From 0af52d7306351ef5647e21bfe7d8e98bbae07282 Mon Sep 17 00:00:00 2001 From: Tasha Snow Date: Thu, 10 Oct 2024 20:33:15 -0700 Subject: [PATCH] Updating ATL15 tutorial with icepyx updates (#90) --- .../IS2_ATL15_surface_height_anomalies.ipynb | 7005 +++++------------ 1 file changed, 1842 insertions(+), 5163 deletions(-) diff --git a/book/tutorials/IS2_ATL15_surface_height_anomalies/IS2_ATL15_surface_height_anomalies.ipynb b/book/tutorials/IS2_ATL15_surface_height_anomalies/IS2_ATL15_surface_height_anomalies.ipynb index 5df5a6c..77af657 100644 --- a/book/tutorials/IS2_ATL15_surface_height_anomalies/IS2_ATL15_surface_height_anomalies.ipynb +++ b/book/tutorials/IS2_ATL15_surface_height_anomalies/IS2_ATL15_surface_height_anomalies.ipynb @@ -39,38 +39,29 @@ "source": [ "We will set up our computing environment with library imports and utility functions\n", "\n", - "Tip: If you need to import a library that is not pre-installed, use `%pip install ` alone within a Jupyter notebook cell to install it for this instance of CryoCloud (the pip installation will not presist between logins. All of the libraries we intend to use are pre-installed so we can skip this step. " + "Tip: If you need to import a library that is not pre-installed, use `%pip install ` alone within a Jupyter notebook cell to install it for this instance of CryoCloud (the pip installation will not persist between logins). " ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "0e226454-c730-4656-ae88-19297a8a855a", "metadata": { "scrolled": true, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Note: you may need to restart the kernel to use updated packages.\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ - "# Pip install libraries that are not pre-installed\n", - "%pip install openpyxl --quiet # Needed for pandas read_excel\n", - "%pip install icepyx --quiet" + "# Needed for pandas read_excel\n", + "%pip install openpyxl" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "1a2db536-38fa-40b2-b01e-6f2f97881789", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [ @@ -83,11 +74,9 @@ " }\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 py_version = '3.3.4'.replace('rc', '-rc.').replace('.dev', '-dev.');\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", @@ -199,17 +188,17 @@ " 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", + " var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.8/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", + " var urls = ['https://cdn.holoviz.org/panel/1.3.8/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", + " var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", " for (var i = 0; i < urls.length; i++) {\n", " skip.push(urls[i])\n", " }\n", @@ -285,7 +274,7 @@ " 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_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.holoviz.org/panel/1.3.8/dist/panel.min.js\"];\n", " var js_modules = [];\n", " var js_exports = {};\n", " var css_urls = [];\n", @@ -298,7 +287,13 @@ " 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", + "\ttry {\n", + " inline_js[i].call(root, root.Bokeh);\n", + "\t} catch(e) {\n", + "\t if (!reloading) {\n", + "\t throw e;\n", + "\t }\n", + "\t}\n", " }\n", " // Cache old bokeh versions\n", " if (Bokeh != undefined && !reloading) {\n", @@ -335,11 +330,10 @@ " } 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", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", "\troot.Bokeh = undefined;\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", @@ -353,7 +347,7 @@ " 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 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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 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}\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\", 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{\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 = '3.3.4'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\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/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.8/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/1.3.8/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/1.3.8/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-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.holoviz.org/panel/1.3.8/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\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\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 root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\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" @@ -630,6 +624,84 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
\n", + "
\n", + "
\n", + "" + ] + }, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "p1002" + } + }, + "output_type": "display_data" + }, { "data": { "application/javascript": [ @@ -639,11 +711,9 @@ " }\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 py_version = '3.3.4'.replace('rc', '-rc.').replace('.dev', '-dev.');\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", @@ -755,17 +825,17 @@ " 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", + " var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.8/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", + " var urls = ['https://cdn.holoviz.org/panel/1.3.8/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", + " var urls = ['https://cdn.holoviz.org/panel/1.3.8/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", " for (var i = 0; i < urls.length; i++) {\n", " skip.push(urls[i])\n", " }\n", @@ -854,7 +924,13 @@ " 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", + "\ttry {\n", + " inline_js[i].call(root, root.Bokeh);\n", + "\t} catch(e) {\n", + "\t if (!reloading) {\n", + "\t throw e;\n", + "\t }\n", + "\t}\n", " }\n", " // Cache old bokeh versions\n", " if (Bokeh != undefined && !reloading) {\n", @@ -891,11 +967,10 @@ " } 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", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " if (!reloading && !bokeh_loaded) {\n", "\troot.Bokeh = undefined;\n", " }\n", " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", @@ -909,7 +984,7 @@ " 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));" + "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 = '3.3.4'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = true;\n var Bokeh = root.Bokeh;\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/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.8/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.8/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/1.3.8/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/1.3.8/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\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\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 root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\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" @@ -1192,8 +1267,6 @@ "\n", "# Import internal libraries\n", "import earthaccess\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", "import geopandas as gpd\n", "import h5py\n", "import hvplot.xarray\n", @@ -1280,7 +1353,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "7a5d4ad8-cef2-4d6e-bc88-2ee1d3673c8e", "metadata": { "tags": [] @@ -1375,7 +1448,7 @@ "4 Howat et al. (2015); Palmer et al. (2015) " ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1394,22 +1467,26 @@ { "cell_type": "markdown", "id": "0b979f8c-8bb5-446c-8511-283025ff945d", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ - "However, since this data has a direct download URL, so we can read the data directly into CryoCloud skipping the download and upload steps, like so: " + "However, this data has a direct download URL, so we can read the data directly into our notebook, skipping the download and upload steps: " ] }, { "cell_type": "markdown", "id": "fff870cc-3b5e-42df-8ce0-a3c256ad4ffd", - "metadata": {}, + "metadata": { + "user_expressions": [] + }, "source": [ "## Open data directly via URL" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "id": "047bbc78-a049-4055-8002-425d6881b70a", "metadata": { "tags": [] @@ -1504,7 +1581,7 @@ "4 Howat et al. (2015); Palmer et al. (2015) " ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -1530,7 +1607,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "0a7195c0-606b-4467-8b2d-1deb94030b04", "metadata": { "tags": [] @@ -1573,7 +1650,7 @@ " -48.000000\n", " NaN\n", " Ekholm et al. (1998)\n", - " POINT (-48 78)\n", + " POINT (-48.00000 78.00000)\n", " \n", " \n", " 1\n", @@ -1591,7 +1668,7 @@ " -68.440875\n", " Stable\n", " Palmer et al. (2013)\n", - " POINT (-68.44088 77.9695)\n", + " POINT (-68.44088 77.96950)\n", " \n", " \n", " 3\n", @@ -1600,7 +1677,7 @@ " -16.580000\n", " Active\n", " Willis et al. (2015)\n", - " POINT (-16.58 81.16)\n", + " POINT (-16.58000 81.16000)\n", " \n", " \n", " 4\n", @@ -1609,7 +1686,7 @@ " -48.709000\n", " Active\n", " Howat et al. (2015); Palmer et al. (2015)\n", - " POINT (-48.709 67.61114)\n", + " POINT (-48.70900 67.61114)\n", " \n", " \n", " ...\n", @@ -1645,7 +1722,7 @@ " -50.188000\n", " Active\n", " Livingstone et al. (2019)\n", - " POINT (-50.188 67.18)\n", + " POINT (-50.18800 67.18000)\n", " \n", " \n", " 62\n", @@ -1654,7 +1731,7 @@ " -50.149000\n", " Active\n", " Livingstone et al. (2019)\n", - " POINT (-50.149 67.178)\n", + " POINT (-50.14900 67.17800)\n", " \n", " \n", " 63\n", @@ -1663,7 +1740,7 @@ " -50.128000\n", " Active\n", " Livingstone et al. (2019)\n", - " POINT (-50.128 67.18)\n", + " POINT (-50.12800 67.18000)\n", " \n", " \n", "\n", @@ -1685,22 +1762,22 @@ "63 Isunguata Sermia 3 67.180000 -50.128000 Active \n", "\n", " References geometry \n", - "0 Ekholm et al. (1998) POINT (-48 78) \n", + "0 Ekholm et al. (1998) POINT (-48.00000 78.00000) \n", "1 Palmer et al. (2013) POINT (-68.39397 78.00502) \n", - "2 Palmer et al. (2013) POINT (-68.44088 77.9695) \n", - "3 Willis et al. (2015) POINT (-16.58 81.16) \n", - "4 Howat et al. (2015); Palmer et al. (2015) POINT (-48.709 67.61114) \n", + "2 Palmer et al. (2013) POINT (-68.44088 77.96950) \n", + "3 Willis et al. (2015) POINT (-16.58000 81.16000) \n", + "4 Howat et al. (2015); Palmer et al. (2015) POINT (-48.70900 67.61114) \n", ".. ... ... \n", "59 Bowling et al. (2019) POINT (-42.05516 69.10984) \n", "60 Bowling et al. (2019) POINT (-41.95437 69.10805) \n", - "61 Livingstone et al. (2019) POINT (-50.188 67.18) \n", - "62 Livingstone et al. (2019) POINT (-50.149 67.178) \n", - "63 Livingstone et al. (2019) POINT (-50.128 67.18) \n", + "61 Livingstone et al. (2019) POINT (-50.18800 67.18000) \n", + "62 Livingstone et al. (2019) POINT (-50.14900 67.17800) \n", + "63 Livingstone et