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September 4, 2024 14:32
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"id": "316315c8-62eb-4841-aaee-b680476b017c", | |
"metadata": {}, | |
"source": [ | |
"# SWOT gradient & interpolation" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"id": "f3e8f6d7-7d72-436b-acf9-f939bc31d99e", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"application/javascript": [ | |
"(function(root) {\n", | |
" function now() {\n", | |
" return new Date();\n", | |
" }\n", | |
"\n", | |
" var force = true;\n", | |
" var py_version = '3.3.3'.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/[email protected]/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/[email protected]/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/1.3.6/dist/bundled/floatpanel/[email protected]/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/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/1.3.6/dist/bundled/gridstack/[email protected]/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/1.3.6/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-3.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.3.min.js\", \"https://cdn.holoviz.org/panel/1.3.6/dist/panel.min.js\"];\n", | |
" var js_modules = [];\n", | |
" var js_exports = {};\n", | |
" var css_urls = [];\n", | |
" var inline_js = [ function(Bokeh) {\n", | |
" Bokeh.set_log_level(\"info\");\n", | |
" },\n", | |
"function(Bokeh) {} // ensure no trailing comma for IE\n", | |
" ];\n", | |
"\n", | |
" function run_inline_js() {\n", | |
" if ((root.Bokeh !== undefined) || (force === true)) {\n", | |
" for (var i = 0; i < inline_js.length; i++) {\n", | |
"\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));" | |
], | |
"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 = '3.3.3'.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/[email protected]/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/[email protected]/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/[email protected]/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/1.3.6/dist/bundled/floatpanel/[email protected]/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.6/dist/bundled/floatpanel/[email protected]/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/1.3.6/dist/bundled/gridstack/[email protected]/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/1.3.6/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n for (var i = 0; i < urls.length; i++) {\n skip.push(urls[i])\n }\n } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; 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}, | |
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], | |
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"text": [ | |
"Warning: could not import utide\n", | |
"Warning: could not import pyTMD\n" | |
] | |
} | |
], | |
"source": [ | |
"import os\n", | |
"from glob import glob\n", | |
"import threading\n", | |
"\n", | |
"import xarray as xr\n", | |
"import pandas as pd\n", | |
"import numpy as np\n", | |
"\n", | |
"%matplotlib inline\n", | |
"from matplotlib import pyplot as plt\n", | |
"\n", | |
"from pyproj import Geod\n", | |
"\n", | |
"import pynsitu as pyn\n", | |
"crs = pyn.maps.crs" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"id": "1d6d7bef-2d45-4c54-9566-6004ebee41ff", | |
"metadata": {}, | |
"outputs": [ | |
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" width: 15px;\n", | |
" text-align: center;\n", | |
"}\n", | |
"\n", | |
".xr-section-summary-in:disabled + label:before {\n", | |
" color: var(--xr-disabled-color);\n", | |
"}\n", | |
"\n", | |
".xr-section-summary-in:checked + label:before {\n", | |
" content: '▼';\n", | |
"}\n", | |
"\n", | |
".xr-section-summary-in:checked + label > span {\n", | |
" display: none;\n", | |
"}\n", | |
"\n", | |
".xr-section-summary,\n", | |
".xr-section-inline-details {\n", | |
" padding-top: 4px;\n", | |
" padding-bottom: 4px;\n", | |
"}\n", | |
"\n", | |
".xr-section-inline-details {\n", | |
" grid-column: 2 / -1;\n", | |
"}\n", | |
"\n", | |
".xr-section-details {\n", | |
" display: none;\n", | |
" grid-column: 1 / -1;\n", | |
" margin-bottom: 5px;\n", | |
"}\n", | |
"\n", | |
".xr-section-summary-in:checked ~ .xr-section-details {\n", | |
" display: contents;\n", | |
"}\n", | |
"\n", | |
".xr-array-wrap {\n", | |
" grid-column: 1 / -1;\n", | |
" display: grid;\n", | |
" grid-template-columns: 20px auto;\n", | |
"}\n", | |
"\n", | |
".xr-array-wrap > label {\n", | |
" grid-column: 1;\n", | |
" vertical-align: top;\n", | |
"}\n", | |
"\n", | |
".xr-preview {\n", | |
" color: var(--xr-font-color3);\n", | |
"}\n", | |
"\n", | |
".xr-array-preview,\n", | |
".xr-array-data {\n", | |
" padding: 0 5px !important;\n", | |
" grid-column: 2;\n", | |
"}\n", | |
"\n", | |
".xr-array-data,\n", | |
".xr-array-in:checked ~ .xr-array-preview {\n", | |
" display: none;\n", | |
"}\n", | |
"\n", | |
".xr-array-in:checked ~ .xr-array-data,\n", | |
".xr-array-preview {\n", | |
" display: inline-block;\n", | |
"}\n", | |
"\n", | |
".xr-dim-list {\n", | |
" display: inline-block !important;\n", | |
" list-style: none;\n", | |
" padding: 0 !important;\n", | |
" margin: 0;\n", | |
"}\n", | |
"\n", | |
".xr-dim-list li {\n", | |
" display: inline-block;\n", | |
" padding: 0;\n", | |
" margin: 0;\n", | |
"}\n", | |
"\n", | |
".xr-dim-list:before {\n", | |
" content: '(';\n", | |
"}\n", | |
"\n", | |
".xr-dim-list:after {\n", | |
" content: ')';\n", | |
"}\n", | |
"\n", | |
".xr-dim-list li:not(:last-child):after {\n", | |
" content: ',';\n", | |
" padding-right: 5px;\n", | |
"}\n", | |
"\n", | |
".xr-has-index {\n", | |
" font-weight: bold;\n", | |
"}\n", | |
"\n", | |
".xr-var-list,\n", | |
".xr-var-item {\n", | |
" display: contents;\n", | |
"}\n", | |
"\n", | |
".xr-var-item > div,\n", | |
".xr-var-item label,\n", | |
".xr-var-item > .xr-var-name span {\n", | |
" background-color: var(--xr-background-color-row-even);\n", | |
" margin-bottom: 0;\n", | |
"}\n", | |
"\n", | |
".xr-var-item > .xr-var-name:hover span {\n", | |
" padding-right: 5px;\n", | |
"}\n", | |
"\n", | |
".xr-var-list > li:nth-child(odd) > div,\n", | |
".xr-var-list > li:nth-child(odd) > label,\n", | |
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", | |
" background-color: var(--xr-background-color-row-odd);\n", | |
"}\n", | |
"\n", | |
".xr-var-name {\n", | |
" grid-column: 1;\n", | |
"}\n", | |
"\n", | |
".xr-var-dims {\n", | |
" grid-column: 2;\n", | |
"}\n", | |
"\n", | |
".xr-var-dtype {\n", | |
" grid-column: 3;\n", | |
" text-align: right;\n", | |
" color: var(--xr-font-color2);\n", | |
"}\n", | |
"\n", | |
".xr-var-preview {\n", | |
" grid-column: 4;\n", | |
"}\n", | |
"\n", | |
".xr-index-preview {\n", | |
" grid-column: 2 / 5;\n", | |
" color: var(--xr-font-color2);\n", | |
"}\n", | |
"\n", | |
".xr-var-name,\n", | |
".xr-var-dims,\n", | |
".xr-var-dtype,\n", | |
".xr-preview,\n", | |
".xr-attrs dt {\n", | |
" white-space: nowrap;\n", | |
" overflow: hidden;\n", | |
" text-overflow: ellipsis;\n", | |
" padding-right: 10px;\n", | |
"}\n", | |
"\n", | |
".xr-var-name:hover,\n", | |
".xr-var-dims:hover,\n", | |
".xr-var-dtype:hover,\n", | |
".xr-attrs dt:hover {\n", | |
" overflow: visible;\n", | |
" width: auto;\n", | |
" z-index: 1;\n", | |
"}\n", | |
"\n", | |
".xr-var-attrs,\n", | |
".xr-var-data,\n", | |
".xr-index-data {\n", | |
" display: none;\n", | |
" background-color: var(--xr-background-color) !important;\n", | |
" padding-bottom: 5px !important;\n", | |
"}\n", | |
"\n", | |
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", | |
".xr-var-data-in:checked ~ .xr-var-data,\n", | |
".xr-index-data-in:checked ~ .xr-index-data {\n", | |
" display: block;\n", | |
"}\n", | |
"\n", | |
".xr-var-data > table {\n", | |
" float: right;\n", | |
"}\n", | |
"\n", | |
".xr-var-name span,\n", | |
".xr-var-data,\n", | |
".xr-index-name div,\n", | |
".xr-index-data,\n", | |
".xr-attrs {\n", | |
" padding-left: 25px !important;\n", | |
"}\n", | |
"\n", | |
".xr-attrs,\n", | |
".xr-var-attrs,\n", | |
".xr-var-data,\n", | |
".xr-index-data {\n", | |
" grid-column: 1 / -1;\n", | |
"}\n", | |
"\n", | |
"dl.xr-attrs {\n", | |
" padding: 0;\n", | |
" margin: 0;\n", | |
" display: grid;\n", | |
