diff --git a/notebooks/ICESat-2_Cloud_Access/ICESat2-CryoSat2-Coincident.ipynb b/notebooks/ICESat-2_Cloud_Access/ICESat2-CryoSat2-Coincident.ipynb
index f4e9c87..c7e5cc8 100644
--- a/notebooks/ICESat-2_Cloud_Access/ICESat2-CryoSat2-Coincident.ipynb
+++ b/notebooks/ICESat-2_Cloud_Access/ICESat2-CryoSat2-Coincident.ipynb
@@ -56,604 +56,10 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "e911afb1-b247-4412-9342-1e8865b3084e",
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "(function(root) {\n",
- " function now() {\n",
- " return new Date();\n",
- " }\n",
- "\n",
- " var force = true;\n",
- " var py_version = '3.4.0'.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': {}, 'shim': {}});\n",
- " root._bokeh_is_loading = css_urls.length + 0;\n",
- " } else {\n",
- " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
- " }\n",
- "\n",
- " 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",
- " } 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-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.1/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 = '3.4.0'.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': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n 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 } 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-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.1/dist/panel.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\n }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));"
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/javascript": [
- "\n",
- "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
- " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
- "}\n",
- "\n",
- "\n",
- " function JupyterCommManager() {\n",
- " }\n",
- "\n",
- " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
- " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
- " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
- " comm_manager.register_target(comm_id, function(comm) {\n",
- " comm.on_msg(msg_handler);\n",
- " });\n",
- " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
- " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
- " comm.onMsg = msg_handler;\n",
- " });\n",
- " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
- " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
- " var messages = comm.messages[Symbol.asyncIterator]();\n",
- " function processIteratorResult(result) {\n",
- " var message = result.value;\n",
- " console.log(message)\n",
- " var content = {data: message.data, comm_id};\n",
- " var buffers = []\n",
- " for (var buffer of message.buffers || []) {\n",
- " buffers.push(new DataView(buffer))\n",
- " }\n",
- " var metadata = message.metadata || {};\n",
- " var msg = {content, buffers, metadata}\n",
- " msg_handler(msg);\n",
- " return messages.next().then(processIteratorResult);\n",
- " }\n",
- " return messages.next().then(processIteratorResult);\n",
- " })\n",
- " }\n",
- " }\n",
- "\n",
- " JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
- " if (comm_id in window.PyViz.comms) {\n",
- " return window.PyViz.comms[comm_id];\n",
- " } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
- " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
- " var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
- " if (msg_handler) {\n",
- " comm.on_msg(msg_handler);\n",
- " }\n",
- " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
- " var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
- " comm.open();\n",
- " if (msg_handler) {\n",
- " comm.onMsg = msg_handler;\n",
- " }\n",
- " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
- " var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
- " comm_promise.then((comm) => {\n",
- " window.PyViz.comms[comm_id] = comm;\n",
- " if (msg_handler) {\n",
- " var messages = comm.messages[Symbol.asyncIterator]();\n",
- " function processIteratorResult(result) {\n",
- " var message = result.value;\n",
- " var content = {data: message.data};\n",
- " var metadata = message.metadata || {comm_id};\n",
- " var msg = {content, metadata}\n",
- " msg_handler(msg);\n",
- " return messages.next().then(processIteratorResult);\n",
- " }\n",
- " return messages.next().then(processIteratorResult);\n",
- " }\n",
- " }) \n",
- " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
- " return comm_promise.then((comm) => {\n",
- " comm.send(data, metadata, buffers, disposeOnDone);\n",
- " });\n",
- " };\n",
- " var comm = {\n",
- " send: sendClosure\n",
- " };\n",
- " }\n",
- " window.PyViz.comms[comm_id] = comm;\n",
- " return comm;\n",
- " }\n",
- " window.PyViz.comm_manager = new JupyterCommManager();\n",
- " \n",
- "\n",
- "\n",
- "var JS_MIME_TYPE = 'application/javascript';\n",
- "var HTML_MIME_TYPE = 'text/html';\n",
- "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
- "var CLASS_NAME = 'output';\n",
- "\n",
- "/**\n",
- " * Render data to the DOM node\n",
- " */\n",
- "function render(props, node) {\n",
- " var div = document.createElement(\"div\");\n",
- " var script = document.createElement(\"script\");\n",
- " node.appendChild(div);\n",
- " node.appendChild(script);\n",
- "}\n",
- "\n",
- "/**\n",
- " * Handle when a new output is added\n",
- " */\n",
- "function handle_add_output(event, handle) {\n",
- " var output_area = handle.output_area;\n",
- " var output = handle.output;\n",
- " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
- " return\n",
- " }\n",
- " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
- " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
- " if (id !== undefined) {\n",
- " var nchildren = toinsert.length;\n",
- " var html_node = toinsert[nchildren-1].children[0];\n",
- " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
- " var scripts = [];\n",
- " var nodelist = html_node.querySelectorAll(\"script\");\n",
- " for (var i in nodelist) {\n",
- " if (nodelist.hasOwnProperty(i)) {\n",
- " scripts.push(nodelist[i])\n",
- " }\n",
- " }\n",
- "\n",
- " scripts.forEach( function (oldScript) {\n",
- " var newScript = document.createElement(\"script\");\n",
- " var attrs = [];\n",
- " var nodemap = oldScript.attributes;\n",
- " for (var j in nodemap) {\n",
- " if (nodemap.hasOwnProperty(j)) {\n",
- " attrs.push(nodemap[j])\n",
- " }\n",
- " }\n",
- " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
- " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
- " oldScript.parentNode.replaceChild(newScript, oldScript);\n",
- " });\n",
- " if (JS_MIME_TYPE in output.data) {\n",
- " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
- " }\n",
- " output_area._hv_plot_id = id;\n",
- " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
- " window.PyViz.plot_index[id] = Bokeh.index[id];\n",
- " } else {\n",
- " window.PyViz.plot_index[id] = null;\n",
- " }\n",
- " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
- " var bk_div = document.createElement(\"div\");\n",
- " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
- " var script_attrs = bk_div.children[0].attributes;\n",
- " for (var i = 0; i < script_attrs.length; i++) {\n",
- " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
- " }\n",
- " // store reference to server id on output_area\n",
- " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
- " }\n",
- "}\n",
- "\n",
- "/**\n",
- " * Handle when an output is cleared or removed\n",
- " */\n",
- "function handle_clear_output(event, handle) {\n",
- " var id = handle.cell.output_area._hv_plot_id;\n",
- " var server_id = handle.cell.output_area._bokeh_server_id;\n",
- " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
- " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
- " if (server_id !== null) {\n",
- " comm.send({event_type: 'server_delete', 'id': server_id});\n",
- " return;\n",
- " } else if (comm !== null) {\n",
- " comm.send({event_type: 'delete', 'id': id});\n",
- " }\n",
- " delete PyViz.plot_index[id];\n",
- " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
- " var doc = window.Bokeh.index[id].model.document\n",
- " doc.clear();\n",
- " const i = window.Bokeh.documents.indexOf(doc);\n",
- " if (i > -1) {\n",
- " window.Bokeh.documents.splice(i, 1);\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "/**\n",
- " * Handle kernel restart event\n",
- " */\n",
- "function handle_kernel_cleanup(event, handle) {\n",
- " delete PyViz.comms[\"hv-extension-comm\"];\n",
- " window.PyViz.plot_index = {}\n",
- "}\n",
- "\n",
- "/**\n",
- " * Handle update_display_data messages\n",
- " */\n",
- "function handle_update_output(event, handle) {\n",
- " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
- " handle_add_output(event, handle)\n",
- "}\n",
- "\n",
- "function register_renderer(events, OutputArea) {\n",
- " function append_mime(data, metadata, element) {\n",
- " // create a DOM node to render to\n",
- " var toinsert = this.create_output_subarea(\n",
- " metadata,\n",
- " CLASS_NAME,\n",
- " EXEC_MIME_TYPE\n",
- " );\n",
- " this.keyboard_manager.register_events(toinsert);\n",
- " // Render to node\n",
- " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
- " render(props, toinsert[0]);\n",
- " element.append(toinsert);\n",
- " return toinsert\n",
- " }\n",
- "\n",
- " events.on('output_added.OutputArea', handle_add_output);\n",
- " events.on('output_updated.OutputArea', handle_update_output);\n",
- " events.on('clear_output.CodeCell', handle_clear_output);\n",
- " events.on('delete.Cell', handle_clear_output);\n",
- " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
- "\n",
- " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
- " safe: true,\n",
- " index: 0\n",
- " });\n",
- "}\n",
- "\n",
- "if (window.Jupyter !== undefined) {\n",
- " try {\n",
- " var events = require('base/js/events');\n",
- " var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
- " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
- " register_renderer(events, OutputArea);\n",
- " }\n",
- " } catch(err) {\n",
- " }\n",
- "}\n"
- ],
- "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n var bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n var script_attrs = bk_div.children[0].attributes;\n for (var i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n"
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.holoviews_exec.v0+json": "",
- "text/html": [
- "
\n",
- ""
- ]
- },
- "metadata": {
- "application/vnd.holoviews_exec.v0+json": {
- "id": "p1002"
- }
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import os\n",
"import platform\n",
@@ -769,14 +175,14 @@
"id": "aeb468e5-7a5a-456c-96cb-6808af3f5eaa",
"metadata": {},
"source": [
- "#### Use `earthaccess` for querying and direct S3 access of ATL10\n",
+ "#### Use `earthaccess` for direct S3 access of ATL10\n",
"\n",
"First we authenticate using `earthaccess`"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "2165876a-c5fa-4df9-b7f5-c82f01e7bdba",
"metadata": {},
"outputs": [],
@@ -794,7 +200,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "faa2297b-2699-466d-a872-1527114dfd46",
"metadata": {},
"outputs": [],
@@ -837,53 +243,10 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "902b81e5-6719-48bb-a66d-03daf8fd7374",