al. (2019) POINT (-50.12800 67.18000) \n", "\n", "[64 rows x 6 columns]" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -1729,7 +1806,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "9d1751a6-f1fa-4e66-9bc4-c0fed6dcf6ba", "metadata": { "tags": [] @@ -1772,7 +1849,7 @@ " -16.580000\n", " Active\n", " Willis et al. (2015)\n", - " POINT (-16.58 81.16)\n", + " POINT (-16.58000 81.16000)\n", " \n", " \n", " 4\n", @@ -1781,7 +1858,7 @@ " -48.709000\n", " Active\n", " Howat et al. (2015); Palmer et al. (2015)\n", - " POINT (-48.709 67.61114)\n", + " POINT (-48.70900 67.61114)\n", " \n", " \n", " 5\n", @@ -1790,7 +1867,7 @@ " -48.450597\n", " Active\n", " Bowling et al. (2019)\n", - " POINT (-48.4506 63.54186)\n", + " POINT (-48.45060 63.54186)\n", " \n", " \n", " 6\n", @@ -1808,7 +1885,7 @@ " -50.188000\n", " Active\n", " Livingstone et al. (2019)\n", - " POINT (-50.188 67.18)\n", + " POINT (-50.18800 67.18000)\n", " \n", " \n", " 62\n", @@ -1817,7 +1894,7 @@ " -50.149000\n", " Active\n", " Livingstone et al. (2019)\n", - " POINT (-50.149 67.178)\n", + " POINT (-50.14900 67.17800)\n", " \n", " \n", " 63\n", @@ -1826,7 +1903,7 @@ " -50.128000\n", " Active\n", " Livingstone et al. (2019)\n", - " POINT (-50.128 67.18)\n", + " POINT (-50.12800 67.18000)\n", " \n", " \n", "\n", @@ -1843,16 +1920,16 @@ "63 Isunguata Sermia 3 67.180000 -50.128000 Active \n", "\n", " References geometry \n", - "3 Willis et al. (2015) POINT (-16.58 81.16) \n", - "4 Howat et al. (2015); Palmer et al. (2015) POINT (-48.709 67.61114) \n", - "5 Bowling et al. (2019) POINT (-48.4506 63.54186) \n", + "3 Willis et al. (2015) POINT (-16.58000 81.16000) \n", + "4 Howat et al. (2015); Palmer et al. (2015) POINT (-48.70900 67.61114) \n", + "5 Bowling et al. (2019) POINT (-48.45060 63.54186) \n", "6 Bowling et al. (2019) POINT (-48.20663 63.26025) \n", - "61 Livingstone et al. (2019) POINT (-50.188 67.18) \n", - "62 Livingstone et al. (2019) POINT (-50.149 67.178) \n", - "63 Livingstone et al. (2019) POINT (-50.128 67.18) " + "61 Livingstone et al. (2019) POINT (-50.18800 67.18000) \n", + "62 Livingstone et al. (2019) POINT (-50.14900 67.17800) \n", + "63 Livingstone et al. (2019) POINT (-50.12800 67.18000) " ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1970,7 +2047,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "24e94b90-a04d-4d8a-9a8b-fb499f9bbc53", "metadata": { "tags": [] @@ -2013,7 +2090,7 @@ " -16.580000\n", " Active\n", " Willis et al. (2015)\n", - " POLYGON ((-16.06723 81.1139, -16.09787 81.1062...\n", + " POLYGON ((-16.06723 81.11390, -16.09787 81.106...\n", " \n", " \n", " 4\n", @@ -2022,7 +2099,7 @@ " -48.709000\n", " Active\n", " Howat et al. (2015); Palmer et al. (2015)\n", - " POLYGON ((-48.4764 67.61446, -48.47613 67.6057...\n", + " POLYGON ((-48.47640 67.61446, -48.47613 67.605...\n", " \n", " \n", " 5\n", @@ -2093,8 +2170,8 @@ "63 Livingstone et al. (2019) \n", "\n", " geometry \n", - "3 POLYGON ((-16.06723 81.1139, -16.09787 81.1062... \n", - "4 POLYGON ((-48.4764 67.61446, -48.47613 67.6057... \n", + "3 POLYGON ((-16.06723 81.11390, -16.09787 81.106... \n", + "4 POLYGON ((-48.47640 67.61446, -48.47613 67.605... \n", "5 POLYGON ((-48.25472 63.54482, -48.25457 63.536... \n", "6 POLYGON ((-48.01287 63.26285, -48.01281 63.254... \n", "61 POLYGON ((-49.96016 67.18562, -49.95932 67.176... \n", @@ -2102,7 +2179,7 @@ "63 POLYGON ((-49.90015 67.18552, -49.89933 67.176... " ] }, - "execution_count": 8, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -2200,7 +2277,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "id": "3f3f1628-0fc1-407c-a8e5-a85cb8735345", "metadata": { "tags": [] @@ -2214,7 +2291,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "id": "3e15602f-3fc1-40de-9519-a75ae5b3b4b1", "metadata": { "tags": [] @@ -2228,7 +2305,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "id": "1a352b60-b203-47d6-9785-2cfdbcfdeb6b", "metadata": { "tags": [] @@ -2251,4258 +2328,858 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "e852347c-fafd-4db0-b81e-e70e3e98ed98", "metadata": { "tags": [] }, + "outputs": [], + "source": [ + "# Visualize area of interest\n", + "region.visualize_spatial_extent()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b68bdc52-4de8-44fa-8f8e-fcdd89da01bf", + "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 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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) 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" - ], - "text/plain": [ - " Size: 2GB\n", - "Dimensions: (x: 1541, y: 2741, time: 21)\n", - "Coordinates:\n", - " * x (x) float64 12kB -6.7e+05 -6.69e+05 ... 8.7e+05\n", - " * y (y) float64 22kB -3.35e+06 -3.349e+06 ... -6.1e+05\n", - " * time (time) datetime64[ns] 168B 2019-01-01T06:00:00 ... 2...\n", - "Data variables:\n", - " Polar_Stereographic int8 1B -127\n", - " ice_area (time, y, x) float32 355MB nan nan nan ... nan nan nan\n", - " delta_h (time, y, x) float32 355MB nan nan nan ... nan nan nan\n", - " delta_h_sigma (time, y, x) float32 355MB nan nan nan ... nan nan nan\n", - " data_count (time, y, x) float32 355MB nan nan nan ... nan nan nan\n", - " misfit_rms (time, y, x) float32 355MB nan nan nan ... nan nan nan\n", - " misfit_scaled_rms (time, y, x) float32 355MB nan nan nan ... nan nan nan\n", - "Attributes:\n", - " description: delta_h group includes variables describing height differen..." - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# (1) Open s3url data file and store in Xarray Dataset\n", - "# This cell takes 10s of secs to load\n", - "with s3.open(s3url,'rb') as f:\n", - " ATL15_dh = xr.open_dataset(f, group='delta_h').load()\n", - "\n", - "# View Xarray Dataset\n", - "ATL15_dh" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "f4e72f9a-c6bc-448c-891a-3b4870c0ecea", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# (2) create a Read object; you'll be asked to authenticate at this step if you haven't already\n", - "reader = ipx.Read(s3url)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "cc752711-13aa-4d0a-b15d-b21a79894b21", - "metadata": { - "scrolled": true, - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['delta_h/Polar_Stereographic',\n", - " 'delta_h/time',\n", - " 'delta_h/x',\n", - " 'delta_h/y',\n", - " 'dhdt_lag1/dhdt/Bands',\n", - " 'dhdt_lag1/dhdt_sigma/Bands',\n", - " 'dhdt_lag1/ice_area/Bands',\n", - " 'dhdt_lag1/Polar_Stereographic',\n", - " 'dhdt_lag1/time',\n", - " 'dhdt_lag1/x',\n", - " 'dhdt_lag1/y',\n", - " 'dhdt_lag12/dhdt/Bands',\n", - " 'dhdt_lag12/dhdt_sigma/Bands',\n", - " 'dhdt_lag12/ice_area/Bands',\n", - " 'dhdt_lag12/Polar_Stereographic',\n", - " 'dhdt_lag12/time',\n", - " 'dhdt_lag12/x',\n", - " 'dhdt_lag12/y',\n", - " 'dhdt_lag16/dhdt/Bands',\n", - " 'dhdt_lag16/dhdt_sigma/Bands',\n", - " 'dhdt_lag16/ice_area/Bands',\n", - " 'dhdt_lag16/Polar_Stereographic',\n", - " 'dhdt_lag16/time',\n", - " 'dhdt_lag16/x',\n", - " 'dhdt_lag16/y',\n", - " 'dhdt_lag20/dhdt/Bands',\n", - " 'dhdt_lag20/dhdt_sigma/Bands',\n", - " 'dhdt_lag20/ice_area/Bands',\n", - " 'dhdt_lag20/Polar_Stereographic',\n", - " 'dhdt_lag20/time',\n", - " 'dhdt_lag20/x',\n", - " 'dhdt_lag20/y',\n", - " 'dhdt_lag4/dhdt/Bands',\n", - " 'dhdt_lag4/dhdt_sigma/Bands',\n", - " 'dhdt_lag4/ice_area/Bands',\n", - " 'dhdt_lag4/Polar_Stereographic',\n", - " 'dhdt_lag4/time',\n", - " 'dhdt_lag4/x',\n", - " 'dhdt_lag4/y',\n", - " 'dhdt_lag8/dhdt/Bands',\n", - " 'dhdt_lag8/dhdt_sigma/Bands',\n", - " 'dhdt_lag8/ice_area/Bands',\n", - " 'dhdt_lag8/Polar_Stereographic',\n", - " 'dhdt_lag8/time',\n", - " 'dhdt_lag8/x',\n", - " 'dhdt_lag8/y',\n", - " 'orbit_info/bounding_polygon_dim1',\n", - " 'orbit_info/bounding_polygon_lat1',\n", - " 'orbit_info/bounding_polygon_lon1',\n", - " 'quality_assessment/phony_dim_1',\n", - " 'quality_assessment/qa_granule_fail_reason',\n", - " 'quality_assessment/qa_granule_pass_fail',\n", - " 'tile_stats/N_bias',\n", - " 'tile_stats/N_data',\n", - " 'tile_stats/Polar_Stereographic',\n", - " 'tile_stats/RMS_bias',\n", - " 'tile_stats/RMS_d2z0dx2',\n", - " 'tile_stats/RMS_d2zdt2',\n", - " 'tile_stats/RMS_d2zdx2dt',\n", - " 'tile_stats/RMS_data',\n", - " 'tile_stats/sigma_tt',\n", - " 'tile_stats/sigma_xx0',\n", - " 'tile_stats/sigma_xxt',\n", - " 'tile_stats/x',\n", - " 'tile_stats/y']" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# (2) see what variables are available\n", - "reader.vars.avail()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "52e93f16-b98e-4690-b273-aeeadb5bfdb4", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'Polar_Stereographic': ['delta_h/Polar_Stereographic'],\n", - " 'time': ['delta_h/time'],\n", - " 'x': ['delta_h/x'],\n", - " 'y': ['delta_h/y']}" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# (2) Indicate which variables you'd like to read in.\n", - "# More information on managing ICESat-2 variables is available in the icepyx documentation and examples.\n", - "reader.vars.append(keyword_list=[\"delta_h\"])\n", - "# view the variables that will be loaded into memory in your DataSet\n", - "reader.vars.wanted" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "5475a92f-4b55-4307-acca-1c16f76144c5", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdin", - "output_type": "stream", - "text": [ - "Do you wish to proceed (not recommended) y/[n]? y\n" - ] - } - ], - "source": [ - "# (2) load your data into memory\n", - "# if you are asked if you want to proceed, enter 'y' and press return/enter\n", - "# Depending on your hub settings, the warning letting you know this operation will take a moment may or may not show up\n", - "ATL15_dh = reader.load()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e589974d-78ec-4747-a4de-223d17252dfe", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset> Size: 2GB\n",
-       "Dimensions:              (x: 1541, y: 2741, time: 21)\n",
-       "Coordinates:\n",
-       "  * x                    (x) float64 12kB -6.7e+05 -6.69e+05 ... 8.7e+05\n",
-       "  * y                    (y) float64 22kB -3.35e+06 -3.349e+06 ... -6.1e+05\n",
-       "  * time                 (time) datetime64[ns] 168B 2019-01-01T06:00:00 ... 2...\n",
-       "Data variables:\n",
-       "    Polar_Stereographic  int8 1B ...\n",
-       "    ice_area             (time, y, x) float32 355MB ...\n",
-       "    delta_h              (time, y, x) float32 355MB ...\n",
-       "    delta_h_sigma        (time, y, x) float32 355MB ...\n",
-       "    data_count           (time, y, x) float32 355MB ...\n",
-       "    misfit_rms           (time, y, x) float32 355MB ...\n",
-       "    misfit_scaled_rms    (time, y, x) float32 355MB ...\n",
-       "Attributes:\n",
-       "    description:  delta_h group includes variables describing height differen...
