diff --git a/catalog/catalog.json b/catalog/catalog.json
index 29c25966468c7027809f6662b836e09e17a70947..c8abc1d2d17799fa840c3e05a7d68c32138cf4c1 100644
--- a/catalog/catalog.json
+++ b/catalog/catalog.json
@@ -329,20 +329,25 @@
"href": "./iclus_hd/collection.json",
"type": "application/json"
},
- {
+ {
"rel": "child",
"href": "./sambi_urb/collection.json",
"type": "application/json"
},
- {
+ {
"rel": "child",
"href": "./serap_slamm/collection.json",
"type": "application/json"
},
- {
+ {
"rel": "child",
"href": "./serap_urb/collection.json",
"type": "application/json"
+ },
+ {
+ "rel": "child",
+ "href": "./red_river_2018/collection.json",
+ "type": "application/json"
}
]
}
\ No newline at end of file
diff --git a/catalog/red_river_2018/collection.json b/catalog/red_river_2018/collection.json
new file mode 100644
index 0000000000000000000000000000000000000000..168462845d746a228d433c8de1fae61b9f0d37f4
--- /dev/null
+++ b/catalog/red_river_2018/collection.json
@@ -0,0 +1,248 @@
+{
+ "type": "Collection",
+ "id": "red_river_2018",
+ "stac_version": "1.0.0",
+ "description": "Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System",
+ "links": [
+ {
+ "rel": "root",
+ "href": "../catalog.json",
+ "type": "application/json"
+ },
+ {
+ "rel": "parent",
+ "href": "../catalog.json",
+ "type": "application/json"
+ }
+ ],
+ "stac_extensions": [
+ "https://stac-extensions.github.io/datacube/v2.2.0/schema.json"
+ ],
+ "cube:dimensions": {
+ "Time": {
+ "type": "temporal",
+ "description": "time",
+ "extent": [
+ "1980-01-01T00:00:00Z",
+ "2015-12-31T23:00:00Z"
+ ],
+ "step": "P0DT1H0M0S"
+ },
+ "west_east": {
+ "type": "spatial",
+ "axis": "x",
+ "description": null,
+ "extent": [
+ -133.01121520996094,
+ -65.226806640625
+ ],
+ "step": 1.0
+ },
+ "south_north": {
+ "type": "spatial",
+ "axis": "y",
+ "description": null,
+ "extent": [
+ 20.994766235351562,
+ 52.848968505859375
+ ],
+ "step": 1.0
+ }
+ },
+ "cube:variables": {
+ "HFX": {
+ "dimensions": [
+ "Time",
+ "south_north",
+ "west_east"
+ ],
+ "type": "data",
+ "description": "UPWARD HEAT FLUX AT THE SURFACE",
+ "unit": "W m-2"
+ },
+ "LH": {
+ "dimensions": [
+ "Time",
+ "south_north",
+ "west_east"
+ ],
+ "type": "data",
+ "description": "LATENT HEAT FLUX AT THE SURFACE",
+ "unit": "W m-2"
+ },
+ "PBLH": {
+ "dimensions": [
+ "Time",
+ "south_north",
+ "west_east"
+ ],
+ "type": "data",
+ "description": "PBL HEIGHT",
+ "unit": "m"
+ },
+ "Q2": {
+ "dimensions": [
+ "Time",
+ "south_north",
+ "west_east"
+ ],
+ "type": "data",
+ "description": "QV at 2 M",
+ "unit": "kg kg-1"
+ },
+ "RAINC": {
+ "dimensions": [
+ "Time",
+ "south_north",
+ "west_east"
+ ],
+ "type": "data",
+ "description": "ACCUMULATED TOTAL CUMULUS PRECIPITATION",
+ "unit": "mm"
+ },
+ "RAINNC": {
+ "dimensions": [
+ "Time",
+ "south_north",
+ "west_east"
+ ],
+ "type": "data",
+ "description": "ACCUMULATED TOTAL GRID SCALE PRECIPITATION",
+ "unit": "mm"
+ },
+ "SWDOWN": {
+ "dimensions": [
+ "Time",
+ "south_north",
+ "west_east"
+ ],
+ "type": "data",
+ "description": "DOWNWARD SHORT WAVE FLUX AT GROUND SURFACE",
+ "unit": "W m-2"
+ },
+ "T2": {
+ "dimensions": [
+ "Time",
+ "south_north",
+ "west_east"
+ ],
+ "type": "data",
+ "description": "TEMP at 2 M",
+ "unit": "K"
+ },
+ "U10": {
+ "dimensions": [
+ "Time",
+ "south_north",
+ "west_east"
+ ],
+ "type": "data",
+ "description": "U at 10 M",
+ "unit": "m s-1"
+ },
+ "V10": {
+ "dimensions": [
+ "Time",
+ "south_north",
+ "west_east"
+ ],
+ "type": "data",
+ "description": "V at 10 M",
+ "unit": "m s-1"
+ },
+ "XLAT": {
+ "dimensions": [
+ "south_north",
+ "west_east"
+ ],
+ "type": "auxiliary",
+ "description": "LATITUDE, SOUTH IS NEGATIVE",
+ "unit": "degree_north"
+ },
+ "XLONG": {
+ "dimensions": [
+ "south_north",
+ "west_east"
+ ],
+ "type": "auxiliary",
+ "description": "LONGITUDE, WEST IS NEGATIVE",
+ "unit": "degree_east"
+ },
+ "crs": {
+ "dimensions": [],
+ "type": "auxiliary",
+ "description": null,
+ "unit": null
+ },
+ "time_char": {
+ "dimensions": [
+ "Time"
+ ],
+ "type": "data",