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "ffece4b13ba24fad9517ad71b7fca2bc",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "QUEUEING TASKS | : 0%| | 0/25 [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "52f63516496b4a2094238b668c46ece5",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "PROCESSING TASKS | : 0%| | 0/25 [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9e15df3b18b947bf922dcf9663397d9a",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "COLLECTING RESULTS | : 0%| | 0/25 [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"def cloud_atl10_urls(on_prem_urls):\n",
" import re\n",
@@ -921,447 +284,10 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "1d1102e5-561e-41e8-81df-e4ff543d19c1",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset> Size: 4MB\n",
- "Dimensions: (delta_time: 65982)\n",
- "Coordinates:\n",
- " * delta_time (delta_time) datetime64[ns] 528kB 2019-11-21T19:48:...\n",
- " latitude (delta_time) float64 528kB ...\n",
- " longitude (delta_time) float64 528kB ...\n",
- "Data variables:\n",
- " beam_fb_confidence (delta_time) float32 264kB ...\n",
- " beam_fb_height (delta_time) float32 264kB ...\n",
- " beam_fb_quality_flag (delta_time) int8 66kB ...\n",
- " beam_fb_unc (delta_time) float32 264kB ...\n",
- " beam_refsurf_ndx (delta_time) int32 264kB ...\n",
- " geoseg_beg (delta_time) int32 264kB ...\n",
- " geoseg_end (delta_time) int32 264kB ...\n",
- " height_segment_id (delta_time) int32 264kB ...\n",
- " seg_dist_x (delta_time) float64 528kB ...\n",
- "Attributes:\n",
- " Description: Contains freeboard estimate and associated parameters compu...\n",
- " data_rate: Data within this group are stored at the variable individua... Dimensions:
Coordinates: (3)
Data variables: (9)
Indexes: (1)
PandasIndex
PandasIndex(DatetimeIndex(['2019-11-21 19:48:52.331834504',\n",
- " '2019-11-21 19:48:52.446915976',\n",
- " '2019-11-21 19:48:52.449103880',\n",
- " '2019-11-21 19:48:52.450941792',\n",
- " '2019-11-21 19:48:52.452535720',\n",
- " '2019-11-21 19:48:52.453978552',\n",
- " '2019-11-21 19:48:52.460776520',\n",
- " '2019-11-21 19:48:52.468344456',\n",
- " '2019-11-21 19:48:52.472513240',\n",
- " '2019-11-21 19:48:52.483181808',\n",
- " ...\n",
- " '2019-11-21 19:54:57.558923392',\n",
- " '2019-11-21 19:54:57.567318104',\n",
- " '2019-11-21 19:54:59.917396776',\n",
- " '2019-11-21 19:54:59.924788784',\n",
- " '2019-11-21 19:54:59.932679856',\n",
- " '2019-11-21 19:54:59.938481432',\n",
- " '2019-11-21 19:54:59.948442072',\n",
- " '2019-11-21 19:54:59.959646624',\n",
- " '2019-11-21 19:54:59.967252120',\n",
- " '2019-11-21 19:54:59.971622344'],\n",
- " dtype='datetime64[ns]', name='delta_time', length=65982, freq=None)) Attributes: (2)
Description : Contains freeboard estimate and associated parameters computed by its beam reference surface section. data_rate : Data within this group are stored at the variable individual height/freeboard segment rate "
- ],
- "text/plain": [
- " Size: 4MB\n",
- "Dimensions: (delta_time: 65982)\n",
- "Coordinates:\n",
- " * delta_time (delta_time) datetime64[ns] 528kB 2019-11-21T19:48:...\n",
- " latitude (delta_time) float64 528kB ...\n",
- " longitude (delta_time) float64 528kB ...\n",
- "Data variables:\n",
- " beam_fb_confidence (delta_time) float32 264kB ...\n",
- " beam_fb_height (delta_time) float32 264kB ...\n",
- " beam_fb_quality_flag (delta_time) int8 66kB ...\n",
- " beam_fb_unc (delta_time) float32 264kB ...\n",
- " beam_refsurf_ndx (delta_time) int32 264kB ...\n",
- " geoseg_beg (delta_time) int32 264kB ...\n",
- " geoseg_end (delta_time) int32 264kB ...\n",
- " height_segment_id (delta_time) int32 264kB ...\n",
- " seg_dist_x (delta_time) float64 528kB ...\n",
- "Attributes:\n",
- " Description: Contains freeboard estimate and associated parameters compu...\n",
- " data_rate: Data within this group are stored at the variable individua..."
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"ds_is2 = xr.open_dataset(icesat2_files[1], group='gt2r/freeboard_segment')\n",
"ds_is2"
@@ -1377,98 +303,10 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"id": "0503dfee-7d66-49fa-8482-ad2dc330ff52",
"metadata": {},
- "outputs": [
- {
- "data": {},
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.holoviews_exec.v0+json": "",
- "text/html": [
- "\n",
- ""
- ],
- "text/plain": [
- ":Scatter [longitude] (beam_fb_height)"
- ]
- },
- "execution_count": 6,
- "metadata": {
- "application/vnd.holoviews_exec.v0+json": {
- "id": "p1004"
- }
- },
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"ds_is2['beam_fb_height'].hvplot(kind='scatter', s=2)"
]
@@ -1485,45 +323,10 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"id": "60c1704a-0b17-4d4f-a7a8-dac8fe5b9701",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['CS_LTA__SIR_SAR_2__20200110T203717_20200110T204612_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200308T174708_20200308T175621_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200204T191657_20200204T192558_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200413T053254_20200413T053659_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20191005T151255_20191005T151621_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20191121T231659_20191121T232817_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200121T094259_20200121T095800_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200215T082253_20200215T083741_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200329T163208_20200329T164044_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20191030T135252_20191030T135600_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200319T065300_20200319T070802_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20191216T215645_20191216T220909_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20191009T150801_20191009T151142_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200315T065755_20200315T071241_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200402T162707_20200402T163602_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200409T053748_20200409T054151_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200114T203033_20200114T204440_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20191024T004201_20191024T005059_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20191103T134759_20191103T135125_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20191128T122800_20191128T123212_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200427T150701_20200427T151544_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200219T081800_20200219T083303_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20191227T110305_20191227T111751_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200304T175209_20200304T180102_E001.nc',\n",
- " 'CS_LTA__SIR_SAR_2__20200208T191154_20200208T192117_E001.nc']"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"downloaded_files = os.listdir(path)\n",
"downloaded_files"
@@ -1539,517 +342,10 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"id": "8657ed36-d583-46b9-8ed2-9b1b57cb5786",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- "\n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- " \n",
- "
<xarray.Dataset> Size: 3MB\n",
- "Dimensions: (time_cor_01: 596, time_20_ku: 11905)\n",
- "Coordinates:\n",
- " lat_01 (time_cor_01) float64 5kB ...\n",
- " lat_poca_20_ku (time_20_ku) float64 95kB ...\n",
- " lon_01 (time_cor_01) float64 5kB ...\n",
- " lon_poca_20_ku (time_20_ku) float64 95kB ...\n",
- " * time_20_ku (time_20_ku) datetime64[ns] 95kB 2020-0...\n",
- " * time_cor_01 (time_cor_01) datetime64[ns] 5kB 2020-0...\n",
- "Data variables: (12/54)\n",
- " alt_01 (time_cor_01) float64 5kB ...\n",
- " echo_avg_numval_20_ku (time_20_ku) float32 48kB ...\n",
- " flag_cor_applied_20_ku (time_20_ku) float64 95kB ...\n",
- " flag_cor_err_01 (time_cor_01) float64 5kB ...\n",
- " flag_instr_conf_rx_str_in_use_01 (time_cor_01) float32 2kB ...\n",
- " flag_instr_mode_op_20_ku (time_20_ku) float32 48kB ...\n",
- " ... ...\n",
- " ssha_interp_20_ku (time_20_ku) float64 95kB ...\n",
- " ssha_interp_numval_20_ku (time_20_ku) float32 48kB ...\n",
- " ssha_interp_rms_20_ku (time_20_ku) float64 95kB ...\n",
- " surf_type_20_ku (time_20_ku) float32 48kB ...\n",
- " swh_ocean_01_ku (time_cor_01) float64 5kB ...\n",
- " wind_speed_alt_01_ku (time_cor_01) float64 5kB ...\n",
- "Attributes: (12/101)\n",
- " product_name: CS_LTA__SIR_SAR_2__20200110T203717_20200110...\n",
- " processing_stage: LTA_\n",
- " reference_document: CS-RS-ACS-ESL-5265 2.1\n",
- " doi: 10.5270/CR2-388fb81\n",
- " acquisition_station: Kiruna \n",
- " mission: Cryosat\n",
- " ... ...\n",
- " xref_surf_type: CS_OPER_AUX_LS_MAP_00000000T000000_99999999...\n",
- " xref_tidal_load: CS_OPER_AUX_TDLOAD_00000000T000000_99999999...\n",
- " xref_u_wind: CS_OPER_AUX_U_WIND_20200110T180000_20200110...\n",
- " xref_uso: CS_OPER_AUX_DORUSO_20100411T040029_20211117...\n",
- " xref_v_wind: CS_OPER_AUX_V_WIND_20200110T180000_20200110...\n",
- " xref_wet_trop: CS_OPER_AUX_WETTRP_20200110T180000_20200110... Dimensions: time_cor_01 : 596time_20_ku : 11905
Coordinates: (6)
Data variables: (54)
alt_01
(time_cor_01)
float64
...
comment : Altitude of satellite CoM above reference ellipsoid (WGS84). long_name : Altitude of CoM above reference ellipsoid standard_name : height_above_reference_ellipsoid units : m [596 values with dtype=float64] echo_avg_numval_20_ku
(time_20_ku)
float32
...
comment : In LRM mode this is the number of echoes which have been averaged to make one measurement (normally). In SAR and SARIn modes this is the number of Doppler beams which have been stacked to derive each measurement. long_name : count of echoes or beams averaged units : count [11905 values with dtype=float32] flag_cor_applied_20_ku
(time_20_ku)
float64
...
comment : This flag indicates which corrections were applied in the computation of the height values in this record. The intent of this is to allow the user to remove applied corrections and substitute their own. flag_masks : [ 1 2 4 8 16 32 64\n",
- " 128 256 512 1024 2048 4096 8192\n",
- " 16384 32768 65536 131072 262144 524288 1048576\n",
- " 2097152 4194304 8388608 16777216 33554432 67108864 134217728\n",
- " 268435456 536870912] flag_meanings : correction_failure ssb_applied sarin_bad_velocity sarin_out_of_range sarin_bad_baseline lrm_slope_model_invalid sarin_ice_bias_applied sarin_ocean_bias_applied sar_ice_bias_applied sar_ocean_bias_applied lrm_ice_bias_applied lrm_ocean_bias_applied lrm_retracker_applied sarin_retracker_applied sar_retracker_applied window_offset_applied slope_doppler_applied pole_tide_applied solid_earth_applied load_tide_applied ocean_tide_equil_applied ocean_tide_applied iono_model_applied iono_gim_applied hf_fluctuations_applied inv_bar_applied model_wet_applied model_dry_applied doppler_applied internal_cal_applied long_name : corrections application flag [11905 values with dtype=float64] flag_cor_err_01
(time_cor_01)
float64
...