" grid-template-columns: 125px auto;\n", | |
"}\n", | |
"\n", | |
".xr-attrs dt,\n", | |
".xr-attrs dd {\n", | |
" padding: 0;\n", | |
" margin: 0;\n", | |
" float: left;\n", | |
" padding-right: 10px;\n", | |
" width: auto;\n", | |
"}\n", | |
"\n", | |
".xr-attrs dt {\n", | |
" font-weight: normal;\n", | |
" grid-column: 1;\n", | |
"}\n", | |
"\n", | |
".xr-attrs dt:hover span {\n", | |
" display: inline-block;\n", | |
" background: var(--xr-background-color);\n", | |
" padding-right: 10px;\n", | |
"}\n", | |
"\n", | |
".xr-attrs dd {\n", | |
" grid-column: 2;\n", | |
" white-space: pre-wrap;\n", | |
" word-break: break-all;\n", | |
"}\n", | |
"\n", | |
".xr-icon-database,\n", | |
".xr-icon-file-text2,\n", | |
".xr-no-icon {\n", | |
" display: inline-block;\n", | |
" vertical-align: middle;\n", | |
" width: 1em;\n", | |
" height: 1.5em !important;\n", | |
" stroke-width: 0;\n", | |
" stroke: currentColor;\n", | |
" fill: currentColor;\n", | |
"}\n", | |
"</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n", | |
"Dimensions: (num_lines: 4161, num_pixels: 519)\n", | |
"Coordinates:\n", | |
" latitude (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" longitude (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
"Dimensions without coordinates: num_lines, num_pixels\n", | |
"Data variables: (12/36)\n", | |
" ancillary_surface_classification_flag (num_lines, num_pixels) float32 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" cross_track_distance (num_lines, num_pixels) float32 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" cvl_dac (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" cvl_distance_to_coast (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" cvl_flag_val (num_lines, num_pixels) float32 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" cvl_ice_conc (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" ... ...\n", | |
" pass_number (num_lines) uint16 dask.array<chunksize=(4161,), meta=np.ndarray>\n", | |
" sig0_karin_2 (num_lines, num_pixels) float32 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" ssh_karin_2_qual (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" time (num_lines) datetime64[ns] dask.array<chunksize=(4161,), meta=np.ndarray>\n", | |
" time_tai (num_lines) datetime64[ns] dask.array<chunksize=(4161,), meta=np.ndarray>\n", | |
" version (num_lines) |S7 dask.array<chunksize=(4161,), meta=np.ndarray>\n", | |
"Attributes:\n", | |
" latc: 39.961324\n", | |
" lonc: 4.3749\n", | |
" phi: 76.80931729750102</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-363662e3-0776-4c9e-b771-a16fb3e50262' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-363662e3-0776-4c9e-b771-a16fb3e50262' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>num_lines</span>: 4161</li><li><span>num_pixels</span>: 519</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ffc5e54a-3c38-4cb7-9b98-33b2519f1583' class='xr-section-summary-in' type='checkbox' checked><label for='section-ffc5e54a-3c38-4cb7-9b98-33b2519f1583' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-d8d2dcfc-f2ff-4602-a695-67fd4efecaa1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8d2dcfc-f2ff-4602-a695-67fd4efecaa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c37387e8-590a-438d-9998-59e187b9cf57' class='xr-var-data-in' type='checkbox'><label for='data-c37387e8-590a-438d-9998-59e187b9cf57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Latitude of measurement [-80,80]. Positive latitude is North latitude, negative latitude is South latitude.</dd><dt><span>long_name :</span></dt><dd>latitude (positive N, negative S)</dd><dt><span>quality_flag :</span></dt><dd>ssh_karin_2_qual</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"120\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Colored Rectangle -->\n", | |
" <polygon points=\"0.0,0.0 39.78272544138227,0.0 39.78272544138227,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", | |
"\n", | |
" <!-- Text -->\n", | |
" <text x=\"19.891363\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >519</text>\n", | |
" <text x=\"59.782725\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,59.782725,60.000000)\">4161</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-7f8791ed-ba5a-43dd-ad63-db3d56d76fbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f8791ed-ba5a-43dd-ad63-db3d56d76fbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae8ab326-8dfd-4b81-8828-5bf728d81348' class='xr-var-data-in' type='checkbox'><label for='data-ae8ab326-8dfd-4b81-8828-5bf728d81348' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Longitude of measurement. East longitude relative to Greenwich meridian.</dd><dt><span>long_name :</span></dt><dd>longitude (degrees East)</dd><dt><span>quality_flag :</span></dt><dd>ssh_karin_2_qual</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"120\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Colored Rectangle -->\n", | |
" <polygon points=\"0.0,0.0 39.78272544138227,0.0 39.78272544138227,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", | |
"\n", | |
" <!-- Text -->\n", | |
" <text x=\"19.891363\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >519</text>\n", | |
" <text x=\"59.782725\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,59.782725,60.000000)\">4161</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c4713e4-c543-40a6-9981-727684293108' class='xr-section-summary-in' type='checkbox' ><label for='section-3c4713e4-c543-40a6-9981-727684293108' class='xr-section-summary' >Data variables: <span>(36)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ancillary_surface_classification_flag</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-cc9cf918-f3ad-4b6b-b498-d4a4cdb157e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc9cf918-f3ad-4b6b-b498-d4a4cdb157e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e2b0d4a-89b2-4941-9dc4-ae558c7e0d23' class='xr-var-data-in' type='checkbox'><label for='data-8e2b0d4a-89b2-4941-9dc4-ae558c7e0d23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>7-state surface type classification computed from a mask built with MODIS and GlobCover data.</dd><dt><span>flag_meanings :</span></dt><dd>open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basin</dd><dt><span>flag_values :</span></dt><dd>[0, 1, 2, 3, 4, 5, 6]</dd><dt><span>institution :</span></dt><dd>European Space Agency</dd><dt><span>long_name :</span></dt><dd>surface classification</dd><dt><span>source :</span></dt><dd>MODIS/GlobCover</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>valid_max :</span></dt><dd>6</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 8.24 MiB </td>\n", | |
" <td> 8.24 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cross_track_distance</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-ae916d87-0807-4482-9e32-b639c66de7f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae916d87-0807-4482-9e32-b639c66de7f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-313042c4-eee1-45a6-9afa-58f8099d032b' class='xr-var-data-in' type='checkbox'><label for='data-313042c4-eee1-45a6-9afa-58f8099d032b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Distance of sample from nadir. Negative values indicate the left side of the swath, and positive values indicate the right side of the swath.</dd><dt><span>long_name :</span></dt><dd>cross track distance</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>75000</dd><dt><span>valid_min :</span></dt><dd>-75000</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 8.24 MiB </td>\n", | |
