commit 92c760f4744bdb40f3f303616b764813b454366e Author: nghiadang Date: Wed Aug 28 05:10:02 2024 +0000 first commit diff --git a/01.train.ipynb b/01.train.ipynb new file mode 100644 index 0000000..77a783f --- /dev/null +++ b/01.train.ipynb @@ -0,0 +1,3601 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "912ed572-1658-406b-976c-cd6de2d4e89e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n", + " require([\"jspanel\"], function(jsPanel) {\n", + "\twindow.jsPanel = jsPanel\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-modal\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-tooltip\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-hint\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-layout\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-contextmenu\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-dock\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"gridstack\"], function(GridStack) {\n", + "\twindow.GridStack = GridStack\n", + "\ton_load()\n", + " })\n", + " require([\"notyf\"], function() {\n", + "\ton_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 9;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " Bokeh = root.Bokeh;\n", + " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " if (!reloading && (!bokeh_loaded || is_dev)) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = false;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n 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plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return 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google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", + " var reloading = true;\n", + " var Bokeh = root.Bokeh;\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n", + " require([\"jspanel\"], function(jsPanel) {\n", + "\twindow.jsPanel = jsPanel\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-modal\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-tooltip\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-hint\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-layout\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-contextmenu\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-dock\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"gridstack\"], function(GridStack) {\n", + "\twindow.GridStack = GridStack\n", + "\ton_load()\n", + " })\n", + " require([\"notyf\"], function() {\n", + "\ton_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 9;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " Bokeh = root.Bokeh;\n", + " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " if (!reloading && (!bokeh_loaded || is_dev)) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = true;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = 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+ " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + 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Dataset size: 210.07 GB

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+       "Dimensions:      (time: 151, y: 8874, x: 9902)\n",
+       "Coordinates:\n",
+       "  * time         (time) datetime64[ns] 2022-09-02T03:35:23.960000 ... 2023-09...\n",
+       "  * y            (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
+       "  * x            (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
+       "    spatial_ref  int32 32648\n",
+       "Data variables:\n",
+       "    blue         (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
+       "    green        (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
+       "    red          (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
+       "    nir          (time, y, x) float32 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
+       "    scl          (time, y, x) uint8 dask.array<chunksize=(1, 2048, 2048), meta=np.ndarray>\n",
+       "Attributes:\n",
+       "    crs:           EPSG:32648\n",
+       "    grid_mapping:  spatial_ref
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<xarray.DataArray 'NDVI' (time: 151, y: 8874, x: 9902)>\n",
+       "dask.array<truediv, shape=(151, 8874, 9902), dtype=float32, chunksize=(1, 2048, 2048), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time         (time) datetime64[ns] 2022-09-02T03:35:23.960000 ... 2023-09...\n",
+       "  * y            (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n",
+       "  * x            (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n",
+       "    spatial_ref  int32 32648
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2022-09-02T03:35:23.960000 ... 2023-09...\n", + " * y (y) float64 1.106e+06 1.106e+06 ... 1.017e+06 1.017e+06\n", + " * x (x) float64 5.548e+05 5.548e+05 ... 6.538e+05 6.538e+05\n", + " spatial_ref int32 32648" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Tiến hành tính toán NDVI\n", + "ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')\n", + "ndvi = ds1[\"NDVI\"]\n", + "display(ndvi)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "84992d28-8e3f-468e-be08-ded511f2c662", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## ảnh ndvi chưa fill nan\n", + "plt.imshow(ndvi.isel(time=6))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "72318b60-532a-4f08-a5f7-94762d08a42c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# đặt thời gian các mùa\n", + "time_split = [slice('2022-09-01', '2023-01-01'), \n", + " slice('2023-01-01', '2023-05-01'),\n", + " slice('2023-05-01', '2023-07-01'),\n", + " slice('2023-07-01', '2023-10-01')]\n", + "\n", + "# fill nan\n", + "fill_nan_ndvi = fill_nan(ndvi, time_split)\n", + "\n", + "## ảnh ndivi đã fill nan\n", + "plt.imshow(fill_nan_ndvi.isel(time=6))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "375b1cfb-37f5-49fe-8061-ea32eb47f9f6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 2.57 s, sys: 8.69 ms, total: 2.58 s\n", + "Wall time: 2.58 s\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "06924ce1ac2847d884f6d8bbe865cac6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "## tính ndvi theo tháng\n", + "average_ndvi = fill_nan_ndvi.resample(time='1M').mean().persist()\n", + "progress(average_ndvi)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d2585562-88aa-4c7d-bf70-1f6affcf65d4", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NoXYLU2022HientrangHT_codegeometry
01.0603860.8191081162.862PomeloCLN5POINT (603860.819 1081162.862)
12.0601306.4101082782.940PomeloCLN5POINT (601306.410 1082782.940)
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45.0602459.0001080946.000PomeloCLN5POINT (602459.000 1080946.000)
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" + ], + "text/plain": [ + " No X Y LU2022 Hientrang HT_code \\\n", + "0 1.0 603860.819 1081162.862 Pomelo CLN 5 \n", + "1 2.0 601306.410 1082782.940 Pomelo CLN 5 \n", + "2 3.0 601084.510 1081351.870 Pomelo CLN 5 \n", + "3 4.0 602193.760 1079205.220 Pomelo CLN 5 \n", + "4 5.0 602459.000 1080946.000 Pomelo CLN 5 \n", + "\n", + " geometry \n", + "0 POINT (603860.819 1081162.862) \n", + "1 POINT (601306.410 1082782.940) \n", + "2 POINT (601084.510 1081351.870) \n", + "3 POINT (602193.760 1079205.220) \n", + "4 POINT (602459.000 1080946.000) " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compute average_ndvi\n", + "average_ndvi = average_ndvi.compute()\n", + "\n", + "## cấu hình dữ liệu train và vh vv file\n", + "train_path = \"train/ST_training data_updated_1130points.shp\" # đường dẫn shp file train\n", + "name_vh = \"vh-0922_0923-full_ST.tif\"\n", + "name_vv = \"vv-0922_0923-full_ST.tif\"\n", + "\n", + "## load dữ liệu điểm train\n", + "train = load_train_data(train_path)\n", + "train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b32cfb3f-e267-4995-853d-8e0e26f5501f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 527 µs, sys: 111 µs, total: 638 µs\n", + "Wall time: 375 µs\n" + ] + } + ], + "source": [ + "%%time\n", + "## tải về dữ liệu sen1\n", + "import os\n", + "if not os.path.exists(name_vh):\n", + " !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vh-0922_0923-full_ST.tif vh-0922_0923-full_ST.tif\n", + "if not os.path.exists(name_vv):\n", + " !aws s3 cp s3://easi-asia-dc-data/staging/ctu/sentinel-1/vv-0922_0923-full_ST.tif vv-0922_0923-full_ST.tif" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "9838697a-2260-45ef-a810-533428a783e9", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning 1: TIFFReadDirectory:Invalid data type for tag StripByteCounts\n", + "Warning 1: TIFFFetchNormalTag:ASCII value for tag \"GeoASCIIParams\" contains null byte in value; value incorrectly truncated during reading due to implementation limitations\n", + "Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n", + "Warning 1: TIFFReadDirectory:Invalid data type for tag StripByteCounts\n", + "Warning 1: TIFFFetchNormalTag:ASCII value for tag \"GeoASCIIParams\" contains null byte in value; value incorrectly truncated during reading due to implementation limitations\n", + "Warning 1: TIFFReadDirectory:Sum of Photometric type-related color channels and ExtraSamples doesn't match SamplesPerPixel. Defining non-color channels as ExtraSamples.\n" + ] + } + ], + "source": [ + "# load dữ liệu sen1\n", + "dsvh, dsvv = load_sen1(name_vh, name_vv)\n", + "\n", + "# xây dựng tập dataset\n", + "datasets = get_data_sen1_and_sen2(train, average_ndvi, dsvh, dsvv)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "aa56f7f4-c646-4097-b2de-a64f8ff92fd1", + "metadata": { + "tags": [] + }, + "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", + "}\n", + "\n", + "# chia tập dữ liệu train, val, test\n", + "X_train, X_val, X_test, y_train, y_val, y_test = split_train_data(train, label_mapping, datasets)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2e955884-d4af-422d-a8e6-d436199540e0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Best Parameters: {'classifier__criterion': 'gini', 'classifier__max_depth': 10, 'classifier__n_estimators': 700}\n", + "Accuracy: 85.0 %\n" + ] + } + ], + "source": [ + "# Huấn luyện mô hình\n", + "grid_search = train_with_rf(X_train, X_val, y_train, y_val)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b2a1e42c-cf1b-4d82-a6af-b06e3496918f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accuracy for test data 88.0 %\n" + ] + } + ], + "source": [ + "# kiểm tra độ chính xác với tập test\n", + "y_pred_test = grid_search.predict(X_test)\n", + "test_accuracy = accuracy_score(y_test, y_pred_test)\n", + "print(f\"Accuracy for test data {round(test_accuracy, 2)*100} %\")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f1a14379-ed6e-4897-9ca4-2669743fab40", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done!\n" + ] + } + ], + "source": [ + "# Lưu mô hình huấn luyện\n", + "save_model(\"model.joblib\", grid_search)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "33dd516d-9824-499e-96b9-5cd9224c194c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# đóng client, cluster\n", + "client.close()\n", + "cluster.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b1b959a-28da-4779-87e2-6b6936b99634", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/02.predict.ipynb b/02.predict.ipynb new file mode 100644 index 0000000..cb713f8 --- /dev/null +++ b/02.predict.ipynb @@ -0,0 +1,3470 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1a452ff0-7c2f-4fa7-abf4-f7c8b09a9932", + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n", + " require([\"jspanel\"], function(jsPanel) {\n", + "\twindow.jsPanel = jsPanel\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-modal\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-tooltip\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-hint\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-layout\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-contextmenu\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-dock\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"gridstack\"], function(GridStack) {\n", + "\twindow.GridStack = GridStack\n", + "\ton_load()\n", + " })\n", + " require([\"notyf\"], function() {\n", + "\ton_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 9;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 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window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " Bokeh = root.Bokeh;\n", + " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " if (!reloading && (!bokeh_loaded || is_dev)) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = false;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && 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autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: 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else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar 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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 toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", + " var reloading = true;\n", + " var Bokeh = root.Bokeh;\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n", + " require([\"jspanel\"], function(jsPanel) {\n", + "\twindow.jsPanel = jsPanel\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-modal\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-tooltip\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-hint\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-layout\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-contextmenu\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-dock\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"gridstack\"], function(GridStack) {\n", + "\twindow.GridStack = GridStack\n", + "\ton_load()\n", + " })\n", + " require([\"notyf\"], function() {\n", + "\ton_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 9;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " Bokeh = root.Bokeh;\n", + " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " if (!reloading && (!bokeh_loaded || is_dev)) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = true;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n Bokeh = root.Bokeh;\n bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n", + " comm.onMsg = msg_handler;\n", + " });\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " console.log(message)\n", + " var content = {data: message.data, comm_id};\n", + " var buffers = []\n", + " for (var buffer of message.buffers || []) {\n", + " buffers.push(new DataView(buffer))\n", + " }\n", + " var metadata = message.metadata || {};\n", + " var msg = {content, buffers, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " })\n", + " }\n", + " }\n", + "\n", + " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n", + " if (comm_id in window.PyViz.comms) {\n", + " return window.PyViz.comms[comm_id];\n", + " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n", + " if (msg_handler) {\n", + " comm.on_msg(msg_handler);\n", + " }\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n", + " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n", + " comm.open();\n", + " if (msg_handler) {\n", + " comm.onMsg = msg_handler;\n", + " }\n", + " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n", + " var comm_promise = google.colab.kernel.comms.open(comm_id)\n", + " comm_promise.then((comm) => {\n", + " window.PyViz.comms[comm_id] = comm;\n", + " if (msg_handler) {\n", + " var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = 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"output_type": "stream", + "text": [ + "CPU times: user 13.5 s, sys: 3.97 s, total: 17.5 s\n", + "Wall time: 11 s\n" + ] + } + ], + "source": [ + "%%time\n", + "%matplotlib inline\n", + "from new_import import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "219d4303-b606-45e1-a3a7-0b52e3cfe279", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting new cluster.\n", + "CPU times: user 798 ms, sys: 20.3 ms, total: 818 ms\n", + "Wall time: 3min 46s\n" + ] + }, + { + "data": { + "text/html": [ + "
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+       "  * y            (y) float64 1.072e+06 1.072e+06 ... 1.067e+06 1.067e+06\n",
+       "  * x            (x) float64 5.95e+05 5.95e+05 5.95e+05 ... 6.033e+05 6.033e+05\n",
+       "    spatial_ref  int32 32648\n",
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+       "Attributes:\n",
+       "    crs:           EPSG:32648\n",
+       "    grid_mapping:  spatial_ref
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<xarray.DataArray 'NDVI' (time: 78, y: 532, x: 830)>\n",
+       "dask.array<truediv, shape=(78, 532, 830), dtype=float32, chunksize=(1, 532, 830), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time         (time) datetime64[ns] 2022-09-02T03:35:38.706000 ... 2023-09...\n",
+       "  * y            (y) float64 1.072e+06 1.072e+06 ... 1.067e+06 1.067e+06\n",
+       "  * x            (x) float64 5.95e+05 5.95e+05 5.95e+05 ... 6.033e+05 6.033e+05\n",
+       "    spatial_ref  int32 32648
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 2022-09-02T03:35:38.706000 ... 2023-09...\n", + " * y (y) float64 1.072e+06 1.072e+06 ... 1.067e+06 1.067e+06\n", + " * x (x) float64 5.95e+05 5.95e+05 5.95e+05 ... 6.033e+05 6.033e+05\n", + " spatial_ref int32 32648" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Tiến hành tính toán NDVI\n", + "ds1 = calculate_indices(result, index='NDVI', satellite_mission='s2')\n", + "ndvi = ds1[\"NDVI\"]\n", + "display(ndvi)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f3930d43-c159-49bf-8260-68b14681729d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "## ảnh ndvi chưa fill nan\n", + "plt.imshow(ndvi.isel(time=50))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5372d50c-7b05-475e-bdce-ed36a8ea794d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# đặt thời gian các mùa\n", + "time_split = [slice('2022-09-01', '2023-01-01'), \n", + " slice('2023-01-01', '2023-05-01'),\n", + " slice('2023-05-01', '2023-07-01'),\n", + " slice('2023-07-01', '2023-10-01')]\n", + "\n", + "# fill nan\n", + "fill_nan_ndvi = fill_nan(ndvi, time_split)\n", + "\n", + "## ảnh ndivi đã fill nan\n", + "plt.imshow(fill_nan_ndvi.isel(time=50))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4e11434c-24ff-48f4-8611-b3baeb300de3", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 74.2 ms, sys: 453 µs, total: 74.7 ms\n", + "Wall time: 73.5 ms\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "74d07ecf73294b8b9d9b943d4d04fee1", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "## tính ndvi theo tháng\n", + "average_ndvi = fill_nan_ndvi.resample(time='1M').mean().persist()\n", + "progress(average_ndvi)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "34e7fd9c-3a16-4c82-b210-2afc4a84772e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# compute average_ndvi\n", + "average_ndvi = average_ndvi.compute()\n", + "\n", + "## cấu hình shapefile ranh giới thuận hòa và vh vv file\n", + "thuanhoa_path = \"ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.shp\"\n", + "name_vh = \"ThuanHoa/ThuanHoa_VH.tif\"\n", + "name_vv = \"ThuanHoa/ThuanHoa_VV.tif\"\n", + "\n", + "# load dữ liệu sen1\n", + "dsvh, dsvv = load_sen1(name_vh, name_vv)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5d29d5cf-31fd-4263-8ae4-2eb9690f739e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# load model RF\n", + "loaded_model = joblib.load(os.path.join(\"model_train\", \"model.joblib\"))\n", + "\n", + "# dự đoán\n", + "data_array = predict(loaded_model, data.rio.crs, average_ndvi, dsvh, dsvv)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "1e17f18d-0878-43d1-acf6-54dfc9f1a0db", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# cấu hình màu cho các loại đất\n", + "colors = [\n", + " \"#abcee9\",\n", + " \"#ffef44\",\n", + " \"#c4ff9e\",\n", + " \"#ffd6a8\",\n", + " \"#93ddda\",\n", + " \"#1aeef7\",\n", + " \"#ffa7f2\",\n", + " \"#33ee33\"\n", + "]\n", + "labels = [\n", + " \"Lúa tôm\",\n", + " \"Lúa\",\n", + " \"CHN\",\n", + " \"CLN\",\n", + " \"TS\",\n", + " \"Sông\",\n", + " \"Đất xây dựng\",\n", + " \"Rừng\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b6e705b1-f1e4-4c94-90aa-4e2ecd56290f", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_289/2294385133.py:5: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", + " cbar.ax.set_yticklabels(labels)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
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\n", + "
\n", + "" + ], + "text/plain": [ + ":DynamicMap []\n", + " :Image [x,y] (value)" + ] + }, + "execution_count": 15, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1003" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# hiển thị kết quả phân loại sử dụng đất\n", + "colorval = list(range(len(colors)))\n", + "options = {\n", + " 'title': 'Phân loại sử dụng đất',\n", + " 'cmap': colors,\n", + " 'clim': (0, 8),\n", + " 'aspect': 'equal',\n", + " 'colorbar_opts': {\n", + " 'major_label_overrides': dict(zip(colorval, labels)),\n", + " 'major_label_text_align': 'left',\n", + " 'ticker': FixedTicker(ticks=colorval),\n", + " },\n", + "}\n", + " \n", + "region_result.hvplot(\n", + " rasterize = True, # Use Datashader, particularly useful for dask arrays\n", + " aggregator = reductions.mode(), # Datashader selects mode value, requires 'hv.Image'\n", + ").options(opts.Image(**options))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "43580ab8-c637-4e79-b842-0131f970bfac", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Lưu lại kết quả\n", + "region_result.rio.to_raster(\"KetQuaPhanLoaiDat.tif\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a23a5bd1-1a8c-448d-b239-8ba2d8e104f2", + "metadata": {}, + "outputs": [], + "source": [ + "# đóng client, cluster\n", + "client.close()\n", + "cluster.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49db198b-588e-403e-8f50-6ffb36b5d3b2", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/03.compare.ipynb b/03.compare.ipynb new file mode 100644 index 0000000..5941d90 --- /dev/null +++ b/03.compare.ipynb @@ -0,0 +1,1401 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1decb47d-81ab-4cd7-bb2c-990d22a37c3d", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n", + " require([\"jspanel\"], function(jsPanel) {\n", + "\twindow.jsPanel = jsPanel\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-modal\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-tooltip\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-hint\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-layout\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-contextmenu\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-dock\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"gridstack\"], function(GridStack) {\n", + "\twindow.GridStack = GridStack\n", + "\ton_load()\n", + " })\n", + " require([\"notyf\"], function() {\n", + "\ton_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 9;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.2.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.2.2.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " Bokeh = root.Bokeh;\n", + " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " if (!reloading && (!bokeh_loaded || is_dev)) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = false;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n var urls = 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existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && 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autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && 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+ " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of 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else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", + " var reloading = true;\n", + " var Bokeh = root.Bokeh;\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n", + " require([\"jspanel\"], function(jsPanel) {\n", + "\twindow.jsPanel = jsPanel\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-modal\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-tooltip\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-hint\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-layout\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-contextmenu\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-dock\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"gridstack\"], function(GridStack) {\n", + "\twindow.GridStack = GridStack\n", + "\ton_load()\n", + " })\n", + " require([\"notyf\"], function() {\n", + "\ton_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 9;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " Bokeh = root.Bokeh;\n", + " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " if (!reloading && (!bokeh_loaded || is_dev)) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.2.2'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = true;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n })\n require([\"jspanel-modal\"], function() {\n\ton_load()\n })\n require([\"jspanel-tooltip\"], function() {\n\ton_load()\n })\n require([\"jspanel-hint\"], function() {\n\ton_load()\n })\n require([\"jspanel-layout\"], function() {\n\ton_load()\n })\n require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n })\n require([\"jspanel-dock\"], function() {\n\ton_load()\n })\n require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n })\n require([\"notyf\"], function() {\n\ton_load()\n })\n root._bokeh_is_loading = css_urls.length + 9;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || 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on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async 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script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) 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var messages = comm.messages[Symbol.asyncIterator]();\n", + " function processIteratorResult(result) {\n", + " var message = result.value;\n", + " var content = {data: message.data};\n", + " var metadata = message.metadata || {comm_id};\n", + " var msg = {content, metadata}\n", + " msg_handler(msg);\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " return messages.next().then(processIteratorResult);\n", + " }\n", + " }) \n", + " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n", + " return comm_promise.then((comm) => {\n", + " comm.send(data, metadata, buffers, disposeOnDone);\n", + " });\n", + " };\n", + " var comm = {\n", + " send: sendClosure\n", + " };\n", + " }\n", + " window.PyViz.comms[comm_id] = comm;\n", + " return comm;\n", + " }\n", + " window.PyViz.comm_manager = new JupyterCommManager();\n", + " \n", + "\n", + "\n", + "var JS_MIME_TYPE = 'application/javascript';\n", + "var HTML_MIME_TYPE = 'text/html';\n", + "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n", + "var CLASS_NAME = 'output';\n", + "\n", + "/**\n", + " * Render data to the DOM node\n", + " */\n", + "function render(props, node) {\n", + " var div = document.createElement(\"div\");\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(div);\n", + " node.appendChild(script);\n", + "}\n", + "\n", + "/**\n", + " * Handle when a new output is added\n", + " */\n", + "function handle_add_output(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + " if (id !== undefined) {\n", + " var nchildren = toinsert.length;\n", + " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n", + " }\n", + " output_area._hv_plot_id = id;\n", + " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n", + " window.PyViz.plot_index[id] = Bokeh.index[id];\n", + " } else {\n", + " window.PyViz.plot_index[id] = null;\n", + " }\n", + " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + "function handle_clear_output(event, handle) {\n", + " var id = handle.cell.output_area._hv_plot_id;\n", + " var server_id = handle.cell.output_area._bokeh_server_id;\n", + " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n", + " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n", + " if (server_id !== null) {\n", + " comm.send({event_type: 'server_delete', 'id': server_id});\n", + " return;\n", + " } else if (comm !== null) {\n", + " comm.send({event_type: 'delete', 'id': id});\n", + " }\n", + " delete PyViz.plot_index[id];\n", + " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n", + " var doc = window.Bokeh.index[id].model.document\n", + " doc.clear();\n", + " const i = window.Bokeh.documents.indexOf(doc);\n", + " if (i > -1) {\n", + " window.Bokeh.documents.splice(i, 1);\n", + " }\n", + " }\n", + "}\n", + "\n", + "/**\n", + " * Handle kernel restart event\n", + " */\n", + "function handle_kernel_cleanup(event, handle) {\n", + " delete PyViz.comms[\"hv-extension-comm\"];\n", + " window.PyViz.plot_index = {}\n", + "}\n", + "\n", + "/**\n", + " * Handle update_display_data messages\n", + " */\n", + "function handle_update_output(event, handle) {\n", + " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n", + " handle_add_output(event, handle)\n", + "}\n", + "\n", + "function register_renderer(events, OutputArea) {\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[0]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " events.on('output_added.OutputArea', handle_add_output);\n", + " events.on('output_updated.OutputArea', handle_update_output);\n", + " events.on('clear_output.CodeCell', handle_clear_output);\n", + " events.on('delete.Cell', handle_clear_output);\n", + " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n", + "\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " safe: true,\n", + " index: 0\n", + " });\n", + "}\n", + "\n", + "if (window.Jupyter !== undefined) {\n", + " try {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " } catch(err) {\n", + " }\n", + "}\n" + ], + "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n 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'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n 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PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Khai báo các thư viện cần thiết\n", + "from new_import import *\n", + "\n", + "# Khai báo đường dẫn đến kết quả phân loại và kết quả thực tế của địa phương\n", + "KD_path = \"ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.shp\"\n", + "KetQuaPhanLoaiDat = \"KetQuaPhanLoaiDat.tif\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9416dfdf-fac0-489b-a8ca-3d88b9c53247", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# khai báo các loại đất ứng với các mã đất phân loại được\n", + "CODE_MAP = {\n", + " \"BHK\": 2,\n", + " \"CLN\": 3,\n", + " \"DGD\": 6,\n", + " \"DGT\": 6,\n", + " \"DNL\": 6,\n", + " \"DRA\": 6,\n", + " \"DSH\": 6,\n", + " \"DTL\": 5,\n", + " \"DTS\": 6,\n", + " \"DYT\": 6,\n", + " \"LUC\": 1,\n", + " \"NKH\": 3,\n", + " \"NTD\": 6,\n", + " \"NTS\": 4,\n", + " \"ONT\": 6,\n", + " \"SKC\": 6,\n", + " \"SKX\": 6,\n", + " \"SON\": 5,\n", + " \"TMD\": 6,\n", + " \"TON\": 6,\n", + " \"TSC\": 6,\n", + "}\n", + "\n", + "# Khai báo các nhãn phân loại đất ứng với 3 loại đất chính\n", + "HT_MAP = {\n", + " \"NN\": {\"name\": \"Đất Nông Nghiệp\", \"data\": [1, 2, 3, 4]},\n", + " \"PNN\": {\"name\": \"Đất Phi Nông Nghiệp\", \"data\": [6]},\n", + " \"TQ\": {\"name\": \"Đất Thổ Quả\", \"data\": [15]},\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5d0d03c0-a2a8-4572-a0de-e08f5cad9842", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "process NN\n", + "process PNN\n", + "process TQ\n" + ] + } + ], + "source": [ + "# Tiến hành chồng lắp\n", + "result = compare(KD_path, KetQuaPhanLoaiDat, CODE_MAP, HT_MAP)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "cb6f13ef-f6b5-401c-8f79-27ac84522fc0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# cấu hình màu cho các loại sử dụng đất\n", + "colors = [\n", + " \"#abcee9\",\n", + " \"#ffffc0\",\n", + " \"#c4ff9e\",\n", + " \"#ffd6a8\",\n", + " \"#93ddda\",\n", + " \"#1aeef7\",\n", + " \"#ffa7f2\",\n", + " \"#33ee33\",\n", + "]\n", + "labels = [\"Lúa tôm\", \"Lúa\", \"CHN\", \"CLN\", \"TS\", \"Sông\", \"Đất xây dựng\", \"Rừng\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f5229c56-8081-4232-b1e4-bbd129bc29c4", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "save ThuanHoa/KetQua/NN.tif\n", + "save ThuanHoa/KetQua/PNN.tif\n", + "save ThuanHoa/KetQua/TQ.tif\n" + ] + } + ], + "source": [ + "# Lưu kết quả\n", + "save_result(result, HT_MAP)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3460451b-4d64-47c6-9f1d-621237b6f89b", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ], + "text/plain": [ + ":DynamicMap [name]\n", + " :Image [y,x] (value)" + ] + }, + "execution_count": 7, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1003" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "# hiển thị kết quả\n", + "xx = []\n", + "\n", + "for k, v in result.items():\n", + " rs = merge_arrays(v, nodata=np.nan)\n", + " xx.append(rs.squeeze(drop=True))\n", + "xx = xr.concat(xx, pd.Index([HT_MAP[x][\"name\"] for x in HT_MAP], name=\"name\"))\n", + "\n", + "colorval = list(range(len(colors)))\n", + "options = {\n", + " \"cmap\": colors,\n", + " \"clim\": (0, 8),\n", + " \"aspect\": \"equal\",\n", + " \"height\": 400,\n", + " \"colorbar_opts\": {\n", + " \"major_label_overrides\": dict(zip(colorval, labels)),\n", + " \"major_label_text_align\": \"left\",\n", + " \"ticker\": FixedTicker(ticks=colorval),\n", + " },\n", + "}\n", + "\n", + "xx.hvplot(\n", + " groupby=\"name\",\n", + " rasterize=True, # Use Datashader, particularly useful for dask arrays\n", + " aggregator=reductions.mode(), # Datashader selects mode value, requires 'hv.Image'\n", + ").options(opts.Image(**options))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e30ff8d-f2d1-465b-96b1-4c0d24006da0", + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/KetQuaPhanLoaiDat.tif b/KetQuaPhanLoaiDat.tif new file mode 100644 index 0000000..2aa523e Binary files /dev/null and b/KetQuaPhanLoaiDat.tif differ diff --git a/ThuanHoa/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/ThuanHoa/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100644 index 0000000..363fcab --- /dev/null +++ b/ThuanHoa/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ThuanHoa/KetQua/NN.tif b/ThuanHoa/KetQua/NN.tif new file mode 100644 index 0000000..6bf92f1 Binary files /dev/null and b/ThuanHoa/KetQua/NN.tif differ diff --git a/ThuanHoa/KetQua/PNN.tif b/ThuanHoa/KetQua/PNN.tif new file mode 100644 index 0000000..621a686 Binary files /dev/null and b/ThuanHoa/KetQua/PNN.tif differ diff --git a/ThuanHoa/KetQua/TQ.tif b/ThuanHoa/KetQua/TQ.tif new file mode 100644 index 0000000..d24b74d Binary files /dev/null and b/ThuanHoa/KetQua/TQ.tif differ diff --git a/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.dbf b/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.dbf new file mode 100644 index 0000000..a806535 Binary files /dev/null and b/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.dbf differ diff --git a/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.prj b/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.prj new file mode 100644 index 0000000..39dec21 --- /dev/null +++ b/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.prj @@ -0,0 +1 @@ +PROJCS["Transverse_Mercator",GEOGCS["GCS_WGS_1984",DATUM["D_unknown",SPHEROID["WGS84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105.5],PARAMETER["scale_factor",0.9999],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["Meter",1]] \ No newline at end of file diff --git a/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.qpj b/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.qpj new file mode 100644 index 0000000..8a1d453 --- /dev/null +++ b/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.qpj @@ -0,0 +1 @@ +PROJCS["unnamed",GEOGCS["WGS 84",DATUM["unknown",SPHEROID["WGS84",6378137,298.257223563],TOWGS84[-192.873,-39.382,-111.202,-0.00205,-0.0005,0.00335,0.0188]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105.5],PARAMETER["scale_factor",0.9999],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["Meter",1]] diff --git a/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.shp b/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.shp new file mode 100644 index 0000000..80519f5 Binary files /dev/null and b/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.shp differ diff --git a/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.shx b/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.shx new file mode 100644 index 0000000..256ef80 Binary files /dev/null and b/ThuanHoa/KhoanhDat/ThuanHoa_KKDD2019.shx differ diff --git a/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.dbf b/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.dbf new file mode 100644 index 0000000..193f541 Binary files /dev/null and b/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.dbf differ diff --git a/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.prj b/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.prj new file mode 100644 index 0000000..39dec21 --- /dev/null +++ b/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.prj @@ -0,0 +1 @@ +PROJCS["Transverse_Mercator",GEOGCS["GCS_WGS_1984",DATUM["D_unknown",SPHEROID["WGS84",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105.5],PARAMETER["scale_factor",0.9999],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["Meter",1]] \ No newline at end of file diff --git a/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.qpj b/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.qpj new file mode 100644 index 0000000..8a1d453 --- /dev/null +++ b/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.qpj @@ -0,0 +1 @@ +PROJCS["unnamed",GEOGCS["WGS 84",DATUM["unknown",SPHEROID["WGS84",6378137,298.257223563],TOWGS84[-192.873,-39.382,-111.202,-0.00205,-0.0005,0.00335,0.0188]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105.5],PARAMETER["scale_factor",0.9999],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["Meter",1]] diff --git a/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.shp b/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.shp new file mode 100644 index 0000000..0bc61ec Binary files /dev/null and b/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.shp differ diff --git a/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.shx b/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.shx new file mode 100644 index 0000000..3965ec7 Binary files /dev/null and b/ThuanHoa/KhoanhDat/ThuanHoa_TKDD2022.shx differ diff --git a/ThuanHoa/ThuanHoa_VH.tif b/ThuanHoa/ThuanHoa_VH.tif new file mode 100644 index 0000000..e007842 Binary files /dev/null and b/ThuanHoa/ThuanHoa_VH.tif differ diff --git a/ThuanHoa/ThuanHoa_VV.tif b/ThuanHoa/ThuanHoa_VV.tif new file mode 100644 index 0000000..4132074 Binary files /dev/null and b/ThuanHoa/ThuanHoa_VV.tif differ diff --git a/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.cpg b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.cpg new file mode 100644 index 0000000..3ad133c --- /dev/null +++ b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.cpg @@ -0,0 +1 @@ +UTF-8 \ No newline at end of file diff --git a/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.dbf b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.dbf new file mode 100644 index 0000000..cb95cc3 Binary files /dev/null and b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.dbf differ diff --git a/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.prj b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.prj new file mode 100644 index 0000000..10ab055 --- /dev/null +++ b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.prj @@ -0,0 +1 @@ +PROJCS["VN-2000_TM-3_105-30",GEOGCS["GCS_VN_2000",DATUM["D_Vietnam_2000",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",105.5],PARAMETER["Scale_Factor",0.9999],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]] \ No newline at end of file diff --git a/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.qmd b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.qmd new file mode 100644 index 0000000..d702e82 --- /dev/null +++ b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.qmd @@ -0,0 +1,27 @@ + + + + + + + + + + + + + + + + + 0 + 0 + + + + + false + + + + diff --git a/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.shp b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.shp new file mode 100644 index 0000000..b003a64 Binary files /dev/null and b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.shp differ diff --git a/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.shx b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.shx new file mode 100644 index 0000000..471d202 Binary files /dev/null and b/ThuanHoa/region/ST_ThuanHoa_Boundaryofficially.shx differ diff --git a/deafrica_tools/__init__.py b/deafrica_tools/__init__.py new file mode 100644 index 0000000..610e60e --- /dev/null +++ b/deafrica_tools/__init__.py @@ -0,0 +1,26 @@ +__locales__ = __path__[0] + '/locales' + + +def set_lang(lang=None): + if lang is None: + import os + os_lang = os.getenv('LANG') + + # Just take the first 2 letters: 'fr' not 'fr_FR.UTF-8' + if os_lang is not None and len(os_lang) >=2: + lang = [os_lang[:2]] + else: + lang = [lang] + + import gettext + try: + translation = gettext.translation( + 'deafrica_tools', + localedir=__locales__, + languages=lang, + fallback=True + ) + translation.install() + + except FileNotFoundError: + print(f'Could not load lang={lang}') diff --git a/deafrica_tools/__pycache__/__init__.cpython-310.pyc b/deafrica_tools/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000..95a646e Binary files /dev/null and b/deafrica_tools/__pycache__/__init__.cpython-310.pyc differ diff --git a/deafrica_tools/__pycache__/bandindices.cpython-310.pyc b/deafrica_tools/__pycache__/bandindices.cpython-310.pyc new file mode 100644 index 0000000..f4eb3d9 Binary files /dev/null and b/deafrica_tools/__pycache__/bandindices.cpython-310.pyc differ diff --git a/deafrica_tools/app/__init__.py b/deafrica_tools/app/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/deafrica_tools/app/animations.py b/deafrica_tools/app/animations.py new file mode 100644 index 0000000..a0ad4fb --- /dev/null +++ b/deafrica_tools/app/animations.py @@ -0,0 +1,942 @@ +# -*- coding: utf-8 -*- +""" +Satellite imagery animation widget, which can be used to interactively +produce animations for multiple DE Africa products. +""" + +# Import required packages + +# Force GeoPandas to use Shapely instead of PyGEOS +# In a future release, GeoPandas will switch to using Shapely by default. +import os +os.environ['USE_PYGEOS'] = '0' + +import fiona +import sys +import datacube +import warnings +import matplotlib.pyplot as plt +from datacube.utils.geometry import CRS +from ipyleaflet import ( + WMSLayer, + basemaps, + basemap_to_tiles, + Map, + DrawControl, + WidgetControl, + LayerGroup, + LayersControl, + GeoData, +) +from traitlets import Unicode +from ipywidgets import ( + GridspecLayout, + Button, + Layout, + HBox, + VBox, + HTML, + Output, +) +import json +import itertools +import numpy as np +import geopandas as gpd +from io import BytesIO +import ipywidgets as widgets +import datetime +from skimage import exposure +from skimage.filters import unsharp_mask + +from datacube.utils import masking +from datacube.utils.geometry import Geometry +from datacube.utils.masking import mask_invalid_data +import deafrica_tools.app.widgetconstructors as deawidgets +from deafrica_tools.dask import create_local_dask_cluster +from deafrica_tools.spatial import reverse_geocode +from deafrica_tools.datahandling import pan_sharpen_brovey + +import warnings + +warnings.filterwarnings("ignore") + + +# WMS params and satellite style bands +sat_params = { + "Landsat": { + "products": ["ls5_sr", "ls7_sr", "ls8_sr", "ls9_sr"], + "styles": { + "True colour": ("true_colour", ["red", "green", "blue"]), + "False colour": ( + "false_colour", + ["swir_1", "nir", "green"], + ), + }, + }, + "Sentinel-2": { + "products": ["s2_l2a"], + "styles": { + "True colour": ("simple_rgb", ["red", "green", "blue"]), + "False colour": ( + "infrared_green", + ["swir_2", "nir_1", "green"], + ), + }, + }, +} + + +def make_box_layout(): + return Layout( + # border='solid 1px black', + margin="0px 10px 10px 0px", + padding="5px 5px 5px 5px", + width="100%", + height="100%", + ) + + +def create_expanded_button(description, button_style): + return Button( + description=description, + button_style=button_style, + layout=Layout(width="auto", height="auto"), + ) + + +def update_map_layers(self): + """ + Updates map to add new DE Africa layers, styles or basemap when selected + using menu options. Triggers data reload by resetting load params + and output arrays. + """ + + # Clear data load params to trigger data re-load + self.timeseries_ds = None + self.load_params = None + self.query_params = None + + # Clear all layers and add basemap + self.map_layers.clear_layers() + self.map_layers.add_layer(self.basemap) + + +def extract_data(self): + + # Connect to datacube database + dc = datacube.Datacube(app="Exporting satellite images") + + # Configure local dask cluster + client = create_local_dask_cluster(return_client=True, display_client=True) + + # Convert to geopolygon + geopolygon = Geometry(geom=self.gdf_drawn.geometry[0], crs=self.gdf_drawn.crs) + + # Create query. + start_date = np.datetime64(self.start_date) + end_date = np.datetime64(self.end_date) + + self.query_params = { + "time": (str(start_date), str(end_date)), + "geopolygon": geopolygon, + } + + # Find matching datasets + dss = [ + dc.find_datasets(product=i, **self.query_params) + for i in sat_params[self.dealayer]["products"] + ] + dss = list(itertools.chain.from_iterable(dss)) + + # If data is found + if len(dss) > 0: + + # Get CRS + crs = str(dss[0].crs) + + self.load_params = { + "measurements": sat_params[self.dealayer]["styles"][self.style][1], + "resolution": (-self.resolution, self.resolution), + "output_crs": crs, + "group_by": "solar_day", + "dask_chunks": {"time": 1, "x": 2048, "y": 2048}, + "resampling": {"*": "cubic", "oa_fmask": "nearest", "fmask": "nearest"}, + } + + # Load data + from deafrica_tools.datahandling import load_ard + + timeseries_ds = load_ard( + dc=dc, + products=sat_params[self.dealayer]["products"], + min_gooddata=1.0 - (self.max_cloud_cover / 100), + ls7_slc_off=False, + mask_pixel_quality=self.cloud_mask, + **self.load_params, + **self.query_params, + ) + + # Set invalid nodata pixels to NaN + timeseries_ds = mask_invalid_data(timeseries_ds) + + # Else if no data is returned, return None + else: + timeseries_ds = None + + # Close down the dask client + client.close() + + return timeseries_ds.compute() + + +def plot_data(self, fname): + + # Data to plot + to_plot = self.timeseries_ds + + # If rolling median specified + if self.rolling_median: + with self.status_info: + print( + f"\nApplying rolling median ({self.rolling_median_window} timesteps window)" + ) + to_plot = to_plot.rolling( + time=int(self.rolling_median_window), center=True, min_periods=1 + ).median() + + # If resampling freq specified + if self.resample_freq: + with self.status_info: + print(f"\nResampling data to {self.resample_freq} frequency") + to_plot = to_plot.resample(time=self.resample_freq).median() + + # Raise by power to dampen bright features and enhance dark. + # Raise vmin and vmax by same amount to ensure proper stretch + if self.power < 1.0: + with self.status_info: + print(f"\nApplying power transformation ({self.power})") + to_plot = to_plot ** self.power + + # Apply unsharp masking to enhance overall dynamic range, + # and improve fine scale detail + if self.unsharp_mask: + with self.status_info: + print( + f"\nApplying unsharp masking with {self.unsharp_mask_radius} " + f"radius and {self.unsharp_mask_amount} amount" + ) + from skimage.exposure import rescale_intensity + + funcs_list = [ + rescale_intensity, + lambda x: unsharp_mask( + x, radius=self.unsharp_mask_radius, amount=self.unsharp_mask_amount + ), + ] + else: + funcs_list = None + + from deafrica_tools.plotting import xr_animation + + xr_animation( + output_path=fname, + ds=to_plot.dropna(dim="time", how="all"), + show_text="", + bands=sat_params[self.dealayer]["styles"][self.style][1], + interval=self.interval, + width_pixels=self.width, + show_gdf=deacoastlines_overlay(to_plot) if self.deacoastlines else None, + gdf_kwargs={"linewidth": 3}, + percentile_stretch=(self.vmin, self.vmax), + image_proc_funcs=funcs_list, + show_date="%Y" if self.resample_freq == "1Y" else "%b %Y", + annotation_kwargs={"fontsize": 75}, + ) + + # Add plot preview below map and finish + plt.show() + with self.status_info: + print(f"\nImage successfully exported to:\n{fname}.") + + +def deacoastlines_overlay(ds): + + import geopandas as gpd + import pandas as pd + import matplotlib + from shapely.geometry import box, Point + from deafrica_tools.coastal import get_coastlines + + # Get bounding box of data + xmin, ymin, xmax, ymax = ds.geobox.geographic_extent.boundingbox + bounds = [xmin, ymin, xmax, ymax] + + # Load data + deacl_gdf = get_coastlines(bbox=bounds) + + # Clip to extent of satellite data + bbox = gpd.GeoDataFrame(geometry=[ds.geobox.extent.geom], crs=ds.geobox.crs) + deacl_gdf = gpd.overlay(deacl_gdf, bbox.to_crs(deacl_gdf.crs)) + deacl_gdf = deacl_gdf.dissolve("year") # values("year", ascending=True) + + # Apply colours + norm = matplotlib.colors.Normalize(vmin=0, vmax=len(deacl_gdf.index)) + cmap = matplotlib.cm.get_cmap("inferno") + rgba = cmap(norm(deacl_gdf.reset_index().index)) + deacl_gdf["color"] = list(rgba) + deacl_gdf["start_time"] = pd.to_datetime(deacl_gdf.index) + pd.DateOffset(months=0) + deacl_gdf = deacl_gdf.sort_index() + + if len(deacl_gdf.index) > 0: + return deacl_gdf + else: + return None + + +class animation_app(HBox): + def __init__(self): + super().__init__() + + ###################### + # INITIAL ATTRIBUTES # + ###################### + + # Basemap + self.basemap_list = [ + ("ESRI World Imagery", basemap_to_tiles(basemaps.Esri.WorldImagery)), + ("Open Street Map", basemap_to_tiles(basemaps.OpenStreetMap.Mapnik)), + ] + self.basemap = self.basemap_list[0][1] + + # Satellite data + end_date = datetime.datetime.today() + start_date = datetime.datetime( + year=end_date.year - 3, month=end_date.month, day=end_date.day + ) + self.start_date = start_date.strftime("%Y-%m-%d") + self.end_date = end_date.strftime("%Y-%m-%d") + self.dealayer_list = [ + ("Landsat", "Landsat"), + ("Sentinel-2", "Sentinel-2"), + ] + self.dealayer = self.dealayer_list[0][1] + + # Styles + self.styles_list = ["True colour", "False colour"] + self.style = self.styles_list[0] + + # Analysis params + self.resolution = 30 + self.vmin = 0.01 + self.vmax = 0.99 + self.power = 1.0 + self.output_list = [("MP4", "mp4"), ("GIF", "gif")] + self.output_format = self.output_list[0][1] + self.rolling_median = False + self.rolling_median_window = 20 + self.unsharp_mask = False + self.unsharp_mask_radius = 20 + self.unsharp_mask_amount = 0.3 + self.max_size = False + self.width = 900 + self.interval = 100 + self.cloud_mask = False + self.max_cloud_cover = 20 + self.resample_list = [ + ("None", False), + ("Monthly", "1M"), + ("Quarterly", "Q-DEC"), + ("Yearly", "1Y"), + ] + self.resample_freq = self.resample_list[0][1] + self.deacoastlines = False + + # Drawing params + self.target = None + self.action = None + self.gdf_drawn = None + + # Data load params + self.timeseries_ds = None + self.load_params = None + self.query_params = None + + ################## + # HEADER FOR APP # + ################## + + # Create the Header widget + header_title_text = ( + "

Digital Earth Africa satellite imagery animations

" + ) + instruction_text = ( + "

Select the desired satellite data, imagery date range " + "and image style, then zoom in and draw a rectangle to " + "select an area export as a satellite imagery time-series " + "animation.