+ "description": null,
+ "unit": null
+ }
+ },
+ "extent": {
+ "spatial": {
+ "bbox": [
+ [
+ -133.01121520996094,
+ 20.994766235351562,
+ -65.226806640625,
+ 52.848968505859375
+ ]
+ ]
+ },
+ "temporal": {
+ "interval": [
+ [
+ "1980-01-01T00:00:00Z",
+ "2015-12-31T23:00:00Z"
+ ]
+ ]
+ }
+ },
+ "license": "Unlicense",
+ "assets": {
+ "zarr-s3-osn": {
+ "href": "s3://mdmf-workspace/gdp/red_river_2018.zarr/",
+ "type": "application/vnd+zarr",
+ "description": "Open Storage Network Pod S3 API access to collection zarr group",
+ "xarray:open_kwargs": {
+ "chunks": {},
+ "engine": "zarr",
+ "consolidated": true
+ },
+ "xarray:storage_options": {
+ "anon": true,
+ "client_kwargs": {
+ "endpoint_url": "https://usgs.osn.mghpcc.org/"
+ }
+ },
+ "roles": [
+ "data",
+ "zarr",
+ "s3"
+ ]
+ },
+ "zarr-s3": {
+ "href": "s3://nhgf-development/workspace/DataConversion/red_river_2018.zarr/",
+ "type": "application/vnd+zarr",
+ "description": "S3 access to collection zarr group",
+ "xarray:open_kwargs": {
+ "chunks": {},
+ "engine": "zarr",
+ "consolidated": true
+ },
+ "xarray:storage_options": {
+ "requester_pays": true
+ },
+ "roles": [
+ "data",
+ "zarr",
+ "s3"
+ ]
+ }
+ }
+}
\ No newline at end of file
diff --git a/workflows/archive/red_river_2018_create_collection_from_zarr.ipynb b/workflows/archive/red_river_2018_create_collection_from_zarr.ipynb
index 20958906fa0675aea82f6b88010cfa0fe181aaa6..1f93dbeba1dcb773b083d92cd3e48ba9fdaf3ab9 100644
--- a/workflows/archive/red_river_2018_create_collection_from_zarr.ipynb
+++ b/workflows/archive/red_river_2018_create_collection_from_zarr.ipynb
@@ -17,604 +17,10 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "201e0945-de55-45ff-b095-c2af009a4e62",
"metadata": {},
- "outputs": [
- {
- "data": {
- "application/javascript": [
- "(function(root) {\n",
- " function now() {\n",
- " return new Date();\n",
- " }\n",
- "\n",
- " var force = true;\n",
- " var py_version = '3.4.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
- " var reloading = false;\n",
- " var Bokeh = root.Bokeh;\n",
- "\n",
- " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
- " root._bokeh_timeout = Date.now() + 5000;\n",
- " root._bokeh_failed_load = false;\n",
- " }\n",
- "\n",
- " function run_callbacks() {\n",
- " try {\n",
- " root._bokeh_onload_callbacks.forEach(function(callback) {\n",
- " if (callback != null)\n",
- " callback();\n",
- " });\n",
- " } finally {\n",
- " delete root._bokeh_onload_callbacks;\n",
- " }\n",
- " console.debug(\"Bokeh: all callbacks have finished\");\n",
- " }\n",
- "\n",
- " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n",
- " if (css_urls == null) css_urls = [];\n",
- " if (js_urls == null) js_urls = [];\n",
- " if (js_modules == null) js_modules = [];\n",
- " if (js_exports == null) js_exports = {};\n",
- "\n",
- " root._bokeh_onload_callbacks.push(callback);\n",
- "\n",
- " if (root._bokeh_is_loading > 0) {\n",
- " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
- " return null;\n",
- " }\n",
- " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
- " run_callbacks();\n",
- " return null;\n",
- " }\n",
- " if (!reloading) {\n",
- " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
- " }\n",
- "\n",
- " function on_load() {\n",
- " root._bokeh_is_loading--;\n",
- " if (root._bokeh_is_loading === 0) {\n",
- " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
- " run_callbacks()\n",
- " }\n",
- " }\n",
- " window._bokeh_on_load = on_load\n",
- "\n",
- " function on_error() {\n",
- " console.error(\"failed to load \" + url);\n",
- " }\n",
- "\n",
- " var skip = [];\n",
- " if (window.requirejs) {\n",
- " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n",
- " root._bokeh_is_loading = css_urls.length + 0;\n",
- " } else {\n",
- " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
- " }\n",
- "\n",
- " var existing_stylesheets = []\n",
- " var links = document.getElementsByTagName('link')\n",
- " for (var i = 0; i < links.length; i++) {\n",
- " var link = links[i]\n",
- " if (link.href != null) {\n",