" <td> 8.24 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"120\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cvl_dac</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-7d3c1b1c-c91e-4659-a65b-17fa35296464' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d3c1b1c-c91e-4659-a65b-17fa35296464' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a385211-15d4-4e97-a98f-e1f2b935d69c' class='xr-var-data-in' type='checkbox'><label for='data-5a385211-15d4-4e97-a98f-e1f2b935d69c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Model estimate of the effect on sea surface topography due to high frequency air pressure and wind effects and the low-frequency height from inverted barometer effect (inv_bar_cor). This value is subtracted from the ssh_karin and ssh_karin_2 to compute ssha_karin and ssha_karin_2, respectively. Use only one of inv_bar_cor and dac.</dd><dt><span>institution :</span></dt><dd>LEGOS/CNES/CLS</dd><dt><span>long_name :</span></dt><dd>dynamic atmospheric correction</dd><dt><span>source :</span></dt><dd>MOG2D</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cvl_distance_to_coast</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-e971b125-a496-496c-9b26-972084ac7bf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e971b125-a496-496c-9b26-972084ac7bf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-661efa30-58e0-4900-a7b9-f11963d9cf55' class='xr-var-data-in' type='checkbox'><label for='data-661efa30-58e0-4900-a7b9-f11963d9cf55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Approximate distance to the nearest coast point along the Earth surface.</dd><dt><span>institution :</span></dt><dd>European Space Agency</dd><dt><span>long_name :</span></dt><dd>distance to coast</dd><dt><span>source :</span></dt><dd>MODIS/GlobCover</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cvl_flag_val</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-d4c768ad-4e6c-4eaa-9de8-e21be2c9df12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4c768ad-4e6c-4eaa-9de8-e21be2c9df12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c52690a-86ea-4911-b028-285020c577c7' class='xr-var-data-in' type='checkbox'><label for='data-2c52690a-86ea-4911-b028-285020c577c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>calval validity flag</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 8.24 MiB </td>\n", | |
" <td> 8.24 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
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"\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cvl_ice_conc</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-61e0b27a-99d0-4178-ae38-bf5dafb0c488' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61e0b27a-99d0-4178-ae38-bf5dafb0c488' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88f12fa1-818d-4ccd-8a86-d8ce1019e2da' class='xr-var-data-in' type='checkbox'><label for='data-88f12fa1-818d-4ccd-8a86-d8ce1019e2da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Concentration of sea ice from model.</dd><dt><span>institution :</span></dt><dd>EUMETSAT</dd><dt><span>long_name :</span></dt><dd>concentration of sea ice</dd><dt><span>source :</span></dt><dd>EUMETSAT Ocean and Sea Ice Satellite Applications Facility</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>%</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
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" </tbody>\n", | |
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" </td>\n", | |
" <td>\n", | |
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" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cvl_mean_dynamic_topography_cnes_cls_22</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-295c1980-1986-437d-90e5-c02881a37fda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-295c1980-1986-437d-90e5-c02881a37fda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-738c20c7-2800-4401-83af-c6823fc32832' class='xr-var-data-in' type='checkbox'><label for='data-738c20c7-2800-4401-83af-c6823fc32832' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Mean dynamic topography CNES/CLS 2022</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
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"\n", | |
" <!-- Vertical lines -->\n", | |
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"\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cvl_mean_sea_surface_cnes_22_hybrid</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-c57368fc-8976-4510-bfd3-83d413e3e644' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c57368fc-8976-4510-bfd3-83d413e3e644' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1797b88-e6e7-47bb-8670-5052a547c8e1' class='xr-var-data-in' type='checkbox'><label for='data-b1797b88-e6e7-47bb-8670-5052a547c8e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Mean sea surface height from MSS hybrid model</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"120\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
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"\n", | |
" <!-- Text -->\n", | |
" <text x=\"19.891363\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >519</text>\n", | |
" <text x=\"59.782725\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,59.782725,60.000000)\">4161</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cvl_ocean_tide_fes_2022</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-6d6ed7b9-8cd3-4610-86d5-c04fafb02b7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d6ed7b9-8cd3-4610-86d5-c04fafb02b7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b15c6d34-b717-4026-b018-b83563571f2a' class='xr-var-data-in' type='checkbox'><label for='data-b15c6d34-b717-4026-b018-b83563571f2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Geocentric ocean tide height from FES2022 unstructured grid. Includes the sum of total ocean tide, the corresponding load tide ans equilibrium long-period ocean tide.</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"120\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
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"\n", | |
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" <text x=\"19.891363\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >519</text>\n", | |
" <text x=\"59.782725\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,59.782725,60.000000)\">4161</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cvl_ssha_reference</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-4d8f4368-807f-43ef-8833-cbbc327f5644' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d8f4368-807f-43ef-8833-cbbc327f5644' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a12b665-982e-47ed-a10d-d5db0e29cff6' class='xr-var-data-in' type='checkbox'><label for='data-8a12b665-982e-47ed-a10d-d5db0e29cff6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>calval reference ssha</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"120\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cvl_swh_model</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-bda59bb8-bced-4367-be99-0f67878df96f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bda59bb8-bced-4367-be99-0f67878df96f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1550d9aa-db94-4b84-96c4-621a040d514b' class='xr-var-data-in' type='checkbox'><label for='data-1550d9aa-db94-4b84-96c4-621a040d514b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Significant wave height from model.</dd><dt><span>institution :</span></dt><dd>ECMWF</dd><dt><span>long_name :</span></dt><dd>significant wave height from wave model</dd><dt><span>source :</span></dt><dd>European Centre for Medium-Range Weather Forecasts</dd><dt><span>standard_name :</span></dt><dd>sea_surface_wave_significant_height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
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" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cycle_number</span></div><div class='xr-var-dims'>(num_lines)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161,), meta=np.ndarray></div><input id='attrs-332e74aa-9387-4153-96e0-4bd1b54d37eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-332e74aa-9387-4153-96e0-4bd1b54d37eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac41500a-02b5-47ae-936e-aa906d7a144e' class='xr-var-data-in' type='checkbox'><label for='data-ac41500a-02b5-47ae-936e-aa906d7a144e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 8.13 kiB </td>\n", | |
" <td> 8.13 kiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161,) </td>\n", | |
" <td> (4161,) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> uint16 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
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"\n", | |