" + ) + self.header = deawidgets.create_html(f"{header_title_text}{instruction_text}") + self.header.layout = make_box_layout() + + ##################################### + # HANDLER FUNCTION FOR DRAW CONTROL # + ##################################### + + # Define the action to take once something is drawn on the map + def update_geojson(target, action, geo_json): + + # Get data from action + self.action = action + + # Clear data load params to trigger data re-load + self.timeseries_ds = None + self.load_params = None + self.query_params = None + + # Convert data to geopandas + json_data = json.dumps(geo_json) + binary_data = json_data.encode() + io = BytesIO(binary_data) + io.seek(0) + gdf = gpd.read_file(io) + gdf.crs = "EPSG:4326" + + # Convert to WGS 84 / NSIDC EASE-Grid 2.0 Global and compute area + gdf_drawn_nsidc = gdf.copy().to_crs("EPSG:6933") + m2_per_ha = 10000 + area = gdf_drawn_nsidc.area.values[0] / m2_per_ha + polyarea_label = "Total area of satellite data to extract" + polyarea_text = f"{polyarea_label}: {area:.2f} ha" + + # Test area size + if self.max_size: + confirmation_text = ( + ' ' + "(Overriding maximum size limit; use with caution as may lead to memory issues)" + ) + self.header.value = ( + header_title_text + + instruction_text + + polyarea_text + + confirmation_text + ) + self.gdf_drawn = gdf + elif area <= 50000: + confirmation_text = ( + ' ' + "(Area to extract falls within " + "recommended 50000 ha limit)" + ) + self.header.value = ( + header_title_text + + instruction_text + + polyarea_text + + confirmation_text + ) + self.gdf_drawn = gdf + else: + warning_text = ( + ' ' + "(Area to extract is too large, " + "please select an area less than 50000 " + "ha)" + ) + self.header.value = ( + header_title_text + instruction_text + polyarea_text + warning_text + ) + self.gdf_drawn = None + + ########################### + # WIDGETS FOR APP OUTPUTS # + ########################### + + self.status_info = Output(layout=make_box_layout()) + self.output_plot = Output(layout=make_box_layout()) + + ######################################### + # MAP WIDGET, DRAWING TOOLS, WMS LAYERS # + ######################################### + + # Create drawing tools + desired_drawtools = ["rectangle"] + draw_control = deawidgets.create_drawcontrol(desired_drawtools) + + # Begin by displaying an empty layer group, and update the group with desired WMS on interaction. + self.map_layers = LayerGroup(layers=()) + self.map_layers.name = "Map Overlays" + + # Create map widget + self.m = deawidgets.create_map(map_center=(5.65, 26.17), zoom_level=13) + self.m.layout = make_box_layout() + + # Add tools to map widget + self.m.add_control(draw_control) + self.m.add_layer(self.map_layers) + + # Update all maps to starting defaults + update_map_layers(self) + + ############################ + # WIDGETS FOR APP CONTROLS # + ############################ + + # Create parameter widgets + dropdown_basemap = deawidgets.create_dropdown( + self.basemap_list, self.basemap_list[0][1] + ) + dropdown_dealayer = deawidgets.create_dropdown( + self.dealayer_list, self.dealayer_list[0][1] + ) + dropdown_output = deawidgets.create_dropdown( + self.output_list, self.output_list[0][1] + ) + date_picker_start = deawidgets.create_datepicker( + value=start_date, + ) + date_picker_end = deawidgets.create_datepicker( + value=end_date, + ) + dropdown_styles = deawidgets.create_dropdown( + self.styles_list, self.styles_list[0] + ) + slider_percentile = widgets.FloatRangeSlider( + value=[0.01, 0.99], + min=0, + max=1, + step=0.001, + description="", + layout={"width": "85%"}, + ) + run_button = create_expanded_button("Generate animation", "info") + + floatslider_max_cloud_cover = widgets.IntSlider( + value=20, + min=0, + max=100, + step=1, + description="", + layout={"width": "85%"}, + ) + + checkbox_rolling_median = deawidgets.create_checkbox( + self.rolling_median, + "Apply rolling median
to produce smooth,
cloud-free animations", + layout={"width": "90%", + "height": "4em"}, + ) + text_rolling_median_window = widgets.IntText( + value=20, + step=1, + description="
Rolling window (timesteps)", + layout={ + "width": "85%", + "margin": "0px", + "padding": "0px", + "display": "none", + }, + ) + + # Expandable advanced section + text_interval = widgets.IntText( + value=100, description="", step=50, layout={"width": "95%"} + ) + text_resolution = widgets.FloatText( + value=30, + description="", + layout={"width": "95%", "margin": "0px", "padding": "0px"}, + ) + text_width = widgets.IntText( + value=900, description="", step=50, layout={"width": "95%"} + ) + dropdown_resampling = deawidgets.create_dropdown( + self.resample_list, + self.resample_freq, + description="", + layout={"width": "95%"}, + ) + checkbox_cloud_mask = deawidgets.create_checkbox( + self.cloud_mask, "Mask out cloudy
pixels", layout={"width": "95%", "height": "auto"} + ) + slider_power = widgets.FloatSlider( + value=1.0, + min=0.01, + max=1.0, + step=0.01, + description="", + layout={"width": "95%"}, + ) + checkbox_unsharp_mask = deawidgets.create_checkbox( + self.unsharp_mask, "Enable", layout={"width": "95%"} + ) + text_unsharp_mask_radius = widgets.FloatText( + value=20, + step=1, + description="Radius", + layout={ + "width": "95%", + "margin": "0px", + "padding": "0px", + "display": "none", + }, + ) + text_unsharp_mask_amount = widgets.FloatText( + value=0.3, + step=0.1, + description="Amount", + layout={ + "width": "95%", + "margin": "0px", + "padding": "0px", + "display": "none", + }, + ) + checkbox_deacoastlines = deawidgets.create_checkbox( + self.deacoastlines, "Add DE Africa Coastlines overlay", layout={"width": "95%"} + ) + checkbox_max_size = deawidgets.create_checkbox( + self.max_size, "Enable", layout={"width": "95%"} + ) + expand_box = widgets.VBox( + [ + HTML("Frame interval (milliseconds):"), + text_interval, + HTML("
Resolution (metres):"), + text_resolution, + HTML("
Width of output animation in pixels:"), + text_width, + HTML("
Apply temporal resampling:"), + dropdown_resampling, + HTML("
"), + checkbox_cloud_mask, + checkbox_deacoastlines, + HTML("
Apply power transformation to darken bright features:"), + slider_power, + HTML("
Apply unsharp masking to sharpen imagery:"), + checkbox_unsharp_mask, + text_unsharp_mask_radius, + text_unsharp_mask_amount, + HTML( + "
Override maximum size limit: (use with caution; may cause memory issues/crashes)" + ), + checkbox_max_size, + ], + ) + + expand = widgets.Accordion( + children=[expand_box], + selected_index=None, + ) + expand.set_title(0, "Advanced") + + # Add specific dialogs to class so they can be modified + self.text_resolution = text_resolution + self.text_unsharp_mask_radius = text_unsharp_mask_radius + self.text_unsharp_mask_amount = text_unsharp_mask_amount + self.text_rolling_median_window = text_rolling_median_window + + #################################### + # UPDATE FUNCTIONS FOR EACH WIDGET # + #################################### + + # Run update functions whenever various widgets are changed. + date_picker_start.observe(self.update_start_date, "value") + date_picker_end.observe(self.update_end_date, "value") + dropdown_basemap.observe(self.update_basemap, "value") + dropdown_dealayer.observe(self.update_dealayer, "value") + dropdown_styles.observe(self.update_styles, "value") + + slider_percentile.observe(self.update_slider_percentile, "value") + floatslider_max_cloud_cover.observe( + self.update_floatslider_max_cloud_cover, "value" + ) + checkbox_rolling_median.observe(self.update_checkbox_rolling_median, "value") + text_rolling_median_window.observe( + self.update_text_rolling_median_window, "value" + ) + dropdown_output.observe(self.update_output, "value") + run_button.on_click(self.run_app) + draw_control.on_draw(update_geojson) + + # Advanced params + text_resolution.observe(self.update_text_resolution, "value") + slider_power.observe(self.update_slider_power, "value") + text_width.observe(self.update_width, "value") + text_interval.observe(self.update_interval, "value") + dropdown_resampling.observe(self.update_dropdown_resampling, "value") + checkbox_cloud_mask.observe(self.update_checkbox_cloud_mask, "value") + checkbox_unsharp_mask.observe(self.update_checkbox_unsharp_mask, "value") + text_unsharp_mask_radius.observe(self.update_text_unsharp_mask_radius, "value") + text_unsharp_mask_amount.observe(self.update_text_unsharp_mask_amount, "value") + checkbox_deacoastlines.observe(self.update_deacoastlines, "value") + checkbox_max_size.observe(self.update_checkbox_max_size, "value") + + ################################## + # COLLECTION OF ALL APP CONTROLS # + ################################## + + parameter_selection = VBox( + [ + HTML("Satellite imagery:"), + dropdown_dealayer, + HTML("Start date:"), + date_picker_start, + HTML("End date:"), + date_picker_end, + HTML("Style:"), + dropdown_styles, + HTML("Colour percentile stretch:"), + slider_percentile, + HTML("Maximum cloud cover (%):"), + floatslider_max_cloud_cover, + checkbox_rolling_median, + text_rolling_median_window, + HTML("
Output file format:"), + dropdown_output, + HTML("
"), + expand, + ] + ) + map_selection = VBox( + [ + HTML("
Map overlay:"), + dropdown_basemap, + ] + ) + parameter_selection.layout = make_box_layout() + map_selection.layout = make_box_layout() + + ############################### + # SPECIFICATION OF APP LAYOUT # + ############################### + + # 0 1 2 3 4 5 6 7 8 9 + # --------------------------------------------- + # 0 | Header | Map sel. | + # |-------------------------------------------| + # 1 | Params | | + # 2 | | | + # 3 | | | + # 4 | | Map | + # 5 | | | + # |--------| | + # 6 | Run | | + # |-------------------------------------------| + # 7 | Status info | Figure/output | + # 8 | | | + # 9 | | | + # 10 | | | + # 11 --------------------------------------------- + + # Create the layout #[rowspan, colspan] + grid = GridspecLayout(12, 10, height="1500px", width="auto") + + # Header and controls + grid[0, :8] = self.header + grid[0, 8:] = map_selection + grid[1:6, 0:2] = parameter_selection + grid[6, 0:2] = run_button + + # Status info, map and plot + grid[1:7, 2:] = self.m # map + grid[7:, 0:4] = self.status_info + grid[7:, 4:] = self.output_plot + + # Display using HBox children attribute + self.children = [grid] + + ###################################### + # DEFINITION OF ALL UPDATE FUNCTIONS # + ###################################### + + # Update date + def update_start_date(self, change): + self.start_date = str(change.new) + + # Clear data load params to trigger data re-load + self.timeseries_ds = None + self.load_params = None + self.query_params = None + + # Update date + def update_end_date(self, change): + self.end_date = str(change.new) + + # Clear data load params to trigger data re-load + self.timeseries_ds = None + self.load_params = None + self.query_params = None + + # Update colour stretch + def update_slider_percentile(self, change): + self.vmin, self.vmax = change.new + + # Update power transform + def update_slider_power(self, change): + self.power = change.new + + # Update good data slider + def update_floatslider_max_cloud_cover(self, change): + self.max_cloud_cover = change.new + + # Clear data load params to trigger data re-load + self.timeseries_ds = None + self.load_params = None + self.query_params = None + + # Enable unsharp masking and show/hide custom params + def update_checkbox_unsharp_mask(self, change): + self.unsharp_mask = change.new + + # Show unsharp masking params in menu if activated + if change.new: + self.text_unsharp_mask_radius.layout.display = "block" + self.text_unsharp_mask_amount.layout.display = "block" + else: + self.text_unsharp_mask_radius.layout.display = "none" + self.text_unsharp_mask_amount.layout.display = "none" + + # Change unsharp masking radius + def update_text_unsharp_mask_radius(self, change): + self.unsharp_mask_radius = change.new + + # Change unsharp masking amount + def update_text_unsharp_mask_amount(self, change): + self.unsharp_mask_amount = change.new + + # Enable rolling median and show/hide custom params + def update_checkbox_rolling_median(self, change): + self.rolling_median = change.new + + # Show rolling median params in menu if activated + if change.new: + self.text_rolling_median_window.layout.display = "block" + else: + self.text_rolling_median_window.layout.display = "none" + + # Change rolling median window + def update_text_rolling_median_window(self, change): + self.rolling_median_window = change.new + + # Override max size limit + def update_checkbox_max_size(self, change): + self.max_size = change.new + + # Add DE Africa Coastlines overlay + def update_deacoastlines(self, change): + self.deacoastlines = change.new + + # Apply cloud mask in load_ard + def update_checkbox_cloud_mask(self, change): + self.cloud_mask = change.new + + # Clear data load params to trigger data re-load + self.timeseries_ds = None + self.load_params = None + self.query_params = None + + # Override min width + def update_width(self, change): + self.width = change.new + + # Override interval + def update_interval(self, change): + self.interval = change.new + + # Update resolution + def update_text_resolution(self, change): + self.resolution = change.new + + # Clear data load params to trigger data re-load + self.timeseries_ds = None + self.load_params = None + self.query_params = None + + # Change layers shown on the map + def update_dealayer(self, change): + self.dealayer = change.new + + if change.new == "Landsat": + self.text_resolution.value = 30 + + else: + self.text_resolution.value = 10 + + # Update basemap + def update_basemap(self, change): + self.basemap = change.new + update_map_layers(self) + + # Set imagery style + def update_styles(self, change): + self.style = change.new + + # Clear data load params to trigger data re-load + self.timeseries_ds = None + self.load_params = None + self.query_params = None + + # Set output file format + def update_output(self, change): + self.output_format = change.new + + # Set output file format + def update_dropdown_resampling(self, change): + self.resample_freq = change.new + + def run_app(self, change): + + # Clear progress bar and output areas before running + self.status_info.clear_output() + self.output_plot.clear_output() + + # Verify that polygon was drawn + if self.gdf_drawn is not None: + + with self.status_info: + + # Load data and add to attribute + if self.timeseries_ds is None: + self.timeseries_ds = extract_data(self) + + else: + print("Using previously loaded data") + + if self.timeseries_ds is not None: + + with self.status_info: + + # Create unique file name + centre_coords = self.gdf_drawn.geometry[0].centroid.coords[0][::-1] + site = reverse_geocode(coords=centre_coords) + fname = ( + f"{self.dealayer}_{site}_{self.start_date}_" + f"{self.end_date}_{self.style}_{self.resolution:.0f}m." + f"{self.output_format}".replace(" ", "") + .replace(",", "") + .lower() + ) + + print( + f"\nExporting animation for {site}.\nThis may take several minutes..." + ) + + ############ + # Plotting # + ############ + + with self.output_plot: + plot_data(self, fname) + + else: + with self.status_info: + print( + "No satellite data found in the selected area. " + "Please select a new rectangle over an area with " + "satellite imagery." + ) + + else: + with self.status_info: + print( + 'Please draw a valid rectangle on the map, then press "Generate animation".' + ) \ No newline at end of file diff --git a/deafrica_tools/app/changefilmstrips.py b/deafrica_tools/app/changefilmstrips.py new file mode 100644 index 0000000..31fb2d8 --- /dev/null +++ b/deafrica_tools/app/changefilmstrips.py @@ -0,0 +1,275 @@ +""" +Loading and interacting with data in the change filmstrips notebook, +inside the Real_world_examples folder. +""" + +# Load modules +import os +import dask +import datacube +import warnings +import numpy as np +import pandas as pd +import xarray as xr +import matplotlib.pyplot as plt +from odc.algo import geomedian_with_mads +from odc.ui import select_on_a_map +from dask.utils import parse_bytes +from datacube.utils.geometry import CRS, assign_crs +from datacube.utils.rio import configure_s3_access +from datacube.utils.dask import start_local_dask +from ipyleaflet import basemaps, basemap_to_tiles + +# Load utility functions +from deafrica_tools.datahandling import load_ard, mostcommon_crs +from deafrica_tools.dask import create_local_dask_cluster + + +def run_filmstrip_app( + output_name, + time_range, + time_step, + tide_range=(0.0, 1.0), + resolution=(-30, 30), + max_cloud=0.5, + ls7_slc_off=False, + size_limit=10000, +): + """ + An interactive app that allows the user to select a region from a + map, then load Digital Earth Africa Landsat data and combine it + using the geometric median ("geomedian") statistic to reveal the + median or 'typical' appearance of the landscape for a series of + time periods. + + The results for each time period are combined into a 'filmstrip' + plot which visualises how the landscape has changed in appearance + across time, with a 'change heatmap' panel highlighting potential + areas of greatest change. + + For coastal applications, the analysis can be customised to select + only satellite images obtained during a specific tidal range + (e.g. low, average or high tide). + + Last modified: April 2020 + + Parameters + ---------- + output_name : str + A name that will be used to name the output filmstrip plot file. + time_range : tuple + A tuple giving the date range to analyse + (e.g. `time_range = ('1988-01-01', '2017-12-31')`). + time_step : dict + This parameter sets the length of the time periods to compare + (e.g. `time_step = {'years': 5}` will generate one filmstrip + plot for every five years of data; `time_step = {'months': 18}` + will generate one plot for each 18 month period etc. Time + periods are counted from the first value given in `time_range`. + tide_range : tuple, optional + An optional parameter that can be used to generate filmstrip + plots based on specific ocean tide conditions. This can be + valuable for analysing change consistently along the coast. + For example, `tide_range = (0.0, 0.2)` will select only + satellite images acquired at the lowest 20% of tides; + `tide_range = (0.8, 1.0)` will select images from the highest + 20% of tides. The default is `tide_range = (0.0, 1.0)` which + will select all images regardless of tide. + resolution : tuple, optional + The spatial resolution to load data. The default is + `resolution = (-30, 30)`, which will load data at 30 m pixel + resolution. Increasing this (e.g. to `resolution = (-100, 100)`) + can be useful for loading large spatial extents. + max_cloud : float, optional + This parameter can be used to exclude satellite images with + excessive cloud. The default is `0.5`, which will keep all images + with less than 50% cloud. + ls7_slc_off : bool, optional + An optional boolean indicating whether to include data from + after the Landsat 7 SLC failure (i.e. SLC-off). Defaults to + False, which removes all Landsat 7 observations > May 31 2003. + size_limit : int, optional + An optional integer (in hectares) specifying the size limit + for the data query. Queries larger than this size will receive + a warning that he data query is too large (and may + therefore result in memory errors). + + + Returns + ------- + ds_geomedian : xarray Dataset + An xarray dataset containing geomedian composites for each + timestep in the analysis. + + """ + + ######################## + # Select and load data # + ######################## + + # Define centre_coords as a global variable + global centre_coords + + # Test if centre_coords is in the global namespace; + # use default value if it isn't + if "centre_coords" not in globals(): + centre_coords = (6.587292, 1.532833) + + # Plot interactive map to select area + basemap = basemap_to_tiles(basemaps.Esri.WorldImagery) + geopolygon = select_on_a_map(height="600px", + layers=(basemap,), + center=centre_coords, + zoom=14) + + # Set centre coords based on most recent selection to re-focus + # subsequent data selections + centre_coords = geopolygon.centroid.points[0][::-1] + + # Test size of selected area + msq_per_hectare = 10000 + area = geopolygon.to_crs(crs=CRS("epsg:6933")).area / msq_per_hectare + radius = np.round(np.sqrt(size_limit), 1) + if area > size_limit: + print(f"Warning: Your selected area is {area:.00f} hectares. " + f"Please select an area of less than {size_limit} hectares." + f"\nTo select a smaller area, re-run the cell " + f"above and draw a new polygon.") + + else: + + print("Starting analysis...") + + # Connect to datacube database + dc = datacube.Datacube(app="Change_filmstrips") + + # Configure local dask cluster + client = create_local_dask_cluster(return_client=True) + + # Obtain native CRS + crs = mostcommon_crs(dc=dc, + product="ls8_sr", + query={ + "time": "2014", + "geopolygon": geopolygon + }) + + # Create query based on time range, area selected, custom params + query = { + "time": time_range, + "geopolygon": geopolygon, + "output_crs": crs, + "resolution": resolution, + "dask_chunks": { + "x": 3000, + "y": 3000 + }, + "align": (resolution[1] / 2.0, resolution[1] / 2.0), + } + + # Load data from all three Landsats + warnings.filterwarnings("ignore") + ds = load_ard( + dc=dc, + measurements=["red", "green", "blue"], + products=["ls5_sr", "ls7_sr", "ls8_sr"], + min_gooddata=max_cloud, + ls7_slc_off=ls7_slc_off, + **query, + ) + + # Optionally calculate tides for each timestep in the satellite + # dataset and drop any observations out side this range + if tide_range != (0.0, 1.0): + from deafrica_tools.coastal import tidal_tag + ds = tidal_tag(ds=ds, tidepost_lat=None, tidepost_lon=None) + min_tide, max_tide = ds.tide_height.quantile(tide_range).values + ds = ds.sel(time=(ds.tide_height >= min_tide) & + (ds.tide_height <= max_tide)) + ds = ds.drop("tide_height") + print(f" Keeping {len(ds.time)} observations with tides " + f"between {min_tide:.2f} and {max_tide:.2f} m") + + # Create time step ranges to generate filmstrips from + bins_dt = pd.date_range(start=time_range[0], + end=time_range[1], + freq=pd.DateOffset(**time_step)) + + # Bin all satellite observations by timestep. If some observations + # fall outside the upper bin, label these with the highest bin + labels = bins_dt.astype("str") + time_steps = (pd.cut(ds.time.values, bins_dt, + labels=labels[:-1]).add_categories( + labels[-1]).fillna(labels[-1])) + + time_steps_var = xr.DataArray(time_steps, [("time", ds.time.values)], + name="timestep") + + # Resample data temporally into time steps, and compute geomedians + ds_geomedian = (ds.groupby(time_steps_var).apply( + lambda ds_subset: geomedian_with_mads( + ds_subset, compute_mads=False, compute_count=False))) + + print("\nGenerating geomedian composites and plotting " + "filmstrips... (click the Dashboard link above for status)") + ds_geomedian = ds_geomedian.compute() + + # Reset CRS that is lost during geomedian compositing + ds_geomedian = assign_crs(ds_geomedian, crs=ds.geobox.crs) + + ############ + # Plotting # + ############ + + # Convert to array and extract vmin/vmax + output_array = ds_geomedian[["red", "green", "blue"]].to_array() + percentiles = output_array.quantile(q=(0.02, 0.98)).values + + # Create the plot with one subplot more than timesteps in the + # dataset. Figure width is set based on the number of subplots + # and aspect ratio + n_obs = output_array.sizes["timestep"] + ratio = output_array.sizes["x"] / output_array.sizes["y"] + fig, axes = plt.subplots(1, + n_obs + 1, + figsize=(5 * ratio * (n_obs + 1), 5)) + fig.subplots_adjust(wspace=0.05, hspace=0.05) + + # Add timesteps to the plot, set aspect to equal to preserve shape + for i, ax_i in enumerate(axes.flatten()[:n_obs]): + output_array.isel(timestep=i).plot.imshow(ax=ax_i, + vmin=percentiles[0], + vmax=percentiles[1]) + ax_i.get_xaxis().set_visible(False) + ax_i.get_yaxis().set_visible(False) + ax_i.set_aspect("equal") + + # Add change heatmap panel to final subplot. Heatmap is computed + # by first taking the log of the array (so change in dark areas + # can be identified), then computing standard deviation between + # all timesteps + (np.log(output_array).std(dim=["timestep"]).mean( + dim="variable").plot.imshow(ax=axes.flatten()[-1], + robust=True, + cmap="magma", + add_colorbar=False)) + axes.flatten()[-1].get_xaxis().set_visible(False) + axes.flatten()[-1].get_yaxis().set_visible(False) + axes.flatten()[-1].set_aspect("equal") + axes.flatten()[-1].set_title("Change heatmap") + + # Export to file + date_string = "_".join(time_range) + ts_v = list(time_step.values())[0] + ts_k = list(time_step.keys())[0] + fig.savefig( + f"filmstrip_{output_name}_{date_string}_{ts_v}{ts_k}.png", + dpi=150, + bbox_inches="tight", + pad_inches=0.1, + ) + + # close dask client + client.shutdown() + + return ds_geomedian diff --git a/deafrica_tools/app/crophealth.py b/deafrica_tools/app/crophealth.py new file mode 100644 index 0000000..f6fd091 --- /dev/null +++ b/deafrica_tools/app/crophealth.py @@ -0,0 +1,337 @@ +# crophealth.py +''' +Functions for loading and interacting with data in the crop health notebook, + inside the Real_world_examples folder. +''' + +# Load modules + +# Force GeoPandas to use Shapely instead of PyGEOS +# In a future release, GeoPandas will switch to using Shapely by default. +import os +os.environ['USE_PYGEOS'] = '0' + +from ipyleaflet import ( + Map, + GeoJSON, + DrawControl, + basemaps +) +import datetime as dt +import datacube +from osgeo import ogr +import matplotlib as mpl +import matplotlib.pyplot as plt +import rasterio +from rasterio.features import geometry_mask +import xarray as xr +from IPython.display import display +import warnings +import ipywidgets as widgets +import json +import geopandas as gpd +from io import BytesIO + +# Load utility functions +from deafrica_tools.datahandling import load_ard +from deafrica_tools.spatial import xr_rasterize +from deafrica_tools.bandindices import calculate_indices + + +def load_crophealth_data(lat, lon, buffer, date): + """ + Loads Sentinel-2 analysis-ready data (ARD) product for the crop health + case-study area over the last two years. + Last modified: April 2020 + + Parameters + ---------- + lat: float + The central latitude to analyse + lon: float + The central longitude to analyse + buffer: + The number of square degrees to load around the central latitude and longitude. + For reasonable loading times, set this as `0.1` or lower. + date: + The most recent date to show data for. + The app will automatically load all data available for the two years prior to this date. + + Returns + ---------- + ds: xarray.Dataset + data set containing combined, masked data + Masked values are set to 'nan' + """ + + # Suppress warnings + warnings.filterwarnings('ignore') + + # Initialise the data cube. 'app' argument is used to identify this app + dc = datacube.Datacube(app='Crophealth-app') + + # Define area to load + latitude = (lat - buffer, lat + buffer) + longitude = (lon - buffer, lon + buffer) + + # Specify the date range + # Calculated as today's date, subtract 730 days to collect two years of data + # Dates are converted to strings as required by loading function below + end_date = dt.datetime.strptime(date, "%Y-%m-%d") + start_date = end_date - dt.timedelta(days=730) + + time = (start_date.strftime("%Y-%m-%d"), end_date.strftime("%Y-%m-%d")) + + # Construct the data cube query + products = ["s2_l2a"] + + query = { + 'x': longitude, + 'y': latitude, + 'time': time, + 'measurements': [ + 'red', + 'green', + 'blue', + 'nir', + 'swir_2' + ], + 'output_crs': 'EPSG:6933', + 'resolution': (-20, 20) + } + + # Load the data and mask out bad quality pixels + ds = load_ard(dc, products=products, min_gooddata=0.5, **query) + + # Calculate the normalised difference vegetation index (NDVI) across + # all pixels for each image. + # This is stored as an attribute of the data + ds = calculate_indices(ds, index='NDVI', satellite_mission='s2') + + # Return the data + return(ds) + + +def run_crophealth_app(ds, lat, lon, buffer): + """ + Plots an interactive map of the crop health case-study area and allows + the user to draw polygons. This returns a plot of the average NDVI value + in the polygon area. + Last modified: January 2020 + + Parameters + ---------- + ds: xarray.Dataset + data set containing combined, masked data + Masked values are set to 'nan' + lat: float + The central latitude corresponding to the area of loaded ds + lon: float + The central longitude corresponding to the area of loaded ds + buffer: + The number of square degrees to load around the central latitude and longitude. + For reasonable loading times, set this as `0.1` or lower. + """ + + # Suppress warnings + warnings.filterwarnings('ignore') + + # Update plotting functionality through rcParams + mpl.rcParams.update({'figure.autolayout': True}) + + # Define polygon bounds + latitude = (lat - buffer, lat + buffer) + longitude = (lon - buffer, lon + buffer) + + # Define the bounding box that will be overlayed on the interactive map + # The bounds are hard-coded to match those from the loaded data + geom_obj = { + "type": "Feature", + "properties": { + "style": { + "stroke": True, + "color": 'red', + "weight": 4, + "opacity": 0.8, + "fill": True, + "fillColor": False, + "fillOpacity": 0, + "showArea": True, + "clickable": True + } + }, + "geometry": { + "type": "Polygon", + "coordinates": [ + [ + [ + longitude[0], + latitude[0] + ], + [ + longitude[1], + latitude[0] + ], + [ + longitude[1], + latitude[1] + ], + [ + longitude[0], + latitude[1] + ], + [ + longitude[0], + latitude[0] + ] + ] + ] + } + } + + # Create a map geometry from the geom_obj dictionary + # center specifies where the background map view should focus on + # zoom specifies how zoomed in the background map should be + loadeddata_geometry = ogr.CreateGeometryFromJson(str(geom_obj['geometry'])) + loadeddata_center = [ + loadeddata_geometry.Centroid().GetY(), + loadeddata_geometry.Centroid().GetX() + ] + loadeddata_zoom = 16 + + # define the study area map + studyarea_map = Map( + center=loadeddata_center, + zoom=loadeddata_zoom, + basemap=basemaps.Esri.WorldImagery + ) + + # define the drawing controls + studyarea_drawctrl = DrawControl( + polygon={"shapeOptions": {"fillOpacity": 0}}, + marker={}, + circle={}, + circlemarker={}, + polyline={}, + ) + + # add drawing controls and data bound geometry to the map + studyarea_map.add_control(studyarea_drawctrl) + studyarea_map.add_layer(GeoJSON(data=geom_obj)) + + # Index to count drawn polygons + polygon_number = 0 + + # Define widgets to interact with + instruction = widgets.Output(layout={'border': '1px solid black'}) + with instruction: + print("Draw a polygon within the red box to view a plot of " + "average NDVI over time in that area.") + + info = widgets.Output(layout={'border': '1px solid black'}) + with info: + print("Plot status:") + + fig_display = widgets.Output(layout=widgets.Layout( + width="50%", # proportion of horizontal space taken by plot + )) + + with fig_display: + plt.ioff() + fig, ax = plt.subplots(figsize=(8, 6)) + ax.set_ylim([0, 1]) + + colour_list = plt.rcParams['axes.prop_cycle'].by_key()['color'] + + # Function to execute each time something is drawn on the map + def handle_draw(self, action, geo_json): + nonlocal polygon_number + + # Execute behaviour based on what the user draws + if geo_json['geometry']['type'] == 'Polygon': + + info.clear_output(wait=True) # wait=True reduces flicker effect + + # Save geojson polygon to io temporary file to be rasterized later + jsonData = json.dumps(geo_json) + binaryData = jsonData.encode() + io = BytesIO(binaryData) + io.seek(0) + + # Read the polygon as a geopandas dataframe + gdf = gpd.read_file(io) + gdf.crs = "EPSG:4326" + + # Convert the drawn geometry to pixel coordinates + xr_poly = xr_rasterize(gdf, ds.NDVI.isel(time=0), crs='EPSG:6933') + + # Construct a mask to only select pixels within the drawn polygon + masked_ds = ds.NDVI.where(xr_poly) + + masked_ds_mean = masked_ds.mean(dim=['x', 'y'], skipna=True) + colour = colour_list[polygon_number % len(colour_list)] + + # Add a layer to the map to make the most recently drawn polygon + # the same colour as the line on the plot + studyarea_map.add_layer( + GeoJSON( + data=geo_json, + style={ + 'color': colour, + 'opacity': 1, + 'weight': 4.5, + 'fillOpacity': 0.0 + } + ) + ) + + # add new data to the plot + xr.plot.plot( + masked_ds_mean, + marker='*', + color=colour, + ax=ax + ) + + # reset titles back to custom + ax.set_title("Average NDVI from Sentinel-2") + ax.set_xlabel("Date") + ax.set_ylabel("NDVI") + + # refresh display + fig_display.clear_output(wait=True) # wait=True reduces flicker effect + with fig_display: + display(fig) + + with info: + print("Plot status: polygon sucessfully added to plot.") + + # Iterate the polygon number before drawing another polygon + polygon_number = polygon_number + 1 + + else: + info.clear_output(wait=True) + with info: + print("Plot status: this drawing tool is not currently " + "supported. Please use the polygon tool.") + + # call to say activate handle_draw function on draw + studyarea_drawctrl.on_draw(handle_draw) + + with fig_display: + # TODO: update with user friendly something + display(widgets.HTML("")) + + # Construct UI: + # +-----------------------+ + # | instruction | + # +-----------+-----------+ + # | map | plot | + # | | | + # +-----------+-----------+ + # | info | + # +-----------------------+ + ui = widgets.VBox([instruction, + widgets.HBox([studyarea_map, fig_display]), + info]) + display(ui) diff --git a/deafrica_tools/app/deacoastlines.py b/deafrica_tools/app/deacoastlines.py new file mode 100644 index 0000000..2b00a48 --- /dev/null +++ b/deafrica_tools/app/deacoastlines.py @@ -0,0 +1,505 @@ +""" +Digital Earth Africa Coastline widget, which can be used to +interactively extract shoreline data using transects. +""" + +# Import required packages + +# Force GeoPandas to use Shapely instead of PyGEOS +# In a future release, GeoPandas will switch to using Shapely by default. +import os +os.environ['USE_PYGEOS'] = '0' + +import fiona +import sys +import datacube +import warnings +import matplotlib.pyplot as plt +from datacube.utils.geometry import CRS +from ipyleaflet import ( + WMSLayer, + basemaps, + basemap_to_tiles, + Map, + DrawControl, + WidgetControl, + LayerGroup, + LayersControl, + GeoData, +) +from traitlets import Unicode +from ipywidgets import ( + GridspecLayout, + Button, + Layout, + HBox, + VBox, + HTML, + Output, +) +import json +import geopandas as gpd +from io import BytesIO +import ipywidgets as widgets + +import deafrica_tools.app.widgetconstructors as deawidgets +from deafrica_tools.coastal import get_coastlines, transect_distances +from owslib.wms import WebMapService + +def make_box_layout(): + return Layout( + # border='solid 1px black', + margin='0px 10px 10px 0px', + padding='5px 5px 5px 5px', + width='100%', + height='100%', + ) + + +def create_expanded_button(description, button_style): + return Button( + description=description, + button_style=button_style, + layout=Layout(width="auto", height="auto"), + ) + + +class transect_app(HBox): + + def __init__(self): + super().__init__() + + ###################### + # INITIAL ATTRIBUTES # + ###################### + + self.output_name = "example_output" + self.export_csv = False + self.export_plot = False + self.product_list = [ + ("ESRI World Imagery", "none"), + ("Open Street Map", "open_street_map"), + ] + self.product = self.product_list[0][1] + self.mode_list = [('Distance', 'distance'), ('Width', 'width')] + self.mode = self.mode_list[0][1] + self.target = None + self.action = None + self.gdf_drawn = None + self.gdf_uploaded = None + + ################## + # HEADER FOR APP # + ################## + + # Create the Header widget + header_title_text = "

Digital Earth Africa Coastlines shoreline transect extraction

" + instruction_text = "Select parameters and draw a transect on the map to extract shoreline data. In distance mode, draw a transect line starting from land that crosses multiple shorelines.
In width mode, draw a transect line that intersects shorelines at least twice. Alternatively, upload an vector file to extract shoreline data for multiple existing transects." + self.header = deawidgets.create_html( + f"{header_title_text}

{instruction_text}

") + self.header.layout = make_box_layout() + + ##################################### + # HANDLER FUNCTION FOR DRAW CONTROL # + ##################################### + + # Define the action to take once something is drawn on the map + def update_geojson(target, action, geo_json): + + # Remove previously uploaded data if present + self.gdf_uploaded = None + fileupload_transects._counter = 0 + + # Get data from action + self.action = action + + # Convert data to geopandas + json_data = json.dumps(geo_json) + binary_data = json_data.encode() + io = BytesIO(binary_data) + io.seek(0) + gdf = gpd.read_file(io) + gdf.crs = "EPSG:4326" + + # Convert to WGS 84 / NSIDC EASE-Grid 2.0 Global and compute area + gdf_drawn_nsidc = gdf.copy().to_crs("EPSG:6933") + m2_per_km2 = 10**6 + area = gdf_drawn_nsidc.envelope.area.values[0] / m2_per_km2 + polyarea_label = 'Total area of DE Africa Coastlines data to extract' + polyarea_text = f"{polyarea_label}: {area:.2f} km2" + + # Test area size + if area <= 50000: + confirmation_text = ' (Area to extract falls within recommended limit; click "Extract shoreline data" to continue)' + self.header.value = header_title_text + polyarea_text + confirmation_text + self.gdf_drawn = gdf + else: + warning_text = ' (Area to extract is too large, please select a smaller transect)' + self.header.value = header_title_text + polyarea_text + warning_text + self.gdf_drawn = None + + ########################### + # WIDGETS FOR APP OUTPUTS # + ########################### + + self.status_info = Output(layout=make_box_layout()) + self.output_plot = Output(layout=make_box_layout()) + + ######################################### + # MAP WIDGET, DRAWING TOOLS, WMS LAYERS # + ######################################### + + # Create drawing tools + desired_drawtools = ['polyline'] + draw_control = deawidgets.create_drawcontrol(desired_drawtools) + + # Load DEACoastLines WMS + deacl_url = "https://geoserver.digitalearth.africa/geoserver/wms" + deacl_layer = "coastlines:DEAfrica_Coastlines" + deacoastlines = WMSLayer( + url=deacl_url, + layers=deacl_layer, + format='image/png', + transparent=True, + attribution='DE Africa Coastlines © 2022 Digital Earth Africa') + + # Begin by displaying an empty layer group, and update the group with desired WMS on interaction. + self.map_layers = LayerGroup(layers=(deacoastlines,)) + self.map_layers.name = 'Map Overlays' + + # Create map widget + self.m = deawidgets.create_map(map_center=(0.5273, 25.1367), + zoom_level=3, + basemap=basemaps.Esri.WorldImagery) + self.m.layout = make_box_layout() + + # Add tools to map widget + self.m.add_control(draw_control) + self.m.add_layer(self.map_layers) + + # Store current basemap for future use + self.basemap = self.m.basemap + + ############################ + # WIDGETS FOR APP CONTROLS # + ############################ + + # Create parameter widgets + text_output_name = deawidgets.create_inputtext(self.output_name, + self.output_name) + checkbox_csv = deawidgets.create_checkbox(self.export_csv, + 'Distance table (.csv)') + checkbox_plot = deawidgets.create_checkbox(self.export_plot, + 'Figure (.png)') + deaoverlay_dropdown = deawidgets.create_dropdown( + self.product_list, self.product_list[0][1]) + mode_dropdown = deawidgets.create_dropdown(self.mode_list, + self.mode_list[0][1]) + run_button = create_expanded_button("Extract shoreline data", "info") + fileupload_transects = widgets.FileUpload(accept='', multiple=True) + + #################################### + # UPDATE FUNCTIONS FOR EACH WIDGET # + #################################### + + # Run update functions whenever various widgets are changed. + text_output_name.observe(self.update_text_output_name, "value") + checkbox_csv.observe(self.update_checkbox_csv, "value") + checkbox_plot.observe(self.update_checkbox_plot, "value") + deaoverlay_dropdown.observe(self.update_deaoverlay, "value") + mode_dropdown.observe(self.update_mode, "value") + run_button.on_click(self.run_app) + draw_control.on_draw(update_geojson) + fileupload_transects.observe(self.update_fileupload_transects, "value") + + ################################## + # COLLECTION OF ALL APP CONTROLS # + ################################## + + parameter_selection = VBox([ + HTML("Output name:"), text_output_name, + HTML( + 'Transect extraction mode:
' + ), + mode_dropdown, + HTML("
Output files:"), + checkbox_plot, + checkbox_csv, + HTML( + "
Advanced
Upload a GeoJSON or ESRI " + "Shapefile (<5 mb) containing one or more transect lines."), + fileupload_transects + ]) + map_selection = VBox([ + HTML("
Map overlay:"), + deaoverlay_dropdown, + ]) + parameter_selection.layout = make_box_layout() + map_selection.layout = make_box_layout() + + ############################### + # SPECIFICATION OF APP LAYOUT # + ############################### + + # 0 1 2 3 4 5 6 7 8 9 + # --------------------------------------------- + # 0 | Header | Map sel. | + # --------------------------------------------- + # 1 | Params | | + # 2 | | | + # 3 | | | + # 4 | | Map | + # 5 | | | + # ---------- | + # 6 | Run | | + # --------------------------------------------- + # 7 | Status info | + # --------------------------------------------- + # 8 | | + # 9 | Output/figure | + # 10 | | + # 11 | ------------------------------------------| + + # Create the layout #[rowspan, colspan] + grid = GridspecLayout(12, 10, height="1350px", width="auto") + + # Header and controls + grid[0, :8] = self.header + grid[0, 8:] = map_selection + grid[1:6, 0:2] = parameter_selection + grid[6, 0:2] = run_button + + # Status info, map and plot + grid[1:7, 2:] = self.m # map + grid[7:8, :] = self.status_info + grid[8:, :] = self.output_plot + + # Display using HBox children attribute + self.children = [grid] + + ###################################### + # DEFINITION OF ALL UPDATE FUNCTIONS # + ###################################### + + # Set the output csv + def update_fileupload_transects(self, change): + + # Clear any drawn data if present + self.gdf_drawn = None + + # Save to file + for uploaded_filename in change.new.keys(): + with open(uploaded_filename, "wb") as output_file: + content = change.new[uploaded_filename]['content'] + output_file.write(content) + + with self.status_info: + + try: + + print('Loading vector data...', end='\r') + valid_files = [ + file for file in change.new.keys() + if file.lower().endswith(('.shp', '.geojson')) + ] + valid_file = valid_files[0] + transect_gdf = (gpd.read_file(valid_file).to_crs( + "EPSG:4326").explode().reset_index(drop=True)) + + # Use ID column if it exists + if 'id' in transect_gdf: + transect_gdf = transect_gdf.set_index('id') + print(f"Uploaded '{valid_file}'; automatically labelling " + "transects using column 'id'.") + else: + print( + f"Uploaded '{valid_file}'; no 'id' column detected, " + f"labelling transects from 0 to {len(transect_gdf.index) - 1}." + ) + + # Create a geodata + geodata = GeoData(geo_dataframe=transect_gdf, + style={ + 'color': 'black', + 'weight': 3 + }) + + # Add to map + xmin, ymin, xmax, ymax = transect_gdf.total_bounds + self.m.fit_bounds([[ymin, xmin], [ymax, xmax]]) + self.m.add_layer(geodata) + + # If completed, add to attribute + self.gdf_uploaded = transect_gdf + + except IndexError: + print( + "Cannot read uploaded files. Please ensure that data is " + "in either GeoJSON or ESRI Shapefile format.", + end='\r') + self.gdf_uploaded = None + + except fiona.errors.DriverError: + print( + "Shapefile is invalid. Please ensure that all shapefile " + "components (e.g. .shp, .shx, .dbf, .prj) are uploaded.", + end='\r') + self.gdf_uploaded = None + + # Set output name + def update_text_output_name(self, change): + self.output_name = change.new + + # Output CSV + def update_checkbox_csv(self, change): + self.export_csv = change.new + + # Output plot + def update_checkbox_plot(self, change): + self.export_plot = change.new + + # Set mode + def update_mode(self, change): + self.mode = change.new + + # Update product + def update_deaoverlay(self, change): + + self.product = change.new + + # Load DE Africa CoastLines WMS + deacl_url = "https://geoserver.digitalearth.africa/geoserver/wms" + deacl_layer = "coastlines:DEAfrica_Coastlines" + deacoastlines = WMSLayer( + url=deacl_url, + layers=deacl_layer, + format="image/png", + transparent=True, + attribution="DE Africa Coastlines © 2022 Digital Earth Africa") + + if self.product == "none": + self.map_layers.clear_layers() + self.map_layers.add_layer(deacoastlines) + + elif self.product == "open_street_map": + self.map_layers.clear_layers() + layer = basemap_to_tiles(basemaps.OpenStreetMap.Mapnik) + self.map_layers.add_layer(layer) + self.map_layers.add_layer(deacoastlines) + + def run_app(self, change): + + # Clear progress bar and output areas before running + self.status_info.clear_output() + self.output_plot.clear_output() + + # Run DE Africa Coastlines analysis + with self.status_info: + warnings.filterwarnings("ignore") + + # Load transects from either map or uploaded files + if self.gdf_uploaded is not None: + transect_gdf = self.gdf_uploaded + run_text = 'uploaded file' + elif self.gdf_drawn is not None: + transect_gdf = self.gdf_drawn + transect_gdf.index = [self.output_name] + run_text = 'selected transect' + else: + print(f'No transect drawn or uploaded. Please select a transect on the map, or upload a GeoJSON or ESRI Shapefile.', + end='\r') + transect_gdf = None + + # If valid data was returned, load DEA Coastlines data + if transect_gdf is not None: + + # Load Coastlines data from WFS + deacl_gdf = get_coastlines(bbox=transect_gdf) + + # Test that data was correctly returned + if len(deacl_gdf.index) > 0: + + # Dissolve by year to remove duplicates, then sort by date + deacl_gdf = deacl_gdf.dissolve(by='year', as_index=False) + deacl_gdf['year'] = deacl_gdf.year.astype(int) + deacl_gdf = deacl_gdf.sort_values('year') + deacl_gdf = deacl_gdf.set_index('year') + + else: + print( + "No annual shoreline data was found near the " + "supplied transect. Please draw or select a new " + "transect.", + end='\r') + deacl_gdf = None + + # If valid DEA Coastlines data returned, calculate distances + if deacl_gdf is not None: + print(f'Analysing transect distances using "{self.mode}" mode...', + end='\r') + dist_df = transect_distances( + transect_gdf.to_crs("EPSG:6933"), + deacl_gdf.to_crs("EPSG:6933"), + mode=self.mode) + + # If valid data was produced: + if dist_df.any(axis=None): + + # Successful output + print(f'DE Africa Coastlines data successfully extracted for {run_text}.') + + # Export distance data + if self.export_csv: + + # Create folder if required and set path + out_dir = 'deacoastlines_outputs' + os.makedirs(out_dir, exist_ok=True) + csv_filename = f"{out_dir}/{self.output_name}.csv" + + # Export to file + dist_df.to_csv(csv_filename, index_label="Transect") + print(f'Distance data exported to "{csv_filename}".') + + # Generate plot + with self.output_plot: + + fig, ax = plt.subplots(constrained_layout=True, + figsize=(15, 5.5)) + dist_df.T.plot(ax=ax, linewidth=3) + + ax.legend(frameon=False, ncol=3, title='Transect') + ax.set_title(f"Digital Earth Africa Coastlines transect extraction - {self.output_name}") + ax.set_ylabel(f"Along-transect {self.mode} (m)") + ax.set_xlim(dist_df.T.index[0], dist_df.T.index[-1]) + + # Hide the right and top spines + ax.spines['right'].set_visible(False) + ax.spines['top'].set_visible(False) + + # Only show ticks on the left and bottom spines + ax.yaxis.set_ticks_position('left') + ax.xaxis.set_ticks_position('bottom') + plt.show() + + # Export plot + with self.status_info: + if self.export_plot: + + # Create folder if required and set path + out_dir = 'deacoastlines_outputs' + os.makedirs(out_dir, exist_ok=True) + figure_filename = f"{out_dir}/{self.output_name}.png" + + # Export to file + fig.savefig(figure_filename) + print(f'Figure exported to "{figure_filename}".') + + else: + print( + "No valid shoreline data intersects with the " + "supplied transect. This can occur if:\n\n" + " - the transect does not intersect with any shorelines\n" + " - the transect intersects with shorelines more than once in 'distance' mode\n" + " - the transect intersects with shorelines only once in 'width' mode\n\n" + "Please draw or upload a new transect.", + end='\r') \ No newline at end of file diff --git a/deafrica_tools/app/forestmonitoring.py b/deafrica_tools/app/forestmonitoring.py new file mode 100644 index 0000000..4e99243 --- /dev/null +++ b/deafrica_tools/app/forestmonitoring.py @@ -0,0 +1,1026 @@ +''' +Functions for loading and interacting with Global Forest Change data in the forest monitoring notebook, inside the Real_world_examples folder. +''' + +# Import required packages + +# Force GeoPandas to use Shapely instead of PyGEOS +# In a future release, GeoPandas will switch to using Shapely by default. +import os +os.environ['USE_PYGEOS'] = '0' + +import json +import warnings +from io import BytesIO + +import deafrica_tools.app.widgetconstructors as deawidgets +import geopandas as gpd +import ipywidgets as widgets +import matplotlib.colors as mcolors +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import rioxarray +import xarray as xr +from deafrica_tools.dask import create_local_dask_cluster +from deafrica_tools.spatial import xr_rasterize +from ipyleaflet import ( + DrawControl, + GeoData, + LayerGroup, + LayersControl, + Map, + WidgetControl, + WMSLayer, + basemap_to_tiles, + basemaps, +) +from ipywidgets import HTML, Button, GridspecLayout, HBox, Layout, Output, VBox +from matplotlib.patches import Patch +from traitlets import Unicode + +# Turn off all warnings. +warnings.filterwarnings("ignore") +warnings.simplefilter("ignore") + +def make_box_layout(): + """ + Defines a number of CSS properties that impact how a widget is laid out. + """ + return Layout( # border='solid 1px black', + margin="0px 10px 10px 0px", + padding="5px 5px 5px 5px", + width="100%", + height="100%", + ) + +def create_expanded_button(description, button_style): + """ + Defines a number of CSS properties to create a button to handle mouse clicks. + """ + return Button( + description=description, + button_style=button_style, + layout=Layout(width="auto", height="auto"), + ) + +def load_gfclayer(gdf_drawn, gfclayer): + """ + Loads the selected Global Forest Change layer for the + area drawn on the map widget. + """ + # Configure local dask cluster. + client = create_local_dask_cluster(return_client=True, display_client=True) + + # Get the coordinates of the top-left corner for each Global Forest Change tile, + # covering the area of interest. + min_lat, max_lat = ( + gdf_drawn.bounds.miny.item(), + gdf_drawn.bounds.maxy.item(), + ) + min_lon, max_lon = ( + gdf_drawn.bounds.minx.item(), + gdf_drawn.bounds.maxx.item(), + ) + + lats = np.arange( + np.floor(min_lat / 10) * 10, np.ceil(max_lat / 10) * 10, 10 + ).astype(int) + lons = np.arange( + np.floor(min_lon / 10) * 10, np.ceil(max_lon / 10) * 10, 10 + ).astype(int) + + coord_list = [] + for lat in lats: + lat = lat + 10 + if lat >= 0: + lat_str = f"{lat:02d}N" + else: + lat_str = f"{abs(lat):02d}S" + for lon in lons: + if lon >= 0: + lon_str = f"{lon:03d}E" + else: + lon_str = f"{abs(lon):03d}W" + coord_str = f"{lat_str}_{lon_str}" + coord_list.append(coord_str) + + # Load each Global Forest Change tile covering the area of interest. + base_url = f"https://storage.googleapis.com/earthenginepartners-hansen/GFC-2021-v1.9/Hansen_GFC-2021-v1.9_{gfclayer}_" + dask_chunks = dict(x=2048, y=2048) + + tile_list = [] + for coord in coord_list: + tile_url = f"{base_url}{coord}.tif" + # Load the tile as an xarray.DataArray. + tile = rioxarray.open_rasterio(tile_url, chunks=dask_chunks).squeeze() + tile_list.append(tile) + + # Merge the tiles into a single xarray.DataArray. + ds = xr.combine_by_coords(tile_list) + # Clip the dataset using the bounds of the area of interest. + ds = ds.rio.clip_box( + minx=min_lon - 0.00025, + miny=min_lat - 0.00025, + maxx=max_lon + 0.00025, + maxy=max_lat + 0.00025, + ) + # Rename the y and x variables for DEA convention on xarray.DataArrays where crs="EPSG:4326". + ds = ds.rename({"y": "latitude", "x": "longitude"}) + + # Mask pixels representing no loss (encoded as 0) in the "lossyear" layer. + if gfclayer == "lossyear": + ds = ds.where(ds != 0) + # Mask pixels representing no gain (encoded as 0) in the "gain" layer. + elif gfclayer == "gain": + ds = ds.where(ds != 0) + # Mask pixels with 0 percentage tree canopy cover. + elif gfclayer == "treecover2000": + ds = ds.where(ds != 0) + + # Create a mask from the area of interest GeoDataFrame. + mask = xr_rasterize(gdf_drawn, ds) + # Mask the dataset. + ds = ds.where(mask) + # Convert the xarray.DataArray to a dataset. + ds = ds.to_dataset(name=gfclayer) + # Compute. + ds = ds.compute() + # Assign the "EPSG:4326" CRS to the dataset. + ds.rio.write_crs(4326, inplace=True) + ds = ds.transpose("latitude", "longitude") + + # Close down the dask client. + client.close() + return ds + +def load_all_gfclayers(gdf_drawn): + gfclayers = ["treecover2000", "gain", "lossyear"] + + dataset_list = [] + for layer in gfclayers: + ds = load_gfclayer(gdf_drawn, gfclayer=layer) + dataset_list.append(ds) + + dataset = xr.merge(dataset_list) + return dataset + +def get_gfclayer_treecover2000(gfclayer_ds, gfclayer="treecover2000"): + """ + Preprocess the Global Forest change "treecover2020" layer. + """ + ds = gfclayer_ds[gfclayer] + + # Check if the dataarray is empty. + condition = ds.isnull().all().item() + + if condition: + return None + else: + # Mask the dataset. + mask = np.isnan(ds) + ds_masked = ds.where(mask, 1) + + # Get the pixel count for each unique pixel value in the layer. + counts = np.unique(ds_masked, return_counts=True) + # Remove the counts for pixels with the value np.nan. + index = np.argwhere(np.isnan(counts[0])) + counts_dict = dict( + zip(np.delete(counts[0], index), np.delete(counts[1], index)) + ) + + # Reproject the dataset to EPSG:6933 which uses metres + ds_reprojected = ds_masked.rio.reproject("EPSG:6933") + # Get the area per pixel. + pixel_length = ds_reprojected.geobox.resolution[1] + m_per_km = 1000 + per_pixel_area = (pixel_length / m_per_km) ** 2 + + # Save the results as a pandas DataFrame. + df = pd.DataFrame( + data={ + "Year": ["2000"], + "Tree Cover in km$^2$": np.fromiter(counts_dict.values(), dtype=float) + * per_pixel_area, + } + ) + + # Get the total area. + print_statement = f'Total Forest Cover in {df["Year"].item()}: {round(df["Tree Cover in km$^2$"].item(), 4)} km2' + + # File name to use when exporting results. + file_name = f"forest_cover_in_2000" + + return ds, df, print_statement, file_name + +def get_gfclayer_gain(gfclayer_ds, gfclayer="gain"): + """ + Preprocess the Global Forest Change "gain" layer. + """ + ds = gfclayer_ds[gfclayer] + + # Check if the dataarray is empty. + condition = ds.isnull().all().item() + + if condition: + return None + else: + # Get the pixel count for each unique pixel value in the layer. + counts = np.unique(ds, return_counts=True) + # Remove the counts for pixels with the value np.nan. + index = np.argwhere(np.isnan(counts[0])) + counts_dict = dict( + zip(np.delete(counts[0], index), np.delete(counts[1], index)) + ) + + # Reproject the dataset to EPSG:6933 which uses metres. + ds_reprojected = ds.rio.reproject("EPSG:6933") + # Get the area per pixel. + pixel_length = ds_reprojected.geobox.resolution[1] + m_per_km = 1000 + per_pixel_area = (pixel_length / m_per_km) ** 2 + + # Save the results as a pandas DataFrame. + df = pd.DataFrame( + data={ + "Year": ["2000-2012"], + "Forest Cover Gain in km$^2$": np.fromiter( + counts_dict.values(), dtype=float + ) + * per_pixel_area, + } + ) + + # Get the total area. + print_statement = f'Total Forest Cover Gain {df["Year"].item()}: {round(df["Forest Cover Gain in km$^2$"].item(), 4)} km2' + + # File name to use when exporting results. + file_name = f"forest_cover_gain_from_2000_to_2012" + + return ds, df, print_statement, file_name + +def get_gfclayer_lossyear(gfclayer_ds, start_year, end_year, gfclayer="lossyear"): + """ + Preprocess the Global Forest Change "lossyear" layer. + """ + + ds = gfclayer_ds[gfclayer] + + # Mask the dataset to the selected time range. + selected_years = list(range(start_year, end_year + 1)) + mask = ds.isin(selected_years) + ds = ds.where(mask) + + # Check if the dataarray is empty. + condition = ds.isnull().all().item() + + if condition: + return None + else: + # Get the pixel count for each unique pixel value in the layer. + counts = np.unique(ds, return_counts=True) + # Remove the counts for pixels with the value np.nan. + index = np.argwhere(np.isnan(counts[0])) + counts_dict = dict( + zip(np.delete(counts[0], index), np.delete(counts[1], index)) + ) + + # Reproject the dataset to EPSG:6933 which uses metres + ds_reprojected = ds.rio.reproject("EPSG:6933") + # Get the area per pixel. + pixel_length = ds_reprojected.geobox.resolution[1] + m_per_km = 1000 + per_pixel_area = (pixel_length / m_per_km) ** 2 + + # For each year get the area of loss. + # Save the results as a pandas DataFrame. + df = pd.DataFrame( + { + "Year": 2000 + np.fromiter(counts_dict.keys(), dtype=int), + "Forest Cover Loss in km$^2$": np.fromiter( + counts_dict.values(), dtype=float + ) + * per_pixel_area, + } + ) + + # Get the total area. + print_statement = f'Total Forest Cover Loss from {start_year + 2000} to {end_year + 2000}: {round(df["Forest Cover Loss in km$^2$"].sum(), 4)} km2' + + # File name to use when exporting results. + file_name = f"forest_cover_loss_from_{start_year + 2000}_to_{end_year + 2000}" + + return ds, df, print_statement, file_name + +def plot_gfclayer_treecover2000(gfclayer_ds, gfclayer="treecover2000"): + """ + Plot the Global Forest Change "treecover2000" layer. + """ + + if get_gfclayer_treecover2000(gfclayer_ds) is None: + print( + f"No Global Forest Change {gfclayer} layer data found in the selected area. Please select a new polygon over an area with data." + ) + else: + ds, df, print_statement, file_name = get_gfclayer_treecover2000(gfclayer_ds) + + # Export the dataframe as a csv. + df.to_csv(f"{file_name}.csv", index=False) + print(f'Table exported to "{file_name}.csv"') + + # Define the plotting parameters. + figure_width = 10 + figure_length = 10 + title = f"Tree Canopy Cover for the Year 2000" + + # Plot the dataset. + fig, ax = plt.subplots(figsize=(figure_width, figure_length)) + im = ds.plot(cmap="Greens", add_colorbar=False, ax=ax) + # Add a colorbar to the plot. + cbar = plt.colorbar(mappable=im) + cbar.set_label( + "Percentage tree canopy cover for year 2000", labelpad=-65, y=0.25 + ) + # Add a title to the plot. + plt.title(title) + # Save the plot. + plt.savefig(f"{file_name}.png") + print(f'Figure exported to "{file_name}.png"') + plt.show() + + print(print_statement) + +def plot_gfclayer_gain(gfclayer_ds, gfclayer="gain"): + """ + Plot the Global Forest Change "gain" layer. + """ + + if get_gfclayer_gain(gfclayer_ds) is None: + print( + f"No Global Forest Change {gfclayer} layer data found in the selected area. Please select a new polygon over an area with data." + ) + else: + ds, df, print_statement, file_name = get_gfclayer_gain(gfclayer_ds) + + # Export the dataframe as a csv. + df.to_csv(f"{file_name}.csv", index=False) + print(f'Table exported to "{file_name}.csv"') + + # Define the plotting parameters. + color = "#6CAE75" + figure_width = 10 + figure_length = 10 + title = f"Forest Cover Gain from 2000 to 2012" + + # Plot the dataset. + fig, ax = plt.subplots(figsize=(figure_width, figure_length)) + im = ds.plot(cmap=mcolors.ListedColormap([color]), add_colorbar=False, ax=ax) + # Add a legend to the plot. + im.axes.legend( + [Patch(facecolor=color)], + ["Global forest cover gain 2000–2012"], + loc="lower left", + bbox_to_anchor=(1.0, 0.5), + frameon=False, + ) + # Add a title to the plot. + plt.title(title) + # Save the plot. + plt.savefig(f"{file_name}.png") + print(f'Figure exported to "{file_name}.png"') + plt.show() + + print(print_statement) + +def plot_gfclayer_lossyear(gfclayer_ds, start_year, end_year, gfclayer="lossyear"): + """ + Plot the Global Forest change "lossyear" layer. + """ + + if ( + get_gfclayer_lossyear(gfclayer_ds, start_year, end_year, gfclayer="lossyear") + is None + ): + print( + f"No Global Forest Change {gfclayer} layer data found in the selected area. Please select a new polygon over an area with data." + ) + else: + ds, df, print_statement, file_name = get_gfclayer_lossyear( + gfclayer_ds, start_year, end_year, gfclayer="lossyear" + ) + + # Export the dataframe as a csv. + df.to_csv(f"{file_name}.csv", index=False) + print(f'Table exported to "{file_name}.csv"') + + # Define the plotting parameters. + figure_width = 10 + figure_length = 15 + nrows = 2 + ncols = 1 + title = f"Forest Cover Loss from {start_year + 2000} to {end_year + 2000}" + + # Location of transition from one color to the next on the colormap. + color_levels = list(np.arange(1 - 0.5, 22, 1)) + # Ticks to be displayed. + ticks = list(np.arange(1, 22)) + tick_labels = list(2000 + np.arange(1, 22)) + + # Define the color map to use when plotting. + color_list = [ + "#e6194b", + "#3cb44b", + "#ffe119", + "#4363d8", + "#f58231", + "#911eb4", + "#46f0f0", + "#f032e6", + "#bcf60c", + "#fabebe", + "#008080", + "#e6beff", + "#9a6324", + "#fffac8", + "#800000", + "#aaffc3", + "#808000", + "#ffd8b1", + "#000075", + "#808080", + "#7A306C", + ] + cmap = mcolors.ListedColormap(colors=color_list, N=21) + norm = mcolors.BoundaryNorm(boundaries=color_levels, ncolors=cmap.N) + + # Plot the dataset. + fig, (ax1, ax2) = plt.subplots( + nrows, ncols, figsize=(figure_width, figure_length) + ) + im = ds.plot(ax=ax1, cmap=cmap, norm=norm, add_colorbar=False) + # Add a title to the subplot. + ax1.set_title(title) + # Add a colorbar to the subplot. + cbar = plt.colorbar(mappable=im, ticks=ticks) + cbar.set_label("Year of gross forest cover loss event", labelpad=-60, y=0.25) + cbar.set_ticklabels(tick_labels) + # Plot the second subplot. + df.plot( + x="Year", + y="Forest Cover Loss in km$^2$", + ylabel="Forest Cover Loss in km$^2$", + title=title, + ax=ax2, + ) + # Save the plot. + plt.savefig(f"{file_name}.png") + print(f'Figure exported to "{file_name}.png"') + plt.show() + + print(print_statement) + +def plot_gfclayer_all(gfclayer_ds, start_year, end_year): + """ + Plot all the Global Forest Change Layers loaded. + """ + + # Define the plotting parameters. + figure_width = 10 + figure_length = 10 + treecover_color = "Greens" + gain_color = "yellow" + lossyear_color = "red" + + print_statement_list = [] + filename_list = ["\nTables exported as: "] + + figure_fn = "global_forest_change_all_layers.png" + + # Define the figure. + fig, ax = plt.subplots(figsize=(figure_width, figure_length)) + if ( + get_gfclayer_treecover2000( + gfclayer_ds[["treecover2000"]], gfclayer="treecover2000" + ) + is None + ): + print( + f"No Global Forest Change 'treecover2000' layer data found in the selected area. Please select a new polygon over an area with data." + ) + else: + ( + ds_treecover2000, + df_treecover2000, + print_statement_treecover2000, + file_name_treecover2000, + ) = get_gfclayer_treecover2000( + gfclayer_ds[["treecover2000"]], gfclayer="treecover2000" + ) + # Plot the treecover2000 layer as the background layer. + background = ds_treecover2000.plot( + cmap=treecover_color, add_colorbar=False, ax=ax + ) + # Add a colorbar to the treecover2000 plot. + cbar = plt.colorbar(mappable=background) + cbar.set_label( + "Percentage tree canopy cover for year 2000", labelpad=-65, y=0.25 + ) + # Export the dataframe as a csv. + df_treecover2000.to_csv(f"{file_name_treecover2000}.csv", index=False) + # Add the print statement to the list. + print_statement_list.append(print_statement_treecover2000) + # Add the file name to the list. + filename_list.append(f'"{file_name_treecover2000}.csv"') + + if get_gfclayer_gain(gfclayer_ds[["gain"]], gfclayer="gain") is None: + print( + f"No Global Forest Change 'gain' layer data found in the selected area. Please select a new polygon over an area with data." + ) + else: + ds_gain, df_gain, print_statement_gain, file_name_gain = get_gfclayer_gain( + gfclayer_ds[["gain"]], gfclayer="gain" + ) + # Plot the gain layer. + ds_gain.plot( + ax=ax, cmap=mcolors.ListedColormap([gain_color]), add_colorbar=False + ) + # Export the dataframe as a csv. + df_gain.to_csv(f"{file_name_gain}.csv", index=False) + # Add the print statement to the list. + print_statement_list.append(print_statement_gain) + # Add the file name to the list. + filename_list.append(f'"{file_name_gain}.csv"') + + if ( + get_gfclayer_lossyear( + gfclayer_ds[["lossyear"]], start_year, end_year, gfclayer="lossyear" + ) + is None + ): + print( + f"No Global Forest Change 'lossyear' layer data found in the selected area. Please select a new polygon over an area with data." + ) + else: + ( + ds_lossyear, + df_lossyear, + print_statement_lossyear, + file_name_lossyear, + ) = get_gfclayer_lossyear( + gfclayer_ds[["lossyear"]], start_year, end_year, gfclayer="lossyear" + ) + # Plot the lossyear layer. + ds_lossyear.plot( + ax=ax, cmap=mcolors.ListedColormap([lossyear_color]), add_colorbar=False + ) + # Export the dataframe as a csv. + df_lossyear.to_csv(f"{file_name_lossyear}.csv", index=False) + # Add the print statement to the list. + print_statement_list.append(print_statement_lossyear) + # Add the file name to the list. + filename_list.append(f'"{file_name_lossyear}.csv"') + + # Add a legend to the plot. + ax.legend( + [Patch(facecolor=gain_color), Patch(facecolor=lossyear_color)], + [ + "Global forest cover \n gain 2000–2012", + f"Global forest cover \n loss {str(2000+start_year)}-{str(2000+end_year)}", + ], + loc="lower right", + bbox_to_anchor=(-0.1, 0.75), + frameon=False, + ) + + plt.title("Global Forest Change Layers") + plt.savefig(figure_fn) + plt.show() + print(*print_statement_list, sep="\n") + print(*filename_list, sep="\n\t") + print(f'\nFigure saved as "{figure_fn}"'); + +def plot_gfclayer(gfclayer_ds, start_year, end_year, gfclayer): + if gfclayer == "treecover2000": + plot_gfclayer_treecover2000(gfclayer_ds, gfclayer) + elif gfclayer == "lossyear": + plot_gfclayer_lossyear(gfclayer_ds, start_year, end_year, gfclayer) + elif gfclayer == "gain": + plot_gfclayer_gain(gfclayer_ds, gfclayer) + elif gfclayer == "alllayers": + plot_gfclayer_all(gfclayer_ds, start_year, end_year) + +def update_map_layers(self): + """ + Updates map widget to add new basemap when selected + using menu options. + """ + # Clear data load parameters to trigger data reload. + self.gfclayer_ds = None + + # Remove all layers from the map_layers Layers Group. + self.map_layers.clear_layers() + # Add the selected basemap to the layer Group. + self.map_layers.add_layer(self.basemap) + +class forest_monitoring_app(HBox): + def __init__(self): + super().__init__() + + ################## + # HEADER FOR APP # + ################## + + # Create the header widget. + header_title_text = "

Digital Earth Africa Forest Change

" + instruction_text = """

Select the desired Global Forest Change layer, then zoom in and draw a polygon to + select an area for which to plot the selected Global Forest Change layer. Alternatively, upload a vector file of the area of interest.