comment : Correction error flag. This flag shows whether the correction models returned an error. flag_masks : [ 1 2 4 8 16 32 64 128 256\n",
- " 512 1024 2048 4096 8192 16384 32768 65536 131072\n",
- " 262144 524288 1048576 2097152 4194304] flag_meanings : surface_type_error pole_tide_error solid_earth_error load_tide_error ocean_tide_equil_error ocean_tide_error iono_model_error iono_gim_error hf_fluctuations_error inv_bar_error model_wet_error model_dry_error ssb_model_error slope_model_error dem_error odle_error geoid_error mss_error snow_density_error snow_depth_error ice_conc_error swh_error wind_error long_name : corrections error flags [596 values with dtype=float64] flag_instr_conf_rx_str_in_use_01
(time_cor_01)
float32
...
comment : Star tracker identification flag showing the source of the platform pointing. 0: No Star Tracker data used. 1: Data from Star Tracker 1 used. 2: Data from Star Tracker 2 used. 3: Data from Star Tracker 3 used. 4: Data from the Star Tracker selected on board by AOCS used. flag_meanings : no_star_tracker tracker_1 tracker_2 tracker_3 attref_file flag_values : [0 1 2 3 4] long_name : star tracker id [596 values with dtype=float32] flag_instr_mode_op_20_ku
(time_20_ku)
float32
...
comment : Instrument measurement mode derived from configuration bits in L0. flag_meanings : lrm sar sarin flag_values : [1 2 3] long_name : measurement mode [11905 values with dtype=float32] flag_prod_status_20_ku
(time_20_ku)
float64
...
comment : Flags indicating the quality of the 20Hz measurement parameters. The surface type discriminated by the SAR chain is also packed within this flag. An error flag for height or backscatter when the corresponding field is not set to _FillValue means that the result should typically be rejected, but could be of use for certain specialised users who prefer degraded data to no data. flag_masks : [ 1 2 4 8 16 32 64\n",
- " 128 256 512 1024 2048 4096 8192\n",
- " 16384 32768 65536 131072 262144 524288 1048576\n",
- " 2097152 4194304 8388608 16777216 33554432 67108864 134217728\n",
- " 268435456] flag_meanings : calibration_warning sarin_bad_velocity sarin_out_of_range sarin_bad_baseline lrm_slope_model_invalid delta_time_error mispointing_error surface_model_unavailable sarin_side_redundant sarin_rx_2_error sarin_rx_1_error sarin_height_ambiguous surf_type_class_undefined surf_type_class_sea_ice surf_type_class_lead surf_type_class_ocean freeboard_error peakiness_error ssha_interp_error sig0_3_error sig0_2_error sig0_1_error height_3_error height_2_error height_1_error orbit_discontinuity orbit_error block_degraded height_sea_ice_error long_name : quality flag [11905 values with dtype=float64] geoid_01
(time_cor_01)
float64
...
comment : Computed from the geoid model with a correction to refer the value to the mean tide system i.e. includes the permanent tide (zero frequency), and referenced to WGS84. See Lemoine, F. G., S. C. Kenyon, J. K. Factor, R.G. Trimmer, N. K. Pavlis, D. S. Chinn, C. M. Cox, S. M. Klosko, S. B. Luthcke, M. H. Torrence, Y. M. Wang, R. G. Williamson, E. C. Pavlis, R. H. Rapp and T. R. Olson (1998). The Development of the Joint NASA GSFC and the National Imagery and Mapping Agency (NIMA) Geopotential Model EGM96. NASA/TP-1998-206861, July 1998. institution : NASA GSFC and NIMA long_name : geoid height source : EGM96 standard_name : geoid_height_above_reference_ellipsoid units : m [596 values with dtype=float64] height_1_20_ku
(time_20_ku)
float64
...
comment : Measured height of the surface above the reference ellipsoid (WGS84) at the coordinate location [lon_poca_20_ku] [lat_poca_20_ku]. All instrumental [instr_cor_range_rx_20_ku] and appropriate geophysical corrections included (see [flag_cor_applied_20_ku] for the set of corrections applied), and system bias. Corrected for surface slope via a slope model in LRM and SARIn degraded mode. Corrected for surface slope via phase information in SARIn mode. Contains the Ocean CFI retracker result in LRM mode, the UCL sea ice retracker results in SAR mode (different method for specular and diffuse echoes), and the UCL margins retracker in SARIn mode. long_name : surface height (retracker 1) standard_name : height_above_reference_ellipsoid units : m [11905 values with dtype=float64] height_2_20_ku
(time_20_ku)
float64
...
comment : Measured height of the surface above the reference ellipsoid (WGS84) at the coordinate location [lon_poca_20_ku] [lat_poca_20_ku]. All instrumental [instr_cor_range_rx_20_ku] and appropriate geophysical corrections included (see [flag_cor_applied_20_ku] for the set of corrections applied), and system bias. Corrected for surface slope via a slope model in LRM. Not currently used in SAR and SARIn modes. Contains the UCL land-ice retracker in LRM mode. long_name : surface height (retracker 2) standard_name : height_above_reference_ellipsoid units : m [11905 values with dtype=float64] height_3_20_ku
(time_20_ku)
float64
...
comment : Measured height of the surface above the reference ellipsoid (WGS84) at the coordinate location [lon_poca_20_ku] [lat_poca_20_ku]. All instrumental [instr_cor_range_rx_20_ku] and appropriate geophysical corrections included (see [flag_cor_applied_20_ku] for the set of corrections applied), and system bias. Corrected for surface slope via a slope model in LRM. Not currently used in SAR and SARIn modes. Contains the OCOG retracker in LRM mode. long_name : surface height (retracker 3) standard_name : height_above_reference_ellipsoid units : m [11905 values with dtype=float64] height_sea_ice_floe_20_ku
(time_20_ku)
float64
...
comment : Measured height of the surface above the reference ellipsoid (WGS84) at the coordinate location [lon_poca_20_ku] [lat_poca_20_ku]. All instrumental [instr_cor_range_rx_20_ku] and appropriate geophysical corrections included (see [flag_cor_applied_20_ku] for the set of corrections applied), and system bias. Only filled if the sea ice floe retracker ran. This retracker is run over all surfaces not discriminated as a sea-ice lead, not just over sea-ice floes. long_name : surface height (sea-ice floe retracker) standard_name : height_above_reference_ellipsoid units : m [11905 values with dtype=float64] height_sea_ice_lead_20_ku
(time_20_ku)
float64
...
comment : Measured height of the surface above the reference ellipsoid (WGS84) at the coordinate location [lon_poca_20_ku] [lat_poca_20_ku]. All instrumental [instr_cor_range_rx_20_ku] and appropriate geophysical corrections included (see [flag_cor_applied_20_ku] for the set of corrections applied), and system bias. Only filled if the sea ice lead retracker ran. long_name : surface height (sea-ice floe retracker) standard_name : height_above_reference_ellipsoid units : m [11905 values with dtype=float64] hf_fluct_total_cor_01
(time_cor_01)
float64
...
comment : High frequency fluctuations of the sea surface topography due to high frequency air pressure and wind effects. Also known as DAC (Dynamical Atmospheric Correction). This 1-way correction is computed at the altimeter [time_cor_01] time-tag from the interpolation of 2 meteorological fields that surround the altimeter time-tag. The inverse barometric correction [inv_bar_cor_01] is included in this field. This correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) in order to account for both the depression of the ocean surface caused by the local barometric pressure and the high-frequency effects caused by wind forcing. This correction is an alternative to [inv_bar_cor_01] and therefore only one should be used. institution : LEGOS/CLS/CNES long_name : dynamic atmospheric correction source : MOG2D 2.1.0 standard_name : sea_surface_height_correction_due_to_air_pressure_and_wind_at_high_frequency units : m [596 values with dtype=float64] ind_first_meas_20hz_01
(time_cor_01)
float64
...
comment : Index of the first 20Hz measurement of the 1Hz packet. long_name : index of the first 20Hz measurement: 1 Hz units : count [596 values with dtype=float64] ind_meas_1hz_20_ku
(time_20_ku)
float32
...
comment : Index of the 1Hz measurement to which belongs the 20Hz measurement. long_name : index of the 1Hz measurement: 20 Hz ku band units : count [11905 values with dtype=float32] inv_bar_cor_01
(time_cor_01)
float64
...
comment : Inverse barometric correction. This 1-way correction is computed at the altimeter [time_cor_01] time-tag from the interpolation of 2 meteorological fields that surround the altimeter time-tag. This correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) in order to correct this range measurement for the depression of the ocean surface caused by the local barometric pressure. This correction is an alternative to [hf_fluct_total_cor_01] and only one should be used. institution : ECMWF long_name : inverse barometric correction source : European Centre for Medium Range Weather Forecasting standard_name : sea_surface_height_correction_due_to_air_pressure_at_low_frequency units : m [596 values with dtype=float64] iono_cor_01
(time_cor_01)
float64
...
comment : Model Ionospheric correction. This 1-way correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) in order to correct this range measurement for ionospheric range delays of the radar pulse. This correction is an alternative to [iono_cor_gim_01] and only one should be used. See S. K. Llewellyn, R. B. Bent, A. S. C. I. H. B. FL, U. S. N. T. I. Service, Space and Missile Systems Organization (U.S.), Documentation and Description of the Bent Ionospheric Model. U.S. Department of Commerce, National Technical Information Service, 1973. institution : Bent long_name : model ionospheric correction source : Bent standard_name : altimeter_range_correction_due_to_ionosphere units : m [596 values with dtype=float64] iono_cor_gim_01
(time_cor_01)
float64
...
comment : GIM Ionospheric correction. This correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) in order to correct this range measurement for ionospheric range delays of the radar pulse. This correction is an alternative to [iono_cor_01] and only one should be used. institution : NASA/JPL long_name : GIM ionospheric correction source : GIM standard_name : altimeter_range_correction_due_to_ionosphere units : m [596 values with dtype=float64] load_tide_01
(time_cor_01)
float64
...
comment : Ocean loading tide. This 1-way correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) to remove the effect of local tidal distortion to the Earth's crust, caused by increasing weight of ocean as local water tide rises. institution : LEGOS/CNES long_name : ocean loading tide height source : FES2004 units : m [596 values with dtype=float64] mean_sea_surf_sea_ice_01
(time_cor_01)
float64
...
comment : Mean sea surface model, referenced to the WGS84 ellipsoid. This model has been optimised for use in computing the surface height anomaly of the polar oceans to derive sea-ice freeboard. The model is a merge of the CLS2011 mean sea-surface and CryoSat data from high latitudes. institution : UCL long_name : mean sea surface height source : UCL13 standard_name : sea_surface_height_above_reference_ellipsoid units : m [596 values with dtype=float64] mod_dry_tropo_cor_01
(time_cor_01)
float64
...
