diff --git a/workflows/archive/red_river_2018_create_collection_from_zarr.ipynb b/workflows/archive/red_river_2018_create_collection_from_zarr.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..20958906fa0675aea82f6b88010cfa0fe181aaa6
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+++ b/workflows/archive/red_river_2018_create_collection_from_zarr.ipynb
@@ -0,0 +1,4371 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "6c10e07b-1e60-4926-af1d-fa75dc78e5d4",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# red_river_2018 Zarr -> Collection Workflow\n",
+ "This is a workflow to build a [STAC collection](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) from the zarr asset for the dataset named above. We use the [datacube extension](https://github.com/stac-extensions/datacube) to define the spatial and temporal dimensions of the zarr store, as well as the variables it contains.\n",
+ "\n",
+ "To simplify this workflow so that it can scale to many datasets, a few simplifying suggestions and assumptions are made:\n",
+ "1. For USGS data, we can use the CC0-1.0 license. For all other data we can use Unlicense. Ref: https://spdx.org/licenses/\n",
+ "2. I am assuming all coordinates are from the WGS84 datum if not specified."
+ ]
+ },
+ {
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+ "execution_count": 1,
+ "id": "201e0945-de55-45ff-b095-c2af009a4e62",
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+ " var messages = comm.messages[Symbol.asyncIterator]();\n",
+ " function processIteratorResult(result) {\n",
+ " var message = result.value;\n",
+ " var content = {data: message.data};\n",
+ " var metadata = message.metadata || {comm_id};\n",
+ " var msg = {content, metadata}\n",
+ " msg_handler(msg);\n",
+ " return messages.next().then(processIteratorResult);\n",
+ " }\n",
+ " return messages.next().then(processIteratorResult);\n",
+ " }\n",
+ " }) \n",
+ " var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
+ " return comm_promise.then((comm) => {\n",
+ " comm.send(data, metadata, buffers, disposeOnDone);\n",
+ " });\n",
+ " };\n",
+ " var comm = {\n",
+ " send: sendClosure\n",
+ " };\n",
+ " }\n",
+ " window.PyViz.comms[comm_id] = comm;\n",
+ " return comm;\n",
+ " }\n",
+ " window.PyViz.comm_manager = new JupyterCommManager();\n",
+ " \n",
+ "\n",
+ "\n",
+ "var JS_MIME_TYPE = 'application/javascript';\n",
+ "var HTML_MIME_TYPE = 'text/html';\n",
+ "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+ "var CLASS_NAME = 'output';\n",
+ "\n",
+ "/**\n",
+ " * Render data to the DOM node\n",
+ " */\n",
+ "function render(props, node) {\n",
+ " var div = document.createElement(\"div\");\n",
+ " var script = document.createElement(\"script\");\n",
+ " node.appendChild(div);\n",
+ " node.appendChild(script);\n",
+ "}\n",
+ "\n",
+ "/**\n",
+ " * Handle when a new output is added\n",
+ " */\n",
+ "function handle_add_output(event, handle) {\n",
+ " var output_area = handle.output_area;\n",
+ " var output = handle.output;\n",
+ " if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
+ " return\n",
+ " }\n",
+ " var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+ " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+ " if (id !== undefined) {\n",
+ " var nchildren = toinsert.length;\n",
+ " var html_node = toinsert[nchildren-1].children[0];\n",
+ " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
+ " var scripts = [];\n",
+ " var nodelist = html_node.querySelectorAll(\"script\");\n",
+ " for (var i in nodelist) {\n",
+ " if (nodelist.hasOwnProperty(i)) {\n",
+ " scripts.push(nodelist[i])\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " scripts.forEach( function (oldScript) {\n",
+ " var newScript = document.createElement(\"script\");\n",
+ " var attrs = [];\n",
+ " var nodemap = oldScript.attributes;\n",
+ " for (var j in nodemap) {\n",
+ " if (nodemap.hasOwnProperty(j)) {\n",
+ " attrs.push(nodemap[j])\n",
+ " }\n",
+ " }\n",
+ " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n",
+ " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n",
+ " oldScript.parentNode.replaceChild(newScript, oldScript);\n",
+ " });\n",
+ " if (JS_MIME_TYPE in output.data) {\n",
+ " toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
+ " }\n",
+ " output_area._hv_plot_id = id;\n",
+ " if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
+ " window.PyViz.plot_index[id] = Bokeh.index[id];\n",
+ " } else {\n",
+ " window.PyViz.plot_index[id] = null;\n",
+ " }\n",
+ " } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+ " var bk_div = document.createElement(\"div\");\n",
+ " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+ " var script_attrs = bk_div.children[0].attributes;\n",
+ " for (var i = 0; i < script_attrs.length; i++) {\n",
+ " toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+ " }\n",
+ " // store reference to server id on output_area\n",
+ " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "/**\n",
+ " * Handle when an output is cleared or removed\n",
+ " */\n",
+ "function handle_clear_output(event, handle) {\n",
+ " var id = handle.cell.output_area._hv_plot_id;\n",
+ " var server_id = handle.cell.output_area._bokeh_server_id;\n",
+ " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
+ " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
+ " if (server_id !== null) {\n",
+ " comm.send({event_type: 'server_delete', 'id': server_id});\n",
+ " return;\n",
+ " } else if (comm !== null) {\n",
+ " comm.send({event_type: 'delete', 'id': id});\n",
+ " }\n",
+ " delete PyViz.plot_index[id];\n",
+ " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
+ " var doc = window.Bokeh.index[id].model.document\n",
+ " doc.clear();\n",
+ " const i = window.Bokeh.documents.indexOf(doc);\n",
+ " if (i > -1) {\n",
+ " window.Bokeh.documents.splice(i, 1);\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "/**\n",
+ " * Handle kernel restart event\n",
+ " */\n",
+ "function handle_kernel_cleanup(event, handle) {\n",
+ " delete PyViz.comms[\"hv-extension-comm\"];\n",
+ " window.PyViz.plot_index = {}\n",
+ "}\n",
+ "\n",
+ "/**\n",
+ " * Handle update_display_data messages\n",
+ " */\n",
+ "function handle_update_output(event, handle) {\n",
+ " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
+ " handle_add_output(event, handle)\n",
+ "}\n",
+ "\n",
+ "function register_renderer(events, OutputArea) {\n",
+ " function append_mime(data, metadata, element) {\n",
+ " // create a DOM node to render to\n",
+ " var toinsert = this.create_output_subarea(\n",
+ " metadata,\n",
+ " CLASS_NAME,\n",
+ " EXEC_MIME_TYPE\n",
+ " );\n",
+ " this.keyboard_manager.register_events(toinsert);\n",
+ " // Render to node\n",
+ " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+ " render(props, toinsert[0]);\n",
+ " element.append(toinsert);\n",
+ " return toinsert\n",
+ " }\n",
+ "\n",
+ " events.on('output_added.OutputArea', handle_add_output);\n",
+ " events.on('output_updated.OutputArea', handle_update_output);\n",
+ " events.on('clear_output.CodeCell', handle_clear_output);\n",
+ " events.on('delete.Cell', handle_clear_output);\n",
+ " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
+ "\n",
+ " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+ " safe: true,\n",
+ " index: 0\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "if (window.Jupyter !== undefined) {\n",
+ " try {\n",
+ " var events = require('base/js/events');\n",
+ " var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+ " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+ " register_renderer(events, OutputArea);\n",
+ " }\n",
+ " } catch(err) {\n",
+ " }\n",
+ "}\n"
+ ],
+ "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n function JupyterCommManager() {\n }\n\n JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n comm_manager.register_target(comm_id, function(comm) {\n comm.on_msg(msg_handler);\n });\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n comm.onMsg = msg_handler;\n });\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n 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": [
+ "<style>*[data-root-id],\n",
+ "*[data-root-id] > * {\n",
+ " box-sizing: border-box;\n",
+ " font-family: var(--jp-ui-font-family);\n",
+ " font-size: var(--jp-ui-font-size1);\n",
+ " color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
+ "}\n",
+ "\n",
+ "/* Override VSCode background color */\n",
+ ".cell-output-ipywidget-background:has(\n",
+ " > .cell-output-ipywidget-background > .lm-Widget > *[data-root-id]\n",
+ " ),\n",
+ ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