""" + self.header = deawidgets.create_html( + value=f"{header_title_text}{instruction_text}" + ) + self.header.layout = make_box_layout() + + ############################ + # WIDGETS FOR APP CONTROLS # + ############################ + + ## Selection widget for selecting the basemap to use for the map widget. + ## and when plotting the Global Forest Change Layer. + # Basemaps available for selection for the map widget. + self.basemap_list = [ + ("Open Street Map", basemap_to_tiles(basemaps.OpenStreetMap.Mapnik)), + ("ESRI World Imagery", basemap_to_tiles(basemaps.Esri.WorldImagery)), + ] + # Set the default basemap to be used for the map widget / initial value for the widget. + self.basemap = self.basemap_list[0][1] + # Dropdown selection widget. + dropdown_basemap = deawidgets.create_dropdown( + options=self.basemap_list, value=self.basemap + ) + # Register the update function to run when a new value is selected + # on the dropdown_basemap widget. + dropdown_basemap.observe(self.update_basemap, "value") + # Text to accompany the dropdown selection widget. + basemap_selection_html = deawidgets.create_html( + value=f"
Map overlay:" + ) + # Combine the basemap_selection_html text and the dropdown_basemap widget in a single container. + basemap_selection = VBox([basemap_selection_html, dropdown_basemap]) + + ## Selection widget for selecting the Global Forest change layer to plot. + # Global Forest Change layers available plotting. + self.gfclayers_list = [ + ("Year of gross forest cover loss event", "lossyear"), + ("Global forest cover gain 2000–2012", "gain"), + ("Tree canopy cover for the year 2000", "treecover2000"), + ("All layers", "alllayers"), + ] + # Set the default GFC layer to be plotted / initial value for the widget. + self.gfclayer = self.gfclayers_list[0][1] + + ## Selection widget for the data time range. + # Set the default time range for which to load data for. + self.start_year = 1 + self.end_year = 21 + + # Create the time range selector. + time_range = list(range(self.start_year, self.end_year + 1)) + time_range_str = [str(2000 + i) for i in time_range] + + timerange_options = tuple(zip(time_range_str, time_range)) + timerange_selection_slide = widgets.SelectionRangeSlider( + options=timerange_options, + value=(self.start_year, self.end_year), + description="", + disabled=False, + ) + # Register the update function to run when a new value is selected on the slider. + timerange_selection_slide.observe(self.update_timerange, "value") + # Text to accompany the timerange_selection widget. + timerange_selection_html = deawidgets.create_html( + value=f"
Forest Cover Loss Time Range:" + ) + # Combine the timerange_selection_text and the timerange_selection_slide in a single container. + timerange_selection = VBox( + [timerange_selection_html, timerange_selection_slide] + ) + + # Set the initial parameter for the GFC layer dataset. + self.gfclayer_ds = None + # Dropdown selection widget. + dropdown_gfclayer = deawidgets.create_dropdown( + options=self.gfclayers_list, value=self.gfclayer + ) + # Register the update function to run when a new value is selected + # on the dropdown_gfclayer widget. + dropdown_gfclayer.observe(self.update_gfclayer, "value") + # Text to accompany the dropdown selection widget. + gfclayer_selection_html = deawidgets.create_html( + value=f"
Global Forest Change Layer:" + ) + # Combine the gfclayer_selection_html text and the dropdown_gfclayer widget in a single container. + gfclayer_selection = VBox([gfclayer_selection_html, dropdown_gfclayer]) + + ## Add a checkbox for whether to overide the limit to the size of polygon drawn on the + ## map widget. + # Initial value of the widget. + self.max_size = False + # CheckBox widget. + checkbox_max_size = deawidgets.create_checkbox( + value=self.max_size, description="Enable", layout={"width": "95%"} + ) + # Text to accompany the CheckBox widget. + checkbox_max_size_html = deawidgets.create_html( + value=f"""
Override maximum size limit: + (use with caution; may cause memory issues/crashes)""" + ) + # Register the update function to run when the checkbox is ticked. + # on the checkbox_max_size CheckBox + checkbox_max_size.observe(self.update_checkbox_max_size, "value") + # # Combine the checkbox_max_size_html text and the checkbox_max_size widget in a single container. + enable_max_size = VBox([checkbox_max_size_html, checkbox_max_size]) + + # Add widget to enable uploading a geojson or ESRI shapefile. + self.gdf_uploaded = None + fileupload_aoi = widgets.FileUpload(accept="", multiple=True) + # Register the update function to be called for the file upload. + fileupload_aoi.observe(self.update_fileupload_aoi, "value") + fileupload_html = deawidgets.create_html(value=f"""
Advanced
Upload a GeoJSON or ESRI Shapefile (<5 mb) containing a single area of interest.""") + fileupload = VBox([fileupload_html, fileupload_aoi]) + + + ## Put the app controls widgets into a single container. + parameter_selection = VBox( + [ + basemap_selection, + gfclayer_selection, + timerange_selection, + enable_max_size, + fileupload + ] + ) + parameter_selection.layout = make_box_layout() + + ## Button to click to run the app. + run_button = create_expanded_button( + description="Generate plot", button_style="info" + ) + # Register the update function to be called when the run_button button + # is clicked. + run_button.on_click(self.run_app) + + + + ########################### + # WIDGETS FOR APP OUTPUTS # + ########################### + + self.status_info = Output(layout=make_box_layout()) + self.output_plot = Output(layout=make_box_layout()) + + ################################# + # MAP WIDGET WITH DRAWING TOOLS # + ################################# + + # Create the map widget. + self.m = deawidgets.create_map( + map_center=(-18.45, 28.93), + zoom_level=11, + ) + self.m.layout = make_box_layout() + + # Create an empty Layer Group. + self.map_layers = LayerGroup(layers=()) + # Name of the Layer Group layer. + self.map_layers.name = "Map Overlays" + # Add the empty Layer Group as a single layer to the map widget. + self.m.add_layer(self.map_layers) + + # Create the desired drawing tools. + desired_drawtools = ["rectangle", "polygon"] + draw_control = deawidgets.create_drawcontrol(desired_drawtools) + # Add drawing tools to the map widget. + self.m.add_control(draw_control) + # Set the initial parameters for the drawing tools. + self.target = None + self.action = None + self.gdf_drawn = None + + ##################################### + # HANDLER FUNCTION FOR DRAW CONTROL # + ##################################### + + def handle_draw(target, action, geo_json): + + """ + Defines the action to take once something is drawn on the + map widget. + """ + # Remove previously uploaded data if present + self.gdf_uploaded = None + fileupload_aoi._counter = 0 + + self.target = target + self.action = action + + # Clear data load parameters to trigger data reload. + self.gfclayer_ds = None + + # Convert the drawn polygon geojson to a GeoDataFrame. + json_data = json.dumps(geo_json) + binary_data = json_data.encode() + io = BytesIO(binary_data) + io.seek(0) + gdf = gpd.read_file(io) + gdf.crs = "EPSG:4326" + + # Convert the GeoDataFrame to WGS 84 / NSIDC EASE-Grid 2.0 Global and compute the area. + gdf_drawn_nsidc = gdf.copy().to_crs("EPSG:6933") + m2_per_ha = 10000 + area = gdf_drawn_nsidc.area.values[0] / m2_per_ha + + polyarea_label = ( + f"Total area of Global Forest Change {self.gfclayer} layer to load" + ) + polyarea_text = f"{polyarea_label}: {area:.2f} ha" + + # Test the size of the polygon drawn. + if self.max_size: + confirmation_text = """ + (Overriding maximum size limit; use with caution as may lead to memory issues)""" + self.header.value = ( + header_title_text + + instruction_text + + polyarea_text + + confirmation_text + ) + self.gdf_drawn = gdf + elif area <= 50000: + confirmation_text = """ + (Area to extract falls within + recommended 50000 ha limit)""" + self.header.value = ( + header_title_text + + instruction_text + + polyarea_text + + confirmation_text + ) + self.gdf_drawn = gdf + else: + warning_text = """ + (Area to extract is too large, + please select an area less than 50000 )""" + self.header.value = ( + header_title_text + instruction_text + polyarea_text + warning_text + ) + self.gdf_drawn = None + + # Register the handler for draw events. + draw_control.on_draw(handle_draw) + + ############################### + # SPECIFICATION OF APP LAYOUT # + ############################### + + # Create the app layout. + grid_rows = 12 + grid_columns = 11 + grid_height = "1500px" + grid_width = "auto" + grid = GridspecLayout( + grid_rows, grid_columns, height=grid_height, width=grid_width + ) + + # Place app widgets and components in app layout. + # [rows, columns] + grid[0, :] = self.header + grid[1:6, 0:4] = parameter_selection + grid[6, 0:4] = run_button + grid[7:, 0:4] = self.status_info + grid[6:, 4:] = self.output_plot + grid[1:6, 4:] = self.m + # Display using HBox children attribute + self.children = [grid] + + ###################################### + # DEFINITION OF ALL UPDATE FUNCTIONS # + ###################################### + + def update_basemap(self, change): + """ + Updates the basemap on the map widget based on the + selected value of the dropdown_basemap widget. + """ + self.basemap = change.new + self.output_plot_basemap = get_basemap(self.basemap.url) + update_map_layers(self) + + def update_gfclayer(self, change): + """ + Updates the Global Forest Change layer to be plotted + based on the selected value of the dropdown_gfclayer widget. + """ + self.gfclayer = change.new + + def update_timerange(self, change): + """Updates the time range of the data to be loaded""" + self.start_year = change.new[0] + self.end_year = change.new[1] + + def update_checkbox_max_size(self, change): + """ + Sets the value of self.max_size to True when the + checkbox_max_size CheckBox is checked. + """ + self.max_size = change.new + + def update_fileupload_aoi(self, change): + + # Clear any drawn data if present + self.gdf_drawn = None + + # Save to file + for uploaded_filename in change.new.keys(): + with open(uploaded_filename, "wb") as output_file: + content = change.new[uploaded_filename]['content'] + output_file.write(content) + + with self.status_info: + + try: + + print('Loading vector data...', end='\r') + valid_files = [ + file for file in change.new.keys() + if file.lower().endswith(('.shp', '.geojson')) + ] + valid_file = valid_files[0] + aoi_gdf = (gpd.read_file(valid_file).to_crs( + "EPSG:4326").explode().reset_index(drop=True)) + + # Create a geodata + geodata = GeoData(geo_dataframe=aoi_gdf, + style={ + 'color': 'black', + 'weight': 3 + }) + + # Add to map + xmin, ymin, xmax, ymax = aoi_gdf.total_bounds + self.m.fit_bounds([[ymin, xmin], [ymax, xmax]]) + self.m.add_layer(geodata) + + # If completed, add to attribute + self.gdf_uploaded = aoi_gdf + + except IndexError: + print( + "Cannot read uploaded files. Please ensure that data is " + "in either GeoJSON or ESRI Shapefile format.", + end='\r') + self.gdf_uploaded = None + + except fiona.errors.DriverError: + print( + "Shapefile is invalid. Please ensure that all shapefile " + "components (e.g. .shp, .shx, .dbf, .prj) are uploaded.", + end='\r') + self.gdf_uploaded = None + + def run_app(self, change): + + # Clear progress bar and output areas before running. + self.status_info.clear_output() + self.output_plot.clear_output() + + with self.status_info: + # Load the area of interest from the map or uploaded files. + if self.gdf_uploaded is not None: + aoi_gdf = self.gdf_uploaded + elif self.gdf_drawn is not None: + aoi_gdf = self.gdf_drawn + else: + print(f'No valid polygon drawn on the map or uploaded. Please draw a valid a transect on the map, or upload a GeoJSON or ESRI Shapefile.', + end='\r') + aoi_gdf = None + + # If valid area of interest data returned. Load the selected Global Forest Change data. + if aoi_gdf is not None: + + if self.gfclayer_ds is None: + if self.gfclayer != "alllayers": + self.gfclayer_ds = load_gfclayer(gdf_drawn=aoi_gdf, gfclayer=self.gfclayer) + else: + self.gfclayer_ds = load_all_gfclayers(gdf_drawn=aoi_gdf) + else: + print("Using previously loaded data") + + # Plot the selected Global Forest Change layer. + if self.gfclayer_ds is not None: + with self.output_plot: + plot_gfclayer(gfclayer_ds=self.gfclayer_ds, + start_year=self.start_year, + end_year=self.end_year, + gfclayer=self.gfclayer) + else: + with self.status_info: + print(f"No Global Forest Change {self.gfclayer} layer data found in the selected area. Please select a new polygon over an area with data.") \ No newline at end of file diff --git a/deafrica_tools/app/geomedian.py b/deafrica_tools/app/geomedian.py new file mode 100644 index 0000000..4c7f45e --- /dev/null +++ b/deafrica_tools/app/geomedian.py @@ -0,0 +1,126 @@ +""" +Geomedian widget: generates an interactive visualisation of +the geomedian summary statistic. +""" + +# Load modules +import ipywidgets as widgets +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +import numpy as np +import xarray as xr +from odc.algo import xr_geomedian + +def run_app(): + + """ + An interactive app that allows users to visualise the difference between the median and geomedian time-series summary statistics. By modifying the red-green-blue values of three timesteps for a given pixel, the user changes the output summary statistics. + + This allows a visual representation of the difference through the output values, RGB colour, as well as showing values plotted as a vector on a 3-dimensional space. + + Last modified: December 2021 + """ + + # Define the red-green-blue sliders for timestep 1 + p1r = widgets.IntSlider(description='Red', max=255, value=58) + p1g = widgets.IntSlider(description='Green', max=255, value=153) + p1b = widgets.IntSlider(description='Blue', max=255, value=68) + + # Define the red-green-blue sliders for timestep 2 + p2r = widgets.IntSlider(description='Red', max=255, value=208) + p2g = widgets.IntSlider(description='Green', max=255, value=221) + p2b = widgets.IntSlider(description='Blue', max=255, value=203) + + # Define the red-green-blue sliders for timestep 3 + p3r = widgets.IntSlider(description='Red', max=255, value=202) + p3g = widgets.IntSlider(description='Green', max=255, value=82) + p3b = widgets.IntSlider(description='Blue', max=255, value=33) + + # Define the median calculation for the timesteps + def f(p1r, p1g, p1b, p2r, p2g, p2b, p3r, p3g, p3b): + print('Red Median = {}'.format(np.median([p1r, p2r, p3r]))) + print('Green Median = {}'.format(np.median([p1g, p2g, p3g]))) + print('Blue Median = {}'.format(np.median([p1b, p2b, p3b]))) + + # Define the geomedian calculation for the timesteps + def g(p1r, p1g, p1b, p2r, p2g, p2b, p3r, p3g, p3b): + print('Red Geomedian = {:.2f}'.format(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).red.values.ravel()[0])) + print('Green Geomedian = {:.2f}'.format(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).green.values.ravel()[0])) + print('Blue Geomedian = {:.2f}'.format(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).blue.values.ravel()[0])) + + # Define the Timestep 1 box colour + def h(p1r, p1g, p1b): + fig1, axes1 = plt.subplots(figsize=(2,2)) + fig1 = plt.imshow([[(p1r, p1g, p1b)]]) + axes1.set_title('Timestep 1') + axes1.axis('off') + plt.show(fig1) + + # Define the Timestep 2 box colour + def hh(p2r, p2g, p2b): + fig2, axes2 = plt.subplots(figsize=(2,2)) + fig2 = plt.imshow([[(p2r, p2g, p2b)]]) + axes2.set_title('Timestep 2') + axes2.axis('off') + plt.show(fig2) + + # Define the Timestep 3 box colour + def hhh(p3r, p3g, p3b): + fig3, axes3 = plt.subplots(figsize=(2,2)) + fig3 = plt.imshow([[(p3r, p3g, p3b)]]) + axes3.set_title('Timestep 3') + axes3.axis('off') + plt.show(fig3) + + # Define the Median RGB colour box + def i(p1r, p1g, p1b, p2r, p2g, p2b, p3r, p3g, p3b): + fig4, axes4 = plt.subplots(figsize=(3,3)) + fig4 = plt.imshow([[(int(np.median([p1r, p2r, p3r])), int(np.median([p1g, p2g, p3g])), int(np.median([p1b, p2b, p3b])))]]) + axes4.set_title('Median RGB - All timesteps') + axes4.axis('off') + plt.show(fig4) + + # Define the Geomedian RGB colour box + def ii(p1r, p1g, p1b, p2r, p2g, p2b, p3r, p3g, p3b): + fig5, axes5 = plt.subplots(figsize=(3,3)) + fig5 = plt.imshow([[(int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).red.values.ravel()[0]), int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).green.values.ravel()[0]), int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).blue.values.ravel()[0]))]]) + axes5.set_title('Geomedian RGB - All timesteps') + axes5.axis('off') + plt.show(fig5) + + # Define 3-D axis to display vectors on + def j(p1r, p1g, p1b, p2r, p2g, p2b, p3r, p3g, p3b): + fig6 = plt.figure() + axes6 = fig6.add_subplot(111, projection='3d') + x = [p1r, p2r, p3r, int(np.median([p1r, p2r, p3r])), int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).red.values.ravel()[0])] + y = [p1g, p2g, p3g, int(np.median([p1g, p2g, p3g])), int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).green.values.ravel()[0])] + z = [p1b, p2b, p3b, int(np.median([p1b, p2b, p3b])), int(xr_geomedian(xr.Dataset({"red": (("x", "y", "time"), [[[np.float32(p1r), np.float32(p2r), np.float32(p3r)]]]), "green": (("x", "y", "time"), [[[np.float32(p1g), np.float32(p2g), np.float32(p3g)]]]), "blue": (("x", "y", "time"), [[[np.float32(p1b), np.float32(p2b), np.float32(p3b)]]])})).blue.values.ravel()[0])] + labels = [' 1', ' 2', ' 3', ' median', ' geomedian'] + axes6.scatter(x, y, z, c=['black','black','black','r', 'blue'], marker='o') + axes6.set_xlabel('Red') + axes6.set_ylabel('Green') + axes6.set_zlabel('Blue') + axes6.set_xlim3d(0, 255) + axes6.set_ylim3d(0, 255) + axes6.set_zlim3d(0, 255) + for ax, ay, az, label in zip(x, y, z, labels): + axes6.text(ax, ay, az, label) + plt.title('Each band represents a dimension.') + plt.show() + + # Define outputs + outf = widgets.interactive_output(f, {'p1r': p1r, 'p2r': p2r,'p3r': p3r, 'p1g': p1g, 'p2g': p2g,'p3g': p3g, 'p1b': p1b, 'p2b': p2b,'p3b': p3b}) + outg = widgets.interactive_output(g, {'p1r': p1r, 'p2r': p2r,'p3r': p3r, 'p1g': p1g, 'p2g': p2g,'p3g': p3g, 'p1b': p1b, 'p2b': p2b,'p3b': p3b}) + + outh = widgets.interactive_output(h, {'p1r': p1r, 'p1g': p1g, 'p1b': p1b}) + outhh = widgets.interactive_output(hh, {'p2r': p2r, 'p2g': p2g, 'p2b': p2b}) + outhhh = widgets.interactive_output(hhh, {'p3r': p3r, 'p3g': p3g, 'p3b': p3b}) + + outi = widgets.interactive_output(i, {'p1r': p1r, 'p2r': p2r,'p3r': p3r, 'p1g': p1g, 'p2g': p2g,'p3g': p3g, 'p1b': p1b, 'p2b': p2b,'p3b': p3b}) + outii = widgets.interactive_output(ii, {'p1r': p1r, 'p2r': p2r,'p3r': p3r, 'p1g': p1g, 'p2g': p2g,'p3g': p3g, 'p1b': p1b, 'p2b': p2b,'p3b': p3b}) + + outj = widgets.interactive_output(j, {'p1r': p1r, 'p2r': p2r,'p3r': p3r, 'p1g': p1g, 'p2g': p2g,'p3g': p3g, 'p1b': p1b, 'p2b': p2b,'p3b': p3b}) + + app_output = widgets.HBox([widgets.VBox([widgets.HBox([outh, widgets.VBox([ p1r, p1g, p1b])]), widgets.HBox([outhh, widgets.VBox([p2r, p2g, p2b])]), widgets.HBox([outhhh, widgets.VBox([ p3r, p3g, p3b])])]), widgets.VBox([widgets.HBox([widgets.VBox([outf, outi]), widgets.VBox([outg, outii])]), outj])]) + + return app_output \ No newline at end of file diff --git a/deafrica_tools/app/imageexport.py b/deafrica_tools/app/imageexport.py new file mode 100644 index 0000000..8b0cf1c --- /dev/null +++ b/deafrica_tools/app/imageexport.py @@ -0,0 +1,372 @@ +""" +Create an interactive map for selecting satellite imagery and exporting image files. +""" + +# Load modules +import datacube +import itertools +import numpy as np +import matplotlib.pyplot as plt +from odc.ui import select_on_a_map +from datacube.utils.geometry import CRS +from datacube.utils import masking +from skimage import exposure +from ipyleaflet import (WMSLayer, basemaps, basemap_to_tiles) +from traitlets import Unicode + +from deafrica_tools.spatial import reverse_geocode +from deafrica_tools.dask import create_local_dask_cluster + + +def select_region_app(date, + satellites, + size_limit=10000): + """ + An interactive app that allows the user to select a region from a + map using imagery from Sentinel-2 and Landsat. The output of this + function is used as the input to :func:`export_image_app` to export high- + resolution satellite images. + + Last modified: September 2021 + + Parameters + ---------- + date : str + The exact date used to plot imagery on the interactive map + (e.g. ``date='1988-01-01'``). + satellites : str + The satellite data to plot on the interactive map. The + following options are supported: + + ``'Landsat-9'``: data from the Landsat 9 satellite + ``'Landsat-8'``: data from the Landsat 8 satellite + ``'Landsat-7'``: data from the Landsat 7 satellite + ``'Landsat-5'``: data from the Landsat 5 satellite + ``'Sentinel-2'``: data from Sentinel-2A and Sentinel-2B + ``'Sentinel-2 geomedian'``: data from the Sentinel-2 annual geomedian + + size_limit : int, optional + An optional size limit for the area selection in sq km. + Defaults to 10000 sq km. + + Returns + ------- + A dictionary containing: + + * 'geopolygon' (defining the area to export imagery from), + * 'date' (date used to export imagery), and + * 'satellites' (the satellites from which to extract imagery). + + These are passed to the :func:`export_image_app` function to export the image. + """ + + ######################## + # Select and load data # + ######################## + + # Load DEA WMS + class TimeWMSLayer(WMSLayer): + time = Unicode('').tag(sync=True, o=True) + + # WMS layers + wms_params = { + 'Landsat-9': 'ls9_sr', + 'Landsat-8': 'ls8_sr', + 'Landsat-7': 'ls7_sr', + 'Landsat-5': 'ls5_sr', + 'Sentinel-2': 's2_l2a', + 'Sentinel-2 geomedian': 'gm_s2_annual' + } + + time_wms = TimeWMSLayer(url='https://ows.digitalearth.africa/', + layers=wms_params[satellites], + time=date, + format='image/png', + transparent=True, + attribution='Digital Earth Africa') + + # Plot interactive map to select area + basemap = basemap_to_tiles(basemaps.OpenStreetMap.Mapnik) + geopolygon = select_on_a_map(height='1000px', + layers=( + basemap, + time_wms, + ), + center=(4, 20), + zoom=4) + + # Test size of selected area + area = geopolygon.to_crs(crs=CRS('epsg:6933')).area / 1000000 + if area > size_limit: + print(f'Warning: Your selected area is {area:.00f} sq km. ' + f'Please select an area of less than {size_limit} sq km.' + f'\nTo select a smaller area, re-run the cell ' + f'above and draw a new polygon.') + + else: + return {'geopolygon': geopolygon, + 'date': date, + 'satellites': satellites} + + +def export_image_app(geopolygon, + date, + satellites, + style='True colour', + resolution=None, + vmin=0, + vmax=2000, + percentile_stretch=None, + power=None, + image_proc_funcs=None, + output_format="jpg", + standardise_name=False): + """ + Exports Digital Earth Africa satellite data as an image file + based on the extent and time period selected using + :func:`select_region_app`. The function supports Sentinel-2 and Landsat + data, creating True and False colour images. + + By default, files are named using: + + ``" - - - .png"`` + + Set ``standardise_name=True`` for a machine-readable name: + + ``"___.png"`` + + Last modified: September 2021 + + Parameters + ---------- + geopolygon : datacube.utils.geometry object + A datacube geopolygon providing the spatial bounds used to load + satellite data. + date : str + The exact date used to extract imagery + (e.g. `date='1988-01-01'`). + satellites : str + The satellite data to be used to extract imagery. The + following options are supported: + + ``'Landsat-9'``: data from the Landsat 9 satellite + ``'Landsat-8'``: data from the Landsat 8 satellite + ``'Landsat-7'``: data from the Landsat 7 satellite + ``'Landsat-5'``: data from the Landsat 5 satellite + ``'Sentinel-2'``: data from Sentinel-2A and Sentinel-2B + ``'Sentinel-2 geomedian'``: data from the Sentinel-2 annual geomedian + + style : str, optional + The style used to produce the image. Two options are currently + supported: + + * ``'True colour'``: Creates a true colour image using the red, + green and blue satellite bands + * ``'False colour'``: Creates a false colour image using + short-wave infrared, infrared and green satellite bands. + The specific bands used vary between Landsat and Sentinel-2. + + resolution : tuple, optional + The spatial resolution to load data. By default, the tool will + automatically set the best possible resolution depending on the + satellites selected (i.e 30 m for Landsat, 10 m for Sentinel-2). + Increasing this (e.g. to ``resolution=(-100, 100)``) can be useful + for loading large spatial extents. + vmin, vmax : int or float + The minimum and maximum surface reflectance values used to + clip the resulting imagery to enhance contrast. + percentile_stretch : tuple of floats, optional + An tuple of two floats (between 0.00 and 1.00) that can be used + to clip the imagery to based on percentiles to get more control + over the brightness and contrast of the image. The default is + ``None``; ``(0.02, 0.98)`` is equivelent to ``robust=True``. If this + parameter is used, ``vmin`` and ``vmax`` will have no effect. + power : float, optional + Raises imagery by a power to reduce bright features and + enhance dark features. This can add extra definition over areas + with extremely bright features like snow, beaches or salt pans. + image_proc_funcs : list of funcs, optional + An optional list containing functions that will be applied to + the output image. This can include image processing functions + such as increasing contrast, unsharp masking, saturation etc. + The function should take AND return a `numpy.ndarray` with + shape ``[y, x, bands]``. If your function has parameters, you + can pass in custom values using a lambda function, e.g.: + ``[lambda x: skimage.filters.unsharp_mask(x, radius=5, amount=0.2)]`` + output_format : str, optional + The output file format of the image. Valid options include ``'jpg'`` + and ``'png'``. Defaults to ``'jpg'``. + standardise_name : bool, optional + Whether to export the image file with a machine-readable + file name (e.g. ``___.png``) + """ + + ########################### + # Set up satellite params # + ########################### + + sat_params = { + 'Landsat-9': { + 'products': ['ls9_sr'], + 'resolution': [-30, 30], + 'styles': { + 'True colour': ['red', 'green', 'blue'], + 'False colour': ['swir_1', 'nir', 'green'] + } + }, + 'Landsat-8': { + 'products': ['ls8_sr'], + 'resolution': [-30, 30], + 'styles': { + 'True colour': ['red', 'green', 'blue'], + 'False colour': ['swir_1', 'nir', 'green'] + } + }, + 'Landsat-7': { + 'products': ['ls7_sr'], + 'resolution': [-30, 30], + 'styles': { + 'True colour': ['red', 'green', 'blue'], + 'False colour': ['swir_1', 'nir', 'green'] + } + }, + 'Landsat-5': { + 'products': ['ls5_sr'], + 'resolution': [-30, 30], + 'styles': { + 'True colour': ['red', 'green', 'blue'], + 'False colour': ['swir_1', 'nir', 'green'] + } + }, + 'Sentinel-2': { + 'products': ['s2_l2a'], + 'resolution': [-10, 10], + 'styles': { + 'True colour': ['red', 'green', 'blue'], + 'False colour': ['swir_2', 'nir_1', 'green'] + } + }, + 'Sentinel-2 geomedian': { + 'products': ['gm_s2_annual'], + 'resolution': [-10, 10], + 'styles': { + 'True colour': ['red', 'green', 'blue'], + 'False colour': ['swir_2', 'nir_1', 'green'] + } + }, + } + + ############# + # Load data # + ############# + + # Connect to datacube database + dc = datacube.Datacube(app='Exporting_satellite_images') + + # Configure local dask cluster + client = create_local_dask_cluster(return_client=True) + + # Create query after adjusting interval time to UTC by + # adding a UTC offset of -10 hours. + start_date = np.datetime64(date) + query_params = { + 'time': (str(start_date)), + 'geopolygon': geopolygon + } + + # Find matching datasets + dss = [ + dc.find_datasets(product=i, **query_params) + for i in sat_params[satellites]['products'] + ] + dss = list(itertools.chain.from_iterable(dss)) + + # Get CRS and sensor + crs = str(dss[0].crs) + + if satellites == 'Sentinel-2 geomedian': + sensor = satellites + else: + sensor = dss[0].metadata_doc['properties']['eo:platform'].capitalize() + sensor = sensor[0:-1].replace('_', '-') + sensor[-1].capitalize() + + # Use resolution if provided, otherwise use default + if resolution: + sat_params[satellites]['resolution'] = resolution + + load_params = { + 'output_crs': crs, + 'resolution': sat_params[satellites]['resolution'], + 'resampling': 'bilinear' + } + + # Load data from datasets + ds = dc.load(datasets=dss, + measurements=sat_params[satellites]['styles'][style], + group_by='solar_day', + dask_chunks={ + 'time': 1, + 'x': 3000, + 'y': 3000 + }, + **load_params, + **query_params) + ds = masking.mask_invalid_data(ds) + + rgb_array = ds.isel(time=0).to_array().values + + ############ + # Plotting # + ############ + + # Create unique file name + centre_coords = geopolygon.centroid.coords[0][::-1] + site = reverse_geocode(coords=centre_coords) + fname = (f"{sensor} - {date} - {site} - {style}, " + f"{load_params['resolution'][1]} m resolution.{output_format}") + + # Remove spaces and commas if requested + if standardise_name: + fname = fname.replace(' - ', '_').replace(', ', + '-').replace(' ', + '-').lower() + + print( + f'\nExporting image to {fname}.\nThis may take several minutes to complete...' + ) + + # Convert to numpy array + rgb_array = np.transpose(rgb_array, axes=[1, 2, 0]) + + # If percentile stretch is supplied, calculate vmin and vmax + # from percentiles + if percentile_stretch: + vmin, vmax = np.nanpercentile(rgb_array, percentile_stretch) + + # Raise by power to dampen bright features and enhance dark. + # Raise vmin and vmax by same amount to ensure proper stretch + if power: + rgb_array = rgb_array**power + vmin, vmax = vmin**power, vmax**power + + # Rescale/stretch imagery between vmin and vmax + rgb_rescaled = exposure.rescale_intensity(rgb_array.astype(float), + in_range=(vmin, vmax), + out_range=(0.0, 1.0)) + + # Apply image processing funcs + if image_proc_funcs: + for i, func in enumerate(image_proc_funcs): + print(f'Applying custom function {i + 1}') + rgb_rescaled = func(rgb_rescaled) + + # Plot RGB + plt.imshow(rgb_rescaled) + + # Export to file + plt.imsave(fname=fname, arr=rgb_rescaled, format=output_format) + + # Close dask client + client.shutdown() + + print('Finished exporting image.') diff --git a/deafrica_tools/app/wetlandsinsighttool.py b/deafrica_tools/app/wetlandsinsighttool.py new file mode 100644 index 0000000..e8b6ce5 --- /dev/null +++ b/deafrica_tools/app/wetlandsinsighttool.py @@ -0,0 +1,388 @@ +""" +Wetlands insight tool widget, which can be used to run an interactive +version of the wetlands insight tool. +""" + +# Import required packages + +# Force GeoPandas to use Shapely instead of PyGEOS +# In a future release, GeoPandas will switch to using Shapely by default. +import os +os.environ['USE_PYGEOS'] = '0' + +import datacube +import warnings +import seaborn as sns +import matplotlib.pyplot as plt +from datacube.utils.geometry import CRS +from ipyleaflet import ( + WMSLayer, + basemaps, + basemap_to_tiles, + Map, + DrawControl, + WidgetControl, + LayerGroup, + LayersControl, +) +from traitlets import Unicode +from ipywidgets import ( + GridspecLayout, + Button, + Layout, + HBox, + VBox, + HTML, + Output, +) +import json +import geopandas as gpd +from io import BytesIO +from dask.diagnostics import ProgressBar + +import deafrica_tools +from deafrica_tools.dask import create_local_dask_cluster +from deafrica_tools.wetlands import WIT_drill +import deafrica_tools.app.widgetconstructors as deawidgets + + +def make_box_layout(): + return Layout( + #border='solid 1px black', + margin='0px 10px 10px 0px', + padding='5px 5px 5px 5px', + width='100%', + height='100%', + ) + + +def create_expanded_button(description, button_style): + return Button( + description=description, + button_style=button_style, + layout=Layout(width="auto", height="auto"), + ) + + +class wit_app(HBox): + def __init__(self, lang=None): + super().__init__() + + deafrica_tools.set_lang(lang) + + ########################################################## + # INITIAL ATTRIBUTES # + + self.startdate = "2020-01-01" + self.enddate = "2020-03-01" + self.mingooddata = 0.0 + self.resamplingfreq = "1M" + self.out_csv = "example_WIT.csv" + self.out_plot = "example_WIT.png" + self.product_list = [ + (_("None"), "none"), + (_("ESRI World Imagery"), "esri_world_imagery"), + (_("Sentinel-2 Geomedian"), "gm_s2_annual"), + (_("Water Observations from Space"), "wofs_ls_summary_annual"), + + ] + self.product = self.product_list[0][1] + self.product_year = "2020-01-01" + self.target = None + self.action = None + self.gdf_drawn = None + + ########################################################## + # HEADER FOR APP # + + # Create the Header widget + header_title_text = _("Wetlands Insight Tool") + instruction_text = _("Select parameters and AOI") + self.header = deawidgets.create_html(f"

{header_title_text}

{instruction_text}

") + self.header.layout = make_box_layout() + + ########################################################## + # HANDLER FUNCTION FOR DRAW CONTROL # + + # Define the action to take once something is drawn on the map + def update_geojson(target, action, geo_json): + + self.action = action + + json_data = json.dumps(geo_json) + binary_data = json_data.encode() + io = BytesIO(binary_data) + io.seek(0) + + gdf = gpd.read_file(io) + gdf.crs = "EPSG:4326" + self.gdf_drawn = gdf + + gdf_drawn_epsg6933 = gdf.copy().to_crs("EPSG:6933") + m2_per_km2 = 10 ** 6 + area = gdf_drawn_epsg6933.area.values[0] / m2_per_km2 + polyarea_label = _('Total polygon area') + polyarea_text = f"

{polyarea_label}: {area:.2f} km2

" + + if area <= 3000: + confirmation_text = '

' + _('Area falls within recommended limit') + '

' + self.header.value = header_title_text + polyarea_text + confirmation_text + else: + warning_text = '

' + _('Area is too large, please update your polygon') + '

' + self.header.value = header_title_text + polyarea_text + warning_text + + ########################################################## + # WIDGETS FOR APP OUTPUTS # + + self.dask_client = Output(layout=make_box_layout()) + self.progress_bar = Output(layout=make_box_layout()) + self.wit_plot = Output(layout=make_box_layout()) + self.progress_header = deawidgets.create_html("") + + ########################################################## + # MAP WIDGET, DRAWING TOOLS, WMS LAYERS # + + # Create drawing tools + desired_drawtools = ['rectangle', 'polygon'] + draw_control = deawidgets.create_drawcontrol(desired_drawtools) + + # Begin by displaying an empty layer group, and update the group with desired WMS on interaction. + self.deafrica_layers = LayerGroup(layers=()) + self.deafrica_layers.name = _('Map Overlays') + + # Create map widget + self.m = deawidgets.create_map() + + self.m.layout = make_box_layout() + + # Add tools to map widget + self.m.add_control(draw_control) + self.m.add_layer(self.deafrica_layers) + + # Store current basemap for future use + self.basemap = self.m.basemap + + ########################################################## + # WIDGETS FOR APP CONTROLS # + + # Create parameter widgets + startdate_picker = deawidgets.create_datepicker() + enddate_picker = deawidgets.create_datepicker() + min_good_data = deawidgets.create_boundedfloattext(self.mingooddata, 0.0, 1.0, 0.05) + resampling_freq = deawidgets.create_inputtext(self.resamplingfreq, self.resamplingfreq) + output_csv = deawidgets.create_inputtext(self.out_csv, self.out_csv) + output_plot = deawidgets.create_inputtext(self.out_plot, self.out_plot) + deaoverlay_dropdown = deawidgets.create_dropdown(self.product_list, self.product_list[0][1]) + run_button = create_expanded_button(_("Run"), "info") + + ########################################################## + # COLLECTION OF ALL APP CONTROLS # + + parameter_selection = VBox( + [ + HTML("" + _("Map Overlay:") + ""), + deaoverlay_dropdown, + HTML("" + _("Start Date:") + ""), + startdate_picker, + HTML("" + _("End Date:") + ""), + enddate_picker, + HTML("" + _("Minimum Good Data:") + ""), + min_good_data, + HTML("" + _("Resampling Frequency:") + ""), + resampling_freq, + HTML("" + _("Output CSV:") + ""), + output_csv, + HTML("" + _("Output Plot:") + ""), + output_plot, + ] + ) + parameter_selection.layout = make_box_layout() + + ########################################################## + # SPECIFICATION OF APP LAYOUT # + + # Create the layout #[rowspan, colspan] + grid = GridspecLayout(11, 10, height="1100px", width="auto") + + # Controls and Status + grid[0, :] = self.header + grid[1:6, 0:2] = parameter_selection + grid[6, 0:2] = run_button + + # Dask and Progress info + grid[1, 7:] = self.dask_client + grid[2:7, 7:] = self.progress_bar + + # Map + grid[1:7, 2:7] = self.m + + # Plot + grid[7:, :] = self.wit_plot + + # Display using HBox children attribute + self.children = [grid] + + ########################################################## + # SPECIFICATION UPDATE FUNCTIONS FOR EACH WIDGET # + + # Run update functions whenever various widgets are changed. + startdate_picker.observe(self.update_startdate, "value") + enddate_picker.observe(self.update_enddate, "value") + min_good_data.observe(self.update_mingooddata, "value") + resampling_freq.observe(self.update_resamplingfreq, "value") + output_csv.observe(self.update_outputcsv, "value") + output_plot.observe(self.update_outputplot, "value") + deaoverlay_dropdown.observe(self.update_deaoverlay, "value") + run_button.on_click(self.run_app) + draw_control.on_draw(update_geojson) + + ############################################################## + # DEFINITION OF ALL UPDATE FUNCTIONS # + + # set the start date to the new edited date + def update_startdate(self, change): + self.startdate = change.new + + # set the end date to the new edited date + def update_enddate(self, change): + self.enddate = change.new + + # set the min good data + def update_mingooddata(self, change): + self.mingooddata = change.new + + # set the resampling frequency + def update_resamplingfreq(self, change): + self.resamplingfreq = change.new + + # set the output csv + def update_outputcsv(self, change): + self.out_csv = change.new + + # set the output plot + def update_outputplot(self, change): + self.out_plot = change.new + + # Update product + def update_deaoverlay(self, change): + + self.product = change.new + + if self.product == "none": + self.deafrica_layers.clear_layers() + elif self.product == "esri_world_imagery": + self.deafrica_layers.clear_layers() + layer = basemap_to_tiles(basemaps.Esri.WorldImagery) + self.deafrica_layers.add_layer(layer) + else: + self.deafrica_layers.clear_layers() + layer = deawidgets.create_dea_wms_layer(self.product, self.product_year) + self.deafrica_layers.add_layer(layer) + + def run_app(self, change): + + # Clear progress bar and output areas before running + self.dask_client.clear_output() + self.progress_bar.clear_output() + self.wit_plot.clear_output() + + # Connect to datacube database + dc = datacube.Datacube(app="wetland_app") + + # Configure local dask cluster + with self.dask_client: + client = create_local_dask_cluster( + return_client=True, display_client=True + ) + + # Set any defaults + TCW_threshold = -0.035 + dask_chunks = dict(x=1000, y=1000, time=1) + + #check resampling freq + if self.resamplingfreq == 'None': + rsf = None + else: + rsf = self.resamplingfreq + + self.progress_header.value = f"

"+_("Progress")+"