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" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4161</text>\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_editing_flag</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-a19f7503-57ae-4d15-8c1e-ee6f58cb744a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a19f7503-57ae-4d15-8c1e-ee6f58cb744a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ad502f3-e502-42dd-a360-5b14d67508f0' class='xr-var-data-in' type='checkbox'><label for='data-6ad502f3-e502-42dd-a360-5b14d67508f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Deduced from L3 DUACS processing.</dd><dt><span>flag_masks :</span></dt><dd>[0, 5, 10, 20, 30, 50, 70, 100, 101, 102]</dd><dt><span>flag_meanings :</span></dt><dd>good local_outliers bad_quality_coast ice soft_outliers extremes mission_event bad_swath_extremities not_on_sea no_data</dd><dt><span>long_name :</span></dt><dd>Data quality flag</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 2.06 MiB </td>\n", | |
" <td> 2.06 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> int8 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
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"\n", | |
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_nadir_orbit_error</span></div><div class='xr-var-dims'>(num_lines)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161,), meta=np.ndarray></div><input id='attrs-b4a0849f-09df-4923-aff2-9d3735640f76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4a0849f-09df-4923-aff2-9d3735640f76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c0b63ed-0ff6-465b-b7a8-7686b637d46b' class='xr-var-data-in' type='checkbox'><label for='data-1c0b63ed-0ff6-465b-b7a8-7686b637d46b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Orbit Error Correction</dd><dt><span>long_name :</span></dt><dd>Orbit Error correction</dd><dt><span>standard_name :</span></dt><dd>orbit_error</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 32.51 kiB </td>\n", | |
" <td> 32.51 kiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161,) </td>\n", | |
" <td> (4161,) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_phase_screen</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-b66f5a11-5385-4303-b05a-7d3a76d16e58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b66f5a11-5385-4303-b05a-7d3a76d16e58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f361278-aad9-4ea0-a563-2a428e9ba416' class='xr-var-data-in' type='checkbox'><label for='data-4f361278-aad9-4ea0-a563-2a428e9ba416' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Estimated with optimal interpolation, field to add to ssha_karin_2</dd><dt><span>long_name :</span></dt><dd>Static calibration of very large scale (one day)</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_phase_screen_orbit</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-32168e43-7caf-4953-bcc5-4f90c8e3112c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32168e43-7caf-4953-bcc5-4f90c8e3112c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec7e1431-e055-443e-b0fb-0857788d767d' class='xr-var-data-in' type='checkbox'><label for='data-ec7e1431-e055-443e-b0fb-0857788d767d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Phase screen correction, orbit-wise component</dd><dt><span>long_name :</span></dt><dd>Sea level anomaly</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
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" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
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"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_phase_screen_static</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-2af78720-b56c-4c29-b64c-f97942efc076' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2af78720-b56c-4c29-b64c-f97942efc076' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae1da5e2-b005-4b57-895a-60751fab831d' class='xr-var-data-in' type='checkbox'><label for='data-ae1da5e2-b005-4b57-895a-60751fab831d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Phase screen correction, static component</dd><dt><span>long_name :</span></dt><dd>Sea level anomaly</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_relative_vorticity</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-baa8b04b-9040-4d23-b9c2-8dd903f8ea26' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-baa8b04b-9040-4d23-b9c2-8dd903f8ea26' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11682513-e9b0-4e71-a4f6-1233fabcf8e1' class='xr-var-data-in' type='checkbox'><label for='data-11682513-e9b0-4e71-a4f6-1233fabcf8e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_speed_meridional</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-0689fa66-c62e-4e10-ad25-9b0a01466625' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0689fa66-c62e-4e10-ad25-9b0a01466625' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99519862-ce2c-4daf-a544-765b4426c11e' class='xr-var-data-in' type='checkbox'><label for='data-99519862-ce2c-4daf-a544-765b4426c11e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geostrophic velocity anomalies: meridian component</dd><dt><span>standard_name :</span></dt><dd>surface_geostrophic_northward_sea_water_velocity_assuming_sea_level_for_geoid</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", | |
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" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
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" <td> </td>\n", | |
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" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
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" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
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" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
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" </tbody>\n", | |
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_speed_meridional_abs</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-566f2d5e-77ac-4ddf-bf53-a68b662c2bb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-566f2d5e-77ac-4ddf-bf53-a68b662c2bb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a87ecea2-ea2a-4f60-8c73-3c567354fdcf' class='xr-var-data-in' type='checkbox'><label for='data-a87ecea2-ea2a-4f60-8c73-3c567354fdcf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Absolute geostrophic velocity: meridian component</dd><dt><span>standard_name :</span></dt><dd>surface_geostrophic_northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", | |
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" <td>\n", | |
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" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
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" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
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" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
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" <tr>\n", | |
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" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_speed_zonal</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-7c5a275d-a31f-4cae-a532-0dde75151b77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c5a275d-a31f-4cae-a532-0dde75151b77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcd59600-c00f-4dc8-8b9e-8558aa50afeb' class='xr-var-data-in' type='checkbox'><label for='data-fcd59600-c00f-4dc8-8b9e-8558aa50afeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Geostrophic velocity anomalies: zonal component</dd><dt><span>standard_name :</span></dt><dd>surface_geostrophic_eastward_sea_water_velocity_assuming_sea_level_for_geoid</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", | |
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" <td>\n", | |
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" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
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" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
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" <tr>\n", | |
" <th> Dask graph </th>\n", | |