+ " background-color: transparent !important;\n",
+ "}\n",
+ "</style>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.holoviews_exec.v0+json": "",
+ "text/html": [
+ "<div id='p1002'>\n",
+ " <div id=\"c2816b04-d08f-41a3-91f5-942d421a79da\" data-root-id=\"p1002\" style=\"display: contents;\"></div>\n",
+ "</div>\n",
+ "<script type=\"application/javascript\">(function(root) {\n",
+ " var docs_json = {\"61c9fa0c-eec6-491a-90b9-89fde3eef5e8\":{\"version\":\"3.4.1\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"p1002\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1003\",\"attributes\":{\"plot_id\":\"p1002\",\"comm_id\":\"a09e345221794016b9e6e93c04fe9e2f\",\"client_comm_id\":\"e6993845f5b049b3a6bcb292b2ffcff1\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
+ " var render_items = [{\"docid\":\"61c9fa0c-eec6-491a-90b9-89fde3eef5e8\",\"roots\":{\"p1002\":\"c2816b04-d08f-41a3-91f5-942d421a79da\"},\"root_ids\":[\"p1002\"]}];\n",
+ " var docs = Object.values(docs_json)\n",
+ " if (!docs) {\n",
+ " return\n",
+ " }\n",
+ " const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
+ " function embed_document(root) {\n",
+ " var Bokeh = get_bokeh(root)\n",
+ " Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+ " for (const render_item of render_items) {\n",
+ " for (const root_id of render_item.root_ids) {\n",
+ "\tconst id_el = document.getElementById(root_id)\n",
+ "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
+ "\t const root_el = id_el.children[0]\n",
+ "\t root_el.id = root_el.id + '-rendered'\n",
+ "\t}\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " function get_bokeh(root) {\n",
+ " if (root.Bokeh === undefined) {\n",
+ " return null\n",
+ " } else if (root.Bokeh.version !== py_version) {\n",
+ " if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
+ "\treturn null\n",
+ " }\n",
+ " return root.Bokeh.versions.get(py_version);\n",
+ " } else if (root.Bokeh.version === py_version) {\n",
+ " return root.Bokeh\n",
+ " }\n",
+ " return null\n",
+ " }\n",
+ " function is_loaded(root) {\n",
+ " var Bokeh = get_bokeh(root)\n",
+ " return (Bokeh != null && Bokeh.Panel !== undefined)\n",
+ " }\n",
+ " if (is_loaded(root)) {\n",
+ " embed_document(root);\n",
+ " } else {\n",
+ " var attempts = 0;\n",
+ " var timer = setInterval(function(root) {\n",
+ " if (is_loaded(root)) {\n",
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+ " clearInterval(timer);\n",
+ "\t var Bokeh = get_bokeh(root)\n",
+ "\t if (Bokeh == null || Bokeh.Panel == null) {\n",
+ " console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
+ "\t } else {\n",
+ "\t console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
+ "\t embed_document(root)\n",
+ "\t }\n",
+ " }\n",
+ " }\n",
+ " }, 25, root)\n",
+ " }\n",
+ "})(window);</script>"
+ ]
+ },
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {
+ "id": "p1002"
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pystac\n",
+ "from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
+ "import xarray as xr\n",
+ "import cf_xarray\n",
+ "import os\n",
+ "import fsspec\n",
+ "import cf_xarray\n",
+ "import hvplot.xarray\n",
+ "import pandas as pd\n",
+ "import json\n",
+ "import numpy as np\n",
+ "import pyproj\n",
+ "from pyproj import Transformer\n",
+ "import cartopy.crs as ccrs\n",
+ "import cfunits\n",
+ "import json\n",
+ "import sys\n",
+ "sys.path.insert(1, '..')\n",
+ "import stac_helpers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a71f9d19-8fb3-4f47-b4c4-447bb80d8dd5",
+ "metadata": {},
+ "source": [
+ "## Collection ID"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# name for STAC collection - should match name of zarr dataset\n",
+ "collection_id = 'red_river_2018'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "116b5837-8e85-4ae7-964a-803533ded714",
+ "metadata": {},
+ "source": [
+ "## Asset Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "dd6fa323-132a-4794-8c80-576933f547a0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url = f's3://mdmf-workspace/gdp/{collection_id}.zarr/'\n",
+ "\n",
+ "# define keyword arguments needed for opening the dataset with xarray\n",
+ "# ref: https://github.com/stac-extensions/xarray-assets\n",
+ "xarray_opendataset_kwargs = {\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":True},\n",
+ " \"xarray:storage_options\": {\"anon\": True, \"client_kwargs\": {\"endpoint_url\":\"https://usgs.osn.mghpcc.org/\"}}}\n",
+ "# description for zarr url asset attached to collection (zarr_url)\n",
+ "asset_description = \"Open Storage Network Pod S3 API access to collection zarr group\"\n",
+ "# roles to tag zarr url asset with\n",
+ "asset_roles = [\"data\",\"zarr\",\"s3\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# url to zarr store that you want to create a collection for\n",
+ "zarr_url2 = f's3://nhgf-development/workspace/DataConversion/{collection_id}.zarr/'\n",
+ "\n",
+ "# define keyword arguments needed for opening the dataset with xarray\n",
+ "# ref: https://github.com/stac-extensions/xarray-assets\n",
+ "xarray_opendataset_kwargs2 = {\"xarray:open_kwargs\":{\"chunks\":{},\"engine\":\"zarr\",\"consolidated\":True},\n",
+ " \"xarray:storage_options\":{\"requester_pays\":True}}\n",
+ "# description for zarr url asset attached to collection (zarr_url)\n",
+ "asset_description2 = \"S3 access to collection zarr group\"\n",
+ "# roles to tag zarr url asset with\n",
+ "asset_roles2 = [\"data\",\"zarr\",\"s3\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b213b74f-ad17-4774-93b6-3b62be616b45",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Data Exploration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "708f2cf5-79ab-49af-8067-de31d0d13ee6",
+ "metadata": {},
+ "outputs": [
+ {
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+ "</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 517GB\n",
+ "Dimensions: (Time: 315576, south_north: 165, west_east: 248)\n",
+ "Coordinates:\n",
+ " * Time (Time) datetime64[ns] 3MB 1980-01-01 ... 2015-12-31T23:00:00\n",
+ " XLAT (south_north, west_east) float32 164kB dask.array<chunksize=(11, 16), meta=np.ndarray>\n",
+ " XLONG (south_north, west_east) float32 164kB dask.array<chunksize=(11, 16), meta=np.ndarray>\n",
+ "Dimensions without coordinates: south_north, west_east\n",
+ "Data variables:\n",
+ " HFX (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " LH (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " PBLH (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " Q2 (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " RAINC (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " RAINNC (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " SWDOWN (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " T2 (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " U10 (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " V10 (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " crs int64 8B ...\n",
+ " time_char (Time) |S64 20MB dask.array<chunksize=(1422,), meta=np.ndarray>\n",
+ "Attributes: (12/27)\n",
+ " DODS.dimName: DateStrLen\n",
+ " DODS.strlen: 19\n",
+ " Metadata_Conventions: Unidata Dataset Discovery v1.0\n",
+ " NCO: 4.6.8\n",
+ " acknowledgment: http://www.caps.ou.edu/micronet/temp/files/CS...\n",
+ " cdm_data_type: Grid\n",
+ " ... ...\n",
+ " publisher_name: David Watkins\n",
+ " publisher_url: https://water.usgs.gov/\n",
+ " summary: The U.S. Great Plains is known for frequent h...\n",
+ " time_coverage_end: 2015-12-31\n",
+ " time_coverage_start: 1980-01-01\n",
+ " title: Very High-Resolution Dynamic Downscaling of R...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dc24223e-436e-4441-910d-903d95f6cdda' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dc24223e-436e-4441-910d-903d95f6cdda' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 315576</li><li><span>south_north</span>: 165</li><li><span>west_east</span>: 248</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-97767eee-6bd5-44db-8c2a-59d7ffb6d7aa' class='xr-section-summary-in' type='checkbox' checked><label for='section-97767eee-6bd5-44db-8c2a-59d7ffb6d7aa' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Time</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1980-01-01 ... 2015-12-31T23:00:00</div><input id='attrs-292d9323-6389-4487-b400-bafc4173b78a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-292d9323-6389-4487-b400-bafc4173b78a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b48ae5b6-69cf-4494-909b-593e8747ba96' class='xr-var-data-in' type='checkbox'><label for='data-b48ae5b6-69cf-4494-909b-593e8747ba96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['1980-01-01T00:00:00.000000000', '1980-01-01T01:00:00.000000000',\n",