" + + # run wetlands polygon drill + with self.progress_bar: +# with ProgressBar(): + warnings.filterwarnings("ignore") + try: + df = WIT_drill( + gdf=self.gdf_drawn, + time=(self.startdate, self.enddate), + min_gooddata=self.mingooddata, + resample_frequency=rsf, + TCW_threshold=TCW_threshold, + export_csv=self.out_csv, + dask_chunks=dask_chunks, + verbose=False, + verbose_progress=True, + ) + print(_("WIT complete")) + except AttributeError: + print(_("No polygon selected")) + + # close down the dask client + client.shutdown() + + # save the csv + if self.out_csv: + df.to_csv(self.out_csv, index_label="Datetime") + + # ---Plotting------------------------------ + + with self.wit_plot: + + fontsize = 17 + plt.rcParams.update({"font.size": fontsize}) + # set up color palette + pal = [ + sns.xkcd_rgb["cobalt blue"], + sns.xkcd_rgb["neon blue"], + sns.xkcd_rgb["grass"], + sns.xkcd_rgb["beige"], + sns.xkcd_rgb["brown"], + ] + + # make a stacked area plot + plt.close("all") + + fig, ax = plt.subplots(constrained_layout=True, figsize=(20, 6)) + + ax.stackplot( + df.index, + df.wofs_area_percent, + df.wet_percent, + df.green_veg_percent, + df.dry_veg_percent, + df.bare_soil_percent, + labels=[ + _("open water"), + _("wet"), + _("green veg"), + _("dry veg"), + _("bare soil"), + ], + colors=pal, + alpha=0.6, + ) + + # set axis limits to the min and max + ax.set_ylim(0, 100) + ax.set_xlim(df.index[0], df.index[-1]) + ax.tick_params(axis="x", labelsize=fontsize) + + # add a legend and a tight plot box + ax.legend(loc="lower left", framealpha=0.6) + ax.set_title(_("Percentage Fractional Cover, Wetness, and Water")) + # plt.tight_layout() + plt.show() + + if self.out_plot: + # save the figure + fig.savefig(f"{self.out_plot}") diff --git a/deafrica_tools/app/widgetconstructors.py b/deafrica_tools/app/widgetconstructors.py new file mode 100644 index 0000000..6881712 --- /dev/null +++ b/deafrica_tools/app/widgetconstructors.py @@ -0,0 +1,367 @@ +""" +Functions for easily defining widgets in the context of DE Africa notebooks. + +These are largely customised wrappers around existing widgets. +""" + +import ipyleaflet as leaflet +from ipyleaflet import LayersControl +import ipywidgets as widgets +from traitlets import Unicode + + +def create_datepicker(description='', value=None, layout={'width': '85%'}): + ''' + Create a DatePicker widget + + Last modified: July 2022 + + Parameters + ---------- + description : string + descirption label to attach + layout : dictionary + any layout commands for the widget + + Returns + ------- + date_picker : ipywidgets.widgets.widget_date.DatePicker + + ''' + + date_picker = widgets.DatePicker( + description=description, + layout=layout, + disabled=False, + value=value + ) + + return date_picker + + +def create_inputtext(value, placeholder, description="", layout={'width': '85%'}): + ''' + Create a Text widget + + Last modified: October 2021 + + Parameters + ---------- + value : string + initial value of the widget + placeholder : string + placeholder text to display to the user before intput + description : string + descirption label to attach + layout : dictionary + any layout commands for the widget + + Returns + ------- + input_text : ipywidgets.widgets.widget_string.Text + + ''' + + input_text = widgets.Text( + value=value, + placeholder=placeholder, + description=description, + layout=layout, + disabled=False + ) + + return input_text + + +def create_boundedfloattext(value, min_val, max_val, step_val, description="", layout={'width': '85%'}): + ''' + Create a BoundedFloatText widget + + Last modified: October 2021 + + Parameters + ---------- + value : float + initial value of the widget + min_val : float + minimum allowed value for the float + max_val : float + maximum allowed value for the float + step_val : float + allowed increment for the float + description : string + descirption label to attach + layout : dictionary + any layout commands for the widget + + Returns + ------- + float_text : ipywidgets.widgets.widget_float.BoundedFloatText + + ''' + + float_text = widgets.BoundedFloatText( + value=value, + min=min_val, + max=max_val, + step=step_val, + description=description, + layout=layout, + disabled=False, + ) + + return float_text + + +def create_dropdown(options, value, description="", layout={'width': '85%'}): + ''' + Create a Dropdown widget + + Last modified: October 2021 + + Parameters + ---------- + options : list + a list of options for the user to select from + value : string + initial value of the widget + description : string + descirption label to attach + layout : dictionary + any layout commands for the widget + + Returns + ------- + dropdown : ipywidgets.widgets.widget_selection.Dropdown + + ''' + + dropdown = widgets.Dropdown( + options=options, + value=value, + description=description, + layout=layout, + disabled=False, + ) + + return dropdown + + +def create_html(value): + ''' + Create a HTML widget + + Last modified: October 2021 + + Parameters + ---------- + value : string + HTML text to display + + Returns + ------- + html : ipywidgets.widgets.widget_string.HTML + + ''' + + html = widgets.HTML( + value=value, + ) + + return html + + +def create_map(map_center=(4, 20), zoom_level=3, basemap=leaflet.basemaps.OpenStreetMap.Mapnik, basemap_name='Open Street Map'): + ''' + Create an interactive ipyleaflet map + + Last modified: October 2021 + + Parameters + ---------- + map_center : tuple + A tuple containing the latitude and longitude to focus on. + Defaults to center of Africa, (4, 20) + zoom_level : integer + Zoom level for the map + Defaults to 3 to view all of Africa + basemap : ipyleaflet basemap (dict) + Basemap to use, can be any from https://ipyleaflet.readthedocs.io/en/latest/api_reference/basemaps.html + Defaults to Open Street Map (basemaps.OpenStreetMap.Mapnik) + basemap_name : string + Layer name for the basemap + + Returns + ------- + m : ipyleaflet.leaflet.Map + interactive ipyleaflet map + + ''' + + basemap_tiles = leaflet.basemap_to_tiles(basemap) + basemap_tiles.name = basemap_name + + m = leaflet.Map(center=map_center, zoom=zoom_level, basemap=basemap_tiles, scroll_wheel_zoom=True) + + return m + + +def create_dea_wms_layer(product, date): + ''' + Create a Digital Earth Africa WMS layer to add to a map + + Last modified: October 2021 + + Parameters + ---------- + product : string + The Digital Earth Africa product to load + (e.g. 'gm_s2_annual') + date : string (yyyy-mm-dd format) + The date to load the product for + + Returns + ------- + time_wms : ipyleaflet WMS layer + + ''' + + + # Load DEA WMS + class TimeWMSLayer(leaflet.WMSLayer): + time = Unicode("").tag(sync=True, o=True) + + time_wms = TimeWMSLayer( + url="https://ows.digitalearth.africa/", + layers=product, + time=date, + format="image/png", + transparent=True, + attribution="Digital Earth Africa", + ) + + return time_wms + + +def create_drawcontrol( + draw_controls = ['rectangle', 'polygon', 'circle', 'polyline', 'marker', 'circlemarker'], + rectangle_options={}, + polygon_options={}, + circle_options={}, + polyline_options={}, + marker_options={}, + circlemarker_options={}, +): + ''' + Create a draw control widget to add to ipyleaflet maps + + Last modified: October 2021 + + Parameters + ---------- + draw_controls : list + List of draw controls to add to the map. Defaults to adding all + Viable options are 'rectangle', 'polygon', 'circle', 'polyline', 'marker', 'circlemarker' + rectangle_options : dict + Options to customise the appearence of the relevant shape + User can supply, or leave blank to get default DE Africa appearence + polygon_options : dict + Options to customise the appearence of the relevant shape + User can supply, or leave blank to get default DE Africa appearence + circle_options : dict + Options to customise the appearence of the relevant shape + User can supply, or leave blank to get default DE Africa appearence + polyline_options : dict + Options to customise the appearence of the relevant shape + User can supply, or leave blank to get default DE Africa appearence + marker_options : dict + Options to customise the appearence of the relevant shape + User can supply, or leave blank to get default DE Africa appearence + circlemarker_options : dict + Options to customise the appearence of the relevant shape + User can supply, or leave blank to get default DE Africa appearence + + + Returns + ------- + draw_control : ipyleaflet.leaflet.DrawControl + + ''' + + # Set defualt DE Africa styling options for polygons + default_shapeoptions = { + "color": "#FFFFFF", + "opacity": 0.8, + "fillColor": "#336699", + "fillOpacity": 0.4, + } + default_drawerror = { + "color": "#FF6633", + "message": "Drawing error, clear all and try again" + } + + # Set draw control appearence to DE Africa defaults + # Do this if user has requested a control, but has not provided a corresponding options dict + + if ('rectangle' in draw_controls) and (not rectangle_options): + rectangle_options = {"shapeOptions": default_shapeoptions} + + if ('polygon' in draw_controls) and (not polygon_options): + polygon_options = { + "shapeOptions": default_shapeoptions, + "drawError": default_drawerror, + "allowIntersection": False, + } + + if ('circle' in draw_controls) and (not circle_options): + circle_options = {"shapeOptions": default_shapeoptions} + + if ('polyline' in draw_controls) and (not polyline_options): + polyline_options = {"shapeOptions": default_shapeoptions} + + if ('marker' in draw_controls) and (not marker_options): + marker_options = {'shapeOptions': {'opacity': 1.0}} + + if ('circlemarker' in draw_controls) and (not circlemarker_options): + circlemarker_options = {"shapeOptions": default_shapeoptions} + + # Instantiate draw control and add options + draw_control = leaflet.DrawControl() + draw_control.rectangle = rectangle_options + draw_control.polygon = polygon_options + draw_control.marker = marker_options + draw_control.circle = circle_options + draw_control.circlemarker = circlemarker_options + draw_control.polyline = polyline_options + + return draw_control + + +def create_checkbox(value, description="", layout={'width': '85%'}): + ''' + Create a Checkbox widget + + Last modified: July 2022 + + Parameters + ---------- + value : string + initial value of the widget; True or False + description : string + description label to attach + layout : dictionary + any layout commands for the widget + + Returns + ------- + dropdown : ipywidgets.widgets.widget_selection.Dropdown + + ''' + + checklist = widgets.Checkbox(value=value, + description=description, + layout=layout, + disabled=False, + indent=False) + + return checklist \ No newline at end of file diff --git a/deafrica_tools/areaofinterest.py b/deafrica_tools/areaofinterest.py new file mode 100644 index 0000000..ed69efa --- /dev/null +++ b/deafrica_tools/areaofinterest.py @@ -0,0 +1,54 @@ +""" +Function for defining an area of interest using either a point and buffer or a vector file. +""" + +# Import required packages + +# Force GeoPandas to use Shapely instead of PyGEOS +# In a future release, GeoPandas will switch to using Shapely by default. +import os +os.environ['USE_PYGEOS'] = '0' + +import geopandas as gpd +from shapely.geometry import box +from geojson import Feature, Point, FeatureCollection + +def define_area(lat=None, lon=None, buffer=None, vector_path=None): + ''' + Define an area of interest using either a point and buffer or a vector. + + Parameters: + ----------- + lat : float, optional + The latitude of the center point of the area of interest. + lon : float, optional + The longitude of the center point of the area of interest. + buffer : float, optional + The buffer around the center point, in degrees. + vector_path : str, optional + The path to a vector defining the area of interest. + + Returns: + -------- + feature_collection : dict + A GeoJSON feature collection representing the area of interest. + ''' + # Define area using point and buffer + if lat is not None and lon is not None and buffer is not None: + lat_range = (lat - buffer, lat + buffer) + lon_range = (lon - buffer, lon + buffer) + box_geom = box(min(lon_range), min(lat_range), max(lon_range), max(lat_range)) + aoi = gpd.GeoDataFrame(geometry=[box_geom], crs='EPSG:4326') + + # Define area using vector + elif vector_path is not None: + aoi = gpd.read_file(vector_path).to_crs("EPSG:4326") + # If neither option is provided, raise an error + else: + raise ValueError("Either lat/lon/buffer or vector_path must be provided.") + + # Convert the GeoDataFrame to a GeoJSON FeatureCollection + features = [Feature(geometry=row["geometry"], properties=row.drop("geometry").to_dict()) for _, row in aoi.iterrows()] + feature_collection = FeatureCollection(features) + + return feature_collection \ No newline at end of file diff --git a/deafrica_tools/bandindices.py b/deafrica_tools/bandindices.py new file mode 100644 index 0000000..0092925 --- /dev/null +++ b/deafrica_tools/bandindices.py @@ -0,0 +1,615 @@ +""" +Functions for computing remote sensing band indices on Digital Earth Africa +data. +""" + +# Import required packages +import warnings +import numpy as np + +# Define custom functions +def calculate_indices( + ds, + index=None, + collection=None, + satellite_mission=None, + custom_varname=None, + normalise=True, + drop=False, + deep_copy=True, +): + """ + Takes an xarray dataset containing spectral bands, calculates one of + a set of remote sensing indices, and adds the resulting array as a + new variable in the original dataset. + + Last modified: July 2022 + + Parameters + ---------- + ds : xarray Dataset + A two-dimensional or multi-dimensional array with containing the + spectral bands required to calculate the index. These bands are + used as inputs to calculate the selected water index. + + index : str or list of strs + A string giving the name of the index to calculate or a list of + strings giving the names of the indices to calculate: + + * ``'ASI'`` (Artificial Surface Index, Yongquan Zhao & Zhe Zhu 2022) + * ``'AWEI_ns'`` (Automated Water Extraction Index, no shadows, Feyisa 2014) + * ``'AWEI_sh'`` (Automated Water Extraction Index, shadows, Feyisa 2014) + * ``'BAEI'`` (Built-Up Area Extraction Index, Bouzekri et al. 2015) + * ``'BAI'`` (Burn Area Index, Martin 1998) + * ``'BSI'`` (Bare Soil Index, Rikimaru et al. 2002) + * ``'BUI'`` (Built-Up Index, He et al. 2010) + * ``'CMR'`` (Clay Minerals Ratio, Drury 1987) + * ``'ENDISI'`` (Enhanced Normalised Difference for Impervious Surfaces Index, Chen et al. 2019) + * ``'EVI'`` (Enhanced Vegetation Index, Huete 2002) + * ``'FMR'`` (Ferrous Minerals Ratio, Segal 1982) + * ``'IOR'`` (Iron Oxide Ratio, Segal 1982) + * ``'LAI'`` (Leaf Area Index, Boegh 2002) + * ``'MBI'`` (Modified Bare Soil Index, Nguyen et al. 2021) + * ``'MNDWI'`` (Modified Normalised Difference Water Index, Xu 1996) + * ``'MSAVI'`` (Modified Soil Adjusted Vegetation Index, Qi et al. 1994) + * ``'NBI'`` (New Built-Up Index, Jieli et al. 2010) + * ``'NBR'`` (Normalised Burn Ratio, Lopez Garcia 1991) + * ``'NDBI'`` (Normalised Difference Built-Up Index, Zha 2003) + * ``'NDCI'`` (Normalised Difference Chlorophyll Index, Mishra & Mishra, 2012) + * ``'NDMI'`` (Normalised Difference Moisture Index, Gao 1996) + * ``'NDSI'`` (Normalised Difference Snow Index, Hall 1995) + * ``'NDTI'`` (Normalised Difference Turbidity Index, Lacaux et al. 2007) + * ``'NDVI'`` (Normalised Difference Vegetation Index, Rouse 1973) + * ``'NDWI'`` (Normalised Difference Water Index, McFeeters 1996) + * ``'SAVI'`` (Soil Adjusted Vegetation Index, Huete 1988) + * ``'TCB'`` (Tasseled Cap Brightness, Crist 1985) + * ``'TCG'`` (Tasseled Cap Greeness, Crist 1985) + * ``'TCW'`` (Tasseled Cap Wetness, Crist 1985) + * ``'WI'`` (Water Index, Fisher 2016) + + collection : str + Deprecated in version 0.1.7. Use `satellite_mission` instead. + + Valid options are: + * ``'c2'`` (for USGS Landsat Collection 2) + If 'c2', then `satellite_mission='ls'`. + * ``'s2'`` (for Sentinel-2) + If 's2', then `satellite_mission='s2'`. + + satellite_mission : str + An string that tells the function which satellite mission's data is + being used to calculate the index. This is necessary because + different satellite missions use different names for bands covering + a similar spectra. + + Valid options are: + + * ``'ls'`` (for USGS Landsat) + * ``'s2'`` (for Copernicus Sentinel-2) + + custom_varname : str, optional + By default, the original dataset will be returned with + a new index variable named after `index` (e.g. 'NDVI'). To + specify a custom name instead, you can supply e.g. + `custom_varname='custom_name'`. Defaults to None, which uses + `index` to name the variable. + + normalise : bool, optional + Some coefficient-based indices (e.g. ``'WI'``, ``'BAEI'``, + ``'AWEI_ns'``, ``'AWEI_sh'``, ``'TCW'``, ``'TCG'``, ``'TCB'``, + ``'EVI'``, ``'LAI'``, ``'SAVI'``, ``'MSAVI'``) + produce different results if surface reflectance values are not + scaled between 0.0 and 1.0 prior to calculating the index. + Setting `normalise=True` first scales values to a 0.0-1.0 range + by dividing by 10000.0. Defaults to True. + + drop : bool, optional + Provides the option to drop the original input data, thus saving + space. If `drop=True`, returns only the index and its values. + + deep_copy: bool, optional + If `deep_copy=False`, calculate_indices will modify the original + array, adding bands to the input dataset and not removing them. + If the calculate_indices function is run more than once, variables + may be dropped incorrectly producing unexpected behaviour. This is + a bug and may be fixed in future releases. This is only a problem + when `drop=True`. + + Returns + ------- + ds : xarray Dataset + The original xarray Dataset inputted into the function, with a + new varible containing the remote sensing index as a DataArray. + If drop = True, the new variable/s as DataArrays in the + original Dataset. + """ + + # Set ds equal to a copy of itself in order to prevent the function + # from editing the input dataset. This is to prevent unexpected + # behaviour though it uses twice as much memory. + if deep_copy: + ds = ds.copy(deep=True) + + # Capture input band names in order to drop these if drop=True + if drop: + bands_to_drop = list(ds.data_vars) + print(f"Dropping bands {bands_to_drop}") + + # Dictionary containing remote sensing index band recipes + index_dict = { + # Normalised Difference Vegation Index, Rouse 1973 + "NDVI": lambda ds: (ds.nir - ds.red) / (ds.nir + ds.red), + # Enhanced Vegetation Index, Huete 2002 + "EVI": lambda ds: ( + 2.5 * ((ds.nir - ds.red) / (ds.nir + 6 * ds.red - 7.5 * ds.blue + 1)) + ), + # Leaf Area Index, Boegh 2002 + "LAI": lambda ds: ( + 3.618 + * ((2.5 * (ds.nir - ds.red)) / (ds.nir + (6 * ds.red) - (7.5 * ds.blue) + 1)) + - 0.118 + ), + # Soil Adjusted Vegetation Index, Huete 1988 + "SAVI": lambda ds: ((1.5 * (ds.nir - ds.red)) / (ds.nir + ds.red + 0.5)), + # Mod. Soil Adjusted Vegetation Index, Qi et al. 1994 + "MSAVI": lambda ds: ( + (2 * ds.nir + 1 - ((2 * ds.nir + 1) ** 2 - 8 * (ds.nir - ds.red)) ** 0.5) + / 2 + ), + # Normalised Difference Moisture Index, Gao 1996 + "NDMI": lambda ds: (ds.nir - ds.swir_1) / (ds.nir + ds.swir_1), + # Normalised Burn Ratio, Lopez Garcia 1991 + "NBR": lambda ds: (ds.nir - ds.swir_2) / (ds.nir + ds.swir_2), + # Burn Area Index, Martin 1998 + "BAI": lambda ds: (1.0 / ((0.10 - ds.red) ** 2 + (0.06 - ds.nir) ** 2)), + # Normalised Difference Chlorophyll Index, + # (Mishra & Mishra, 2012) + "NDCI": lambda ds: (ds.red_edge_1 - ds.red) / (ds.red_edge_1 + ds.red), + # Normalised Difference Snow Index, Hall 1995 + "NDSI": lambda ds: (ds.green - ds.swir_1) / (ds.green + ds.swir_1), + # Normalised Difference Water Index, McFeeters 1996 + "NDWI": lambda ds: (ds.green - ds.nir) / (ds.green + ds.nir), + # Modified Normalised Difference Water Index, Xu 2006 + "MNDWI": lambda ds: (ds.green - ds.swir_1) / (ds.green + ds.swir_1), + # Normalised Difference Built-Up Index, Zha 2003 + "NDBI": lambda ds: (ds.swir_1 - ds.nir) / (ds.swir_1 + ds.nir), + # Built-Up Index, He et al. 2010 + "BUI": lambda ds: ((ds.swir_1 - ds.nir) / (ds.swir_1 + ds.nir)) + - ((ds.nir - ds.red) / (ds.nir + ds.red)), + # Built-up Area Extraction Index, Bouzekri et al. 2015 + "BAEI": lambda ds: (ds.red + 0.3) / (ds.green + ds.swir_1), + # New Built-up Index, Jieli et al. 2010 + "NBI": lambda ds: (ds.swir_1 + ds.red) / ds.nir, + # Bare Soil Index, Rikimaru et al. 2002 + "BSI": lambda ds: ((ds.swir_1 + ds.red) - (ds.nir + ds.blue)) + / ((ds.swir_1 + ds.red) + (ds.nir + ds.blue)), + # Automated Water Extraction Index (no shadows), Feyisa 2014 + "AWEI_ns": lambda ds: ( + 4 * (ds.green - ds.swir_1) - (0.25 * ds.nir * +2.75 * ds.swir_2) + ), + # Automated Water Extraction Index (shadows), Feyisa 2014 + "AWEI_sh": lambda ds: ( + ds.blue + 2.5 * ds.green - 1.5 * (ds.nir + ds.swir_1) - 0.25 * ds.swir_2 + ), + # Water Index, Fisher 2016 + "WI": lambda ds: ( + 1.7204 + + 171 * ds.green + + 3 * ds.red + - 70 * ds.nir + - 45 * ds.swir_1 + - 71 * ds.swir_2 + ), + # Tasseled Cap Wetness, Crist 1985 + "TCW": lambda ds: ( + 0.0315 * ds.blue + + 0.2021 * ds.green + + 0.3102 * ds.red + + 0.1594 * ds.nir + + -0.6806 * ds.swir_1 + + -0.6109 * ds.swir_2 + ), + # Tasseled Cap Greeness, Crist 1985 + "TCG": lambda ds: ( + -0.1603 * ds.blue + + -0.2819 * ds.green + + -0.4934 * ds.red + + 0.7940 * ds.nir + + -0.0002 * ds.swir_1 + + -0.1446 * ds.swir_2 + ), + # Tasseled Cap Brightness, Crist 1985 + "TCB": lambda ds: ( + 0.2043 * ds.blue + + 0.4158 * ds.green + + 0.5524 * ds.red + + 0.5741 * ds.nir + + 0.3124 * ds.swir_1 + + -0.2303 * ds.swir_2 + ), + # Clay Minerals Ratio, Drury 1987 + "CMR": lambda ds: (ds.swir_1 / ds.swir_2), + # Ferrous Minerals Ratio, Segal 1982 + "FMR": lambda ds: (ds.swir_1 / ds.nir), + # Iron Oxide Ratio, Segal 1982 + "IOR": lambda ds: (ds.red / ds.blue), + # Normalized Difference Turbidity Index, Lacaux, J.P. et al. 2007 + "NDTI": lambda ds: (ds.red - ds.green) / (ds.red + ds.green), + # Modified Bare Soil Index, Nguyen et al. 2021 + "MBI": lambda ds: ((ds.swir_1 - ds.swir_2 - ds.nir) / (ds.swir_1 + ds.swir_2 + ds.nir)) + 0.5, + } + + # Enhanced Normalised Difference Impervious Surfaces Index, Chen et al. 2019 + def mndwi(ds): + return (ds.green - ds.swir_1) / (ds.green + ds.swir_1) + def swir_diff(ds): + return ds.swir_1/ds.swir_2 + def alpha(ds): + return (2*(np.mean(ds.blue)))/(np.mean(swir_diff(ds)) + np.mean(mndwi(ds)**2)) + def ENDISI(ds): + m = mndwi(ds) + s = swir_diff(ds) + a = alpha(ds) + return (ds.blue - (a)*(s + m**2))/(ds.blue + (a)*(s + m**2)) + + index_dict["ENDISI"] = ENDISI + + ## Artificial Surface Index, Yongquan Zhao & Zhe Zhu 2022 + def af(ds): + AF = (ds.nir - ds.blue) / (ds.nir + ds.blue) + AF_norm = (AF - AF.min(dim=["y","x"]))/(AF.max(dim=["y","x"]) - AF.min(dim=["y","x"])) + return AF_norm + def ndvi(ds): + return (ds.nir - ds.red) / (ds.nir + ds.red) + def msavi(ds): + return ((2 * ds.nir + 1 - ((2 * ds.nir + 1) ** 2 - 8 * (ds.nir - ds.red)) ** 0.5) / 2 ) + def vsf(ds): + NDVI = ndvi(ds) + MSAVI = msavi(ds) + VSF = 1 - NDVI * MSAVI + VSF_norm = (VSF - VSF.min(dim=["y","x"]))/(VSF.max(dim=["y","x"]) - VSF.min(dim=["y","x"])) + return VSF_norm + def mbi(ds): + return ((ds.swir_1 - ds.swir_2 - ds.nir) / (ds.swir_1 + ds.swir_2 + ds.nir)) + 0.5 + def embi(ds): + MBI = mbi(ds) + MNDWI = mndwi(ds) + return (MBI - MNDWI - 0.5) / (MBI + MNDWI + 1.5) + def ssf(ds): + EMBI = embi(ds) + SSF = 1 - EMBI + SSF_norm = (SSF - SSF.min(dim=["y","x"]))/(SSF.max(dim=["y","x"]) - SSF.min(dim=["y","x"])) + return SSF_norm + # Overall modulation using the Modulation Factor (MF). + def mf(ds): + MF = ((ds.blue + ds.green) - (ds.nir + ds.swir_1)) / ((ds.blue + ds.green) + (ds.nir + ds.swir_1)) + MF_norm = (MF - MF.min(dim=["y","x"]))/(MF.max(dim=["y","x"]) - MF.min(dim=["y","x"])) + return MF_norm + def ASI(ds): + AF = af(ds) + VSF = vsf(ds) + SSF = ssf(ds) + MF = mf(ds) + return AF * VSF * SSF * MF + + index_dict["ASI"] = ASI + + # If index supplied is not a list, convert to list. This allows us to + # iterate through either multiple or single indices in the loop below + indices = index if isinstance(index, list) else [index] + + # calculate for each index in the list of indices supplied (indexes) + for index in indices: + + # Select an index function from the dictionary + index_func = index_dict.get(str(index)) + + # If no index is provided or if no function is returned due to an + # invalid option being provided, raise an exception informing user to + # choose from the list of valid options + if index is None: + + raise ValueError( + f"No remote sensing `index` was provided. Please " + "refer to the function \ndocumentation for a full " + "list of valid options for `index` (e.g. 'NDVI')" + ) + + elif ( + index + in [ + "WI", + "BAEI", + "AWEI_ns", + "AWEI_sh", + "EVI", + "LAI", + "SAVI", + "MSAVI", + ] + and not normalise + ): + + warnings.warn( + f"\nA coefficient-based index ('{index}') normally " + "applied to surface reflectance values in the \n" + "0.0-1.0 range was applied to values in the 0-10000 " + "range. This can produce unexpected results; \nif " + "required, resolve this by setting `normalise=True`" + ) + + elif index_func is None: + + raise ValueError( + f"The selected index '{index}' is not one of the " + "valid remote sensing index options. \nPlease " + "refer to the function documentation for a full " + "list of valid options for `index`" + ) + + # Deprecation warning if `collection` is specified instead of `satellite_mission`. + if collection is not None: + warnings.warn('`collection` was deprecated in version 0.1.7. Use `satelite_mission` instead.', + DeprecationWarning, + stacklevel=2) + # Map the collection values to the valid satellite_mission values. + if collection == "c2": + satellite_mission = "ls" + elif collection == "s2": + satellite_mission = "s2" + # Raise error if no valid collection name is provided: + else: + raise ValueError( + f"'{collection}' is not a valid option for " + "`collection`. Please specify either \n" + "'c2' or 's2'.") + + + # Rename bands to a consistent format if depending on what satellite mission + # is specified in `satellite_mission`. This allows the same index calculations + # to be applied to all satellite missions. If no satellite mission was provided, + # raise an exception. + if satellite_mission is None: + + raise ValueError( + "No `satellite_mission` was provided. Please specify " + "either 'ls' or 's2' to ensure the \nfunction " + "calculates indices using the correct spectral " + "bands." + ) + + elif satellite_mission == "ls": + sr_max = 1.0 + # Dictionary mapping full data names to simpler alias names + # This only applies to properly-scaled "ls" data i.e. from + # the Landsat geomedians. calculate_indices will not show + # correct output for raw (unscaled) Landsat data (i.e. default + # outputs from dc.load) + bandnames_dict = { + "SR_B1": "blue", + "SR_B2": "green", + "SR_B3": "red", + "SR_B4": "nir", + "SR_B5": "swir_1", + "SR_B7": "swir_2", + } + + # Rename bands in dataset to use simple names (e.g. 'red') + bands_to_rename = { + a: b for a, b in bandnames_dict.items() if a in ds.variables + } + + elif satellite_mission == "s2": + sr_max = 10000 + # Dictionary mapping full data names to simpler alias names + bandnames_dict = { + "nir_1": "nir", + "B02": "blue", + "B03": "green", + "B04": "red", + "B05": "red_edge_1", + "B06": "red_edge_2", + "B07": "red_edge_3", + "B08": "nir", + "B11": "swir_1", + "B12": "swir_2", + } + + # Rename bands in dataset to use simple names (e.g. 'red') + bands_to_rename = { + a: b for a, b in bandnames_dict.items() if a in ds.variables + } + + # Raise error if no valid satellite_mission name is provided: + else: + raise ValueError( + f"'{satellite_mission}' is not a valid option for " + "`satellite_mission`. Please specify either \n" + "'ls' or 's2'" + ) + + # Apply index function + try: + # If normalised=True, divide data by 10,000 before applying func + mult = sr_max if normalise else 1.0 + index_array = index_func(ds.rename(bands_to_rename) / mult) + + except AttributeError: + raise ValueError( + f"Please verify that all bands required to " + f"compute {index} are present in `ds`." + ) + + # Add as a new variable in dataset + output_band_name = custom_varname if custom_varname else index + ds[output_band_name] = index_array + + # Once all indexes are calculated, drop input bands if drop=True + if drop: + ds = ds.drop(bands_to_drop) + + # Return input dataset with added water index variable + return ds + +def dualpol_indices( + ds, + co_pol='vv', + cross_pol='vh', + index=None, + custom_varname=None, + drop=False, + deep_copy=True, +): + """ + Takes an xarray dataset containing dual-polarization radar backscatter, + calculates one or a set of indices, and adds the resulting array as a + new variable in the original dataset. + + Last modified: July 2021 + + Parameters + ---------- + ds : xarray Dataset + A two-dimensional or multi-dimensional array containing the + two polarization bands. + + co_pol: str + Measurement name for the co-polarization band. + Default is 'vv' for Sentinel-1. + + cross_pol: str + Measurement name for the cross-polarization band. + Default is 'vh' for Sentinel-1. + + index : str or list of strs + A string giving the name of the index to calculate or a list of + strings giving the names of the indices to calculate: + + * ``'RVI'`` (Radar Vegetation Index for dual-pol, Trudel et al. 2012; Nasirzadehdizaji et al., 2019; Gururaj et al., 2019) + * ``'VDDPI'`` (Vertical dual depolarization index, Periasamy 2018) + * ``'theta'`` (pseudo scattering-type, Bhogapurapu et al. 2021) + * ``'entropy'`` (pseudo scattering entropy, Bhogapurapu et al. 2021) + * ``'purity'`` (co-pol purity, Bhogapurapu et al. 2021) + * ``'ratio'`` (cross-pol/co-pol ratio) + + custom_varname : str, optional + By default, the original dataset will be returned with + a new index variable named after `index` (e.g. 'RVI'). To + specify a custom name instead, you can supply e.g. + `custom_varname='custom_name'`. Defaults to None, which uses + `index` to name the variable. + + drop : bool, optional + Provides the option to drop the original input data, thus saving + space. If `drop=True`, returns only the index and its values. + + deep_copy: bool, optional + If `deep_copy=False`, calculate_indices will modify the original + array, adding bands to the input dataset and not removing them. + If the calculate_indices function is run more than once, variables + may be dropped incorrectly producing unexpected behaviour. This is + a bug and may be fixed in future releases. This is only a problem + when `drop=True`. + + Returns + ------- + ds : xarray Dataset + The original xarray Dataset inputted into the function, with a + new varible containing the remote sensing index as a DataArray. + If drop = True, the new variable/s as DataArrays in the + original Dataset. + """ + + if not co_pol in list(ds.data_vars): + raise ValueError(f"{co_pol} measurement is not in the dataset") + if not cross_pol in list(ds.data_vars): + raise ValueError(f"{cross_pol} measurement is not in the dataset") + + # Set ds equal to a copy of itself in order to prevent the function + # from editing the input dataset. This is to prevent unexpected + # behaviour though it uses twice as much memory. + if deep_copy: + ds = ds.copy(deep=True) + + # Capture input band names in order to drop these if drop=True + if drop: + bands_to_drop = list(ds.data_vars) + print(f"Dropping bands {bands_to_drop}") + + def ratio(ds): + return ds[cross_pol] / ds[co_pol] + + def purity(ds): + return (1 - ratio(ds)) / (1 + ratio(ds)) + + def theta(ds): + return np.arctan((1 - ratio(ds))**2 / (1 + ratio(ds)**2 - ratio(ds))) + + def P1(ds): + return 1 / (1 + ratio(ds)) + + def P2(ds): + return 1 - P1(ds) + + def entropy(ds): + return P1(ds)*np.log2(P1(ds)) + P2(ds)*np.log2(P2(ds)) + + # Dictionary containing remote sensing index band recipes + index_dict = { + # Radar Vegetation Index for dual-pol, Trudel et al. 2012 + "RVI": lambda ds: 4*ds[cross_pol] / (ds[co_pol] + ds[cross_pol]), + # Vertical dual depolarization index, Periasamy 2018 + "VDDPI": lambda ds: (ds[co_pol] + ds[cross_pol]) / ds[co_pol], + # cross-pol/co-pol ratio + "ratio": ratio, + # co-pol purity, Bhogapurapu et al. 2021 + "purity": purity, + # pseudo scattering-type, Bhogapurapu et al. 2021 + "theta": theta, + # pseudo scattering entropy, Bhogapurapu et al. 2021 + "entropy": entropy, + } + + # If index supplied is not a list, convert to list. This allows us to + # iterate through either multiple or single indices in the loop below + indices = index if isinstance(index, list) else [index] + + # calculate for each index in the list of indices supplied (indexes) + for index in indices: + + # Select an index function from the dictionary + index_func = index_dict.get(str(index)) + + # If no index is provided or if no function is returned due to an + # invalid option being provided, raise an exception informing user to + # choose from the list of valid options + if index is None: + + raise ValueError( + f"No radar `index` was provided. Please " + "refer to the function \ndocumentation for a full " + "list of valid options for `index` (e.g. 'RVI')" + ) + + elif index_func is None: + + raise ValueError( + f"The selected index '{index}' is not one of the " + "valid remote sensing index options. \nPlease " + "refer to the function documentation for a full " + "list of valid options for `index`" + ) + + # Apply index function + index_array = index_func(ds) + + # Add as a new variable in dataset + output_band_name = custom_varname if custom_varname else index + ds[output_band_name] = index_array + + # Once all indexes are calculated, drop input bands if drop=True + if drop: + ds = ds.drop(bands_to_drop) + + # Return input dataset with added water index variable + return ds diff --git a/deafrica_tools/classification.py b/deafrica_tools/classification.py new file mode 100644 index 0000000..027ba95 --- /dev/null +++ b/deafrica_tools/classification.py @@ -0,0 +1,1674 @@ +""" +Machine learning functions for classification of remote sensing data contained +in an Open Data Cube instance. +""" + +import multiprocessing as mp +import os +import sys +import time +import warnings +from abc import ABCMeta, abstractmethod +from copy import deepcopy + +import dask.array as da +import dask.distributed as dd +import joblib +import numpy as np +import pandas as pd +import xarray as xr +from dask_ml.wrappers import ParallelPostFit +from datacube.utils import geometry +from datacube.utils.geometry import assign_crs +from deafrica_tools.spatial import xr_rasterize +from sklearn.base import ClusterMixin +from sklearn.cluster import AgglomerativeClustering +from sklearn.cluster import KMeans +from sklearn.mixture import GaussianMixture +from sklearn.model_selection import BaseCrossValidator +from sklearn.model_selection import KFold, ShuffleSplit +from sklearn.utils import check_random_state +from tqdm.auto import tqdm + + +def sklearn_flatten(input_xr): + """ + Reshape a DataArray or Dataset with spatial (and optionally + temporal) structure into an np.array with the spatial and temporal + dimensions flattened into one dimension. + + This flattening procedure enables DataArrays and Datasets to be used + to train and predict with sklearn models. + + Last modified: September 2019 + + Parameters + ---------- + input_xr : xarray.DataArray or xarray.Dataset + Must have dimensions 'x' and 'y', may have dimension 'time'. + Dimensions other than 'x', 'y' and 'time' are unaffected by the + flattening. + + Returns + ---------- + input_np : numpy.array + A numpy array corresponding to input_xr.data (or + input_xr.to_array().data), with dimensions 'x','y' and 'time' + flattened into a single dimension, which is the first axis of + the returned array. input_np contains no NaNs. + + """ + # cast input Datasets to DataArray + if isinstance(input_xr, xr.Dataset): + input_xr = input_xr.to_array() + + # stack across pixel dimensions, handling timeseries if necessary + if "time" in input_xr.dims: + stacked = input_xr.stack(z=["x", "y", "time"]) + else: + stacked = input_xr.stack(z=["x", "y"]) + + # finding 'bands' dimensions in each pixel - these will not be + # flattened as their context is important for sklearn + pxdims = [] + for dim in stacked.dims: + if dim != "z": + pxdims.append(dim) + + # mask NaNs - we mask pixels with NaNs in *any* band, because + # sklearn cannot accept NaNs as input + mask = np.isnan(stacked) + if len(pxdims) != 0: + mask = mask.any(dim=pxdims) + + # turn the mask into a numpy array (boolean indexing with xarrays + # acts weird) + mask = mask.data + + # the dimension we are masking along ('z') needs to be the first + # dimension in the underlying np array for the boolean indexing to work + stacked = stacked.transpose("z", *pxdims) + input_np = stacked.data[~mask] + + return input_np + + +def sklearn_unflatten(output_np, input_xr): + """ + Reshape a numpy array with no 'missing' elements (NaNs) and + 'flattened' spatiotemporal structure into a DataArray matching the + spatiotemporal structure of the DataArray + + This enables an sklearn model's prediction to be remapped to the + correct pixels in the input DataArray or Dataset. + + Last modified: September 2019 + + Parameters + ---------- + output_np : numpy.array + The first dimension's length should correspond to the number of + valid (non-NaN) pixels in input_xr. + input_xr : xarray.DataArray or xarray.Dataset + Must have dimensions 'x' and 'y', may have dimension 'time'. + Dimensions other than 'x', 'y' and 'time' are unaffected by the + flattening. + + Returns + ---------- + output_xr : xarray.DataArray + An xarray.DataArray with the same dimensions 'x', 'y' and 'time' + as input_xr, and the same valid (non-NaN) pixels. These pixels + are set to match the data in output_np. + + """ + + # the output of a sklearn model prediction should just be a numpy array + # with size matching x*y*time for the input DataArray/Dataset. + + # cast input Datasets to DataArray + if isinstance(input_xr, xr.Dataset): + input_xr = input_xr.to_array() + + # generate the same mask we used to create the input to the sklearn model + if "time" in input_xr.dims: + stacked = input_xr.stack(z=["x", "y", "time"]) + else: + stacked = input_xr.stack(z=["x", "y"]) + + pxdims = [] + for dim in stacked.dims: + if dim != "z": + pxdims.append(dim) + + mask = np.isnan(stacked) + if len(pxdims) != 0: + mask = mask.any(dim=pxdims) + + # handle multivariable output + output_px_shape = () + if len(output_np.shape[1:]): + output_px_shape = output_np.shape[1:] + + # use the mask to put the data in all the right places + output_ma = np.ma.empty((len(stacked.z), *output_px_shape)) + output_ma[~mask] = output_np + output_ma[mask] = np.ma.masked + + # set the stacked coordinate to match the input + output_xr = xr.DataArray( + output_ma, + coords={"z": stacked["z"]}, + dims=["z", *["output_dim_" + str(idx) for idx in range(len(output_px_shape))]], + ) + + output_xr = output_xr.unstack() + + return output_xr + + +def fit_xr(model, input_xr): + """ + Utilise our wrappers to fit a vanilla sklearn model. + + Last modified: September 2019 + + Parameters + ---------- + model : scikit-learn model or compatible object + Must have a fit() method that takes numpy arrays. + input_xr : xarray.DataArray or xarray.Dataset. + Must have dimensions 'x' and 'y', may have dimension 'time'. + + Returns + ---------- + model : a scikit-learn model which has been fitted to the data in + the pixels of input_xr. + + """ + + model = model.fit(sklearn_flatten(input_xr)) + return model + + +def predict_xr( + model, + input_xr, + chunk_size=None, + persist=False, + proba=False, + clean=True, + return_input=False, +): + """ + Using dask-ml ParallelPostfit(), runs the parallel + predict and predict_proba methods of sklearn + estimators. Useful for running predictions + on a larger-than-RAM datasets. + + Last modified: September 2020 + + Parameters + ---------- + model : scikit-learn model or compatible object + Must have a .predict() method that takes numpy arrays. + input_xr : xarray.DataArray or xarray.Dataset. + Must have dimensions 'x' and 'y' + chunk_size : int + The dask chunk size to use on the flattened array. If this + is left as None, then the chunks size is inferred from the + .chunks method on the `input_xr` + persist : bool + If True, and proba=True, then 'input_xr' data will be + loaded into distributed memory. This will ensure data + is not loaded twice for the prediction of probabilities, + but this will only work if the data is not larger than + distributed RAM. + proba : bool + If True, predict probabilities + clean : bool + If True, remove Infs and NaNs from input and output arrays + return_input : bool + If True, then the data variables in the 'input_xr' dataset will + be appended to the output xarray dataset. + + Returns + ---------- + output_xr : xarray.Dataset + An xarray.Dataset containing the prediction output from model. + if proba=True then dataset will also contain probabilites, and + if return_input=True then dataset will have the input feature layers. + Has the same spatiotemporal structure as input_xr. + + """ + # if input_xr isn't dask, coerce it + dask = True + if not bool(input_xr.chunks): + dask = False + input_xr = input_xr.chunk({"x": len(input_xr.x), "y": len(input_xr.y)}) + + # set chunk size if not supplied + if chunk_size is None: + chunk_size = int(input_xr.chunks["x"][0]) * int(input_xr.chunks["y"][0]) + + def _predict_func(model, input_xr, persist, proba, clean, return_input): + x, y, crs = input_xr.x, input_xr.y, input_xr.geobox.crs + + input_data = [] + + for var_name in input_xr.data_vars: + input_data.append(input_xr[var_name]) + + input_data_flattened = [] + + for arr in input_data: + data = arr.data.flatten().rechunk(chunk_size) + input_data_flattened.append(data) + + # reshape for prediction + input_data_flattened = da.array(input_data_flattened).transpose() + + if clean == True: + input_data_flattened = da.where( + da.isfinite(input_data_flattened), input_data_flattened, 0 + ) + + if (proba == True) & (persist == True): + # persisting data so we don't require loading all the data twice + input_data_flattened = input_data_flattened.persist() + + # apply the classification + print("predicting...") + out_class = model.predict(input_data_flattened) + + # Mask out NaN or Inf values in results + if clean == True: + out_class = da.where(da.isfinite(out_class), out_class, 0) + + # Reshape when writing out + out_class = out_class.reshape(len(y), len(x)) + + # stack back into xarray + output_xr = xr.DataArray(out_class, coords={"x": x, "y": y}, dims=["y", "x"]) + + output_xr = output_xr.to_dataset(name="Predictions") + + if proba == True: + print(" probabilities...") + out_proba = model.predict_proba(input_data_flattened) + + # convert to % + out_proba = da.max(out_proba, axis=1) * 100.0 + + if clean == True: + out_proba = da.where(da.isfinite(out_proba), out_proba, 0) + + out_proba = out_proba.reshape(len(y), len(x)) + + out_proba = xr.DataArray( + out_proba, coords={"x": x, "y": y}, dims=["y", "x"] + ) + output_xr["Probabilities"] = out_proba + + if return_input == True: + print(" input features...") + # unflatten the input_data_flattened array and append + # to the output_xr containin the predictions + arr = input_xr.to_array() + stacked = arr.stack(z=["y", "x"]) + + # handle multivariable output + output_px_shape = () + if len(input_data_flattened.shape[1:]): + output_px_shape = input_data_flattened.shape[1:] + + output_features = input_data_flattened.reshape( + (len(stacked.z), *output_px_shape) + ) + + # set the stacked coordinate to match the input + output_features = xr.DataArray( + output_features, + coords={"z": stacked["z"]}, + dims=[ + "z", + *["output_dim_" + str(idx) for idx in range(len(output_px_shape))], + ], + ).unstack() + + # convert to dataset and rename arrays + output_features = output_features.to_dataset(dim="output_dim_0") + data_vars = list(input_xr.data_vars) + output_features = output_features.rename( + {i: j for i, j in zip(output_features.data_vars, data_vars)} + ) + + # merge with predictions + output_xr = xr.merge([output_xr, output_features], compat="override") + + return assign_crs(output_xr, str(crs)) + + if dask == True: + # convert model to dask predict + model = ParallelPostFit(model) + with joblib.parallel_backend("dask", wait_for_workers_timeout=20): + output_xr = _predict_func( + model, input_xr, persist, proba, clean, return_input + ) + + else: + output_xr = _predict_func( + model, input_xr, persist, proba, clean, return_input + ).compute() + + return output_xr + + +class HiddenPrints: + """ + For concealing unwanted print statements called by other functions + """ + + def __enter__(self): + self._original_stdout = sys.stdout + sys.stdout = open(os.devnull, "w") + + def __exit__(self, exc_type, exc_val, exc_tb): + sys.stdout.close() + sys.stdout = self._original_stdout + + +def _get_training_data_for_shp( + gdf, + index, + row, + out_arrs, + out_vars, + dc_query, + return_coords, + feature_func=None, + field=None, + zonal_stats=None, +): + """ + This is the core function that is triggered by `collect_training_data`. + The `collect_training_data` function loops through geometries in a geopandas + geodataframe and runs the code within `_get_training_data_for_shp`. + Parameters are inherited from `collect_training_data`. + See that function for information on the other params not listed below. + + Parameters + ---------- + index, row : iterables inherited from geopandas object + out_arrs : list + An empty list into which the training data arrays are stored. + out_vars : list + An empty list into which the data varaible names are stored. + + + Returns + -------- + Two lists, a list of numpy.arrays containing classes and extracted data for + each pixel or polygon, and another containing the data variable names. + + """ + + # prevent function altering dictionary kwargs + dc_query = deepcopy(dc_query) + + # remove dask chunks if supplied as using + # mulitprocessing for parallization + if "dask_chunks" in dc_query.keys(): + dc_query.pop("dask_chunks", None) + + # set up query based on polygon + geom = geometry.Geometry(geom=gdf.iloc[index].geometry, crs=gdf.crs) + q = {"geopolygon": geom} + + # merge polygon query with user supplied query params + dc_query.update(q) + + # Use input feature function + data = feature_func(dc_query) + + # create polygon mask + mask = xr_rasterize(gdf.iloc[[index]], data) + data = data.where(mask) + + # Check that feature_func has removed time + if "time" in data.dims: + t = data.dims["time"] + if t > 1: + raise ValueError( + "After running the feature_func, the dataset still has " + + str(t) + + " time-steps, dataset must only have" + + " x and y dimensions." + ) + + if return_coords == True: + # turn coords into a variable in the ds + data["x_coord"] = data.x + 0 * data.y + data["y_coord"] = data.y + 0 * data.x + + # append ID measurement to dataset for tracking failures + band = [m for m in data.data_vars][0] + _id = xr.zeros_like(data[band]) + data["id"] = _id + data["id"] = data["id"] + gdf.iloc[index]["id"] + + # If no zonal stats were requested then extract all pixel values + if zonal_stats is None: + flat_train = sklearn_flatten(data) + flat_val = np.repeat(row[field], flat_train.shape[0]) + stacked = np.hstack((np.expand_dims(flat_val, axis=1), flat_train)) + + elif zonal_stats in ["mean", "median", "max", "min"]: + method_to_call = getattr(data, zonal_stats) + flat_train = method_to_call() + flat_train = flat_train.to_array() + stacked = np.hstack((row[field], flat_train)) + + else: + raise Exception( + zonal_stats + + " is not one of the supported" + + " reduce functions ('mean','median','max','min')" + ) + + out_arrs.append(stacked) + out_vars.append([field] + list(data.data_vars)) + + +def _get_training_data_parallel( + gdf, dc_query, ncpus, return_coords, feature_func=None, field=None, zonal_stats=None +): + """ + Function passing the '_get_training_data_for_shp' function + to a mulitprocessing.Pool. + Inherits variables from 'collect_training_data'. + + """ + # Check if dask-client is running + try: + zx = None + zx = dd.get_client() + except: + pass + + if zx is not None: + raise ValueError( + "You have a Dask Client running, which prevents \n" + "this function from multiprocessing. Close the client." + ) + + # instantiate lists that can be shared across processes + manager = mp.Manager() + results = manager.list() + column_names = manager.list() + + # progress bar + pbar = tqdm(total=len(gdf)) + + def update(*a): + pbar.update() + + with mp.Pool(ncpus) as pool: + for index, row in gdf.iterrows(): + pool.apply_async( + _get_training_data_for_shp, + [ + gdf, + index, + row, + results, + column_names, + dc_query, + return_coords, + feature_func, + field, + zonal_stats, + ], + callback=update, + ) + + pool.close() + pool.join() + pbar.close() + + return column_names, results + + +def collect_training_data( + gdf, + dc_query, + ncpus=1, + return_coords=False, + feature_func=None, + field=None, + zonal_stats=None, + clean=True, + fail_threshold=0.02, + fail_ratio=0.5, + max_retries=3, +): + """ + This function provides methods for gathering training data from the ODC over + geometries stored within a geopandas geodataframe. The function will return a + 'model_input' array containing stacked training data arrays with all NaNs & Infs removed. + In the instance where ncpus > 1, a parallel version of the function will be run + (functions are passed to a mp.Pool()). This function can conduct zonal statistics if + the supplied shapefile contains polygons. The 'feature_func' parameter defines what + features to produce. + + Parameters + ---------- + gdf : geopandas geodataframe + geometry data in the form of a geopandas geodataframe + dc_query : dictionary + Datacube query object, should not contain lat and long (x or y) + variables as these are supplied by the 'gdf' variable + ncpus : int + The number of cpus/processes over which to parallelize the gathering + of training data (only if ncpus is > 1). Use 'mp.cpu_count()' to determine the number of + cpus available on a machine. Defaults to 1. + return_coords : bool + If True, then the training data will contain two extra columns 'x_coord' and + 'y_coord' corresponding to the x,y coordinate of each sample. This variable can + be useful for handling spatial autocorrelation between samples later in the ML workflow. + feature_func : function + A function for generating feature layers that is applied to the data within + the bounds of the input geometry. The 'feature_func' must accept a 'dc_query' + object, and return a single xarray.Dataset or xarray.DataArray containing + 2D coordinates (i.e x and y, without a third dimension). + e.g.:: + + def feature_function(query): + dc = datacube.Datacube(app='feature_layers') + ds = dc.load(**query) + ds = ds.mean('time') + return ds + + field : str + Name of the column in the gdf that contains the class labels + zonal_stats : string, optional + An optional string giving the names of zonal statistics to calculate + for each polygon. Default is None (all pixel values are returned). Supported + values are 'mean', 'median', 'max', 'min'. + clean : bool + Whether or not to remove missing values in the training dataset. If True, + training labels with any NaNs or Infs in the feature layers will be dropped + from the dataset. + fail_threshold : float, default 0.02 + Silent read fails on S3 during mulitprocessing can result in some rows + of the returned data containing NaN values. + The'fail_threshold' fraction specifies a % of acceptable fails. + e.g. Setting 'fail_threshold' to 0.05 means if >5% of the samples in the training dataset + fail then those samples will be returned to the multiprocessing queue. Below this fraction + the function will accept the failures and return the results. + fail_ratio: float + A float between 0 and 1 that defines if a given training sample has failed. + Default is 0.5, which means if 50 % of the measurements in a given sample return null + values, and the number of total fails is more than the 'fail_threshold', the sample + will be passed to the retry queue. + max_retries: int, default 3 + Maximum number of times to retry collecting samples. This number is invoked + if the 'fail_threshold' is not reached. + + Returns + -------- + Two objects are returned: + `columns_names`: a list of variable (feature) names + `model_input`: a numpy.array containing the data values for each feature extracted + + """ + + # check the dtype of the class field + if gdf[field].dtype != int: + raise ValueError( + 'The "field" column of the input vector must contain integer dtypes' + ) + + # set up some print statements + if feature_func is None: + raise ValueError( + "Please supply a feature layer function through the " + +"parameter 'feature_func'" + ) + + if zonal_stats is not None: + print("Taking zonal statistic: " + zonal_stats) + + # add unique id to gdf to help with indexing failed rows + # during multiprocessing + # if zonal_stats is not None: + gdf["id"] = range(0, len(gdf)) + + if ncpus == 1: + # progress indicator + print("Collecting training data in serial mode") + i = 0 + + # list to store results + results = [] + column_names = [] + + # loop through polys and extract training data + for index, row in gdf.iterrows(): + print(" Feature {:04}/{:04}\r".format(i + 1, len(gdf)), end="") + + _get_training_data_for_shp( + gdf, + index, + row, + results, + column_names, + dc_query, + return_coords, + feature_func, + field, + zonal_stats, + ) + i += 1 + + else: + print("Collecting training data in parallel mode") + column_names, results = _get_training_data_parallel( + gdf=gdf, + dc_query=dc_query, + ncpus=ncpus, + return_coords=return_coords, + feature_func=feature_func, + field=field, + zonal_stats=zonal_stats, + ) + + # column names are appended during each iteration + # but they are identical, grab only the first instance + column_names = column_names[0] + + # Stack the extracted training data for each feature into a single array + model_input = np.vstack(results) + + # this code block below iteratively retries failed rows + # up to max_retries or until fail_threshold is + # reached - whichever occurs first + if ncpus > 1: + i = 1 + while i <= max_retries: + # Find % of fails (null values) in data. Use Pandas for simplicity + df = pd.DataFrame(data=model_input[:, 0:-1], index=model_input[:, -1]) + # how many nan values per id? + num_nans = df.isnull().sum(axis=1) + num_nans = num_nans.groupby(num_nans.index).sum() + # how many valid values per id? + num_valid = df.notnull().sum(axis=1) + num_valid = num_valid.groupby(num_valid.index).sum() + # find fail rate + perc_fail = num_nans / (num_nans + num_valid) + fail_ids = perc_fail[perc_fail > fail_ratio] + fail_rate = len(fail_ids) / len(gdf) + + print( + "Percentage of possible fails after run " + + str(i) + + " = " + + str(round(fail_rate * 100, 2)) + + " %" + ) + + if fail_rate > fail_threshold: + print("Recollecting samples that failed") + + fail_ids = list(fail_ids.index) + # keep only the ids in model_input object that didn't fail + model_input = model_input[~np.isin(model_input[:, -1], fail_ids)] + + # index out the fail_ids from the original gdf + gdf_rerun = gdf.loc[gdf["id"].isin(fail_ids)] + gdf_rerun = gdf_rerun.reset_index(drop=True) + + time.sleep(5) # sleep for 5s to rest api + + # recollect failed rows + column_names_again, results_again = _get_training_data_parallel( + gdf=gdf_rerun, + dc_query=dc_query, + ncpus=ncpus, + return_coords=return_coords, + feature_func=feature_func, + field=field, + zonal_stats=zonal_stats, + ) + + # Stack the extracted training data for each feature into a single array + model_input_again = np.vstack(results_again) + + # merge results of the re-run with original run + model_input = np.vstack((model_input, model_input_again)) + + i += 1 + + else: + break + + # ----------------------------------------------- + + # remove id column + idx_var = column_names[0:-1] + model_col_indices = [column_names.index(var_name) for var_name in idx_var] + model_input = model_input[:, model_col_indices] + + if clean == True: + num = np.count_nonzero(np.isnan(model_input).any(axis=1)) + model_input = model_input[~np.isnan(model_input).any(axis=1)] + model_input = model_input[~np.isinf(model_input).any(axis=1)] + print("Removed " + str(num) + " rows wth NaNs &/or Infs") + print("Output shape: ", model_input.shape) + + else: + print("Returning data without cleaning") + print("Output shape: ", model_input.shape) + + return column_names[0:-1], model_input + + +class KMeans_tree(ClusterMixin): + """ + A hierarchical KMeans unsupervised clustering model. This class is + a clustering model, so it inherits scikit-learn's ClusterMixin + base class. + + Parameters + ---------- + n_levels : integer, default 2 + number of levels in the tree of clustering models. + n_clusters : integer, default 3 + Number of clusters in each of the constituent KMeans models in + the tree. + **kwargs : optional + Other keyword arguments to be passed directly to the KMeans + initialiser. + + """ + + def __init__(self, n_levels=2, n_clusters=3, **kwargs): + + assert n_levels >= 1 + + self.base_model = KMeans(n_clusters=3, **kwargs) + self.n_levels = n_levels + self.n_clusters = n_clusters + # make child models + if n_levels > 1: + self.branches = [ + KMeans_tree(n_levels=n_levels - 1, n_clusters=n_clusters, **kwargs) + for _ in range(n_clusters) + ] + + def fit(self, X, y=None, sample_weight=None): + """ + Fit the tree of KMeans models. All parameters mimic those + of KMeans.fit(). + + Parameters + ---------- + X : array-like or sparse matrix, shape=(n_samples, n_features) + Training instances to cluster. It must be noted that the + data will be converted to C ordering, which will cause a + memory copy if the given data is not C-contiguous. + y : Ignored + not used, present here for API consistency by convention. + sample_weight : array-like, shape (n_samples,), optional + The weights for each observation in X. If None, all + observations are assigned equal weight (default: None) + """ + + self.labels_ = self.base_model.fit(X, sample_weight=sample_weight).labels_ + + if self.n_levels > 1: + labels_old = np.copy(self.labels_) + # make room to add the sub-cluster labels + self.labels_ *= (self.n_clusters) ** (self.n_levels - 1) + + for clu in range(self.n_clusters): + # fit child models on their corresponding partition of the training set + self.branches[clu].fit( + X[labels_old == clu], + sample_weight=( + sample_weight[labels_old == clu] + if sample_weight is not None + else None + ), + ) + self.labels_[labels_old == clu] += self.branches[clu].labels_ + + return self + + def predict(self, X, sample_weight=None): + """ + Send X through the KMeans tree and predict the resultant + cluster. Compatible with KMeans.predict(). + + Parameters + ---------- + X : {array-like, sparse matrix}, shape = [n_samples, n_features] + New data to predict. + sample_weight : array-like, shape (n_samples,), optional + The weights for each observation in X. If None, all + observations are assigned equal weight (default: None) + + Returns + ------- + labels : array, shape [n_samples,] + Index of the cluster each sample belongs to. + """ + + result = self.base_model.predict(X, sample_weight=sample_weight) + + if self.n_levels > 1: + rescpy = np.copy(result) + + # make room to add the sub-cluster labels + result *= (self.n_clusters) ** (self.n_levels - 1) + + for clu in range(self.n_clusters): + result[rescpy == clu] += self.branches[clu].predict( + X[rescpy == clu], + sample_weight=( + sample_weight[rescpy == clu] + if sample_weight is not None + else None + ), + ) + + return result + + +def spatial_clusters( + coordinates, + method="Hierarchical", + max_distance=None, + n_groups=None, + verbose=False, + **kwargs +): + """ + Create spatial groups on coorindate data using either KMeans clustering + or a Gaussian Mixture model + + Last modified: September 2020 + + Parameters + ---------- + n_groups : int + The number of groups to create. This is passed as ``n_clusters=n_groups`` + for the KMeans algo, and ``n_components=n_groups`` for the GMM. If using + method=``'Hierarchical'`` then this parameter is ignored. + coordinates : np.array + A numpy array of coordinate values e.g.:: + + np.array([[3337270., 262400.], + [3441390., -273060.], ...]) + + method : str + Which algorithm to use to seperate data points. + Either ``'KMeans'``, ``'GMM'``, or ``'Hierarchical'``. + If using ``'Hierarchical'`` then must set max_distance. + max_distance : int + If method is set to ``'Hierarchical'`` then maximum distance describes the + maximum euclidean distances between all observations in a cluster. 