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" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_speed_zonal_abs</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-09a6218c-9cf3-471b-a9d8-263162d785b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09a6218c-9cf3-471b-a9d8-263162d785b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c478f8ff-a10d-4c27-b983-84124c89adc8' class='xr-var-data-in' type='checkbox'><label for='data-c478f8ff-a10d-4c27-b983-84124c89adc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Absolute geostrophic velocity: zonal component</dd><dt><span>standard_name :</span></dt><dd>surface_geostrophic_eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
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" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
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" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
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" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
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" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_ssha_karin_2_calibrated</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-4400e637-cd6c-4be7-8239-b62af22d0ea7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4400e637-cd6c-4be7-8239-b62af22d0ea7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08dcdef4-afac-4106-80e6-ff9b288bf217' class='xr-var-data-in' type='checkbox'><label for='data-08dcdef4-afac-4106-80e6-ff9b288bf217' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Height of the sea surface anomaly with all corrections applied but without editing.</dd><dt><span>long_name :</span></dt><dd>sea surface height anomaly</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_reference_ellipsoid</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
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" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
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" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
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" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
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" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_ssha_karin_2_filtered</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-a5452a25-51ce-432f-b8b9-5bc462324031' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5452a25-51ce-432f-b8b9-5bc462324031' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c26b5d8-27b9-425d-af7f-662c257dfd5c' class='xr-var-data-in' type='checkbox'><label for='data-0c26b5d8-27b9-425d-af7f-662c257dfd5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Height of the sea surface anomaly with all corrections applied and denoised using Unet model.</dd><dt><span>long_name :</span></dt><dd>sea surface height anomaly without noise</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_reference_ellipsoid</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
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" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
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" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_strain</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-2cdb0e60-c773-4339-a37f-0c4b3a98acd0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cdb0e60-c773-4339-a37f-0c4b3a98acd0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cbf26b2-0300-4413-8e46-2d42d71b5cd4' class='xr-var-data-in' type='checkbox'><label for='data-3cbf26b2-0300-4413-8e46-2d42d71b5cd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s-1</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
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"\n", | |
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_xcal</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-425efb5d-bb77-4538-a471-a27bb51f7785' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-425efb5d-bb77-4538-a471-a27bb51f7785' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92a1c6fc-f72c-4c03-9baa-0826ae28e7df' class='xr-var-data-in' type='checkbox'><label for='data-92a1c6fc-f72c-4c03-9baa-0826ae28e7df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Estimated from large scale of optimal interpolation, field to add to ssha_karin_2</dd><dt><span>long_name :</span></dt><dd>Calibration of large scale</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"120\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
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"\n", | |
" <!-- Text -->\n", | |
" <text x=\"19.891363\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >519</text>\n", | |
" <text x=\"59.782725\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,59.782725,60.000000)\">4161</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_xcal_qual</span></div><div class='xr-var-dims'>(num_lines)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161,), meta=np.ndarray></div><input id='attrs-921a495a-ffb1-4040-9f12-15cc8d775b91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-921a495a-ffb1-4040-9f12-15cc8d775b91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45c4ab60-42fa-46a2-963c-8ea2065a2d79' class='xr-var-data-in' type='checkbox'><label for='data-45c4ab60-42fa-46a2-963c-8ea2065a2d79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>flag_masks :</span></dt><dd>[0, 1, 2]</dd><dt><span>flag_meanings :</span></dt><dd>good suspect bad</dd><dt><span>long_name :</span></dt><dd>Calibration of large scale quality</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>valid_max :</span></dt><dd>2</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.25 kiB </td>\n", | |
" <td> 16.25 kiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161,) </td>\n", | |
" <td> (4161,) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
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"\n", | |
" <!-- Text -->\n", | |
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4161</text>\n", | |
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>duacs_xcal_status_flag</span></div><div class='xr-var-dims'>(num_lines)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161,), meta=np.ndarray></div><input id='attrs-b943788c-7df8-4340-b0b9-6c22a7101c0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b943788c-7df8-4340-b0b9-6c22a7101c0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0933f3de-bec4-4b1a-9f34-485acbf84eef' class='xr-var-data-in' type='checkbox'><label for='data-0933f3de-bec4-4b1a-9f34-485acbf84eef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Whether the xcal value is estimated (True) or interpolated (False)</dd><dt><span>long_name :</span></dt><dd>Indicator of the xcal value origin</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 4.06 kiB </td>\n", | |
" <td> 4.06 kiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161,) </td>\n", | |
" <td> (4161,) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> bool numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_nadir</span></div><div class='xr-var-dims'>(num_lines)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161,), meta=np.ndarray></div><input id='attrs-7745ffcb-077a-4cf6-bce1-6413e8d2cc62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7745ffcb-077a-4cf6-bce1-6413e8d2cc62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60cffd61-8e10-4a4a-85b8-fe884511e9c1' class='xr-var-data-in' type='checkbox'><label for='data-60cffd61-8e10-4a4a-85b8-fe884511e9c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Geodetic latitude [-80,80] (degrees north of equator) of the satellite nadir point.</dd><dt><span>long_name :</span></dt><dd>latitude of satellite nadir point</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 32.51 kiB </td>\n", | |
" <td> 32.51 kiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161,) </td>\n", | |
" <td> (4161,) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
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"\n", | |
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" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_nadir</span></div><div class='xr-var-dims'>(num_lines)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161,), meta=np.ndarray></div><input id='attrs-dcf8da28-335d-414b-80e6-944270c2217c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcf8da28-335d-414b-80e6-944270c2217c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f780ae8f-c3de-41c2-87ba-deec2097cdd3' class='xr-var-data-in' type='checkbox'><label for='data-f780ae8f-c3de-41c2-87ba-deec2097cdd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Longitude (degrees east of Grenwich meridian) of the satellite nadir point.</dd><dt><span>long_name :</span></dt><dd>longitude of satellite nadir point</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 32.51 kiB </td>\n", | |