- "\texisting_stylesheets.push(link.href)\n",
- " }\n",
- " }\n",
- " for (var i = 0; i < css_urls.length; i++) {\n",
- " var url = css_urls[i];\n",
- " if (existing_stylesheets.indexOf(url) !== -1) {\n",
- "\ton_load()\n",
- "\tcontinue;\n",
- " }\n",
- " const element = document.createElement(\"link\");\n",
- " element.onload = on_load;\n",
- " element.onerror = on_error;\n",
- " element.rel = \"stylesheet\";\n",
- " element.type = \"text/css\";\n",
- " element.href = url;\n",
- " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
- " document.body.appendChild(element);\n",
- " } var existing_scripts = []\n",
- " var scripts = document.getElementsByTagName('script')\n",
- " for (var i = 0; i < scripts.length; i++) {\n",
- " var script = scripts[i]\n",
- " if (script.src != null) {\n",
- "\texisting_scripts.push(script.src)\n",
- " }\n",
- " }\n",
- " for (var i = 0; i < js_urls.length; i++) {\n",
- " var url = js_urls[i];\n",
- " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
- "\tif (!window.requirejs) {\n",
- "\t on_load();\n",
- "\t}\n",
- "\tcontinue;\n",
- " }\n",
- " var element = document.createElement('script');\n",
- " element.onload = on_load;\n",
- " element.onerror = on_error;\n",
- " element.async = false;\n",
- " element.src = url;\n",
- " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
- " document.head.appendChild(element);\n",
- " }\n",
- " for (var i = 0; i < js_modules.length; i++) {\n",
- " var url = js_modules[i];\n",
- " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n",
- "\tif (!window.requirejs) {\n",
- "\t on_load();\n",
- "\t}\n",
- "\tcontinue;\n",
- " }\n",
- " var element = document.createElement('script');\n",
- " element.onload = on_load;\n",
- " element.onerror = on_error;\n",
- " element.async = false;\n",
- " element.src = url;\n",
- " element.type = \"module\";\n",
- " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
- " document.head.appendChild(element);\n",
- " }\n",
- " for (const name in js_exports) {\n",
- " var url = js_exports[name];\n",
- " if (skip.indexOf(url) >= 0 || root[name] != null) {\n",
- "\tif (!window.requirejs) {\n",
- "\t on_load();\n",
- "\t}\n",
- "\tcontinue;\n",
- " }\n",
- " var element = document.createElement('script');\n",
- " element.onerror = on_error;\n",
- " element.async = false;\n",
- " element.type = \"module\";\n",
- " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
- " element.textContent = `\n",
- " import ${name} from \"${url}\"\n",
- " window.${name} = ${name}\n",
- " window._bokeh_on_load()\n",
- " `\n",
- " document.head.appendChild(element);\n",
- " }\n",
- " if (!js_urls.length && !js_modules.length) {\n",
- " on_load()\n",
- " }\n",
- " };\n",
- "\n",
- " function inject_raw_css(css) {\n",
- " const element = document.createElement(\"style\");\n",
- " element.appendChild(document.createTextNode(css));\n",
- " document.body.appendChild(element);\n",
- " }\n",
- "\n",
- " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.1.min.js\", \"https://cdn.holoviz.org/panel/1.4.1/dist/panel.min.js\"];\n",
- " var js_modules = [];\n",
- " var js_exports = {};\n",
- " var css_urls = [];\n",
- " var inline_js = [ function(Bokeh) {\n",
- " Bokeh.set_log_level(\"info\");\n",
- " },\n",
- "function(Bokeh) {} // ensure no trailing comma for IE\n",
- " ];\n",
- "\n",
- " function run_inline_js() {\n",
- " if ((root.Bokeh !== undefined) || (force === true)) {\n",
- " for (var i = 0; i < inline_js.length; i++) {\n",
- "\ttry {\n",
- " inline_js[i].call(root, root.Bokeh);\n",
- "\t} catch(e) {\n",
- "\t if (!reloading) {\n",
- "\t throw e;\n",
- "\t }\n",
- "\t}\n",
- " }\n",
- " // Cache old bokeh versions\n",
- " if (Bokeh != undefined && !reloading) {\n",
- "\tvar NewBokeh = root.Bokeh;\n",
- "\tif (Bokeh.versions === undefined) {\n",
- "\t Bokeh.versions = new Map();\n",
- "\t}\n",
- "\tif (NewBokeh.version !== Bokeh.version) {\n",
- "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n",
- "\t}\n",
- "\troot.Bokeh = Bokeh;\n",
- " }} else if (Date.now() < root._bokeh_timeout) {\n",