" - ], - "text/plain": [ - " Size: 2GB\n", - "Dimensions: (x: 1541, y: 2741, time: 21)\n", - "Coordinates:\n", - " * x (x) float64 12kB -6.7e+05 -6.69e+05 ... 8.7e+05\n", - " * y (y) float64 22kB -3.35e+06 -3.349e+06 ... -6.1e+05\n", - " * time (time) datetime64[ns] 168B 2019-01-01T06:00:00 ... 2...\n", - "Data variables:\n", - " Polar_Stereographic int8 1B ...\n", - " ice_area (time, y, x) float32 355MB ...\n", - " delta_h (time, y, x) float32 355MB ...\n", - " delta_h_sigma (time, y, x) float32 355MB ...\n", - " data_count (time, y, x) float32 355MB ...\n", - " misfit_rms (time, y, x) float32 355MB ...\n", - " misfit_scaled_rms (time, y, x) float32 355MB ...\n", - "Attributes:\n", - " description: delta_h group includes variables describing height differen..." - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ATL15_dh" - ] - }, - { - "cell_type": "markdown", - "id": "3b0e5f8a-a7fb-4f76-b70d-cd9c55c94f08", - "metadata": {}, - "source": [ - "We can acquaint ourselves with this dataset in a few ways: \n", - "- The data product's [overview page](https://doi.org/10.5067/ATLAS/ATL15.002) (Smith and others, 2022) to get the very basics such as geographic coverage, CRS, and what the data product tells us (quarterly height changes).\n", - "- The Xarray Dataset read-in metadata: clicking on the written document icon of each data variable will expand metadata including a data variable's dimensions, datatype, etc. \n", - "- The data product's [data dictionary](https://nsidc.org/sites/default/files/documents/technical-reference/icesat2_atl15_data_dict_v002.pdf) (Smith and others, 2021) to do a deep dive on what individual variables tell us. \n", - "- The data product's [Algorithm Theoretical Basis Document](https://icesat-2.gsfc.nasa.gov/sites/default/files/page_files/ICESat2_ATL14_ATL15_ATBD_r001.pdf)\n", - "\n", - "We'll be plotting the delta_h data variable in this tutorial, here's what we can learn about from these sources:\n", - "- [ATL14/15's overview page](https://doi.org/10.5067/ATLAS/ATL15.002): this is likely the 'quarterly height changes' described, but let's dive deeper to be sure\n", - "- ATL14/15's Xarray Dataset imbedded metadata tells us a couple things: delta_h =height change at 1 km (the resolution selected earlier) and height change relative to the datum (Jan 1, 2020) surface\n", - "- [ATL14/15's data dictionary](https://nsidc.org/sites/default/files/documents/technical-reference/icesat2_atl15_data_dict_v002.pdf): delta_h = quarterly height change at 40 km\n", - "\n", - "Ok, since the data is relative to a datum, we have two options: \n", - "1) Difference individual time slices to subtract out the datum, like so: \n", - "\n", - " (time$_0$ - datum) - (time$_1$ - datum) = time$_0$ - datum - time$_1$ + datum = time$_0$ - time$_1$\n", - "\n", - "2) Subtract out the datum directly. The datum is the complementary dataset high-resolution DEM surface contained in tha accompanying dataset ATL14.\n", - "\n", - "In this tutorial we'll use the first method. We'll use some explanatory data analysis to illustrate this. " - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "8e79268f-cfe8-498f-a695-b98bf0177c1c", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6a20165c02744810b7ca5ee4ecb96ebd", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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Why? Check out the bounds of the colorbar, we've got some pretty extreme values (colorbar is defaulting to ±10 m!) that appear to be along the margin. It's making more sense now. We can change the bounds of the colorbar to plot see more of the smaller scale change in the continental interior." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "1e2b2eae-00fc-4a4b-8343-28b38688433a", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-5.8791504\n", - "6.1810913\n" - ] - } - ], - "source": [ - "# Let's calculate some basic stats to determine appropriate coloarbar bounds\n", - "print(dhdt.min().values)\n", - "print(dhdt.max().values)" - ] - }, - { - "cell_type": "markdown", - "id": "f5eb944c-b268-44cb-a4f3-690e8a7ae8ef", - "metadata": {}, - "source": [ - "We can use a TwoSlopeNorm to achieve different mapping for positive and negative values while still keeping the center at zero:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "faf7223f-02fa-4570-9704-9c631b3b996b", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - 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", 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<xarray.DataArray 'delta_h' (quantile: 2)> Size: 16B\n",
-       "array([-0.28271484,  2.78289673])\n",
-       "Coordinates:\n",
-       "  * quantile  (quantile) float64 16B 0.01 0.99
" - ], "text/plain": [ - " Size: 16B\n", - "array([-0.28271484, 2.78289673])\n", - "Coordinates:\n", - " * quantile (quantile) float64 16B 0.01 0.99" + "{'Number of available granules': 4,\n", + " 'Average size of granules (MB)': 226.04341173171997,\n", + " 'Total size of all granules (MB)': 904.1736469268799}" ] }, - "execution_count": 31, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Let's use the Xarray DataArray quantile method to find the 1% and 99% quantiles (Q1 and Q3) of the data\n", - "dhdt.quantile([0.01,0.99])" + "# Let's find out some information about the available data granuales (files)\n", + "region.avail_granules()" ] }, { - "cell_type": "markdown", - "id": "f0ff22ef-89ee-4f85-9b14-e46dfe528978", - "metadata": {}, + "cell_type": "code", + "execution_count": 9, + "id": "87dc30f0-dc2e-4dc4-a13b-305944246691", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[['ATL15_GL_0321_40km_004_01.nc',\n", + " 'ATL15_GL_0321_20km_004_01.nc',\n", + " 'ATL15_GL_0321_10km_004_01.nc',\n", + " 'ATL15_GL_0321_01km_004_01.nc'],\n", + " ['s3://nsidc-cumulus-prod-protected/ATLAS/ATL15/004/ATL15_GL_0321_40km_004_01.nc',\n", + " 's3://nsidc-cumulus-prod-protected/ATLAS/ATL15/004/ATL15_GL_0321_20km_004_01.nc',\n", + " 's3://nsidc-cumulus-prod-protected/ATLAS/ATL15/004/ATL15_GL_0321_10km_004_01.nc',\n", + " 's3://nsidc-cumulus-prod-protected/ATLAS/ATL15/004/ATL15_GL_0321_01km_004_01.nc']]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "We can use these quantiles as the colorbar bounds so that we see the data variability by plotting the most extreme values at the maxed out value of the colorbar. We'll adjust the caps of the colorbar (using `extend`) to express that there are data values beyond the bounds. " + "# Let's see the granule IDs and cloud access urls\n", + "gran_ids = region.avail_granules(ids=True, cloud=True)\n", + "gran_ids" ] }, { "cell_type": "code", - "execution_count": 42, - "id": "8320be7d-73dd-45ec-8a7f-7efdb9b629d3", + "execution_count": 10, + "id": "bc3e741b-9f35-4598-accf-827e4eb6b447", "metadata": { "tags": [] }, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fca96d0b9d224d49b2942d4fd21669ad", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", - "text/html": [ - "\n", - "
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\n", - " " - ], "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + "'s3://nsidc-cumulus-prod-protected/ATLAS/ATL15/004/ATL15_GL_0321_01km_004_01.nc'" ] }, + "execution_count": 10, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" + } + ], + "source": [ + "# Let's grab the s3 URL of the highest resolution available product\n", + "s3url = gran_ids[1][3]\n", + "s3url" + ] + }, + { + "cell_type": "markdown", + "id": "3bd249ae-dbe8-49c9-90e4-b291613659bf", + "metadata": {}, + "source": [ + "You can manually find s3 URL's for cloud-hosted data from [NASA Earth Data](https://www.earthdata.nasa.gov/)\n", + "\n", + "Learn more about finding cloud-hosted data from NASA Earth data cloud [here](https://nsidc.org/data/user-resources/help-center/nasa-earthdata-cloud-data-access-guide)" + ] + }, + { + "cell_type": "markdown", + "id": "3ba938e7-0ac0-4c6f-99f1-854a57ac2779", + "metadata": {}, + "source": [ + "The next step (accessing data in the cloud) requires a NASA Earthdata user account.\n", + "You can register for a free account [here](https://www.earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/earthdata-login).\n", + "We provide two options for reading in your data: (1) by setting up an s3 file system and using Xarray directly; or (2) by using icepyx (which uses Xarray under the hood).\n", + "Currently, the read time is similar with both methods.\n", + "The h5coro library will soon be available to help speed up this process.\n", + "\n", + "The file system method requires you complete a login step.\n", + "icepyx will automatically ask for your credentials when you perform a task that needs them.\n", + "If you do not have them stored as environment variables or in a .netrc file, you will be prompted to enter them." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b2af9f40-c9a8-48ed-9f81-c93d52d960e6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your Earthdata Login username: icepyx_devteam\n", + "Enter your Earthdata password: ········\n" + ] } ], "source": [ - "# Let's make the same plot but using the quantiles as the colorbar bounds\n", - "fig, ax = plt.subplots(figsize=(4,5), tight_layout=True)\n", - "dhdt = ATL15_dh['delta_h'][1,:,:] - ATL15_dh['delta_h'][0,:,:]\n", - "divnorm = colors.TwoSlopeNorm(vmin=dhdt.quantile(0.01), vcenter=0, vmax=dhdt.quantile(0.99))\n", - "cb = ax.imshow(dhdt, origin='lower', norm=divnorm, cmap='coolwarm_r', \n", - " aspect='auto',\n", - " extent = [greenland_extent[0], # minx (west)\n", - " greenland_extent[1], # maxx (east)\n", - " greenland_extent[2], # miny (south)\n", - " greenland_extent[3]] # maxy (north)\n", - " )\n", - "ax.set_xlabel('longitude'); ax.set_ylabel('latitude')\n", - "plt.colorbar(cb, fraction=0.02, extend='both', label='height change [m]', ticks=[-0.25,0,1,2])\n", - "plt.tight_layout()\n", - "plt.show()" + "# (1) authenticate\n", + "auth = earthaccess.login()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e16e81f6-9092-4523-8565-34a75d8631f1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# (1) set up our s3 file system using our credentials\n", + "s3 = earthaccess.get_s3fs_session(daac='NSIDC')" ] }, { "cell_type": "code", - "execution_count": 33, - "id": "b53dab15-820d-45d9-bf17-72a8702af057", + "execution_count": null, + "id": "7377ae88-8552-4ee8-8595-d15b786b003c", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# Let's make the same plot but for all the available time slices and let's turn it in a function so that we can reuse this code for a smaller subset of data\n", - "# create empty lists to store data\n", - "def plot_icesat2_atl15(xmin, xmax, ymin, ymax, dataset):\n", - " # subset data using bounding box in epsg:3134 x,y\n", - " mask_x = (dataset.x >= xmin) & (dataset.x <= xmax)\n", - " mask_y = (dataset.y >= ymin) & (dataset.y <= ymax)\n", - " ds_sub = dataset.where(mask_x & mask_y, drop=True)\n", - " \n", - " # Create empty lists to store data\n", - " vmins_maxs = []\n", - "\n", - " # Find the min's and max's of each inter time slice comparison and store into lists\n", - " for idx in range(len(ds_sub['time'].values)-1): \n", - " dhdt = ds_sub['delta_h'][idx+1,:,:] - ds_sub['delta_h'][idx,:,:]\n", - " vmin=dhdt.quantile(0.01)\n", - " vmins_maxs += [vmin]\n", - " vmax=dhdt.quantile(0.99)\n", - " vmins_maxs += [vmax]\n", - " if (min(vmins_maxs)<0) & (max(vmins_maxs)>0):\n", - " vcenter = 0\n", - " else: \n", - " vcenter = max(vmins_maxs) - min(vmins_maxs)\n", - " divnorm = colors.TwoSlopeNorm(vmin=min(vmins_maxs), vcenter=vcenter, vmax=max(vmins_maxs))\n", - "\n", - " # create fig, ax\n", - " fig, axs = plt.subplots(7,2, sharex=True, sharey=True, figsize=(10,10))\n", + "# (1) Open s3url data file and store in Xarray Dataset\n", + "# This cell takes 10s of secs to load\n", + "with s3.open(s3url,'rb') as f:\n", + " ATL15_dh = xr.open_dataset(f, group='delta_h').load()\n", "\n", - " idx = 0\n", - " for ax in axs.ravel(): \n", - " ax.set_aspect('equal')\n", - " dhdt = ds_sub['delta_h'][idx+1,:,:] - ds_sub['delta_h'][idx,:,:]\n", - " cb = ax.imshow(dhdt, origin='lower', norm=divnorm, cmap='coolwarm_r', \n", - " extent=[xmin[0], xmax[0], ymin[0], ymax[0]])\n", - " # Change polar stereographic m to km\n", - " km_scale = 1e3\n", - " ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/km_scale))\n", - " ax.xaxis.set_major_formatter(ticks_x)\n", - " ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/km_scale))\n", - " ax.yaxis.set_major_formatter(ticks_y)\n", - " # Create common axes labels\n", - " fig.supxlabel('easting (km)'); fig.supylabel('northing (km)')\n", - " # Increment the idx\n", - " idx = idx + 1\n", - " \n", - " fig.colorbar(cb, extend='both', ax=axs.ravel().tolist(), label='height change [m]')\n", - " plt.show()" + "# View Xarray Dataset\n", + "ATL15_dh" ] }, { - "cell_type": "markdown", - "id": "277c6fa1-5013-46d8-80a9-1ca545e17611", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "id": "f4e72f9a-c6bc-448c-891a-3b4870c0ecea", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "Let's zoom into an individual active lake to see more detail. First let's remind ourselves of the Greenland active subglacial lakes by filtering on lake type:" + "# (2) create a Read object; you'll be asked to authenticate at this step if you haven't already\n", + "reader = ipx.Read(s3url)" ] }, { "cell_type": "code", - "execution_count": 34, - "id": "7eb6cefc-7794-45e3-a5d6-3900d7204d85", + "execution_count": 12, + "id": "cc752711-13aa-4d0a-b15d-b21a79894b21", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [ { "data": { - "text/html": [ - "
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Name / LocationLat. oNLon. oELake TypeReferencesgeometry
3Flade Isblink ice cap81.160000-16.580000ActiveWillis et al. (2015)POLYGON ((-16.06723 81.1139, -16.09787 81.1062...