+ " '1980-01-01T02:00:00.000000000', ..., '2015-12-31T21:00:00.000000000',\n",
+ " '2015-12-31T22:00:00.000000000', '2015-12-31T23:00:00.000000000'],\n",
+ " dtype='datetime64[ns]')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLAT</span></div><div class='xr-var-dims'>(south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(11, 16), meta=np.ndarray></div><input id='attrs-fc2b690b-4f80-4701-bbb8-faa509755cf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc2b690b-4f80-4701-bbb8-faa509755cf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00c13b68-1e2d-4671-aea3-8faf8d5ef5c9' class='xr-var-data-in' type='checkbox'><label for='data-00c13b68-1e2d-4671-aea3-8faf8d5ef5c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>cell_methods :</span></dt><dd>Time: mean</dd><dt><span>grid_mapping :</span></dt><dd>crs: XLAT XLONG</dd><dt><span>long_name :</span></dt><dd>LATITUDE, SOUTH IS NEGATIVE</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 159.84 kiB </td>\n",
+ " <td> 704 B </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (165, 248) </td>\n",
+ " <td> (11, 16) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 240 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
+ " <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ " </table>\n",
+ " </td>\n",
+ " <td>\n",
+ " <svg width=\"170\" height=\"129\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+ "\n",
+ " <!-- Horizontal lines -->\n",
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+ "</svg>\n",
+ " </td>\n",
+ " </tr>\n",
+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XLONG</span></div><div class='xr-var-dims'>(south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(11, 16), meta=np.ndarray></div><input id='attrs-04b64ea5-7ee1-4f9b-bf78-6d85a4d9e803' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04b64ea5-7ee1-4f9b-bf78-6d85a4d9e803' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9a71c52-3d4e-4b67-bc36-7c277e8f8876' class='xr-var-data-in' type='checkbox'><label for='data-d9a71c52-3d4e-4b67-bc36-7c277e8f8876' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>cell_methods :</span></dt><dd>Time: mean</dd><dt><span>grid_mapping :</span></dt><dd>crs: XLAT XLONG</dd><dt><span>long_name :</span></dt><dd>LONGITUDE, WEST IS NEGATIVE</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 159.84 kiB </td>\n",
+ " <td> 704 B </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (165, 248) </td>\n",
+ " <td> (11, 16) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 240 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
+ " <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ " </table>\n",
+ " </td>\n",
+ " <td>\n",
+ " <svg width=\"170\" height=\"129\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+ "</svg>\n",
+ " </td>\n",
+ " </tr>\n",
+ "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7436c2d7-0a7d-4aa1-8821-ef1a000b0c2a' class='xr-section-summary-in' type='checkbox' checked><label for='section-7436c2d7-0a7d-4aa1-8821-ef1a000b0c2a' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>HFX</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1422, 11, 16), meta=np.ndarray></div><input id='attrs-05db783c-f06f-48b4-9494-0047651e69b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05db783c-f06f-48b4-9494-0047651e69b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c92deb9-34b7-4384-847a-04ca3db2c2d0' class='xr-var-data-in' type='checkbox'><label for='data-4c92deb9-34b7-4384-847a-04ca3db2c2d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>grid_mapping :</span></dt><dd>crs: XLAT XLONG</dd><dt><span>long_name :</span></dt><dd>UPWARD HEAT FLUX AT THE SURFACE</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 48.11 GiB </td>\n",
+ " <td> 0.95 MiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (315576, 165, 248) </td>\n",
+ " <td> (1422, 11, 16) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 53280 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
+ " <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ " </table>\n",
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+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>LH</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1422, 11, 16), meta=np.ndarray></div><input id='attrs-78aa51df-3b32-45ed-9ccd-8a92e2eb1ec8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78aa51df-3b32-45ed-9ccd-8a92e2eb1ec8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4cb1e95a-74b2-4dfa-9a94-99226c67f720' class='xr-var-data-in' type='checkbox'><label for='data-4cb1e95a-74b2-4dfa-9a94-99226c67f720' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>grid_mapping :</span></dt><dd>crs: XLAT XLONG</dd><dt><span>long_name :</span></dt><dd>LATENT HEAT FLUX AT THE SURFACE</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 48.11 GiB </td>\n",
+ " <td> 0.95 MiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (315576, 165, 248) </td>\n",
+ " <td> (1422, 11, 16) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 53280 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
+ " <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PBLH</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1422, 11, 16), meta=np.ndarray></div><input id='attrs-f13b7de8-9a26-44dc-af95-16b7efbad6a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f13b7de8-9a26-44dc-af95-16b7efbad6a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1287ac9c-2650-483b-be10-ba93774b1854' class='xr-var-data-in' type='checkbox'><label for='data-1287ac9c-2650-483b-be10-ba93774b1854' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>grid_mapping :</span></dt><dd>crs: XLAT XLONG</dd><dt><span>long_name :</span></dt><dd>PBL HEIGHT</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 48.11 GiB </td>\n",
+ " <td> 0.95 MiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (315576, 165, 248) </td>\n",
+ " <td> (1422, 11, 16) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 53280 chunks in 2 graph layers </td>\n",
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+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 48.11 GiB </td>\n",
+ " <td> 0.95 MiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (315576, 165, 248) </td>\n",
+ " <td> (1422, 11, 16) </td>\n",
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+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 53280 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
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+ " <tr>\n",
+ " <td>\n",
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+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 48.11 GiB </td>\n",
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+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (315576, 165, 248) </td>\n",
+ " <td> (1422, 11, 16) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
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+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RAINNC</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1422, 11, 16), meta=np.ndarray></div><input id='attrs-e2247fdd-2e0a-4036-835a-eb261e89915c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2247fdd-2e0a-4036-835a-eb261e89915c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b479bcf1-1a58-4fbc-8a95-3db5ac2cfd46' class='xr-var-data-in' type='checkbox'><label for='data-b479bcf1-1a58-4fbc-8a95-3db5ac2cfd46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>grid_mapping :</span></dt><dd>crs: XLAT XLONG</dd><dt><span>long_name :</span></dt><dd>ACCUMULATED TOTAL GRID SCALE PRECIPITATION</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><table>\n",
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+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
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+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
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+ " \n",
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+ " <th> Shape </th>\n",
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+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SWDOWN</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1422, 11, 16), meta=np.ndarray></div><input id='attrs-48707204-a3fa-4f97-8169-2886d1e8e95c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48707204-a3fa-4f97-8169-2886d1e8e95c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0dda31cb-96ac-431b-980a-5d6f37080bc6' class='xr-var-data-in' type='checkbox'><label for='data-0dda31cb-96ac-431b-980a-5d6f37080bc6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>grid_mapping :</span></dt><dd>crs: XLAT XLONG</dd><dt><span>long_name :</span></dt><dd>DOWNWARD SHORT WAVE FLUX AT GROUND SURFACE</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>units :</span></dt><dd>W m-2</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 48.11 GiB </td>\n",