- " setTimeout(run_inline_js, 100);\n",
- " } else if (!root._bokeh_failed_load) {\n",
- " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
- " root._bokeh_failed_load = true;\n",
- " }\n",
- " root._bokeh_is_initializing = false\n",
- " }\n",
- "\n",
- " function load_or_wait() {\n",
- " // Implement a backoff loop that tries to ensure we do not load multiple\n",
- " // versions of Bokeh and its dependencies at the same time.\n",
- " // In recent versions we use the root._bokeh_is_initializing flag\n",
- " // to determine whether there is an ongoing attempt to initialize\n",
- " // bokeh, however for backward compatibility we also try to ensure\n",
- " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n",
- " // before older versions are fully initialized.\n",
- " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n",
- " root._bokeh_is_initializing = false;\n",
- " root._bokeh_onload_callbacks = undefined;\n",
- " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n",
- " load_or_wait();\n",
- " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n",
- " setTimeout(load_or_wait, 100);\n",
- " } else {\n",
- " root._bokeh_is_initializing = true\n",
- " root._bokeh_onload_callbacks = []\n",
- " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
- " if (!reloading && !bokeh_loaded) {\n",
- "\troot.Bokeh = undefined;\n",
- " }\n",
- " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n",
- "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
- "\trun_inline_js();\n",
- " });\n",
- " }\n",
- " }\n",
- " // Give older versions of the autoload script a head-start to ensure\n",
- " // they initialize before we start loading newer version.\n",
- " setTimeout(load_or_wait, 100)\n",
- "}(window));"
- ],
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- }
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+ "outputs": [],
"source": [
"import pystac\n",
"from pystac.extensions.datacube import CollectionDatacubeExtension, AssetDatacubeExtension, AdditionalDimension, DatacubeExtension\n",
@@ -634,7 +40,11 @@
"import json\n",
"import sys\n",
"sys.path.insert(1, '..')\n",
- "import stac_helpers"
+ "import stac_helpers\n",
+ "import dask\n",
+ "import dask.array as da\n",
+ "from dask.distributed import Client, LocalCluster\n",
+ "import pyproj"
]
},
{
@@ -647,7 +57,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "15ee060d-3127-4024-a1ad-6aa0648667e1",
"metadata": {},
"outputs": [],
@@ -666,7 +76,7 @@
},
{
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- "execution_count": 3,
+ "execution_count": null,
"id": "dd6fa323-132a-4794-8c80-576933f547a0",
"metadata": {
"tags": []
@@ -688,7 +98,7 @@
},
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- "execution_count": 4,
+ "execution_count": null,
"id": "e1441cd4-e94c-4902-af46-8f1af470eb6b",
"metadata": {
"tags": []
@@ -720,2434 +130,13 @@
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+ "execution_count": null,
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- " font-size: 11px;\n",
- " width: 15px;\n",
- " text-align: center;\n",
- "}\n",
- "\n",
- ".xr-section-summary-in:disabled + label:before {\n",
- " color: var(--xr-disabled-color);\n",
- "}\n",
- "\n",
- ".xr-section-summary-in:checked + label:before {\n",
- " content: 'â–¼';\n",
- "}\n",
- "\n",
- ".xr-section-summary-in:checked + label > span {\n",
- " display: none;\n",
- "}\n",
- "\n",
- ".xr-section-summary,\n",
- ".xr-section-inline-details {\n",
- " padding-top: 4px;\n",
- " padding-bottom: 4px;\n",
- "}\n",
- "\n",
- ".xr-section-inline-details {\n",
- " grid-column: 2 / -1;\n",
- "}\n",
- "\n",
- ".xr-section-details {\n",
- " display: none;\n",
- " grid-column: 1 / -1;\n",
- " margin-bottom: 5px;\n",
- "}\n",
- "\n",
- ".xr-section-summary-in:checked ~ .xr-section-details {\n",
- " display: contents;\n",
- "}\n",
- "\n",
- ".xr-array-wrap {\n",
- " grid-column: 1 / -1;\n",
- " display: grid;\n",
- " grid-template-columns: 20px auto;\n",
- "}\n",
- "\n",
- ".xr-array-wrap > label {\n",
- " grid-column: 1;\n",