comment : Model dry tropospheric correction. This 1-way correction is computed at the [time_cor_01] altimeter time-tag from the interpolation of 2 meteorological fields that surround the altimeter time-tag. This correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) in order to correct for the propagation delay to the radar pulse, caused by the dry-gas component of the Earth's atmosphere. institution : ECMWF long_name : dry tropospheric correction source : European Centre for Medium Range Weather Forecasting standard_name : altimeter_range_correction_due_to_dry_troposphere units : m [596 values with dtype=float64] mod_wet_tropo_cor_01
(time_cor_01)
float64
...
comment : Model Wet Tropospheric Correction. This 1-way correction is computed at the time_cor_01 altimeter time-tag from the interpolation of 2 meteorological fields that surround the altimeter time-tag. This correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) in order to correct for the propagation delay to the radar pulse, caused by the H2O component of the Earth's atmosphere. institution : ECMWF long_name : wet tropospheric correction source : European Centre for Medium Range Weather Forecasting standard_name : altimeter_range_correction_due_to_wet_troposphere units : m [596 values with dtype=float64] num_valid_01
(time_cor_01)
float32
...
comment : Count of the number of records in the meas_ind dimension that are valid. long_name : number of valid measurements units : count [596 values with dtype=float32] ocean_tide_01
(time_cor_01)
float64
...
comment : Ocean tide. This 1-way correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) to remove the effect of local tide and adjust the measurement to the mean sea surface. This is the pure ocean tide, not including the corresponding loading tide [load_tide_01] or the equilibrium long-period ocean tide height [ocean_tide_eq_01]. The permanent tide (zero frequency) is not included in this parameter because it is included in the geoid [geoid_01] and mean sea surface [mean_sea_surf_sea_ice_01]. institution : LEGOS/CNES long_name : elastic ocean tide source : FES2004 units : m [596 values with dtype=float64] ocean_tide_eq_01
(time_cor_01)
float64
...
comment : Long Period equilibrium ocean tide. This correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) to remove the effect of the oceanic response to the single tidal forcing. institution : LEGOS/CNES long_name : long period equilibrium ocean tide source : FES2004 standard_name : sea_surface_height_amplitude_due_to_equilibrium_ocean_tide units : m [596 values with dtype=float64] odle_01
(time_cor_01)
float64
...
comment : Ocean depth and land elevation model. The model is a merge of ACE land elevation data and Smith and Sandwell ocean bathymetry. See P. A. M. Berry, R. A. Pinnock, R. D. Hilton, and C. P. D. Johnson, ACE: a new GDEM incorporating satellite altimeter derived heights, ESA Pub. SP-461, 9pp, 2000 and W. H. F Smith, and D. T. Sandwell, Global seafloor topography from satellite altimetry and ship depth soundings, Science, v. 277, p. 1957-1962, 26 Sept., 1997. institution : ESA/ESRIN long_name : ocean depth/land elevation source : MACESS units : m [596 values with dtype=float64] off_nadir_pitch_angle_str_01
(time_cor_01)
float64
...
comment : Pitch angle with respect to the nadir pointing, measured by the STRs and post-processed by the ground facility. long_name : antenna bench pitch angle units : degrees [596 values with dtype=float64] off_nadir_roll_angle_str_01
(time_cor_01)
float64
...
comment : Roll angle with respect to the nadir pointing, measured by the STRs and post-processed by the ground facility. long_name : antenna bench roll angle units : degrees [596 values with dtype=float64] off_nadir_yaw_angle_str_01
(time_cor_01)
float64
...
comment : Yaw angle with respect to the nadir pointing, measured by the STRs and post-processed by the ground facility. long_name : antenna bench yaw angle units : degrees [596 values with dtype=float64] peakiness_20_ku
(time_20_ku)
float64
...
comment : Waveform peakiness. Note that this will require different interpretation for SAR and SARIn echoes which do not have the typical pulse-limited echo shape. For LRM and SARIn, the traditional ENVISAT derivation is used. This is the ratio of the maximum power in the waveform to the average of the waveform power to the right hand side of the expected waveform leading edge location. For SAR mode, waveform bins with no power are excluded from the average to account for the specular nature of some waveforms. long_name : waveform peakiness [11905 values with dtype=float64] pole_tide_01
(time_cor_01)
float64
...
comment : Geocentric polar tide. This 1-way correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) to remove a long-period distortion of the Earth's crust. Although called a 'tide' this is in fact caused by variations in centrifugal force as the Earth's rotational axis moves its geographic location. institution : IERS/CNES long_name : geocentric pole tide source : Wahr [1985] Deformation of the Earth induced by polar motion - J. Geophys. Res. (Solid Earth), 90, 9363-9368. standard_name : sea_surface_height_amplitude_due_to_pole_tide units : m [596 values with dtype=float64] radar_freeboard_20_ku
(time_20_ku)
float64
...
comment : Radar freeboard. Computed as [radar_freeboard_20_ku] = [height_1_20_ku] - [ssha_interp_20_ku]. Estimates of snow depth [snow_depth_20_ku] and density [snow_density_20_ku] are provided for the convenience of the product user. A correction for pulse delay due to snow depth is provided in [snow_depth_cor_20_ku] but is not applied. Note that freeboard can be a small negative value due to the effect of random noise (in both the height estimation and the interpolation of the sea surface) on small freeboard values. Unused in LRM mode. long_name : radar freeboard units : m [11905 values with dtype=float64] range_1_20_ku
(time_20_ku)
float64
...
comment : Measured range from the satellite CoM to the surface at the coordinate location [lon_poca_20_ku] [lat_poca_20_ku]. All instrumental corrections applied. Contains the Ocean CFI retracker result in LRM mode, the UCL sea-ice retracker results in SAR mode (different method for specular and diffuse echoes), and the UCL margins retracker in SARIn mode. Does not include geophysical corrections. long_name : range to surface (retracker 1) standard_name : altimeter_range units : m [11905 values with dtype=float64] range_2_20_ku
(time_20_ku)
float64
...
comment : Measured range from the satellite CoM to the surface at the coordinate location [lon_poca_20_ku] [lat_poca_20_ku]. All instrumental corrections applied. Not currently used in SAR and SARIn modes. Contains the UCL land-ice retracker in LRM mode. Does not include geophysical corrections. long_name : range to surface (retracker 2) standard_name : altimeter_range units : m [11905 values with dtype=float64] range_3_20_ku
(time_20_ku)
float64
...
comment : Measured range from the satellite CoM to the surface at the coordinate location [lon_poca_20_ku] [lat_poca_20_ku]. All instrumental corrections applied. Not currently used in SAR and SARIn modes. Contains the OCOG retracker in LRM mode. Does not include geophysical corrections. long_name : range to surface (retracker 3) standard_name : altimeter_range units : m [11905 values with dtype=float64] retracker_1_quality_20_ku
(time_20_ku)
float64
...
comment : The quality metric (chi^2 of fitted model) computed by the Ocean CFI retracker in LRM mode, the UCL sea-ice retracker in SAR mode (over leads only) and the UCL margins retracker in SARIn mode. long_name : quality metric (retracker 1) [11905 values with dtype=float64] retracker_2_quality_20_ku
(time_20_ku)
float64
...
comment : The quality metric (chi^2 of fitted model) computed by the UCL land-ice retracker in LRM mode. Unused in SAR and SARIn modes. long_name : quality metric (retracker 2) [11905 values with dtype=float64] retracker_3_quality_20_ku
(time_20_ku)
float64
...
comment : Unused in LRM, SAR, and SARIn modes. long_name : quality metric (retracker 3) [11905 values with dtype=float64] sea_ice_concentration_01
(time_cor_01)
float64
...
comment : Sea ice concentration derived from NSIDC SSM/I data. Unused in LRM mode. institution : UCL long_name : sea ice area fraction source : UCL standard_name : sea_ice_area_fraction units : percent [596 values with dtype=float64] sea_state_bias_01_ku
(time_cor_01)
float64
...
comment : Sea State Bias Correction. This sea state bias has been accounted for (if indicated by [flag_cor_applied_20_ku]) in the height estimates over open-ocean in LRM mode. Not used in SAR/SARIn modes. institution : CLS long_name : sea state bias correction source : Labroue2007 standard_name : sea_surface_height_bias_due_to_sea_surface_roughness units : m [596 values with dtype=float64] sig0_1_20_ku
(time_20_ku)
float64
...
comment : The measured backscatter from the surface, corrected for instrument effects [instr_cor_gain_rx_20_ku], and including a system bias that calibrates the results against previous missions. The backscatter is computed from the amplitude of the waveform in Watts, as measured by the Ocean CFI retracker in LRM mode. The measured power is used to solve the radar equation to recover the value for backscatter. For SAR and SARIn mode, the power waveform is first convolved with a function to produce an LRM-like waveform, that is then retracked with OCOG to produce the amplitude estimate. long_name : backscatter coefficient (retracker 1) standard_name : surface_backwards_scattering_coefficient_of_radar_wave units : dB [11905 values with dtype=float64] sig0_2_20_ku
(time_20_ku)
float64
...
comment : The measured backscatter from the surface, corrected for instrument effects [instr_cor_gain_rx_20_ku], and including a system bias that calibrates the results against previous missions. The backscatter is computed from the amplitude of the waveform in Watts, as measured by the UCL land-ice retracker. The measured power is used to solve the radar equation to recover the value for backscatter. Not currently used in SAR and SARIn modes. long_name : backscatter coefficient (retracker 2) standard_name : surface_backwards_scattering_coefficient_of_radar_wave units : dB [11905 values with dtype=float64] sig0_3_20_ku
(time_20_ku)
float64
...
comment : The measured backscatter from the surface, corrected for instrument effects [instr_cor_gain_rx_20_ku], and including a system bias that calibrates the results against previous missions. The backscatter is computed from the amplitude of the waveform in Watts, as measured by the OCOG retracker. The measured power is used to solve the radar equation to recover the value for backscatter. Not currently used in SAR and SARIn modes. long_name : backscatter coefficient (retracker 3) standard_name : surface_backwards_scattering_coefficient_of_radar_wave units : dB [11905 values with dtype=float64] snow_density_01
(time_cor_01)
float64
...
comment : Snow density. Currently set to a fixed average value for all records. The intention is to replace this with a model in future. Unused in LRM mode. institution : UCL long_name : snow density source : UCL standard_name : snow_density units : kg/m^3 [596 values with dtype=float64] snow_depth_01
(time_cor_01)
float64
...