+ " <td> 0.95 MiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (315576, 165, 248) </td>\n",
+ " <td> (1422, 11, 16) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 53280 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
+ " <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ " </table>\n",
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+ " </tr>\n",
+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>T2</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1422, 11, 16), meta=np.ndarray></div><input id='attrs-12467b62-9009-41de-85bd-a527cf954353' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12467b62-9009-41de-85bd-a527cf954353' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bdd7301-0b0b-41a3-bd37-f68418022212' class='xr-var-data-in' type='checkbox'><label for='data-1bdd7301-0b0b-41a3-bd37-f68418022212' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>grid_mapping :</span></dt><dd>crs: XLAT XLONG</dd><dt><span>long_name :</span></dt><dd>TEMP at 2 M</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 48.11 GiB </td>\n",
+ " <td> 0.95 MiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (315576, 165, 248) </td>\n",
+ " <td> (1422, 11, 16) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
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+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U10</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1422, 11, 16), meta=np.ndarray></div><input id='attrs-b693df28-1bd1-4ef4-bda6-b996c5526a44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b693df28-1bd1-4ef4-bda6-b996c5526a44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3154b52c-f337-4a97-8a0a-c952e5a46171' class='xr-var-data-in' type='checkbox'><label for='data-3154b52c-f337-4a97-8a0a-c952e5a46171' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>grid_mapping :</span></dt><dd>crs: XLAT XLONG</dd><dt><span>long_name :</span></dt><dd>U at 10 M</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 48.11 GiB </td>\n",
+ " <td> 0.95 MiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (315576, 165, 248) </td>\n",
+ " <td> (1422, 11, 16) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 53280 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
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+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>V10</span></div><div class='xr-var-dims'>(Time, south_north, west_east)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1422, 11, 16), meta=np.ndarray></div><input id='attrs-aa3886dc-beff-4423-8aa5-9df89a95f9fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa3886dc-beff-4423-8aa5-9df89a95f9fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c7cbf31-bf5c-40f5-98f3-13236eb50aba' class='xr-var-data-in' type='checkbox'><label for='data-4c7cbf31-bf5c-40f5-98f3-13236eb50aba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>FieldType :</span></dt><dd>104</dd><dt><span>MemoryOrder :</span></dt><dd>XY </dd><dt><span>grid_mapping :</span></dt><dd>crs: XLAT XLONG</dd><dt><span>long_name :</span></dt><dd>V at 10 M</dd><dt><span>stagger :</span></dt><dd></dd><dt><span>units :</span></dt><dd>m s-1</dd></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 48.11 GiB </td>\n",
+ " <td> 0.95 MiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (315576, 165, 248) </td>\n",
+ " <td> (1422, 11, 16) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 53280 chunks in 2 graph layers </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Data type </th>\n",
+ " <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+ "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>crs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5e43c3ae-2e9d-4359-b4af-3315e2ef1f92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e43c3ae-2e9d-4359-b4af-3315e2ef1f92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fbfa524-a4ce-44ee-9255-bca9244c7ff0' class='xr-var-data-in' type='checkbox'><label for='data-8fbfa524-a4ce-44ee-9255-bca9244c7ff0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984 ensemble</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_char</span></div><div class='xr-var-dims'>(Time)</div><div class='xr-var-dtype'>|S64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1422,), meta=np.ndarray></div><input id='attrs-8b292e1e-c3fe-4a6b-9bfd-1d2ff5e59838' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8b292e1e-c3fe-4a6b-9bfd-1d2ff5e59838' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-871f0357-5657-41aa-8cb5-70f81ded2b43' class='xr-var-data-in' type='checkbox'><label for='data-871f0357-5657-41aa-8cb5-70f81ded2b43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
+ " <tr>\n",
+ " <td>\n",
+ " <table style=\"border-collapse: collapse;\">\n",
+ " <thead>\n",
+ " <tr>\n",
+ " <td> </td>\n",
+ " <th> Array </th>\n",
+ " <th> Chunk </th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Bytes </th>\n",
+ " <td> 19.26 MiB </td>\n",
+ " <td> 88.88 kiB </td>\n",
+ " </tr>\n",
+ " \n",
+ " <tr>\n",
+ " <th> Shape </th>\n",
+ " <td> (315576,) </td>\n",
+ " <td> (1422,) </td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th> Dask graph </th>\n",
+ " <td colspan=\"2\"> 222 chunks in 2 graph layers </td>\n",
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+ " <tr>\n",
+ " <th> Data type </th>\n",
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+ " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+ "\n",
+ " <!-- Colored Rectangle -->\n",
+ " <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
+ "\n",
+ " <!-- Text -->\n",
+ " <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >315576</text>\n",
+ " <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
+ "</svg>\n",
+ " </td>\n",
+ " </tr>\n",
+ "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-14060a67-016e-4e50-8d6d-710e2b7dac0e' class='xr-section-summary-in' type='checkbox' ><label for='section-14060a67-016e-4e50-8d6d-710e2b7dac0e' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>Time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-61987bd5-3acd-4579-85c4-dcf4fe74cfea' class='xr-index-data-in' type='checkbox'/><label for='index-61987bd5-3acd-4579-85c4-dcf4fe74cfea' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['1980-01-01 00:00:00', '1980-01-01 01:00:00',\n",
+ " '1980-01-01 02:00:00', '1980-01-01 03:00:00',\n",
+ " '1980-01-01 04:00:00', '1980-01-01 05:00:00',\n",
+ " '1980-01-01 06:00:00', '1980-01-01 07:00:00',\n",
+ " '1980-01-01 08:00:00', '1980-01-01 09:00:00',\n",
+ " ...\n",
+ " '2015-12-31 14:00:00', '2015-12-31 15:00:00',\n",
+ " '2015-12-31 16:00:00', '2015-12-31 17:00:00',\n",
+ " '2015-12-31 18:00:00', '2015-12-31 19:00:00',\n",
+ " '2015-12-31 20:00:00', '2015-12-31 21:00:00',\n",
+ " '2015-12-31 22:00:00', '2015-12-31 23:00:00'],\n",
+ " dtype='datetime64[ns]', name='Time', length=315576, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e54bdf48-eea3-455f-8667-561254b42130' class='xr-section-summary-in' type='checkbox' ><label for='section-e54bdf48-eea3-455f-8667-561254b42130' class='xr-section-summary' >Attributes: <span>(27)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>DODS.dimName :</span></dt><dd>DateStrLen</dd><dt><span>DODS.strlen :</span></dt><dd>19</dd><dt><span>Metadata_Conventions :</span></dt><dd>Unidata Dataset Discovery v1.0</dd><dt><span>NCO :</span></dt><dd>4.6.8</dd><dt><span>acknowledgment :</span></dt><dd>http://www.caps.ou.edu/micronet/temp/files/CSC-CAPS-final-report-2018Feb_xhu.pdf</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>conventions :</span></dt><dd>CF-1.7.3</dd><dt><span>creator_email :</span></dt><dd>xhu@ou.edu</dd><dt><span>creator_name :</span></dt><dd>Xiaoming Hu</dd><dt><span>date_issued :</span></dt><dd></dd><dt><span>geospatial_lat_max :</span></dt><dd>52.848969</dd><dt><span>geospatial_lat_min :</span></dt><dd>20.994766</dd><dt><span>geospatial_lon_max :</span></dt><dd>-65.226807</dd><dt><span>geospatial_lon_min :</span></dt><dd>-133.011215</dd><dt><span>history :</span></dt><dd></dd><dt><span>history_of_appended_files :</span></dt><dd>Tue Jun 26 14:07:18 2018: Appended file ../times_1980.nc had no "history" attribute\n",