- " vertical-align: top;\n",
- "}\n",
- "\n",
- ".xr-preview {\n",
- " color: var(--xr-font-color3);\n",
- "}\n",
- "\n",
- ".xr-array-preview,\n",
- ".xr-array-data {\n",
- " padding: 0 5px !important;\n",
- " grid-column: 2;\n",
- "}\n",
- "\n",
- ".xr-array-data,\n",
- ".xr-array-in:checked ~ .xr-array-preview {\n",
- " display: none;\n",
- "}\n",
- "\n",
- ".xr-array-in:checked ~ .xr-array-data,\n",
- ".xr-array-preview {\n",
- " display: inline-block;\n",
- "}\n",
- "\n",
- ".xr-dim-list {\n",
- " display: inline-block !important;\n",
- " list-style: none;\n",
- " padding: 0 !important;\n",
- " margin: 0;\n",
- "}\n",
- "\n",
- ".xr-dim-list li {\n",
- " display: inline-block;\n",
- " padding: 0;\n",
- " margin: 0;\n",
- "}\n",
- "\n",
- ".xr-dim-list:before {\n",
- " content: '(';\n",
- "}\n",
- "\n",
- ".xr-dim-list:after {\n",
- " content: ')';\n",
- "}\n",
- "\n",
- ".xr-dim-list li:not(:last-child):after {\n",
- " content: ',';\n",
- " padding-right: 5px;\n",
- "}\n",
- "\n",
- ".xr-has-index {\n",
- " font-weight: bold;\n",
- "}\n",
- "\n",
- ".xr-var-list,\n",
- ".xr-var-item {\n",
- " display: contents;\n",
- "}\n",
- "\n",
- ".xr-var-item > div,\n",
- ".xr-var-item label,\n",
- ".xr-var-item > .xr-var-name span {\n",
- " background-color: var(--xr-background-color-row-even);\n",
- " margin-bottom: 0;\n",
- "}\n",
- "\n",
- ".xr-var-item > .xr-var-name:hover span {\n",
- " padding-right: 5px;\n",
- "}\n",
- "\n",
- ".xr-var-list > li:nth-child(odd) > div,\n",
- ".xr-var-list > li:nth-child(odd) > label,\n",
- ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
- " background-color: var(--xr-background-color-row-odd);\n",
- "}\n",
- "\n",
- ".xr-var-name {\n",
- " grid-column: 1;\n",
- "}\n",
- "\n",
- ".xr-var-dims {\n",
- " grid-column: 2;\n",
- "}\n",
- "\n",
- ".xr-var-dtype {\n",
- " grid-column: 3;\n",
- " text-align: right;\n",
- " color: var(--xr-font-color2);\n",
- "}\n",
- "\n",
- ".xr-var-preview {\n",
- " grid-column: 4;\n",
- "}\n",
- "\n",
- ".xr-index-preview {\n",
- " grid-column: 2 / 5;\n",
- " color: var(--xr-font-color2);\n",
- "}\n",
- "\n",
- ".xr-var-name,\n",
- ".xr-var-dims,\n",
- ".xr-var-dtype,\n",
- ".xr-preview,\n",
- ".xr-attrs dt {\n",
- " white-space: nowrap;\n",
- " overflow: hidden;\n",
- " text-overflow: ellipsis;\n",
- " padding-right: 10px;\n",
- "}\n",
- "\n",
- ".xr-var-name:hover,\n",
- ".xr-var-dims:hover,\n",
- ".xr-var-dtype:hover,\n",
- ".xr-attrs dt:hover {\n",
- " overflow: visible;\n",
- " width: auto;\n",
- " z-index: 1;\n",
- "}\n",
- "\n",
- ".xr-var-attrs,\n",
- ".xr-var-data,\n",
- ".xr-index-data {\n",
- " display: none;\n",
- " background-color: var(--xr-background-color) !important;\n",
- " padding-bottom: 5px !important;\n",
- "}\n",
- "\n",
- ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
- ".xr-var-data-in:checked ~ .xr-var-data,\n",
- ".xr-index-data-in:checked ~ .xr-index-data {\n",
- " display: block;\n",
- "}\n",
- "\n",
- ".xr-var-data > table {\n",
- " float: right;\n",
- "}\n",
- "\n",
- ".xr-var-name span,\n",
- ".xr-var-data,\n",
- ".xr-index-name div,\n",
- ".xr-index-data,\n",
- ".xr-attrs {\n",
- " padding-left: 25px !important;\n",
- "}\n",
- "\n",
- ".xr-attrs,\n",
- ".xr-var-attrs,\n",
- ".xr-var-data,\n",
- ".xr-index-data {\n",
- " grid-column: 1 / -1;\n",
- "}\n",
- "\n",
- "dl.xr-attrs {\n",
- " padding: 0;\n",
- " margin: 0;\n",
- " display: grid;\n",
- " grid-template-columns: 125px auto;\n",
- "}\n",
- "\n",
- ".xr-attrs dt,\n",
- ".xr-attrs dd {\n",
- " padding: 0;\n",
- " margin: 0;\n",
- " float: left;\n",
- " padding-right: 10px;\n",
- " width: auto;\n",
- "}\n",
- "\n",
- ".xr-attrs dt {\n",
- " font-weight: normal;\n",
- " grid-column: 1;\n",
- "}\n",
- "\n",
- ".xr-attrs dt:hover span {\n",
- " display: inline-block;\n",
- " background: var(--xr-background-color);\n",
- " padding-right: 10px;\n",
- "}\n",
- "\n",
- ".xr-attrs dd {\n",
- " grid-column: 2;\n",
- " white-space: pre-wrap;\n",
- " word-break: break-all;\n",
- "}\n",
- "\n",
- ".xr-icon-database,\n",