comment : Snow depth from Modified Warren climatology. Unused in LRM mode. See S. G. Warren, I. G. Rigor, and N. Untersteiner, Snow depth on Arctic sea ice.,1999. institution : UCL long_name : snow depth source : Warren standard_name : surface_snow_thickness units : m [596 values with dtype=float64] snow_depth_cor_20_ku
(time_20_ku)
float64
...
comment : Snow depth correction. This one-way correction corrects for the propagation delay caused by snow present on top of sea ice. To apply, add to range or subtract from height or freeboard. The correction in m is directly proportional to snow depth (-0.25 x snow_depth). long_name : snow depth correction units : m [11905 values with dtype=float64] solid_earth_tide_01
(time_cor_01)
float64
...
comment : Solid earth tide. This 1-way correction has been accounted for during the computation of height (see [flag_cor_applied_20_ku] to determine if it was applied) to remove the effect of local tidal distortion to the Earth's crust, in particular by the sun and moon. long_name : solid earth tide source : Cartwright and Edden [1973] Corrected tables of tidal harmonics - J. Geophys. J. R. Astr. Soc., 33, 253-264 standard_name : sea_surface_height_amplitude_due_to_earth_tide units : m [596 values with dtype=float64] ssha_interp_20_ku
(time_20_ku)
float64
...
comment : Sea surface height anomaly computed using sea surface height interpolated to the current location. Unused in LRM mode. long_name : interpolated sea-surface height anomaly units : m [11905 values with dtype=float64] ssha_interp_numval_20_ku
(time_20_ku)
float32
...
comment : Number of SSHA points used in the interpolation of [ssha_interp_20_ku] at this location. Unused in LRM mode. long_name : number of ssha interpolation points units : count [11905 values with dtype=float32] ssha_interp_rms_20_ku
(time_20_ku)
float64
...
comment : Estimated error in [ssha_interp_20_ku]. Unused in LRM mode. long_name : ssha interpolation error units : m [11905 values with dtype=float64] surf_type_20_ku
(time_20_ku)
float32
...
comment : A 4-state surface type mask for Cryosat2 data for the surface type at the nadir location. Computed by combining data from different sources: GMT, GlobCover, Modis Mosaic of Antarctica, and Water body outlines from LEGOS flag_meanings : ocean lake_enclosed_sea ice land flag_values : [0 1 2 3] institution : CLS/CNES long_name : surface type from mask source : GMT, GlobCover, Modis Mosaic of Antarctica, and Water body outlines from LEGOS [11905 values with dtype=float32] swh_ocean_01_ku
(time_cor_01)
float64
...
comment : Computed directly from sigma c as computed by the Ocean CFI retracker in LRM mode only. No bias correction to cross-calibrate with previous missions applied. Unused in SAR and SARIn modes. long_name : 1 Hz averaged significant waveheight standard_name : sea_surface_significant_wave_height units : m [596 values with dtype=float64] wind_speed_alt_01_ku
(time_cor_01)
float64
...
comment : Computed directly from backscatter via a CFI call using the Chelton model for ENVISAT. No bias correction to cross-calibrate with previous missions applied. Not currently used in SAR and SARIn modes. long_name : 1 Hz averaged altimeter wind speed standard_name : wind_speed units : m/s [596 values with dtype=float64] Indexes: (2)
PandasIndex
PandasIndex(DatetimeIndex(['2020-01-10 20:37:53.967812992',\n",
- " '2020-01-10 20:37:54.012692992',\n",
- " '2020-01-10 20:37:54.057572992',\n",
- " '2020-01-10 20:37:54.102452992',\n",
- " '2020-01-10 20:37:54.147332992',\n",
- " '2020-01-10 20:37:54.192213120',\n",
- " '2020-01-10 20:37:54.237092992',\n",
- " '2020-01-10 20:37:54.281972992',\n",
- " '2020-01-10 20:37:54.326852992',\n",
- " '2020-01-10 20:37:54.371732992',\n",
- " ...\n",
- " '2020-01-10 20:46:49.062520960',\n",
- " '2020-01-10 20:46:49.107507968',\n",
- " '2020-01-10 20:46:49.152494976',\n",
- " '2020-01-10 20:46:49.197481984',\n",
- " '2020-01-10 20:46:49.242468992',\n",
- " '2020-01-10 20:46:49.287456',\n",
- " '2020-01-10 20:46:49.332443008',\n",
- " '2020-01-10 20:46:49.377430016',\n",
- " '2020-01-10 20:46:49.422417024',\n",
- " '2020-01-10 20:46:49.467404032'],\n",
- " dtype='datetime64[ns]', name='time_20_ku', length=11905, freq=None)) PandasIndex
PandasIndex(DatetimeIndex(['2020-01-10 20:37:53.967812992',\n",
- " '2020-01-10 20:37:54.865412992',\n",
- " '2020-01-10 20:37:55.763026944',\n",
- " '2020-01-10 20:37:56.660652032',\n",
- " '2020-01-10 20:37:57.558291968',\n",
- " '2020-01-10 20:37:58.455947008',\n",
- " '2020-01-10 20:37:59.353615104',\n",
- " '2020-01-10 20:38:00.251294976',\n",
- " '2020-01-10 20:38:01.148993024',\n",
- " '2020-01-10 20:38:02.046700032',\n",
- " ...\n",
- " '2020-01-10 20:46:41.189558016',\n",
- " '2020-01-10 20:46:42.089355008',\n",
- " '2020-01-10 20:46:42.989136',\n",
- " '2020-01-10 20:46:43.888915968',\n",
- " '2020-01-10 20:46:44.788686976',\n",
- " '2020-01-10 20:46:45.688448',\n",
- " '2020-01-10 20:46:46.588208',\n",
- " '2020-01-10 20:46:47.487968',\n",
- " '2020-01-10 20:46:48.387716096',\n",
- " '2020-01-10 20:46:49.287456'],\n",
- " dtype='datetime64[ns]', name='time_cor_01', length=596, freq=None)) Attributes: (101)
product_name : CS_LTA__SIR_SAR_2__20200110T203717_20200110T204612_E001 processing_stage : LTA_ reference_document : CS-RS-ACS-ESL-5265 2.1 doi : 10.5270/CR2-388fb81 acquisition_station : Kiruna mission : Cryosat processing_centre : PDS creation_time : UTC=2023-03-17T22:47:56 sensing_start : 10-JAN-2020 20:37:16.967813 sensing_stop : 10-JAN-2020 20:46:12.287456 software_version : IPF2SAR/7.1 phase : 2 cycle_number : 11 rel_orbit_number : 5243 abs_orbit_number : 51726 state_vector_time : UTC=2020-01-10T20:41:44.627635 delta_ut1 : 0.0 x_position : -549065.4375 y_position : -340840.09375 z_position : 7055642.5 x_velocity : -7409.91845703125 y_velocity : -1110.4063720703125 z_velocity : -624.103759765625 vector_source : doris_precise leap_utc : leap_sign : 0 leap_err : 0 product_err : 0 first_record_time : TAI=2020-01-10T20:37:53.967813 last_record_time : TAI=2020-01-10T20:46:49.287456 abs_orbit_start : 51726 rel_time_acs_node_start : 1295.9014892578125 abs_orbit_stop : 51726 rel_time_acs_node_stop : 1831.2210693359375 equator_cross_time : UTC=2020-01-10T20:15:41.042779 equator_cross_long : 15551958 ascending_flag : A first_record_lat : 78476904 first_record_lon : 173419 last_record_lat : 68969026 last_record_lon : -166827194 instr_id : A open_ocean_percent : 10000 close_sea_percent : 0 continent_ice_percent : 0 land_percent : 0 lrm_mode_percent : 0 sar_mode_percent : 10000 sarin_mode_percent : 0 l2_prod_status : 0 l2_proc_flag : 0 l2_processing_quality : 9987 l2_proc_thresh : 500 l1b_proc_flag : 0 l1b_proc_thresh : 500 l1b_processing_quality : 10000 l1b_prod_status : 0 sir_configuration : RX_1 sir_op_mode : SAR xref_cal1 : CS_OFFL_SIR1SAC11B_20191231T115924_20210101T115924_E100.DBL xref_cal1_sarin : CS_OFFL_SIR_SICC1B_20200110T115924_20200110T115924_D100.DBL xref_cal2 : CS_OFFL_SIR1SAC21B_20200101T115924_20200101T115924_D100.DBL xref_constants : Geophysical_Constants_5.0.xml xref_dip_map : CS_OPER_AUX_DIPMAP_00000000T000000_99999999T999999_0002.DBL xref_earth_tide : CS_OPER_AUX_CARTWR_00000000T000000_99999999T999999_0002.DBL xref_geoid : CS_OPER_AUX_GEOID__00000000T000000_99999999T999999_0002.DBL xref_gim : CS_OPER_AUX_IONGIM_20200110T000000_20200110T235959_0001.DBL xref_iono_cor : CS_OPER_AUX_MICOEF_00000000T000000_99999999T999999_0002.DBL xref_mean_pressure : CS_OPER_AUX_SEAMPS_20200110T180000_20200110T180000_0001.DBL,CS_OPER_AUX_SEAMPS_20200111T000000_20200111T000000_0001.DBL xref_meteo : CS_OPER_AUX_ALTGRD_20110504T100000_20301231T235959_0003.DBL xref_mog2d : CS_OPER_AUX_MOG_2D_20200110T180000_20200110T180000_0001.DBL,CS_OPER_AUX_MOG_2D_20200111T000000_20200111T000000_0001.DBL xref_mss : CS_OPER_AUX_MSSURF_00000000T000000_99999999T999999_0007.DBL xref_ocean_tide : CS_OPER_AUX_OCTIDE_00000000T000000_99999999T999999_0003.DBL xref_odle : CS_OPER_AUX_ODLE___00000000T000000_99999999T999999_0002.DBL xref_orbit : CS_OPER_AUX_ORBDOR_20200109T215523_20200111T002323_F001.EEF,CS_OPER_MPL_ORBPRE_20200110T001000_20200209T001000_0001.EEF xref_orbit_scenario : CS_OPER_MPL_ORBREF_20190711T045745_20200713T104253_0002.EEF xref_pconf : CS_OPER_PCONF_IPF1_20161001T000000_99999999T000000_0001.XML,CS_OPER_PCONF_IPF2_20150427T000000_99999999T999999_0010.XML xref_pole_location : CS_OPER_AUX_POLLOC_19870101T000000_20230111T000000_0001.DBL xref_s1_tide_amplitude : CS_OPER_AUX_S1AMPL_00000000T000000_99999999T999999_0001.DBL xref_s1_tide_phase : CS_OPER_AUX_S1PHAS_00000000T000000_99999999T999999_0001.DBL xref_s1s2_pressure_00h : CS_OPER_AUX_PRSS00_00000000T000000_99999999T999999_0001.DBL xref_s1s2_pressure_06h : CS_OPER_AUX_PRSS06_00000000T000000_99999999T999999_0001.DBL xref_s1s2_pressure_12h : CS_OPER_AUX_PRSS12_00000000T000000_99999999T999999_0001.DBL xref_s1s2_pressure_18h : CS_OPER_AUX_PRSS18_00000000T000000_99999999T999999_0001.DBL xref_s2_tide_amplitude : CS_OPER_AUX_S2AMPL_00000000T000000_99999999T999999_0001.DBL xref_s2_tide_phase : CS_OPER_AUX_S2PHAS_00000000T000000_99999999T999999_0001.DBL xref_sai : CS_OPER_AUX_SUNACT_19910101T000000_20230101T000000_0001.DBL xref_sea_ice : CS_OPER_AUX_SEA_IC_20200109T000000_20200111T235959_0003.DBL xref_sea_ice_type_north : CS_OPER_AUX_DSITNH_20200111T000000_20200111T235959_0001.DBL xref_siral_characterisation : CS_OPER_AUX_IPFDBA_20100701T000000_99999999T999999_0003.EEF xref_siral_l0 : CS_OPER_SIR1SAR_0__20200110T203717_20200110T204613_0001.DBL,CS_OPER_SIR1TKSA0__20200110T203717_20200110T204613_0001.DBL xref_siral_l1b : CS_LTA__SIR_SAR_1B_20200110T203717_20200110T204612_E001.DBL xref_snow_depth : CS_OPER_AUX_SDC_01_00000000T000000_99999999T999999_0005.DBL xref_star_tracker_attref : CS_OFFL_STR_ATTREF_20200109T215523_20200111T002321_E001.EEF xref_surf_pressure : CS_OPER_AUX_SURFPS_20200110T180000_20200110T180000_0001.DBL,CS_OPER_AUX_SURFPS_20200111T000000_20200111T000000_0001.DBL xref_surf_type : CS_OPER_AUX_LS_MAP_00000000T000000_99999999T999999_0002.DBL xref_tidal_load : CS_OPER_AUX_TDLOAD_00000000T000000_99999999T999999_0003.DBL xref_u_wind : CS_OPER_AUX_U_WIND_20200110T180000_20200110T180000_0001.DBL,CS_OPER_AUX_U_WIND_20200111T000000_20200111T000000_0001.DBL xref_uso : CS_OPER_AUX_DORUSO_20100411T040029_20211117T033313_0001.DBL xref_v_wind : CS_OPER_AUX_V_WIND_20200110T180000_20200110T180000_0001.DBL,CS_OPER_AUX_V_WIND_20200111T000000_20200111T000000_0001.DBL xref_wet_trop : CS_OPER_AUX_WETTRP_20200110T180000_20200110T180000_0001.DBL,CS_OPER_AUX_WETTRP_20200111T000000_20200111T000000_0001.DBL "
- ],
- "text/plain": [
- " Size: 3MB\n",
- "Dimensions: (time_cor_01: 596, time_20_ku: 11905)\n",
- "Coordinates:\n",
- " lat_01 (time_cor_01) float64 5kB ...\n",
- " lat_poca_20_ku (time_20_ku) float64 95kB ...\n",
- " lon_01 (time_cor_01) float64 5kB ...\n",
- " lon_poca_20_ku (time_20_ku) float64 95kB ...\n",
- " * time_20_ku (time_20_ku) datetime64[ns] 95kB 2020-0...\n",
- " * time_cor_01 (time_cor_01) datetime64[ns] 5kB 2020-0...\n",
- "Data variables: (12/54)\n",
- " alt_01 (time_cor_01) float64 5kB ...\n",
- " echo_avg_numval_20_ku (time_20_ku) float32 48kB ...\n",
- " flag_cor_applied_20_ku (time_20_ku) float64 95kB ...\n",
- " flag_cor_err_01 (time_cor_01) float64 5kB ...\n",
- " flag_instr_conf_rx_str_in_use_01 (time_cor_01) float32 2kB ...\n",
- " flag_instr_mode_op_20_ku (time_20_ku) float32 48kB ...\n",
- " ... ...\n",
- " ssha_interp_20_ku (time_20_ku) float64 95kB ...\n",
- " ssha_interp_numval_20_ku (time_20_ku) float32 48kB ...\n",
- " ssha_interp_rms_20_ku (time_20_ku) float64 95kB ...\n",
- " surf_type_20_ku (time_20_ku) float32 48kB ...\n",
- " swh_ocean_01_ku (time_cor_01) float64 5kB ...\n",
- " wind_speed_alt_01_ku (time_cor_01) float64 5kB ...\n",