+ "Tue Jun 26 13:45:09 2018: Appended file ../times_1980.nc had no "history" attribute\n",
+ "Tue Jun 26 13:23:25 2018: Appended file ../times_1980.nc had no "history" attribute\n",
+ "</dd><dt><span>id :</span></dt><dd>cida.usgs.gov/red_river_2018</dd><dt><span>license :</span></dt><dd>Freely available</dd><dt><span>naming_authority :</span></dt><dd>cida.usgs.gov</dd><dt><span>project :</span></dt><dd>Informing Hydrologic Planning in the Red River Valley through Improved Regional Climate Projections</dd><dt><span>publisher_email :</span></dt><dd>wwatkins@usgs.gov</dd><dt><span>publisher_name :</span></dt><dd>David Watkins</dd><dt><span>publisher_url :</span></dt><dd>https://water.usgs.gov/</dd><dt><span>summary :</span></dt><dd>The U.S. Great Plains is known for frequent hazardous convective weather and climate extremes. Across this region, climate change is expected to cause more severe droughts, more intense heavy rainfall events, and subsequently more flooding episodes. These potential changes in climate will adversely affect habitats, ecosystems, and landscapes as well as the fish and wildlife they support. Better understanding and simulation of regional precipitation can help natural resource managers mitigate and adapt to these adverse impacts. In this project, we aim to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model and use the high quality dynamic downscaling results (with a 4km horizontal resolution) to investigate the precipitation variability near the Edwards Plateau and Balcones Escarpment in Texas, an area prone to heavy rain and devastating flood events. To this end, WRF simulations with different physics schemes and nudging strategies are first conducted for a representative warm season. Results show that simply choosing different physics schemes is not enough to alleviate the dry bias over the southern Great Plains, which is related to an anticyclonic circulation anomaly over the central and western parts of continental U.S. in the simulations. Spectral nudging emerges as an effective solution for alleviating the precipitation bias. As a result, a better precipitation downscaling is achieved. With the carefully designed configurations, WRF downscaling is conducted for 1980-2015. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthly/yearly variability, showing improvement over at least two previously published precipitation downscaling studies. With the improved precipitation downscaling, a better hydrological simulation over the trans-state Oologah watershed is also achieved. In addition, analyzing the high-resolution (4 km) downscaling outputs leads to a better understanding regarding the precipitation variability in Texas. </dd><dt><span>time_coverage_end :</span></dt><dd>2015-12-31</dd><dt><span>time_coverage_start :</span></dt><dd>1980-01-01</dd><dt><span>title :</span></dt><dd>Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System</dd></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.Dataset> Size: 517GB\n",
+ "Dimensions: (Time: 315576, south_north: 165, west_east: 248)\n",
+ "Coordinates:\n",
+ " * Time (Time) datetime64[ns] 3MB 1980-01-01 ... 2015-12-31T23:00:00\n",
+ " XLAT (south_north, west_east) float32 164kB dask.array<chunksize=(11, 16), meta=np.ndarray>\n",
+ " XLONG (south_north, west_east) float32 164kB dask.array<chunksize=(11, 16), meta=np.ndarray>\n",
+ "Dimensions without coordinates: south_north, west_east\n",
+ "Data variables:\n",
+ " HFX (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " LH (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " PBLH (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " Q2 (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " RAINC (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " RAINNC (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " SWDOWN (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " T2 (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " U10 (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " V10 (Time, south_north, west_east) float32 52GB dask.array<chunksize=(1422, 11, 16), meta=np.ndarray>\n",
+ " crs int64 8B ...\n",
+ " time_char (Time) |S64 20MB dask.array<chunksize=(1422,), meta=np.ndarray>\n",
+ "Attributes: (12/27)\n",
+ " DODS.dimName: DateStrLen\n",
+ " DODS.strlen: 19\n",
+ " Metadata_Conventions: Unidata Dataset Discovery v1.0\n",
+ " NCO: 4.6.8\n",
+ " acknowledgment: http://www.caps.ou.edu/micronet/temp/files/CS...\n",
+ " cdm_data_type: Grid\n",
+ " ... ...\n",
+ " publisher_name: David Watkins\n",
+ " publisher_url: https://water.usgs.gov/\n",
+ " summary: The U.S. Great Plains is known for frequent h...\n",
+ " time_coverage_end: 2015-12-31\n",
+ " time_coverage_start: 1980-01-01\n",
+ " title: Very High-Resolution Dynamic Downscaling of R..."
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# open and view zarr dataset\n",
+ "fs2 = fsspec.filesystem('s3', profile='osn-mdmf-workspace', endpoint_url='https://usgs.osn.mghpcc.org/')\n",
+ "ds = xr.open_dataset(fs2.get_mapper(zarr_url), engine='zarr', \n",
+ " backend_kwargs={'consolidated':True}, chunks={})\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "996e60ba-13e4-453a-8534-e62ce747f0fa",
+ "metadata": {},
+ "source": [
+ "## Collection Metadata Input"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "482d204d-b5b6-40e5-ac42-55b459be1097",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "collection description: Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System\n"
+ ]
+ }
+ ],
+ "source": [
+ "# description of STAC collection\n",
+ "collection_description = ds.attrs['title']\n",
+ "print(f'collection description: {collection_description}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "91129d65-a614-4fe4-86b6-105b1f121f55",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "license in dataset attrs: \"Freely available\"\n",
+ "\n",
+ "For USGS data, we can use \"\u001b[1mCC0-1.0\u001b[0m\" as the license. For all other data we can use \"\u001b[1mUnlicense\u001b[0m\".\n",
+ "Ref: https://spdx.org/licenses/\n",
+ "\n",
+ "license automatically chosen: \u001b[1mUnlicense\n"
+ ]
+ }
+ ],
+ "source": [
+ "# license for dataset\n",
+ "collection_license = stac_helpers.license_picker(ds.attrs['license'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0bc7e9b3-ad62-4b10-a18e-66b7ed2d35dc",
+ "metadata": {},
+ "source": [
+ "## Identify x, y, t dimensions of dataset\n",
+ "May require user input if dimensions cannot be auto-detected.\n",
+ "\n",
+ "**X and Y dims are indices only, but we will enter the geospatial vars of XLONG and XLAT here to detect the bounds of the dataset**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dimension dictionary: {'X': 'XLONG', 'Y': 'XLAT', 'T': 'Time'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# dims_auto_extract = ['X', 'Y', 'T']\n",
+ "# dim_names_dict = {}\n",
+ "# for d in dims_auto_extract:\n",
+ "# dim_names_dict[d] = stac_helpers.extract_dim(ds, d)\n",
+ "dim_names_dict = {'X': 'XLONG', 'Y': 'XLAT', 'T':'Time'}\n",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810d7480-165d-41c0-bd09-163656a14003",
+ "metadata": {},
+ "source": [
+ "## Get crs info\n",
+ "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "crs_var = 'crs'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# use pyproj to automatically extract crs info\n",
+ "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
+ "\n",
+ "# alternatively, create the appropriate cartopy projection\n",
+ "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
+ "# central_latitude=crs_info.latitude_of_projection_origin,\n",
+ "# standard_parallels=crs_info.standard_parallel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "282c689e-07f0-48ee-8e3d-35876e8c5094",
+ "metadata": {},
+ "source": [
+ "### Compare dataset crs var to generated proj4 string to make sure it looks ok"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "4cee13ba-487d-483e-a013-b65685137502",
+ "metadata": {},
+ "outputs": [
+ {
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+ ".xr-array-data {\n",
+ " padding: 0 5px !important;\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-array-data,\n",
+ ".xr-array-in:checked ~ .xr-array-preview {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ ".xr-array-in:checked ~ .xr-array-data,\n",
+ ".xr-array-preview {\n",
+ " display: inline-block;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list {\n",
+ " display: inline-block !important;\n",
+ " list-style: none;\n",
+ " padding: 0 !important;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li {\n",