4Inuppaat Quuat67.611136-48.709000ActiveHowat et al. (2015); Palmer et al. (2015)POLYGON ((-48.4764 67.61446, -48.47613 67.6057...
5Sioqqap Sermia, [SS1]63.541856-48.450597ActiveBowling et al. (2019)POLYGON ((-48.25472 63.54482, -48.25457 63.536...
6Sioqqap Sermia, [SS2]63.260248-48.206633ActiveBowling et al. (2019)POLYGON ((-48.01287 63.26285, -48.01281 63.254...
61Isunguata Sermia 167.180000-50.188000ActiveLivingstone et al. (2019)POLYGON ((-49.96016 67.18562, -49.95932 67.176...
62Isunguata Sermia 267.178000-50.149000ActiveLivingstone et al. (2019)POLYGON ((-49.92117 67.18356, -49.92035 67.174...
63Isunguata Sermia 367.180000-50.128000ActiveLivingstone et al. (2019)POLYGON ((-49.90015 67.18552, -49.89933 67.176...
\n", - "
" - ], "text/plain": [ - " Name / Location Lat. oN Lon. oE Lake Type \\\n", - "3 Flade Isblink ice cap 81.160000 -16.580000 Active \n", - "4 Inuppaat Quuat 67.611136 -48.709000 Active \n", - "5 Sioqqap Sermia, [SS1] 63.541856 -48.450597 Active \n", - "6 Sioqqap Sermia, [SS2] 63.260248 -48.206633 Active \n", - "61 Isunguata Sermia 1 67.180000 -50.188000 Active \n", - "62 Isunguata Sermia 2 67.178000 -50.149000 Active \n", - "63 Isunguata Sermia 3 67.180000 -50.128000 Active \n", - "\n", - " References \\\n", - "3 Willis et al. (2015) \n", - "4 Howat et al. (2015); Palmer et al. (2015) \n", - "5 Bowling et al. (2019) \n", - "6 Bowling et al. (2019) \n", - "61 Livingstone et al. (2019) \n", - "62 Livingstone et al. (2019) \n", - "63 Livingstone et al. (2019) \n", - "\n", - " geometry \n", - "3 POLYGON ((-16.06723 81.1139, -16.09787 81.1062... \n", - "4 POLYGON ((-48.4764 67.61446, -48.47613 67.6057... \n", - "5 POLYGON ((-48.25472 63.54482, -48.25457 63.536... \n", - "6 POLYGON ((-48.01287 63.26285, -48.01281 63.254... \n", - "61 POLYGON ((-49.96016 67.18562, -49.95932 67.176... \n", - "62 POLYGON ((-49.92117 67.18356, -49.92035 67.174... \n", - "63 POLYGON ((-49.90015 67.18552, -49.89933 67.176... " + "['delta_h/Polar_Stereographic',\n", + " 'delta_h/time',\n", + " 'delta_h/x',\n", + " 'delta_h/y',\n", + " 'dhdt_lag1/dhdt/Bands',\n", + " 'dhdt_lag1/dhdt_sigma/Bands',\n", + " 'dhdt_lag1/ice_area/Bands',\n", + " 'dhdt_lag1/Polar_Stereographic',\n", + " 'dhdt_lag1/time',\n", + " 'dhdt_lag1/x',\n", + " 'dhdt_lag1/y',\n", + " 'dhdt_lag12/dhdt/Bands',\n", + " 'dhdt_lag12/dhdt_sigma/Bands',\n", + " 'dhdt_lag12/ice_area/Bands',\n", + " 'dhdt_lag12/Polar_Stereographic',\n", + " 'dhdt_lag12/time',\n", + " 'dhdt_lag12/x',\n", + " 'dhdt_lag12/y',\n", + " 'dhdt_lag16/dhdt/Bands',\n", + " 'dhdt_lag16/dhdt_sigma/Bands',\n", + " 'dhdt_lag16/ice_area/Bands',\n", + " 'dhdt_lag16/Polar_Stereographic',\n", + " 'dhdt_lag16/time',\n", + " 'dhdt_lag16/x',\n", + " 'dhdt_lag16/y',\n", + " 'dhdt_lag20/dhdt/Bands',\n", + " 'dhdt_lag20/dhdt_sigma/Bands',\n", + " 'dhdt_lag20/ice_area/Bands',\n", + " 'dhdt_lag20/Polar_Stereographic',\n", + " 'dhdt_lag20/time',\n", + " 'dhdt_lag20/x',\n", + " 'dhdt_lag20/y',\n", + " 'dhdt_lag4/dhdt/Bands',\n", + " 'dhdt_lag4/dhdt_sigma/Bands',\n", + " 'dhdt_lag4/ice_area/Bands',\n", + " 'dhdt_lag4/Polar_Stereographic',\n", + " 'dhdt_lag4/time',\n", + " 'dhdt_lag4/x',\n", + " 'dhdt_lag4/y',\n", + " 'dhdt_lag8/dhdt/Bands',\n", + " 'dhdt_lag8/dhdt_sigma/Bands',\n", + " 'dhdt_lag8/ice_area/Bands',\n", + " 'dhdt_lag8/Polar_Stereographic',\n", + " 'dhdt_lag8/time',\n", + " 'dhdt_lag8/x',\n", + " 'dhdt_lag8/y',\n", + " 'orbit_info/bounding_polygon_dim1',\n", + " 'orbit_info/bounding_polygon_lat1',\n", + " 'orbit_info/bounding_polygon_lon1',\n", + " 'quality_assessment/phony_dim_1',\n", + " 'quality_assessment/qa_granule_fail_reason',\n", + " 'quality_assessment/qa_granule_pass_fail',\n", + " 'tile_stats/N_bias',\n", + " 'tile_stats/N_data',\n", + " 'tile_stats/Polar_Stereographic',\n", + " 'tile_stats/RMS_bias',\n", + " 'tile_stats/RMS_d2z0dx2',\n", + " 'tile_stats/RMS_d2zdt2',\n", + " 'tile_stats/RMS_d2zdx2dt',\n", + " 'tile_stats/RMS_data',\n", + " 'tile_stats/sigma_tt',\n", + " 'tile_stats/sigma_xx0',\n", + " 'tile_stats/sigma_xxt',\n", + " 'tile_stats/x',\n", + " 'tile_stats/y']" ] }, - "execution_count": 34, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "gdf_polys_active = gdf_polys[gdf_polys['Lake Type'] == 'Active']\n", - "gdf_polys_active" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "eb54bca1-def0-46cd-b2c0-d08a1e79611d", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# We can call the bounds of the geometry of the Shapely Polygon we created earlier\n", - "gdf_sub = gdf_polys_active[gdf_polys_active['Name / Location'] == 'Inuppaat Quuat']" - ] - }, - { - "cell_type": "markdown", - "id": "64cb4599-4193-4c0e-8a9e-e96a01fcdb01", - "metadata": {}, - "source": [ - "Now using these geometry bounds we can plot the area immediately around the active subglacial lake. " + "# (2) see what variables are available\n", + "reader.vars.avail()" ] }, { "cell_type": "code", - "execution_count": 41, - "id": "5e949e39-f84f-42ff-8e7f-7b45270d9a39", + "execution_count": 13, + "id": "52e93f16-b98e-4690-b273-aeeadb5bfdb4", "metadata": { "tags": [] }, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4b1a5302b72d4aca8d02a263055e6e90", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " " - ], "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + "{'Polar_Stereographic': ['delta_h/Polar_Stereographic'],\n", + " 'time': ['delta_h/time'],\n", + " 'x': ['delta_h/x'],\n", + " 'y': ['delta_h/y']}" ] }, + "execution_count": 13, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "# If the figure doesn't pop up, it is because the holoviews plot was run immediately after and interferred with \n", - "# matplotlib. Just rerun this cell and it will be fixed\n", - "%matplotlib widget\n", - "\n", - "# Assigning the min, max lon, lat coords of the lower-left and upper-right corners of our bounding box \n", - "# around the lake's buffer polygon\n", - "lon_min = gdf_sub.geometry.bounds.minx\n", - "lon_max = gdf_sub.geometry.bounds.maxx\n", - "lat_min = gdf_sub.geometry.bounds.miny\n", - "lat_max = gdf_sub.geometry.bounds.maxy\n", - "\n", - "# Now we re-project the lon, lat into x, y coords in the 3413 CRS, which ATL15 uses\n", - "xmin, ymin = ll2ps(lon_min, lat_min)\n", - "xmax, ymax = ll2ps(lon_max, lat_max)\n", - "\n", - "# Use our plotting fuction to plot up the data based on min, max x, y bounds\n", - "plot_icesat2_atl15(xmin, xmax, ymin, ymax, ATL15_dh)" + "# (2) Indicate which variables you'd like to read in.\n", + "# More information on managing ICESat-2 variables is available in the icepyx documentation and examples.\n", + "reader.vars.append(keyword_list=[\"delta_h\"])\n", + "# view the variables that will be loaded into memory in your DataSet\n", + "reader.vars.wanted" ] }, { "cell_type": "code", - "execution_count": 37, - "id": "f6a59c54-792e-4a6b-8cbb-590d555405a3", + "execution_count": null, + "id": "5475a92f-4b55-4307-acca-1c16f76144c5", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# (2) load your data into memory\n", + "# if you are asked if you want to proceed, enter 'y' and press return/enter\n", + "# Depending on your hub settings, the warning letting you know this operation will take a moment may or may not show up\n", + "ATL15_dh = reader.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e589974d-78ec-4747-a4de-223d17252dfe", "metadata": { "tags": [] }, "outputs": [ { "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", + "text/html": [ + "
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+       "Dimensions:              (x: 1541, y: 2741, time: 21)\n",
+       "Coordinates:\n",
+       "  * x                    (x) float64 -6.7e+05 -6.69e+05 ... 8.69e+05 8.7e+05\n",
+       "  * y                    (y) float64 -3.35e+06 -3.349e+06 ... -6.11e+05 -6.1e+05\n",
+       "  * time                 (time) datetime64[ns] 2019-01-01T06:00:00 ... 2024-0...\n",
+       "Data variables:\n",
+       "    Polar_Stereographic  int8 ...\n",
+       "    ice_area             (time, y, x) float32 ...\n",
+       "    delta_h              (time, y, x) float32 ...\n",
+       "    delta_h_sigma        (time, y, x) float32 ...\n",
+       "    data_count           (time, y, x) float32 ...\n",
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" ], "text/plain": [ - "Column\n", - " [0] HoloViews(DynamicMap, widget_location='bottom', widget_type='scrubber')\n", - " [1] WidgetBox(align=('center', 'end'))\n", - " [0] Player(end=20, width=550)" + "\n", + "Dimensions: (x: 1541, y: 2741, time: 21)\n", + "Coordinates:\n", + " * x (x) float64 -6.7e+05 -6.69e+05 ... 8.69e+05 8.7e+05\n", + " * y (y) float64 -3.35e+06 -3.349e+06 ... -6.11e+05 -6.1e+05\n", + " * time (time) datetime64[ns] 2019-01-01T06:00:00 ... 2024-0...\n", + "Data variables:\n", + " Polar_Stereographic int8 ...\n", + " ice_area (time, y, x) float32 ...\n", + " delta_h (time, y, x) float32 ...\n", + " delta_h_sigma (time, y, x) float32 ...\n", + " data_count (time, y, x) float32 ...\n", + " misfit_rms (time, y, x) float32 ...\n", + " misfit_scaled_rms (time, y, x) float32 ...