" <td> 32.51 kiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161,) </td>\n", | |
" <td> (4161,) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
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"\n", | |
" <!-- Text -->\n", | |
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4161</text>\n", | |
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pass_number</span></div><div class='xr-var-dims'>(num_lines)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161,), meta=np.ndarray></div><input id='attrs-d1797825-f4ac-47a7-9127-657622dd41c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1797825-f4ac-47a7-9127-657622dd41c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cc2befe-f96f-4781-a309-152f211cf595' class='xr-var-data-in' type='checkbox'><label for='data-7cc2befe-f96f-4781-a309-152f211cf595' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 8.13 kiB </td>\n", | |
" <td> 8.13 kiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161,) </td>\n", | |
" <td> (4161,) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> uint16 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Colored Rectangle -->\n", | |
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", | |
"\n", | |
" <!-- Text -->\n", | |
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4161</text>\n", | |
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sig0_karin_2</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-af0d2621-8bd8-42d6-96ba-1b02364a495d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af0d2621-8bd8-42d6-96ba-1b02364a495d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-067fbf98-248d-4b89-af04-b3bcebf6af7a' class='xr-var-data-in' type='checkbox'><label for='data-067fbf98-248d-4b89-af04-b3bcebf6af7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Normalized radar cross section (sigma0) from KaRIn in real, linear units (not decibels). The value may be negative due to noise subtraction. The value is corrected for instrument calibration and atmospheric attenuation. A meteorological model provides the atmospheric attenuation (sig0_cor_atmos_model).</dd><dt><span>long_name :</span></dt><dd>normalized radar cross section (sigma0) from KaRIn</dd><dt><span>quality_flag :</span></dt><dd>sig0_karin_2_qual</dd><dt><span>standard_name :</span></dt><dd>surface_backwards_scattering_coefficient_of_radar_wave</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>10000000.0</dd><dt><span>valid_min :</span></dt><dd>-1000.0</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 8.24 MiB </td>\n", | |
" <td> 8.24 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"120\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
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" <polygon points=\"0.0,0.0 39.78272544138227,0.0 39.78272544138227,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", | |
"\n", | |
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" <text x=\"59.782725\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,59.782725,60.000000)\">4161</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssh_karin_2_qual</span></div><div class='xr-var-dims'>(num_lines, num_pixels)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161, 519), meta=np.ndarray></div><input id='attrs-c17a4329-3d92-4843-a3e7-0db928efcead' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c17a4329-3d92-4843-a3e7-0db928efcead' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41ca18cf-08ae-4326-9d0c-34f7f9d5a9db' class='xr-var-data-in' type='checkbox'><label for='data-41ca18cf-08ae-4326-9d0c-34f7f9d5a9db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Quality flag for sea surface height from KaRIn in ssh_karin_2 variable.</dd><dt><span>flag_masks :</span></dt><dd>[1, 2, 4, 8, 16, 64, 512, 1024, 2048, 4096, 8192, 32768, 65536, 131072, 262144, 524288, 16777216, 33554432, 536870912, 1073741824, 2147483648]</dd><dt><span>flag_meanings :</span></dt><dd>suspect_large_ssh_delta suspect_large_ssh_std suspect_large_ssh_window_std suspect_beam_used suspect_less_than_nine_beams suspect_ssb_out_of_range suspect_karin_telem suspect_orbit_control suspect_sc_event_flag suspect_tvp_qual suspect_volumetric_corr degraded_ssb_not_computable degraded_media_delays_missing degraded_beam_used degraded_large_attitude degraded_karin_ifft_overflow bad_karin_telem bad_very_large_attitude bad_outside_of_range degraded bad_not_usable</dd><dt><span>long_name :</span></dt><dd>quality flag for sea surface height from KaRIn</dd><dt><span>standard_name :</span></dt><dd>status_flag</dd><dt><span>valid_max :</span></dt><dd>3809459807</dd><dt><span>valid_min :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 16.48 MiB </td>\n", | |
" <td> 16.48 MiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161, 519) </td>\n", | |
" <td> (4161, 519) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"89\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"39\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
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"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"120\" style=\"stroke-width:2\" />\n", | |
"\n", | |
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" <text x=\"19.891363\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >519</text>\n", | |
" <text x=\"59.782725\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,59.782725,60.000000)\">4161</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(num_lines)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161,), meta=np.ndarray></div><input id='attrs-f70aa906-6a52-4a66-a8aa-b6c5fc5bdb47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f70aa906-6a52-4a66-a8aa-b6c5fc5bdb47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cba01e5-4e45-4cef-a094-2c648249f886' class='xr-var-data-in' type='checkbox'><label for='data-9cba01e5-4e45-4cef-a094-2c648249f886' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.</dd><dt><span>leap_second :</span></dt><dd>0000-00-00T00:00:00Z</dd><dt><span>long_name :</span></dt><dd>time in UTC</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>tai_utc_difference :</span></dt><dd>37.0</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 32.51 kiB </td>\n", | |
" <td> 32.51 kiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161,) </td>\n", | |
" <td> (4161,) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
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"\n", | |
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"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_tai</span></div><div class='xr-var-dims'>(num_lines)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161,), meta=np.ndarray></div><input id='attrs-c435a15a-038b-4381-a48e-065fbdc8ba27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c435a15a-038b-4381-a48e-065fbdc8ba27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c92d17c3-755b-447a-bcd6-eb8f85873c7d' class='xr-var-data-in' type='checkbox'><label for='data-c92d17c3-755b-447a-bcd6-eb8f85873c7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [time:tai_utc_difference].</dd><dt><span>long_name :</span></dt><dd>time in TAI</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>tai_utc_difference :</span></dt><dd>37.0</dd></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 32.51 kiB </td>\n", | |
" <td> 32.51 kiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161,) </td>\n", | |
" <td> (4161,) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Colored Rectangle -->\n", | |
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", | |
"\n", | |
" <!-- Text -->\n", | |
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4161</text>\n", | |