+ " display: inline-block;\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:before {\n",
+ " content: '(';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list:after {\n",
+ " content: ')';\n",
+ "}\n",
+ "\n",
+ ".xr-dim-list li:not(:last-child):after {\n",
+ " content: ',';\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-has-index {\n",
+ " font-weight: bold;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list,\n",
+ ".xr-var-item {\n",
+ " display: contents;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > div,\n",
+ ".xr-var-item label,\n",
+ ".xr-var-item > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-even);\n",
+ " margin-bottom: 0;\n",
+ "}\n",
+ "\n",
+ ".xr-var-item > .xr-var-name:hover span {\n",
+ " padding-right: 5px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-list > li:nth-child(odd) > div,\n",
+ ".xr-var-list > li:nth-child(odd) > label,\n",
+ ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+ " background-color: var(--xr-background-color-row-odd);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name {\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dims {\n",
+ " grid-column: 2;\n",
+ "}\n",
+ "\n",
+ ".xr-var-dtype {\n",
+ " grid-column: 3;\n",
+ " text-align: right;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-preview {\n",
+ " grid-column: 4;\n",
+ "}\n",
+ "\n",
+ ".xr-index-preview {\n",
+ " grid-column: 2 / 5;\n",
+ " color: var(--xr-font-color2);\n",
+ "}\n",
+ "\n",
+ ".xr-var-name,\n",
+ ".xr-var-dims,\n",
+ ".xr-var-dtype,\n",
+ ".xr-preview,\n",
+ ".xr-attrs dt {\n",
+ " white-space: nowrap;\n",
+ " overflow: hidden;\n",
+ " text-overflow: ellipsis;\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name:hover,\n",
+ ".xr-var-dims:hover,\n",
+ ".xr-var-dtype:hover,\n",
+ ".xr-attrs dt:hover {\n",
+ " overflow: visible;\n",
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+ " z-index: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-data {\n",
+ " display: none;\n",
+ " background-color: var(--xr-background-color) !important;\n",
+ " padding-bottom: 5px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+ ".xr-var-data-in:checked ~ .xr-var-data,\n",
+ ".xr-index-data-in:checked ~ .xr-index-data {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ ".xr-var-data > table {\n",
+ " float: right;\n",
+ "}\n",
+ "\n",
+ ".xr-var-name span,\n",
+ ".xr-var-data,\n",
+ ".xr-index-name div,\n",
+ ".xr-index-data,\n",
+ ".xr-attrs {\n",
+ " padding-left: 25px !important;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs,\n",
+ ".xr-var-attrs,\n",
+ ".xr-var-data,\n",
+ ".xr-index-data {\n",
+ " grid-column: 1 / -1;\n",
+ "}\n",
+ "\n",
+ "dl.xr-attrs {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " display: grid;\n",
+ " grid-template-columns: 125px auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt,\n",
+ ".xr-attrs dd {\n",
+ " padding: 0;\n",
+ " margin: 0;\n",
+ " float: left;\n",
+ " padding-right: 10px;\n",
+ " width: auto;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt {\n",
+ " font-weight: normal;\n",
+ " grid-column: 1;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dt:hover span {\n",
+ " display: inline-block;\n",
+ " background: var(--xr-background-color);\n",
+ " padding-right: 10px;\n",
+ "}\n",
+ "\n",
+ ".xr-attrs dd {\n",
+ " grid-column: 2;\n",
+ " white-space: pre-wrap;\n",
+ " word-break: break-all;\n",
+ "}\n",
+ "\n",
+ ".xr-icon-database,\n",
+ ".xr-icon-file-text2,\n",
+ ".xr-no-icon {\n",
+ " display: inline-block;\n",
+ " vertical-align: middle;\n",
+ " width: 1em;\n",
+ " height: 1.5em !important;\n",
+ " stroke-width: 0;\n",
+ " stroke: currentColor;\n",
+ " fill: currentColor;\n",
+ "}\n",
+ "</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'crs' ()> Size: 8B\n",
+ "[1 values with dtype=int64]\n",
+ "Attributes:\n",
+ " crs_wkt: GEOGCRS["WGS 84",ENSEMBLE["World Geodetic S...\n",
+ " geographic_crs_name: WGS 84\n",
+ " grid_mapping_name: latitude_longitude\n",
+ " horizontal_datum_name: World Geodetic System 1984 ensemble\n",
+ " inverse_flattening: 298.257223563\n",
+ " longitude_of_prime_meridian: 0.0\n",
+ " prime_meridian_name: Greenwich\n",
+ " reference_ellipsoid_name: WGS 84\n",
+ " semi_major_axis: 6378137.0\n",
+ " semi_minor_axis: 6356752.314245179</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'crs'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c86479f4-569d-4ad8-bcce-e2964cca6d48' class='xr-array-in' type='checkbox' checked><label for='section-c86479f4-569d-4ad8-bcce-e2964cca6d48' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[1 values with dtype=int64]</pre></div></div></li><li class='xr-section-item'><input id='section-1cefda1c-8f61-4310-997f-61312b840d9b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1cefda1c-8f61-4310-997f-61312b840d9b' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e895191d-ea78-4297-9b98-1cd457db25e6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e895191d-ea78-4297-9b98-1cd457db25e6' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-798f500c-8ae2-4ac7-8d57-7c8ef113e558' class='xr-section-summary-in' type='checkbox' ><label for='section-798f500c-8ae2-4ac7-8d57-7c8ef113e558' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCRS["WGS 84",ENSEMBLE["World Geodetic System 1984 ensemble",MEMBER["World Geodetic System 1984 (Transit)"],MEMBER["World Geodetic System 1984 (G730)"],MEMBER["World Geodetic System 1984 (G873)"],MEMBER["World Geodetic System 1984 (G1150)"],MEMBER["World Geodetic System 1984 (G1674)"],MEMBER["World Geodetic System 1984 (G1762)"],MEMBER["World Geodetic System 1984 (G2139)"],ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ENSEMBLEACCURACY[2.0]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433]],CS[ellipsoidal,2],AXIS["geodetic latitude (Lat)",north,ORDER[1],ANGLEUNIT["degree",0.0174532925199433]],AXIS["geodetic longitude (Lon)",east,ORDER[2],ANGLEUNIT["degree",0.0174532925199433]],USAGE[SCOPE["Horizontal component of 3D system."],AREA["World."],BBOX[-90,-180,90,180]],ID["EPSG",4326]]</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>horizontal_datum_name :</span></dt><dd>World Geodetic System 1984 ensemble</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS 84</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd></dl></div></li></ul></div></div>"
+ ],
+ "text/plain": [
+ "<xarray.DataArray 'crs' ()> Size: 8B\n",
+ "[1 values with dtype=int64]\n",
+ "Attributes:\n",
+ " crs_wkt: GEOGCRS[\"WGS 84\",ENSEMBLE[\"World Geodetic S...\n",
+ " geographic_crs_name: WGS 84\n",
+ " grid_mapping_name: latitude_longitude\n",
+ " horizontal_datum_name: World Geodetic System 1984 ensemble\n",
+ " inverse_flattening: 298.257223563\n",
+ " longitude_of_prime_meridian: 0.0\n",
+ " prime_meridian_name: Greenwich\n",
+ " reference_ellipsoid_name: WGS 84\n",
+ " semi_major_axis: 6378137.0\n",
+ " semi_minor_axis: 6356752.314245179"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ds[crs_var]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "f7bc73db-7717-450e-9679-525f7be0c910",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/conda/global/1499438d-1713798099-3-pangeo/lib/python3.11/site-packages/pyproj/crs/crs.py:1286: UserWarning: You will likely lose important projection information when converting to a PROJ string from another format. See: https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems\n",
+ " proj = self._crs.to_proj4(version=version)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'+proj=longlat +datum=WGS84 +no_defs +type=crs'"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "crs.to_proj4()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a8c3ed37-8564-400b-a7fb-25bd5e43d21c",
+ "metadata": {},
+ "source": [
+ "## Create Collection Extent"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "69f0d837-68a5-4fed-9a14-5d75cfbb0da4",
+ "metadata": {},
+ "source": [
+ "### Spatial Extent\n",
+ "##### WARNING - make sure data type is **float** NOT **numpy.float64**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[-133.01121520996094, 20.994766235351562, -65.226806640625, 52.848968505859375]\n",
+ "\n",
+ "spatial_bounds data type: <class 'float'>\n"
+ ]
+ }
+ ],
+ "source": [
+ "# pull out lat/lon bbox for data\n",
+ "# coordinates must be from WGS 84 datum\n",
+ "# left, bottom, right, top\n",
+ "\n",
+ "# Note: try changing around the commented out lines below to get type float rather than a numpy float\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float), ds[dim_names_dict['Y']].data.min().compute().astype(float), ds[dim_names_dict['X']].data.max().compute().astype(float), ds[dim_names_dict['Y']].data.max().compute().astype(float)]\n",