\n", + "Attributes:\n", + " description: delta_h group includes variables describing height differen..." ] }, - "execution_count": 37, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "p1084" - } - }, + "execution_count": 15, + "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Let's explore plotting the data interactively using Xarray and Holoviews\n", - "hvplot.extension('matplotlib')\n", - "divnorm = colors.TwoSlopeNorm(vmin=-0.25, vcenter=0, vmax=1.25)\n", - "ATL15_dh['delta_h'].hvplot(groupby='time', cmap='coolwarm_r', norm=divnorm, invert=True, \n", - " width=(ATL15_dh['x'].max()-ATL15_dh['x'].min())/3e3, height=(ATL15_dh['y'].max()-ATL15_dh['y'].min())/3e3, \n", - " widget_type='scrubber', widget_location='bottom')" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "64f4a56e-23dd-492d-b433-14412e22590a", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# clean up environment by deleting intermediary files\n", - "os.remove('Flade_Isblink_poly.gpkg')" - ] - }, - { - "cell_type": "markdown", - "id": "373dba30-f76d-4eea-8a04-8105531fab0a", - "metadata": {}, - "source": [ - "## Streaming cloud-hosted data via earthaccess\n", - "We will next use earthaccess to authenicate for NASA EarthData, and search and stream cloud-hosted data directly. " - ] - }, - { - "cell_type": "markdown", - "id": "869a0c7c-cb2f-4594-90f0-1210e0c8f812", - "metadata": {}, - "source": [ - "\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "339bcb88-0bcc-40bc-8a83-fdef598b0c91", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using earthaccess v0.10.0\n" - ] - } - ], - "source": [ - "# Import earthaccess Library and view version\n", - "import earthaccess\n", - "print(f\"Using earthaccess v{earthaccess.__version__}\")" - ] - }, - { - "cell_type": "markdown", - "id": "9669aa7c-325b-4ce3-850f-01d272c73b01", - "metadata": {}, - "source": [ - "### Log into NASA's Earthdata using the earthaccess package" - ] - }, - { - "cell_type": "markdown", - "id": "16c436c5-9a3c-487c-b196-a67e80e01b3f", - "metadata": {}, - "source": [ - "There are multiple ways to provide your Earthdata credentials via [earthaccess](https://nsidc.github.io/earthaccess/). The [earthaccess authentication class](https://nsidc.github.io/earthaccess/tutorials/restricted-datasets/#auth) automatically tries three methods for getting user credentials to log in:\n", - "1) with `EARTHDATA_USERNAME` and `EARTHDATA_PASSWORD` environment variables\n", - "2) through an interactive, in-notebook login (used below); passwords are not shown plain text\n", - "3) with stored credentials in a .netrc file (not recommended for security reasons)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "4be86745-179a-4010-b033-3e2e10438fdd", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - } - ], - "source": [ - "# Try to authenticate\n", - "auth = earthaccess.login()\n", - "print(auth.authenticated)" - ] - }, - { - "cell_type": "markdown", - "id": "51385d73-f828-48ba-9b69-a93473adfea0", - "metadata": {}, - "source": [ - "### Search for cloud-available datasets from NASA" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "c0e74d67-7dc5-4404-a8fe-fbec06801bca", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'concept-id': 'C1595422627-ASF',\n", - " 'file-type': '',\n", - " 'get-data': [],\n", - " 'short-name': 'SENTINEL-1_INTERFEROGRAMS',\n", - " 'version': '1'}\n", - "{'cloud-info': {'Region': 'us-west-2',\n", - " 'S3BucketAndObjectPrefixNames': ['s3://lp-prod-protected/HLSS30.020',\n", - " 's3://lp-prod-public/HLSS30.020'],\n", - " 'S3CredentialsAPIDocumentationURL': 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME',\n", - " 'S3CredentialsAPIEndpoint': 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials'},\n", - " 'concept-id': 'C2021957295-LPCLOUD',\n", - " 'file-type': \"[{'Format': 'Cloud Optimized GeoTIFF (COG)', 'FormatType': \"\n", - " \"'Native', 'Media': ['Earthdata Cloud', 'HTTPS'], \"\n", - " \"'AverageFileSize': 20, 'AverageFileSizeUnit': 'MB', \"\n", - " \"'TotalCollectionFileSizeBeginDate': \"\n", - " \"'2015-11-28T00:00:00.000Z'}]\",\n", - " 'get-data': ['https://search.earthdata.nasa.gov/search?q=C2021957295-LPCLOUD',\n", - " 'https://appeears.earthdatacloud.nasa.gov/'],\n", - " 'short-name': 'HLSS30',\n", - " 'version': '2.0'}\n" - ] - } - ], - "source": [ - "# Using a keyword search\n", - "\n", - "from pprint import pprint\n", - "datasets = earthaccess.search_datasets(keyword=\"SENTINEL\",\n", - " cloud_hosted=True)\n", - "\n", - "for dataset in datasets[0:2]:\n", - " pprint(dataset.summary())" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "197a6bb3-9ca7-4353-99e8-b3acb0691f53", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'cloud-info': {'Region': 'us-west-2',\n", - " 'S3BucketAndObjectPrefixNames': ['s3://lp-prod-protected/HLSS30.020',\n", - " 's3://lp-prod-public/HLSS30.020'],\n", - " 'S3CredentialsAPIDocumentationURL': 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME',\n", - " 'S3CredentialsAPIEndpoint': 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials'},\n", - " 'concept-id': 'C2021957295-LPCLOUD',\n", - " 'file-type': \"[{'Format': 'Cloud Optimized GeoTIFF (COG)', 'FormatType': \"\n", - " \"'Native', 'Media': ['Earthdata Cloud', 'HTTPS'], \"\n", - " \"'AverageFileSize': 20, 'AverageFileSizeUnit': 'MB', \"\n", - " \"'TotalCollectionFileSizeBeginDate': \"\n", - " \"'2015-11-28T00:00:00.000Z'}]\",\n", - " 'get-data': ['https://search.earthdata.nasa.gov/search?q=C2021957295-LPCLOUD',\n", - " 'https://appeears.earthdatacloud.nasa.gov/'],\n", - " 'short-name': 'HLSS30',\n", - " 'version': '2.0'}\n", - "('The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface '\n", - " 'reflectance data from the Operational Land Imager (OLI) aboard the joint '\n", - " 'NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard '\n", - " 'Europe’s Copernicus Sentinel-2A and Sentinel-2B satellites. The combined '\n", - " 'measurement enables global observations of the land every 2–3 days at '\n", - " '30-meter (m) spatial resolution. The HLS project uses a set of algorithms to '\n", - " 'obtain seamless products from OLI and MSI that include atmospheric '\n", - " 'correction, cloud and cloud-shadow masking, spatial co-registration and '\n", - " 'common gridding, illumination and view angle normalization, and spectral '\n", - " 'bandpass adjustment. \\r\\n'\n", - " '\\r\\n'\n", - " 'The HLSS30 product provides 30-m Nadir Bidirectional Reflectance '\n", - " 'Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from '\n", - " 'Sentinel-2A and Sentinel-2B MSI data products. The HLSS30 and HLSL30 '\n", - " 'products are gridded to the same resolution and Military Grid Reference '\n", - " 'System (MGRS) '\n", - " '(https://hls.gsfc.nasa.gov/products-description/tiling-system/) tiling '\n", - " 'system, and thus are “stackable” for time series analysis.\\r\\n'\n", - " '\\r\\n'\n", - " 'The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and '\n", - " 'each band is distributed as a separate COG. There are 13 bands included in '\n", - " 'the HLSS30 product along with four angle bands and a quality assessment (QA) '\n", - " 'band. See the User Guide for a more detailed description of the individual '\n", - " 'bands provided in the HLSS30 product.\\r\\n')\n" - ] - } - ], - "source": [ - "# Using a known short name\n", - "datasets = earthaccess.search_datasets(short_name=\"HLSS30\",\n", - " cloud_hosted=True)\n", - "for dataset in datasets:\n", - " pprint(dataset.summary())\n", - " pprint(dataset.abstract())" + "ATL15_dh" ] }, { "cell_type": "markdown", - "id": "86364ad2-5b96-4619-b283-82e56e9b6790", + "id": "3b0e5f8a-a7fb-4f76-b70d-cd9c55c94f08", "metadata": {}, "source": [ - "### Searching for granules (files) from a given collection (dataset)\n", - "earthaccess has two different ways of querying for data:\n", - "1) We can build a query object or \n", - "2) we can use the top level API. \n", + "We can acquaint ourselves with this dataset in a few ways: \n", + "- The data product's [overview page](https://doi.org/10.5067/ATLAS/ATL15.002) (Smith and others, 2022) to get the very basics such as geographic coverage, CRS, and what the data product tells us (quarterly height changes).\n", + "- The Xarray Dataset read-in metadata: clicking on the written document icon of each data variable will expand metadata including a data variable's dimensions, datatype, etc. \n", + "- The data product's [data dictionary](https://nsidc.org/sites/default/files/documents/technical-reference/icesat2_atl15_data_dict_v002.pdf) (Smith and others, 2021) to do a deep dive on what individual variables tell us. \n", + "- The data product's [Algorithm Theoretical Basis Document](https://icesat-2.gsfc.nasa.gov/sites/default/files/page_files/ICESat2_ATL14_ATL15_ATBD_r001.pdf)\n", "\n", - "The difference is that the query object is a bit more flexible and we don't retrieve the metadata from CMR until we execute the `.get()` or `.get_all()` methods." - ] - }, - { - "cell_type": "markdown", - "id": "76f6b3bc-7c40-43af-b03d-31616f0fae7d", - "metadata": {}, - "source": [ - "Let's use [bboxfinder.com](http://bboxfinder.com) to get the extent of our bounding box and enter it into our bounding box input below. You are also welcome to put in a bounding box you already have available. You can uncomment the Iceland bounding box if you don't want to find your own. Order should be: `min_lon, min_lat, max_lon, max_lat`" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "c61b74b9-6a08-4938-8ed1-3ef8545eae43", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'concept_id': ['C2021957295-LPCLOUD'],\n", - " 'bounding_box': '-22.1649,63.3052,-11.9366,65.597',\n", - " 'temporal': ['2020-01-01T00:00:00Z,2023-01-01T23:59:59Z']}" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Using a specific concept-id, which is unique to the specific product and version\n", + "We'll be plotting the delta_h data variable in this tutorial, here's what we can learn about from these sources:\n", + "- [ATL14/15's overview page](https://doi.org/10.5067/ATLAS/ATL15.002): this is likely the 'quarterly height changes' described, but let's dive deeper to be sure\n", + "- ATL14/15's Xarray Dataset imbedded metadata tells us a couple things: delta_h =height change at 1 km (the resolution selected earlier) and height change relative to the datum (Jan 1, 2020) surface\n", + "- [ATL14/15's data dictionary](https://nsidc.org/sites/default/files/documents/technical-reference/icesat2_atl15_data_dict_v002.pdf): delta_h = quarterly height change at 40 km\n", "\n", - "# bbox for ~Iceland is -22.1649, 63.3052, -11.9366, 65.5970 \n", - "granules_query = earthaccess.granule_query().cloud_hosted(True) \\\n", - " .concept_id(\"C2021957295-LPCLOUD\") \\\n", - " .bounding_box(-22.1649, 63.3052, -11.9366, 65.5970) \\\n", - " .temporal(\"2020-01-01\",\"2023-01-01\")\n", - "granules_query.params" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "ef5ba481-f579-4a5c-a34b-8f19097ae1b6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "5380" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "granules_query.hits()" - ] - }, - { - "cell_type": "markdown", - "id": "38559fcd-4e61-4b7c-bcfb-f8e15ac3b6e6", - "metadata": {}, - "source": [ - "Earthaccess has many methods we can use for our search. For a complete list of the parameters we can use, go to [https://nsidc.github.io/earthaccess/user-reference/granules/granules-query/](https://nsidc.github.io/earthaccess/user-reference/granules/granules-query/)" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "c92d48d0-524c-4a08-8845-4c1a8326c4d1", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B09.