- ".xr-icon-file-text2,\n",
- ".xr-no-icon {\n",
- " display: inline-block;\n",
- " vertical-align: middle;\n",
- " width: 1em;\n",
- " height: 1.5em !important;\n",
- " stroke-width: 0;\n",
- " stroke: currentColor;\n",
- " fill: currentColor;\n",
- "}\n",
- "</style><pre class='xr-text-repr-fallback'><xarray.Dataset> 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",
- " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
- " <line x1=\"0\" y1=\"5\" x2=\"120\" y2=\"5\" />\n",
- " <line x1=\"0\" y1=\"10\" x2=\"120\" y2=\"10\" />\n",
- " <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n",
- " <line x1=\"0\" y1=\"21\" x2=\"120\" y2=\"21\" />\n",
- " <line x1=\"0\" y1=\"26\" x2=\"120\" y2=\"26\" />\n",
- " <line x1=\"0\" y1=\"31\" x2=\"120\" y2=\"31\" />\n",
- " <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n",
- " <line x1=\"0\" y1=\"42\" x2=\"120\" y2=\"42\" />\n",
- " <line x1=\"0\" y1=\"47\" x2=\"120\" y2=\"47\" />\n",
- " <line x1=\"0\" y1=\"53\" x2=\"120\" y2=\"53\" />\n",
- " <line x1=\"0\" y1=\"58\" x2=\"120\" y2=\"58\" />\n",
- " <line x1=\"0\" y1=\"63\" x2=\"120\" y2=\"63\" />\n",
- " <line x1=\"0\" y1=\"69\" x2=\"120\" y2=\"69\" />\n",
- " <line x1=\"0\" y1=\"74\" x2=\"120\" y2=\"74\" />\n",
- " <line x1=\"0\" y1=\"79\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
- "\n",
- " <!-- Vertical lines -->\n",
- " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"79\" style=\"stroke-width:2\" />\n",
- " <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"79\" />\n",
- " <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"79\" />\n",
- " <line x1=\"23\" y1=\"0\" x2=\"23\" y2=\"79\" />\n",
- " <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"79\" />\n",
- " <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"79\" />\n",
- " <line x1=\"46\" y1=\"0\" x2=\"46\" y2=\"79\" />\n",
- " <line x1=\"54\" y1=\"0\" x2=\"54\" y2=\"79\" />\n",
- " <line x1=\"61\" y1=\"0\" x2=\"61\" y2=\"79\" />\n",
- " <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"79\" />\n",
- " <line x1=\"77\" y1=\"0\" x2=\"77\" y2=\"79\" />\n",
- " <line x1=\"85\" y1=\"0\" x2=\"85\" y2=\"79\" />\n",
- " <line x1=\"92\" y1=\"0\" x2=\"92\" y2=\"79\" />\n",
- " <line x1=\"100\" y1=\"0\" x2=\"100\" y2=\"79\" />\n",
- " <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"79\" />\n",
- " <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"79\" />\n",
- " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"79\" style=\"stroke-width:2\" />\n",
- "\n",
- " <!-- Colored Rectangle -->\n",
- " <polygon points=\"0.0,0.0 120.0,0.0 120.0,79.83870967741936 0.0,79.83870967741936\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
- "\n",
- " <!-- Text -->\n",
- " <text x=\"60.000000\" y=\"99.838710\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >248</text>\n",
- " <text x=\"140.000000\" y=\"39.919355\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,39.919355)\">165</text>\n",
- "</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",
- "\n",
- " <!-- Horizontal lines -->\n",
- " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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- " <th> Chunk </th>\n",
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- " <tbody>\n",
- " \n",
- " <tr>\n",
- " <th> Bytes </th>\n",
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- " <th> Shape </th>\n",
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- " <tbody>\n",
- " \n",
- " <tr>\n",
- " <th> Bytes </th>\n",
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- " \n",
- " <tr>\n",
- " <th> Bytes </th>\n",
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- " <tbody>\n",
- " \n",
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- " <th> Array </th>\n",
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- " <tbody>\n",
- " \n",
- " <tr>\n",
- " <th> Bytes </th>\n",
- " <td> 48.11 GiB </td>\n",
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- " <tr>\n",
- " <th> Shape </th>\n",
- " <td> (315576, 165, 248) </td>\n",