'n_groups' + is ignored in this case. + **kwargs : optional, + Additional keyword arguments to pass to ``sklearn.cluster.Kmeans`` or + ``sklearn.mixture.GuassianMixture`` depending on the 'method' argument. + Returns + ------- + labels : array, shape [n_samples,] + Index of the cluster each sample belongs to. + """ + if method not in ["Hierarchical", "KMeans", "GMM"]: + raise ValueError("method must be one of: 'Hierarchical','KMeans' or 'GMM'") + + if (method in ["GMM", "KMeans"]) & (n_groups is None): + raise ValueError( + "The 'GMM' and 'KMeans' methods requires explicitly setting 'n_groups'" + ) + + if (method == "Hierarchical") & (max_distance is None): + raise ValueError("The 'Hierarchical' method requires setting max_distance") + + if method == "Hierarchical": + cluster_label = AgglomerativeClustering( + n_clusters=None, + linkage="complete", + distance_threshold=max_distance, + **kwargs + ).fit_predict(coordinates) + + if method == "KMeans": + cluster_label = KMeans(n_clusters=n_groups, **kwargs).fit_predict(coordinates) + + if method == "GMM": + cluster_label = GaussianMixture(n_components=n_groups, **kwargs).fit_predict( + coordinates + ) + if verbose: + print("n clusters = " + str(len(np.unique(cluster_label)))) + + return cluster_label + + +def SKCV( + coordinates, + n_splits, + cluster_method, + kfold_method, + test_size, + balance, + n_groups=None, + max_distance=None, + train_size=None, + random_state=None, + **kwargs +): + """ + Generate spatial k-fold cross validation indices using coordinate data. + + This function wraps the ``SpatialShuffleSplit`` and ``SpatialKFold`` classes. + These classes ingest coordinate data in the form of an + ``np.array([[eastings, northings]])`` and assign samples to a spatial cluster + using either a KMeans, Gaussain Mixture, or Agglomerative Clustering algorithm. + This cross-validator is preferred over other sklearn.model_selection methods + for spatial data to avoid overestimating cross-validation scores. + This can happen because of the inherent spatial autocorrelation that is usually + associated with this type of data. + + Last modified: Dec 2020 + + Parameters + ---------- + coordinates : np.array + A numpy array of coordinate values e.g.:: + + np.array([[3337270., 262400.], + [3441390., -273060.], ...]) + + n_splits : int + The number of test-train cross validation splits to generate. + cluster_method : str + Which algorithm to use to separate data points. Either ``'KMeans'``, + ``'GMM'``, or ``'Hierarchical'`` + kfold_method : str + One of either ``'SpatialShuffleSplit'`` or ``'SpatialKFold'``. See the docs + under class:_SpatialShuffleSplit and class:_SpatialKFold for more + information on these options. + test_size : float, int, None + If float, should be between 0.0 and 1.0 and represent the proportion + of the dataset to include in the test split. If int, represents the + absolute number of test samples. If None, the value is set to the + complement of the train size. If ``train_size`` is also None, it will + be set to 0.15. + balance : int or bool + if setting kfold_method to ``'SpatialShuffleSplit'``: int + The number of splits generated per iteration to try to balance the + amount of data in each set so that *test_size* and *train_size* are + respected. If 1, then no extra splits are generated (essentially + disabling the balacing). Must be >= 1. + + if setting kfold_method to ``'SpatialKFold'``: bool + Whether or not to split clusters into fold with approximately equal + number of data points. If False, each fold will have the same number of + clusters (which can have different number of data points in them). + + n_groups : int + The number of groups to create. This is passed as 'n_clusters=n_groups' + for the KMeans algo, and 'n_components=n_groups' for the GMM. If using + cluster_method='Hierarchical' then this parameter is ignored. + max_distance : int + If method is set to 'hierarchical' then maximum distance describes the + maximum euclidean distances between all observations in a cluster. 'n_groups' + is ignored in this case. + train_size : float, int, or None + If float, should be between 0.0 and 1.0 and represent the + proportion of the dataset to include in the train split. If + int, represents the absolute number of train samples. If None, + the value is automatically set to the complement of the test size. + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by ``np.random``. + **kwargs : optional, + Additional keyword arguments to pass to sklearn.cluster.Kmeans or + sklearn.mixture.GuassianMixture depending on the cluster_method argument. + Returns + -------- + generator object _BaseSpatialCrossValidator.split + + """ + # intiate a method + if kfold_method == "SpatialShuffleSplit": + splitter = _SpatialShuffleSplit( + n_groups=n_groups, + method=cluster_method, + coordinates=coordinates, + max_distance=max_distance, + test_size=test_size, + train_size=train_size, + n_splits=n_splits, + random_state=random_state, + balance=balance, + **kwargs + ) + + if kfold_method == "SpatialKFold": + splitter = _SpatialKFold( + n_groups=n_groups, + coordinates=coordinates, + max_distance=max_distance, + method=cluster_method, + test_size=test_size, + n_splits=n_splits, + random_state=random_state, + balance=balance, + **kwargs + ) + + return splitter + + +def spatial_train_test_split( + X, + y, + coordinates, + cluster_method, + kfold_method, + balance, + test_size=None, + n_splits=None, + n_groups=None, + max_distance=None, + train_size=None, + random_state=None, + **kwargs +): + """ + Split arrays into random train and test subsets. Similar to + `sklearn.model_selection.train_test_split` but instead works on + spatial coordinate data. Coordinate data is grouped according + to either a KMeans, Gaussain Mixture, or Agglomerative Clustering algorthim. + Grouping by spatial clusters is preferred over plain random splits for + spatial data to avoid overestimating validation scores due to spatial + autocorrelation. + + Parameters + ---------- + X : np.array + Training data features + y : np.array + Training data labels + coordinates : np.array + A numpy array of coordinate values e.g.:: + + np.array([[3337270., 262400.], + [3441390., -273060.], ...]) + + cluster_method : str + Which algorithm to use to seperate data points. + Either ``'KMeans'``, ``'GMM'``, or ``'Hierarchical'`` + kfold_method : str + One of either ``'SpatialShuffleSplit'`` or ``'SpatialKFold'``. + See the docs under class:_SpatialShuffleSplit and + class: _SpatialKFold for more information on these options. + balance : int or bool + if setting kfold_method to ''`SpatialShuffleSplit`'': int + The number of splits generated per iteration to try to balance the + amount of data in each set so that *test_size* and *train_size* are + respected. If 1, then no extra splits are generated (essentially + disabling the balacing). Must be >= 1. + + if setting kfold_method to ''`SpatialKFold`'': bool + Whether or not to split clusters into fold with approximately equal + number of data points. If False, each fold will have the same number of + clusters (which can have different number of data points in them). + + test_size : float, int, None + If float, should be between 0.0 and 1.0 and represent the proportion + of the dataset to include in the test split. If int, represents the + absolute number of test samples. If None, the value is set to the + complement of the train size. If ``train_size`` is also None, it will + be set to 0.15. + n_splits : int + This parameter is invoked for the 'SpatialKFold' folding method, use this + number to satisfy the train-test size ratio desired, as the 'test_size' + parameter for the KFold method often fails to get the ratio right. + n_groups : int + The number of groups to create. This is passed as 'n_clusters=n_groups' + for the KMeans algo, and 'n_components=n_groups' for the GMM. If using + cluster_method='Hierarchical' then this parameter is ignored. + max_distance : int + If method is set to 'hierarchical' then maximum distance describes the + maximum euclidean distances between all observations in a cluster. 'n_groups' + is ignored in this case. + train_size : float, int, or None + If float, should be between 0.0 and 1.0 and represent the + proportion of the dataset to include in the train split. If + int, represents the absolute number of train samples. If None, + the value is automatically set to the complement of the test size. + random_state : int, + RandomState instance or None, optional + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. + **kwargs : optional, + Additional keyword arguments to pass to sklearn.cluster.Kmeans or + sklearn.mixture.GuassianMixture depending on the cluster_method argument. + + Returns + ------- + Tuple : + Contains four arrays in the following order: + X_train, X_test, y_train, y_test + + """ + + if kfold_method == "SpatialShuffleSplit": + splitter = _SpatialShuffleSplit( + n_groups=n_groups, + method=cluster_method, + coordinates=coordinates, + max_distance=max_distance, + test_size=test_size, + train_size=train_size, + n_splits=1 if n_splits is None else n_splits, + random_state=random_state, + balance=balance, + **kwargs + ) + + if kfold_method == "SpatialKFold": + if n_splits is None: + raise ValueError( + "n_splits parameter requires an integer value, eg. 'n_splits=5'" + ) + if (test_size is not None) or (train_size is not None): + warnings.warn( + "With the 'SpatialKFold' method, controlling the test/train ratio " + "is better achieved using the 'n_splits' parameter" + ) + + splitter = _SpatialKFold( + n_groups=n_groups, + coordinates=coordinates, + max_distance=max_distance, + method=cluster_method, + n_splits=n_splits, + random_state=random_state, + balance=balance, + **kwargs + ) + + lst = [] + for train, test in splitter.split(coordinates): + X_tr, X_tt = X[train, :], X[test, :] + y_tr, y_tt = y[train], y[test] + lst.extend([X_tr, X_tt, y_tr, y_tt]) + + return (lst[0], lst[1], lst[2], lst[3]) + + +def _partition_by_sum(array, parts): + """ + Partition an array into parts of approximately equal sum. + Does not change the order of the array elements. + Produces the partition indices on the array. Use :func:`numpy.split` to + divide the array along these indices. + Parameters + ---------- + array : array or array-like + The 1D array that will be partitioned. The array will be raveled before + computations. + parts : int + Number of parts to split the array. Can be at most the number of + elements in the array. + Returns + ------- + indices : array + The indices in which the array should be split. + Notes + ----- + Solution from https://stackoverflow.com/a/54024280 + """ + array = np.atleast_1d(array).ravel() + if parts > array.size: + raise ValueError( + "Cannot partition an array of size {} into {} parts of equal sum.".format( + array.size, parts + ) + ) + cumulative_sum = array.cumsum() + # Ideally, we want each part to have the same number of points (total / + # parts). + ideal_sum = cumulative_sum[-1] // parts + # If the parts are ideal, the cumulative sum of each part will be this + ideal_cumsum = np.arange(1, parts) * ideal_sum + indices = np.searchsorted(cumulative_sum, ideal_cumsum, side="right") + # Check for repeated split points, which indicates that there is no way to + # split the array. + if np.unique(indices).size != indices.size: + raise ValueError( + "Could not find partition points to split the array into {} parts " + "of equal sum.".format(parts) + ) + return indices + + +class _BaseSpatialCrossValidator(BaseCrossValidator, metaclass=ABCMeta): + """ + Base class for spatial cross-validators. + Parameters + ---------- + n_groups : int + The number of groups to create. This is passed as 'n_clusters=n_groups' + for the KMeans algo, and 'n_components=n_groups' for the GMM. + coordinates : np.array + A numpy array of coordinate values e.g. + np.array([[3337270., 262400.], + [3441390., -273060.], ..., + method : str + Which algorithm to use to seperate data points. Either 'KMeans' or 'GMM' + n_splits : int + Number of splitting iterations. + """ + + def __init__( + self, + n_groups=None, + coordinates=None, + method=None, + max_distance=None, + n_splits=None, + ): + + self.n_groups = n_groups + self.coordinates = coordinates + self.method = method + self.max_distance = max_distance + self.n_splits = n_splits + + def split(self, X, y=None, groups=None): + """ + Generate indices to split data into training and test set. + Parameters + ---------- + X : array-like, shape (n_samples, 2) + Columns should be the easting and northing coordinates of data + points, respectively. + y : array-like, shape (n_samples,) + The target variable for supervised learning problems. Always + ignored. + groups : array-like, with shape (n_samples,), optional + Group labels for the samples used while splitting the dataset into + train/test set. Always ignored. + Yields + ------ + train : ndarray + The training set indices for that split. + test : ndarray + The testing set indices for that split. + """ + if X.shape[1] != 2: + raise ValueError( + "X (the coordinate data) must have exactly 2 columns ({} given).".format( + X.shape[1] + ) + ) + for train, test in super().split(X, y, groups): + yield train, test + + def get_n_splits(self, X=None, y=None, groups=None): + """ + Returns the number of splitting iterations in the cross-validator + Parameters + ---------- + X : object + Always ignored, exists for compatibility. + y : object + Always ignored, exists for compatibility. + groups : object + Always ignored, exists for compatibility. + Returns + ------- + n_splits : int + Returns the number of splitting iterations in the cross-validator. + """ + return self.n_splits + + @abstractmethod + def _iter_test_indices(self, X=None, y=None, groups=None): + """ + Generates integer indices corresponding to test sets. + MUST BE IMPLEMENTED BY DERIVED CLASSES. + Parameters + ---------- + X : array-like, shape (n_samples, 2) + Columns should be the easting and northing coordinates of data + points, respectively. + y : array-like, shape (n_samples,) + The target variable for supervised learning problems. Always + ignored. + groups : array-like, with shape (n_samples,), optional + Group labels for the samples used while splitting the dataset into + train/test set. Always ignored. + Yields + ------ + test : ndarray + The testing set indices for that split. + """ + + +class _SpatialShuffleSplit(_BaseSpatialCrossValidator): + """ + Random permutation of spatial cross-validator. + Yields indices to split data into training and test sets. Data are first + grouped into clusters using either a KMeans or GMM algorithm + and are then split into testing and training sets randomly. + The proportion of clusters assigned to each set is controlled by *test_size* + and/or *train_size*. However, the total amount of actual data points in + each set could be different from these values since clusters can have + a different number of data points inside them. To guarantee that the + proportion of actual data is as close as possible to the proportion of + clusters, this cross-validator generates an extra number of splits and + selects the one with proportion of data points in each set closer to the + desired amount. The number of balance splits per + iteration is controlled by the *balance* argument. + This cross-validator is preferred over `sklearn.model_selection.ShuffleSplit` + for spatial data to avoid overestimating cross-validation scores. + This can happen because of the inherent spatial autocorrelation. + Parameters + ---------- + n_groups : int + The number of groups to create. This is passed as 'n_clusters=n_groups' + for the KMeans algo, and 'n_components=n_groups' for the GMM. If using + cluster_method='Hierarchical' then this parameter is ignored. + coordinates : np.array + A numpy array of coordinate values e.g. + np.array([[3337270., 262400.], + [3441390., -273060.], ...]) + cluster_method : str + Which algorithm to use to seperate data points. Either 'KMeans', 'GMM', or + 'Hierarchical' + max_distance : int + If method is set to 'hierarchical' then maximum distance describes the + maximum euclidean distances between all observations in a cluster. 'n_groups' + is ignored in this case. + n_splits : int, + Number of re-shuffling & splitting iterations. + test_size : float, int, None + If float, should be between 0.0 and 1.0 and represent the proportion + of the dataset to include in the test split. If int, represents the + absolute number of test samples. If None, the value is set to the + complement of the train size. If ``train_size`` is also None, it will + be set to 0.1. + train_size : float, int, or None + If float, should be between 0.0 and 1.0 and represent the + proportion of the dataset to include in the train split. If + int, represents the absolute number of train samples. If None, + the value is automatically set to the complement of the test size. + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. + balance : int + The number of splits generated per iteration to try to balance the + amount of data in each set so that *test_size* and *train_size* are + respected. If 1, then no extra splits are generated (essentially + disabling the balacing). Must be >= 1. + **kwargs : optional, + Additional keyword arguments to pass to sklearn.cluster.Kmeans or + sklearn.mixture.GuassianMixture depending on the cluster_method argument. + Returns + -------- + generator + containing indices to split data into training and test sets + """ + + def __init__( + self, + n_groups=None, + coordinates=None, + method="Heirachical", + max_distance=None, + n_splits=None, + test_size=0.15, + train_size=None, + random_state=None, + balance=10, + **kwargs + ): + super().__init__( + n_groups=n_groups, + coordinates=coordinates, + method=method, + max_distance=max_distance, + n_splits=n_splits, + **kwargs + ) + if balance < 1: + raise ValueError( + "The *balance* argument must be >= 1. To disable balance, use 1." + ) + self.test_size = test_size + self.train_size = train_size + self.random_state = random_state + self.balance = balance + self.kwargs = kwargs + + def _iter_test_indices(self, X=None, y=None, groups=None): + """ + Generates integer indices corresponding to test sets. + Runs several iterations until a split is found that yields clusters with + the right amount of data points in it. + Parameters + ---------- + X : array-like, shape (n_samples, 2) + Columns should be the easting and northing coordinates of data + points, respectively. + y : array-like, shape (n_samples,) + The target variable for supervised learning problems. Always + ignored. + groups : array-like, with shape (n_samples,), optional + Group labels for the samples used while splitting the dataset into + train/test set. Always ignored. + Yields + ------ + test : ndarray + The testing set indices for that split. + """ + labels = spatial_clusters( + n_groups=self.n_groups, + coordinates=self.coordinates, + method=self.method, + max_distance=self.max_distance, + **self.kwargs + ) + + cluster_ids = np.unique(labels) + # Generate many more splits so that we can pick and choose the ones + # that have the right balance of training and testing data. + shuffle = ShuffleSplit( + n_splits=self.n_splits * self.balance, + test_size=self.test_size, + train_size=self.train_size, + random_state=self.random_state, + ).split(cluster_ids) + + for _ in range(self.n_splits): + test_sets, balance = [], [] + for _ in range(self.balance): + # This is a false positive in pylint which is why the warning + # is disabled at the top of this file: + # https://github.com/PyCQA/pylint/issues/1830 + # pylint: disable=stop-iteration-return + train_clusters, test_clusters = next(shuffle) + # pylint: enable=stop-iteration-return + train_points = np.where(np.isin(labels, cluster_ids[train_clusters]))[0] + test_points = np.where(np.isin(labels, cluster_ids[test_clusters]))[0] + # The proportion of data points assigned to each group should + # be close the proportion of clusters assigned to each group. + balance.append( + abs( + train_points.size / test_points.size + - train_clusters.size / test_clusters.size + ) + ) + test_sets.append(test_points) + best = np.argmin(balance) + yield test_sets[best] + + +class _SpatialKFold(_BaseSpatialCrossValidator): + """ + Spatial K-Folds cross-validator. + Yields indices to split data into training and test sets. Data are first + grouped into clusters using either a KMeans or GMM algorithm + clusters. The clusters are then split into testing and training sets iteratively + along k folds of the data (k is given by *n_splits*). + By default, the clusters are split into folds in a way that makes each fold + have approximately the same number of data points. Sometimes this might not + be possible, which can happen if the number of splits is close to the + number of clusters. In these cases, each fold will have the same number of + clusters regardless of how many data points are in each cluster. This + behaviour can also be disabled by setting ``balance=False``. + This cross-validator is preferred over `sklearn.model_selection.KFold` for + spatial data to avoid overestimating cross-validation scores. This can happen + because of the inherent autocorrelation that is usually associated with + this type of data. + Parameters + ---------- + n_groups : int + The number of groups to create. This is passed as 'n_clusters=n_groups' + for the KMeans algo, and 'n_components=n_groups' for the GMM. If using + cluster_method='Hierarchical' then this parameter is ignored. + coordinates : np.array + A numpy array of coordinate values e.g. + np.array([[3337270., 262400.], + [3441390., -273060.], ...]) + cluster_method : str + Which algorithm to use to seperate data points. Either 'KMeans', 'GMM', or + 'Hierarchical' + max_distance : int + If method is set to 'hierarchical' then maximum distance describes the + maximum euclidean distances between all observations in a cluster. 'n_groups' + is ignored in this case. + n_splits : int + Number of folds. Must be at least 2. + shuffle : bool + Whether to shuffle the data before splitting into batches. + random_state : int, RandomState instance or None, optional (defasult=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. + balance : bool + Whether or not to split clusters into fold with approximately equal + number of data points. If False, each fold will have the same number of + clusters (which can have different number of data points in them). + **kwargs : optional, + Additional keyword arguments to pass to sklearn.cluster.Kmeans or + sklearn.mixture.GuassianMixture depending on the cluster_method argument. + """ + + def __init__( + self, + n_groups=None, + coordinates=None, + method="Heirachical", + max_distance=None, + n_splits=5, + test_size=0.15, + train_size=None, + shuffle=True, + random_state=None, + balance=True, + **kwargs + ): + super().__init__( + n_groups=n_groups, + coordinates=coordinates, + method=method, + max_distance=max_distance, + n_splits=n_splits, + **kwargs + ) + + if n_splits < 2: + raise ValueError( + "Number of splits must be >=2 for clusterKFold. Given {}.".format( + n_splits + ) + ) + self.test_size = test_size + self.shuffle = shuffle + self.random_state = random_state + self.balance = balance + self.kwargs = kwargs + + def _iter_test_indices(self, X=None, y=None, groups=None): + """ + Generates integer indices corresponding to test sets. + Parameters + ---------- + X : array-like, shape (n_samples, 2) + Columns should be the easting and northing coordinates of data + points, respectively. + y : array-like, shape (n_samples,) + The target variable for supervised learning problems. Always + ignored. + groups : array-like, with shape (n_samples,), optional + Group labels for the samples used while splitting the dataset into + train/test set. Always ignored. + Yields + ------ + test : ndarray + The testing set indices for that split. + """ + labels = spatial_clusters( + n_groups=self.n_groups, + coordinates=self.coordinates, + method=self.method, + max_distance=self.max_distance, + **self.kwargs + ) + + cluster_ids = np.unique(labels) + if self.n_splits > cluster_ids.size: + raise ValueError( + "Number of k-fold splits ({}) cannot be greater than the number of " + "clusters ({}). Either decrease n_splits or increase the number of " + "clusters.".format(self.n_splits, cluster_ids.size) + ) + if self.shuffle: + check_random_state(self.random_state).shuffle(cluster_ids) + if self.balance: + cluster_sizes = [np.isin(labels, i).sum() for i in cluster_ids] + try: + split_points = _partition_by_sum(cluster_sizes, parts=self.n_splits) + folds = np.split(np.arange(cluster_ids.size), split_points) + except ValueError: + warnings.warn( + "Could not balance folds to have approximately the same " + "number of data points. Dividing into folds with equal " + "number of clusters instead. Decreasing n_splits or increasing " + "the number of clusters may help.", + UserWarning, + ) + folds = [i for _, i in KFold(n_splits=self.n_splits).split(cluster_ids)] + else: + folds = [i for _, i in KFold(n_splits=self.n_splits).split(cluster_ids)] + for test_clusters in folds: + test_points = np.where(np.isin(labels, cluster_ids[test_clusters]))[0] + yield test_points diff --git a/deafrica_tools/coastal.py b/deafrica_tools/coastal.py new file mode 100644 index 0000000..c76d3db --- /dev/null +++ b/deafrica_tools/coastal.py @@ -0,0 +1,1123 @@ +""" +Coastal analyses on Digital Earth Africa data. +""" + +# Import required packages + +# Force GeoPandas to use Shapely instead of PyGEOS +# In a future release, GeoPandas will switch to using Shapely by default. +import os +os.environ['USE_PYGEOS'] = '0' + +import requests +import numpy as np +import xarray as xr +import pandas as pd +import geopandas as gpd +import matplotlib.pyplot as plt +from scipy import stats +from otps import TimePoint +from otps import predict_tide +from shapely.geometry import box +from datacube.utils.geometry import CRS +from owslib.wfs import WebFeatureService + +from deafrica_tools.datahandling import parallel_apply + + +# Fix converters for tidal plot +from pandas.plotting import register_matplotlib_converters +register_matplotlib_converters() + + +# URL for the DE Africa Coastlines data on Geoserver. +WFS_ADDRESS = "https://geoserver.digitalearth.africa/geoserver/wfs" + +def model_tides( + x, + y, + time, + model="FES2014", + directory="/var/share/tide_models", + epsg=4326, + method="bilinear", + extrapolate=True, + cutoff=10.0, +): + """ + Compute tides at points and times using tidal harmonics. + If multiple x, y points are provided, tides will be + computed for all timesteps at each point. + + This function supports any tidal model supported by + `pyTMD`, including the FES2014 Finite Element Solution + tide model, and the TPXO8-atlas and TPXO9-atlas-v5 + TOPEX/POSEIDON global tide models. + + This function requires access to tide model data files + to work. These should be placed in a folder with + subfolders matching the formats specified by `pyTMD`: + https://pytmd.readthedocs.io/en/latest/getting_started/Getting-Started.html#directories + + For FES2014 (https://www.aviso.altimetry.fr/es/data/products/auxiliary-products/global-tide-fes/description-fes2014.html): + - {directory}/fes2014/ocean_tide/ + {directory}/fes2014/load_tide/ + + For TPXO8-atlas (https://www.tpxo.net/tpxo-products-and-registration): + - {directory}/tpxo8_atlas/ + + For TPXO9-atlas-v5 (https://www.tpxo.net/tpxo-products-and-registration): + - {directory}/TPXO9_atlas_v5/ + + This function is a minor modification of the `pyTMD` + package's `compute_tide_corrections` function, adapted + to process multiple timesteps for multiple input point + locations. For more info: + https://pytmd.readthedocs.io/en/stable/user_guide/compute_tide_corrections.html + + Parameters: + ----------- + x, y : float or list of floats + One or more x and y coordinates used to define + the location at which to model tides. By default these + coordinates should be lat/lon; use `epsg` if they + are in a custom coordinate reference system. + time : A datetime array or pandas.DatetimeIndex + An array containing 'datetime64[ns]' values or a + 'pandas.DatetimeIndex' providing the times at which to + model tides in UTC time. + model : string + The tide model used to model tides. Options include: + - "FES2014" (only pre-configured option on DEA Sandbox) + - "TPXO8-atlas" + - "TPXO9-atlas-v5" + directory : string + The directory containing tide model data files. These + data files should be stored in sub-folders for each + model that match the structure provided by `pyTMD`: + https://pytmd.readthedocs.io/en/latest/getting_started/Getting-Started.html#directories + For example: + - {directory}/fes2014/ocean_tide/ + {directory}/fes2014/load_tide/ + - {directory}/tpxo8_atlas/ + - {directory}/TPXO9_atlas_v5/ + epsg : int + Input coordinate system for 'x' and 'y' coordinates. + Defaults to 4326 (WGS84). + method : string + Method used to interpolate tidal contsituents + from model files. Options include: + - bilinear: quick bilinear interpolation + - spline: scipy bivariate spline interpolation + - linear, nearest: scipy regular grid interpolations + extrapolate : bool + Whether to extrapolate tides for locations outside of + the tide modelling domain using nearest-neighbor + cutoff : int or float + Extrapolation cutoff in kilometers. Set to `np.inf` + to extrapolate for all points. + + Returns + ------- + A pandas.DataFrame containing tide heights for every + combination of time and point coordinates. + """ + + import os + import pyproj + import numpy as np + import pyTMD.time + import pyTMD.model + import pyTMD.utilities + from pyTMD.calc_delta_time import calc_delta_time + from pyTMD.infer_minor_corrections import infer_minor_corrections + from pyTMD.predict_tide_drift import predict_tide_drift + from pyTMD.read_tide_model import extract_tidal_constants + from pyTMD.read_netcdf_model import extract_netcdf_constants + from pyTMD.read_GOT_model import extract_GOT_constants + from pyTMD.read_FES_model import extract_FES_constants + + # Check that tide directory is accessible + try: + os.access(directory, os.F_OK) + except: + raise FileNotFoundError("Invalid tide directory") + + # Get parameters for tide model + model = pyTMD.model(directory, format="netcdf", compressed=False).elevation(model) + + # If time passed as a single Timestamp, convert to datetime64 + if isinstance(time, pd.Timestamp): + time = time.to_datetime64() + + # Handle numeric or array inputs + x = np.atleast_1d(x) + y = np.atleast_1d(y) + time = np.atleast_1d(time) + + # Determine point and time counts + assert len(x) == len(y), "x and y must be the same length" + n_points = len(x) + n_times = len(time) + + # Converting x,y from EPSG to latitude/longitude + try: + # EPSG projection code string or int + crs1 = pyproj.CRS.from_string("epsg:{0:d}".format(int(epsg))) + except (ValueError, pyproj.exceptions.CRSError): + # Projection SRS string + crs1 = pyproj.CRS.from_string(epsg) + + crs2 = pyproj.CRS.from_string("epsg:{0:d}".format(4326)) + transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True) + lon, lat = transformer.transform(x.flatten(), y.flatten()) + + # Assert delta time is an array and convert datetime + time = np.atleast_1d(time) + t = pyTMD.time.convert_datetime(time, epoch=(1992, 1, 1, 0, 0, 0)) / 86400.0 + + # Delta time (TT - UT1) file + delta_file = pyTMD.utilities.get_data_path(["data", "merged_deltat.data"]) + + # Read tidal constants and interpolate to grid points + if model.format in ("OTIS", "ATLAS"): + amp, ph, D, c = extract_tidal_constants( + lon, + lat, + model.grid_file, + model.model_file, + model.projection, + TYPE=model.type, + METHOD=method, + EXTRAPOLATE=extrapolate, + CUTOFF=cutoff, + GRID=model.format, + ) + deltat = np.zeros_like(t) + + elif model.format == "netcdf": + amp, ph, D, c = extract_netcdf_constants( + lon, + lat, + model.grid_file, + model.model_file, + TYPE=model.type, + METHOD=method, + EXTRAPOLATE=extrapolate, + CUTOFF=cutoff, + SCALE=model.scale, + GZIP=model.compressed, + ) + deltat = np.zeros_like(t) + + elif model.format == "GOT": + amp, ph, c = extract_GOT_constants( + lon, + lat, + model.model_file, + METHOD=method, + EXTRAPOLATE=extrapolate, + CUTOFF=cutoff, + SCALE=model.scale, + GZIP=model.compressed, + ) + + # Interpolate delta times from calendar dates to tide time + deltat = calc_delta_time(delta_file, t) + + elif model.format == "FES": + amp, ph = extract_FES_constants( + lon, + lat, + model.model_file, + TYPE=model.type, + VERSION=model.version, + METHOD=method, + EXTRAPOLATE=extrapolate, + CUTOFF=cutoff, + SCALE=model.scale, + GZIP=model.compressed, + ) + + # Available model constituents + c = model.constituents + + # Interpolate delta times from calendar dates to tide time + deltat = calc_delta_time(delta_file, t) + + # Calculate complex phase in radians for Euler's + cph = -1j * ph * np.pi / 180.0 + + # Calculate constituent oscillation + hc = amp * np.exp(cph) + + # Repeat constituents to length of time and number of input + # coords before passing to `predict_tide_drift` + t, hc, deltat = ( + np.tile(t, n_points), + hc.repeat(n_times, axis=0), + np.tile(deltat, n_points), + ) + + # Predict tidal elevations at time and infer minor corrections + npts = len(t) + tide = np.ma.zeros((npts), fill_value=np.nan) + tide.mask = np.any(hc.mask, axis=1) + + # Depending on pyTMD version (<=1.06 vs > 1.06), use different params + # TODO: Remove once Sandbox is updated to use pyTMD version 1.0.9 + try: + tide.data[:] = predict_tide_drift( + t, hc, c, deltat=deltat, corrections=model.format + ) + minor = infer_minor_corrections( + t, hc, c, deltat=deltat, corrections=model.format + ) + except: + tide.data[:] = predict_tide_drift( + t, hc, c, DELTAT=deltat, CORRECTIONS=model.format + ) + minor = infer_minor_corrections( + t, hc, c, DELTAT=deltat, CORRECTIONS=model.format + ) + tide.data[:] += minor.data[:] + + # Replace invalid values with fill value + tide.data[tide.mask] = tide.fill_value + + # Export data as a dataframe + return pd.DataFrame( + { + "time": np.tile(time, n_points), + "x": np.repeat(x, n_times), + "y": np.repeat(y, n_times), + "tide_m": tide, + } + ).set_index("time") + + +def pixel_tides( + ds, + times=None, + resample=True, + calculate_quantiles=None, + resolution=None, + buffer=None, + resample_method="bilinear", + **model_tides_kwargs, +): + """ + Obtain tide heights for each pixel in a dataset by modelling + tides into a low-resolution grid surrounding the dataset, + then (optionally) spatially resample this low-res data back + into the original higher resolution dataset extent and resolution. + + Parameters: + ----------- + ds : xarray.Dataset + A dataset whose geobox (`ds.odc.geobox`) will be used to define + the spatial extent of the low resolution tide modelling grid. + times : pandas.DatetimeIndex or list of pandas.Timestamps, optional + By default, the function will model tides using the times + contained in the `time` dimension of `ds`. Alternatively, this + param can be used to model tides for a custom set of times + instead. For example: + `times=pd.date_range(start="2000", end="2001", freq="5h")` + resample : bool, optional + Whether to resample low resolution tides back into `ds`'s original + higher resolution grid. Set this to `False` if you do not want + low resolution tides to be re-projected back to higher resolution. + calculate_quantiles : list or np.array, optional + Rather than returning all individual tides, low-resolution tides + can be first aggregated using a quantile calculation by passing in + a list or array of quantiles to compute. For example, this could + be used to calculate the min/max tide across all times: + `calculate_quantiles=[0.0, 1.0]`. + resolution: int, optional + The desired resolution of the low-resolution grid used for tide + modelling. The default None will create a 5000 m resolution grid + if `ds` has a projected CRS (i.e. metre units), or a 0.05 degree + resolution grid if `ds` has a geographic CRS (e.g. degree units). + Note: higher resolutions do not necessarily provide better + tide modelling performance, as results will be limited by the + resolution of the underlying global tide model (e.g. 1/16th + degree / ~5 km resolution grid for FES2014). + buffer : int, optional + The amount by which to buffer the higher resolution grid extent + when creating the new low resolution grid. This buffering is + important as it ensures that ensure pixel-based tides are seamless + across dataset boundaries. This buffer will eventually be clipped + away when the low-resolution data is re-projected back to the + resolution and extent of the higher resolution dataset. To + ensure that at least two pixels occur outside of the dataset + bounds, the default None applies a 12000 m buffer if `ds` has a + projected CRS (i.e. metre units), or a 0.12 degree buffer if + `ds` has a geographic CRS (e.g. degree units). + resample_method : string, optional + If resampling is requested (see `resample` above), use this + resampling method when converting from low resolution to high + resolution pixels. Defaults to "bilinear"; valid options include + "nearest", "cubic", "min", "max", "average" etc. + **model_tides_kwargs : + Optional parameters passed to the `dea_tools.coastal.model_tides` + function. Important parameters include "model" and "directory", + used to specify the tide model to use and the location of its files. + + Returns: + -------- + If `resample` is True: + + tides_lowres : xr.DataArray + A low resolution data array giving either tide heights every + timestep in `ds` (if `times` is None), tide heights at every + time in `times` (if `times` is not None), or tide height quantiles + for every quantile provided by `calculate_quantiles`. + + If `resample` is False: + + tides_highres, tides_lowres : tuple of xr.DataArrays + In addition to `tides_lowres` (see above), a high resolution + array of tide heights will be generated that matches the + exact spatial resolution and extent of `ds`. This will contain + either tide heights every timestep in `ds` (if `times` is None), + tide heights at every time in `times` (if `times` is not None), + or tide height quantiles for every quantile provided by + `calculate_quantiles`. + """ + + import odc.geo.xr + from odc.geo.geobox import GeoBox + + # First test if no time dimension and nothing passed to `times` + if ('time' not in ds.dims) & (times is None): + raise ValueError( + "`ds` does not contain a 'time' dimension. Times are required " + "for modelling tides: please pass in a set of custom tides " + "using the `times` parameter. For example: " + "`times=pd.date_range(start='2000', end='2001', freq='5h')`" + ) + + # If custom times are provided, convert them to a consistent + # pandas.DatatimeIndex format + if times is not None: + if isinstance(times, list): + time_coords = pd.DatetimeIndex(times) + elif isinstance(times, pd.Timestamp): + time_coords = pd.DatetimeIndex([times]) + else: + time_coords = times + + # Otherwise, use times from `ds` directly + else: + time_coords = ds.coords["time"] + + # Determine spatial dimensions + y_dim, x_dim = ds.odc.spatial_dims + + # Determine resolution and buffer, using different defaults for + # geographic (i.e. degrees) and projected (i.e. metres) CRSs: + crs_units = ds.odc.geobox.crs.units[0][0:6] + if ds.odc.geobox.crs.geographic: + if resolution is None: + resolution = 0.05 + elif resolution > 360: + raise ValueError(f"A resolution of greater than 360 was " + f"provided, but `ds` has a geographic CRS " + f"in {crs_units} units. Did you accidently " + f"provide a resolution in projected " + f"(i.e. metre) units?") + if buffer is None: + buffer = 0.12 + else: + if resolution is None: + resolution = 5000 + elif resolution < 1: + raise ValueError(f"A resolution of less than 1 was provided, " + f"but `ds` has a projected CRS in " + f"{crs_units} units. Did you accidently " + f"provide a resolution in geographic " + f"(degree) units?") + if buffer is None: + buffer = 12000 + + # Raise error if resolution is less than dataset resolution + dataset_res = ds.odc.geobox.resolution.x + if resolution < dataset_res: + raise ValueError(f"The resolution of the low-resolution tide " + f"modelling grid ({resolution:.2f}) is less " + f"than `ds`'s pixel resolution ({dataset_res:.2f}). " + f"This can cause extremely slow tide modelling " + f"performance. Please select provide a resolution " + f"greater than {dataset_res:.2f} using " + f"`pixel_tides`'s 'resolution' parameter.") + + # Create a new reduced resolution tide modelling grid after + # first buffering the grid + print(f"Creating reduced resolution {resolution} x {resolution} " + f"{crs_units} tide modelling array") + buffered_geobox = ds.odc.geobox.buffered(buffer) + rescaled_geobox = GeoBox.from_bbox( + bbox=buffered_geobox.boundingbox, resolution=resolution + ) + rescaled_ds = odc.geo.xr.xr_zeros(rescaled_geobox) + + # Flatten grid to 1D, then add time dimension + flattened_ds = rescaled_ds.stack(z=(x_dim, y_dim)) + flattened_ds = flattened_ds.expand_dims(dim={"time": time_coords.values}) + + # Model tides for each timestep + model = ( + "FES2014" if "model" not in model_tides_kwargs else model_tides_kwargs["model"] + ) + print(f"Modelling tides using {model} tide model") + tide_df = model_tides( + x=flattened_ds[x_dim], + y=flattened_ds[y_dim], + time=flattened_ds.time, + epsg=ds.odc.geobox.crs.epsg, + **model_tides_kwargs, + ) + + # Rename x and y coordinates to match satellite array + tide_df = tide_df.rename({"x": x_dim, "y": y_dim}, axis=1) + + # Insert modelled tide values back into flattened array, then unstack + # back to 3D (y, x, time) + tides_lowres = ( + + # Convert dataframe to xarray format + tide_df.set_index([x_dim, y_dim], append=True) + .to_xarray() + + # Re-index and transpose back into 3D + .tide_m.reindex_like(rescaled_ds) + .transpose("time", y_dim, x_dim) + .astype(np.float32) + ) + + # Optionally calculate and return quantiles rather than raw data + if calculate_quantiles is not None: + + print("Computing tide quantiles") + tides_lowres = tides_lowres.quantile(q=calculate_quantiles, dim="time") + reproject_dim = "quantile" + + else: + reproject_dim = "time" + + # Ensure CRS is present + tides_lowres = tides_lowres.odc.assign_crs(ds.odc.geobox.crs) + + # Reproject each timestep into original high resolution grid + if resample: + + print("Reprojecting tides into original array") + tides_highres = parallel_apply( + tides_lowres, + reproject_dim, + odc.algo.xr_reproject, + ds.odc.geobox.compat, + resample_method, + ) + + return tides_highres, tides_lowres + + else: + print("Returning low resolution tide array") + return tides_lowres + +def tidal_tag( + ds, + ebb_flow=False, + swap_dims=False, + tidepost_lat=None, + tidepost_lon=None, + return_tideposts=False, + **model_tides_kwargs, +): + """ + Takes an xarray.Dataset and returns the same dataset with a new + `tide_m` variable giving the height of the tide at the exact + moment of each satellite acquisition. + + The function models tides at the centroid of the dataset by default, + but a custom tidal modelling location can be specified using + `tidepost_lat` and `tidepost_lon`. + + The default settings use the FES2014 global tidal model, implemented + using the pyTMD Python package. FES2014 was produced by NOVELTIS, + LEGOS, CLS Space Oceanography Division and CNES. It is distributed + by AVISO, with support from CNES (http://www.aviso.altimetry.fr/). + + Parameters + ---------- + ds : xarray.Dataset + An xarray.Dataset object with x, y and time dimensions + ebb_flow : bool, optional + An optional boolean indicating whether to compute if the + tide phase was ebbing (falling) or flowing (rising) for each + observation. The default is False; if set to True, a new + `ebb_flow` variable will be added to the dataset with each + observation labelled with 'Ebb' or 'Flow'. + swap_dims : bool, optional + An optional boolean indicating whether to swap the `time` + dimension in the original xarray.Dataset to the new + `tide_m` variable. Defaults to False. + tidepost_lat, tidepost_lon : float or int, optional + Optional coordinates used to model tides. The default is None, + which uses the centroid of the dataset as the tide modelling + location. + return_tideposts : bool, optional + An optional boolean indicating whether to return the `tidepost_lat` + and `tidepost_lon` location used to model tides in addition to the + xarray.Dataset. Defaults to False. + **model_tides_kwargs : + Optional parameters passed to the `dea_tools.coastal.model_tides` + function. Important parameters include "model" and "directory", + used to specify the tide model to use and the location of its files. + + Returns + ------- + The original xarray.Dataset with a new `tide_m` variable giving + the height of the tide (and optionally, its ebb-flow phase) at the + exact moment of each satellite acquisition (if `return_tideposts=True`, + the function will also return the `tidepost_lon` and `tidepost_lat` + location used in the analysis). + + """ + + import odc.geo.xr + + # If custom tide modelling locations are not provided, use the + # dataset centroid + if not tidepost_lat or not tidepost_lon: + + tidepost_lon, tidepost_lat = ds.odc.geobox.geographic_extent.centroid.coords[0] + print( + f"Setting tide modelling location from dataset centroid: " + f"{tidepost_lon:.2f}, {tidepost_lat:.2f}" + ) + + else: + print( + f"Using user-supplied tide modelling location: " + f"{tidepost_lon:.2f}, {tidepost_lat:.2f}" + ) + + # Use tidal model to compute tide heights for each observation: + model = ( + "FES2014" if "model" not in model_tides_kwargs else model_tides_kwargs["model"] + ) + print(f"Modelling tides using {model} tidal model") + tide_df = model_tides( + x=tidepost_lon, + y=tidepost_lat, + time=ds.time, + epsg="EPSG:4326", + **model_tides_kwargs, + ) + + # If tides cannot be successfully modeled (e.g. if the centre of the + # xarray dataset is located is over land), raise an exception + if tide_df.tide_m.isnull().all(): + + raise ValueError( + f"Tides could not be modelled for dataset centroid located " + f"at {tidepost_lon:.2f}, {tidepost_lat:.2f}. This can occur if " + f"this coordinate occurs over land. Please manually specify " + f"a tide modelling location located over water using the " + f"`tidepost_lat` and `tidepost_lon` parameters." + ) + + # Assign tide heights to the dataset as a new variable + ds["tide_m"] = xr.DataArray(tide_df.tide_m, coords=[ds.time]) + + # Optionally calculate the tide phase for each observation + if ebb_flow: + + # Model tides for a time 15 minutes prior to each previously + # modelled satellite acquisition time. This allows us to compare + # tide heights to see if they are rising or falling. + print("Modelling tidal phase (e.g. ebb or flow)") + tide_pre_df = model_tides( + x=tidepost_lon, + y=tidepost_lat, + time=(ds.time - pd.Timedelta("15 min")), + epsg="EPSG:4326", + **model_tides_kwargs, + ) + + # Compare tides computed for each timestep. If the previous tide + # was higher than the current tide, the tide is 'ebbing'. If the + # previous tide was lower, the tide is 'flowing' + tidal_phase = [ + "Ebb" if i else "Flow" + for i in tide_pre_df.tide_m.values > tide_df.tide_m.values + ] + + # Assign tide phase to the dataset as a new variable + ds["ebb_flow"] = xr.DataArray(tidal_phase, coords=[ds.time]) + + # If swap_dims = True, make tide height the primary dimension + # instead of time + if swap_dims: + + # Swap dimensions and sort by tide height + ds = ds.swap_dims({"time": "tide_m"}) + ds = ds.sortby("tide_m") + ds = ds.drop_vars("time") + + if return_tideposts: + return ds, tidepost_lon, tidepost_lat + else: + return ds + + +def tidal_stats( + ds, + tidepost_lat=None, + tidepost_lon=None, + plain_english=True, + plot=True, + modelled_freq="2h", + linear_reg=False, + round_stats=3, + **model_tides_kwargs, +): + """ + Takes an xarray.Dataset and statistically compares the tides + modelled for each satellite observation against the full modelled + tidal range. This comparison can be used to evaluate whether the + tides observed by satellites (e.g. Landsat) are biased compared to + the natural tidal range (e.g. fail to observe either the highest or + lowest tides etc). + + For more information about the tidal statistics computed by this + function, refer to Figure 8 in Bishop-Taylor et al. 2018: + https://www.sciencedirect.com/science/article/pii/S0272771418308783#fig8 + + The function models tides at the centroid of the dataset by default, + but a custom tidal modelling location can be specified using + `tidepost_lat` and `tidepost_lon`. + + The default settings use the FES2014 global tidal model, implemented + using the pyTMD Python package. FES2014 was produced by NOVELTIS, + LEGOS, CLS Space Oceanography Division and CNES. It is distributed + by AVISO, with support from CNES (http://www.aviso.altimetry.fr/). + + Parameters + ---------- + ds : xarray.Dataset + An xarray.Dataset object with x, y and time dimensions + tidepost_lat, tidepost_lon : float or int, optional + Optional coordinates used to model tides. The default is None, + which uses the centroid of the dataset as the tide modelling + location. + plain_english : bool, optional + An optional boolean indicating whether to print a plain english + version of the tidal statistics to the screen. Defaults to True. + plot : bool, optional + An optional boolean indicating whether to plot how satellite- + observed tide heights compare against the full tidal range. + Defaults to True. + modelled_freq : str, optional + An optional string giving the frequency at which to model tides + when computing the full modelled tidal range. Defaults to '2h', + which computes a tide height for every two hours across the + temporal extent of `ds`. + linear_reg: bool, optional + Experimental: whether to return linear regression stats that + assess whether dstellite-observed and all available tides show + any decreasing or increasing trends over time. Not currently + recommended as all observed regressions always return as + significant due to far larger sample size. + round_stats : int, optional + The number of decimal places used to round the output statistics. + Defaults to 3. + **model_tides_kwargs : + Optional parameters passed to the `dea_tools.coastal.model_tides` + function. Important parameters include "model" and "directory", + used to specify the tide model to use and the location of its files. + + Returns + ------- + A pandas.Series object containing the following statistics: + + tidepost_lat: latitude used for modelling tide heights + tidepost_lon: longitude used for modelling tide heights + observed_min_m: minimum tide height observed by the satellite + all_min_m: minimum tide height from all available tides + observed_max_m: maximum tide height observed by the satellite + all_max_m: maximum tide height from all available tides + observed_range_m: tidal range observed by the satellite + all_range_m: full astronomical tidal range based on all + available tides + spread_m: proportion of the full astronomical tidal range observed + by the satellite (see Bishop-Taylor et al. 2018) + low_tide_offset: proportion of the lowest tides never observed + by the satellite (see Bishop-Taylor et al. 2018) + high_tide_offset: proportion of the highest tides never observed + by the satellite (see Bishop-Taylor et al. 2018) + + If `linear_reg = True`, the output will also contain: + + observed_slope: slope of any relationship between observed tide + heights and time + all_slope: slope of any relationship between all available tide + heights and time + observed_pval: significance/p-value of any relationship between + observed tide heights and time + all_pval: significance/p-value of any relationship between + all available tide heights and time + + """ + + # Model tides for each observation in the supplied xarray object + ds_tides, tidepost_lon, tidepost_lat = tidal_tag( + ds, + tidepost_lat=tidepost_lat, + tidepost_lon=tidepost_lon, + return_tideposts=True, + **model_tides_kwargs, + ) + + # Drop spatial ref for nicer plotting + if "spatial_ref" in ds_tides: + ds_tides = ds_tides.drop_vars("spatial_ref") + + # Generate range of times covering entire period of satellite record + all_timerange = pd.date_range( + start=ds_tides.time.min().item(), + end=ds_tides.time.max().item(), + freq=modelled_freq, + ) + + # Model tides for each timestep + all_tides_df = model_tides( + x=tidepost_lon, + y=tidepost_lat, + time=all_timerange, + epsg="EPSG:4326", + **model_tides_kwargs, + ) + + # Get coarse statistics on all and observed tidal ranges + obs_mean = ds_tides.tide_m.mean().item() + all_mean = all_tides_df.tide_m.mean() + obs_min, obs_max = ds_tides.tide_m.quantile([0.0, 1.0]).values + all_min, all_max = all_tides_df.tide_m.quantile([0.0, 1.0]).values + + # Calculate tidal range + obs_range = obs_max - obs_min + all_range = all_max - all_min + + # Calculate Bishop-Taylor et al. 2018 tidal metrics + spread = obs_range / all_range + low_tide_offset = abs(all_min - obs_min) / all_range + high_tide_offset = abs(all_max - obs_max) / all_range + + # Extract x (time in decimal years) and y (distance) values + all_x = ( + all_tides_df.index.year + + ((all_tides_df.index.dayofyear - 1) / 365) + + ((all_tides_df.index.hour - 1) / 24) + ) + all_y = all_tides_df.tide_m.values.astype(np.float32) + time_period = all_x.max() - all_x.min() + + # Extract x (time in decimal years) and y (distance) values + obs_x = ( + ds_tides.time.dt.year + + ((ds_tides.time.dt.dayofyear - 1) / 365) + + ((ds_tides.time.dt.hour - 1) / 24) + ) + obs_y = ds_tides.tide_m.values.astype(np.float32) + + + # Compute linear regression + obs_linreg = stats.linregress(x=obs_x, y=obs_y) + all_linreg = stats.linregress(x=all_x, y=all_y) + + if plain_english: + + print( + f"\n{spread:.0%} of the {all_range:.2f} m modelled astronomical " + f"tidal range is observed at this location.\nThe lowest " + f"{low_tide_offset:.0%} and highest {high_tide_offset:.0%} " + f"of astronomical tides are never observed.