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B05.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B10.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B03.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B08.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B06.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B07.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.Fmask.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B8A.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.SAA.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.VAA.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.SZA.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B02.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B01.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B11.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B04.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.VZA.tif',\n", - " 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2020048T131259.v2.0/HLS.S30.T27VWL.2020048T131259.v2.0.B12.tif']" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "granule = granules_query.get(1)[0]\n", - "granule.data_links()" - ] - }, - { - "cell_type": "markdown", - "id": "4065b490-7f8d-46be-b35e-3b5e90c734b4", - "metadata": {}, - "source": [ - "### Downloading granules" - ] - }, - { - "cell_type": "markdown", - "id": "873ef4ad-f8b9-4032-b9e2-0272f096b9a9", - "metadata": { - "jp-MarkdownHeadingCollapsed": true, - "tags": [] - }, - "source": [ - "**IMPORTANT**: Some datasets will require users to accept an EULA (end user license agreement), it is advisable trying to download a single granule using our browser first and see if we get redirected to a NASA form.\n", - "![NASA end user license agreement)](images/EULA.png)" - ] - }, - { - "cell_type": "markdown", - "id": "945e661c-baf1-4d5a-8821-07ef9446829a", - "metadata": {}, - "source": [ - "### Streaming data with earthaccess\n", - "If we have enough RAM (memory), we can load our granules from an S3 bucket into memory. Earthaccess works with fsspec (xarray, h5netcdf) at the moment, so this is task is better suited for Level 3 and Level 4 netcdf datasets.\n", + "Ok, since the data is relative to a datum, we have two options: \n", + "1) Difference individual time slices to subtract out the datum, like so: \n", "\n", - "We are going to select a few granules for the same day in February for 5 years." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "a7f173ef-fb02-4d83-8c35-2fc1b4efd699", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Querying 2018\n", - "Querying 2019\n", - "Querying 2020\n", - "Querying 2021\n", - "Querying 2022\n" - ] - } - ], - "source": [ - "iceland_bbox = (-22.1649, 63.3052, -11.9366, 65.5970)\n", - "# We are going to save our granules for each year on this list\n", - "granule_list = []\n", + " (time$_0$ - datum) - (time$_1$ - datum) = time$_0$ - datum - time$_1$ + datum = time$_0$ - time$_1$\n", "\n", - "for year in range(2018, 2023):\n", - " print(f\"Querying {year}\")\n", - " granules = earthaccess.search_data(\n", - " short_name = \"HLSS30\",\n", - " bounding_box = iceland_bbox,\n", - " temporal = (f\"{year}-02-17\", f\"{year}-02-18\")\n", - " )\n", - " granule_list.extend(granules)" + "2) Subtract out the datum directly. The datum is the complementary dataset high-resolution DEM surface contained in tha accompanying dataset ATL14.\n", + "\n", + "In this tutorial we'll use the first method. We'll use some explanatory data analysis to illustrate this. " ] }, { "cell_type": "code", - "execution_count": 53, - "id": "55302fcc-8396-420f-b3ea-f0587ee5efa1", + "execution_count": 17, + "id": "8e79268f-cfe8-498f-a695-b98bf0177c1c", "metadata": { "tags": [] }, "outputs": [ { "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6be65ae59e894857bb6185ac7b1194b4", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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Direct access link will allow you to stream the data, but you can also open from HTTPS into memory if it's the right file format (generally formats that Xarray can open). " + "Hmmm...doesn't look like much change over this quarter. Why? Check out the bounds of the colorbar, we've got some pretty extreme values (colorbar is defaulting to ±10 m!) that appear to be along the margin. It's making more sense now. We can change the bounds of the colorbar to plot see more of the smaller scale change in the continental interior." ] }, { "cell_type": "code", - "execution_count": 54, - "id": "1fcca3fa-1319-4a1c-94de-50fb071e06c0", + "execution_count": 18, + "id": "1e2b2eae-00fc-4a4b-8343-28b38688433a", "metadata": { "tags": [] }, @@ -6511,20 +3188,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "Direct access links: s3://lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2018048T131249.v2.0/HLS.S30.T27VWL.2018048T131249.v2.0.VAA.tif\n", - "External links: https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSS30.020/HLS.S30.T27VWL.2018048T131249.v2.0/HLS.S30.T27VWL.2018048T131249.v2.0.VAA.tif\n" + "-5.8791504\n", + "6.1810913\n" ] } ], "source": [ - "print(\"Direct access links: \", scene.data_links(access=\"direct\")[0])\n", - "print(\"External links: \", scene.data_links(access=\"external\")[0])" + "# Let's calculate some basic stats to determine appropriate coloarbar bounds\n", + "print(dhdt.min().values)\n", + "print(dhdt.max().values)" + ] + }, + { + "cell_type": "markdown", + "id": "f5eb944c-b268-44cb-a4f3-690e8a7ae8ef", + "metadata": {}, + "source": [ + "We can use a TwoSlopeNorm to achieve different mapping for positive and negative values while still keeping the center at zero:" ] }, { "cell_type": "code", - "execution_count": 55, - "id": "41e1cbfc-8bdc-4a10-b7de-14185ab86c1d", + "execution_count": 20, + "id": "faf7223f-02fa-4570-9704-9c631b3b996b", "metadata": { "tags": [] }, @@ -6532,60 +3218,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fce0aef2e83e4ccfa85c97903c2e9188", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/18 [00:00\n", "
\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -6598,73 +3242,35 @@ } ], "source": [ - "%matplotlib widget\n", - "import rasterio as rio\n", - "from rasterio.plot import show\n", - "import xarray as xr\n", - "\n", - "# Read and plot with grid coordinates \n", - "with rio.open(earthaccess.open(granule_list[1:2])[0]) as src:\n", - " fig, ax = plt.subplots(figsize=(9,8))\n", - "\n", - " # To plot\n", - " show(src,1)\n", + "# We can make that same plot with more representative colorbar bounds\n", + "fig, ax = plt.subplots(figsize=(4,5), tight_layout=True)\n", + "dhdt = ATL15_dh['delta_h'][1,:,:] - ATL15_dh['delta_h'][0,:,:]\n", + "divnorm = colors.TwoSlopeNorm(vmin=dhdt.min(), vcenter=0, vmax=dhdt.max())\n", + "cb = ax.imshow(dhdt, origin='lower', norm=divnorm, cmap='coolwarm_r', \n", + " aspect='auto'\n", + " )\n", "\n", - " # To open data into a numpy array\n", - " profile = src.profile\n", - " arr = src.read(1)" + "ax.set_xlabel('longitude'); ax.set_ylabel('latitude')\n", + "plt.colorbar(cb, fraction=0.02, label='height change [m]')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6d3b76be-6df8-4cb1-b475-d962a7d7eb06", + "metadata": {}, + "source": [ + "Now the colorbar bounds are more representative, but we still have the issue that extreme values along the margin are swapping any signals we might see in the continental interior. " ] }, { "cell_type": "code", - "execution_count": 56, - "id": "d55122f3-3989-4589-9b0f-9c378a782e21", + "execution_count": 21, + "id": "82678fa1-fd9a-469f-9eac-2254624f5e3e", "metadata": { "tags": [] }, "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5fd5ae84821a4b8e92882b549a2e4924", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/18 [00:00
<xarray.Dataset> Size: 54MB\n",
-       "Dimensions:      (band: 1, x: 3660, y: 3660)\n",
+       "
<xarray.DataArray 'delta_h' (quantile: 2)>\n",
+       "array([-0.28271484,  2.78289673])\n",
        "Coordinates:\n",
-       "  * band         (band) int64 8B 1\n",
-       "  * x            (x) float64 29kB 4e+05 4e+05 4e+05 ... 5.097e+05 5.097e+05\n",
-       "  * y            (y) float64 29kB 7.1e+06 7.1e+06 7.1e+06 ... 6.99e+06 6.99e+06\n",
-       "    spatial_ref  int64 8B ...\n",
-       "Data variables:\n",
-       "    band_data    (band, y, x) float32 54MB ...