- "Attributes: (12/101)\n",
- " product_name: CS_LTA__SIR_SAR_2__20200110T203717_20200110...\n",
- " processing_stage: LTA_\n",
- " reference_document: CS-RS-ACS-ESL-5265 2.1\n",
- " doi: 10.5270/CR2-388fb81\n",
- " acquisition_station: Kiruna \n",
- " mission: Cryosat\n",
- " ... ...\n",
- " xref_surf_type: CS_OPER_AUX_LS_MAP_00000000T000000_99999999...\n",
- " xref_tidal_load: CS_OPER_AUX_TDLOAD_00000000T000000_99999999...\n",
- " xref_u_wind: CS_OPER_AUX_U_WIND_20200110T180000_20200110...\n",
- " xref_uso: CS_OPER_AUX_DORUSO_20100411T040029_20211117...\n",
- " xref_v_wind: CS_OPER_AUX_V_WIND_20200110T180000_20200110...\n",
- " xref_wet_trop: CS_OPER_AUX_WETTRP_20200110T180000_20200110..."
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"ds_cs2 = xr.open_dataset(path + downloaded_files[0])\n",
"ds_cs2"
@@ -2057,98 +353,10 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "795b0bdd-0895-4de4-8008-b6f15fcf2b3e",
"metadata": {},
- "outputs": [
- {
- "data": {},
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.holoviews_exec.v0+json": "",
- "text/html": [
- "\n",
- ""
- ],
- "text/plain": [
- ":Scatter [time_20_ku] (radar_freeboard_20_ku)"
- ]
- },
- "execution_count": 9,
- "metadata": {
- "application/vnd.holoviews_exec.v0+json": {
- "id": "p1067"
- }
- },
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"ds_cs2['radar_freeboard_20_ku'].hvplot(kind='scatter', s=2)"
]
@@ -2171,31 +379,10 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"id": "11476d13-0f1f-44f7-a364-4a949554d8c2",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"projection = ccrs.Stereographic(central_latitude=90.,\n",
" central_longitude=-45.,\n",
@@ -2231,105 +418,6 @@
"fig.colorbar(cs2_img, label='Cryosat-2 Radar Freeboard (m)')\n",
"fig.colorbar(is2_img, label='ICESat-2 Lidar Freeboard (m)')"
]
- },
- {
- "cell_type": "markdown",
- "id": "392bb8fd-8ec6-4fd4-b6db-dc0386094c7f",
- "metadata": {},
- "source": [
- "Here we're plotting several ICESat-2 and CryoSat-2 files at a time. This takes a few minutes to render."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "4815c13b-88c3-4d10-850e-02cf766d1844",
- "metadata": {},
- "outputs": [
- {
- "ename": "ValueError",
- "evalue": "'c' argument has 11905 elements, which is inconsistent with 'x' and 'y' with size 120.",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4486\u001b[0m, in \u001b[0;36mAxes._parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m 4485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;66;03m# Is 'c' acceptable as PathCollection facecolors?\u001b[39;00m\n\u001b[0;32m-> 4486\u001b[0m colors \u001b[38;5;241m=\u001b[39m \u001b[43mmcolors\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_rgba_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4487\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/matplotlib/colors.py:505\u001b[0m, in \u001b[0;36mto_rgba_array\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 505\u001b[0m rgba \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([to_rgba(cc) \u001b[38;5;28;01mfor\u001b[39;00m cc \u001b[38;5;129;01min\u001b[39;00m c])\n\u001b[1;32m 507\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m alpha \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/matplotlib/colors.py:505\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 504\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 505\u001b[0m rgba \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[43mto_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcc\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m cc \u001b[38;5;129;01min\u001b[39;00m c])\n\u001b[1;32m 507\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m alpha \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/matplotlib/colors.py:302\u001b[0m, in \u001b[0;36mto_rgba\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m rgba \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[38;5;66;03m# Suppress exception chaining of cache lookup failure.\u001b[39;00m\n\u001b[0;32m--> 302\u001b[0m rgba \u001b[38;5;241m=\u001b[39m \u001b[43m_to_rgba_no_colorcycle\u001b[49m\u001b[43m(\u001b[49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/matplotlib/colors.py:391\u001b[0m, in \u001b[0;36m_to_rgba_no_colorcycle\u001b[0;34m(c, alpha)\u001b[0m\n\u001b[1;32m 390\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m np\u001b[38;5;241m.\u001b[39miterable(c):\n\u001b[0;32m--> 391\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid RGBA argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00morig_c\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 392\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(c) \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m]:\n",
- "\u001b[0;31mValueError\u001b[0m: Invalid RGBA argument: nan",
- "\nThe above exception was the direct cause of the following exception:\n",
- "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
- "File \u001b[0;32m:22\u001b[0m\n",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/matplotlib/pyplot.py:3699\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 3680\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mscatter)\n\u001b[1;32m 3681\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscatter\u001b[39m(\n\u001b[1;32m 3682\u001b[0m x: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3697\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3698\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m PathCollection:\n\u001b[0;32m-> 3699\u001b[0m __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3700\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3701\u001b[0m \u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3702\u001b[0m \u001b[43m \u001b[49m\u001b[43ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3703\u001b[0m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3704\u001b[0m \u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmarker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3705\u001b[0m \u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcmap\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3706\u001b[0m \u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3707\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3708\u001b[0m \u001b[43m \u001b[49m\u001b[43mvmax\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvmax\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3709\u001b[0m \u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3710\u001b[0m \u001b[43m \u001b[49m\u001b[43mlinewidths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlinewidths\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3711\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3712\u001b[0m \u001b[43m \u001b[49m\u001b[43mplotnonfinite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mplotnonfinite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3713\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3714\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3715\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3716\u001b[0m sci(__ret)\n\u001b[1;32m 3717\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m __ret\n",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/cartopy/mpl/geoaxes.py:315\u001b[0m, in \u001b[0;36m_add_transform..wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInvalid transform: Spherical \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 311\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis not supported - consider using \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPlateCarree/RotatedPole.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 314\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m transform\n\u001b[0;32m--> 315\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/cartopy/mpl/geoaxes.py:1696\u001b[0m, in \u001b[0;36mGeoAxes.scatter\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1688\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 1689\u001b[0m InterProjectionTransform) \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m 1690\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39msource_projection\u001b[38;5;241m.\u001b[39mis_geodetic()):\n\u001b[1;32m 1691\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCartopy cannot currently do spherical \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1692\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscatter. The source CRS cannot be a \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1693\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgeodetic, consider using the cyllindrical form \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1694\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m(PlateCarree or RotatedPole).\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m-> 1696\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1697\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mautoscale_view()\n\u001b[1;32m 1698\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data..inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 1464\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1467\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 1468\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m 1469\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4673\u001b[0m, in \u001b[0;36mAxes.scatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4670\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m edgecolors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4671\u001b[0m orig_edgecolor \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 4672\u001b[0m c, colors, edgecolors \u001b[38;5;241m=\u001b[39m \\\n\u001b[0;32m-> 4673\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_scatter_color_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 4674\u001b[0m \u001b[43m \u001b[49m\u001b[43mc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medgecolors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4675\u001b[0m \u001b[43m \u001b[49m\u001b[43mget_next_color_func\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_patches_for_fill\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_next_color\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4677\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m plotnonfinite \u001b[38;5;129;01mand\u001b[39;00m colors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4678\u001b[0m c \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mma\u001b[38;5;241m.\u001b[39mmasked_invalid(c)\n",
- "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4492\u001b[0m, in \u001b[0;36mAxes._parse_scatter_color_args\u001b[0;34m(c, edgecolors, kwargs, xsize, get_next_color_func)\u001b[0m\n\u001b[1;32m 4490\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 4491\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m valid_shape:\n\u001b[0;32m-> 4492\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m invalid_shape_exception(c\u001b[38;5;241m.\u001b[39msize, xsize) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 4493\u001b[0m \u001b[38;5;66;03m# Both the mapping *and* the RGBA conversion failed: pretty\u001b[39;00m\n\u001b[1;32m 4494\u001b[0m \u001b[38;5;66;03m# severe failure => one may appreciate a verbose feedback.\u001b[39;00m\n\u001b[1;32m 4495\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 4496\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m argument must be a color, a sequence of colors, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4497\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor a sequence of numbers, not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mc\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n",
- "\u001b[0;31mValueError\u001b[0m: 'c' argument has 11905 elements, which is inconsistent with 'x' and 'y' with size 120."