\n" + ) + + if linear_reg: + + if obs_linreg.pvalue > 0.05: + print( + f"Observed tides show no significant trends " + f"over the ~{time_period:.0f} year period." + ) + else: + obs_slope_desc = "decrease" if obs_linreg.slope < 0 else "increase" + print( + f"Observed tides {obs_slope_desc} significantly " + f"(p={obs_linreg.pvalue:.3f}) over time by " + f"{obs_linreg.slope:.03f} m per year (i.e. a " + f"~{time_period * obs_linreg.slope:.2f} m " + f"{obs_slope_desc} over the ~{time_period:.0f} year period)." + ) + + if all_linreg.pvalue > 0.05: + print( + f"All tides show no significant trends " + f"over the ~{time_period:.0f} year period." + ) + else: + all_slope_desc = "decrease" if all_linreg.slope < 0 else "increase" + print( + f"All tides {all_slope_desc} significantly " + f"(p={all_linreg.pvalue:.3f}) over time by " + f"{all_linreg.slope:.03f} m per year (i.e. a " + f"~{time_period * all_linreg.slope:.2f} m " + f"{all_slope_desc} over the ~{time_period:.0f} year period)." + ) + + if plot: + + # Create plot and add all time and observed tide data + fig, ax = plt.subplots(figsize=(10, 5)) + all_tides_df.tide_m.plot(ax=ax, alpha=0.4) + ds_tides.tide_m.plot.line( + ax=ax, marker="o", linewidth=0.0, color="black", markersize=2 + ) + + # Add horizontal lines for spread/offsets + ax.axhline(obs_min, color="black", linestyle=":", linewidth=1) + ax.axhline(obs_max, color="black", linestyle=":", linewidth=1) + ax.axhline(all_min, color="black", linestyle=":", linewidth=1) + ax.axhline(all_max, color="black", linestyle=":", linewidth=1) + + # Add text annotations for spread/offsets + ax.annotate( + f" High tide\n offset ({high_tide_offset:.0%})", + xy=(all_timerange.max(), np.mean([all_max, obs_max])), + va="center", + ) + ax.annotate( + f" Spread\n ({spread:.0%})", + xy=(all_timerange.max(), np.mean([obs_min, obs_max])), + va="center", + ) + ax.annotate( + f" Low tide\n offset ({low_tide_offset:.0%})", + xy=(all_timerange.max(), np.mean([all_min, obs_min])), + ) + + # Remove top right axes and add labels + ax.spines["right"].set_visible(False) + ax.spines["top"].set_visible(False) + ax.set_ylabel("Tide height (m)") + ax.set_xlabel("") + ax.margins(x=0.015) + + # Export pandas.Series containing tidal stats + output_stats = { + "tidepost_lat": tidepost_lat, + "tidepost_lon": tidepost_lon, + "observed_mean_m": obs_mean, + "all_mean_m": all_mean, + "observed_min_m": obs_min, + "all_min_m": all_min, + "observed_max_m": obs_max, + "all_max_m": all_max, + "observed_range_m": obs_range, + "all_range_m": all_range, + "spread": spread, + "low_tide_offset": low_tide_offset, + "high_tide_offset": high_tide_offset, + } + + if linear_reg: + output_stats.update( + { + "observed_slope": obs_linreg.slope, + "all_slope": all_linreg.slope, + "observed_pval": obs_linreg.pvalue, + "all_pval": all_linreg.pvalue, + } + ) + + return pd.Series(output_stats).round(round_stats) + + +def transect_distances(transects_gdf, lines_gdf, mode='distance'): + """ + Take a set of transects (e.g. shore-normal beach survey lines), and + determine the distance along the transect to each object in a set of + lines (e.g. shorelines). Distances are measured in the CRS of the + input datasets. + + For coastal applications, transects should be drawn from land to + water (with the first point being on land so that it can be used + as a consistent location from which to measure distances. + + The distance calculation can be performed using two modes: + - 'distance': Distances are measured from the start of the + transect to where it intersects with each line. Any transect + that intersects a line more than once is ignored. This mode is + useful for measuring e.g. the distance to the shoreline over + time from a consistent starting location. + - 'width' Distances are measured between the first and last + intersection between a transect and each line. Any transect + that intersects a line only once is ignored. This is useful + for e.g. measuring the width of a narrow area of coastline over + time, e.g. the neck of a spit or tombolo. + + Parameters + ---------- + transects_gdf : geopandas.GeoDataFrame + A GeoDataFrame containing one or multiple vector profile lines. + The GeoDataFrame's index column will be used to name the rows in + the output distance table. + lines_gdf : geopandas.GeoDataFrame + A GeoDataFrame containing one or multiple vector line features + that intersect the profile lines supplied to `transects_gdf`. + The GeoDataFrame's index column will be used to name the columns + in the output distance table. + mode : string, optional + Whether to use 'distance' (for measuring distances from the + start of a profile) or 'width' mode (for measuring the width + between two profile intersections). See docstring above for more + info; defaults to 'distance'. + + Returns + ------- + distance_df : pandas.DataFrame + A DataFrame containing distance measurements for each profile + line (rows) and line feature (columns). + """ + + import warnings + from shapely.errors import ShapelyDeprecationWarning + from shapely.geometry import Point + + def _intersect_dist(transect_gdf, lines_gdf, mode=mode): + """ + Take an individual transect, and determine the distance along + the transect to each object in a set of lines (e.g. shorelines). + """ + + # Identify intersections between transects and lines + intersect_points = lines_gdf.apply( + lambda x: x.geometry.intersection(transect_gdf.geometry), axis=1) + + # In distance mode, identify transects with one intersection only, + # and use this as the end point and the start of the transect as the + # start point when measuring distances + if mode == 'distance': + start_point = Point(transect_gdf.geometry.coords[0]) + point_df = intersect_points.apply( + lambda x: pd.Series({'start': start_point, 'end': x}) + if x.type == 'Point' + else pd.Series({'start': None, 'end': None})) + + # In width mode, identify transects with multiple intersections, and + # use the first intersection as the start point and the second + # intersection for the end point when measuring distances + if mode == 'width': + point_df = intersect_points.apply( + lambda x: pd.Series({'start': x.geoms[0], 'end': x.geoms[-1]}) + if x.type == 'MultiPoint' + else pd.Series({'start': None, 'end': None})) + + # Calculate distances between valid start and end points + distance_df = point_df.apply( + lambda x: x.start.distance(x.end) if x.start else None, axis=1) + + return distance_df + + # Run code after ignoring Shapely pre-v2.0 warnings + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning) + + # Assert that both datasets use the same CRS + assert transects_gdf.crs == lines_gdf.crs, ('Please ensure both ' + 'input datasets use the same CRS.') + + # Run distance calculations + distance_df = transects_gdf.apply( + lambda x: _intersect_dist(x, lines_gdf), axis=1) + + return pd.DataFrame(distance_df) + + +def get_coastlines(bbox: tuple, + crs="EPSG:4326", + layer="shorelines", + drop_wms=True) -> gpd.GeoDataFrame: + """ + Get DE Africa Coastlines data for a provided bounding box using WFS. + + For a full description of the DE Africa Coastlines dataset, refer to the + official Digital Earth Africa product description: + + Parameters + ---------- + bbox : (xmin, ymin, xmax, ymax), or geopandas object + Bounding box expressed as a tuple. Alternatively, a bounding + box can be automatically extracted by suppling a + geopandas.GeoDataFrame or geopandas.GeoSeries. + crs : str, optional + Optional CRS for the bounding box. This is ignored if `bbox` + is provided as a geopandas object. + layer : str, optional + Which DE Africa Coastlines layer to load. Options include the annual + shoreline vectors ("shorelines") and the rates of change + statistics points ("statistics"). Defaults to "shorelines". + drop_wms : bool, optional + Whether to drop WMS-specific attribute columns from the data. + These columns are used for visualising the dataset on DE Africa Maps, + and are unlikely to be useful for scientific analysis. Defaults + to True. + + Returns + ------- + gpd.GeoDataFrame + A GeoDataFrame containing shoreline or point features and + associated metadata. + """ + + # If bbox is a geopandas object, convert to bbox. + try: + crs = str(bbox.crs) + bbox = bbox.total_bounds + except: + pass + + # Get the available layers in the coastlines:DEAfrica_Coastlines group. + describe_layer_url = "https://geoserver.digitalearth.africa/geoserver/wms?service=WMS&version=1.1.1&request=DescribeLayer&layers=coastlines:DEAfrica_Coastlines&outputFormat=application/json" + describe_layer_response = requests.get(describe_layer_url).json() + available_layers = [layer["layerName"] for layer in describe_layer_response['layerDescriptions']] + + # Get the layer name. + if layer == "shorelines": + layer_name = [i for i in available_layers if "shorelines" in i] + else: + layer_name = [i for i in available_layers if "rates_of_change" in i] + + # Query WFS. + wfs = WebFeatureService(url=WFS_ADDRESS, version="1.1.0") + response = wfs.getfeature(typename=layer_name, + bbox=tuple(bbox) + (crs,), + outputFormat="json") + + # Load data as a geopandas.GeoDataFrame. + coastlines_gdf = gpd.read_file(response) + + # Clip to extent of bounding box. + extent = gpd.GeoSeries(box(*bbox), crs=crs).to_crs(coastlines_gdf.crs) + coastlines_gdf = coastlines_gdf.clip(extent) + + # Optionally drop WMS-specific columns. + if drop_wms: + coastlines_gdf = coastlines_gdf.loc[:, ~coastlines_gdf.columns.str.contains("wms_")] + + return coastlines_gdf + diff --git a/deafrica_tools/dask.py b/deafrica_tools/dask.py new file mode 100644 index 0000000..c29ed54 --- /dev/null +++ b/deafrica_tools/dask.py @@ -0,0 +1,106 @@ +""" +Functions for simplifying the creation of a local dask cluster. +""" + +from importlib.util import find_spec +import os +import dask +from aiohttp import ClientConnectionError +from datacube.utils.dask import start_local_dask +from datacube.utils.rio import configure_s3_access + +_HAVE_PROXY = bool(find_spec('jupyter_server_proxy')) +_IS_AWS = ('AWS_ACCESS_KEY_ID' in os.environ or + 'AWS_DEFAULT_REGION' in os.environ) + + +def create_local_dask_cluster(spare_mem='3Gb', display_client=True, return_client=False): + """ + Using the datacube utils function `start_local_dask`, generate + a local dask cluster. Automatically detects if on AWS or NCI. + + Parameters + ---------- + spare_mem : String, optional + The amount of memory, in Gb, to leave for the notebook to run. + This memory will not be used by the cluster. e.g '3Gb' + display_client : Bool, optional + An optional boolean indicating whether to display a summary of + the dask client, including a link to monitor progress of the + analysis. Set to False to hide this display. + return_client : Bool, optional + An optional boolean indicating whether to return the dask client + object. + + """ + + if _HAVE_PROXY: + # Configure dashboard link to go over proxy + prefix = os.environ.get('JUPYTERHUB_SERVICE_PREFIX', '/') + dask.config.set({"distributed.dashboard.link": + prefix + "proxy/{port}/status"}) + + # Start up a local cluster + client = start_local_dask(mem_safety_margin=spare_mem) + + if _IS_AWS: + # Configure GDAL for s3 access + configure_s3_access(aws_unsigned=True, + client=client) + + # Show the dask cluster settings + if display_client: + from IPython.display import display + display(client) + + # return the client as an object + if return_client: + return client + + +try: + from dask_gateway import Gateway + + def create_dask_gateway_cluster(profile='r5_L', workers=2): + """ + Create a cluster in our internal dask cluster. + + Parameters + ---------- + profile : str + Possible values are: + - r5_L (2 cores, 15GB memory) + - r5_XL (4 cores, 31GB memory) + - r5_2XL (8 cores, 63GB memory) + - r5_4XL (16 cores, 127GB memory) + + workers : int + Number of workers in the cluster. + """ + try: + gateway = Gateway() + + # Close any existing clusters + cluster_names = gateway.list_clusters() + if len(cluster_names) > 0: + print("Cluster(s) still running:", cluster_names) + for n in cluster_names: + cluster = gateway.connect(n.name) + cluster.shutdown() + + options = gateway.cluster_options() + options['profile'] = profile + + # limit username to alphanumeric characters + # kubernetes pods won't launch if labels contain anything other than [a-Z, -, _] + options['jupyterhub_user'] = ''.join(c if c.isalnum() else '-' for c in os.getenv('JUPYTERHUB_USER')) + + cluster = gateway.new_cluster(options) + cluster.scale(workers) + return cluster + except ClientConnectionError: + raise ConnectionError("access to dask gateway cluster unauthorized") + +except ImportError: + def create_dask_gateway_cluster(*args, **kwargs): + raise NotImplementedError \ No newline at end of file diff --git a/deafrica_tools/datahandling.py b/deafrica_tools/datahandling.py new file mode 100644 index 0000000..085952a --- /dev/null +++ b/deafrica_tools/datahandling.py @@ -0,0 +1,1523 @@ +""" +Functions for loading and handling Digital Earth Africa data. +""" + +# Import required packages +import os +from osgeo import gdal +import requests +import zipfile +import warnings +import numpy as np +import xarray as xr +import pandas as pd +import datetime +import pytz + +from collections import Counter +from datacube.utils import masking +from scipy.ndimage import binary_dilation +from odc.algo import mask_cleanup +from copy import deepcopy +import odc.algo + +from skimage.morphology import binary_erosion,binary_dilation,disk +from scipy.ndimage.filters import uniform_filter +from scipy.ndimage.measurements import variance +from datetime import datetime +from dateutil import parser +from deafrica_tools.bandindices import calculate_indices + +def _dc_query_only(**kw): + """ + Remove load-only parameters, the rest + can be passed to Query + + Returns + ======= + + dict of query parameters + """ + + def _impl( + measurements=None, + output_crs=None, + resolution=None, + resampling=None, + skip_broken_datasets=None, + dask_chunks=None, + fuse_func=None, + align=None, + datasets=None, + progress_cbk=None, + group_by=None, + **query, + ): + return query + + return _impl(**kw) + + +def _common_bands(dc, products): + """ + Takes a list of products and returns a list of measurements/bands + that are present in all products + Returns + ------- + List of band names + """ + common = None + bands = None + + for p in products: + p = dc.index.products.get_by_name(p) + if common is None: + common = set(p.measurements) + bands = list(p.measurements) + else: + common = common.intersection(set(p.measurements)) + return [band for band in bands if band in common] + + +def load_ard( + dc, + products=None, + min_gooddata=0.0, + categories_to_mask_ls=dict( + cloud="high_confidence", cloud_shadow="high_confidence" + ), + categories_to_mask_s2=[ + "cloud high probability", + "cloud medium probability", + "thin cirrus", + "cloud shadows", + "saturated or defective", + ], + categories_to_mask_s1=["invalid data"], + mask_filters=None, + mask_pixel_quality=True, + ls7_slc_off=True, + predicate=None, + dtype="auto", + verbose=True, + **kwargs, +): + """ + Loads analysis ready data. + + Loads and combines Landsat USGS Collections 2, Sentinel-2, and Sentinel-1 for + multiple sensors (i.e. ls5t, ls7e, ls8c and ls9 for Landsat; s2a and s2b for Sentinel-2), + optionally applies pixel quality masks, and drops time steps that + contain greater than a minimum proportion of good quality (e.g. non- + cloudy or shadowed) pixels. + + The function supports loading the following DE Africa products: + + Landsat: + * ls5_sr ('sr' denotes surface reflectance) + * ls7_sr + * ls8_sr + * ls9_sr + * ls5_st ('st' denotes surface temperature) + * ls7_st + * ls8_st + * ls9_st + + Sentinel-2: + * s2_l2a + + Sentinel-1: + * s1_rtc + + Last modified: Feb 2021 + + Parameters + ---------- + dc : datacube Datacube object + The Datacube to connect to, i.e. `dc = datacube.Datacube()`. + This allows you to also use development datacubes if required. + products : list + A list of product names to load data from. For example: + + * Landsat C2: ``['ls5_sr', 'ls7_sr', 'ls8_sr', 'ls9_sr']`` + * Sentinel-2: ``['s2_l2a']`` + * Sentinel-1: ``['s1_rtc']`` + + min_gooddata : float, optional + An optional float giving the minimum percentage of good quality + pixels required for a satellite observation to be loaded. + Defaults to 0.0 which will return all observations regardless of + pixel quality (set to e.g. 0.99 to return only observations with + more than 99% good quality pixels). + categories_to_mask_ls : dict, optional + An optional dictionary that is used to identify poor quality pixels + for masking. This mask is used for both masking out low + quality pixels (e.g. cloud or shadow), and for dropping + observations entirely based on the `min_gooddata` calculation. + categories_to_mask_s2 : list, optional + An optional list of Sentinel-2 Scene Classification Layer (SCL) names + that identify poor quality pixels for masking. + categories_to_mask_s1 : list, optional + An optional list of Sentinel-1 mask names that identify poor + quality pixels for masking. + mask_filters : iterable of tuples, optional + Iterable tuples of morphological operations - ("", ) + to apply on mask, where: + + operation: string, can be one of these morphological operations: + * ``'closing'`` = remove small holes in cloud - morphological closing + * ``'opening'`` = shrinks away small areas of the mask + * ``'dilation'`` = adds padding to the mask + * ``'erosion'`` = shrinks bright regions and enlarges dark regions + + radius: int + e.g. ``mask_filters=[('erosion', 5),("opening", 2),("dilation", 2)]`` + mask_pixel_quality : bool, optional + An optional boolean indicating whether to apply the poor data + mask to all observations that were not filtered out for having + less good quality pixels than ``min_gooddata``. E.g. if + ``min_gooddata=0.99``, the filtered observations may still contain + up to 1% poor quality pixels. The default of ``False`` simply + returns the resulting observations without masking out these + pixels; ``True`` masks them and sets them to NaN using the poor data + mask. This will convert numeric values to floating point values + which can cause memory issues, set to False to prevent this. + ls7_slc_off : bool, optional + An optional boolean indicating whether to include data from + after the Landsat 7 SLC failure (i.e. SLC-off). Defaults to + ``True``, which keeps all Landsat 7 observations > May 31 2003. + predicate : function, optional + An optional function that can be passed in to restrict the + datasets that are loaded by the function. A filter function + should take a `datacube.model.Dataset` object as an input (i.e. + as returned from `dc.find_datasets`), and return a boolean. + For example, a filter function could be used to return True on + only datasets acquired in January: + ``dataset.time.begin.month == 1`` + dtype : string, optional + An optional parameter that controls the data type/dtype that + layers are coerced to after loading. Valid values: ''`native`'', + ``'auto'``, ``'float{16|32|64}'``. + When ``'auto'`` is used, the data will be + converted to ``'float32'`` if masking is used, otherwise data will + be returned in the native data type of the data. Be aware that + if data is loaded in its native dtype, nodata and masked + pixels will be returned with the data's native nodata value + (typically ``-999``), not ``NaN``. + NOTE: If loading Landsat, the data is automatically rescaled so + 'native' dtype will return a value error. + verbose : bool, optional + If True, print progress statements during loading + **kwargs : dict, optional + A set of keyword arguments to ``dc.load`` that define the + spatiotemporal query used to extract data. This typically + includes ``measurements``, ``x`, ``y``, ``time``, ``resolution``, + ``resampling``, ``group_by`` and ``crs``. Keyword arguments can + either be listed directly in the ``load_ard`` call like any + other parameter (e.g. ``measurements=['red']``), or by + passing in a query kwarg dictionary (e.g. ``**query``). For a + list of possible options, see the ``dc.load`` documentation: + https://datacube-core.readthedocs.io/en/latest/dev/api/generate/datacube.Datacube.load.html + + Returns + ------- + combined_ds : xarray Dataset + An xarray dataset containing only satellite observations that + contains greater than `min_gooddata` proportion of good quality + pixels. + + """ + + ######### + # Setup # + ######### + # prevent function altering original query object + kwargs = deepcopy(kwargs) + + # We deal with `dask_chunks` separately + dask_chunks = kwargs.pop("dask_chunks", None) + requested_measurements = kwargs.pop("measurements", None) + + # Warn user if they combine lazy load with min_gooddata + if verbose: + if (min_gooddata > 0.0) and dask_chunks is not None: + warnings.warn( + "Setting 'min_gooddata' percentage to > 0.0 " + "will cause dask arrays to compute when " + "loading pixel-quality data to calculate " + "'good pixel' percentage. This can " + "slow the return of your dataset." + ) + + # Verify that products were provided and determine if Sentinel-2 + # or Landsat data is being loaded + if not products: + raise ValueError( + "Please provide a list of product names to load data from. " + "Valid options are: Landsat C2 SR: ['ls5_sr', 'ls7_sr', 'ls8_sr', 'ls9_sr'], or " + "Landsat C2 ST: ['ls5_st', 'ls7_st', 'ls8_st', 'ls9_st'], or " + "Sentinel-2: ['s2_l2a'], or" + "Sentinel-1: ['s1_rtc'], or" + ) + + # convert products to list if user passed as a string + if type(products) == str: + products=[products] + + if all(["ls" in product for product in products]): + product_type = "ls" + elif all(["s2" in product for product in products]): + product_type = "s2" + elif all(["s1" in product for product in products]): + product_type = "s1" + + # check if the landsat product is surface temperature + st = False + if (product_type == "ls") & (all(["st" in product for product in products])): + st = True + + # Check some parameters before proceeding + if (product_type == "ls") & (dtype == "native"): + raise ValueError( + "Cannot load Landsat bands in native dtype " + "as values require rescaling which converts dtype to float" + ) + + if product_type == "ls": + if any(k in categories_to_mask_ls for k in ("cirrus", "cirrus_confidence")): + raise ValueError( + "'cirrus' categories for the pixel quality mask" + " are not supported by load_ard" + ) + + # If `measurements` are specified but do not include pixel quality bands, + # add these to `measurements` according to collection + if product_type == "ls": + if verbose: + print("Using pixel quality parameters for USGS Collection 2") + fmask_band = "pixel_quality" + + elif product_type == "s2": + if verbose: + print("Using pixel quality parameters for Sentinel 2") + fmask_band = "SCL" + + elif product_type == "s1": + if verbose: + print("Using pixel quality parameters for Sentinel 1") + fmask_band = "mask" + + measurements = requested_measurements.copy() if requested_measurements else None + + # define a list of acceptable aliases to load landsat. We can't rely on 'common' + # measurements as native band names have the same name for different measurements. + ls_aliases = ["pixel_quality", "radiometric_saturation"] + if st: + ls_aliases = [ + "surface_temperature", + "surface_temperature_quality", + "atmospheric_transmittance", + "thermal_radiance", + "emissivity", + "emissivity_stddev", + "cloud_distance", + "upwell_radiance", + "downwell_radiance", + ] + ls_aliases + else: + ls_aliases = ["red", "green", "blue", "nir", "swir_1", "swir_2"] + ls_aliases + + if measurements is not None: + if product_type == "ls": + + # check we aren't loading aerosol bands from LS8 + aerosol_bands = [ + "aerosol_qa", + "qa_aerosol", + "atmos_opacity", + "coastal_aerosol", + "SR_QA_AEROSOL", + ] + if any(b in aerosol_bands for b in measurements): + raise ValueError( + "load_ard doesn't support loading aerosol or " + "atmospeheric opacity related bands " + "for Landsat, instead use dc.load()" + ) + + # check measurements are in acceptable aliases list for landsat + if set(measurements).issubset(ls_aliases): + pass + else: + raise ValueError( + "load_ard does not support all band aliases for Landsat, " + "use only the following band names to load Landsat data: " + + str(ls_aliases) + ) + + # Deal with "load all" case: pick a set of bands common across + # all products + if measurements is None: + if product_type == "ls": + measurements = ls_aliases + else: + measurements = _common_bands(dc, products) + + # If `measurements` are specified but do not include pq, add. + if measurements: + if fmask_band not in measurements: + measurements.append(fmask_band) + + # Get list of data and mask bands so that we can later exclude + # mask bands from being masked themselves (also handle the case of rad_sat) + data_bands = [ + band + for band in measurements + if band not in (fmask_band, "radiometric_saturation") + ] + mask_bands = [band for band in measurements if band not in data_bands] + + ################# + # Find datasets # + ################# + + # Pull out query params only to pass to dc.find_datasets + query = _dc_query_only(**kwargs) + + # Extract datasets for each product using subset of dcload_kwargs + dataset_list = [] + + # Get list of datasets for each product + if verbose: + print("Finding datasets") + for product in products: + + # Obtain list of datasets for product + if verbose: + print(f" {product}") + + if product_type == "ls": + # handle LS seperately to S2/S1 due to collection_category + # force the user to load Tier 1 + datasets = dc.find_datasets( + product=product, collection_category='T1', **query + ) + else: + datasets = dc.find_datasets(product=product, **query) + + # Remove Landsat 7 SLC-off observations if ls7_slc_off=False + if not ls7_slc_off and product in ["ls7_sr"]: + if verbose: + print(" Ignoring SLC-off observations for ls7") + datasets = [ + i + for i in datasets + if i.time.begin < datetime.datetime(2003, 5, 31, tzinfo=pytz.UTC) + ] + + # Add any returned datasets to list + dataset_list.extend(datasets) + + # Raise exception if no datasets are returned + if len(dataset_list) == 0: + raise ValueError( + "No data available for query: ensure that " + "the products specified have data for the " + "time and location requested" + ) + + # If predicate is specified, use this function to filter the list + # of datasets prior to load (this now redundant as dc.load now supports + # a predicate filter) + if predicate: + if verbose: + print(f"Filtering datasets using filter function") + dataset_list = [ds for ds in dataset_list if predicate(ds)] + + # Raise exception if filtering removes all datasets + if len(dataset_list) == 0: + raise ValueError("No data available after filtering with " "filter function") + + ############# + # Load data # + ############# + + # Note we always load using dask here so that + # we can lazy load data before filtering by good data + ds = dc.load( + datasets=dataset_list, + measurements=measurements, + dask_chunks={} if dask_chunks is None else dask_chunks, + **kwargs, + ) + #print(ds) + #################### + # Filter good data # + #################### + + # need to distinguish between products due to different + # pq band properties + + # collection 2 USGS + if product_type == "ls": + mask, _ = masking.create_mask_value( + ds[fmask_band].attrs["flags_definition"], **categories_to_mask_ls + ) + + pq_mask = (ds[fmask_band] & mask) != 0 + + # only run if data bands are present + if len(data_bands) > 0: + + # identify pixels that will become negative after rescaling (but not 0 values) + invalid = ( + ((ds[data_bands] < (-1.0 * -0.2 / 0.0000275)) & (ds[data_bands] > 0)) + .to_array(dim="band") + .any(dim="band") + ) + + #merge masks + pq_mask = np.logical_or(pq_mask, pq_mask) + + # sentinel 2 + if product_type == "s2": + pq_mask = odc.algo.enum_to_bool(mask=ds[fmask_band], + categories=categories_to_mask_s2) + + # sentinel 1 + if product_type == "s1": + pq_mask = odc.algo.enum_to_bool(mask=ds[fmask_band], + categories=categories_to_mask_s1) + #print(pq_mask) + # The good data percentage calculation has to load in all `fmask` + # data, which can be slow. If the user has chosen no filtering + # by using the default `min_gooddata = 0`, we can skip this step + # completely to save processing time + if min_gooddata > 0.0: + + # Compute good data for each observation as % of total pixels. + # Inveerting the pq_mask for this because cloud=True in pq_mask + # and we want to sum good pixels + if verbose: + print("Counting good quality pixels for each time step") + data_perc = (~pq_mask).sum(axis=[1, 2], dtype="int32") / ( + pq_mask.shape[1] * pq_mask.shape[2] + ) + + keep = (data_perc >= min_gooddata).persist() + + # Filter by `min_gooddata` to drop low quality observations + total_obs = len(ds.time) + ds = ds.sel(time=keep) + pq_mask = pq_mask.sel(time=keep) + + if verbose: + print( + f"Filtering to {len(ds.time)} out of {total_obs} " + f"time steps with at least {min_gooddata:.1%} " + f"good quality pixels" + ) + + # morpholigcal filtering on cloud masks + if (mask_filters is not None) & (mask_pixel_quality): + if verbose: + print(f"Applying morphological filters to pq mask {mask_filters}") + pq_mask = mask_cleanup(pq_mask, mask_filters=mask_filters) + + ############### + # Apply masks # + ############### + + # Generate good quality data mask + mask = None + if mask_pixel_quality: + if verbose: + print("Applying pixel quality/cloud mask") + mask = pq_mask + + # Split into data/masks bands, as conversion to float and masking + # should only be applied to data bands + ds_data = ds[data_bands] + ds_masks = ds[mask_bands] + + # Remove sentinel-2 pixels valued 1 (scene edges, terrain shadow) + if product_type == "s2": + valid_data_mask = (ds_data > 1).to_array(dim="band").all(dim="band") + ds_data = odc.algo.keep_good_only(ds_data, where=valid_data_mask) + + # Mask data if either of the above masks were generated + if mask is not None: + ds_data = odc.algo.erase_bad(ds_data, where=mask) + + # Automatically set dtype to either native or float32 depending + # on whether masking was requested + if dtype == "auto": + dtype = "native" if mask is None else "float32" + + # Set nodata values using odc.algo tools to reduce peak memory + # use when converting data dtype + if dtype != "native": + ds_data = odc.algo.to_float(ds_data, dtype=dtype) + + # Put data and mask bands back together + attrs = ds.attrs + ds = xr.merge([ds_data, ds_masks]) + ds.attrs.update(attrs) + + ############### + # Return data # + ############### + + # Drop bands not originally requested by user + if requested_measurements: + ds = ds[requested_measurements] + + # Apply the scale and offset factors to Collection 2 Landsat. We need + # different factors for different bands. Also handle the case where + # masking_pixel_quaity = False, in which case the dtype is still + # in int, so we convert it to float + if product_type == "ls": + if verbose: + print("Re-scaling Landsat C2 data") + + sr_bands = ["red", "green", "blue", "nir", "swir_1", "swir_2"] + radiance_bands = ["thermal_radiance", "upwell_radiance", "downwell_radiance"] + trans_emiss = ["atmospheric_transmittance", "emissivity", "emissivity_stddev"] + qa = ["pixel_quality", "radiometric_saturation"] + + if mask_pixel_quality == False: + # set nodata to NaNs before rescaling + # in the case where masking hasn't already done this + for band in ds.data_vars: + if band not in qa: + ds[band] = odc.algo.to_f32(ds[band]) + + for band in ds.data_vars: + if band == "cloud_distance": + ds[band] = 0.01 * ds[band] + + if band == "surface_temperature_quality": + ds[band] = 0.01 * ds[band] + + if band in radiance_bands: + ds[band] = 0.001 * ds[band] + + if band in trans_emiss: + ds[band] = 0.0001 * ds[band] + + if band in sr_bands: + ds[band] = 2.75e-5 * ds[band] - 0.2 + + if band == "surface_temperature": + ds[band] = ds[band] * 0.00341802 + 149.0 + + # add back attrs that are lost during scaling calcs + for band in ds.data_vars: + ds[band].attrs.update(attrs) + + # If user supplied dask_chunks, return data as a dask array without + # actually loading it in + if dask_chunks is not None: + if verbose: + print(f"Returning {len(ds.time)} time steps as a dask array") + return ds + else: + if verbose: + print(f"Loading {len(ds.time)} time steps") + return ds.compute() + + +def array_to_geotiff( + fname, data, geo_transform, projection, nodata_val=0, dtype=gdal.GDT_Float32 +): + """ + Create a single band GeoTIFF file with data from an array. + + Because this works with simple arrays rather than xarray datasets + from DEA, it requires geotransform info (`(upleft_x, x_size, + x_rotation, upleft_y, y_rotation, y_size)`) and projection data + (in "WKT" format) for the output raster. These are typically + obtained from an existing raster using the following GDAL calls: + + >>> from osgeo import gdal + >>> gdal_dataset = gdal.Open(raster_path) + >>> geotrans = gdal_dataset.GetGeoTransform() + >>> prj = gdal_dataset.GetProjection() + + or alternatively, directly from an xarray dataset: + + >>> geotrans = xarraydataset.geobox.transform.to_gdal() + >>> prj = xarraydataset.geobox.crs.wkt + + + Parameters + ---------- + fname : str + Output geotiff file path including extension + data : numpy array + Input array to export as a geotiff + geo_transform : tuple + Geotransform for output raster; e.g. `(upleft_x, x_size, + x_rotation, upleft_y, y_rotation, y_size)` + projection : str + Projection for output raster (in "WKT" format) + nodata_val : int, optional + Value to convert to nodata in the output raster; default 0 + dtype : gdal dtype object, optional + Optionally set the dtype of the output raster; can be + useful when exporting an array of float or integer values. + Defaults to `gdal.GDT_Float32` + + """ + + # Set up driver + driver = gdal.GetDriverByName("GTiff") + + # Create raster of given size and projection + rows, cols = data.shape + dataset = driver.Create(fname, cols, rows, 1, dtype) + dataset.SetGeoTransform(geo_transform) + dataset.SetProjection(projection) + + # Write data to array and set nodata values + band = dataset.GetRasterBand(1) + band.WriteArray(data) + band.SetNoDataValue(nodata_val) + + # Close file + dataset = None + + +def mostcommon_crs(dc, product, query): + """ + Takes a given query and returns the most common CRS for observations + returned for that spatial extent. This can be useful when your study + area lies on the boundary of two UTM zones, forcing you to decide + which CRS to use for your `output_crs` in `dc.load`. + + Parameters + ---------- + dc : datacube Datacube object + The Datacube to connect to, i.e. `dc = datacube.Datacube()`. + This allows you to also use development datacubes if required. + product : str + A product name to load CRSs from + query : dict + A datacube query including x, y and time range to assess for the + most common CRS + + Returns + ------- + str + A EPSG string giving the most common CRS from all datasets returned + by the query above + + """ + + # remove dask_chunks & align to prevent func failing + # prevent function altering dictionary kwargs + query = deepcopy(query) + if "dask_chunks" in query: + query.pop("dask_chunks", None) + + if "align" in query: + query.pop("align", None) + + # List of matching products + matching_datasets = dc.find_datasets(product=product, **query) + + # Extract all CRSs + crs_list = [str(i.crs) for i in matching_datasets] + + # Identify most common CRS + crs_counts = Counter(crs_list) + crs_mostcommon = crs_counts.most_common(1)[0][0] + + # Warn user if multiple CRSs are encountered + if len(crs_counts.keys()) > 1: + + warnings.warn( + f"Multiple UTM zones {list(crs_counts.keys())} " + f"were returned for this query. Defaulting to " + f"the most common zone: {crs_mostcommon}", + UserWarning, + ) + + return crs_mostcommon + + +def download_unzip(url, output_dir=None, remove_zip=True): + """ + Downloads and unzips a .zip file from an external URL to a local + directory. + + Parameters + ---------- + url : str + A string giving a URL path to the zip file you wish to download + and unzip + output_dir : str, optional + An optional string giving the directory to unzip files into. + Defaults to None, which will unzip files in the current working + directory + remove_zip : bool, optional + An optional boolean indicating whether to remove the downloaded + .zip file after files are unzipped. Defaults to True, which will + delete the .zip file. + + """ + + # Get basename for zip file + zip_name = os.path.basename(url) + + # Raise exception if the file is not of type .zip + if not zip_name.endswith(".zip"): + raise ValueError( + f"The URL provided does not point to a .zip " + f"file (e.g. {zip_name}). Please specify a " + f"URL path to a valid .zip file" + ) + + # Download zip file + print(f"Downloading {zip_name}") + r = requests.get(url) + with open(zip_name, "wb") as f: + f.write(r.content) + + # Extract into output_dir + with zipfile.ZipFile(zip_name, "r") as zip_ref: + zip_ref.extractall(output_dir) + print( + f"Unzipping output files to: " + f"{output_dir if output_dir else os.getcwd()}" + ) + + # Optionally cleanup + if remove_zip: + os.remove(zip_name) + + +def wofs_fuser(dest, src): + """ + Fuse two WOfS water measurements represented as `ndarray` objects. + + Note: this is a copy of the function located here: + https://github.com/GeoscienceAustralia/digitalearthau/blob/develop/digitalearthau/utils.py + """ + empty = (dest & 1).astype(bool) + both = ~empty & ~((src & 1).astype(bool)) + dest[empty] = src[empty] + dest[both] |= src[both] + + +def dilate(array, dilation=10, invert=True): + """ + Dilate a binary array by a specified nummber of pixels using a + disk-like radial dilation. + + By default, invalid (e.g. False or 0) values are dilated. This is + suitable for applications such as cloud masking (e.g. creating a + buffer around cloudy or shadowed pixels). This functionality can + be reversed by specifying `invert=False`. + + Parameters + ---------- + array : array + The binary array to dilate. + dilation : int, optional + An optional integer specifying the number of pixels to dilate + by. Defaults to 10, which will dilate `array` by 10 pixels. + invert : bool, optional + An optional boolean specifying whether to invert the binary + array prior to dilation. The default is True, which dilates the + invalid values in the array (e.g. False or 0 values). + + Returns + ------- + array + An array of the same shape as `array`, with valid data pixels + dilated by the number of pixels specified by `dilation`. + """ + + y, x = np.ogrid[ + -dilation : (dilation + 1), + -dilation : (dilation + 1), + ] + + # disk-like radial dilation + kernel = (x * x) + (y * y) <= (dilation + 0.5) ** 2 + + # If invert=True, invert True values to False etc + if invert: + array = ~array + + return ~binary_dilation( + array.astype(bool), structure=kernel.reshape((1,) + kernel.shape) + ) + + +def _select_along_axis(values, idx, axis): + other_ind = np.ix_(*[np.arange(s) for s in idx.shape]) + sl = other_ind[:axis] + (idx,) + other_ind[axis:] + return values[sl] + + +def first(array: xr.DataArray, dim: str, index_name: str = None) -> xr.DataArray: + """ + Finds the first occuring non-null value along the given dimension. + + Parameters + ---------- + array : xr.DataArray + The array to search. + dim : str + The name of the dimension to reduce by finding the first non-null value. + + Returns + ------- + reduced : xr.DataArray + An array of the first non-null values. + The `dim` dimension will be removed, and replaced with a coord of the + same name, containing the value of that dimension where the last value + was found. + """ + axis = array.get_axis_num(dim) + idx_first = np.argmax(~pd.isnull(array), axis=axis) + reduced = array.reduce(_select_along_axis, idx=idx_first, axis=axis) + reduced[dim] = array[dim].isel({dim: xr.DataArray(idx_first, dims=reduced.dims)}) + if index_name is not None: + reduced[index_name] = xr.DataArray(idx_first, dims=reduced.dims) + return reduced + + +def last(array: xr.DataArray, dim: str, index_name: str = None) -> xr.DataArray: + """ + Finds the last occuring non-null value along the given dimension. + + Parameters + ---------- + array : xr.DataArray + The array to search. + dim : str + The name of the dimension to reduce by finding the last non-null value. + index_name : str, optional + If given, the name of a coordinate to be added containing the index + of where on the dimension the nearest value was found. + + Returns + ------- + reduced : xr.DataArray + An array of the last non-null values. + The `dim` dimension will be removed, and replaced with a coord of the + same name, containing the value of that dimension where the last value + was found. + """ + axis = array.get_axis_num(dim) + rev = (slice(None),) * axis + (slice(None, None, -1),) + idx_last = -1 - np.argmax(~pd.isnull(array)[rev], axis=axis) + reduced = array.reduce(_select_along_axis, idx=idx_last, axis=axis) + reduced[dim] = array[dim].isel({dim: xr.DataArray(idx_last, dims=reduced.dims)}) + if index_name is not None: + reduced[index_name] = xr.DataArray(idx_last, dims=reduced.dims) + return reduced + + +def nearest( + array: xr.DataArray, dim: str, target, index_name: str = None +) -> xr.DataArray: + """ + Finds the nearest values to a target label along the given dimension, for + all other dimensions. + + E.g. For a DataArray with dimensions ('time', 'x', 'y') + + nearest_array = nearest(array, 'time', '2017-03-12') + + will return an array with the dimensions ('x', 'y'), with non-null values + found closest for each (x, y) pixel to that location along the time + dimension. + + The returned array will include the 'time' coordinate for each x,y pixel + that the nearest value was found. + + Parameters + ---------- + array : xr.DataArray + The array to search. + dim : str + The name of the dimension to look for the target label. + target : same type as array[dim] + The value to look up along the given dimension. + index_name : str, optional + If given, the name of a coordinate to be added containing the index + of where on the dimension the nearest value was found. + + Returns + ------- + nearest_array : xr.DataArray + An array of the nearest non-null values to the target label. + The `dim` dimension will be removed, and replaced with a coord of the + same name, containing the value of that dimension closest to the + given target label. + """ + before_target = slice(None, target) + after_target = slice(target, None) + + da_before = array.sel({dim: before_target}) + da_after = array.sel({dim: after_target}) + + da_before = last(da_before, dim, index_name) if da_before[dim].shape[0] else None + da_after = first(da_after, dim, index_name) if da_after[dim].shape[0] else None + + if da_before is None and da_after is not None: + return da_after + if da_after is None and da_before is not None: + return da_before + + target = array[dim].dtype.type(target) + is_before_closer = abs(target - da_before[dim]) < abs(target - da_after[dim]) + nearest_array = xr.where(is_before_closer, da_before, da_after) + nearest_array[dim] = xr.where(is_before_closer, da_before[dim], da_after[dim]) + if index_name is not None: + nearest_array[index_name] = xr.where( + is_before_closer, da_before[index_name], da_after[index_name] + ) + return nearest_array + +def parallel_apply(ds, dim, func, *args): + """ + Applies a custom function in parallel along the dimension of an + xarray.Dataset or xarray.DataArray. + + The function can be any function that can be applied to an + individual xarray.Dataset or xarray.DataArray (e.g. data for a + single timestep). The function should also return data in + xarray.Dataset or xarray.DataArray format. + + This function is useful as a simple method for parallising code + that cannot easily be parallised using Dask. + + Parameters + ---------- + ds : xarray.Dataset or xarray.DataArray + xarray data with a dimension `dim` to apply the custom function + along. + dim : string + The dimension along which the custom function will be applied. + func : function + The function that will be applied in parallel to each array + along dimension `dim`. The first argument passed to this + function should be the array along `dim`. + *args : + Any number of arguments that will be passed to `func`. + + Returns + ------- + xarray.Dataset + A concatenated dataset containing an output for each array + along the input `dim` dimension. + """ + + from concurrent.futures import ProcessPoolExecutor + from tqdm import tqdm + from itertools import repeat + + with ProcessPoolExecutor() as executor: + + # Apply func in parallel + groups = [group for (i, group) in ds.groupby(dim)] + to_iterate = (groups, *(repeat(i, len(groups)) for i in args)) + out_list = list(tqdm(executor.map(func, *to_iterate), total=len(groups))) + + # Combine to match the original dataset + return xr.concat(out_list, dim=ds[dim]) + + +def pan_sharpen_brovey(band_1, band_2, band_3, pan_band): + """ + Brovey pan sharpening on surface reflectance input using numexpr + and return three xarrays. + Parameters + ---------- + band_1, band_2, band_3 : xarray.DataArray or numpy.array + Three input multispectral bands, either as xarray.DataArrays or + numpy.arrays. These bands should have already been resampled to + the spatial resolution of the panchromatic band. + pan_band : xarray.DataArray or numpy.array + A panchromatic band corresponding to the above multispectral + bands that will be used to pan-sharpen the data. + Returns + ------- + band_1_sharpen, band_2_sharpen, band_3_sharpen : numpy.arrays + Three numpy arrays equivelent to `band_1`, `band_2` and `band_3` + pan-sharpened to the spatial resolution of `pan_band`. + """ + # Calculate total + exp = 'band_1 + band_2 + band_3' + total = numexpr.evaluate(exp) + + # Perform Brovey Transform in form of: band/total*panchromatic + exp = 'a/b*c' + band_1_sharpen = numexpr.evaluate(exp, local_dict={'a': band_1, + 'b': total, + 'c': pan_band}) + band_2_sharpen = numexpr.evaluate(exp, local_dict={'a': band_2, + 'b': total, + 'c': pan_band}) + band_3_sharpen = numexpr.evaluate(exp, local_dict={'a': band_3, + 'b': total, + 'c': pan_band}) + + return band_1_sharpen, band_2_sharpen, band_3_sharpen + +def load_s1_by_orbits(dc,query): + ''' + Function to query and load ascending and descending Sentinel-1 data + and add a variable to denote acquisition orbits + + Parameters: + dc: connected datacube + query: a query dictionary to define spatial extent, measurements, time range and spatial resolution + + Returns: + Queried dataset with variable 'is_ascending' added to denote orbit path + + ''' + # load ascending data + print('\nQuerying and loading Sentinel-1 ascending data...') + ds_s1_ascending=load_ard(dc=dc,products=['s1_rtc'],resampling='bilinear', + dtype='native',sat_orbit_state='ascending',**query) + # add an variable denoting data source + ds_s1_ascending['is_ascending']=xr.DataArray(np.ones(len(ds_s1_ascending.time)), + dims=('time'),coords={'time': ds_s1_ascending.time}) + + # load descending data + print('\nQuerying and loading Sentinel-1 descending data...') + ds_s1_descending=load_ard(dc=dc,products=['s1_rtc'],resampling='bilinear', + dtype='native',sat_orbit_state='descending',**query) + # add an variable denoting data source + ds_s1_descending['is_ascending']=xr.DataArray(np.zeros(len(ds_s1_descending.time)), + dims=('time'),coords={'time': ds_s1_descending.time}) + + # merge datasets together + ds_s1=xr.concat([ds_s1_ascending,ds_s1_descending],dim='time').sortby('time') + + return ds_s1 + +def filter_obs_by_orbit(ds_s1): + ''' + Function to impliment per-pixel filtering of Sentinel-1 observations + to keep only observations from the orbit (ascending/descending) with higher frequency over time. + + Each of the Sentinel-1 observations was acquired from either a descending or ascending orbit, + which has impacts on the local incidence angle and backscattering value. + Here we do the filtering to minimise the effects of inconsistent looking angle and obit direction for each individual pixel. + + Parameters: + ds_s1: xarray.Dataset + Time-series observations of Sentinel-1 data, + with two required variables: 'is_ascending' denoting orbit path and 'mask' to identify acquisition exent + + Returns: + ds_s1_filtered: xarray.Dataset + Filtered dataset + ''' + + print('\nFiltering Sentinel-1 product by orbit...') + cnt_ascending=((ds_s1["is_ascending"]==1)&(ds_s1['mask']!=0)).sum(dim='time') + cnt_descending=((ds_s1["is_ascending"]==0)&(ds_s1['mask']!=0)).sum(dim='time') + + ds_s1_filtered=ds_s1.where(((cnt_ascending>=cnt_descending)&(ds_s1["is_ascending"]==1))| + ((cnt_ascending=0 + thresholded_ds = thresholded_ds.where(~nodata) + # use 20% ~ 80% wet frequency to identify potential coastal zone + coastal_mask=(thresholded_ds.mean(dim='time') >= 0.2)&(thresholded_ds.mean(dim='time') <= 0.8) + # buffering + print('\nApplying buffering of {} Sentinel-2 pixels (parameter buffer_pixels)...'.format(buffer_pixels)) + coastal_mask=xr.apply_ufunc(binary_dilation,coastal_mask.compute(),disk(buffer_pixels)) + return coastal_mask + +def choose_product(ds_ls,ds_s2,ds_s1,ds_ls_s2,time_step,**kwargs): + ''' + Rule-based guide on choosing the best availabel dataset in a given time step and optionally within a coastal zone mask + + Parameters: + ds_ls: xarray.Dataset + Time series Landsat data + ds_s2: xarray.Dataset + Time series Sentinel-2 data + ds_s1: xarray.Dataset + Time series Sentinel-1 data + ds_ls_s2: xarray.Dataset or None + Time series of combined Landsat and Sentinel-2 data. + time_step: string + Time step for temporal composition + **kwargs: A set of optional parameters including: + thresh_n_valid: integer + Threhold of minimum average number of valid observations within each time step + thresh_freq: float + Threshold of minimum frequency of valid observations within each time step + buffer_pixels: integer + Number of pixels to buffer coastal zone + coastal_masking: Boolean + whether to calculate a coastal zone mask and restrict the comparison of the products within the mask + Returns: + Xarray.Dataset of the best product + String of the best product name: 'ls', 's2', 's1' or 'ls_s2' + ''' + + # check if optional parameters are defined otherwise set default values + thresh_n_valid=10 if "thresh_n_valid" not in kwargs else kwargs["thresh_n_valid"] + thresh_freq=0.2 if "thresh_freq" not in kwargs else kwargs["thresh_freq"] + buffer_pixels=100 if "buffer_pixels" not in kwargs else kwargs["buffer_pixels"] + print('\nThreshold number of valid observations (parameter thresh_n_valid): {}'.format(thresh_n_valid)) + print('\nThreshold frequency of valid observations (parameter thresh_freq): {}'.format(thresh_freq)) + + # create mask if requested + coastal_masking=False if "coastal_masking" not in kwargs else kwargs["coastal_masking"] + if coastal_masking==True: + # calculate index + ds_s2 = calculate_indices(ds_s2, index='MNDWI', satellite_mission='s2') + mask=create_coastal_mask(ds_s2['MNDWI'],buffer_pixels) + else: + print('\nNo coastal masking required, using all pixels within the selected region...') + mask=None + + # calculate mean number and fraction of clear observations within each timestep and the mask + print('\nCalculating number and frequency of valid observations...') + + n_valid_obs_s2,freq_valid_s2=get_mean_number_freq_valid_obs(ds_s2['green'],mask,time_step) + print('\nSentinel-2: Average number and frequency of valid observations: {:.0f} and {:.2f}'.format(n_valid_obs_s2.mean().values,freq_valid_s2.mean().values)) + + n_valid_obs_ls,freq_valid_ls=get_mean_number_freq_valid_obs(ds_ls['green'],mask,time_step) + print('\nLandsat: Average number and frequency of valid observations: {:.0f} and {:.2f}'.format(n_valid_obs_ls.mean().values,freq_valid_ls.mean().values)) + +# n_valid_obs_s1,freq_valid_s1=get_mean_number_freq_valid_obs(ds_s1['vh'],mask,time_step) # dont need this as sentinel-1 will only be chosen when optical datasets are not sufficient + if not ds_ls_s2 is None: + n_valid_obs_ls_s2,freq_valid_ls_s2=get_mean_number_freq_valid_obs(ds_ls_s2['green'],mask,time_step) + print('\nCombined Landsat and Sentinel-2 product: Average number and frequency of valid observations: {:.0f} and {:.2f}'.format(n_valid_obs_ls_s2.mean().values,freq_valid_ls_s2.mean().values)) + + # apply decision rules + print('\nApplying rules to choose product...') + + # if Sentinel-2 meets requirements + if ((n_valid_obs_s2>=thresh_n_valid).all()) and ((freq_valid_s2>=thresh_freq).all()): + print('\nSentinel-2 product has met the minimum required average number and frequency of valid observations within all time periods') + # if combined product is available, choose combined product if it has both higher number and frequency + if not ds_ls_s2 is None: + if ((n_valid_obs_ls_s2>n_valid_obs_s2).all()) and ((freq_valid_ls_s2>freq_valid_s2).all()): + ds_selected, product_name=ds_ls_s2,'ls_s2' + print('\nChoosing combined Landsat and Sentinel-2 product as it has both higher number and frequency of valid observations within all time periods') + else: + ds_selected, product_name=ds_s2,'s2' + print('\nChoosing Sentinel-2 product as neither Landsat or the combined product meets both requirements or is significantly better than Sentinel-2') + # if combined product is unavailable, choose Landsat if it has both higher number and frequency + elif ((n_valid_obs_ls>=n_valid_obs_s2).all()) and ((freq_valid_ls>=freq_valid_s2).all()): + ds_selected, product_name=ds_ls,'ls' + print('\nChoosing Landsat product as it has both higher average number and frequency of valid observations within all time periods') + # otherwise choose Sentinel-2 + else: + ds_selected, product_name=ds_s2,'s2' + print('\nChoosing Sentinel-2 product as Landsat product does not meet both requirements or is not significantly better than Sentinel-2') + # if Sentinel-2 doesn't meet both requirements,but Landsat does, either choose Landsat or combined product if available + elif ((n_valid_obs_ls>=thresh_n_valid).all()) and ((freq_valid_ls>=thresh_freq).all()): + print('\nSentinel-2 does not meet the minimum required average number and frequency of valid observations within all time periods, but Landsat does') + if not ds_ls_s2 is None: + ds_selected, product_name=ds_ls_s2,'ls_s2' + print('\nChoosing combined Landsat and Sentinel-2 product as it has both higher number and frequency of valid observations within all time periods') + else: + ds_selected, product_name=ds_ls,'ls' + print('\nChoosing Landsat product') + # if neither Sentinel-2 or Landsat meet both requirements, choose combined product if it meets requirements + elif not ds_ls_s2 is None: + print('\nNeither Sentinel-2 or Landsat meets the minimum required average number and frequency of valid observations within all time periods') + # but the combined product meet requirements + if ((n_valid_obs_ls_s2>=thresh_n_valid).all()) and ((freq_valid_ls_s2>=thresh_freq).all()): + ds_selected, product_name=ds_ls_s2,'ls_s2' + print('\nChoosing combined Landsat and Sentinel-2 product as it meets the minimum required average number and frequency of valid observations within all time periods') + else: + ds_selected, product_name=ds_s1,'s1' + print('\nChoosing Sentinel-1 as no other products available that meet the requirements') + # otherwise choose Sentinel-1 + else: + print('\nNeither Sentinel-2 or Landsat meets the minimum required average number and frequency of valid observations within all time periods') + ds_selected, product_name=ds_s1,'s1' + print('\nChoosing Sentinel-1 product as no other products available that meet the requirements') + + print('\nBest available product: ',product_name) + return ds_selected, product_name + +def load_combined_ls_s2(dc,query): + '''function to query and load combined Landsat and Sentinel-2 data + + Parameters: + dc: connected datacube + query: a query dictionary to define spatial extent, time range, measurements and spatial resolution for both datasets + + Returns: + ds_combined: Combined data as xarray.Dataset + ''' + print('Querying and loading combined Landsat and Sentinel-2 products...') + # Load available Landsat data resampled to Sentinel-2 resolution + ds_ls = load_ard(dc=dc, products=['ls8_sr', 'ls9_sr'],align=(10, 10), + resampling='bilinear',**query) + + # add an variable denoting data source (for future analysis) + is_ls=xr.DataArray(np.ones(len(ds_ls.time)),dims=('time'),coords={'time': ds_ls.time}) + ds_ls['is_ls'] = is_ls + + # Load Sentinel-2 data + ds_s2 = load_ard(dc=dc,products=['s2_l2a'],resampling='bilinear', + align=(10, 10),mask_filters=[("opening", 2), ("dilation", 5)],**query) + # add an variable denoting data source (for future analysis) + is_ls=xr.DataArray(np.zeros(len(ds_s2.time)),dims=('time'),coords={'time': ds_s2.time}) + ds_s2['is_ls'] = is_ls + + # merge two datasets together + ds_combined=xr.concat([ds_ls,ds_s2],dim='time').sortby('time') + + return ds_combined + +def load_best_available_ds(dc, lat_range, lon_range, time_range, time_step, **kwargs): + ''' + Function to query, load and compare different products, select and return the best available product + + Parameters: + dc: connected datacube + lat_range: range of latitudes in tuple or list + lon_range: range of longitude in tuple or list + time_range: range of time to query the data in tuple or list + time_step: string, pre-defined time step for temporal aggregation, e.g. '1Y' + **kwargs: A set of optional parameters on data query or comparison between products which may include: + combine_ls_s2: A boolean value indicating whether to include merged/stacked Landsat and Sentinel-2 products as an option. Default to False. + set_resolution: integer of spatial resolution in metres to query all products + coastal_masking: A boolean value indicating whether to calculate a mask + and restrict the comparison of the products within the masked zone. + set_product: Set this to only query and load a pre-selected product, 'ls','s2','ls_s2' or 's1' + i.e. no other products will be queried or compared. + thresh_n_valid: Threhold of minimum average number of valid observations within each time step, integer + thresh_freq: Threshold of minimum frequency of valid observations within each time step, float between 0~1 + buffer_pixels: Number of pixels to buffer coastal zone, integer + + Returns: + ds_selected: selected product as xarray.Dataset + product_name: name of selected product in string format, i.e. 'ls','s2','ls_s2','s1' + ''' + # parse input time range to accommodate queries before and after 2017 + min_time=min(parser.parse(time_range_i,default=datetime(1987,1,1,0,0)) + for time_range_i in time_range) + if min_time ds.lat.max(): +# warnings.warn( +# "Lats must be in range {} .. {}. Got: {}".format( +# ds.lat.min().values, ds.lat.max().values, lat +# ) +# ) +# if min(lon) < ds.lon.min() or max(lon) > ds.lon.max(): +# warnings.warn( +# "Lons must be in range {} .. {}. Got: {}".format( +# ds.lon.min().values, ds.lon.max().values, lon +# ) +# ) +# # Find existing coords between min&max +# lats = ds.lat[np.logical_and(ds.lat >= min(lat), ds.lat <= max(lat))].values +# # If there was nothing between, just plan to grab closest +# if len(lats) == 0: +# lats = np.unique(ds.lat.sel(lat=np.array(lat), method="nearest")) +# lons = ds.lon[np.logical_and(ds.lon >= min(lon), ds.lon <= max(lon))].values +# if len(lons) == 0: +# lons = np.unique(ds.lon.sel(lon=np.array(lon), method="nearest")) +# # crop and keep attrs +# output = ds.sel(lat=lats, lon=lons) +# output.attrs = ds.attrs +# for var in output.data_vars: +# output[var].attrs = ds[var].attrs +# return output + + +# def era5_area_nearest(ds, lat, lon): +# """ +# Crop a dataset containing EAR5 variables to a location. +# The output spatial grid is snapped to the nearest input grid points. + +# Parameters +# ---------- +# ds : xarray dataset +# A dataset containing ERA5 variables of interest. + +# lat: tuple or list +# Latitude range for query. + +# lon: tuple or list +# Longitude range for query. + +# Returns +# ------- +# An xarray dataset containing ERA5 variables for the selected location. + +# """ + +# if min(lon) < 0: +# # re-order along longitude to go from -180 to 180 +# ds = ds.assign_coords({"lon": (((ds.lon + 180) % 360) - 180)}) +# ds = ds.reindex({"lon": np.sort(ds.lon)}) + +# # find the nearest lat lon boundary points +# test = ds.sel(lat=lat, lon=lon, method="nearest") +# # define the lat/lon grid +# lat_range = slice(test.lat.max().values, test.lat.min().values) +# lon_range = slice(test.lon.min().values, test.lon.max().values) +# # crop and keep attrs +# output = ds.sel(lat=lat_range, lon=lon_range) +# output.attrs = ds.attrs +# for var in output.data_vars: +# output[var].attrs = ds[var].attrs +# return output + + +# def load_era5_netcdf(var, lat, lon, time, grid="nearest", **kwargs): +# """ +# Returns a ERA5 variable for a selected location and time window. + +# Parameters +# ---------- +# var : string +# Name of the ERA5 climate variable to download, e.g "air_temperature_at_2_metres" + +# lat: tuple or list +# Latitude range for query. + +# lon: tuple or list +# Longitude range for query. + +# time: tuple or list +# Time range for query. + +# grid: string +# Option for output spatial gridding. +# The default is 'nearest', for which output spatial grid is snapped to the nearest ERA5 input grid points. +# Alternatively, output spatial grid will either include input grid points within lat/lon boundaries or the nearest point if none is within the search location. + +# Returns +# ------- +# An xarray dataset containing the variable for the selected location and time window. + +# """ + +# ds = get_era5_daily(var, time[0], time[1], **kwargs) +# if grid == "nearest": +# return era5_area_nearest(ds, lat, lon).compute() +# else: +# return era5_area_crop(ds, lat, lon).compute() diff --git a/deafrica_tools/load_isda.py b/deafrica_tools/load_isda.py new file mode 100644 index 0000000..2e9cca1 --- /dev/null +++ b/deafrica_tools/load_isda.py @@ -0,0 +1,92 @@ +""" +Functions to retrieve iSDAsoil data. +""" + +import numpy as np +import pandas as pd +import xarray as xr +import matplotlib.pyplot as plt +import matplotlib.patches as mpatches +import rasterio as rio +from pyproj import Transformer +import matplotlib.pyplot as plt +import os +import numpy as np + +from urllib.parse import urlparse +import boto3 +from pystac import stac_io, Catalog + +#this function allows us to directly query the data on s3, adapted from iSDA tutorial https://github.com/iSDA-Africa/isdasoil-tutorial/blob/main/iSDAsoil-tutorial.ipynb +def my_read_method(uri): + parsed = urlparse(uri) + if parsed.scheme == 's3': + bucket = parsed.netloc + key = parsed.path[1:] + s3 = boto3.resource('s3') + obj = s3.Object(bucket, key) + return obj.get()['Body'].read().decode('utf-8') + else: + return stac_io.default_read_text_method(uri) + +stac_io.read_text_method = my_read_method + +catalog = Catalog.from_file("https://isdasoil.s3.amazonaws.com/catalog.json") + +assets = {} + +for root, catalogs, items in catalog.walk(): + for item in items: + str(f"Type: {item.get_parent().title}") + # save all items to a dictionary as we go along + assets[item.id] = item + for asset in item.assets.values(): + if asset.roles == ['data']: + str(f"Title: {asset.title}") + str(f"Description: {asset.description}") + str(f"URL: {asset.href}") + str("------------") + +# define load_isda() function + +def load_isda(var, lat, lon): + """ + Download and return iSDA variable with number of bands corresponding to number of iSDA layers. + Parameters + ---------- + var : string + Name of the iSDA variable to download, e.g "ph" + lat: tuple or list + Latitude range for query. + lon: tuple or list + Longitude range for query. + """ + + bands = assets[var].assets["image"].extra_fields.get('eo:bands') + bands = [val['description'] for val in bands] + + if len(np.unique(bands)) > 1: + + ds = xr.open_dataset(assets[var].assets["image"].href, engine="rasterio").rio.clip_box( + minx=lon[0], + miny=lat[0], + maxx=lon[1], + maxy=lat[1], + crs="EPSG:4326", + ) + + ds_layered = ds.drop_dims('band') + for x in np.unique(ds.band): + ds_layered[bands[x-1]] = ds.sel(band=x).to_array(dim='band').squeeze() + + else: + + ds_layered = xr.open_dataset(assets[var].assets["image"].href, engine="rasterio").rio.clip_box( + minx=lon[0], + miny=lat[0], + maxx=lon[1], + maxy=lat[1], + crs="EPSG:4326", + ).squeeze() + + return ds_layered diff --git a/deafrica_tools/load_soil_moisture.py b/deafrica_tools/load_soil_moisture.py new file mode 100644 index 0000000..4fb1a58 --- /dev/null +++ b/deafrica_tools/load_soil_moisture.py @@ -0,0 +1,39 @@ +import xarray as xr +import numpy as np + +# function to load soil moisture data + +def load_soil_moisture(lat, lon, time, product = 'surface', grid = 'nearest'): + product_baseurl = 'https://dapds00.nci.org.au/thredds/dodsC/ub8/global/GRAFS/' + assert product in ['surface', 'rootzone'], 'product parameter must be surface or root-zone' + # lat, lon grid + if grid == 'nearest': + # select lat/lon range from data; snap to nearest grid + lat_range, lon_range = None, None + else: + # define a grid that covers the entire area of interest + lat_range = np.arange(np.max(np.ceil(np.array(lat)*10.+0.5)/10.-0.05), np.min(np.floor(np.array(lat)*10.-0.5)/10.+0.05)-0.05, -0.1) + lon_range = np.arange(np.min(np.floor(np.array(lon)*10.-0.5)/10.+0.05), np.max(np.ceil(np.array(lon)*10.+0.5)/10.