"
+       "  * quantile  (quantile) float64 0.01 0.99
" ], "text/plain": [ - " Size: 54MB\n", - "Dimensions: (band: 1, x: 3660, y: 3660)\n", + "\n", + "array([-0.28271484, 2.78289673])\n", "Coordinates:\n", - " * band (band) int64 8B 1\n", - " * x (x) float64 29kB 4e+05 4e+05 4e+05 ... 5.097e+05 5.097e+05\n", - " * y (y) float64 29kB 7.1e+06 7.1e+06 7.1e+06 ... 6.99e+06 6.99e+06\n", - " spatial_ref int64 8B ...\n", - "Data variables:\n", - " band_data (band, y, x) float32 54MB ..." + " * quantile (quantile) float64 0.01 0.99" ] }, - "execution_count": 56, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "hls_scene = xr.open_dataset(earthaccess.open(granule_list[1:2])[0], engine='rasterio')\n", - "hls_scene" - ] - }, - { - "cell_type": "markdown", - "id": "ee74d663-1699-4ea6-88e2-14eeba18f97b", - "metadata": {}, - "source": [ - "### Working with legacy data formats" - ] - }, - { - "cell_type": "markdown", - "id": "4f915ebe-ff0f-425d-8594-ba89cd9dbb52", - "metadata": {}, - "source": [ - "**MOD07_L2** is an HDF EOS dataset providing atmospheric profiles from the MODerate Resolution Imaging Spectroradiometer instrument on the Terra satelite. You could use this data for providing an atmospheric correction for imagery to get a surface reflectance measurement. \n", - "\n", - "It is in a very old HDF data format and although streaming is technically possible, the only libraries that can open HDF EOS expect file paths not remote file systems like the one used by xarray (fsspec). So instead will access the netCDF endpoint via Opendap to download the granules instead of streaming them." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "b5577154-f15c-464c-b68a-57a280d484d1", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "iceland_bbox = (-22.1649, 63.3052, -11.9366, 65.5970)\n", - "\n", - "granules = earthaccess.search_data(\n", - " short_name = \"MOD07_L2\",\n", - " bounding_box = iceland_bbox,\n", - " temporal = (f\"2020-01-01\", f\"2020-01-02\")\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "a89d0974-ecfd-45c5-9c6d-7a1595c167b2", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Direct access link: ['s3://prod-lads/MOD07_L2/MOD07_L2.A2020001.0015.061.2020002183420.hdf']\n", - "External link: ['https://data.laadsdaac.earthdatacloud.nasa.gov/prod-lads/MOD07_L2/MOD07_L2.A2020001.0015.061.2020002183420.hdf']\n" - ] - } - ], - "source": [ - "print(\"Direct access link: \", granules[0].data_links(access=\"direct\"))\n", - "print(\"External link: \", granules[0].data_links(access=\"external\"))" + "# Let's use the Xarray DataArray quantile method to find the 1% and 99% quantiles (Q1 and Q3) of the data\n", + "dhdt.quantile([0.01,0.99])" ] }, { "cell_type": "markdown", - "id": "cfbfb54d-798a-4bda-88c0-6461bb2c026c", + "id": "f0ff22ef-89ee-4f85-9b14-e46dfe528978", "metadata": {}, "source": [ - "If we try to open these HDF files in Xarray:" + "We can use these quantiles as the colorbar bounds so that we see the data variability by plotting the most extreme values at the maxed out value of the colorbar. We'll adjust the caps of the colorbar (using `extend`) to express that there are data values beyond the bounds. " ] }, { "cell_type": "code", - "execution_count": 59, - "id": "53a520f7-fb86-40ba-aee6-6a98e7615ce7", + "execution_count": 22, + "id": "8320be7d-73dd-45ec-8a7f-7efdb9b629d3", "metadata": { "tags": [] }, @@ -7151,150 +3678,277 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6ed6a7ed1e5141afb7d8649ce521cfc0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/3 [00:00\n", + "
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"File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/api.py:1077\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1074\u001b[0m open_ \u001b[38;5;241m=\u001b[39m open_dataset\n\u001b[1;32m 1075\u001b[0m getattr_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m\n\u001b[0;32m-> 1077\u001b[0m datasets \u001b[38;5;241m=\u001b[39m [\u001b[43mopen_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mopen_kwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m paths]\n\u001b[1;32m 1078\u001b[0m closers \u001b[38;5;241m=\u001b[39m [getattr_(ds, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_close\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m ds \u001b[38;5;129;01min\u001b[39;00m datasets]\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m preprocess \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/api.py:588\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 576\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m 577\u001b[0m decode_cf,\n\u001b[1;32m 578\u001b[0m open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 584\u001b[0m decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m 585\u001b[0m )\n\u001b[1;32m 587\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 588\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 589\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 590\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 592\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 593\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 594\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m 595\u001b[0m backend_ds,\n\u001b[1;32m 596\u001b[0m filename_or_obj,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 606\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 607\u001b[0m )\n\u001b[1;32m 608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/rioxarray/xarray_plugin.py:58\u001b[0m, in \u001b[0;36mRasterioBackend.open_dataset\u001b[0;34m(self, filename_or_obj, drop_variables, parse_coordinates, lock, masked, mask_and_scale, variable, group, default_name, decode_coords, decode_times, decode_timedelta, band_as_variable, open_kwargs)\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m open_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 57\u001b[0m open_kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m---> 58\u001b[0m rds \u001b[38;5;241m=\u001b[39m \u001b[43m_io\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_rasterio\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 60\u001b[0m \u001b[43m \u001b[49m\u001b[43mparse_coordinates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparse_coordinates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 61\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 62\u001b[0m \u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 63\u001b[0m \u001b[43m \u001b[49m\u001b[43mmasked\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmasked\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 64\u001b[0m \u001b[43m \u001b[49m\u001b[43mmask_and_scale\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmask_and_scale\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 65\u001b[0m \u001b[43m \u001b[49m\u001b[43mvariable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvariable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 66\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 67\u001b[0m \u001b[43m \u001b[49m\u001b[43mdefault_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 68\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_times\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_times\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecode_timedelta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdecode_timedelta\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 70\u001b[0m \u001b[43m \u001b[49m\u001b[43mband_as_variable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mband_as_variable\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 71\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mopen_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 72\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 73\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(rds, xarray\u001b[38;5;241m.\u001b[39mDataArray):\n\u001b[1;32m 74\u001b[0m dataset \u001b[38;5;241m=\u001b[39m rds\u001b[38;5;241m.\u001b[39mto_dataset()\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/rioxarray/_io.py:1128\u001b[0m, in \u001b[0;36mopen_rasterio\u001b[0;34m(filename, parse_coordinates, chunks, cache, lock, masked, mask_and_scale, variable, group, default_name, decode_times, decode_timedelta, band_as_variable, **open_kwargs)\u001b[0m\n\u001b[1;32m 1126\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1127\u001b[0m manager \u001b[38;5;241m=\u001b[39m URIManager(file_opener, filename, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m, kwargs\u001b[38;5;241m=\u001b[39mopen_kwargs)\n\u001b[0;32m-> 1128\u001b[0m riods \u001b[38;5;241m=\u001b[39m \u001b[43mmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1129\u001b[0m captured_warnings \u001b[38;5;241m=\u001b[39m rio_warnings\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 1131\u001b[0m \u001b[38;5;66;03m# raise the NotGeoreferencedWarning if applicable\u001b[39;00m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/rioxarray/_io.py:263\u001b[0m, in \u001b[0;36mURIManager.acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_local\u001b[38;5;241m.\u001b[39mthread_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_local\u001b[38;5;241m.\u001b[39mthread_manager \u001b[38;5;241m=\u001b[39m ThreadURIManager(\n\u001b[1;32m 261\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_opener, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_args, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode, kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_kwargs\n\u001b[1;32m 262\u001b[0m )\n\u001b[0;32m--> 263\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_local\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthread_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfile_handle\u001b[49m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/rioxarray/_io.py:219\u001b[0m, in \u001b[0;36mThreadURIManager.file_handle\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file_handle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file_handle\n\u001b[0;32m--> 219\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file_handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_file_handle\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/rasterio/env.py:451\u001b[0m, in \u001b[0;36mensure_env_with_credentials..wrapper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 448\u001b[0m session \u001b[38;5;241m=\u001b[39m DummySession()\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m env_ctor(session\u001b[38;5;241m=\u001b[39msession):\n\u001b[0;32m--> 451\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/rasterio/__init__.py:242\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(fp, mode, driver, width, height, count, crs, transform, dtype, nodata, sharing, **kwargs)\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(fp, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mread\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m have_vsi_plugin:\n\u001b[0;32m--> 242\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mFilePath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfp\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdriver\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdriver\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msharing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 244\u001b[0m memfile \u001b[38;5;241m=\u001b[39m MemoryFile(fp\u001b[38;5;241m.\u001b[39mread())\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/rasterio/env.py:398\u001b[0m, in \u001b[0;36mensure_env..wrapper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 395\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m 396\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds):\n\u001b[1;32m 397\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m local\u001b[38;5;241m.\u001b[39m_env:\n\u001b[0;32m--> 398\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 399\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 400\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Env\u001b[38;5;241m.\u001b[39mfrom_defaults():\n", - "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/rasterio/io.py:236\u001b[0m, in \u001b[0;36m_FilePath.open\u001b[0;34m(self, driver, sharing, **kwargs)\u001b[0m\n\u001b[1;32m 233\u001b[0m \u001b[38;5;66;03m# Assume we were given a non-empty file-like object\u001b[39;00m\n\u001b[1;32m 234\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVSI path: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(mempath\u001b[38;5;241m.\u001b[39mpath))\n\u001b[0;32m--> 236\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDatasetReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmempath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdriver\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdriver\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msharing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32mrasterio/_base.pyx:312\u001b[0m, in \u001b[0;36mrasterio._base.DatasetBase.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mRasterioIOError\u001b[0m: '/vsipythonfilelike/a05cc600-c967-4d42-83ff-7c3dedcf286b/a05cc600-c967-4d42-83ff-7c3dedcf286b' not recognized as being in a supported file format." - ] } ], "source": [ - "# This creates an anticipated error\n", - "mod07 = xr.open_mfdataset(earthaccess.open(granules[0:3]), engine='rasterio')" + "# Let's make the same plot but using the quantiles as the colorbar bounds\n", + "fig, ax = plt.subplots(figsize=(4,5), tight_layout=True)\n", + "dhdt = ATL15_dh['delta_h'][1,:,:] - ATL15_dh['delta_h'][0,:,:]\n", + "divnorm = colors.TwoSlopeNorm(vmin=dhdt.quantile(0.01), vcenter=0, vmax=dhdt.quantile(0.99))\n", + "cb = ax.imshow(dhdt, origin='lower', norm=divnorm, cmap='coolwarm_r', \n", + " aspect='auto'\n", + " )\n", + "ax.set_xlabel('longitude'); ax.set_ylabel('latitude')\n", + "plt.colorbar(cb, fraction=0.02, extend='both', label='height change [m]', ticks=[-0.25,0,1,2])\n", + "plt.tight_layout()\n", + "plt.show()" ] }, { - "cell_type": "markdown", - "id": "1d208b07-e4cf-4eb7-bb40-7a3da46db3e4", - "metadata": {}, + "cell_type": "code", + "execution_count": 23, + "id": "b53dab15-820d-45d9-bf17-72a8702af057", + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "We notice that the access occurs quickly, but xarray is unable to recognize the legacy file format. HDF files are remarkably hard to open. You must download the files and open using pyhdf to open them using code like this: \\\n", - "`from pyhdf.SD import SD,SDC` \\\n", - "`mod07_l2 = SD(MODfile, SDC.READ)`" + "# Let's make the same plot but for all the available time slices and let's turn it in a function so that we can reuse this code for a smaller subset of data\n", + "# create empty lists to store data\n", + "def plot_icesat2_atl15(xmin, xmax, ymin, ymax, dataset):\n", + " # subset data using bounding box in epsg:3134 x,y\n", + " mask_x = (dataset.x >= xmin) & (dataset.x <= xmax)\n", + " mask_y = (dataset.y >= ymin) & (dataset.y <= ymax)\n", + " ds_sub = dataset.where(mask_x & mask_y, drop=True)\n", + " \n", + " # Create empty lists to store data\n", + " vmins_maxs = []\n", + "\n", + " # Find the min's and max's of each inter time slice comparison and store into lists\n", + " for idx in range(len(ds_sub['time'].values)-1): \n", + " dhdt = ds_sub['delta_h'][idx+1,:,:] - ds_sub['delta_h'][idx,:,:]\n", + " vmin=dhdt.quantile(0.01)\n", + " vmins_maxs += [vmin]\n", + " vmax=dhdt.quantile(0.99)\n", + " vmins_maxs += [vmax]\n", + " if (min(vmins_maxs)<0) & (max(vmins_maxs)>0):\n", + " vcenter = 0\n", + " else: \n", + " vcenter = max(vmins_maxs) - min(vmins_maxs)\n", + " divnorm = colors.TwoSlopeNorm(vmin=min(vmins_maxs), vcenter=vcenter, vmax=max(vmins_maxs))\n", + "\n", + " # create fig, ax\n", + " fig, axs = plt.subplots(7,2, sharex=True, sharey=True, figsize=(10,10))\n", + "\n", + " idx = 0\n", + " for ax in axs.ravel(): \n", + " ax.set_aspect('equal')\n", + " dhdt = ds_sub['delta_h'][idx+1,:,:] - ds_sub['delta_h'][idx,:,:]\n", + " cb = ax.imshow(dhdt, origin='lower', norm=divnorm, cmap='coolwarm_r', \n", + " extent=[xmin[0], xmax[0], ymin[0], ymax[0]])\n", + " # Change polar stereographic m to km\n", + " km_scale = 1e3\n", + " ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/km_scale))\n", + " ax.xaxis.set_major_formatter(ticks_x)\n", + " ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/km_scale))\n", + " ax.yaxis.set_major_formatter(ticks_y)\n", + " # Create common axes labels\n", + " fig.supxlabel('easting (km)'); fig.supylabel('northing (km)')\n", + " # Increment the idx\n", + " idx = idx + 1\n", + " \n", + " fig.colorbar(cb, extend='both', ax=axs.ravel().tolist(), label='height change [m]')\n", + " plt.show()" ] }, { "cell_type": "markdown", - "id": "ec910c79-4b11-43d9-9dbd-7c1d5113ebd3", + "id": "277c6fa1-5013-46d8-80a9-1ca545e17611", "metadata": {}, "source": [ - "We do have another option to convert hdf to nc4 files during download so that we can open the files in Xarray." + "Let's zoom into an individual active lake to see more detail. First let's remind ourselves of the Greenland active subglacial lakes by filtering on lake type:" ] }, { "cell_type": "code", - "execution_count": 60, - "id": "05800373-0640-44e2-9f7b-09c39ce03059", + "execution_count": 24, + "id": "7eb6cefc-7794-45e3-a5d6-3900d7204d85", "metadata": { "tags": [] }, "outputs": [ { "data": { + "text/html": [ + "
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Name / LocationLat. oNLon. oELake TypeReferencesgeometry
3Flade Isblink ice cap81.160000-16.580000ActiveWillis et al. (2015)POLYGON ((-16.06723 81.11390, -16.09787 81.106...