- ]
- },
- {
- "data": {
- "image/png": "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",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "%%time\n",
- "\n",
- "# NSIDC WGS84 Polar Stereographic \n",
- "projection = ccrs.Stereographic(central_latitude=90.,\n",
- " central_longitude=-45.,\n",
- " true_scale_latitude=70.)\n",
- "extent = [-2500000.000, 2500000., -2500000., 2500000.000]\n",
- "\n",
- "fig = plt.figure(figsize=(10,10))\n",
- "ax = fig.add_subplot(projection=projection)\n",
- "ax.set_extent(extent, projection)\n",
- "# ax.coastlines()\n",
- "ax.add_feature(cfeature.OCEAN)\n",
- "ax.add_feature(cfeature.LAND)\n",
- "\n",
- "ax.gridlines(draw_labels=True)\n",
- "\n",
- "vmin = 0.\n",
- "vmax = 1.\n",
- "\n",
- "# Plot CryoSat-2 freeboards\n",
- "for fp in downloaded_files:\n",
- " ds = xr.open_dataset(path + fp)\n",
- " cs2= plt.scatter(ds.lon_poca_20_ku[::100], ds.lat_poca_20_ku[::100], 5,\n",
- " c=ds.radar_freeboard_20_ku[::100], cmap=\"Reds\",\n",
- " vmin=vmin, vmax=vmax,\n",
- " transform=ccrs.PlateCarree())\n",
- "\n",
- "# Plot ICESat-2 freeboards\n",
- "for fp in icesat2_files:\n",
- " ds = xr.open_dataset(fp, group='gt2r/freeboard_segment')\n",
- " is2 = plt.scatter(ds.longitude[::100], ds.latitude[::100], 5,\n",
- " c=ds.beam_fb_height[::100], cmap=\"Purples\",\n",
- " vmin=vmin, vmax=vmax,\n",
- " transform=ccrs.PlateCarree())\n",
- " \n",
- "fig.colorbar(cs2, label=\"CryoSat-2 Radar Freeboard (m)\")\n",
- "fig.colorbar(is2, label=\"ICESat-2 Lidar Freeboard (m)\")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a4681430-14b4-4a97-9a0a-9f3f587dfb8a",
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
diff --git a/notebooks/ICESat-2_Cloud_Access/ICESat2-CryoSat2.ipynb b/notebooks/ICESat-2_Cloud_Access/ICESat2-CryoSat2.ipynb
deleted file mode 100644
index b08d9b3..0000000
--- a/notebooks/ICESat-2_Cloud_Access/ICESat2-CryoSat2.ipynb
+++ /dev/null
@@ -1,478 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "id": "de81ea4e-1dad-4f1d-af4b-416dd256af6f",
- "metadata": {},
- "source": [
- "# **Plotting ICESat-2 and CryoSat-2 Freeboards**\n",
- "\n",
- "\n",
- "\n",
- "\n",
- "### **Credits**\n",
- "This notebook was created by Mikala Beig and Andy Barrett, NSIDC\n",
- "\n",
- "### **Learning Objectives** \n",
- "\n",
- "1. use `earthaccess` to search for ICESat-2 ATL10 data using a spatial filter\n",
- "2. open cloud-hosted files using direct access to the ICESat-2 S3 bucket; \n",
- "3. use cs2eo script to download files into your hub instance\n",
- "3. load an HDF5 group into an `xarray.Dataset`; \n",
- "4. visualize freeboards using `hvplot`.\n",
- "5. map the locations of ICESat-2 and CryoSat-2 freeboards using `cartopy`\n",
- "\n",
- "### **Prerequisites**\n",
- "\n",
- "1. An EC2 instance in the us-west-2 region. **NASA cloud-hosted data are in Amazon Region us-west2. So you also need an EC2 instance in the us-west-2 region.** .\n",
- "2. An Earthdata Login is required for data access. If you don't have one, you can register for one [here](https://urs.earthdata.nasa.gov/).\n",
- "3. Experience using cs2eo to query for coincident data.\n",
- "4. A cs2eo download script for CryoSat-2 data.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a2db2b3c-97bf-42aa-8fd1-0eb588afa80e",
- "metadata": {},
- "source": [
- "### **Tutorial Steps**\n",
- "\n",
- "#### Query for coincident ICESat-2 and CryoSat-2 data\n",
- "\n",
- "Using the cs2eo coincident data explorer, query for ATL10 and CryoSat-2, L2, SAR, POCA, Baseline E data products using a spatial and temporal filter.\n",
- "\n",
- "**Download the basic result metadata and the raw access scripts.** Upload the ESA download script (SIR_SAR_L2_E_download_script.py) into the folder from which you are running this notebook.\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "id": "94cbed67-e58a-4579-b153-c9fcbe1e2ab4",
- "metadata": {},
- "source": [
- "#### Import Packages"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e911afb1-b247-4412-9342-1e8865b3084e",
- "metadata": {},
- "outputs": [],
- "source": [
- "import os\n",
- "import platform\n",
- "from ftplib import FTP\n",
- "import sys\n",
- "\n",
- "\n",
- "# For searching and accessing NASA data\n",
- "import earthaccess\n",
- "\n",
- "# For reading data, analysis and plotting\n",
- "import xarray as xr\n",
- "import hvplot.xarray\n",
- "\n",
- "# For nice printing of python objects\n",
- "import pprint \n",
- "\n",
- "# For plotting\n",
- "import matplotlib.pyplot as plt\n",
- "import cartopy.crs as ccrs\n",
- "import cartopy.feature as cfeature\n",
- "\n",
- "#downloading files using cs2eo script\n",
- "from SIR_SAR_L2_E_download_script import download_files\n",
- "\n",
- "## use your own email here\n",
- "user_email = 'your email here'\n",
- "path = './data/'"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "58c3ef53-7223-42da-afd4-e14e87caa85d",
- "metadata": {},
- "source": [
- "#### Download CryoSat-2 data to your hub instance\n",
- "\n",
- "Copy the list of ESA files from within SIR_SAR_L2_E_download_script.py "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ac8e451e-b687-4c13-a5b8-9b700a519a46",
- "metadata": {},
- "outputs": [],
- "source": [
- "esa_files = ['SIR_SAR_L2/2019/12/CS_LTA__SIR_SAR_2__20191227T110305_20191227T111751_E001.nc', \n",
- " 'SIR_SAR_L2/2020/03/CS_LTA__SIR_SAR_2__20200329T163208_20200329T164044_E001.nc', \n",
- " 'SIR_SAR_L2/2020/01/CS_LTA__SIR_SAR_2__20200114T203033_20200114T204440_E001.nc', \n",
- " 'SIR_SAR_L2/2019/11/CS_LTA__SIR_SAR_2__20191103T134759_20191103T135125_E001.nc', \n",
- " 'SIR_SAR_L2/2020/02/CS_LTA__SIR_SAR_2__20200204T191657_20200204T192558_E001.nc', \n",
- " 'SIR_SAR_L2/2019/12/CS_LTA__SIR_SAR_2__20191216T215645_20191216T220909_E001.nc', \n",
- " 'SIR_SAR_L2/2020/03/CS_LTA__SIR_SAR_2__20200315T065755_20200315T071241_E001.nc', \n",
- " 'SIR_SAR_L2/2019/10/CS_LTA__SIR_SAR_2__20191030T135252_20191030T135600_E001.nc', \n",
- " 'SIR_SAR_L2/2020/02/CS_LTA__SIR_SAR_2__20200219T081800_20200219T083303_E001.nc', \n",
- " 'SIR_SAR_L2/2020/01/CS_LTA__SIR_SAR_2__20200110T203717_20200110T204612_E001.nc', \n",
- " 'SIR_SAR_L2/2020/04/CS_LTA__SIR_SAR_2__20200409T053748_20200409T054151_E001.nc', \n",
- " 'SIR_SAR_L2/2020/04/CS_LTA__SIR_SAR_2__20200413T053254_20200413T053659_E001.nc', \n",
- " 'SIR_SAR_L2/2020/02/CS_LTA__SIR_SAR_2__20200208T191154_20200208T192117_E001.nc', \n",
- " 'SIR_SAR_L2/2020/03/CS_LTA__SIR_SAR_2__20200319T065300_20200319T070802_E001.nc', \n",
- " 'SIR_SAR_L2/2020/03/CS_LTA__SIR_SAR_2__20200304T175209_20200304T180102_E001.nc', \n",
- " 'SIR_SAR_L2/2019/11/CS_LTA__SIR_SAR_2__20191128T122800_20191128T123212_E001.nc', \n",
- " 'SIR_SAR_L2/2019/10/CS_LTA__SIR_SAR_2__20191009T150801_20191009T151142_E001.nc', \n",
- " 'SIR_SAR_L2/2019/11/CS_LTA__SIR_SAR_2__20191121T231659_20191121T232817_E001.nc', \n",
- " 'SIR_SAR_L2/2020/02/CS_LTA__SIR_SAR_2__20200215T082253_20200215T083741_E001.nc', \n",
- " 'SIR_SAR_L2/2020/01/CS_LTA__SIR_SAR_2__20200121T094259_20200121T095800_E001.nc', \n",
- " 'SIR_SAR_L2/2019/10/CS_LTA__SIR_SAR_2__20191005T151255_20191005T151621_E001.nc', \n",
- " 'SIR_SAR_L2/2020/04/CS_LTA__SIR_SAR_2__20200427T150701_20200427T151544_E001.nc', \n",
- " 'SIR_SAR_L2/2019/10/CS_LTA__SIR_SAR_2__20191024T004201_20191024T005059_E001.nc', \n",
- " 'SIR_SAR_L2/2020/03/CS_LTA__SIR_SAR_2__20200308T174708_20200308T175621_E001.nc', \n",
- " 'SIR_SAR_L2/2020/04/CS_LTA__SIR_SAR_2__20200402T162707_20200402T163602_E001.nc']"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "eaa73f90-84f0-4429-854a-784c95afde66",
- "metadata": {},
- "source": [
- "Download the CryoSat-2 files into your hub instance by calling the download_files function you imported from the script."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "ecef7122-b3d5-4ca0-b207-7ad3c35ca34d",
- "metadata": {},
- "outputs": [],
- "source": [
- "download_files(user_email, esa_files)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6e9d27c7-d9d9-40ae-aab6-34888aedfe8f",
