samples_leaf=2, min_samples_split=5,\n", " class_weight=\"balanced\", random_state=42, max_depth=d,\n", " )\n", " m.fit(X_fit, y_fit)\n", " train_accs_d.append(m.score(X_fit, y_fit))\n", " val_accs_d.append( m.score(X_val_np, y_val_np))\n", "\n", "train_accs_d = np.array(train_accs_d)\n", "val_accs_d = np.array(val_accs_d)\n", "improvements = np.diff(val_accs_d)\n", "\n", "# ── Tìm điểm hội tụ ───────────────────────────────────────────────────────────\n", "best_depth = DEPTH_RANGE[int(np.argmax(val_accs_d))]\n", "best_val_acc = float(np.max(val_accs_d))\n", "\n", "# Điểm hội tụ sớm: lần đầu cải thiện < threshold\n", "conv_depth = None\n", "for i, imp in enumerate(improvements):\n", " if abs(imp) < THRESHOLD:\n", " conv_depth = DEPTH_RANGE[i + 1]\n", " break\n", "\n", "# Điểm overfit: val_acc bắt đầu giảm so với peak\n", "overfit_depth = None\n", "peak_idx = int(np.argmax(val_accs_d))\n", "for i in range(peak_idx + 1, len(val_accs_d)):\n", " if val_accs_d[i] < best_val_acc - 0.005: # giảm > 0.5%\n", " overfit_depth = DEPTH_RANGE[i]\n", " break\n", "\n", "# Khoảng cách train-val (generalization gap)\n", "gap = train_accs_d - val_accs_d\n", "\n", "# ── Vẽ đồ thị ─────────────────────────────────────────────────────────────────\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", "\n", "# --- Trái: accuracy curves ---\n", "axes[0].plot(DEPTH_RANGE, train_accs_d, \"b-o\", markersize=3, label=\"Train\")\n", "axes[0].plot(DEPTH_RANGE, val_accs_d, \"g-o\", markersize=3, label=\"Val\")\n", "axes[0].axvline(x=best_depth, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", " label=f\"Best depth={best_depth} ({best_val_acc*100:.2f}%)\")\n", "if conv_depth:\n", " axes[0].axvline(x=conv_depth, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", " label=f\"Hội tụ depth={conv_depth}\")\n", "if overfit_depth:\n", " axes[0].axvline(x=overfit_depth, color=\"purple\", linestyle=\"-.\", linewidth=1.5,\n", " label=f\"Overfit depth={overfit_depth}\")\n", "axes[0].set_xlabel(\"max_depth\")\n", "axes[0].set_ylabel(\"Accuracy\")\n", "axes[0].set_title(\"Train / Val Accuracy vs max_depth\")\n", "axes[0].legend(fontsize=8)\n", "axes[0].grid(True, alpha=0.3)\n", "\n", "# --- Giữa: marginal improvement ---\n", "axes[1].bar(DEPTH_RANGE[1:], improvements * 100,\n", " color=[\"green\" if v > 0 else \"red\" for v in improvements], alpha=0.7)\n", "axes[1].axhline(y=0, color=\"black\", linewidth=0.8)\n", "axes[1].axhline(y=THRESHOLD * 100, color=\"orange\", linestyle=\"--\",\n", " label=f\"Threshold={THRESHOLD*100:.2f}%\")\n", "if conv_depth:\n", " axes[1].axvline(x=conv_depth, color=\"orange\", linestyle=\":\", linewidth=1.5,\n", " label=f\"Hội tụ depth={conv_depth}\")\n", "axes[1].set_xlabel(\"max_depth\")\n", "axes[1].set_ylabel(\"ΔVal Accuracy (%)\")\n", "axes[1].set_title(\"Marginal Val Improvement per Depth Step\")\n", "axes[1].legend(fontsize=8)\n", "axes[1].grid(True, alpha=0.3)\n", "\n", "# --- Phải: generalization gap ---\n", "axes[2].fill_between(DEPTH_RANGE, gap * 100, alpha=0.5, color=\"tomato\", label=\"Gap = Train − Val\")\n", "axes[2].plot(DEPTH_RANGE, gap * 100, \"r-o\", markersize=3)\n", "if best_depth:\n", " axes[2].axvline(x=best_depth, color=\"red\", linestyle=\"--\", linewidth=1.5,\n", " label=f\"Best depth={best_depth}\")\n", "axes[2].set_xlabel(\"max_depth\")\n", "axes[2].set_ylabel(\"Gap (%)\")\n", "axes[2].set_title(\"Generalization Gap (Overfitting