+ "spatial_bounds = [ds[dim_names_dict['X']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.min().compute().astype(float).tolist(), ds[dim_names_dict['X']].data.max().compute().astype(float).tolist(), ds[dim_names_dict['Y']].data.max().compute().astype(float).tolist()]\n",
+ "#spatial_bounds = [ds[dim_names_dict['X']].data.min().astype(float).item(), ds[dim_names_dict['Y']].data.min().astype(float).item(), ds[dim_names_dict['X']].data.max().astype(float).item(), ds[dim_names_dict['Y']].data.max().astype(float).item()]\n",
+ "print(spatial_bounds)\n",
+ "print(f'\\nspatial_bounds data type: {type(spatial_bounds[0])}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "f16fdb9e-7ed8-40fb-a4f1-9ecabdebc0a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "XX, YY = np.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "074fc23c-f4d9-4427-80d3-fbf691e6d411",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
+ "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5345c975-9fe3-48e1-a663-0275cdf275dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
+ "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e0a5a222-743d-403a-9411-2406374803cf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spatial extent object \n",
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min(lon).item(), min(lat).item(), max(lon).item(), max(lat).item()]])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a04c8fca-1d33-43ac-9e2b-62d7be2887f7",
+ "metadata": {},
+ "source": [
+ "### Temporal Extent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "41a84995-867c-4152-8c57-85e3758bbb77",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# pull out first and last timestamps\n",
+ "temporal_extent_lower = pd.Timestamp(ds[dim_names_dict['T']].data.min())\n",
+ "temporal_extent_upper = pd.Timestamp(ds[dim_names_dict['T']].data.max())\n",
+ "# if you get an error:\n",
+ "# Cannot convert input [] of type <class 'cftime._cftime.DatetimeNoLeap'> to Timestamp\n",
+ "# use the following instead:\n",
+ "#temporal_extent_lower = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().min())\n",
+ "#temporal_extent_upper = pd.Timestamp(ds.indexes[dim_names_dict['T']].to_datetimeindex().max())\n",
+ "\n",
+ "print(f'min: {temporal_extent_lower} \\nmax: {temporal_extent_upper}')\n",
+ "# create a temporal extent object\n",
+ "temporal_extent = pystac.TemporalExtent(intervals=[[temporal_extent_lower, temporal_extent_upper]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1e37c4-5348-46ad-abc9-e005b5d6c02b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "collection_extent = pystac.Extent(spatial=spatial_extent, temporal=temporal_extent)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "20b00e88-5a13-46b3-9787-d9ac2d4e7bd6",
+ "metadata": {},
+ "source": [
+ "## Open up STAC Catalog and create a collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "adf6c59d-58cd-48b1-a5fd-3bb205a3ef56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# define folder location where your STAC catalog json file is\n",
+ "catalog_path = os.path.join('..', '..', 'catalog')\n",
+ "# open catalog\n",
+ "catalog = pystac.Catalog.from_file(os.path.join(catalog_path, 'catalog.json'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7e96811b-95ae-406a-9728-55fc429d4e1f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.get_child(collection_id):\n",
+ " collection = catalog.get_child(collection_id)\n",
+ " print(\"existing collection opened\")\n",
+ " collection.extent=collection_extent\n",
+ " collection.description=collection_description\n",
+ " collection.license=collection_license\n",
+ "else:\n",
+ " collection = pystac.Collection(id=collection_id,\n",
+ " description=collection_description,\n",
+ " extent=collection_extent,\n",
+ " license=collection_license)\n",
+ " print(\"new collection created\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a21c76e8-cd57-4eb5-a33f-7c668a3b3205",
+ "metadata": {},
+ "source": [
+ "## Add zarr url asset to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "094832af-d22b-4359-b0f6-cf687acce5cc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "asset_id = \"zarr-s3-osn\"\n",
+ "asset = pystac.Asset(href=zarr_url,\n",
+ " description=asset_description,\n",
+ " media_type=\"application/vnd+zarr\",\n",
+ " roles=asset_roles,\n",
+ " extra_fields = xarray_opendataset_kwargs)\n",
+ "collection.add_asset(asset_id, asset)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0c298d07-f234-4a08-986d-87f4a39e9ae6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "asset_id2 = \"zarr-s3\"\n",
+ "asset2 = pystac.Asset(href=zarr_url2,\n",
+ " description=asset_description2,\n",
+ " media_type=\"application/vnd+zarr\",\n",
+ " roles=asset_roles2,\n",
+ " extra_fields = xarray_opendataset_kwargs2)\n",
+ "collection.add_asset(asset_id2, asset2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f67cd5c9-db33-45c2-bc21-480cd67354f4",
+ "metadata": {},
+ "source": [
+ "## Add datacube extension to collection"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "90271372-f3ca-4806-ab56-f184d15e20e9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# reset dims to true dims\n",
+ "dim_names_dict = {'X': 'west_east', 'Y': 'south_north', 'T':'Time'}\n",
+ "print(f\"Dimension dictionary: {dim_names_dict}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fc00946d-2880-491d-9b3b-3aeeb4414d6c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# instantiate extention on collection\n",
+ "dc = DatacubeExtension.ext(collection, add_if_missing=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8bdd77a2-7587-485e-afb7-42af3a822241",
+ "metadata": {},
+ "source": [
+ "### Add cube dimensions (required field for extension)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7dc357c-91ec-49ae-83e5-400f791f9792",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### compare crs information to the projjson to make sure it looks correct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ea452f62-5644-49b6-8a4e-7dc4f649fd1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print out crs information in dataset\n",
+ "crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b1d05ff-8e43-44a7-8343-178b112c4ad6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # the datacube extension can accept reference_system information as a numerical EPSG code, \n",
+ "# # WKT2 (ISO 19162) string or PROJJSON object.\n",
+ "# # we will use a projjson, as was done by Microsoft Planetary Computer here:\n",
+ "# # https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# # https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# projjson = json.loads(lcc.to_json())\n",
+ "\n",
+ "# alternatively, I think we could do this:\n",
+ "projjson = crs.to_json()\n",
+ "print(crs.to_json(pretty=True))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6b88ee9-60c2-4d91-af74-c1c56b094826",
+ "metadata": {},
+ "source": [
+ "#### user review needed\n",
+ "#### look at the spatial and temporal steps, make sure they are all successfully pulled and they look correct"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9e2bbcc5-e45a-4b8c-9d60-601f345e8134",
+ "metadata": {},
+ "source": [
+ "**Time**\n",
+ "\n",
+ "If you need to manually construct this field, here is a helpful reference: https://en.wikipedia.org/wiki/ISO_8601#Durations"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "82f1e9bd-52ee-46f5-9e95-c2359d95fcf3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "time_step = pd.Timedelta(stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True)).isoformat()\n",
+ "# if time is yearly or monthly, you will need to manually construct it:\n",
+ "#time_step = \"P0Y1M0DT0H0M0S\"\n",
+ "print(f'time step: {time_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "64be65b2-de20-447a-a9c2-bd8eca3e440e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for time steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# time_step = stac_helpers.get_step(ds, dim_names_dict['T'], time_dim=True, debug=True, step_ix=4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bc8dff39-2a2e-44a0-9b30-987107c2d1e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for time steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 11\n",
+ "# ds.isel(time=slice(ix-1,ix+3)).time"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
+ "metadata": {},
+ "source": [
+ "**X/lon**\n",