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- " <tbody>\n",
- " \n",
- " <tr>\n",
- " <th> Bytes </th>\n",
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- " \n",
- " <tr>\n",
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- " \n",
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- " <th> Bytes </th>\n",
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- " <tbody>\n",
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- " <th> Shape </th>\n",
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- " </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",
- " </tr>\n",
- " <tr>\n",
- " <th> Data type </th>\n",
- " <td colspan=\"2\"> |S64 numpy.ndarray </td>\n",
- " </tr>\n",
- " </tbody>\n",
- " </table>\n",
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- " <td>\n",
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- " </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"
- }
- ],
+ "outputs": [],
"source": [
"# open and view zarr dataset\n",
- "fs2 = fsspec.filesystem('s3', profile='osn-mdmf-workspace', endpoint_url='https://usgs.osn.mghpcc.org/')\n",
+ "fs2 = fsspec.filesystem('s3', anon=True, 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"
@@ -3163,18 +152,10 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# description of STAC collection\n",
"collection_description = ds.attrs['title']\n",
@@ -3183,25 +164,12 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"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"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# license for dataset\n",
"collection_license = stac_helpers.license_picker(ds.attrs['license'])"
@@ -3220,18 +188,10 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"id": "ab91268f-7200-4cb1-979a-c7d75531d2c0",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Dimension dictionary: {'X': 'XLONG', 'Y': 'XLAT', 'T': 'Time'}\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# dims_auto_extract = ['X', 'Y', 'T']\n",
"# dim_names_dict = {}\n",
@@ -3252,7 +212,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "239d3b00-77f9-4178-954b-ba81a2b34512",
"metadata": {},
"outputs": [],
@@ -3262,7 +222,7 @@
},
{
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- "execution_count": 10,
+ "execution_count": null,
"id": "b03d52f3-1367-4255-a561-52ee4fc9e92d",
"metadata": {},
"outputs": [],
@@ -3286,440 +246,20 @@
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- ".xr-attrs dd {\n",
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- "}\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",
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- " 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>"
- ],
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- "<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",
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- " semi_major_axis: 6378137.0\n",
- " semi_minor_axis: 6356752.314245179"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
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+ "outputs": [],
"source": [
"ds[crs_var]"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"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"
- }
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+ "outputs": [],
"source": [
"crs.to_proj4()"
]
@@ -3743,20 +283,10 @@
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{
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+ "execution_count": null,
"id": "d46805e0-8e94-4ebe-aa01-d9a2d7051459",
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- "\n",
- "spatial_bounds data type: <class 'float'>\n"
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+ "outputs": [],
"source": [
"# pull out lat/lon bbox for data\n",
"# coordinates must be from WGS 84 datum\n",
@@ -3772,12 +302,13 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"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)"
+ "# uncomment if you wish to use dask\n",
+ "XX, YY = dask.array.meshgrid(ds[dim_names_dict['X']].data, ds[dim_names_dict['Y']].data)"
]
},
{
@@ -3787,8 +318,73 @@
"metadata": {},