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>version</span></div><div class='xr-var-dims'>(num_lines)</div><div class='xr-var-dtype'>|S7</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(4161,), meta=np.ndarray></div><input id='attrs-453daa9f-0227-427c-aa18-b24051784c2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-453daa9f-0227-427c-aa18-b24051784c2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d50cfaa-051b-4451-9ec8-267c9de44f4a' class='xr-var-data-in' type='checkbox'><label for='data-6d50cfaa-051b-4451-9ec8-267c9de44f4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n", | |
" <tr>\n", | |
" <td>\n", | |
" <table style=\"border-collapse: collapse;\">\n", | |
" <thead>\n", | |
" <tr>\n", | |
" <td> </td>\n", | |
" <th> Array </th>\n", | |
" <th> Chunk </th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Bytes </th>\n", | |
" <td> 28.44 kiB </td>\n", | |
" <td> 28.44 kiB </td>\n", | |
" </tr>\n", | |
" \n", | |
" <tr>\n", | |
" <th> Shape </th>\n", | |
" <td> (4161,) </td>\n", | |
" <td> (4161,) </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Dask graph </th>\n", | |
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th> Data type </th>\n", | |
" <td colspan=\"2\"> |S7 numpy.ndarray </td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
" </table>\n", | |
" </td>\n", | |
" <td>\n", | |
" <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", | |
"\n", | |
" <!-- Horizontal lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Vertical lines -->\n", | |
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n", | |
"\n", | |
" <!-- Colored Rectangle -->\n", | |
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", | |
"\n", | |
" <!-- Text -->\n", | |
" <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4161</text>\n", | |
" <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n", | |
"</svg>\n", | |
" </td>\n", | |
" </tr>\n", | |
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d0b1ee61-0290-44d0-8b27-c4c3aeaa96a9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d0b1ee61-0290-44d0-8b27-c4c3aeaa96a9' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8a29977b-dec8-485d-917c-2f36dcce9341' class='xr-section-summary-in' type='checkbox' checked><label for='section-8a29977b-dec8-485d-917c-2f36dcce9341' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>latc :</span></dt><dd>39.961324</dd><dt><span>lonc :</span></dt><dd>4.3749</dd><dt><span>phi :</span></dt><dd>76.80931729750102</dd></dl></div></li></ul></div></div>" | |
], | |
"text/plain": [ | |
"<xarray.Dataset>\n", | |
"Dimensions: (num_lines: 4161, num_pixels: 519)\n", | |
"Coordinates:\n", | |
" latitude (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" longitude (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
"Dimensions without coordinates: num_lines, num_pixels\n", | |
"Data variables: (12/36)\n", | |
" ancillary_surface_classification_flag (num_lines, num_pixels) float32 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" cross_track_distance (num_lines, num_pixels) float32 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" cvl_dac (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" cvl_distance_to_coast (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" cvl_flag_val (num_lines, num_pixels) float32 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" cvl_ice_conc (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" ... ...\n", | |
" pass_number (num_lines) uint16 dask.array<chunksize=(4161,), meta=np.ndarray>\n", | |
" sig0_karin_2 (num_lines, num_pixels) float32 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" ssh_karin_2_qual (num_lines, num_pixels) float64 dask.array<chunksize=(4161, 519), meta=np.ndarray>\n", | |
" time (num_lines) datetime64[ns] dask.array<chunksize=(4161,), meta=np.ndarray>\n", | |
" time_tai (num_lines) datetime64[ns] dask.array<chunksize=(4161,), meta=np.ndarray>\n", | |
" version (num_lines) |S7 dask.array<chunksize=(4161,), meta=np.ndarray>\n", | |
"Attributes:\n", | |
" latc: 39.961324\n", | |
" lonc: 4.3749\n", | |
" phi: 76.80931729750102" | |
] | |
}, | |
"execution_count": 2, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"zarr = \"/home/datawork-lops-osi/aponte/swot/cswot/swot/l3_v1.0.1/3_500.zarr\"\n", | |
"ds = xr.open_zarr(zarr)\n", | |
"ds" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"id": "801d7fe1-f65f-471d-98c1-76db70f340e3", | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# get grid orientation and metrics\n", | |
"\n", | |
"def add_grid_metrics(ds):\n", | |
" \"\"\" add grid spatial metrics \"\"\"\n", | |
"\n", | |
" geod = Geod(ellps=\"WGS84\")\n", | |
"\n", | |
" lon, lat = ds.longitude, ds.latitude\n", | |
" dims = lon.dims\n", | |
" \n", | |
" # d/dx where x is cross-track\n", | |
" az12, az21, dx = geod.inv(\n", | |
" lon, lat, lon.shift(num_pixels=-1), lat.shift(num_pixels=-1),\n", | |
" )\n", | |
" \n", | |
" ds = ds.assign_coords(dx=(dims, dx), phi=(dims, az12*np.pi/180))\n", | |
" \n", | |
" ds[\"dx\"] = (\n", | |
" ds[\"dx\"]\n", | |
" .ffill(\"num_pixels\")\n", | |
" .where(ds[\"duacs_editing_flag\"]<5)\n", | |
" )\n", | |
"\n", | |
" # phi is cross-track direction from north\n", | |
" ds[\"phi\"] = (\n", | |
" ds[\"phi\"]\n", | |
" .ffill(\"num_pixels\")\n", | |
" .where(ds[\"duacs_editing_flag\"]<5)\n", | |
" )\n", | |
"\n", | |
" # d/dy where y is along-track\n", | |
" \n", | |
" az12, az21, dy = geod.inv(\n", | |
" lon, lat, lon.shift(num_lines=-1), lat.shift(num_lines=-1),\n", | |
" )\n", | |
" \n", | |
" ds = ds.assign_coords(dy=(dims, dy))\n", | |
" \n", | |
" ds[\"dy\"] = (\n", | |
" ds[\"dy\"]\n", | |
" .ffill(\"num_lines\")\n", | |
" .where(ds[\"duacs_editing_flag\"]<5)\n", | |
" )\n", | |
" \n", | |
" return ds\n", | |
"\n", | |
"ds = add_grid_metrics(ds)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"id": "f8711672-646e-4e33-bb87-05b293abb14b", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.collections.QuadMesh at 0x14c247037c10>" | |
] | |
}, | |
"execution_count": 4, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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", | |
"text/plain": [ | |
"<Figure size 1000x500 with 4 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"fig, axes = plt.subplots(1,2, figsize=(10,5))\n", | |
"ds[\"dx\"].plot(ax=axes[0])\n", | |
"(ds[\"phi\"]*180/np.pi).plot(ax=axes[1])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"id": "7410b9f4-dfc0-4e40-abc3-b88fefa79f08", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"(<xarray.DataArray 'dx' ()>\n", | |
" array(249.88069072),\n", | |
" <xarray.DataArray 'dx' ()>\n", | |
" array(0.55453346))" | |
] | |
}, | |
"execution_count": 5, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"ds[\"dx\"].mean().compute(), ds[\"dx\"].std().compute()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"id": "e4664170-7aee-4f36-a53e-5f01a8a135fe", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"0.002" | |
] | |
}, | |
"execution_count": 6, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"0.5/250" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"id": "98268a67-2e1f-4875-ac51-f75dc6387287", | |
"metadata": {}, | |
"source": [ | |
"## gradient" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 64, | |
"id": "d8739c8d-6481-46c0-9244-f741b32d2eae", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.collections.QuadMesh at 0x14c158cef3a0>" | |
] | |
}, | |
"execution_count": 64, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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", | |
"text/plain": [ | |
"<Figure size 640x480 with 2 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"da = ds.duacs_ssha_karin_2_filtered\n", | |
"\n", | |
"# handle NaNs to mitigate propagation\n", | |
"#da = da.fillna(0.)\n", | |
"# or interpolate with da.interpolate_na\n", | |
"\n", | |
"da.plot()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 65, | |
"id": "c2546bb9-fc1a-4eec-b1d1-478bb6553583", | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# \"naive\" approach, noise will have an impact\n", | |
"dx = 250 # meters\n", | |
"da_dx = da.differentiate(\"num_pixels\")/dx" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 66, | |
"id": "e1c0b764-716c-46b7-b080-1ade53441217", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"\u001b[0;31mSignature:\u001b[0m\n", | |