+ "\n",
+ "had to round to 13th decimal due to slight variations in rounding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8ba7695-ca45-4db2-bd46-c465f4e37eff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
+ "print(f'x step: {x_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fac4c9f2-a952-4c7f-aa32-862957372d6f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# x_dim=dim_names_dict['X']\n",
+ "# x_step = stac_helpers.get_step(ds, x_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d0b5a2d-dc58-4ad6-b890-859ce6bb08de",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(x=slice(ix-1,ix+3)).x"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
+ "metadata": {},
+ "source": [
+ "**Y/lat**\n",
+ "\n",
+ "had to round to 13th decimal due to slight variations in rounding"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7405583b-ecb9-44b0-8815-048e42e55a42",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "# a common issue that causes the spatial step not to be identified comes from rounding errors in the step calculation\n",
+ "# use the debugging cells below to identify if this is the issue, if so, use the round_dec argument to round to a higher decimal place:\n",
+ "y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
+ "print(f'y step: {y_step}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ece0fe37-b54c-4721-aa9b-33d2998d191b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # optional debugging for spatial steps:\n",
+ "# # check all step sizes (step_list), get number of occurences of each (step_count), and get index locations where each step size occurs in the dataset so you can manually inspect the values, if needed\n",
+ "# # please specify the index of the step in step_list with the step_ix field - this will return the indices in the dataset where this step size occurred\n",
+ "# y_dim=dim_names_dict['Y']\n",
+ "# y_step = stac_helpers.get_step(ds, y_dim, debug=True, step_ix=0)\n",
+ "# print(f'\\nx dim name (for next cell): {x_dim}')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "abdafb8f-5217-4b82-91b6-eec8183c9128",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# # debugging for spatial steps, cont:\n",
+ "# # please choose one of the index locations printed above\n",
+ "# # this will print the time steps adjacent to it\n",
+ "# ix = 5\n",
+ "# ds.isel(y=slice(ix-1,ix+3)).y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a5e041-081d-428d-ac2e-75d16de205e6",
+ "metadata": {},
+ "source": [
+ "#### user input needed\n",
+ "#### you will need to copy all of the dimensions printed below into the dict and fill in the appropriate attributes (type, axis, extent, etc.):\n",
+ "\n",
+ "Please see [datacube spec](https://github.com/stac-extensions/datacube?tab=readme-ov-file#dimension-object) for details on required fields.\n",
+ "\n",
+ "If you have a dimension like \"bnds\" or \"nv\" that is used on variables like time_bnds, lon_bnds, lat_bnds to choose either the lower or upper bound, you can use and [additional dimension object](https://github.com/stac-extensions/datacube?tab=readme-ov-file#additional-dimension-object). We recommend making the type \"count\" as Microsoft Planetary Computer did [here](https://github.com/stac-extensions/datacube/blob/9e74fa706c9bdd971e01739cf18dcc53bdd3dd4f/examples/daymet-hi-annual.json#L76).\n",
+ "\n",
+ "Here is an example:\n",
+ "\n",
+ "```\n",
+ "dims_dict = {\n",
+ " 'bnds': pystac.extensions.datacube.Dimension({'type': 'count', 'description': stac_helpers.get_long_name(ds, 'bnds'), 'extent': [ds.bnds.min().item(), ds.bnds.max().item()]})\n",
+ " }\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acd45d3c-7845-47e6-9b7d-e35627a7ca9a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "dims = list(ds.dims)\n",
+ "print(dims)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a443497-67a9-4dce-a8e9-b08d31a88223",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a dictionary of datacube dimensions you would like to assign to this dataset\n",
+ "# dimension name should come from the dims printed in above cell\n",
+ "\n",
+ "# x, y, t dimension info is pulled out automatically using the dim dict we created above\n",
+ "# all other dims listed in above cell need to be manually written in\n",
+ "\n",
+ "# we do not recommend including redundant dimensions (do not include x,y if you have lon,lat)\n",
+ "# note that the extent of each dimension should be pulled from the dataset\n",
+ "dims_dict = {dim_names_dict['T']: pystac.extensions.datacube.Dimension({'type': 'temporal', 'description': stac_helpers.get_long_name(ds, dim_names_dict['T']), 'extent': [temporal_extent_lower.strftime('%Y-%m-%dT%XZ'), temporal_extent_upper.strftime('%Y-%m-%dT%XZ')], 'step':time_step}),\n",
+ " dim_names_dict['X']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'x', 'description': stac_helpers.get_long_name(ds, dim_names_dict['X']), 'extent': [spatial_bounds[0], spatial_bounds[2]], 'step': x_step}),\n",
+ " dim_names_dict['Y']: pystac.extensions.datacube.Dimension({'type': 'spatial', 'axis': 'y', 'description': stac_helpers.get_long_name(ds, dim_names_dict['Y']), 'extent': [spatial_bounds[1], spatial_bounds[3]], 'step': y_step}),\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ab85b09-eb38-404c-910c-13349d5e2234",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# make sure you added all the right dims\n",
+ "assert sorted(list(dims_dict.keys())) == sorted(dims)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f277883-a3fd-425f-966a-ca2140d0ef2f",
+ "metadata": {},
+ "source": [
+ "### Add cube variables (optional field for extension)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e9272931-fc0b-4f2a-9546-283033e9cde8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# drop metpy_crs coordinate we have added\n",
+ "if 'metpy_crs' in ds.coords:\n",
+ " ds = ds.drop_vars('metpy_crs')\n",
+ "\n",
+ "# pull list of vars from dataset\n",
+ "vars = list(ds.variables)\n",
+ "\n",
+ "# spec says that the keys of cube:dimensions and cube:variables should be unique together; a key like lat should not be both a dimension and a variable.\n",
+ "# we will drop all values in dims from vars\n",
+ "vars = [v for v in vars if v not in dims]\n",
+ "\n",
+ "# Microsoft Planetary Computer includes coordinates and crs as variables here:\n",
+ "# https://planetarycomputer.microsoft.com/dataset/daymet-annual-na\n",
+ "# https://planetarycomputer.microsoft.com/api/stac/v1/collections/daymet-annual-na\n",
+ "# we will keep those in the var list\n",
+ "\n",
+ "# create dictionary of dataset variables and associated dimensions\n",
+ "vars_dict={}\n",
+ "for v in vars:\n",
+ " unit = stac_helpers.get_unit(ds, v)\n",
+ " var_type = stac_helpers.get_var_type(ds, v, crs_var)\n",
+ " long_name = stac_helpers.get_long_name(ds, v)\n",
+ " vars_dict[v] = pystac.extensions.datacube.Variable({'dimensions':list(ds[v].dims), 'type': var_type, 'description': long_name, 'unit': unit})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "11ad5352-884c-4472-8864-4570a96f66e5",
+ "metadata": {},
+ "source": [
+ "### Finalize extension"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "10141fd4-91d6-491d-878b-02653720891d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# add dimesions and variables to collection extension\n",
+ "dc.apply(dimensions=dims_dict, variables=vars_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "615ca168-75fb-4135-9941-0ef5fe4fd1cb",
+ "metadata": {},
+ "source": [
+ "## Add STAC Collection to Catalog and Save"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e2120a55-3d04-4122-a93f-29afcdb8cb1b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# # helper to find items of wrong type\n",
+ "# d = collection.to_dict()\n",
+ "# print(*stac_helpers.find_paths(d))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b75791b-6b2d-40be-b7c6-330a60888fb5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if catalog.get_child(collection_id):\n",
+ " collection.normalize_and_save(root_href=os.path.join(catalog_path, collection_id), catalog_type=pystac.CatalogType.SELF_CONTAINED)\n",
+ "else:\n",
+ " catalog.add_child(collection)\n",
+ " catalog.normalize_and_save(root_href=catalog_path, catalog_type=pystac.CatalogType.SELF_CONTAINED)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6f676b5-e892-4bfb-8d73-2828addd838c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "global-global-pangeo",
+ "language": "python",
+ "name": "conda-env-global-global-pangeo-py"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}