-0.05)+0.05, 0.1) + # split time window into years + day_range = np.array(time).astype("M8[D]") + year_range = np.array(time).astype("M8[Y]") + if product == 'surface': + product_name = 'GRAFS_TopSoilRelativeWetness_' + else: product_name = 'GRAFS_RootzoneSoilWaterIndex_' + datasets = [] + for year in np.arange(year_range[0], year_range[1]+1, np.timedelta64(1, 'Y')): + start = np.max([day_range[0], year.astype("M8[D]")]) + end = np.min([day_range[1], (year+1).astype("M8[D]")-1]) + product_url = product_baseurl + product_name +'%s.nc'%str(year) + print(product_url) + # data is loaded lazily through OPeNDAP + ds = xr.open_dataset(product_url) + if lat_range is None: + # select lat/lon range from data if not specified; snap to nearest grid + test = ds.sel(lat=list(lat), lon=list(lon), method='nearest') + lat_range = slice(test.lat.values[0], test.lat.values[1]) + lon_range = slice(test.lon.values[0], test.lon.values[1]) + # slice before return + ds = ds.sel(lat=lat_range, lon=lon_range, time=slice(start, end)).compute() + datasets.append(ds) + return xr.merge(datasets) \ No newline at end of file diff --git a/deafrica_tools/locales/fr/LC_MESSAGES/deafrica_tools.mo b/deafrica_tools/locales/fr/LC_MESSAGES/deafrica_tools.mo new file mode 100644 index 0000000..6b7f6ac Binary files /dev/null and b/deafrica_tools/locales/fr/LC_MESSAGES/deafrica_tools.mo differ diff --git a/deafrica_tools/locales/fr/LC_MESSAGES/deafrica_tools.po b/deafrica_tools/locales/fr/LC_MESSAGES/deafrica_tools.po new file mode 100644 index 0000000..06d9e91 --- /dev/null +++ b/deafrica_tools/locales/fr/LC_MESSAGES/deafrica_tools.po @@ -0,0 +1,117 @@ +msgid "" +msgstr "" +"MIME-Version: 1.0\n" +"Content-Type: text/plain; charset=UTF-8\n" +"Content-Transfer-Encoding: 8bit\n" +"X-Generator: POEditor.com\n" +"Project-Id-Version: deafrica_tools\n" +"Language: fr\n" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:83 +msgid "None" +msgstr "Aucun" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:84 +msgid "ESRI World Imagery" +msgstr "Imagerie mondiale ESRI" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:85 +msgid "Sentinel-2 Geomedian" +msgstr "Sentinel-2 Geomedian" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:86 +msgid "Water Observations from Space" +msgstr "Observations de l'eau depuis l'espace-WOfS" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:99 +msgid "Wetlands Insight Tool" +msgstr "Outil d'analyse des zones humides" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:100 +msgid "Select parameters and AOI" +msgstr "Sélectionner les paramètres et la zone d'intérêt" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:124 +msgid "Total polygon area" +msgstr "Superficie totale du polygone" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:128 +msgid "Area falls within recommended limit" +msgstr "La zone se situe dans la limite recommandée" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:131 +msgid "Area is too large, please update your polygon" +msgstr "La zone est trop grande, veuillez réduire votre polygone" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:151 +msgid "Map Overlays" +msgstr "Superpositions de cartes" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:176 +msgid "Run" +msgstr "Exécuter" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:183 +msgid "Map Overlay:" +msgstr "Carte superposée :" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:185 +msgid "Start Date:" +msgstr "Date de début :" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:187 +msgid "End Date:" +msgstr "Date de fin :" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:189 +msgid "Minimum Good Data:" +msgstr "Minimum de bonnes données :" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:191 +msgid "Resampling Frequency:" +msgstr "Fréquence de rééchantillonnage :" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:193 +msgid "Output CSV:" +msgstr "Sortie CSV :" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:195 +msgid "Output Plot:" +msgstr "Tracé de sortie :" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:308 +msgid "Progress" +msgstr "Progrès" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:326 +msgid "WIT complete" +msgstr "WIT achevée" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:328 +msgid "No polygon selected" +msgstr "Aucun polygone sélectionné" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:365 +msgid "open water" +msgstr "eau libre" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:366 +msgid "wet" +msgstr "humide" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:367 +msgid "green veg" +msgstr "végétation verts" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:368 +msgid "dry veg" +msgstr "végétation seche" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:369 +msgid "bare soil" +msgstr "sol nu" + +#: Tools/deafrica_tools/app/wetlandsinsighttool.py:382 +msgid "Percentage Fractional Cover, Wetness, and Water" +msgstr "Pourcentage de couverture fractionnée, humidité et eau" + diff --git a/deafrica_tools/plotting.py b/deafrica_tools/plotting.py new file mode 100644 index 0000000..d554a4c --- /dev/null +++ b/deafrica_tools/plotting.py @@ -0,0 +1,1272 @@ +""" +Functions for plotting Digital Earth Africa data. +""" + +# Import required packages + +# Force GeoPandas to use Shapely instead of PyGEOS +# In a future release, GeoPandas will switch to using Shapely by default. +import os +os.environ['USE_PYGEOS'] = '0' + +import math +import folium +import ipywidgets +import branca +import numpy as np +import geopandas as gpd +import matplotlib as mpl +import matplotlib.patheffects as PathEffects +import matplotlib.pyplot as plt +import matplotlib.animation as animation +from datetime import datetime +import matplotlib.cm as cm +from matplotlib import colors as mcolours +from pyproj import Transformer +from IPython.display import display +from matplotlib.colors import ListedColormap +from mpl_toolkits.axes_grid1.inset_locator import inset_axes +from mpl_toolkits.axes_grid1 import make_axes_locatable +from ipyleaflet import Map, Marker, Popup, GeoJSON, basemaps, Choropleth +from skimage import exposure +from branca.colormap import linear +from odc.ui import image_aspect + +from matplotlib.animation import FuncAnimation +import pandas as pd +from pathlib import Path +from shapely.geometry import box +from skimage.exposure import rescale_intensity +from tqdm.auto import tqdm +import warnings + + +def rgb( + ds, + bands=["red", "green", "blue"], + index=None, + index_dim="time", + robust=True, + percentile_stretch=None, + col_wrap=4, + size=6, + aspect=None, + savefig_path=None, + savefig_kwargs={}, + **kwargs, +): + + """ + Takes an xarray dataset and plots RGB images using three imagery + bands (e.g ['red', 'green', 'blue']). The `index` + parameter allows easily selecting individual or multiple images for + RGB plotting. Images can be saved to file by specifying an output + path using `savefig_path`. + This function was designed to work as an easier-to-use wrapper + around xarray's `.plot.imshow()` functionality. + + Last modified: April 2021 + + Parameters + ---------- + ds : xarray Dataset + A two-dimensional or multi-dimensional array to plot as an RGB + image. If the array has more than two dimensions (e.g. multiple + observations along a 'time' dimension), either use `index` to + select one (`index=0`) or multiple observations + (`index=[0, 1]`), or create a custom faceted plot using e.g. + `col="time"`. + bands : list of strings, optional + A list of three strings giving the band names to plot. Defaults + to '['red', 'green', 'blue']'. If the dataset does not contain + bands named `'red', 'green', 'blue'`, then `bands` must be + specified. + index : integer or list of integers, optional + `index` can be used to select one (`index=0`) or multiple + observations (`index=[0, 1]`) from the input dataset for + plotting. If multiple images are requested these will be plotted + as a faceted plot. + index_dim : string, optional + The dimension along which observations should be plotted if + multiple observations are requested using `index`. Defaults to + `time`. + robust : bool, optional + Produces an enhanced image where the colormap range is computed + with 2nd and 98th percentiles instead of the extreme values. + Defaults to True. + percentile_stretch : tuple of floats + An tuple of two floats (between 0.00 and 1.00) that can be used + to clip the colormap range to manually specified percentiles to + get more control over the brightness and contrast of the image. + The default is None; '(0.02, 0.98)' is equivelent to + `robust=True`. If this parameter is used, `robust` will have no + effect. + col_wrap : integer, optional + The number of columns allowed in faceted plots. Defaults to 4. + size : integer, optional + The height (in inches) of each plot. Defaults to 6. + aspect : integer, optional + Aspect ratio of each facet in the plot, so that aspect * size + gives width of each facet in inches. Defaults to None, which + will calculate the aspect based on the x and y dimensions of + the input data. + savefig_path : string, optional + Path to export image file for the RGB plot. Defaults to None, + which does not export an image file. + savefig_kwargs : dict, optional + A dict of keyword arguments to pass to + `matplotlib.pyplot.savefig` when exporting an image file. For + all available options, see: + https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html + **kwargs : optional + Additional keyword arguments to pass to `xarray.plot.imshow()`. + For example, the function can be used to plot into an existing + matplotlib axes object by passing an `ax` keyword argument. + For more options, see: + http://xarray.pydata.org/en/stable/generated/xarray.plot.imshow.html + Returns + ------- + An RGB plot of one or multiple observations, and optionally an image + file written to file. + """ + + # If bands are not in the dataset + ds_vars = list(ds.data_vars) + if set(bands).issubset(ds_vars) == False: + raise ValueError( + "rgb() bands do not match band names in dataset. " + "Note the default rgb() bands are ['red', 'green', 'blue']." + ) + + # If ax is supplied via kwargs, ignore aspect and size + if "ax" in kwargs: + + # Create empty aspect size kwarg that will be passed to imshow + aspect_size_kwarg = {} + else: + # Compute image aspect + if not aspect: + aspect = image_aspect(ds) + + # Populate aspect size kwarg with aspect and size data + aspect_size_kwarg = {"aspect": aspect, "size": size} + + # If no value is supplied for `index` (the default), plot using default + # values and arguments passed via `**kwargs` + if index is None: + + # Select bands and convert to DataArray + da = ds[bands].to_array() + + # If percentile_stretch == True, clip plotting to percentile vmin, vmax + if percentile_stretch: + vmin, vmax = da.compute().quantile(percentile_stretch).values + kwargs.update({"vmin": vmin, "vmax": vmax}) + + # If there are more than three dimensions and the index dimension == 1, + # squeeze this dimension out to remove it + if (len(ds.dims) > 2) and ("col" not in kwargs) and (len(da[index_dim]) == 1): + + da = da.squeeze(dim=index_dim) + + # If there are more than three dimensions and the index dimension + # is longer than 1, raise exception to tell user to use 'col'/`index` + elif (len(ds.dims) > 2) and ("col" not in kwargs) and (len(da[index_dim]) > 1): + + raise Exception( + f"The input dataset `ds` has more than two dimensions: " + "{list(ds.dims.keys())}. Please select a single observation " + "using e.g. `index=0`, or enable faceted plotting by adding " + 'the arguments e.g. `col="time", col_wrap=4` to the function ' + "call" + ) + da = da.compute() + img = da.plot.imshow( + robust=robust, col_wrap=col_wrap, **aspect_size_kwarg, **kwargs + ) + + # If values provided for `index`, extract corresponding observations and + # plot as either single image or facet plot + else: + + # If a float is supplied instead of an integer index, raise exception + if isinstance(index, float): + raise Exception( + f"Please supply `index` as either an integer or a list of " "integers" + ) + + # If col argument is supplied as well as `index`, raise exception + if "col" in kwargs: + raise Exception( + f"Cannot supply both `index` and `col`; please remove one and " + "try again" + ) + + # Convert index to generic type list so that number of indices supplied + # can be computed + index = index if isinstance(index, list) else [index] + + # Select bands and observations and convert to DataArray + da = ds[bands].isel(**{index_dim: index}).to_array().compute() + + # If percentile_stretch == True, clip plotting to percentile vmin, vmax + if percentile_stretch: + vmin, vmax = da.compute().quantile(percentile_stretch).values + kwargs.update({"vmin": vmin, "vmax": vmax}) + + # If multiple index values are supplied, plot as a faceted plot + if len(index) > 1: + + img = da.plot.imshow( + robust=robust, + col=index_dim, + col_wrap=col_wrap, + **aspect_size_kwarg, + **kwargs, + ) + + # If only one index is supplied, squeeze out index_dim and plot as a + # single panel + else: + + img = da.squeeze(dim=index_dim).plot.imshow( + robust=robust, **aspect_size_kwarg, **kwargs + ) + + # If an export path is provided, save image to file. Individual and + # faceted plots have a different API (figure vs fig) so we get around this + # using a try statement: + if savefig_path: + + print(f"Exporting image to {savefig_path}") + + try: + img.fig.savefig(savefig_path, **savefig_kwargs) + except: + img.figure.savefig(savefig_path, **savefig_kwargs) + + +def display_map(x, y, crs="EPSG:4326", margin=-0.5, zoom_bias=0): + """ + Given a set of x and y coordinates, this function generates an + interactive map with a bounded rectangle overlayed on Google Maps + imagery. + + Last modified: September 2019 + + Modified from function written by Otto Wagner available here: + https://github.com/ceos-seo/data_cube_utilities/tree/master/data_cube_utilities + + Parameters + ---------- + x : (float, float) + A tuple of x coordinates in (min, max) format. + y : (float, float) + A tuple of y coordinates in (min, max) format. + crs : string, optional + A string giving the EPSG CRS code of the supplied coordinates. + The default is 'EPSG:4326'. + margin : float + A numeric value giving the number of degrees lat-long to pad + the edges of the rectangular overlay polygon. A larger value + results more space between the edge of the plot and the sides + of the polygon. Defaults to -0.5. + zoom_bias : float or int + A numeric value allowing you to increase or decrease the zoom + level by one step. Defaults to 0; set to greater than 0 to zoom + in, and less than 0 to zoom out. + Returns + ------- + folium.Map : A map centered on the supplied coordinate bounds. A + rectangle is drawn on this map detailing the perimeter of the x, y + bounds. A zoom level is calculated such that the resulting + viewport is the closest it can possibly get to the centered + bounding rectangle without clipping it. + """ + + # Convert each corner coordinates to lat-lon + all_x = (x[0], x[1], x[0], x[1]) + all_y = (y[0], y[0], y[1], y[1]) + transformer = Transformer.from_crs(crs, "EPSG:4326") + all_longitude, all_latitude = transformer.transform(all_x, all_y) + + # Calculate zoom level based on coordinates + lat_zoom_level = ( + _degree_to_zoom_level(min(all_latitude), max(all_latitude), margin=margin) + + zoom_bias + ) + lon_zoom_level = ( + _degree_to_zoom_level(min(all_longitude), max(all_longitude), margin=margin) + + zoom_bias + ) + zoom_level = min(lat_zoom_level, lon_zoom_level) + + # Identify centre point for plotting + center = [np.mean(all_latitude), np.mean(all_longitude)] + + # Create map + interactive_map = folium.Map( + location=center, + zoom_start=zoom_level, + tiles="http://mt1.google.com/vt/lyrs=y&z={z}&x={x}&y={y}", + attr="Google", + ) + + # Create bounding box coordinates to overlay on map + line_segments = [ + (all_latitude[0], all_longitude[0]), + (all_latitude[1], all_longitude[1]), + (all_latitude[3], all_longitude[3]), + (all_latitude[2], all_longitude[2]), + (all_latitude[0], all_longitude[0]), + ] + + # Add bounding box as an overlay + interactive_map.add_child( + folium.features.PolyLine(locations=line_segments, color="red", opacity=0.8) + ) + + # Add clickable lat-lon popup box + interactive_map.add_child(folium.features.LatLngPopup()) + + return interactive_map + + +def map_shapefile( + gdf, + attribute, + continuous=False, + cmap="viridis", + basemap=basemaps.Esri.WorldImagery, + default_zoom=None, + hover_col=True, + **style_kwargs, +): + """ + Plots a geopandas GeoDataFrame over an interactive ipyleaflet + basemap, with features coloured based on attribute column values. + Optionally, can be set up to print selected data from features in + the GeoDataFrame. + + Last modified: February 2020 + + Parameters + ---------- + gdf : geopandas.GeoDataFrame + A GeoDataFrame containing the spatial features to be plotted + over the basemap. + attribute: string, required + An required string giving the name of any column in the + GeoDataFrame you wish to have coloured on the choropleth. + continuous: boolean, optional + Whether to plot data as a categorical or continuous variable. + Defaults to remapping the attribute which is suitable for + categorical data. For continuous data set `continuous` to True. + cmap : string, optional + A string giving the name of a `matplotlib.cm` colormap that will + be used to style the features in the GeoDataFrame. Features will + be coloured based on the selected attribute. Defaults to the + 'viridis' colormap. + basemap : ipyleaflet.basemaps object, optional + An optional `ipyleaflet.basemaps` object used as the basemap for + the interactive plot. Defaults to `basemaps.Esri.WorldImagery`. + default_zoom : int, optional + An optional integer giving a default zoom level for the + interactive ipyleaflet plot. Defaults to None, which infers + the zoom level from the extent of the data. + hover_col : boolean or str, optional + If True (the default), the function will print values from the + GeoDataFrame's `attribute` column above the interactive map when + a user hovers over the features in the map. Alternatively, a + custom shapefile field can be specified by supplying a string + giving the name of the field to print. Set to False to prevent + any attributes from being printed. + **style_kwargs : + Optional keyword arguments to pass to the `style` paramemter of + the `ipyleaflet.Choropleth` function. This can be used to + control the appearance of the shapefile, for example 'stroke' + and 'weight' (controlling line width), 'fillOpacity' (polygon + transparency) and 'dashArray' (whether to plot lines/outlines + with dashes). For more information: + https://ipyleaflet.readthedocs.io/en/latest/api_reference/choropleth.html + """ + + def on_hover(event, id, properties): + with dbg: + text = properties.get(hover_col, "???") + lbl.value = f"{hover_col}: {text}" + + # Verify that attribute exists in shapefile + if attribute not in gdf.columns: + raise ValueError( + f"The `attribute` {attribute} does not exist " + f"in the geopandas.GeoDataFrame. " + f"Valid attributes include {gdf.columns.values}." + ) + + # If hover_col is True, use 'attribute' as the default hover attribute. + # Otherwise, hover_col will use the supplied attribute field name + if hover_col and (hover_col is True): + hover_col = attribute + + # If a custom string if supplied to hover_col, check this exists + elif hover_col and (type(hover_col) == str): + if hover_col not in gdf.columns: + raise ValueError( + f"The `hover_col` field {hover_col} does " + f"not exist in the geopandas.GeoDataFrame. " + f"Valid attributes include " + f"{gdf.columns.values}." + ) + + # Convert to WGS 84 and GeoJSON format + gdf_wgs84 = gdf.to_crs(epsg=4326) + data_geojson = gdf_wgs84.__geo_interface__ + + # If continuous is False, remap categorical classes for visualisation + if not continuous: + + # Zip classes data together to make a dictionary + classes_uni = list(gdf[attribute].unique()) + classes_clean = list(range(0, len(classes_uni))) + classes_dict = dict(zip(classes_uni, classes_clean)) + + # Get values to colour by as a list + classes = gdf[attribute].map(classes_dict).tolist() + + # If continuous is True then do not remap + else: + + # Get values to colour by as a list + classes = gdf[attribute].tolist() + + # Create the dictionary to colour map by + keys = gdf.index + id_class_dict = dict(zip(keys.astype(str), classes)) + + # Get centroid to focus map on + lon1, lat1, lon2, lat2 = gdf_wgs84.total_bounds + lon = (lon1 + lon2) / 2 + lat = (lat1 + lat2) / 2 + + if default_zoom is None: + + # Calculate default zoom from latitude of features + default_zoom = _degree_to_zoom_level(lat1, lat2, margin=-0.5) + + # Plot map + m = Map( + center=(lat, lon), + zoom=default_zoom, + basemap=basemap, + layout=dict(width="800px", height="600px"), + ) + + # Define default plotting parameters for the choropleth map. + # The nested dict structure sets default values which can be + # overwritten/customised by `choropleth_kwargs` values + style_kwargs = dict({"fillOpacity": 0.8}, **style_kwargs) + + # Get `branca.colormap` object from matplotlib string + cm_cmap = cm.get_cmap(cmap, 30) + colormap = branca.colormap.LinearColormap( + [cm_cmap(i) for i in np.linspace(0, 1, 30)] + ) + + # Create the choropleth + choropleth = Choropleth( + geo_data=data_geojson, + choro_data=id_class_dict, + colormap=colormap, + style=style_kwargs, + ) + + # If the vector data contains line features, they will not be + # be coloured by default. To resolve this, we need to manually copy + # across the 'fillColor' attribute to the 'color' attribute for each + # feature, then plot the data as a GeoJSON layer rather than the + # choropleth layer that we use for polygon data. + linefeatures = any( + x in ["LineString", "MultiLineString"] for x in gdf.geometry.type.values + ) + if linefeatures: + + # Copy colour from fill to line edge colour + for i in keys: + choropleth.data["features"][i]["properties"]["style"][ + "color" + ] = choropleth.data["features"][i]["properties"]["style"]["fillColor"] + + # Add GeoJSON layer to map + feature_layer = GeoJSON(data=choropleth.data, style=style_kwargs) + m.add_layer(feature_layer) + + else: + + # Add Choropleth layer to map + m.add_layer(choropleth) + + # If a column is specified by `hover_col`, print data from the + # hovered feature above the map + if hover_col and not linefeatures: + + # Use cholopleth object if data is polygon + lbl = ipywidgets.Label() + dbg = ipywidgets.Output() + choropleth.on_hover(on_hover) + display(lbl) + + else: + + lbl = ipywidgets.Label() + dbg = ipywidgets.Output() + feature_layer.on_hover(on_hover) + display(lbl) + + # Display the map + display(m) + + +def xr_animation( + ds, + bands=None, + output_path="animation.mp4", + width_pixels=500, + interval=100, + percentile_stretch=(0.02, 0.98), + image_proc_funcs=None, + show_gdf=None, + show_date="%d %b %Y", + show_text=None, + show_colorbar=True, + gdf_kwargs={}, + annotation_kwargs={}, + imshow_kwargs={}, + colorbar_kwargs={}, + limit=None, +): + """ + Takes an `xarray` timeseries and animates the data as either a + three-band (e.g. true or false colour) or single-band animation, + allowing changes in the landscape to be compared across time. + + Animations can be customised to include text and date annotations + or use specific combinations of input bands. Vector data can be + overlaid and animated on top of imagery, and custom image + processing functions can be applied to each frame. + Supports .mp4 (ideal for Twitter/social media) and .gif (ideal + for all purposes, but can have large file sizes) format files. + + Last modified: October 2020 + + Parameters + ---------- + ds : xarray.Dataset + An xarray dataset with multiple time steps (i.e. multiple + observations along the `time` dimension). + bands : list of strings + An list of either one or three band names to be plotted, + all of which must exist in `ds`. + output_path : str, optional + A string giving the output location and filename of the + resulting animation. File extensions of '.mp4' and '.gif' are + accepted. Defaults to 'animation.mp4'. + width_pixels : int, optional + An integer defining the output width in pixels for the + resulting animation. The height of the animation is set + automatically based on the dimensions/ratio of the input + xarray dataset. Defaults to 500 pixels wide. + interval : int, optional + An integer defining the milliseconds between each animation + frame used to control the speed of the output animation. Higher + values result in a slower animation. Defaults to 100 + milliseconds between each frame. + percentile_stretch : tuple of floats, optional + An optional tuple of two floats that can be used to clip one or + three-band arrays by percentiles to produce a more vibrant, + visually attractive image that is not affected by outliers/ + extreme values. The default is `(0.02, 0.98)` which is + equivalent to xarray's `robust=True`. This parameter is ignored + completely if `vmin` and `vmax` are provided as kwargs to + `imshow_kwargs`. + image_proc_funcs : list of funcs, optional + An optional list containing functions that will be applied to + each animation frame (timestep) prior to animating. This can + include image processing functions such as increasing contrast, + unsharp masking, saturation etc. The function should take AND + return a `numpy.ndarray` with shape [y, x, bands]. If your + function has parameters, you can pass in custom values using + a lambda function: + `image_proc_funcs=[lambda x: custom_func(x, param1=10)]`. + show_gdf: geopandas.GeoDataFrame, optional + Vector data (e.g. ESRI shapefiles or GeoJSON) can be optionally + plotted over the top of imagery by supplying a + `geopandas.GeoDataFrame` object. To customise colours used to + plot the vector features, create a new column in the + GeoDataFrame called 'colors' specifying the colour used to plot + each feature: e.g. `gdf['colors'] = 'red'`. + To plot vector features at specific moments in time during the + animation, create new 'start_time' and/or 'end_time' columns in + the GeoDataFrame that define the time range used to plot each + feature. Dates can be provided in any string format that can be + converted using the `pandas.to_datetime()`. e.g. + `gdf['end_time'] = ['2001', '2005-01', '2009-01-01']` + show_date : string or bool, optional + An optional string or bool that defines how (or if) to plot + date annotations for each animation frame. Defaults to + '%d %b %Y'; can be customised to any format understood by + strftime (https://strftime.org/). Set to False to remove date + annotations completely. + show_text : str or list of strings, optional + An optional string or list of strings with a length equal to + the number of timesteps in `ds`. This can be used to display a + static text annotation (using a string), or a dynamic title + (using a list) that displays different text for each timestep. + By default, no text annotation will be plotted. + show_colorbar : bool, optional + An optional boolean indicating whether to include a colourbar + for single-band animations. Defaults to True. + gdf_kwargs : dict, optional + An optional dictionary of keyword arguments to customise the + appearance of a `geopandas.GeoDataFrame` supplied to + `show_gdf`. Keyword arguments are passed to `GeoSeries.plot` + (see http://geopandas.org/reference.html#geopandas.GeoSeries.plot). + For example: `gdf_kwargs = {'linewidth': 2}`. + annotation_kwargs : dict, optional + An optional dict of keyword arguments for controlling the + appearance of text annotations. Keyword arguments are passed + to `plt.annotate` from `matplotlib`. + (see https://matplotlib.org/api/_as_gen/matplotlib.pyplot.annotate.html + for options). For example, + `annotation_kwargs={'fontsize':20, 'color':'red', 'family':'serif'}`. + imshow_kwargs : dict, optional + An optional dict of keyword arguments for controlling the + appearance of arrays passed to `matplotlib`'s `plt.imshow` + (see https://matplotlib.org/api/_as_gen/matplotlib.pyplot.imshow.html + for options). For example, a green colour scheme and custom + stretch could be specified using: + `onebandplot_kwargs={'cmap':'Greens`, 'vmin':0.2, 'vmax':0.9}`. + (some parameters like 'cmap' will only have an effect for + single-band animations, not three-band RGB animations). + colorbar_kwargs : dict, optional + An optional dict of keyword arguments used to control the + appearance of the colourbar. Keyword arguments are passed to + `matplotlib.pyplot.tick_params` + (see https://matplotlib.org/api/_as_gen/matplotlib.pyplot.tick_params.html + for options). This can be used to customise the colourbar + ticks, e.g. changing tick label colour depending on the + background of the animation: + `colorbar_kwargs={'colors': 'black'}`. + limit: int, optional + An optional integer specifying how many animation frames to + render (e.g. `limit=50` will render the first 50 frames). This + can be useful for quickly testing animations without rendering + the entire time-series. + """ + + def _start_end_times(gdf, ds): + """ + Converts 'start_time' and 'end_time' columns in a + `geopandas.GeoDataFrame` to datetime objects to allow vector + features to be plotted at specific moments in time during an + animation, and sets default values based on the first + and last time in `ds` if this information is missing from the + dataset. + """ + + # Make copy of gdf so we do not modify original data + gdf = gdf.copy() + + # Get min and max times from input dataset + minmax_times = pd.to_datetime(ds.time.isel(time=[0, -1]).values) + + # Update both `start_time` and `end_time` columns + for time_col, time_val in zip(["start_time", "end_time"], minmax_times): + + # Add time_col if it does not exist + if time_col not in gdf: + gdf[time_col] = np.nan + + # Convert values to datetimes and fill gaps with relevant time value + gdf[time_col] = pd.to_datetime(gdf[time_col], errors="ignore") + gdf[time_col] = gdf[time_col].fillna(time_val) + + return gdf + + def _add_colorbar(fig, ax, vmin, vmax, imshow_defaults, colorbar_defaults): + """ + Adds a new colorbar axis to the animation with custom minimum + and maximum values and styling. + """ + + # Create new axis object for colorbar + cax = fig.add_axes([0.02, 0.02, 0.96, 0.03]) + + # Initialise color bar using plot min and max values + img = ax.imshow(np.array([[vmin, vmax]]), **imshow_defaults) + fig.colorbar( + img, cax=cax, orientation="horizontal", ticks=np.linspace(vmin, vmax, 2) + ) + + # Fine-tune appearance of colorbar + cax.xaxis.set_ticks_position("top") + cax.tick_params(axis="x", **colorbar_defaults) + cax.get_xticklabels()[0].set_horizontalalignment("left") + cax.get_xticklabels()[-1].set_horizontalalignment("right") + + def _frame_annotation(times, show_date, show_text): + """ + Creates a custom annotation for the top-right of the animation + by converting a `xarray.DataArray` of times into strings, and + combining this with a custom text annotation. Handles cases + where `show_date=False/None`, `show_text=False/None`, or where + `show_text` is a list of strings. + """ + + # Test if show_text is supplied as a list + is_sequence = isinstance(show_text, (list, tuple, np.ndarray)) + + # Raise exception if it is shorter than number of dates + if is_sequence and (len(show_text) == 1): + show_text, is_sequence = show_text[0], False + elif is_sequence and (len(show_text) < len(times)): + raise ValueError( + f"Annotations supplied via `show_text` must have " + f"either a length of 1, or a length >= the number " + f"of timesteps in `ds` (n={len(times)})" + ) + + times_list = ( + times.dt.strftime(show_date).values if show_date else [None] * len(times) + ) + text_list = show_text if is_sequence else [show_text] * len(times) + annotation_list = [ + "\n".join([str(i) for i in (a, b) if i]) + for a, b in zip(times_list, text_list) + ] + + return annotation_list + + def _update_frames( + i, + ax, + extent, + annotation_text, + gdf, + gdf_defaults, + annotation_defaults, + imshow_defaults, + ): + """ + Animation called by `matplotlib.animation.FuncAnimation` to + animate each frame in the animation. Plots array and any text + annotations, as well as a temporal subset of `gdf` data based + on the times specified in 'start_time' and 'end_time' columns. + """ + + # Clear previous frame to optimise render speed and plot imagery + ax.clear() + ax.imshow( + array[i, ...].clip(0.0, 1.0), + extent=extent, + vmin=0.0, + vmax=1.0, + **imshow_defaults, + ) + + # Add annotation text + ax.annotate(annotation_text[i], **annotation_defaults) + + # Add geodataframe annotation + if show_gdf is not None: + + # Obtain start and end times to filter geodataframe features + time_i = ds.time.isel(time=i).values + + # Subset geodataframe using start and end dates + gdf_subset = show_gdf.loc[ + (show_gdf.start_time <= time_i) & (show_gdf.end_time >= time_i) + ] + + if len(gdf_subset.index) > 0: + + # Set color to geodataframe field if supplied + if ("color" in gdf_subset) and ("color" not in gdf_kwargs): + gdf_defaults.update({"color": gdf_subset["color"].tolist()}) + + gdf_subset.plot(ax=ax, **gdf_defaults) + + # Remove axes to show imagery only + ax.axis("off") + + # Update progress bar + progress_bar.update(1) + + # Test if bands have been supplied, or convert to list to allow + # iteration if a single band is provided as a string + if bands is None: + raise ValueError( + f"Please use the `bands` parameter to supply " + f"a list of one or three bands that exist as " + f"variables in `ds`, e.g. {list(ds.data_vars)}" + ) + elif isinstance(bands, str): + bands = [bands] + + # Test if bands exist in dataset + missing_bands = [b for b in bands if b not in ds.data_vars] + if missing_bands: + raise ValueError( + f"Band(s) {missing_bands} do not exist as " + f"variables in `ds` {list(ds.data_vars)}" + ) + + # Test if time dimension exists in dataset + if "time" not in ds.dims: + raise ValueError( + f"`ds` does not contain a 'time' dimension " + f"required for generating an animation" + ) + + # Set default parameters + outline = [PathEffects.withStroke(linewidth=2.5, foreground="black")] + annotation_defaults = { + "xy": (1, 1), + "xycoords": "axes fraction", + "xytext": (-5, -5), + "textcoords": "offset points", + "horizontalalignment": "right", + "verticalalignment": "top", + "fontsize": 20, + "color": "white", + "path_effects": outline, + } + imshow_defaults = {"cmap": "magma", "interpolation": "nearest"} + colorbar_defaults = {"colors": "white", "labelsize": 12, "length": 0} + gdf_defaults = {"linewidth": 1.5} + + # Update defaults with kwargs + annotation_defaults.update(annotation_kwargs) + imshow_defaults.update(imshow_kwargs) + colorbar_defaults.update(colorbar_kwargs) + gdf_defaults.update(gdf_kwargs) + + # Get info on dataset dimensions + height, width = ds.geobox.shape + scale = width_pixels / width + left, bottom, right, top = ds.geobox.extent.boundingbox + + # Prepare annotations + annotation_list = _frame_annotation(ds.time, show_date, show_text) + + # Prepare geodataframe + if show_gdf is not None: + show_gdf = show_gdf.to_crs(ds.geobox.crs) + show_gdf = gpd.clip(show_gdf, mask=box(left, bottom, right, top)) + show_gdf = _start_end_times(show_gdf, ds) + + # Convert data to 4D numpy array of shape [time, y, x, bands] + ds = ds[bands].to_array().transpose(..., "variable")[0:limit, ...] + array = ds.astype(np.float32).values + + # Optionally apply image processing along axis 0 (e.g. to each timestep) + bar_format = ( + "{l_bar}{bar}| {n_fmt}/{total_fmt} ({remaining_s:.1f} " + "seconds remaining at {rate_fmt}{postfix})" + ) + if image_proc_funcs: + print("Applying custom image processing functions") + for i, array_i in tqdm( + enumerate(array), + total=len(ds.time), + leave=False, + bar_format=bar_format, + unit=" frames", + ): + for func in image_proc_funcs: + array_i = func(array_i) + array[i, ...] = array_i + + # Clip to percentiles and rescale between 0.0 and 1.0 for plotting + vmin, vmax = np.quantile(array[np.isfinite(array)], q=percentile_stretch) + + # Replace with vmin and vmax if present in `imshow_defaults` + if "vmin" in imshow_defaults: + vmin = imshow_defaults.pop("vmin") + if "vmax" in imshow_defaults: + vmax = imshow_defaults.pop("vmax") + + # Rescale between 0 and 1 + array = rescale_intensity(array, in_range=(vmin, vmax), out_range=(0.0, 1.0)) + array = np.squeeze(array) # remove final axis if only one band + + # Set up figure + fig, ax = plt.subplots() + fig.set_size_inches(width * scale / 72, height * scale / 72, forward=True) + fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0) + + # Optionally add colorbar + if show_colorbar & (len(bands) == 1): + _add_colorbar(fig, ax, vmin, vmax, imshow_defaults, colorbar_defaults) + + # Animate + print(f"Exporting animation to {output_path}") + anim = FuncAnimation( + fig=fig, + func=_update_frames, + fargs=( + ax, # axis to plot into + [left, right, bottom, top], # imshow extent + annotation_list, # list of text annotations + show_gdf, # geodataframe to plot over imagery + gdf_defaults, # any kwargs used to plot gdf + annotation_defaults, # kwargs for annotations + imshow_defaults, + ), # kwargs for imshow + frames=len(ds.time), + interval=interval, + repeat=False, + ) + + # Set up progress bar + progress_bar = tqdm(total=len(ds.time), unit=" frames", bar_format=bar_format) + + # Export animation to file + if Path(output_path).suffix == ".gif": + anim.save(output_path, writer="pillow") + else: + anim.save(output_path, dpi=72) + + # Update progress bar to fix progress bar moving past end + if progress_bar.n != len(ds.time): + progress_bar.n = len(ds.time) + progress_bar.last_print_n = len(ds.time) + + +def _degree_to_zoom_level(l1, l2, margin=0.0): + """ + Helper function to set zoom level for `display_map` + """ + + degree = abs(l1 - l2) * (1 + margin) + zoom_level_int = 0 + if degree != 0: + zoom_level_float = math.log(360 / degree) / math.log(2) + zoom_level_int = int(zoom_level_float) + else: + zoom_level_int = 18 + return zoom_level_int + + +def plot_wofs(wofs, legend=True, **plot_kwargs): + """Plot a water observation bit flag image. + + Parameters + ---------- + wofs : xr.DataArray + A DataArray containing water observation bit flags. + legend : bool + Whether to plot a legend. Default True. + plot_kwargs : dict + Keyword arguments passed on to DataArray.plot. + + Returns + ------- + plot + """ + cmap = mcolours.ListedColormap( + [ + np.array([150, 150, 110]) / 255, # dry - 0 + np.array([0, 0, 0]) / 255, # nodata, - 1 + np.array([119, 104, 87]) / 255, # terrain - 16 + np.array([89, 88, 86]) / 255, # cloud_shadow - 32 + np.array([216, 215, 214]) / 255, # cloud - 64 + np.array([242, 220, 180]) / 255, # cloudy terrain - 80 + np.array([79, 129, 189]) / 255, # water - 128 + np.array([51, 82, 119]) / 255, # shady water - 160 + np.array([186, 211, 242]) / 255, # cloudy water - 192 + ] + ) + bounds = [ + 0, + 1, + 16, + 32, + 64, + 80, + 128, + 160, + 192, + ] + norm = mcolours.BoundaryNorm(np.array(bounds) - 0.1, cmap.N) + cblabels = [ + "dry", + "nodata", + "terrain", + "cloud shadow", + "cloud", + "cloudy terrain", + "water", + "shady water", + "cloudy water", + ] + + try: + im = wofs.plot.imshow(cmap=cmap, norm=norm, add_colorbar=legend, **plot_kwargs) + except AttributeError: + im = wofs.plot(cmap=cmap, norm=norm, add_colorbar=legend, **plot_kwargs) + + if legend: + try: + cb = im.colorbar + except AttributeError: + cb = im.cbar + ticks = cb.get_ticks() + cb.set_ticks(ticks + np.diff(ticks, append=193) / 2) + cb.set_ticklabels(cblabels) + return im + + +def plot_lulc(lulc, product=None, legend=True, **plot_kwargs): + """Plot a LULC image. + + Parameters + ---------- + lulc : xr.DataArray + A DataArray containing LULC bit flags. + product : str + 'ESA', 'IO', 'CGLS', or 'CCI', 'ESRI' + legend : bool + Whether to plot a legend. Default True. + plot_kwargs : dict + Keyword arguments passed on to DataArray.plot. + + Returns + ------- + plot + """ + + if "ESRI" in product: + # this is for the orignal ESRI/IO 10 class product for 2020 + try: + cmap = mcolours.ListedColormap( + [ + np.array([0, 0, 0]) / 255, + np.array([65, 155, 223]) / 255, + np.array([57, 125, 73]) / 255, + np.array([136, 176, 83]) / 255, + np.array([122, 135, 198]) / 255, + np.array([228, 150, 53]) / 255, + np.array([223, 195, 90]) / 255, + np.array([196, 40, 27]) / 255, + np.array([165, 155, 143]) / 255, + np.array([168, 235, 255]) / 255, + np.array([97, 97, 97]) / 255, + ] + ) + bounds = range(0, 12) + norm = mcolours.BoundaryNorm(np.array(bounds), cmap.N) + cblabels = [ + "no data", + "water", + "trees", + "grass", + "flooded vegetation", + "crops", + "scrub/shrub", + "built area", + "bare ground", + "snow/ice", + "clouds", + ] + except: + AttributeError + + if "IO" in product: + # this is for the ESRI/IO 9 class multiyear product; same color as preview + try: + cmap = mcolours.ListedColormap( + [ + np.array([0, 0, 0]) / 255, + np.array([65, 155, 223]) / 255, + np.array([57, 125, 73]) / 255, + np.array([122, 135, 198]) / 255, + np.array([228, 150, 53]) / 255, + np.array([196, 40, 27]) / 255, + np.array([165, 155, 143]) / 255, + np.array([168, 235, 255]) / 255, + np.array([97, 97, 97]) / 255, + np.array([227, 226, 195]) / 255, + ] + ) + bounds = [-0.5, 0.5, 1.5, 3, 4.5, 6, 7.5, 8.5, 9.5, 10.5, 11.5] + norm = mcolours.BoundaryNorm(np.array(bounds), cmap.N) + cblabels = [ + "no data", + "water", + "trees", + "flooded vegetation", + "crops", + "built area", + "bare ground", + "snow/ice", + "clouds", + "rangeland", + ] + ticks = list(np.mean((bounds[i+1], val)) for i, val in enumerate(bounds[:-1])) + except: + AttributeError + + if "ESA" in product: + try: + cmap = mcolours.ListedColormap( + [ + np.array([0, 0, 0]) / 255, + np.array([0, 100, 0]) / 255, + np.array([255, 187, 34]) / 255, + np.array([255, 255, 76]) / 255, + np.array([240, 150, 255]) / 255, + np.array([250, 0, 0]) / 255, + np.array([180, 180, 180]) / 255, + np.array([240, 240, 240]) / 255, + np.array([0, 100, 200]) / 255, + np.array([0, 150, 160]) / 255, + np.array([0, 207, 117]) / 255, + np.array([250, 230, 160]) / 255, + ] + ) + bounds = [-5, 5, 15, 25, 35, 45, 55, 65, 75, 85, 92, 98, 105] + norm = mcolours.BoundaryNorm(np.array(bounds), cmap.N) + cblabels = [ + "no data", + "tree cover", + "shrubland", + "grassland", + "cropland", + "built up", + "bare/sparse vegetation", + "snow and ice", + "permanent water bodies", + "herbaceous wetland", + "mangroves", + "moss and lichen", + ] + except: + AttributeError + + if "CGLS" in product: + try: + labels = {0: {'color': '#282828', 'flag': 'unknown'}, + 20: {'color': '#FFBB22', 'flag': 'shrubs'}, + 30: {'color': '#FFFF4C', 'flag': 'herbaceous_vegetation'}, + 40: {'color': '#F096FF', 'flag': 'cultivated_and_managed_vegetation_or_agriculture'}, + 50: {'color': '#FA0000', 'flag': 'urban_or_built_up'}, + 60: {'color': '#B4B4B4', 'flag': 'bare_or_sparse_vegetation'}, + 70: {'color': '#F0F0F0', 'flag': 'snow_and_ice'}, + 80: {'color': '#0032C8', 'flag': 'permanent_water_bodies'}, + 90: {'color': '#0096A0', 'flag': 'herbaceous_wetland'}, + 100: {'color': '#FAE6A0', 'flag': 'moss_and_lichen'}, + 111: {'color': '#58481F', 'flag': 'closed_forest_evergreen_needle_leaf'}, + 112: {'color': '#009900', 'flag': 'closed_forest_evergreen_broad_leaf'}, + 113: {'color': '#70663E', 'flag': 'closed_forest_deciduous_needle_leaf'}, + 114: {'color': '#00CC00', 'flag': 'closed_forest_deciduous_broad_leaf'}, + 115: {'color': '#4E751F', 'flag': 'closed_forest_mixed'}, + 116: {'color': '#007800', 'flag': 'closed_forest_not_matching_any_of_the_other_definitions'}, + 121: {'color': '#666000', 'flag': 'open_forest_evergreen_needle_leaf'}, + 122: {'color': '#8DB400', 'flag': 'open_forest_evergreen_broad_leaf'}, + 123: {'color': '#8D7400', 'flag': 'open_forest_deciduous_needle_leaf'}, + 124: {'color': '#A0DC00', 'flag': 'open_forest_deciduous_broad_leaf'}, + 125: {'color': '#929900', 'flag': 'open_forest_mixed'}, + 126: {'color': '#648C00', 'flag': 'open_forest_not_matching_any_of_the_other_definitions'}, + 200: {'color': '#000080', 'flag': 'oceans_seas'}} + + colors = [label['color'] for label in labels.values()] + cmap = ListedColormap([label['color'] for label in labels.values()]) + norm = mcolours.BoundaryNorm(list(labels.keys())+[201], cmap.N+1, extend='max') + ticks = list(np.mean((list(list(labels.keys())+[201])[i+1], val)) for i, val in enumerate(list(labels.keys()))) + cblabels=[label['flag'] for label in labels.values()] + + except: + AttributeError + if 'CCI' in product: + try: + labels = {0: {'color': '#282828', 'flag': 'no data'}, + 10: {'color': '#EBEB34', 'flag': 'cropland, rainfed'}, + 11: {'color': '#D9EB34', 'flag': 'cropland, rainfed, herbaceous cover'}, + 12: {'color': '#EBDF34', 'flag': 'cropland, rainfed, tree or shrub cover'}, + 20: {'color': '#34EBE2', 'flag': 'cropland, irrigated or post-flooding'}, + 30: {'color': '#EBBD34', 'flag': 'mosaic cropland/natural vegetation'}, + 40: {'color': '#eba534', 'flag': 'mosaic natural vegetation/cropland'}, + 50: {'color': '#34eb46', 'flag': 'tree cover, broadleaved, evergreen, closed to open'}, + 60: {'color': '#21750e', 'flag': 'tree cover, broadleaved, deciduous, closed to open'}, + 61: {'color': '#449432', 'flag': 'tree cover, broadleaved, deciduous, closed'}, + 62: {'color': '#5da64c', 'flag': 'tree cover, broadleaved, deciduous, open'}, + 70: {'color': '#16470b', 'flag': 'tree cover, needleleaved, evergreen, closed to open'}, + 71: {'color': '#237012', 'flag': 'tree cover, needleleaved, evergreen, closed'}, + 72: {'color': '#237012', 'flag': 'tree cover, needleleaved, evergreen, open'}, + 80: {'color': '#31a317', 'flag': 'tree cover, needleleaved, deciduous, closed to open'}, + 81: {'color': '#57ed34', 'flag': 'tree cover, needleleaved, deciduous, closed'}, + 82: {'color': '#81f765', 'flag': 'tree cover, needleleaved, deciduous, open'}, + 90: {'color': '#b6ed64', 'flag': 'tree cover, mixed leaf type'}, + 100: {'color': '#6f8f3f', 'flag': 'mosaic tree and shrub/herbaceous cover'}, + 110: {'color': '#ad950c', 'flag': 'mosaic herbaceous cover/tree and shrub'}, + 120: {'color': '#5e5209', 'flag': 'shrubland'}, + 121: {'color': '#292302', 'flag': 'shrubland, evergreen'}, + 122: {'color': '#a89008', 'flag': 'shrubland, deciduous'}, + 130: {'color': '#f7bf07', 'flag': 'grassland'}, + 140: {'color': '#f57feb', 'flag': 'lichens and mosses'}, + 150: {'color': '#f57feb', 'flag': 'sparse vegetation'}, + 151: {'color': '#fcf7a4', 'flag': 'sparse tree'}, + 152: {'color': '#d4cf87', 'flag': 'sparse shrub'}, + 153: {'color': '#b0aa54', 'flag': 'sparse herbaceous cover'}, + 160: {'color': '#159638', 'flag': 'tree cover, flooded, fresh or brakish water'}, + 170: {'color': '#22bf81', 'flag': 'tree cover, flooded, saline water'}, + 180: {'color': '#44eba9', 'flag': 'shrub or herbaceous cover, flooded, fresh/saline/brakish water'}, + 190: {'color': '#a3273c', 'flag': 'urban areas'}, + 200: {'color': '#fffbcc', 'flag': 'bare areas'}, + 201: {'color': '#b0afa4', 'flag': 'consolidated bare areas'}, + 202: {'color': '#d6d4b6', 'flag': 'unconsolidated bare areas'}, + 210: {'color': '#1A3EF0', 'flag': 'water bodies'}, + 220: {'color': '#ffffff', 'flag': 'permanent snow and ice'}} + + colors = [label['color'] for label in labels.values()] + cmap = ListedColormap([label['color'] for label in labels.values()]) + norm = mcolours.BoundaryNorm(list(labels.keys())+[221], cmap.N+1, extend='max') + ticks = list(np.mean((list(list(labels.keys())+[221])[i+1], val)) for i, val in enumerate(list(labels.keys()))) + cblabels=[label['flag'] for label in labels.values()] + + except: + AttributeError + + try: + im = lulc.plot.imshow(cmap=cmap, norm=norm, add_colorbar=legend, **plot_kwargs) + except AttributeError: + im = lulc.plot(cmap=cmap, norm=norm, add_colorbar=legend, **plot_kwargs) + + if legend: + try: + cb = im.colorbar + except AttributeError: + cb = im.cbar + + if "ESRI" in product: + cb.set_ticks(np.arange(0, 11, 1)+0.5) + cb.set_ticklabels(cblabels) + + if "IO" in product: + cb.set_ticks(ticks) + cb.set_ticklabels(cblabels) + + if "ESA" in product: + cb.set_ticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 88.5, 95, 101.5]) + cb.set_ticklabels(cblabels) + + if "CGLS" in product: + cb.set_ticks(ticks) + cb.set_ticklabels(cblabels) + + if "CCI" in product: + cb.set_ticks(ticks) + cb.set_ticklabels(cblabels) + + return im diff --git a/deafrica_tools/spatial.py b/deafrica_tools/spatial.py new file mode 100644 index 0000000..de454f2 --- /dev/null +++ b/deafrica_tools/spatial.py @@ -0,0 +1,949 @@ +''' +Spatial analyses functions for Digital Earth Africa data. +''' + +# Force GeoPandas to use Shapely instead of PyGEOS +# In a future release, GeoPandas will switch to using Shapely by default. +import os + +os.environ['USE_PYGEOS'] = '0' + +import multiprocessing as mp + +import dask +import fiona +import geopandas as gpd +import numpy as np +import odc.geo.xr # adds `.odc.x` attributes to our xarray objects. +import pandas as pd +import rasterio.features +import scipy.interpolate +import xarray as xr +from datacube.api.query import query_group_by +from datacube.model.utils import xr_apply +from datacube.utils.cog import write_cog +from datacube.utils.geometry import CRS, Geometry +from geopy.geocoders import Nominatim +from rasterstats import zonal_stats +from shapely.geometry import LineString, MultiLineString, mapping, shape +from skimage.measure import find_contours, label + + +def add_geobox(ds, crs=None): + """ + Ensure that an xarray DataArray has a GeoBox and .odc.* accessor + using `odc.geo`. + + If `ds` is missing a Coordinate Reference System (CRS), this can be + supplied using the `crs` param. + + Parameters + ---------- + ds : xarray.Dataset or xarray.DataArray + Input xarray object that needs to be checked for spatial + information. + crs : str, optional + Coordinate Reference System (CRS) information for the input `ds` + array. If `ds` already has a CRS, then `crs` is not required. + Default is None. + + Returns + ------- + xarray.Dataset or xarray.DataArray + The input xarray object with added `.odc.x` attributes to access + spatial information. + + """ + # If a CRS is not found, use custom provided CRS + if ds.odc.crs is None and crs is not None: + ds = ds.odc.assign_crs(crs) + elif ds.odc.crs is None and crs is None: + raise ValueError( + "Unable to determine `ds`'s coordinate " + "reference system (CRS). Please provide a " + "CRS using the `crs` parameter " + "(e.g. `crs='EPSG:3577'`)." + ) + + return ds + + +def xr_vectorize( + da, + attribute_col=None, + crs=None, + dtype="float32", + output_path=None, + verbose=True, + **rasterio_kwargs, +): + """ + Vectorises a raster ``xarray.DataArray`` into a vector + ``geopandas.GeoDataFrame``. + + Parameters + ---------- + da : xarray.DataArray + The input ``xarray.DataArray`` data to vectorise. + attribute_col : str, optional + Name of the attribute column in the resulting + ``geopandas.GeoDataFrame``. Values from ``da`` converted + to polygons will be assigned to this column. If None, + the column name will default to 'attribute'. + crs : str or CRS object, optional + If ``da``'s coordinate reference system (CRS) cannot be + determined, provide a CRS using this parameter. + (e.g. 'EPSG:3577'). + dtype : str, optional + Data type of must be one of int16, int32, uint8, uint16, + or float32 + output_path : string, optional + Provide an optional string file path to export the vectorised + data to file. Supports any vector file formats supported by + ``geopandas.GeoDataFrame.to_file()``. + verbose : bool, optional + Print debugging messages. Default True. + **rasterio_kwargs : + A set of keyword arguments to ``rasterio.features.shapes``. + Can include `mask` and `connectivity`. + + Returns + ------- + gdf : geopandas.GeoDataFrame + + """ + + # Add GeoBox and odc.* accessor to array using `odc-geo` + da = add_geobox(da, crs) + + # Run the vectorizing function + vectors = rasterio.features.shapes( + source=da.data.astype(dtype), transform=da.odc.transform, **rasterio_kwargs + ) + + # Convert the generator into a list + vectors = list(vectors) + + # Extract the polygon coordinates and values from the list + polygons = [polygon for polygon, value in vectors] + values = [value for polygon, value in vectors] + + # Convert polygon coordinates into polygon shapes + polygons = [shape(polygon) for polygon in polygons] + + # Create a geopandas dataframe populated with the polygon shapes + attribute_name = attribute_col if attribute_col is not None else "attribute" + gdf = gpd.GeoDataFrame( + data={attribute_name: values}, geometry=polygons, crs=da.odc.crs + ) + + # If a file path is supplied, export to file + if output_path is not None: + if verbose: + print(f"Exporting vector data to {output_path}") + gdf.to_file(output_path) + + return gdf + + +def xr_rasterize( + gdf, + da, + attribute_col=None, + crs=None, + name=None, + output_path=None, + verbose=True, + **rasterio_kwargs, +): + """ + Rasterizes a vector ``geopandas.GeoDataFrame`` into a + raster ``xarray.DataArray``. + + Parameters + ---------- + gdf : geopandas.GeoDataFrame + A ``geopandas.GeoDataFrame`` object containing the vector + data you want to rasterise. + da : xarray.DataArray or xarray.Dataset + The shape, coordinates, dimensions, and transform of this object + are used to define the array that ``gdf`` is rasterized into. + It effectively provides a spatial template. + attribute_col : string, optional + Name of the attribute column in ``gdf`` containing values for + each vector feature that will be rasterized. If None, the + output will be a boolean array of 1's and 0's. + crs : str or CRS object, optional + If ``da``'s coordinate reference system (CRS) cannot be + determined, provide a CRS using this parameter. + (e.g. 'EPSG:3577'). + name : str, optional + An optional name used for the output ``xarray.DataArray`. + output_path : string, optional + Provide an optional string file path to export the rasterized + data as a GeoTIFF file. + verbose : bool, optional + Print debugging messages. Default True. + **rasterio_kwargs : + A set of keyword arguments to ``rasterio.features.rasterize``. + Can include: 'all_touched', 'merge_alg', 'dtype'. + + Returns + ------- + da_rasterized : xarray.DataArray + The rasterized vector data. + """ + + # Add GeoBox and odc.* accessor to array using `odc-geo` + da = add_geobox(da, crs) + + # Reproject vector data to raster's CRS + gdf_reproj = gdf.to_crs(crs=da.odc.crs) + + # If an attribute column is specified, rasterise using vector + # attribute values. Otherwise, rasterise into a boolean array + if attribute_col is not None: + # Use the geometry and attributes from `gdf` to create an iterable + shapes = zip(gdf_reproj.geometry, gdf_reproj[attribute_col]) + else: + # Use geometry directly (will produce a boolean numpy array) + shapes = gdf_reproj.geometry + + # Rasterise shapes into a numpy array + im = rasterio.features.rasterize( + shapes=shapes, + out_shape=da.odc.geobox.shape, + transform=da.odc.geobox.transform, + **rasterio_kwargs, + ) + + # Convert numpy array to a full xarray.DataArray + # and set array name if supplied + da_rasterized = odc.geo.xr.wrap_xr(im=im, gbox=da.odc.geobox) + da_rasterized = da_rasterized.rename(name) + + # If a file path is supplied, export to file + if output_path is not None: + if verbose: + print(f"Exporting raster data to {output_path}") + write_cog(da_rasterized, output_path, overwrite=True) + + return da_rasterized + + +def subpixel_contours( + da, + z_values=[0.0], + crs=None, + attribute_df=None, + output_path=None, + min_vertices=2, + dim="time", + time_format="%Y-%m-%d", + errors="ignore", + verbose=True, +): + """ + Uses `skimage.measure.find_contours` to extract multiple z-value + contour lines from a two-dimensional array (e.g. multiple elevations + from a single DEM), or one z-value for each array along a specified + dimension of a multi-dimensional array (e.g. to map waterlines + across time by extracting a 0 NDWI contour from each individual + timestep in an xarray timeseries). + + Contours are returned as a geopandas.GeoDataFrame with one row per + z-value or one row per array along a specified dimension. The + `attribute_df` parameter can be used to pass custom attributes + to the output contour features. + + Last modified: May 2023 + + Parameters + ---------- + da : xarray DataArray + A two-dimensional or multi-dimensional array from which + contours are extracted. If a two-dimensional array is provided, + the analysis will run in 'single array, multiple z-values' mode + which allows you to specify multiple `z_values` to be extracted. + If a multi-dimensional array is provided, the analysis will run + in 'single z-value, multiple arrays' mode allowing you to + extract contours for each array along the dimension specified + by the `dim` parameter. + z_values : int, float or list of ints, floats + An individual z-value or list of multiple z-values to extract + from the array. If operating in 'single z-value, multiple + arrays' mode specify only a single z-value. + crs : string or CRS object, optional + If ``da``'s coordinate reference system (CRS) cannot be + determined, provide a CRS using this parameter. + (e.g. 'EPSG:3577'). + output_path : string, optional + The path and filename for the output shapefile. + attribute_df : pandas.Dataframe, optional + A pandas.Dataframe containing attributes to pass to the output + contour features. The dataframe must contain either the same + number of rows as supplied `z_values` (in 'multiple z-value, + single array' mode), or the same number of rows as the number + of arrays along the `dim` dimension ('single z-value, multiple + arrays mode'). + min_vertices : int, optional + The minimum number of vertices required for a contour to be + extracted. The default (and minimum) value is 2, which is the + smallest number required to produce a contour line (i.e. a start + and end point). Higher values remove smaller contours, + potentially removing noise from the output dataset. + dim : string, optional + The name of the dimension along which to extract contours when + operating in 'single z-value, multiple arrays' mode. The default + is 'time', which extracts contours for each array along the time + dimension. + time_format : string, optional + The format used to convert `numpy.datetime64` values to strings + if applied to data with a "time" dimension. Defaults to + "%Y-%m-%d". + errors : string, optional + If 'raise', then any failed contours will raise an exception. + If 'ignore' (the default), a list of failed contours will be + printed. If no contours are returned, an exception will always + be raised. + verbose : bool, optional + Print debugging messages. Default is True. + + Returns + ------- + output_gdf : geopandas geodataframe + A geopandas geodataframe object with one feature per z-value + ('single array, multiple z-values' mode), or one row per array + along the dimension specified by the `dim` parameter ('single + z-value, multiple arrays' mode). If `attribute_df` was + provided, these values will be included in the shapefile's + attribute table. + """ + + def _contours_to_multiline(da_i, z_value, min_vertices=2): + """ + Helper function to apply marching squares contour extraction + to an array and return a data as a shapely MultiLineString. + The `min_vertices` parameter allows you to drop small contours + with less than X vertices. + """ + + # Extracts contours from array, and converts each discrete + # contour into a Shapely LineString feature. If the function + # returns a KeyError, this may be due to an unresolved issue in + # scikit-image: https://github.com/scikit-image/scikit-image/issues/4830 + # A temporary workaround is to peturb the z-value by a tiny + # amount (1e-12) before using it to extract the contour. + try: + line_features = [ + LineString(i[:, [1, 0]]) + for i in find_contours(da_i.data, z_value) + if i.shape[0] >= min_vertices + ] + except KeyError: + line_features = [ + LineString(i[:, [1, 0]]) + for i in find_contours(da_i.data, z_value + 1e-12) + if i.shape[0] >= min_vertices + ] + + # Output resulting lines into a single combined MultiLineString + return MultiLineString(line_features) + + def _time_format(i, time_format): + """ + Converts numpy.datetime64 into formatted strings; + otherwise returns data as-is. + """ + if isinstance(i, np.datetime64): + ts = pd.to_datetime(str(i)) + i = ts.strftime(time_format) + return i + + # Verify input data is a xr.DataArray + if not isinstance(da, xr.DataArray): + raise ValueError( + "The input `da` is not an xarray.DataArray. " + "If you supplied an xarray.Dataset, pass in one " + "of its data variables using the syntax " + "`da=ds.`." + ) + + # Add GeoBox and odc.* accessor to array using `odc-geo` + da = add_geobox(da, crs) + + # If z_values is supplied is not a list, convert to list: + z_values = ( + z_values + if (isinstance(z_values, list) or isinstance(z_values, np.ndarray)) + else [z_values] + ) + + # If dask collection, load into memory + if dask.is_dask_collection(da): + if verbose: + print("Loading data into memory using Dask") + da = da.compute() + + # Test number of dimensions in supplied data array + if len(da.shape) == 2: + if verbose: + print("Operating in multiple z-value, single array mode") + dim = "z_value" + contour_arrays = { + _time_format(i, time_format): _contours_to_multiline(da, i, min_vertices) + for i in z_values + } + + else: + # Test if only a single z-value is given when operating in + # single z-value, multiple arrays mode + if verbose: + print("Operating in single z-value, multiple arrays mode") + if len(z_values) > 1: + raise ValueError( + "Please provide a single z-value when operating " + "in single z-value, multiple arrays mode" + ) + + contour_arrays = { + _time_format(i, time_format): _contours_to_multiline( + da_i, z_values[0], min_vertices + ) + for i, da_i in da.groupby(dim) + } + + # If attributes are provided, add the contour keys to that dataframe + if attribute_df is not None: + try: + attribute_df.insert(0, dim, contour_arrays.keys()) + + # If this fails, it is due to the applied attribute table not + # matching the structure of the loaded data + except ValueError: + if len(da.shape) == 2: + raise ValueError( + f"The provided `attribute_df` contains a different " + f"number of rows ({len(attribute_df.index)}) " + f"than the number of supplied `z_values` " + f"({len(z_values)})." + ) + else: + raise ValueError( + f"The provided `attribute_df` contains a different " + f"number of rows ({len(attribute_df.index)}) " + f"than the number of arrays along the '{dim}' " + f"dimension ({len(da[dim])})." + ) + + # Otherwise, use the contour keys as the only main attributes + else: + attribute_df = list(contour_arrays.keys()) + + # Convert output contours to a geopandas.GeoDataFrame + contours_gdf = gpd.GeoDataFrame( + data=attribute_df, geometry=list(contour_arrays.values()), crs=da.odc.crs + ) + + # Define affine and use to convert array coords to geographic coords. + # We need to add 0.5 x pixel size to the x and y to obtain the centre + # point of our pixels, rather than the top-left corner + affine = da.odc.geobox.transform + shapely_affine = [ + affine.a, + affine.b, + affine.d, + affine.e, + affine.xoff + affine.a / 2.0, + affine.yoff + affine.e / 2.0, + ] + contours_gdf["geometry"] = contours_gdf.affine_transform(shapely_affine) + + # Rename the data column to match the dimension + contours_gdf = contours_gdf.rename({0: dim}, axis=1) + + # Drop empty timesteps + empty_contours = contours_gdf.geometry.is_empty + failed = ", ".join(map(str, contours_gdf[empty_contours][dim].to_list())) + contours_gdf = contours_gdf[~empty_contours] + + # Raise exception if no data is returned, or if any contours fail + # when `errors='raise'. Otherwise, print failed contours + if empty_contours.all() and errors == "raise": + raise ValueError( + "Failed to generate any valid contours; verify that " + "values passed to `z_values` are valid and present " + "in `da`" + ) + elif empty_contours.all() and errors == "ignore": + if verbose: + print( + "Failed to generate any valid contours; verify that " + "values passed to `z_values` are valid and present " + "in `da`" + ) + elif empty_contours.any() and errors == "raise": + raise Exception(f"Failed to generate contours: {failed}") + elif empty_contours.any() and errors == "ignore": + if verbose: + print(f"Failed to generate contours: {failed}") + + # If asked to write out file, test if GeoJSON or ESRI Shapefile. If + # GeoJSON, convert to EPSG:4326 before exporting. + if output_path and output_path.endswith(".geojson"): + if verbose: + print(f"Writing contours to {output_path}") + contours_gdf.to_crs("EPSG:4326").to_file(filename=output_path) + + if output_path and output_path.endswith(".shp"): + if verbose: + print(f"Writing contours to {output_path}") + contours_gdf.to_file(filename=output_path) + + return contours_gdf + + +def interpolate_2d(ds, + x_coords, + y_coords, + z_coords, + method='linear', + factor=1, + verbose=False, + **kwargs): + + """ + This function takes points with X, Y and Z coordinates, and + interpolates Z-values across the extent of an existing xarray + dataset. This can be useful for producing smooth surfaces from point + data that can be compared directly against satellite data derived + from an OpenDataCube query. + + Supported interpolation methods include 'linear', 'nearest' and + 'cubic (using `scipy.interpolate.griddata`), and 'rbf' (using + `scipy.interpolate.Rbf`). + + Last modified: February 2020 + + Parameters + ---------- + ds : xarray DataArray or Dataset + A two-dimensional or multi-dimensional array from which x and y + dimensions will be copied and used for the area in which to + interpolate point data. + x_coords, y_coords : numpy array + Arrays containing X and Y coordinates for all points (e.g. + longitudes and latitudes). + z_coords : numpy array + An array containing Z coordinates for all points (e.g. + elevations). These are the values you wish to interpolate + between. + method : string, optional + The method used to interpolate between point values. This string + is either passed to `scipy.interpolate.griddata` (for 'linear', + 'nearest' and 'cubic' methods), or used to specify Radial Basis + Function interpolation using `scipy.interpolate.Rbf` ('rbf'). + Defaults to 'linear'. + factor : int, optional + An optional integer that can be used to subsample the spatial + interpolation extent to obtain faster interpolation times, then + up-sample this array back to the original dimensions of the + data as a final step. For example, setting `factor=10` will + interpolate data into a grid that has one tenth of the + resolution of `ds`. This approach will be significantly faster + than interpolating at full resolution, but will potentially + produce less accurate or reliable results. + verbose : bool, optional + Print debugging messages. Default False. + **kwargs : + Optional keyword arguments to pass to either + `scipy.interpolate.griddata` (if `method` is 'linear', 'nearest' + or 'cubic'), or `scipy.interpolate.Rbf` (is `method` is 'rbf'). + + Returns + ------- + interp_2d_array : xarray DataArray + An xarray DataArray containing with x and y coordinates copied + from `ds_array`, and Z-values interpolated from the points data. + """ + + # Extract xy and elev points + points_xy = np.vstack([x_coords, y_coords]).T + + # Extract x and y coordinates to interpolate into. + # If `factor` is greater than 1, the coordinates will be subsampled + # for faster run-times. If the last x or y value in the subsampled + # grid aren't the same as the last x or y values in the original + # full resolution grid, add the final full resolution grid value to + # ensure data is interpolated up to the very edge of the array + if ds.x[::factor][-1].item() == ds.x[-1].item(): + x_grid_coords = ds.x[::factor].values + else: + x_grid_coords = ds.x[::factor].values.tolist() + [ds.x[-1].item()] + + if ds.y[::factor][-1].item() == ds.y[-1].item(): + y_grid_coords = ds.y[::factor].values + else: + y_grid_coords = ds.y[::factor].values.tolist() + [ds.y[-1].item()] + + # Create grid to interpolate into + grid_y, grid_x = np.meshgrid(x_grid_coords, y_grid_coords) + + # Apply scipy.interpolate.griddata interpolation methods + if method in ('linear', 'nearest', 'cubic'): + + # Interpolate x, y and z values + interp_2d = scipy.interpolate.griddata(points=points_xy, + values=z_coords, + xi=(grid_y, grid_x), + method=method, + **kwargs) + + # Apply Radial Basis Function interpolation + elif method == 'rbf': + + # Interpolate x, y and z values + rbf = scipy.interpolate.Rbf(x_coords, y_coords, z_coords, **kwargs) + interp_2d = rbf(grid_y, grid_x) + + # Create xarray dataarray from the data and resample to ds coords + interp_2d_da = xr.DataArray(interp_2d, + coords=[y_grid_coords, x_grid_coords], + dims=['y', 'x']) + + # If factor is greater than 1, resample the interpolated array to + # match the input `ds` array + if factor > 1: + interp_2d_da = interp_2d_da.interp_like(ds) + + return interp_2d_da + + +def contours_to_arrays(gdf, col): + """ + This function converts a polyline shapefile into an array with three + columns giving the X, Y and Z coordinates of each vertex. This data + can then be used as an input to interpolation procedures (e.g. using + a function like `interpolate_2d`. + + Last modified: October 2021 + + Parameters + ---------- + gdf : Geopandas GeoDataFrame + A GeoPandas GeoDataFrame of lines to convert into point + coordinates. + col : str + A string giving the name of the GeoDataFrame field to use as + Z-values. + + Returns + ------- + A numpy array with three columns giving the X, Y and Z coordinates + of each vertex in the input GeoDataFrame. + + """ + + # Explode multi-part geometries into multiple single geometries. + gdf = gdf.explode(ignore_index=True) + + coords_zvals = [] + + for i in range(0, len(gdf)): + val = gdf.iloc[i][col] + + try: + coords = np.concatenate( + [np.vstack(x.coords.xy).T for x in gdf.iloc[i].geometry.geoms] + ) + except Exception: + coords = np.vstack(gdf.iloc[i].geometry.coords.xy).T + + coords_zvals.append( + np.column_stack((coords, np.full(np.shape(coords)[0], fill_value=val))) + ) + + return np.concatenate(coords_zvals) + + +def largest_region(bool_array, **kwargs): + + ''' + Takes a boolean array and identifies the largest contiguous region of + connected True values. This is returned as a new array with cells in + the largest region marked as True, and all other cells marked as False. + + Parameters + ---------- + bool_array : boolean array + A boolean array (numpy or xarray.DataArray) with True values for + the areas that will be inspected to find the largest group of + connected cells + **kwargs : + Optional keyword arguments to pass to `measure.label` + + Returns + ------- + largest_region : boolean array + A boolean array with cells in the largest region marked as True, + and all other cells marked as False. + + ''' + + # First, break boolean array into unique, discrete regions/blobs + blobs_labels = label(bool_array, background=0, **kwargs) + + # Count the size of each blob, excluding the background class (0) + ids, counts = np.unique(blobs_labels[blobs_labels > 0], + return_counts=True) + + # Identify the region ID of the largest blob + largest_region_id = ids[np.argmax(counts)] + + # Produce a boolean array where 1 == the largest region + largest_region = blobs_labels == largest_region_id + + return largest_region + + +def transform_geojson_wgs_to_epsg(geojson, EPSG): + """ + Takes a geojson dictionary and converts it from WGS84 (EPSG:4326) to desired EPSG + + Parameters + ---------- + geojson: dict + a geojson dictionary containing a 'geometry' key, in WGS84 coordinates + EPSG: int + numeric code for the EPSG coordinate referecnce system to transform into + + Returns + ------- + transformed_geojson: dict + a geojson dictionary containing a 'coordinates' key, in the desired CRS + + """ + gg = Geometry(geojson['geometry'], CRS('epsg:4326')) + gg = gg.to_crs(CRS(f'epsg:{EPSG}')) + return gg.