"\u001b[0mgaussian_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m \u001b[0msigma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'reflect'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m \u001b[0mcval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m \u001b[0mtruncate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m \u001b[0mradius\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", | |
"\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", | |
"\u001b[0;31mDocstring:\u001b[0m\n", | |
"Multidimensional Gaussian filter.\n", | |
"\n", | |
"Parameters\n", | |
"----------\n", | |
"input : array_like\n", | |
" The input array.\n", | |
"sigma : scalar or sequence of scalars\n", | |
" Standard deviation for Gaussian kernel. The standard\n", | |
" deviations of the Gaussian filter are given for each axis as a\n", | |
" sequence, or as a single number, in which case it is equal for\n", | |
" all axes.\n", | |
"order : int or sequence of ints, optional\n", | |
" The order of the filter along each axis is given as a sequence\n", | |
" of integers, or as a single number. An order of 0 corresponds\n", | |
" to convolution with a Gaussian kernel. A positive order\n", | |
" corresponds to convolution with that derivative of a Gaussian.\n", | |
"output : array or dtype, optional\n", | |
" The array in which to place the output, or the dtype of the\n", | |
" returned array. By default an array of the same dtype as input\n", | |
" will be created.\n", | |
"mode : str or sequence, optional\n", | |
" The `mode` parameter determines how the input array is extended\n", | |
" when the filter overlaps a border. By passing a sequence of modes\n", | |
" with length equal to the number of dimensions of the input array,\n", | |
" different modes can be specified along each axis. Default value is\n", | |
" 'reflect'. The valid values and their behavior is as follows:\n", | |
"\n", | |
" 'reflect' (`d c b a | a b c d | d c b a`)\n", | |
" The input is extended by reflecting about the edge of the last\n", | |
" pixel. This mode is also sometimes referred to as half-sample\n", | |
" symmetric.\n", | |
"\n", | |
" 'constant' (`k k k k | a b c d | k k k k`)\n", | |
" The input is extended by filling all values beyond the edge with\n", | |
" the same constant value, defined by the `cval` parameter.\n", | |
"\n", | |
" 'nearest' (`a a a a | a b c d | d d d d`)\n", | |
" The input is extended by replicating the last pixel.\n", | |
"\n", | |
" 'mirror' (`d c b | a b c d | c b a`)\n", | |
" The input is extended by reflecting about the center of the last\n", | |
" pixel. This mode is also sometimes referred to as whole-sample\n", | |
" symmetric.\n", | |
"\n", | |
" 'wrap' (`a b c d | a b c d | a b c d`)\n", | |
" The input is extended by wrapping around to the opposite edge.\n", | |
"\n", | |
" For consistency with the interpolation functions, the following mode\n", | |
" names can also be used:\n", | |
"\n", | |
" 'grid-constant'\n", | |
" This is a synonym for 'constant'.\n", | |
"\n", | |
" 'grid-mirror'\n", | |
" This is a synonym for 'reflect'.\n", | |
"\n", | |
" 'grid-wrap'\n", | |
" This is a synonym for 'wrap'.\n", | |
"cval : scalar, optional\n", | |
" Value to fill past edges of input if `mode` is 'constant'. Default\n", | |
" is 0.0.\n", | |
"truncate : float, optional\n", | |
" Truncate the filter at this many standard deviations.\n", | |
" Default is 4.0.\n", | |
"radius : None or int or sequence of ints, optional\n", | |
" Radius of the Gaussian kernel. The radius are given for each axis\n", | |
" as a sequence, or as a single number, in which case it is equal\n", | |
" for all axes. If specified, the size of the kernel along each axis\n", | |
" will be ``2*radius + 1``, and `truncate` is ignored.\n", | |
" Default is None.\n", | |
"axes : tuple of int or None, optional\n", | |
" If None, `input` is filtered along all axes. Otherwise,\n", | |
" `input` is filtered along the specified axes. When `axes` is\n", | |
" specified, any tuples used for `sigma`, `order`, `mode` and/or `radius`\n", | |
" must match the length of `axes`. The ith entry in any of these tuples\n", | |
" corresponds to the ith entry in `axes`.\n", | |
"\n", | |
"Returns\n", | |
"-------\n", | |
"gaussian_filter : ndarray\n", | |
" Returned array of same shape as `input`.\n", | |
"\n", | |
"Notes\n", | |
"-----\n", | |
"The multidimensional filter is implemented as a sequence of\n", | |
"1-D convolution filters. The intermediate arrays are\n", | |
"stored in the same data type as the output. Therefore, for output\n", | |
"types with a limited precision, the results may be imprecise\n", | |
"because intermediate results may be stored with insufficient\n", | |
"precision.\n", | |
"\n", | |
"The Gaussian kernel will have size ``2*radius + 1`` along each axis. If\n", | |
"`radius` is None, the default ``radius = round(truncate * sigma)`` will be\n", | |
"used.\n", | |
"\n", | |
"Examples\n", | |
"--------\n", | |
">>> from scipy.ndimage import gaussian_filter\n", | |
">>> import numpy as np\n", | |
">>> a = np.arange(50, step=2).reshape((5,5))\n", | |
">>> a\n", | |
"array([[ 0, 2, 4, 6, 8],\n", | |
" [10, 12, 14, 16, 18],\n", | |
" [20, 22, 24, 26, 28],\n", | |
" [30, 32, 34, 36, 38],\n", | |
" [40, 42, 44, 46, 48]])\n", | |
">>> gaussian_filter(a, sigma=1)\n", | |
"array([[ 4, 6, 8, 9, 11],\n", | |
" [10, 12, 14, 15, 17],\n", | |
" [20, 22, 24, 25, 27],\n", | |
" [29, 31, 33, 34, 36],\n", | |
" [35, 37, 39, 40, 42]])\n", | |
"\n", | |
">>> from scipy import datasets\n", | |
">>> import matplotlib.pyplot as plt\n", | |
">>> fig = plt.figure()\n", | |
">>> plt.gray() # show the filtered result in grayscale\n", | |
">>> ax1 = fig.add_subplot(121) # left side\n", | |
">>> ax2 = fig.add_subplot(122) # right side\n", | |
">>> ascent = datasets.ascent()\n", | |
">>> result = gaussian_filter(ascent, sigma=5)\n", | |
">>> ax1.imshow(ascent)\n", | |
">>> ax2.imshow(result)\n", | |
">>> plt.show()\n", | |
"\u001b[0;31mFile:\u001b[0m /home1/datawork/aponte/miniconda3/envs/equinox/lib/python3.10/site-packages/scipy/ndimage/_filters.py\n", | |
"\u001b[0;31mType:\u001b[0m function" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"from scipy.ndimage import gaussian_filter\n", | |
"gaussian_filter?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 67, | |
"id": "11856c4f-3f11-46fb-9920-fe94af03e88e", | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# gaussian derivative\n", | |
"\n", | |
"def gradient_gauss(da, cutoff = 1e3, dx = 250, **kwargs):\n", | |
"\n", | |
" # cross-track\n", | |
" i = da.get_axis_num(\"num_pixels\")\n", | |
" order = [0,0]\n", | |
" order[i] = 1\n", | |
" da_dx_gauss = xr.DataArray(gaussian_filter(da, sigma=cutoff/dx, order=order, **kwargs), dims=da.dims) /dx\n", | |
" #da_dx = da_dx.where(da)\n", | |
" \n", | |
" # cross-track\n", | |
" i = da.get_axis_num(\"num_lines\")\n", | |
" order = [0,0]\n", | |
" order[i] = 1\n", | |
" da_dy_gauss = xr.DataArray(gaussian_filter(da, sigma=cutoff/dx, order=order, **kwargs), dims=da.dims) /dx\n", | |
" #da_dx = da_dx.where(da)\n", | |
"\n", | |
" return da_dx_gauss, da_dy_gauss\n", | |
"\n", | |
"\n", | |
"da_dx_gauss, da_dy_gauss = gradient_gauss(da)\n", | |
"da_dx_gaussb, da_dy_gaussb = gradient_gauss(da, cutoff=5e3, truncate=2.)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 68, | |
"id": "06672ddc-1ee8-410c-8196-c01df7d0e40c", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.collections.QuadMesh at 0x14c150698490>" | |
] | |
}, | |
"execution_count": 68, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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", | |
"text/plain": [ | |
"<Figure size 1000x500 with 4 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"fig, axes = plt.subplots(1,2,figsize=(10,5))\n", | |
"\n", | |
"ax = axes[0]\n", | |
"da_dx.plot(ax=ax, vmax=1e-5)\n", | |
"\n", | |
"ax = axes[1]\n", | |
"da_dx_gauss.plot(ax=ax, vmax=1e-5)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 69, | |
"id": "c56dabe4-d4e2-43c3-8890-36b060509020", | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.collections.QuadMesh at 0x14c14a22a320>" | |
] | |
}, | |
"execution_count": 69, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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