- "metadata": {},
- "source": [
- "Stashing the files in a data folder to keep our notebook directory less cluttered."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2f6c0779-77cc-4d60-a7a6-cb4a0ce1ea25",
- "metadata": {},
- "outputs": [],
- "source": [
- "!mv CS_LTA__SIR*.nc data"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "aeb468e5-7a5a-456c-96cb-6808af3f5eaa",
- "metadata": {},
- "source": [
- "#### Use `earthaccess` for querying and direct S3 access of ATL10\n",
- "\n",
- "First we authenticate using `earthaccess`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2165876a-c5fa-4df9-b7f5-c82f01e7bdba",
- "metadata": {},
- "outputs": [],
- "source": [
- "auth = earthaccess.login()"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "fb7adbf2-1148-4075-8578-91e01fbeacfc",
- "metadata": {},
- "source": [
- "Then we use a spatial filter to search for ATL10 granules that intersect our area of interest. This is the same area we used in our cs2eo query above."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "37139d13-43e4-4563-b436-2879c76677ec",
- "metadata": {},
- "outputs": [],
- "source": [
- "results = earthaccess.search_data(\n",
- " short_name = 'ATL10',\n",
- " version = '006',\n",
- " cloud_hosted = True,\n",
- " bounding_box = (-17, 79, 12, 83),\n",
- " temporal = ('2019-10-01','2020-04-30'),\n",
- " count = 10\n",
- ")\n",
- "#note that with count=10 we're limiting the number of files we actually access for this tutorial."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "b883fc7f-1c26-471c-88c4-664fa59bed16",
- "metadata": {},
- "outputs": [],
- "source": [
- "display(results[1])"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "e58e1111-1b07-47ec-952b-036290346f4c",
- "metadata": {},
- "source": [
- "We use earthaccess.open() to directly access the ATL10 files within their S3 bucket. earthaccess.open() creates a file-like object, which is required because AWS S3 uses object storage, and we need to create a virtual file system to work with the HDF5 library."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a80212ab-ee66-4d06-8a4a-47169092159e",
- "metadata": {},
- "outputs": [],
- "source": [
- "%time\n",
- "icesat2_files = earthaccess.open(results) \n"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "d24f9d50-3f95-4cdf-b877-1ac06be4471b",
- "metadata": {},
- "source": [
- "We can use xarray to examine the contents of our files (one group at a time)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1d1102e5-561e-41e8-81df-e4ff543d19c1",
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_is2 = xr.open_dataset(icesat2_files[1], group='gt2r/freeboard_segment')\n",
- "ds_is2"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "c603f6a6-7dcb-4a6f-9130-0bab933ff3c6",
- "metadata": {},
- "source": [
- "And we can use hvplot to plot one of the variables within that group."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "0503dfee-7d66-49fa-8482-ad2dc330ff52",
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_is2['beam_fb_height'].hvplot(kind='scatter', s=2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "6a89c207-028f-47df-b7d9-5723bd5e836f",
- "metadata": {},
- "source": [
- "#### Open and plot downloaded CryoSat-2 data \n",
- "\n",
- "We need a list of the downloaded CryoSat-2 files."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "60c1704a-0b17-4d4f-a7a8-dac8fe5b9701",
- "metadata": {},
- "outputs": [],
- "source": [
- "downloaded_files = os.listdir(path)\n",
- "downloaded_files"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "2d280de0-5de7-4a0c-9986-c94ab6b6d05a",
- "metadata": {},
- "source": [
- "We use xarray to access the contents of our netcdf file. In this case, we are not \"streaming\" data from an S3 bucket, but are accessing the data locally."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "8657ed36-d583-46b9-8ed2-9b1b57cb5786",
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_cs2 = xr.open_dataset(path + downloaded_files[0])\n",
- "ds_cs2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "795b0bdd-0895-4de4-8008-b6f15fcf2b3e",
- "metadata": {},
- "outputs": [],
- "source": [
- "ds_cs2['radar_freeboard_20_ku'].hvplot(kind='scatter', s=2)"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "40a8164d-f5c1-4049-aefe-aeb08295092b",
- "metadata": {},
- "source": [
- "#### Plot ICESat-2 and CryoSat-2 Freeboards on same map"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "ad5f7f2c-2ff8-47cc-8ea8-337f9183c20e",
- "metadata": {},
- "source": [
- "Here we're plotting one file from each data set to save time."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "11476d13-0f1f-44f7-a364-4a949554d8c2",
- "metadata": {},
- "outputs": [],
- "source": [
- "projection = ccrs.Stereographic(central_latitude=90.,\n",
- " central_longitude=-45.,\n",
- " true_scale_latitude=70.)\n",
- "extent = [-2500000.000, 2500000., -2500000., 2500000.000]\n",
- "\n",
- "\n",
- "fig = plt.figure(figsize=(10,10))\n",
- "ax = fig.add_subplot(projection=projection)\n",
- "ax.set_extent(extent, projection)\n",
- "ax.add_feature(cfeature.OCEAN)\n",
- "ax.add_feature(cfeature.LAND)\n",
- "ax.gridlines(draw_labels=True)\n",
- "\n",
- "vmin = 0.\n",
- "vmax = 1.\n",
- "\n",
- "# Plot Cryosat freeboard\n",
- "cs2_img = ax.scatter(ds_cs2.lon_poca_20_ku, ds_cs2.lat_poca_20_ku, 5,\n",
- " c=ds_cs2.radar_freeboard_20_ku, \n",
- " vmin=vmin, vmax=vmax, # Set max and min values for plotting\n",
- " cmap='Reds', # shading='auto' to avoid warning\n",
- " transform=ccrs.PlateCarree()) # coords are lat,lon but map if NPS \n",
- "\n",
- "# Plot IS2 freeboard \n",
- "is2_img = ax.scatter(ds_is2.longitude, ds_is2.latitude, 5,\n",
- " c=ds_is2.beam_fb_height, \n",
- " vmin=vmin, vmax=vmax, \n",
- " cmap='Purples', \n",
- " transform=ccrs.PlateCarree())\n",
- "\n",
- "# Add colorbars\n",
- "fig.colorbar(cs2_img, label='Cryosat-2 Radar Freeboard (m)')\n",
- "fig.colorbar(is2_img, label='ICESat-2 Lidar Freeboard (m)')"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "392bb8fd-8ec6-4fd4-b6db-dc0386094c7f",
- "metadata": {},
- "source": [
- "Here we're plotting several ICESat-2 and CryoSat-2 files at a time. This takes a few minutes to render."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "4815c13b-88c3-4d10-850e-02cf766d1844",
- "metadata": {},
- "outputs": [],
- "source": [
- "%%time\n",
- "\n",
- "# NSIDC WGS84 Polar Stereographic \n",
- "projection = ccrs.Stereographic(central_latitude=90.,\n",
- " central_longitude=-45.,\n",
- " true_scale_latitude=70.)\n",
- "extent = [-2500000.000, 2500000., -2500000., 2500000.000]\n",
- "\n",
- "fig = plt.figure(figsize=(10,10))\n",
- "ax = fig.add_subplot(projection=projection)\n",
- "ax.set_extent(extent, projection)\n",
- "# ax.coastlines()\n",
- "ax.add_feature(cfeature.OCEAN)\n",
- "ax.add_feature(cfeature.LAND)\n",
- "\n",
- "ax.gridlines(draw_labels=True)\n",
- "\n",
- "vmin = 0.\n",
- "vmax = 1.\n",
- "\n",
- "# Plot CryoSat-2 freeboards\n",
- "for fp in downloaded_files:\n",
- " ds = xr.open_dataset(path + fp)\n",
- " cs2= plt.scatter(ds.lon_poca_20_ku, ds.lat_poca_20_ku, 5,\n",
- " c=ds.radar_freeboard_20_ku, cmap=\"Reds\",\n",
- " vmin=vmin, vmax=vmax,\n",
- " transform=ccrs.PlateCarree())\n",
- "\n",
- "# Plot ICESat-2 freeboards\n",
- "for fp in icesat2_files:\n",
- " ds = xr.open_dataset(fp, group='gt2r/freeboard_segment')\n",
- " is2 = plt.scatter(ds.longitude, ds.latitude, 5,\n",
- " c=ds.beam_fb_height, cmap=\"Purples\",\n",
- " vmin=vmin, vmax=vmax,\n",
- " transform=ccrs.PlateCarree())\n",
- " \n",
- "fig.colorbar(cs2, label=\"CryoSat-2 Radar Freeboard (m)\")\n",
- "fig.colorbar(is2, label=\"ICESat-2 Lidar Freeboard (m)\")\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "a4681430-14b4-4a97-9a0a-9f3f587dfb8a",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.14"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}