4Inuppaat Quuat67.611136-48.709000ActiveHowat et al. (2015); Palmer et al. (2015)POLYGON ((-48.47640 67.61446, -48.47613 67.605...
5Sioqqap Sermia, [SS1]63.541856-48.450597ActiveBowling et al. (2019)POLYGON ((-48.25472 63.54482, -48.25457 63.536...
6Sioqqap Sermia, [SS2]63.260248-48.206633ActiveBowling et al. (2019)POLYGON ((-48.01287 63.26285, -48.01281 63.254...
61Isunguata Sermia 167.180000-50.188000ActiveLivingstone et al. (2019)POLYGON ((-49.96016 67.18562, -49.95932 67.176...
62Isunguata Sermia 267.178000-50.149000ActiveLivingstone et al. (2019)POLYGON ((-49.92117 67.18356, -49.92035 67.174...
63Isunguata Sermia 367.180000-50.128000ActiveLivingstone et al. (2019)POLYGON ((-49.90015 67.18552, -49.89933 67.176...
\n", + "
" + ], "text/plain": [ - "['https://ladsweb.modaps.eosdis.nasa.gov/opendap/RemoteResources/laads/allData/61/MOD07_L2/2020/001/MOD07_L2.A2020001.0015.061.2020002183420.hdf.html']" + " Name / Location Lat. oN Lon. oE Lake Type \\\n", + "3 Flade Isblink ice cap 81.160000 -16.580000 Active \n", + "4 Inuppaat Quuat 67.611136 -48.709000 Active \n", + "5 Sioqqap Sermia, [SS1] 63.541856 -48.450597 Active \n", + "6 Sioqqap Sermia, [SS2] 63.260248 -48.206633 Active \n", + "61 Isunguata Sermia 1 67.180000 -50.188000 Active \n", + "62 Isunguata Sermia 2 67.178000 -50.149000 Active \n", + "63 Isunguata Sermia 3 67.180000 -50.128000 Active \n", + "\n", + " References \\\n", + "3 Willis et al. (2015) \n", + "4 Howat et al. (2015); Palmer et al. (2015) \n", + "5 Bowling et al. (2019) \n", + "6 Bowling et al. (2019) \n", + "61 Livingstone et al. (2019) \n", + "62 Livingstone et al. (2019) \n", + "63 Livingstone et al. (2019) \n", + "\n", + " geometry \n", + "3 POLYGON ((-16.06723 81.11390, -16.09787 81.106... \n", + "4 POLYGON ((-48.47640 67.61446, -48.47613 67.605... \n", + "5 POLYGON ((-48.25472 63.54482, -48.25457 63.536... \n", + "6 POLYGON ((-48.01287 63.26285, -48.01281 63.254... \n", + "61 POLYGON ((-49.96016 67.18562, -49.95932 67.176... \n", + "62 POLYGON ((-49.92117 67.18356, -49.92035 67.174... \n", + "63 POLYGON ((-49.90015 67.18552, -49.89933 67.176... " ] }, - "execution_count": 60, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# We are going to retrieve the html endpoint link for one granule\n", - "granules[0]._filter_related_links(\"USE SERVICE API\")" + "gdf_polys_active = gdf_polys[gdf_polys['Lake Type'] == 'Active']\n", + "gdf_polys_active" ] }, { "cell_type": "code", - "execution_count": 61, - "id": "82568336-a8be-4fa2-9135-c5bebb4148c2", + "execution_count": 25, + "id": "eb54bca1-def0-46cd-b2c0-d08a1e79611d", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://ladsweb.modaps.eosdis.nasa.gov/opendap/RemoteResources/laads/allData/61/MOD07_L2/2020/001/MOD07_L2.A2020001.0015.061.2020002183420.hdf.nc4'" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], + "source": [ + "# We can call the bounds of the geometry of the Shapely Polygon we created earlier\n", + "gdf_sub = gdf_polys_active[gdf_polys_active['Name / Location'] == 'Inuppaat Quuat']" + ] + }, + { + "cell_type": "markdown", + "id": "64cb4599-4193-4c0e-8a9e-e96a01fcdb01", + "metadata": {}, "source": [ - "netcdf_list = [g._filter_related_links(\"USE SERVICE API\")[0].replace(\".html\", \".nc4\") for g in granules]\n", - "netcdf_list[0]" + "Now using these geometry bounds we can plot the area immediately around the active subglacial lake. " ] }, { "cell_type": "code", - "execution_count": 62, - "id": "5116ef2f-e27f-49a2-9e3f-07f70337226f", + "execution_count": 26, + "id": "5e949e39-f84f-42ff-8e7f-7b45270d9a39", "metadata": { "tags": [] }, @@ -7302,783 +3956,810 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "762be43aa6f840fcad009c45b5b57b33", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "QUEUEING TASKS | : 0%| | 0/3 [00:00\n", + "
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-       "                                        Cell_Along_Swath: 406,\n",
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-       "                                        Output_Parameter: 10,\n",
-       "                                        Water_Vapor_QA_Bytes: 5)\n",
-       "Coordinates:\n",
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-       "    Latitude                           (Cell_Along_Swath, Cell_Across_Swath) float32 438kB ...\n",
-       "    Longitude                          (Cell_Along_Swath, Cell_Across_Swath) float32 438kB ...\n",
-       "  * Output_Parameter                   (Output_Parameter) int32 40B 0 1 ... 8 9\n",
-       "  * Water_Vapor_QA_Bytes               (Water_Vapor_QA_Bytes) int32 20B 0 ... 4\n",
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-       "    Solar_Azimuth                      (Cell_Along_Swath, Cell_Across_Swath) float64 877kB ...\n",
-       "    Sensor_Zenith                      (Cell_Along_Swath, Cell_Across_Swath) float64 877kB ...\n",
-       "    Sensor_Azimuth                     (Cell_Along_Swath, Cell_Across_Swath) float64 877kB ...\n",
-       "    ...                                 ...\n",
-       "    Water_Vapor                        (Cell_Along_Swath, Cell_Across_Swath) float64 877kB ...\n",
-       "    Water_Vapor_Direct                 (Cell_Along_Swath, Cell_Across_Swath) float64 877kB ...\n",
-       "    Water_Vapor_Low                    (Cell_Along_Swath, Cell_Across_Swath) float64 877kB ...\n",
-       "    Water_Vapor_High                   (Cell_Along_Swath, Cell_Across_Swath) float64 877kB ...\n",
-       "    Quality_Assurance                  (Cell_Along_Swath, Cell_Across_Swath, Output_Parameter) float64 9MB ...\n",
-       "    Quality_Assurance_Infrared         (Cell_Along_Swath, Cell_Across_Swath, Water_Vapor_QA_Bytes) float64 4MB ...\n",
-       "Attributes:\n",
-       "    HDFEOSVersion:                      HDFEOS_V2.19\n",
-       "    ScaleFactor_AddOffset_Application:  Value=scale_factor*(stored integer - ...\n",
-       "    Pressure_Levels:                    5, 10, 20, 30, 50, 70, 100, 150, 200,...\n",
-       "    title:                              MODIS Level 2 Atmospheric Profiles   ...\n",
-       "    identifier_product_doi:             10.5067/MODIS/MOD07_L2.061\n",
-       "    identifier_product_doi_authority:   http://dx.doi.org\n",
-       "    history:                            $Id: MOD07.V2.CDL,v 1.1 2005/12/14 16...\n",
-       "    history_json:                       [{"$schema":"https:\\/\\/harmony.earthd...
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You’ve completed the tutorial. In this tutorial you have gained the skills to:\n", "\n", - "- Transfrom Coordinate Reference Systems, \n", - "\n", - "- Open data into Pandas, GeoPandas and Xarray DataFrames/Arrays,\n", + "- Transfrom Coordinate Reference Systems.\n", "\n", - "- Use Shapely geometries to define an area of interest and subset data,\n", + "- Open data into Pandas, GeoPandas and Xarray DataFrames/Arrays.\n", "\n", - "- Learned to use icepyx and earthaccess for streamlining data access, \n", + "- Use Shapely geometries to define an area of interest and subset data.\n", "\n", - "- Search and access optimized and non-optimized cloud data, non-cloud-hosted data " + "- Learned to use icepyx for streamlining data access." ] }, { @@ -8120,9 +4799,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "icepyx", "language": "python", - "name": "python3" + "name": "icepyx" }, "language_info": { "codemirror_mode": { @@ -8134,7 +4813,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.1" } }, "nbformat": 4,