__geo_interface__ + + +def zonal_stats_parallel(shp, + raster, + statistics, + out_shp, + ncpus, + **kwargs): + + """ + Summarizing raster datasets based on vector geometries in parallel. + Each cpu recieves an equal chunk of the dataset. + Utilizes the perrygeo/rasterstats package. + + Parameters + ---------- + shp : str + Path to shapefile that contains polygons over + which zonal statistics are calculated + raster: str + Path to the raster from which the statistics are calculated. + This can be a virtual raster (.vrt). + statistics: list + list of statistics to calculate. e.g. + ['min', 'max', 'median', 'majority', 'sum'] + out_shp: str + Path to export shapefile containing zonal statistics. + ncpus: int + number of cores to parallelize the operations over. + kwargs: + Any other keyword arguments to rasterstats.zonal_stats() + See https://github.com/perrygeo/python-rasterstats for + all options + + Returns + ------- + Exports a shapefile to disk containing the zonal statistics requested + + """ + + # yields n sized chunks from list l (used for splitting task to multiple processes) + def chunks(l, n): + for i in range(0, len(l), n): + yield l[i:i + n] + + # calculates zonal stats and adds results to a dictionary + def worker(z, raster, d): + z_stats = zonal_stats(z, raster, stats=statistics, **kwargs) + for i in range(0, len(z_stats)): + d[z[i]['id']] = z_stats[i] + + # write output polygon + def write_output(zones, out_shp, d): + # copy schema and crs from input and add new fields for each statistic + schema = zones.schema.copy() + crs = zones.crs + for stat in statistics: + schema['properties'][stat] = 'float' + + with fiona.open(out_shp, 'w', 'ESRI Shapefile', schema, crs) as output: + for elem in zones: + for stat in statistics: + elem['properties'][stat] = d[elem['id']][stat] + output.write({'properties': elem['properties'], 'geometry': mapping(shape(elem['geometry']))}) + + with fiona.open(shp) as zones: + jobs = [] + + # create manager dictionary (polygon ids=keys, stats=entries) + # where multiple processes can write without conflicts + man = mp.Manager() + d = man.dict() + + # split zone polygons into 'ncpus' chunks for parallel processing + # and call worker() for each + split = chunks(zones, len(zones)//ncpus) + for z in split: + p = mp.Process(target=worker, args=(z, raster, d)) + p.start() + jobs.append(p) + + # wait that all chunks are finished + [j.join() for j in jobs] + + write_output(zones, out_shp, d) + + +def reverse_geocode(coords, site_classes=None, state_classes=None): + """ + Takes a latitude and longitude coordinate, and performs a reverse + geocode to return a plain-text description of the location in the + form: + + Site, State + + E.g.: `reverse_geocode(coords=(-35.282163, 149.128835))` + + 'Canberra, Australian Capital Territory' + + Parameters + ---------- + coords : tuple of floats + A tuple of (latitude, longitude) coordinates used to perform + the reverse geocode. + site_classes : list of strings, optional + A list of strings used to define the site part of the plain + text location description. Because the contents of the geocoded + address can vary greatly depending on location, these strings + are tested against the address one by one until a match is made. + + Defaults to: + + ``['city', 'town', 'village', 'suburb', 'hamlet', 'county', 'municipality']`` + + state_classes : list of strings, optional + A list of strings used to define the state part of the plain + text location description. These strings are tested against the + address one by one until a match is made. Defaults to: + `['state', 'territory']`. + Returns + ------- + If a valid geocoded address is found, a plain text location + description will be returned: + + 'Site, State' + + If no valid address is found, formatted coordinates will be returned + instead: + + 'XX.XX S, XX.XX E' + """ + + # Run reverse geocode using coordinates + geocoder = Nominatim(user_agent='Digital Earth Africa') + out = geocoder.reverse(coords) + + # Create plain text-coords as fall-back + lat = f'{-coords[0]:.2f} S' if coords[0] < 0 else f'{coords[0]:.2f} N' + lon = f'{-coords[1]:.2f} W' if coords[1] < 0 else f'{coords[1]:.2f} E' + + try: + + # Get address from geocoded data + address = out.raw['address'] + + # Use site and state classes if supplied; else use defaults + default_site_classes = ['city', 'town', 'village', 'suburb', 'hamlet', + 'county', 'municipality'] + default_state_classes = ['state', 'territory'] + site_classes = site_classes if site_classes else default_site_classes + state_classes = state_classes if state_classes else default_state_classes + + # Return the first site or state class that exists in address dict + site = next((address[k] for k in site_classes if k in address), None) + state = next((address[k] for k in state_classes if k in address), None) + + # If site and state exist in the data, return this. + # Otherwise, return N/E/S/W coordinates. + if site and state: + + # Return as site, state formatted string + return f'{site}, {state}' + + else: + + # If no geocoding result, return N/E/S/W coordinates + print('No valid geocoded location; returning coordinates instead') + return f'{lat}, {lon}' + + except (KeyError, AttributeError): + + # If no geocoding result, return N/E/S/W coordinates + print('No valid geocoded location; returning coordinates instead') + return f'{lat}, {lon}' + + +def sun_angles(dc, query): + """ + For a given spatiotemporal query, calculate mean sun + azimuth and elevation for each satellite observation, and + return these as a new `xarray.Dataset` with 'sun_elevation' + and 'sun_azimuth' variables. + + Parameters: + ----------- + dc : datacube.Datacube object + Datacube instance used to load data. + query : dict + A dictionary containing query parameters used to identify + satellite observations and load metadata. + + Returns: + -------- + sun_angles_ds : xarray.Dataset + An `xarray.set` containing a 'sun_elevation' and + 'sun_azimuth' variables. + """ + # Identify satellite datasets and group outputs using the + # same approach used to group satellite imagery (i.e. solar day) + gb = query_group_by(**query) + datasets = dc.find_datasets(**query) + dataset_array = dc.group_datasets(datasets, gb) + + # Load and take the mean of metadata from each product + sun_azimuth = xr_apply( + dataset_array, + lambda t, dd: np.mean([d.metadata.eo_sun_azimuth for d in dd]), + dtype=float, + ) + sun_elevation = xr_apply( + dataset_array, + lambda t, dd: np.mean([d.metadata.eo_sun_elevation for d in dd]), + dtype=float, + ) + + # Combine into new xarray.Dataset + sun_angles_ds = xr.merge( + [sun_elevation.rename("sun_elevation"), sun_azimuth.rename("sun_azimuth")] + ) + + return sun_angles_ds diff --git a/deafrica_tools/temporal.py b/deafrica_tools/temporal.py new file mode 100644 index 0000000..6539451 --- /dev/null +++ b/deafrica_tools/temporal.py @@ -0,0 +1,576 @@ +""" +Functions for calculating per-pixel temporal summary statistics on a +timeseries stored in a xarray.DataArray. + +The key functions are: + +.. autosummary:: + :caption: Primary functions + :nosignatures: + :toctree: gen + + xr_phenology + temporal_statistics + +.. autosummary:: + :nosignatures: + :toctree: gen + +""" + +import sys +import dask +import numpy as np +import xarray as xr +import hdstats +from packaging import version +from datacube.utils.geometry import assign_crs + + +def allNaN_arg(da, dim, stat): + """ + Calculate da.argmax() or da.argmin() while handling + all-NaN slices. Fills all-NaN locations with an + float and then masks the offending cells. + + Parameters + ---------- + da : xarray.DataArray + dim : str + Dimension over which to calculate argmax, argmin e.g. 'time' + stat : str + The statistic to calculte, either 'min' for argmin() + or 'max' for .argmax() + + Returns + ------- + xarray.DataArray + """ + # generate a mask where entire axis along dimension is NaN + mask = da.isnull().all(dim) + + if stat == "max": + y = da.fillna(float(da.min() - 1)) + y = y.argmax(dim=dim, skipna=True).where(~mask) + return y + + if stat == "min": + y = da.fillna(float(da.max() + 1)) + y = y.argmin(dim=dim, skipna=True).where(~mask) + return y + + +def _vpos(da): + """ + vPOS = Value at peak of season + """ + return da.max("time") + + +def _pos(da): + """ + POS = DOY of peak of season + """ + return da.isel(time=da.argmax("time")).time.dt.dayofyear + + +def _trough(da): + """ + Trough = Minimum value + """ + return da.min("time") + + +def _aos(vpos, trough): + """ + AOS = Amplitude of season + """ + return vpos - trough + + +def _vsos(da, pos, method_sos="first"): + """ + vSOS = Value at the start of season + Params + ----- + da : xarray.DataArray + method_sos : str, + If 'first' then vSOS is estimated + as the first positive slope on the + greening side of the curve. If 'median', + then vSOS is estimated as the median value + of the postive slopes on the greening side + of the curve. + """ + # select timesteps before peak of season (AKA greening) + greenup = da.where(da.time < pos.time) + # find the first order slopes + green_deriv = greenup.differentiate("time") + # find where the first order slope is postive + pos_green_deriv = green_deriv.where(green_deriv > 0) + # positive slopes on greening side + pos_greenup = greenup.where(~np.isnan(pos_green_deriv)) + # find the median + median = pos_greenup.median("time") + # distance of values from median + distance = pos_greenup - median + + if method_sos == "first": + # find index (argmin) where distance is most negative + idx = allNaN_arg(distance, "time", "min").astype("int16") + + if method_sos == "median": + # find index (argmin) where distance is smallest absolute value + idx = allNaN_arg(np.fabs(distance), "time", "min").astype("int16") + + return pos_greenup.isel(time=idx) + + +def _sos(vsos): + """ + SOS = DOY for start of season + """ + return vsos.time.dt.dayofyear + + +def _veos(da, pos, method_eos="last"): + """ + vEOS = Value at the end of season + Params + ----- + method_eos : str + If 'last' then vEOS is estimated + as the last negative slope on the + senescing side of the curve. If 'median', + then vEOS is estimated as the 'median' value + of the negative slopes on the senescing + side of the curve. + """ + # select timesteps before peak of season (AKA greening) + senesce = da.where(da.time > pos.time) + # find the first order slopes + senesce_deriv = senesce.differentiate("time") + # find where the fst order slope is negative + neg_senesce_deriv = senesce_deriv.where(~np.isnan(senesce_deriv < 0)) + # negative slopes on senescing side + neg_senesce = senesce.where(neg_senesce_deriv) + # find medians + median = neg_senesce.median("time") + # distance to the median + distance = neg_senesce - median + + if method_eos == "last": + # index where last negative slope occurs + idx = allNaN_arg(distance, "time", "min").astype("int16") + + if method_eos == "median": + # index where median occurs + idx = allNaN_arg(np.fabs(distance), "time", "min").astype("int16") + + return neg_senesce.isel(time=idx) + + +def _eos(veos): + """ + EOS = DOY for end of seasonn + """ + return veos.time.dt.dayofyear + + +def _los(da, eos, sos): + """ + LOS = Length of season (in DOY) + """ + los = eos - sos + #handle negative values + los = xr.where( + los >= 0, + los, + da.time.dt.dayofyear.values[-1] + (eos.where(los < 0) - sos.where(los < 0)), + ) + + return los + + +def _rog(vpos, vsos, pos, sos): + """ + ROG = Rate of Greening (Days) + """ + return (vpos - vsos) / (pos - sos) + + +def _ros(veos, vpos, eos, pos): + """ + ROG = Rate of Senescing (Days) + """ + return (veos - vpos) / (eos - pos) + + +def xr_phenology( + da, + stats=[ + "SOS", + "POS", + "EOS", + "Trough", + "vSOS", + "vPOS", + "vEOS", + "LOS", + "AOS", + "ROG", + "ROS", + ], + method_sos="first", + method_eos="last", + verbose=True +): + """ + Obtain land surface phenology metrics from an + xarray.DataArray containing a timeseries of a + vegetation index like NDVI. + + last modified June 2020 + + Parameters + ---------- + da : xarray.DataArray + DataArray should contain a 2D or 3D time series of a + vegetation index like NDVI, EVI + stats : list + list of phenological statistics to return. Regardless of + the metrics returned, all statistics are calculated + due to inter-dependencies between metrics. + Options include: + + * `SOS` = DOY of start of season + * `POS` = DOY of peak of season + * `EOS` = DOY of end of season + * `vSOS` = Value at start of season + * `vPOS` = Value at peak of season + * `vEOS` = Value at end of season + * `Trough` = Minimum value of season + * `LOS` = Length of season (DOY) + * `AOS` = Amplitude of season (in value units) + * `ROG` = Rate of greening + * `ROS` = Rate of senescence + + method_sos : str + If 'first' then vSOS is estimated as the first positive + slope on the greening side of the curve. If 'median', + then vSOS is estimated as the median value of the postive + slopes on the greening side of the curve. + method_eos : str + If 'last' then vEOS is estimated as the last negative slope + on the senescing side of the curve. If 'median', then vEOS is + estimated as the 'median' value of the negative slopes on the + senescing side of the curve. + + Returns + ------- + xarray.Dataset + Dataset containing variables for the selected + phenology statistics + + """ + # Check inputs before running calculations + if dask.is_dask_collection(da): + if version.parse(xr.__version__) < version.parse("0.16.0"): + raise TypeError( + "Dask arrays are not currently supported by this function, " + + "run da.compute() before passing dataArray." + ) + stats_dtype = { + "SOS": np.int16, + "POS": np.int16, + "EOS": np.int16, + "Trough": np.float32, + "vSOS": np.float32, + "vPOS": np.float32, + "vEOS": np.float32, + "LOS": np.int16, + "AOS": np.float32, + "ROG": np.float32, + "ROS": np.float32, + } + da_template = da.isel(time=0).drop("time") + template = xr.Dataset( + { + var_name: da_template.astype(var_dtype) + for var_name, var_dtype in stats_dtype.items() + if var_name in stats + } + ) + da_all_time = da.chunk({"time": -1}) + + lazy_phenology = da_all_time.map_blocks( + xr_phenology, + kwargs=dict( + stats=stats, + method_sos=method_sos, + method_eos=method_eos, + ), + template=xr.Dataset(template), + ) + + try: + crs = da.geobox.crs + lazy_phenology = assign_crs(lazy_phenology, str(crs)) + except: + pass + + return lazy_phenology + + if method_sos not in ("median", "first"): + raise ValueError("method_sos should be either 'median' or 'first'") + + if method_eos not in ("median", "last"): + raise ValueError("method_eos should be either 'median' or 'last'") + + # If stats supplied is not a list, convert to list. + stats = stats if isinstance(stats, list) else [stats] + + # try to grab the crs info + try: + crs = da.geobox.crs + except: + pass + + # remove any remaining all-NaN pixels + mask = da.isnull().all("time") + da = da.where(~mask, other=0) + + # calculate the statistics + if verbose: + print(" Phenology...") + vpos = _vpos(da) + pos = _pos(da) + trough = _trough(da) + aos = _aos(vpos, trough) + vsos = _vsos(da, pos, method_sos=method_sos) + sos = _sos(vsos) + veos = _veos(da, pos, method_eos=method_eos) + eos = _eos(veos) + los = _los(da, eos, sos) + rog = _rog(vpos, vsos, pos, sos) + ros = _ros(veos, vpos, eos, pos) + + # Dictionary containing the statistics + stats_dict = { + "SOS": sos.astype(np.int16), + "EOS": eos.astype(np.int16), + "vSOS": vsos.astype(np.float32), + "vPOS": vpos.astype(np.float32), + "Trough": trough.astype(np.float32), + "POS": pos.astype(np.int16), + "vEOS": veos.astype(np.float32), + "LOS": los.astype(np.int16), + "AOS": aos.astype(np.float32), + "ROG": rog.astype(np.float32), + "ROS": ros.astype(np.float32), + } + + # intialise dataset with first statistic + ds = stats_dict[stats[0]].to_dataset(name=stats[0]) + + # add the other stats to the dataset + for stat in stats[1:]: + if verbose: + print(" " + stat) + stats_keep = stats_dict.get(stat) + ds[stat] = stats_dict[stat] + + try: + ds = assign_crs(ds, str(crs)) + except: + pass + + return ds.drop("time") + + +def temporal_statistics(da, stats): + """ + Calculate various generic summary statistics on any timeseries. + + This function uses the hdstats temporal library: + https://github.com/daleroberts/hdstats/blob/master/hdstats/ts.pyx + + last modified June 2020 + + Parameters + ---------- + da : xarray.DataArray + DataArray should contain a 3D time series. + stats : list + list of temporal statistics to calculate. + Options include: + + * 'discordance' = + * 'f_std' = std of discrete fourier transform coefficients, returns + three layers: f_std_n1, f_std_n2, f_std_n3 + * 'f_mean' = mean of discrete fourier transform coefficients, returns + three layers: f_mean_n1, f_mean_n2, f_mean_n3 + * 'f_median' = median of discrete fourier transform coefficients, returns + three layers: f_median_n1, f_median_n2, f_median_n3 + * 'mean_change' = mean of discrete difference along time dimension + * 'median_change' = median of discrete difference along time dimension + * 'abs_change' = mean of absolute discrete difference along time dimension + * 'complexity' = + * 'central_diff' = + * 'num_peaks' : The number of peaks in the timeseries, defined with a local + window of size 10. NOTE: This statistic is very slow + + Returns + ------- + xarray.Dataset + Dataset containing variables for the selected + temporal statistics + + """ + + # if dask arrays then map the blocks + if dask.is_dask_collection(da): + if version.parse(xr.__version__) < version.parse("0.16.0"): + raise TypeError( + "Dask arrays are only supported by this function if using, " + + "xarray v0.16, run da.compute() before passing dataArray." + ) + + # create a template that matches the final datasets dims & vars + arr = da.isel(time=0).drop("time") + + # deal with the case where fourier is first in the list + if stats[0] in ("f_std", "f_median", "f_mean"): + template = xr.zeros_like(arr).to_dataset(name=stats[0] + "_n1") + template[stats[0] + "_n2"] = xr.zeros_like(arr) + template[stats[0] + "_n3"] = xr.zeros_like(arr) + + for stat in stats[1:]: + if stat in ("f_std", "f_median", "f_mean"): + template[stat + "_n1"] = xr.zeros_like(arr) + template[stat + "_n2"] = xr.zeros_like(arr) + template[stat + "_n3"] = xr.zeros_like(arr) + else: + template[stat] = xr.zeros_like(arr) + else: + template = xr.zeros_like(arr).to_dataset(name=stats[0]) + + for stat in stats: + if stat in ("f_std", "f_median", "f_mean"): + template[stat + "_n1"] = xr.zeros_like(arr) + template[stat + "_n2"] = xr.zeros_like(arr) + template[stat + "_n3"] = xr.zeros_like(arr) + else: + template[stat] = xr.zeros_like(arr) + try: + template = template.drop("spatial_ref") + except: + pass + + # ensure the time chunk is set to -1 + da_all_time = da.chunk({"time": -1}) + + # apply function across chunks + lazy_ds = da_all_time.map_blocks( + temporal_statistics, kwargs={"stats": stats}, template=template + ) + + try: + crs = da.geobox.crs + lazy_ds = assign_crs(lazy_ds, str(crs)) + except: + pass + + return lazy_ds + + # If stats supplied is not a list, convert to list. + stats = stats if isinstance(stats, list) else [stats] + + # grab all the attributes of the xarray + x, y, time, attrs = da.x, da.y, da.time, da.attrs + + # deal with any all-NaN pixels by filling with 0's + mask = da.isnull().all("time") + da = da.where(~mask, other=0) + + # ensure dim order is correct for functions + da = da.transpose("y", "x", "time").values + + stats_dict = { + "discordance": lambda da: hdstats.discordance(da, n=10), + "f_std": lambda da: hdstats.fourier_std(da, n=3, step=5), + "f_mean": lambda da: hdstats.fourier_mean(da, n=3, step=5), + "f_median": lambda da: hdstats.fourier_median(da, n=3, step=5), + "mean_change": lambda da: hdstats.mean_change(da), + "median_change": lambda da: hdstats.median_change(da), + "abs_change": lambda da: hdstats.mean_abs_change(da), + "complexity": lambda da: hdstats.complexity(da), + "central_diff": lambda da: hdstats.mean_central_diff(da), + "num_peaks": lambda da: hdstats.number_peaks(da, 10), + } + + print(" Statistics:") + # if one of the fourier functions is first (or only) + # stat in the list then we need to deal with this + if stats[0] in ("f_std", "f_median", "f_mean"): + print(" " + stats[0]) + stat_func = stats_dict.get(str(stats[0])) + zz = stat_func(da) + n1 = zz[:, :, 0] + n2 = zz[:, :, 1] + n3 = zz[:, :, 2] + + # intialise dataset with first statistic + ds = xr.DataArray( + n1, attrs=attrs, coords={"x": x, "y": y}, dims=["y", "x"] + ).to_dataset(name=stats[0] + "_n1") + + # add other datasets + for i, j in zip([n2, n3], ["n2", "n3"]): + ds[stats[0] + "_" + j] = xr.DataArray( + i, attrs=attrs, coords={"x": x, "y": y}, dims=["y", "x"] + ) + else: + # simpler if first function isn't fourier transform + first_func = stats_dict.get(str(stats[0])) + print(" " + stats[0]) + ds = first_func(da) + + # convert back to xarray dataset + ds = xr.DataArray( + ds, attrs=attrs, coords={"x": x, "y": y}, dims=["y", "x"] + ).to_dataset(name=stats[0]) + + # loop through the other functions + for stat in stats[1:]: + print(" " + stat) + + # handle the fourier transform examples + if stat in ("f_std", "f_median", "f_mean"): + stat_func = stats_dict.get(str(stat)) + zz = stat_func(da) + n1 = zz[:, :, 0] + n2 = zz[:, :, 1] + n3 = zz[:, :, 2] + + for i, j in zip([n1, n2, n3], ["n1", "n2", "n3"]): + ds[stat + "_" + j] = xr.DataArray( + i, attrs=attrs, coords={"x": x, "y": y}, dims=["y", "x"] + ) + + else: + # Select a stats function from the dictionary + # and add to the dataset + stat_func = stats_dict.get(str(stat)) + ds[stat] = xr.DataArray( + stat_func(da), attrs=attrs, coords={"x": x, "y": y}, dims=["y", "x"] + ) + + # try to add back the geobox + try: + crs = da.geobox.crs + ds = assign_crs(ds, str(crs)) + except: + pass + + return ds diff --git a/deafrica_tools/untitled.txt b/deafrica_tools/untitled.txt new file mode 100644 index 0000000..e69de29 diff --git a/deafrica_tools/wetlands.py b/deafrica_tools/wetlands.py new file mode 100644 index 0000000..4d7377a --- /dev/null +++ b/deafrica_tools/wetlands.py @@ -0,0 +1,732 @@ +""" +Functions for working with the Wetlands Insight Tool (WIT) +""" + +# Import required packages + +# Force GeoPandas to use Shapely instead of PyGEOS +# In a future release, GeoPandas will switch to using Shapely by default. +import os +os.environ['USE_PYGEOS'] = '0' + +import warnings +import numpy as np +import pandas as pd +import geopandas as gpd +import seaborn as sns +import xarray as xr +import matplotlib.pyplot as plt +from skimage import exposure +import matplotlib.animation as animation +import matplotlib.patheffects as PathEffects +from mpl_toolkits.axes_grid1.inset_locator import inset_axes +from dask.distributed import progress + +import datacube +from datacube.utils import masking +from datacube.utils import geometry + +from deafrica_tools.bandindices import calculate_indices +from deafrica_tools.datahandling import load_ard, wofs_fuser +from deafrica_tools.spatial import xr_rasterize +from deafrica_tools.classification import HiddenPrints + + +def WIT_drill( + gdf, + time, + min_gooddata=0.85, + TCW_threshold=-0.035, + resample_frequency=None, + export_csv=None, + dask_chunks=None, + verbose=False, + verbose_progress=False, +): + """ + The Wetlands Insight Tool run onver an extent covered by a polygon. + This function loads FC, WOfS, and Landsat data, and calculates tasseled + cap wetness, in order to determine the dominant land cover class + within a polygon at each satellite observation. + + The output is a pandas dataframe containing a timeseries of the relative + fractions of each class at each time-step. This forms the input to produce + a stacked line-plot. + + Last modified: Oct 2021 + + Parameters + ---------- + gdf : geopandas.GeoDataFrame + The dataframe must only contain a single row, + containing the polygon you wish to interrograte. + time : tuple + a tuple containing the time range over which to run the WIT. + e.g. ('2015-01' , '2019-12') + min_gooddata : Float, optional + A number between 0 and 1 (e.g 0.8) indicating the minimum percentage + of good quality pixels required for a satellite observation to be loaded + and therefore included in the WIT plot. This number should, at a minimum, + be set to 0.80 to limit biases in the result if not resampling the time-series. + If resampling the data using the parameter `resample_frequency`, then + setting this number to 0 (or a low float number) is acceptable. + TCW_threshold : Int, optional + The tasseled cap wetness threshold, beyond which a pixel will be + considered 'wet'. Defaults to -0.035. + resample_frequency : str + Option for resampling time-series of input datasets. This option is useful + for either smoothing the WIT plot, or because the area of analysis is larger + than a scene width and therefore requires composites. Options include any + str accepted by `xarray.resample(time=)`. The resampling method used is .max() + export_csv : str, optional + To save the returned pandas dataframe as a .csv file, pass a + a location string (e.g. 'output/results.csv') + dask_chunks : dict, optional + To lazily load the datasets using dask, pass a dictionary containing + the dimensions over which to chunk e.g. {'time':-1, 'x':250, 'y':250}. + verbose: bool, optional + If true, print statements are putput detailing the progress of the tool. + verbose_progress: bool, optional + For use with Dask progress bar + + Returns + ------- + df : Pandas.Dataframe + A pandas dataframe containing the timeseries of relative fractions + of each land cover class (WOfs, FC, TCW) + + """ + # add geom to dc query dict + if isinstance(gdf, datacube.utils.geometry._base.Geometry): + gdf = gpd.GeoDataFrame({'col1':['name'],'geometry':gdf.geom}, crs=gdf.crs) + geom = geometry.Geometry(geom=gdf.iloc[0].geometry, crs=gdf.crs) + query = {"geopolygon": geom, "time": time} + + # Create a datacube instance + dc = datacube.Datacube(app="wetlands insight tool") + + # load landsat 5,7,8 data + warnings.filterwarnings("ignore") + + if verbose_progress: + print("Loading Landsat data") + ds_ls = load_ard( + dc=dc, + products=["ls8_sr", "ls7_sr", "ls5_sr"], + output_crs="epsg:6933", + min_gooddata=min_gooddata, + mask_filters=(['opening', 3], ['dilation', 3]), + measurements=["red", "green", "blue", "nir", "swir_1", "swir_2"], + dask_chunks=dask_chunks, + group_by="solar_day", + resolution=(-30, 30), + verbose=verbose, + **query, + ) + + # create polygon mask + mask = xr_rasterize(gdf.iloc[[0]], ds_ls) + ds_ls = ds_ls.where(mask) + + # calculate tasselled cap wetness within masked AOI + if verbose: + print("calculating tasseled cap wetness index ") + + with HiddenPrints(): #suppres the prints from this func + tcw = calculate_indices( + ds_ls, index=["TCW"], normalise=False, satellite_mission="ls", drop=True + ) + + if resample_frequency is not None: + if verbose: + print('Resampling TCW to '+ resample_frequency) + tcw = tcw.resample(time=resample_frequency).max() + + tcw = tcw.TCW >= TCW_threshold + tcw = tcw.where(mask, 0) + tcw = tcw.persist() + + if verbose: + print("Loading WOfS layers ") + + wofls = dc.load( + product="wofs_ls", + like=ds_ls, + fuse_func=wofs_fuser, + dask_chunks=dask_chunks, + collection_category="T1", + ) + + # boolean of wet/dry + wofls_wet = masking.make_mask(wofls.water, wet=True) + + if resample_frequency is not None: + if verbose: + print('Resampling WOfS to '+ resample_frequency) + wofls_wet = wofls_wet.resample(time=resample_frequency).max() + + # mask sure wofs matches other datasets + wofls_wet = wofls_wet.where(wofls_wet.time == tcw.time) + + # apply the polygon mask + wofls_wet = wofls_wet.where(mask) + + # load Fractional cover + if verbose: + print("Loading fractional Cover") + + # load fractional cover + fc_ds = dc.load( + product="fc_ls", + time=time, + dask_chunks=dask_chunks, + like=ds_ls, + measurements=["pv", "npv", "bs"], + collection_category="T1", + ) + + # use wofls mask to cloud mask FC + clear_and_dry = masking.make_mask(wofls, dry=True).water + fc_ds = fc_ds.where(clear_and_dry) + + if resample_frequency is not None: + if verbose: + print('Resampling FC to '+ resample_frequency) + fc_ds = fc_ds.resample(time=resample_frequency).max() + + # mask sure fc matches other datasets + fc_ds = fc_ds.where(fc_ds.time == tcw.time) + + # mask with polygon + fc_ds = fc_ds.where(mask) + + # mask with TC wetness + fc_ds_noTCW = fc_ds.where(tcw == False) + + if verbose: + print("Generating classification") + + # Cast the dataset to a dataarray + fc_ds_noTCW = fc_ds_noTCW.to_array(dim="variable", name="fc_ds_noTCW") + + # turn FC array into integer only as nanargmax doesn't + # seem to handle floats the way we want it to + fc_int = fc_ds_noTCW.astype("int8") + + # use nanargmax to get the index of the maximum value + BSPVNPV = fc_int.argmax(dim="variable") + + #int dytype remocves NaNs so we need to create mask again + FC_mask = np.isfinite(fc_ds_noTCW).all(dim="variable") + BSPVNPV = BSPVNPV.where(FC_mask) + + # Restack the Fractional cover dataset all together + # CAUTION:ARGMAX DEPENDS ON ORDER OF VARIABALES IN + # DATASET. NEED TO ADJUST BELOW DEPENDING ON ORDER OF FC VARIABLES + + FC_dominant = xr.Dataset( + { + "bs": (BSPVNPV == 2).where(FC_mask), + "pv": (BSPVNPV == 0).where(FC_mask), + "npv": (BSPVNPV == 1).where(FC_mask), + } + ) + + # pixel counts + pixels = mask.sum(dim=["x", "y"]) + + + if verbose_progress: + print("Computing wetness") + tcw_pixel_count = tcw.sum(dim=["x", "y"]).compute() + + if verbose_progress: + print("Computing green veg, dry veg, and bare soil") + FC_count = FC_dominant.sum(dim=["x", "y"]).compute() + + if verbose_progress: + print("Computing open water") + wofs_pixels = wofls_wet.sum(dim=["x", "y"]).compute() + + # count percentages + wofs_area_percent = (wofs_pixels / pixels) * 100 + tcw_area_percent = (tcw_pixel_count / pixels) * 100 + tcw_less_wofs = tcw_area_percent - wofs_area_percent # wet not wofs + + # Fractional cover pixel count method + # Get number of FC pixels, divide by total number of pixels per polygon + # Work out the number of nodata pixels in the data + BS_percent = (FC_count.bs / pixels) * 100 + PV_percent = (FC_count.pv / pixels) * 100 + NPV_percent = (FC_count.npv / pixels) * 100 + NoData_count = (( + 100 - wofs_area_percent - tcw_less_wofs - PV_percent - NPV_percent - BS_percent + ) / 100) * pixels + + # re-do percentages but now handling any no-data pixels within polygon + BS_percent = (FC_count.bs / (pixels - NoData_count)) * 100 + PV_percent = (FC_count.pv / (pixels - NoData_count)) * 100 + NPV_percent = (FC_count.npv / (pixels - NoData_count)) * 100 + wofs_area_percent = (wofs_pixels / (pixels - NoData_count)) * 100 + tcw_area_percent = (tcw_pixel_count / (pixels - NoData_count)) * 100 + tcw_less_wofs = tcw_area_percent - wofs_area_percent + + # Sometimes when we resample datastes, WOfS extent can be + # greater than the wetness extent, thus make negative values == zero + tcw_less_wofs = tcw_less_wofs.where(tcw_less_wofs>=0, 0) + + # start setup of dataframe by adding only one dataset + df = pd.DataFrame( + data=wofs_area_percent.data, + index=wofs_area_percent.time.values, + columns=["wofs_area_percent"], + ) + + # add data into pandas dataframe for export + df["wet_percent"] = tcw_less_wofs.data + df["green_veg_percent"] = PV_percent.data + df["dry_veg_percent"] = NPV_percent.data + df["bare_soil_percent"] = BS_percent.data + + # round numbers + df = df.round(2) + + # save the csv of the output data used to create the stacked plot for the polygon drill + if export_csv: + if verbose: + print("exporting csv: " + export_csv) + df.to_csv(export_csv, index_label="Datetime") + + return df + + +def animated_timeseries_WIT( + ds, + df, + output_path, + width_pixels=1000, + interval=200, + bands=["red", "green", "blue"], + percentile_stretch=(0.02, 0.98), + image_proc_func=None, + title=False, + show_date=True, + annotation_kwargs={}, + onebandplot_cbar=True, + onebandplot_kwargs={}, + shapefile_path=None, + shapefile_kwargs={}, + pandasplot_kwargs={}, + time_dim="time", + x_dim="x", + y_dim="y", +): + + ############### + # Setup steps # + ############### + + # Test if all dimensions exist in dataset + if time_dim in ds and x_dim in ds and y_dim in ds: + + # Test if there is one or three bands, and that all exist in both datasets: + if ((len(bands) == 3) | (len(bands) == 1)) & all( + [(b in ds.data_vars) for b in bands] + ): + + # Import xarrays as lists of three band numpy arrays + imagelist, vmin, vmax = _ds_to_arrraylist( + ds, + bands=bands, + time_dim=time_dim, + x_dim=x_dim, + y_dim=y_dim, + percentile_stretch=percentile_stretch, + image_proc_func=image_proc_func, + ) + + # Get time, x and y dimensions of dataset and calculate width vs height of plot + timesteps = len(ds[time_dim]) + width = len(ds[x_dim]) + height = len(ds[y_dim]) + width_ratio = float(width) / float(height) + height = 10.0 / width_ratio + + # If title is supplied as a string, multiply out to a list with one string per timestep. + # Otherwise, use supplied list for plot titles. + if isinstance(title, str) or isinstance(title, bool): + title_list = [title] * timesteps + else: + title_list = title + + # Set up annotation parameters that plt.imshow plotting for single band array images. + # The nested dict structure sets default values which can be overwritten/customised by the + # manually specified `onebandplot_kwargs` + onebandplot_kwargs = dict( + { + "cmap": "Greys", + "interpolation": "bilinear", + "vmin": vmin, + "vmax": vmax, + "tick_colour": "black", + "tick_fontsize": 11, + }, + **onebandplot_kwargs, + ) + + # Use pop to remove the two special tick kwargs from the onebandplot_kwargs dict, and save individually + onebandplot_tick_colour = onebandplot_kwargs.pop("tick_colour") + onebandplot_tick_fontsize = onebandplot_kwargs.pop("tick_fontsize") + + # Set up annotation parameters that control font etc. The nested dict structure sets default + # values which can be overwritten/customised by the manually specified `annotation_kwargs` + annotation_kwargs = dict( + { + "xy": (1, 1), + "xycoords": "axes fraction", + "xytext": (-5, -5), + "textcoords": "offset points", + "horizontalalignment": "right", + "verticalalignment": "top", + "fontsize": 15, + "color": "white", + "path_effects": [ + PathEffects.withStroke(linewidth=3, foreground="black") + ], + }, + **annotation_kwargs, + ) + + # Define default plotting parameters for the overlaying shapefile(s). The nested dict structure sets + # default values which can be overwritten/customised by the manually specified `shapefile_kwargs` + shapefile_kwargs = dict( + {"linewidth": 2, "edgecolor": "black", "facecolor": "#00000000"}, + **shapefile_kwargs, + ) + + # Define default plotting parameters for the right-hand line plot. The nested dict structure sets + # default values which can be overwritten/customised by the manually specified `pandasplot_kwargs` + pandasplot_kwargs = dict({}, **pandasplot_kwargs) + + ################### + # Initialise plot # + ################### + + # Set up figure + fig, (ax1, ax2) = plt.subplots( + ncols=2, gridspec_kw={"width_ratios": [1, 2]} + ) + fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0.2, hspace=0) + fig.set_size_inches(10.0, height * 0.5, forward=True) + ax1.axis("off") + ax2.margins(x=0.01) + ax2.xaxis.label.set_visible(False) + + # Initialise axesimage objects to be updated during animation, setting extent from dims + extents = [ + float(ds[x_dim].min()), + float(ds[x_dim].max()), + float(ds[y_dim].min()), + float(ds[y_dim].max()), + ] + im = ax1.imshow(imagelist[0], extent=extents, **onebandplot_kwargs) + + # Initialise right panel and set y axis limits + # set up color palette + pal = [ + sns.xkcd_rgb["cobalt blue"], + sns.xkcd_rgb["neon blue"], + sns.xkcd_rgb["grass"], + sns.xkcd_rgb["beige"], + sns.xkcd_rgb["brown"], + ] + + # make a stacked area plot + ax2.stackplot( + df.index, + df.wofs_area_percent, + df.wet_percent, + df.green_veg_percent, + df.dry_veg_percent, + df.bare_soil_percent, + labels=["open water", "wet", "green veg", "dry veg", "bare soil"], + colors=pal, + alpha=0.6, + **pandasplot_kwargs, + ) + + ax2.legend(loc="lower left", framealpha=0.6) + + df1 = pd.DataFrame( + { + "wofs_area_percent": df.wofs_area_percent, + "wet_percent": df.wofs_area_percent + df.wet_percent, + "green_veg_percent": df.wofs_area_percent + + df.wet_percent + + df.green_veg_percent, + "dry_veg_percent": df.wofs_area_percent + + df.wet_percent + + df.green_veg_percent + + df.dry_veg_percent, + "bare_soil_percent": df.dry_veg_percent + + df.green_veg_percent + + df.wofs_area_percent + + df.wet_percent + + df.bare_soil_percent, + } + ) + df1 = df1.set_index(df.index) + + line_test = df1.plot( + ax=ax2, legend=False, color="black", **pandasplot_kwargs + ) + + # set axis limits to the min and max + ax2.set(xlim=(df.index[0], df.index[-1]), ylim=(0, 100)) + + # add a legend and a tight plot box + + ax2.set_title("Fractional Cover, Wetness, and Water") + + # Initialise annotation objects to be updated during animation + t = ax1.annotate("", **annotation_kwargs) + + ######################### + # Add optional overlays # + ######################### + + # Optionally add shapefile overlay(s) from either string path or list of string paths + if isinstance(shapefile_path, str): + + shapefile = gpd.read_file(shapefile_path) + shapefile.plot(**shapefile_kwargs, ax=ax1) + + elif isinstance(shapefile_path, list): + + # Iterate through list of string paths + for shapefile in shapefile_path: + + shapefile = gpd.read_file(shapefile) + shapefile.plot(**shapefile_kwargs, ax=ax1) + + # After adding shapefile, fix extents of plot + ax1.set_xlim(extents[0], extents[1]) + ax1.set_ylim(extents[2], extents[3]) + + # Optionally add colourbar for one band images + if (len(bands) == 1) & onebandplot_cbar: + _add_colourbar( + ax1, + im, + tick_fontsize=onebandplot_tick_fontsize, + tick_colour=onebandplot_tick_colour, + vmin=onebandplot_kwargs["vmin"], + vmax=onebandplot_kwargs["vmax"], + ) + + ######################################## + # Create function to update each frame # + ######################################## + + # Function to update figure + + def update_figure(frame_i): + + #################### + # Plot image panel # + #################### + + # If possible, extract dates from time dimension + try: + + # Get human-readable date info (e.g. "16 May 1990") + ts = ds[time_dim][{time_dim: frame_i}].dt + year = ts.year.item() + month = ts.month.item() + day = ts.day.item() + date_string = "{} {} {}".format( + day, calendar.month_abbr[month], year + ) + + except: + + date_string = ds[time_dim][{time_dim: frame_i}].values.item() + + # Create annotation string based on title and date specifications: + title = title_list[frame_i] + if title and show_date: + title_date = "{}\n{}".format(date_string, title) + elif title and not show_date: + title_date = "{}".format(title) + elif show_date and not title: + title_date = "{}".format(date_string) + else: + title_date = "" + + # Update left panel with annotation and image + im.set_array(imagelist[frame_i]) + t.set_text(title_date) + + ######################## + # Plot linegraph panel # + ######################## + + # Create list of artists to return + artist_list = [im, t] + + # Update right panel with temporal line subset, adding each new line into artist_list + for i, line in enumerate(line_test.lines): + + # Clip line data to current time, and get x and y values + y = df1[ + df1.index + <= datetime(year=year, month=month, day=day, hour=23, minute=59) + ].iloc[:, i] + x = df1[ + df1.index + <= datetime(year=year, month=month, day=day, hour=23, minute=59) + ].index + + # Plot lines after stripping NaNs (this produces continuous, unbroken lines) + line.set_data(x[y.notnull()], y[y.notnull()]) + artist_list.extend([line]) + + # Return the artists set + return artist_list + + # Nicely space subplots + fig.tight_layout() + + ############################## + # Generate and run animation # + ############################## + + # Generate animation + ani = animation.FuncAnimation( + fig=fig, + func=update_figure, + frames=timesteps, + interval=interval, + blit=True, + ) + + # Export as either MP4 or GIF + if output_path[-3:] == "mp4": + print(" Exporting animation to {}".format(output_path)) + ani.save(output_path, dpi=width_pixels / 10.0) + + elif output_path[-3:] == "wmv": + print(" Exporting animation to {}".format(output_path)) + ani.save( + output_path, + dpi=width_pixels / 10.0, + writer=animation.FFMpegFileWriter( + fps=1000 / interval, bitrate=4000, codec="wmv2" + ), + ) + + elif output_path[-3:] == "gif": + print(" Exporting animation to {}".format(output_path)) + ani.save(output_path, dpi=width_pixels / 10.0, writer="imagemagick") + + else: + print(" Output file type must be either .mp4, .wmv or .gif") + + else: + print( + "Please select either one or three bands that all exist in the input dataset" + ) + + else: + print( + "At least one x, y or time dimension does not exist in the input dataset. Please use the `time_dim`," + "`x_dim` or `y_dim` parameters to override the default dimension names used for plotting" + ) + + +# Define function to convert xarray dataset to list of one or three band numpy arrays + + +def _ds_to_arrraylist( + ds, bands, time_dim, x_dim, y_dim, percentile_stretch, image_proc_func=None +): + """ + Converts an xarray dataset to a list of numpy arrays for plt.imshow plotting + """ + + # Compute percents + p_low, p_high = ds[bands].to_array().quantile(percentile_stretch).values + + array_list = [] + for i, timestep in enumerate(ds[time_dim]): + + # Select single timestep from the data array + ds_i = ds[{time_dim: i}] + + # Get shape of array + x = len(ds[x_dim]) + y = len(ds[y_dim]) + + if len(bands) == 1: + + # Create new one band array + img_toshow = exposure.rescale_intensity( + ds_i[bands[0]].values, in_range=(p_low, p_high), out_range="image" + ) + + else: + + # Create new three band array + rawimg = np.zeros((y, x, 3), dtype=np.float32) + + # Add xarray bands into three dimensional numpy array + for band, colour in enumerate(bands): + + rawimg[:, :, band] = ds_i[colour].values + + # Stretch contrast using percentile values + img_toshow = exposure.rescale_intensity( + rawimg, in_range=(p_low, p_high), out_range=(0, 1.0) + ) + + # Optionally image processing + if image_proc_func: + + img_toshow = image_proc_func(img_toshow).clip(0, 1) + + array_list.append(img_toshow) + + return array_list, p_low, p_high + + +def _add_colourbar( + ax, im, vmin, vmax, cmap="Greys", tick_fontsize=15, tick_colour="black" +): + """ + Add a nicely formatted colourbar to an animation panel + """ + + # Add colourbar + axins2 = inset_axes(ax, width="97%", height="4%", loc=8, borderpad=1) + plt.gcf().colorbar( + im, cax=axins2, orientation="horizontal", ticks=np.linspace(vmin, vmax, 3) + ) + axins2.xaxis.set_ticks_position("top") + axins2.tick_params(axis="x", colors=tick_colour, labelsize=tick_fontsize) + + # Justify left and right labels to edge of plot + axins2.get_xticklabels()[0].set_horizontalalignment("left") + axins2.get_xticklabels()[-1].set_horizontalalignment("right") + labels = [item.get_text() for item in axins2.get_xticklabels()] + labels[0] = " " + labels[0] + labels[-1] = labels[-1] + " " + + +if __name__ == "__main__": + # print that we are running the testing + print("Testing..") + # import doctest to test our module for documentation + import doctest + + doctest.testmod() + print("Testing done") diff --git a/model_train/model.joblib b/model_train/model.joblib new file mode 100644 index 0000000..ca3ce1a Binary files /dev/null and b/model_train/model.joblib differ diff --git a/new_import.py b/new_import.py new file mode 100644 index 0000000..a49a430 --- /dev/null +++ b/new_import.py @@ -0,0 +1,320 @@ +import matplotlib.pyplot as plt + +# Common imports and settings +import os, sys +os.environ['USE_PYGEOS'] = '0' +from IPython.display import Markdown +import pandas as pd +pd.set_option("display.max_rows", None) +import xarray as xr + +# Datacube +import datacube +from datacube.utils.rio import configure_s3_access +from datacube.utils import masking +from datacube.utils.cog import write_cog +# https://github.com/GeoscienceAustralia/dea-notebooks/tree/develop/Tools +from dea_tools.plotting import display_map, rgb +from dea_tools.datahandling import mostcommon_crs + +# EASI defaults +easinotebooksrepo = '/home/jovyan/easi-notebooks' +if easinotebooksrepo not in sys.path: sys.path.append(easinotebooksrepo) +from easi_tools import EasiDefaults, xarray_object_size, notebook_utils, unset_cachingproxy +from easi_tools.load_s2l2a import load_s2l2a_with_offset +from dask.distributed import progress + +# Data tools +import numpy as np +from datetime import datetime + +# Datacube +from datacube.utils import masking # https://github.com/opendatacube/datacube-core/blob/develop/datacube/utils/masking.py +from odc.algo import enum_to_bool # https://github.com/opendatacube/odc-algo/blob/main/odc/algo/_masking.py +from odc.algo import xr_reproject # https://github.com/opendatacube/odc-algo/blob/main/odc/algo/_warp.py +from datacube.utils.geometry import GeoBox, box # https://github.com/opendatacube/datacube-core/blob/develop/datacube/utils/geometry/_base.py + +# Holoviews, Datashader and Bokeh +import hvplot.pandas +import hvplot.xarray +import holoviews as hv +import panel as pn +import colorcet as cc +import cartopy.crs as ccrs +from datashader import reductions +from holoviews import opts +from utils import load_data_geo +import rasterio +import rioxarray +# import geoviews as gv +# from holoviews.operation.datashader import rasterize +hv.extension('bokeh', logo=False) + +from deafrica_tools.bandindices import calculate_indices +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score, classification_report +from sklearn.preprocessing import LabelEncoder + +from sklearn.pipeline import Pipeline +from sklearn.ensemble import RandomForestClassifier +from sklearn.impute import SimpleImputer +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import GridSearchCV +from sklearn.model_selection import train_test_split +from sklearn.metrics import accuracy_score +from shapely.geometry import Point, Polygon +import geopandas as gpd +from pyproj import CRS +from matplotlib.colors import ListedColormap +from holoviews import opts +from datashader import reductions +from bokeh.models.tickers import FixedTicker +from rioxarray.merge import merge_arrays + +import joblib + + +def load_data(dc, date_range, longtitude_range, latitude_range): + product = 's2_l2a' + query = { + 'product': product, # Product name + 'x': longtitude_range, # "x" axis bounds + 'y': latitude_range, # "y" axis bounds + 'time': date_range, # Any parsable date strings + } + native_crs = notebook_utils.mostcommon_crs(dc, query) + print(f'Most common native CRS: {native_crs}') + measurements = ['blue', 'green', 'red', 'nir', 'scl'] + + load_params = { + 'measurements': measurements, # Selected measurement or alias names + 'output_crs': native_crs, # Target EPSG code + 'resolution': (-10, 10), # Target resolution + 'group_by': 'solar_day', # Scene grouping + 'dask_chunks': {'x': 2048, 'y': 2048}, # Dask chunks + } + data = load_s2l2a_with_offset( + dc, + query | load_params # Combine the two dicts that contain our search and load parameters + ) + return data + + +def mask_clean(data): + flag_name = 'scl' + flag_desc = masking.describe_variable_flags(data[flag_name]) # Pandas dataframe + display(flag_desc) + display(flag_desc.loc['qa'].values[1]) + # Create a "data quality" Mask layer + flags_def = flag_desc.loc['qa'].values[1] + good_pixel_flags = [flags_def[str(i)] for i in [2, 4, 5, 6]] # To pass strings to enum_to_bool() + + # enum_to_bool calculates the pixel-wise "or" of each set of pixels given by good_pixel_flags + # 1 = good data + # 0 = "bad" data + good_pixel_mask = enum_to_bool(data[flag_name], good_pixel_flags) + data_layer_names = [x for x in data.data_vars if x != 'scl'] + # Apply good pixel mask to blue, green, red and nir. + result = data[data_layer_names].where(good_pixel_mask).persist() + return result + + +def fill_nan(ndvi, time_split): + rs = [] + for times in time_split: + tmp = ndvi.sel(time=times) + fill_ds = tmp.sel(time=times).bfill(dim='time') + fill_ds = fill_ds.sel(time=times).ffill(dim='time') + rs.append(fill_ds) + merged_ndvi = xr.concat([i for i in rs], dim="time") + fill_m = merged_ndvi.bfill(dim="time") + fill_m = fill_m.ffill(dim="time") + return fill_m + + +def load_train_data(train_path): + train = load_data_geo(train_path) + return train + + +def load_sen1(name_vh, name_vv): + dsvv = rioxarray.open_rasterio(name_vv) + dsvh = rioxarray.open_rasterio(name_vh) + return dsvh, dsvv + + +def get_data_sen1_and_sen2(train, average_ndvi, dsvh, dsvv): + loaded_datasets = {} + for idx, point in train.iterrows(): + key = f"point_{idx + 1}" + try: + ndvi_data = average_ndvi.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values + vh_data = dsvh.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values + vv_data = dsvv.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values + loaded_datasets[key] = { + "data": np.concatenate((ndvi_data, vh_data, vv_data)), + "label": point.HT_code + } + except Exception as e: + # loaded_datasets[key] = None + print(e) + return loaded_datasets + + +def split_train_data(train, label_mapping, datasets): + label_encoder = LabelEncoder() + + # Fit and transform the labels + labels = train.Hientrang.values + numeric_labels = label_encoder.fit_transform([label_mapping[label] for label in labels]) + X = [] + x_new = [] + lb_new = [] + for k, v in datasets.items(): + X.append(v) + for i in range(len(X)): + if X[i] is not None: + x_new.append(X[i]["data"]) + lb_new.append(numeric_labels[i]) + X_train, X_temp, y_train, y_temp= train_test_split(x_new, lb_new, test_size=0.4, random_state=42) + X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42) + return X_train, X_val, X_test, y_train, y_val, y_test + + +def train_with_rf(X_train, X_val, y_train, y_val): + # Takes 1-2 minutes to complete + + # Tạo RandomForestClassifier mặc định để sử dụng làm mô hình ban đầu trong pipeline + base_model = RandomForestClassifier(random_state=42, n_jobs=-1) + + # Tạo pipeline + pipeline = Pipeline([ + # ('imputer', SimpleImputer(strategy='mean')), + ('scaler', StandardScaler()), + ('classifier', base_model), + ]) + # Thiết lập các tham số bạn muốn tối ưu hóa + param_grid = { + 'classifier__n_estimators': [100, 300, 500, 700, 1000], + 'classifier__max_depth': [6, 8, 10, 15, 20], + 'classifier__criterion': ['gini', 'entropy'], + } + + # Sử dụng GridSearchCV để tìm bộ tham số tốt nhất + grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy', n_jobs=-1) + grid_search.fit(X_train, y_train) + + # In ra bộ tham số tốt nhất + best_params = grid_search.best_params_ + print("Best Parameters:", best_params) + + # Dự đoán trên tập kiểm tra + y_pred = grid_search.predict(X_val) + + # Đánh giá kết quả + accuracy = accuracy_score(y_val, y_pred) + print(f"Accuracy: {round(accuracy, 2)*100} %") + return grid_search + + +def save_model(name_file, grid_search): + dir_save_model = "model_train" + if not os.path.exists(dir_save_model): + os.mkdir(dir_save_model) + joblib.dump(grid_search, os.path.join(dir_save_model, name_file)) + print("Done!") + + +def predict(model, data_crs, ndvi, vh, vv): + data_predict = [] + for i in range(ndvi.shape[1]): + ndvi_tmp = ndvi.isel(y=i).values + vh_data = vh.sel(y=ndvi.y.values[i], method='nearest').values + vv_data = vv.sel(y=ndvi.y.values[i], method='nearest').values + all_tmp = np.concatenate((ndvi_tmp, vh_data, vv_data), axis=0) + data_predict.extend(all_tmp.T) + y_pred = model.predict(data_predict) + final_label = y_pred.reshape(ndvi.y.shape[0], ndvi.x.shape[0]) + + final_xarray_save = xr.DataArray(final_label, dims=("y", "x")) + final_xarray_save = final_xarray_save.rio.write_crs(data_crs) + + x_values = ndvi.x.values + y_values = ndvi.y.values + + data_array = xr.DataArray(final_xarray_save, + coords={'x': x_values, 'y': y_values}, + dims=['y', 'x']) + data_array = data_array.rio.write_crs(ndvi.rio.crs) + return data_array + + +def cut_according_shp(thuanhoa_path, average_ndvi, data_array): + gdf = gpd.read_file(thuanhoa_path) + gdf = gdf.to_crs(average_ndvi.rio.crs) + polygon_coords = list(gdf.geometry.values[0].exterior.coords) + polygon_coordinates = [(x, y) for x, y in polygon_coords] + + geometries = [ + { + 'type': 'Polygon', + 'coordinates': [polygon_coordinates] + } + ] + region_result = data_array.rio.clip(geometries, data_array.rio.crs, drop=False) + region_result = region_result.where(region_result >= 0, float('nan')) + return region_result + + +def compare(KD_path, KetQuaPhanLoaiDat, CODE_MAP, HT_MAP): + gdf = gpd.read_file(KD_path, crs="EPSG:9209") + polygon = gdf.geometry.values + label = gdf.tenchu.values + ouput_image = rioxarray.open_rasterio(KetQuaPhanLoaiDat) + code_tq = HT_MAP["TQ"]["data"][0] + code_pnn = HT_MAP["PNN"]["data"][0] + result = {} + for key, values in HT_MAP.items(): + print(f"process {key}") + array_list = [] + for i in range(len(polygon)): + po = polygon[i] + lb = label[i] + code_lb = CODE_MAP.get(lb, code_tq) + try: + qr = ouput_image.rio.clip([po], "EPSG:9209") + if code_lb in values["data"]: + if code_lb == code_pnn: + qr = qr.where((qr != float(code_pnn)), np.nan) + # qr = qr.where((qr != 3.0), np.nan) + elif code_lb == code_tq: + qr = qr.where((qr != float(code_pnn)), np.nan) + qr = qr.where((qr != 3.0), np.nan) + else: + qr = qr.where(qr != float(code_lb), np.nan) + else: + qr.values[:, :, :] = np.nan + array_list.append(qr) + except Exception as e: + pass + result.update({key: array_list}) + return result + + +def save_result(result, HT_MAP): + # cmap = ListedColormap(colors) + save_path = "ThuanHoa/KetQua" + if not os.path.exists(save_path): + os.mkdir(save_path) + + for k, v in result.items(): + rs = merge_arrays(v, nodata = np.nan) + rs.rio.to_raster(f"{save_path}/{k}.tif") + print(f"save {save_path}/{k}.tif") + # img = rs.plot(cmap=cmap, add_colorbar=False) + # cbar = plt.colorbar(img) + # cbar.ax.set_yticklabels(labels) + # plt.title(f'{HT_MAP[k]["name"]}') + # plt.axis('off') + # plt.show() \ No newline at end of file diff --git a/region/ST_region.dbf b/region/ST_region.dbf new file mode 100644 index 0000000..6782865 Binary files /dev/null and b/region/ST_region.dbf differ diff --git a/region/ST_region.prj b/region/ST_region.prj new file mode 100644 index 0000000..0ee7d78 --- /dev/null +++ b/region/ST_region.prj @@ -0,0 +1 @@ +GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433],AUTHORITY["EPSG",4326]] \ No newline at end of file diff --git a/region/ST_region.shp b/region/ST_region.shp new file mode 100644 index 0000000..bed95f1 Binary files /dev/null and b/region/ST_region.shp differ diff --git a/region/ST_region.shx b/region/ST_region.shx new file mode 100644 index 0000000..83e5a7b Binary files /dev/null and b/region/ST_region.shx differ diff --git a/train/ST_training data_updated_1130points.dbf b/train/ST_training data_updated_1130points.dbf new file mode 100644 index 0000000..85bfa7c Binary files /dev/null and b/train/ST_training data_updated_1130points.dbf differ diff --git a/train/ST_training data_updated_1130points.prj b/train/ST_training data_updated_1130points.prj new file mode 100644 index 0000000..0202b8e --- /dev/null +++ b/train/ST_training data_updated_1130points.prj @@ -0,0 +1 @@ +PROJCS["WGS_1984_UTM_Zone_48N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["Meter",1]] \ No newline at end of file diff --git a/train/ST_training data_updated_1130points.qml b/train/ST_training data_updated_1130points.qml new file mode 100644 index 0000000..6bda571 --- /dev/null +++ b/train/ST_training data_updated_1130points.qml @@ -0,0 +1,835 @@ + + + + 1 + 1 + 1 + 0 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 0 + 0 + 1 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 0 + + + 0 + generatedlayout + + + + + + + + + + + + + + + + + + + + + + + + + + + "No" + + 0 + diff --git a/train/ST_training data_updated_1130points.qpj b/train/ST_training data_updated_1130points.qpj new file mode 100644 index 0000000..18131bc --- /dev/null +++ b/train/ST_training data_updated_1130points.qpj @@ -0,0 +1 @@ +PROJCS["WGS 84 / UTM zone 48N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",105],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32648"]] diff --git a/train/ST_training data_updated_1130points.shp b/train/ST_training data_updated_1130points.shp new file mode 100644 index 0000000..050307b Binary files /dev/null and b/train/ST_training data_updated_1130points.shp differ diff --git a/train/ST_training data_updated_1130points.shx b/train/ST_training data_updated_1130points.shx new file mode 100644 index 0000000..18f235d Binary files /dev/null and b/train/ST_training data_updated_1130points.shx differ diff --git a/utils.py b/utils.py new file mode 100644 index 0000000..57b36fc --- /dev/null +++ b/utils.py @@ -0,0 +1,6 @@ +import geopandas as gpd + + +def load_data_geo(path: str): + gdf = gpd.read_file(path) + return gdf \ No newline at end of file