"outputs": [],
"source": [
- "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)\n",
- "lon, lat = transformer.transform(XX.ravel(), YY.ravel())"
+ "# choose a size for the chunks - these are square chunks that are chunk_len x chunk_len\n",
+ "# this size worked on a dask cluster on my local computer\n",
+ "# I haven't been able to get it optimized to work on Nebari, so this will crash if you run the delayed function below\n",
+ "chunk_len = 10000\n",
+ "XX_chunked = XX.rechunk((chunk_len, chunk_len)).ravel()\n",
+ "YY_chunked = YY.rechunk((chunk_len, chunk_len)).ravel()\n",
+ "XX_chunked"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "83141040",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "transformer = Transformer.from_crs(crs, \"EPSG:4326\", always_xy=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ac6f075a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# uncomment if you wish to use dask\n",
+ "cluster = LocalCluster(threads_per_worker=os.cpu_count())\n",
+ "client = Client(cluster)\n",
+ "print(f\"The link to view the client dashboard is:\\n> {client.dashboard_link}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "dedb64c9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#lon, lat = transformer.transform(XX.ravel(), YY.ravel())\n",
+ "@dask.delayed\n",
+ "def proc_func(XX_chunked, YY_chunked):\n",
+ " lon, lat = transformer.transform(XX_chunked, YY_chunked)\n",
+ " min_lon = lon.min()\n",
+ " min_lat = lat.min()\n",
+ " max_lon = lon.max()\n",
+ " max_lat = lat.max()\n",
+ " return min_lon, min_lat, max_lon, max_lat"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "349e50ac",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# commented out because this will crash on nebari, but results from local computer are copied in the cell below\n",
+ "# result = proc_func(XX_chunked, YY_chunked).compute()\n",
+ "# min_lon = result[0]\n",
+ "# min_lat = result[1]\n",
+ "# max_lon = result[2]\n",
+ "# max_lat = result[3]\n",
+ "min_lon = -133.01121520996094\n",
+ "min_lat = 20.994766235351562\n",
+ "max_lon = -65.226806640625\n",
+ "max_lat = 52.848968505859375"
]
},
{
@@ -3798,8 +394,8 @@
"metadata": {},
"outputs": [],
"source": [
- "print(f'lower left coordinates (WGS84): {min(lon)}, {min(lat)}')\n",
- "print(f'upper right coordinates (WGS84): {max(lon)}, {max(lat)}')"
+ "print(f'lower left coordinates (WGS84): {min_lon}, {min_lat}')\n",
+ "print(f'upper right coordinates (WGS84): {max_lon}, {max_lat}')"
]
},
{
@@ -3810,7 +406,7 @@
"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()]])"
+ "spatial_extent = pystac.SpatialExtent(bboxes=[[min_lon, min_lat, max_lon, max_lat]])"
]
},
{
@@ -4078,9 +674,7 @@
"id": "9aa6c8ff-8d9b-40a7-a281-39b502bd5a3d",
"metadata": {},
"source": [
- "**X/lon**\n",
- "\n",
- "had to round to 13th decimal due to slight variations in rounding"
+ "**X/lon**"
]
},
{
@@ -4090,10 +684,10 @@
"metadata": {},
"outputs": [],
"source": [
- "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'])\n",
+ "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",
+ "#x_step = stac_helpers.get_step(ds, dim_names_dict['X'], round_dec=13)\n",
"print(f'x step: {x_step}')"
]
},
@@ -4131,9 +725,7 @@
"id": "21b5cca4-8bb4-498d-ae6b-6b8545fffe56",
"metadata": {},
"source": [
- "**Y/lat**\n",
- "\n",
- "had to round to 13th decimal due to slight variations in rounding"
+ "**Y/lat**"
]
},
{
@@ -4143,10 +735,10 @@
"metadata": {},
"outputs": [],
"source": [
- "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'])\n",
+ "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",
+ "#y_step = stac_helpers.get_step(ds, dim_names_dict['Y'], round_dec=13)\n",
"print(f'y step: {y_step}')"
]
},
@@ -4349,9 +941,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "global-global-pangeo",
+ "display_name": "geo",
"language": "python",
- "name": "conda-env-global-global-pangeo-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -4363,7 +955,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.6"
+ "version": "3.10.0"
}
},
"nbformat": 4,