Risk)\")\n", "axes[2].legend(fontsize=8)\n", "axes[2].grid(True, alpha=0.3)\n", "\n", "plt.suptitle(\"Decision Tree — Convergence Analysis\", fontsize=13, fontweight=\"bold\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# ── Tổng kết ──────────────────────────────────────────────────────────────────\n", "print(f\"\\n{'═'*58}\")\n", "print(f\" Độ sâu TỐI ƯU (best val acc) : max_depth = {best_depth} ({best_val_acc*100:.4f}%)\")\n", "if conv_depth:\n", " print(f\" Điểm HỘI TỤ (Δacc < {THRESHOLD*100:.1f}%) : max_depth = {conv_depth}\")\n", "if overfit_depth:\n", " print(f\" Điểm OVERFIT bắt đầu : max_depth ≥ {overfit_depth}\")\n", " print(f\" → Nên dùng max_depth ≤ {best_depth} để tránh overfit\")\n", "print(f\"{'═'*58}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5660e2ec", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "# ── Đánh giá trên tập test ─────────────────────────────────────────────────────\n", "y_pred = model.predict(X_test_np)\n", "\n", "acc = accuracy_score(y_test_np, y_pred)\n", "print(f\"Test Accuracy : {acc:.4f} ({acc*100:.2f}%)\\n\")\n", "print(classification_report(y_test_np, y_pred, digits=4))\n", "\n", "# ── Feature importance ──────────────────────────────────────────────────────────\n", "feat_imp = model.feature_importances_\n", "idx = feat_imp.argsort()[::-1][:20]\n", "plt.figure(figsize=(12, 4))\n", "plt.bar(range(len(idx)), feat_imp[idx])\n", "plt.xticks(range(len(idx)), idx, rotation=45)\n", "plt.title(\"Top-20 Feature Importances\")\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# ── Confusion matrix ────────────────────────────────────────────────────────────\n", "class_names = list(label_mapping.keys())\n", "cm = confusion_matrix(y_test_np, y_pred)\n", "plt.figure(figsize=(9, 7))\n", "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Greens\",\n", " xticklabels=class_names, yticklabels=class_names)\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.title(\"Confusion Matrix — Decision Tree\")\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e416c2aa", "metadata": {}, "outputs": [], "source": [ "import joblib, json\n", "from datetime import datetime\n", "\n", "# ── Lưu mô hình ────────────────────────────────────────────────────────────────\n", "model_path = \"model_decision_tree_land_use.joblib\"\n", "joblib.dump(model, model_path)\n", "print(f\"✅ Model saved → {model_path}\")\n", "\n", "# ── Lưu thông tin mô hình ──────────────────────────────────────────────────────\n", "info = {\n", " \"model_type\": \"DecisionTree\",\n", " \"max_depth\": model.get_depth(),\n", " \"n_leaves\": model.get_n_leaves(),\n", " \"class_weight\": \"balanced\",\n", " \"n_features\": int(X_fit.shape[1]),\n", " \"label_mapping\": label_mapping,\n", " \"test_accuracy\": float(acc),\n", " \"train_samples\": int(len(X_fit)),\n", " \"test_samples\": int(len(X_test_np)),\n", " \"saved_at\": datetime.now().isoformat(),\n", "}\n", "info_path = \"model_decision_tree_land_use_info.json\"\n", "with open(info_path, \"w\") as f:\n", " json.dump(info, f, indent=2, ensure_ascii=False)\n", "print(f\"✅ Info saved → {info_path}\")\n", "print(json.dumps(info, indent=2, ensure_ascii=False))\n", "\n", "# ── Đóng kết nối Dask ──────────────────────────────────────────────────────────\n", "try:\n", " client.close()\n", " cluster.close()\n", " print(\"✅ Dask cluster closed.\")\n", "except Exception:\n", " pass\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }