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",
- "text/plain": "<xarray.DataArray 'node_lon' (n_node: 5402)>\narray([-45. , 45. , 135. , ..., 141.09968961,\n 138.03317102, 135. ])\nDimensions without coordinates: n_node
\n
",
- "text/plain": "<xarray.UxDataArray 'psi' (n_face: 5400)>\narray([1.35131705, 1.33091472, 1.31013985, ..., 0.69088338, 0.66998883,\n 0.64946691])\nDimensions without coordinates: n_face
\n
",
- "text/plain": "<xarray.UxDataArray 'psi' ()>\narray(1.)
\n
\n\n\n 
\n \n\n\n\n\n
\n"
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {},
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.holoviews_exec.v0+json": "",
- "text/html": "\n\n\n \n \n\n\n\n\n\n \n
\n",
- "text/plain": ":Image [x,y] (x_y psi)"
- },
- "execution_count": 9,
- "metadata": {
- "application/vnd.holoviews_exec.v0+json": {
- "id": "p1002"
- }
- },
- "output_type": "execute_result"
- }
- ],
- "source": [
- "uxds[\"psi\"].plot()"
- ]
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -316,14 +214,14 @@
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 2
+ "version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "2.7.6"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/area_calc.ipynb b/docs/user-guide/area_calc.ipynb
index f7c016dd9..6c1fe63cc 100644
--- a/docs/user-guide/area_calc.ipynb
+++ b/docs/user-guide/area_calc.ipynb
@@ -50,7 +50,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "We will be using the `outCSne30.ug` grid file, which is encoded in the UGRID convention."
+ "We will be using the `outCSne30.ug` grid file, which is encoded in the UGRID convention. In the user guide, this file is loaded through `ux.tutorial.open_grid(\"outCSne30\")` for convenience, and its underlying path can be resolved with `ux.tutorial.file_path(\"outCSne30\", \"grid\")`."
]
},
{
@@ -64,11 +64,7 @@
},
"outputs": [],
"source": [
- "base_path = \"../../test/meshfiles/\"\n",
- "grid_path = base_path + \"/ugrid/outCSne30/outCSne30.ug\"\n",
- "\n",
- "ugrid = ux.open_grid(grid_path)\n",
- "ugrid"
+ "ugrid = ux.tutorial.open_grid(\"outCSne30\")"
]
},
{
@@ -766,7 +762,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -780,7 +776,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.6"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/azimuthal-average.ipynb b/docs/user-guide/azimuthal-average.ipynb
index d5f0085a1..2f439adae 100644
--- a/docs/user-guide/azimuthal-average.ipynb
+++ b/docs/user-guide/azimuthal-average.ipynb
@@ -49,10 +49,7 @@
"metadata": {},
"outputs": [],
"source": [
- "uxds = ux.open_dataset(\n",
- " \"../../test/meshfiles/ugrid/outCSne30/outCSne30.ug\",\n",
- " \"../../test/meshfiles/ugrid/outCSne30/outCSne30_vortex.nc\",\n",
- ")"
+ "uxds = ux.tutorial.open_dataset(\"outCSne30-vortex\")"
]
},
{
@@ -202,10 +199,7 @@
"outputs": [],
"source": [
"clon, clat = 114.54, -17.66\n",
- "tcds = ux.open_dataset(\n",
- " \"../../test/meshfiles/ugrid/ne120_TCsubset/ne120_TCsubset.ug\",\n",
- " \"../../test/meshfiles/ugrid/ne120_TCsubset/ne120_TCsubset.nc\",\n",
- ").squeeze()"
+ "tcds = ux.tutorial.open_dataset(\"ne120-tcsubset\").squeeze()"
]
},
{
@@ -296,7 +290,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -310,7 +304,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.14.3"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/cross-sections.ipynb b/docs/user-guide/cross-sections.ipynb
index 349b1039e..29e8e2922 100644
--- a/docs/user-guide/cross-sections.ipynb
+++ b/docs/user-guide/cross-sections.ipynb
@@ -14,174 +14,10 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "16db9f880115ac2b",
"metadata": {},
- "outputs": [
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- "data": {
- "text/html": [
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- "application/vnd.holoviews_load.v0+json": ""
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/javascript": "\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 = 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window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n let retries = 0;\n const open = () => {\n if (comm.active) {\n comm.open();\n } else if (retries > 3) {\n console.warn('Comm target never activated')\n } else {\n retries += 1\n setTimeout(open, 500)\n }\n }\n if (comm.active) {\n comm.open();\n } else {\n setTimeout(open, 500)\n }\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 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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",
- "application/vnd.holoviews_load.v0+json": ""
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
@@ -213,7 +49,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "720c4a345d659fd3",
"metadata": {},
"outputs": [],
@@ -244,1559 +80,12 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "2a42a1aa9249a94f",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "
"
- ],
- "text/plain": [
- "<xarray.UxDataArray 'RELHUM' (lev: 72, n_face: 21600)> Size: 6MB\n", - "[1555200 values with dtype=float32]\n", - "Coordinates:\n", - " * lev (lev) float64 576B 0.1238 0.1828 0.2699 ... 986.2 993.8 998.5\n", - " time object 8B ...\n", - "Dimensions without coordinates: n_face\n", - "Attributes:\n", - " mdims: 1\n", - " units: percent\n", - " long_name: Relative humidity\n", - " standard_name: relative_humidity\n", - " cell_methods: time: mean
\n",
- "
"
- ],
- "text/plain": [
- "<xarray.UxDataArray 'psi' (time: 6, n_face: 5400)> Size: 259kB\n", - "array([[ 0. , 0. , 0. , ..., 0. ,\n", - " 0. , 0. ],\n", - " [ 0.5 , 0.50999999, 0.51999998, ..., 54.47000122,\n", - " 54.47999954, 54.49000168],\n", - " [ 2. , 2.01999998, 2.03999996, ..., 109.94000244,\n", - " 109.95999908, 109.98000336],\n", - " [ 4.5 , 4.52999997, 4.55999994, ..., 166.41000366,\n", - " 166.43999863, 166.47000504],\n", - " [ 8. , 8.0400002 , 8.07999992, ..., 223.88000488,\n", - " 223.91999817, 223.96000671],\n", - " [ 12.5 , 12.55000043, 12.5999999 , ..., 282.3500061 ,\n", - " 282.39999771, 282.45000839]], shape=(6, 5400))\n", - "Coordinates:\n", - " * time (time) datetime64[ns] 48B 2018-04-28 ... 2018-04-28T05:00:00\n", - "Dimensions without coordinates: n_face
\n",
- " \n",
- "
\n",
- ""
- ],
- "text/plain": [
- ":Image [x,y] (x_y psi)"
- ]
- },
- "execution_count": 21,
- "metadata": {
- "application/vnd.holoviews_exec.v0+json": {
- "id": "b28eccfa-fba1-49ad-a8d5-f80a7b6d9da9"
- }
- },
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"time_index = 2\n",
"cumulative_time0 = cumulative.isel(time=time_index, drop=True)\n",
@@ -3703,7 +301,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -3717,7 +315,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.3"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/data-structures.ipynb b/docs/user-guide/data-structures.ipynb
index 330de971f..b22880aae 100644
--- a/docs/user-guide/data-structures.ipynb
+++ b/docs/user-guide/data-structures.ipynb
@@ -30,14 +30,14 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {},
+ "outputs": [],
"source": [
"import xarray as xr\n",
"\n",
"import uxarray as ux"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -60,11 +60,26 @@
"quad-hexagon\n",
"│ grid.nc\n",
"│ data.nc\n",
- "```"
+ "```\n"
]
},
{
"cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
+ },
+ "outputs": [],
+ "source": [
+ "grid_path = ux.tutorial.file_path(\"quad-hexagon\", \"grid\")\n",
+ "data_path = ux.tutorial.file_path(\"quad-hexagon\", \"data\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
"metadata": {
"collapsed": false,
"jupyter": {
@@ -72,11 +87,31 @@
}
},
"source": [
- "grid_path = \"../../test/meshfiles/ugrid/quad-hexagon/grid.nc\"\n",
- "data_path = \"../../test/meshfiles/ugrid/quad-hexagon/data.nc\""
- ],
+ "\n",
+ "```{note}\n",
+ "In the user guide, we use `ux.tutorial.open_dataset(...)` and `ux.tutorial.open_grid(...)` for convenience, so the examples can run without relying on repository-relative file paths.\n",
+ "\n",
+ "With your own files, the usual patterns are:\n",
+ "\n",
+ "```python\n",
+ "uxds = ux.open_dataset(grid_path, data_path)\n",
+ "uxgrid = ux.open_grid(grid_path)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
+ },
"outputs": [],
- "execution_count": null
+ "source": [
+ "uxds = ux.tutorial.open_dataset(\"quad-hexagon\")"
+ ]
},
{
"cell_type": "markdown",
@@ -102,17 +137,29 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"multiple_data_paths = [data_path for i in range(3)]"
- ],
- "outputs": [],
- "execution_count": null
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": false,
+ "jupyter": {
+ "outputs_hidden": false
+ }
+ },
+ "source": [
+ "If you are working with one of the built-in tutorial datasets that includes multiple data files, you can alternatively load it with `ux.tutorial.open_mfdataset(...)`."
+ ]
},
{
"cell_type": "markdown",
@@ -144,18 +191,18 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
- "uxgrid = ux.open_grid(grid_path)\n",
+ "uxgrid = uxds.uxgrid\n",
"uxgrid"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -174,59 +221,59 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxgrid.dims"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxgrid.sizes"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxgrid.coordinates"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxgrid.connectivity"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -242,31 +289,31 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxgrid.node_lon"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxgrid[\"node_lon\"]"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -304,17 +351,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxgrid.connectivity"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -332,17 +379,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxgrid.face_edge_connectivity"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -358,17 +405,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxgrid.connectivity"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -431,18 +478,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
- "uxds = ux.open_dataset(grid_path, data_path)\n",
"uxds"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -460,20 +506,20 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxds_multi = ux.open_mfdataset(\n",
" grid_path, multiple_data_paths, combine=\"nested\", concat_dim=\"time\"\n",
")\n",
"uxds_multi"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -491,17 +537,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxds.uxgrid"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -517,17 +563,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxds.uxgrid.dims"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -553,17 +599,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxds[\"t2m\"]"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -579,17 +625,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"type(uxds), type(uxds[\"t2m\"])"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -605,17 +651,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxds[\"t2m\"].uxgrid"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -631,17 +677,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxds[\"t2m\"].uxgrid == uxds.uxgrid"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -669,31 +715,31 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxds[\"t2m\"].min()"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxds[\"t2m\"].mean()"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -709,17 +755,17 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"uxds[\"t2m\"].gradient()"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
@@ -749,40 +795,30 @@
},
{
"cell_type": "code",
+ "execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
+ "outputs": [],
"source": [
"issubclass(ux.UxDataset, xr.Dataset)"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "code",
- "metadata": {
- "collapsed": false,
- "jupyter": {
- "outputs_hidden": false
- }
- },
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"issubclass(ux.UxDataArray, xr.DataArray)"
- ],
- "outputs": [],
- "execution_count": null
+ ]
},
{
"cell_type": "markdown",
- "metadata": {
- "collapsed": false,
- "jupyter": {
- "outputs_hidden": false
- }
- },
+ "metadata": {},
"source": [
"## Overloaded Methods\n",
"\n",
@@ -793,20 +829,17 @@
},
{
"cell_type": "code",
- "metadata": {
- "collapsed": false,
- "jupyter": {
- "outputs_hidden": false
- }
- },
- "source": "uxds[\"t2m\"].plot(cmap=\"viridis\", backend=\"bokeh\")",
+ "execution_count": null,
+ "metadata": {},
"outputs": [],
- "execution_count": null
+ "source": [
+ "uxds[\"t2m\"].plot(cmap=\"viridis\", backend=\"bokeh\")"
+ ]
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -820,7 +853,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.9"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/healpix.ipynb b/docs/user-guide/healpix.ipynb
index eac51187f..ea1a3e94a 100644
--- a/docs/user-guide/healpix.ipynb
+++ b/docs/user-guide/healpix.ipynb
@@ -175,10 +175,7 @@
"metadata": {},
"outputs": [],
"source": [
- "source_uxds = ux.open_dataset(\n",
- " \"../../test/meshfiles/ugrid/outCSne30/outCSne30.ug\",\n",
- " \"../../test/meshfiles/ugrid/outCSne30/outCSne30_vortex.nc\",\n",
- ")\n",
+ "source_uxds = ux.tutorial.open_dataset(\"outCSne30-vortex\")\n",
"\n",
"# source data & grid\n",
"source_uxds[\"psi\"].plot(\n",
@@ -443,7 +440,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -457,7 +454,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.6"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/holoviz.ipynb b/docs/user-guide/holoviz.ipynb
index 5337058b2..15ff90c0a 100644
--- a/docs/user-guide/holoviz.ipynb
+++ b/docs/user-guide/holoviz.ipynb
@@ -59,12 +59,7 @@
},
"outputs": [],
"source": [
- "base_path = \"../../test/meshfiles/ugrid/outCSne30/\"\n",
- "grid_path = base_path + \"outCSne30.ug\"\n",
- "data_path = base_path + \"outCSne30_vortex.nc\"\n",
- "\n",
- "uxds = ux.open_dataset(grid_path, data_path)\n",
- "\n",
+ "uxds = ux.tutorial.open_dataset(\"outCSne30-vortex\")\n",
"uxds"
]
},
@@ -444,7 +439,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -458,7 +453,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.9"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/mpl.ipynb b/docs/user-guide/mpl.ipynb
index 2dfd8c1ed..0a65ca607 100644
--- a/docs/user-guide/mpl.ipynb
+++ b/docs/user-guide/mpl.ipynb
@@ -37,11 +37,7 @@
"metadata": {},
"outputs": [],
"source": [
- "base_path = \"../../test/meshfiles/mpas/QU/480/\"\n",
- "grid_path = base_path + \"grid.nc\"\n",
- "data_path = base_path + \"data.nc\"\n",
- "\n",
- "uxds = ux.open_dataset(grid_path, data_path)"
+ "uxds = ux.tutorial.open_dataset(\"mpas-QU-480\")"
]
},
{
@@ -532,11 +528,19 @@
"ax.set_title(\"LineCollection\")\n",
"plt.show()"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a76de3b5-4919-4f1c-8f17-977609e018d7",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "uxarray-viz-testing",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -550,7 +554,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.13.11"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/plotting.ipynb b/docs/user-guide/plotting.ipynb
index f2da15838..f3fd4649f 100644
--- a/docs/user-guide/plotting.ipynb
+++ b/docs/user-guide/plotting.ipynb
@@ -48,15 +48,7 @@
"metadata": {},
"outputs": [],
"source": [
- "grid_path = \"../../test/meshfiles/ugrid/quad-hexagon/grid.nc\"\n",
- "\n",
- "data_paths = [\n",
- " \"../../test/meshfiles/ugrid/quad-hexagon/random-node-data.nc\",\n",
- " \"../../test/meshfiles/ugrid/quad-hexagon/random-edge-data.nc\",\n",
- " \"../../test/meshfiles/ugrid/quad-hexagon/random-face-data.nc\",\n",
- "]\n",
- "\n",
- "uxds = ux.open_mfdataset(grid_path, data_paths)\n",
+ "uxds = ux.tutorial.open_mfdataset(\"quad-hexagon-random\")\n",
"uxgrid = uxds.uxgrid\n",
"\n",
"uxds"
@@ -369,7 +361,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -383,7 +375,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.8"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/spatial-hashing.ipynb b/docs/user-guide/spatial-hashing.ipynb
index ea5f6a02b..96ef2df37 100644
--- a/docs/user-guide/spatial-hashing.ipynb
+++ b/docs/user-guide/spatial-hashing.ipynb
@@ -30,8 +30,7 @@
"metadata": {},
"outputs": [],
"source": [
- "grid_path = \"../../test/meshfiles/ugrid/quad-hexagon/grid.nc\"\n",
- "uxgrid = ux.open_grid(grid_path)"
+ "uxgrid = ux.tutorial.open_grid(\"quad-hexagon\")"
]
},
{
@@ -80,11 +79,18 @@
"print(f\"face ids : {face_ids}\")\n",
"print(f\"Barycentric Coordinates: {bcoords}\")"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "uxarray-dev",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -98,9 +104,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.16"
+ "version": "3.14.4"
}
},
"nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
}
diff --git a/docs/user-guide/topological-aggregations.ipynb b/docs/user-guide/topological-aggregations.ipynb
index f6c3a28ef..5a1d516a5 100644
--- a/docs/user-guide/topological-aggregations.ipynb
+++ b/docs/user-guide/topological-aggregations.ipynb
@@ -133,16 +133,7 @@
},
"outputs": [],
"source": [
- "grid_path = \"../../test/meshfiles/ugrid/quad-hexagon/grid.nc\"\n",
- "\n",
- "data_paths = [\n",
- " \"../../test/meshfiles/ugrid/quad-hexagon/random-node-data.nc\",\n",
- " \"../../test/meshfiles/ugrid/quad-hexagon/random-edge-data.nc\",\n",
- " \"../../test/meshfiles/ugrid/quad-hexagon/random-face-data.nc\",\n",
- "]\n",
- "\n",
- "uxds = ux.open_mfdataset(grid_path, data_paths)\n",
- "\n",
+ "uxds = ux.tutorial.open_mfdataset(\"quad-hexagon-random\")\n",
"uxds"
]
},
@@ -475,7 +466,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -489,7 +480,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.8"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/tree_structures.ipynb b/docs/user-guide/tree_structures.ipynb
index 7bbb6e09a..7332ec28d 100644
--- a/docs/user-guide/tree_structures.ipynb
+++ b/docs/user-guide/tree_structures.ipynb
@@ -63,8 +63,7 @@
"metadata": {},
"outputs": [],
"source": [
- "grid_path = \"../../test/meshfiles/ugrid/quad-hexagon/grid.nc\"\n",
- "uxgrid = ux.open_grid(grid_path)"
+ "uxgrid = ux.tutorial.open_grid(\"quad-hexagon\")"
]
},
{
@@ -300,7 +299,9 @@
"width": 100
}
},
- "source": "The KDTree structure is a binary search tree useful for low-dimensional data. Its implementation is almost identical to BallTree, and the parameters are identical. An important note is that the different allowed inputs for `distance_metric` change between the trees. For KDTree allowed `distance_metrics` can be found [here](https://uxarray.readthedocs.io/en/latest/generated/uxarray.Grid.get_kd_tree.html). We can call it using `get_kd_tree()`. Generally, KDTree is going to be slower than BallTree, and it is recommended to use BallTree for most im"
+ "source": [
+ "The KDTree structure is a binary search tree useful for low-dimensional data. Its implementation is almost identical to BallTree, and the parameters are identical. An important note is that the different allowed inputs for `distance_metric` change between the trees. For KDTree allowed `distance_metrics` can be found [here](https://uxarray.readthedocs.io/en/latest/generated/uxarray.Grid.get_kd_tree.html). We can call it using `get_kd_tree()`. Generally, KDTree is going to be slower than BallTree, and it is recommended to use BallTree for most im"
+ ]
},
{
"cell_type": "code",
@@ -357,11 +358,19 @@
"source": [
"kd_tree.query_radius([1.0, 0.0, 0.0], r=5, return_distance=True)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0aaf1030-2539-4197-9da3-878968b43518",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -375,7 +384,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.11.9"
+ "version": "3.14.4"
},
"panel-cell-order": [
"48760981b4ed3d9b",
diff --git a/docs/user-guide/vector_calculus.ipynb b/docs/user-guide/vector_calculus.ipynb
index ad7de645d..af411e816 100644
--- a/docs/user-guide/vector_calculus.ipynb
+++ b/docs/user-guide/vector_calculus.ipynb
@@ -59,10 +59,7 @@
"metadata": {},
"outputs": [],
"source": [
- "base_path = \"../../test/meshfiles/mpas/dyamond-30km/\"\n",
- "grid_path = base_path + \"gradient_grid_subset.nc\"\n",
- "data_path = base_path + \"gradient_data_subset.nc\"\n",
- "uxds = ux.open_dataset(grid_path, data_path)\n",
+ "uxds = ux.tutorial.open_dataset(\"mpas-dyamond-30km-gradient\")\n",
"uxds"
]
},
@@ -753,14 +750,6 @@
"print(f\" - Curl: {np.abs(curl_radial).max().values:.2e} (≈ 0, irrotational)\")\n",
"print(f\" - Divergence: {div_radial.mean().values:.2f} (≈ 2, expanding)\")"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "40",
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -779,7 +768,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.12.2"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/weighted_mean.ipynb b/docs/user-guide/weighted_mean.ipynb
index 0103830a6..b15fdb5b2 100644
--- a/docs/user-guide/weighted_mean.ipynb
+++ b/docs/user-guide/weighted_mean.ipynb
@@ -44,16 +44,8 @@
"metadata": {},
"outputs": [],
"source": [
- "grid_path = \"../../test/meshfiles/ugrid/quad-hexagon/grid.nc\"\n",
- "quad_hex_data_path_edge_centered = (\n",
- " \"../../test/meshfiles/ugrid/quad-hexagon/random-edge-data.nc\"\n",
- ")\n",
- "quad_hex_data_path_face_centered = (\n",
- " \"../../test/meshfiles/ugrid/quad-hexagon/random-face-data.nc\"\n",
- ")\n",
- "\n",
- "uxds_face = ux.open_mfdataset(grid_path, quad_hex_data_path_face_centered)\n",
- "uxds_edge = ux.open_mfdataset(grid_path, quad_hex_data_path_edge_centered)"
+ "uxds_face = ux.tutorial.open_dataset(\"quad-hexagon-random-face\")\n",
+ "uxds_edge = ux.tutorial.open_dataset(\"quad-hexagon-random-edge\")"
]
},
{
@@ -102,7 +94,9 @@
"cell_type": "markdown",
"id": "def2b952ba7f1740",
"metadata": {},
- "source": "We can simply call `UxDataArray.weighted_mean()` on the UXDataArray to compute the weighted mean. The differences between the weighted mean and the regular mean is small since the area differences across the faces are small. "
+ "source": [
+ "We can simply call `UxDataArray.weighted_mean()` on the UXDataArray to compute the weighted mean. The differences between the weighted mean and the regular mean is small since the area differences across the faces are small. "
+ ]
},
{
"cell_type": "code",
@@ -160,7 +154,9 @@
"cell_type": "markdown",
"id": "647630d11f001fcf",
"metadata": {},
- "source": "The differences between weighted and unweighted mean is more drastic (~0.1 value difference) since the edge lengths have a larger variance. "
+ "source": [
+ "The differences between weighted and unweighted mean is more drastic (~0.1 value difference) since the edge lengths have a larger variance. "
+ ]
},
{
"cell_type": "code",
@@ -187,7 +183,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -201,7 +197,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.12"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/docs/user-guide/zonal-average.ipynb b/docs/user-guide/zonal-average.ipynb
index 9031f6d51..2eab4a091 100644
--- a/docs/user-guide/zonal-average.ipynb
+++ b/docs/user-guide/zonal-average.ipynb
@@ -46,10 +46,7 @@
"\n",
"import uxarray as ux\n",
"\n",
- "uxds = ux.open_dataset(\n",
- " \"../../test/meshfiles/ugrid/outCSne30/outCSne30.ug\",\n",
- " \"../../test/meshfiles/ugrid/outCSne30/outCSne30_vortex.nc\",\n",
- ")"
+ "uxds = ux.tutorial.open_dataset(\"outCSne30-vortex\")"
]
},
{
@@ -588,10 +585,8 @@
"metadata": {},
"outputs": [],
"source": [
- "grid_path = Path(\"../../test/meshfiles/scrip/ne30pg2/grid.nc\")\n",
- "data_path = Path(\"../../test/meshfiles/scrip/ne30pg2/data.nc\")\n",
+ "ne30_ds = ux.tutorial.open_dataset(\"scrip-ne30pg2\")\n",
"\n",
- "ne30_ds = ux.open_dataset(grid_path, data_path)\n",
"relhum = ne30_ds[\"RELHUM\"]\n",
"\n",
"for dim in (\"time\", \"t\", \"step\"):\n",
@@ -754,7 +749,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -768,7 +763,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.13.11"
+ "version": "3.14.4"
}
},
"nbformat": 4,
diff --git a/test/core/test_tutorial.py b/test/core/test_tutorial.py
new file mode 100644
index 000000000..67623930c
--- /dev/null
+++ b/test/core/test_tutorial.py
@@ -0,0 +1,47 @@
+import pytest
+import uxarray as ux
+
+
+def test_available_datasets():
+ assert "quad-hexagon" in ux.tutorial.available_datasets()
+
+
+def test_file_path():
+ path = ux.tutorial.file_path("quad-hexagon", component="grid")
+ assert path.exists()
+
+
+def test_open_dataset():
+ uxds = ux.tutorial.open_dataset("quad-hexagon")
+ assert uxds.uxgrid is not None
+
+
+def test_open_grid():
+ uxgrid = ux.tutorial.open_grid("outCSne30")
+ assert uxgrid.n_face > 0
+
+
+def test_unknown_dataset():
+ with pytest.raises(ValueError, match="Unknown tutorial dataset"):
+ ux.tutorial.open_dataset("not-a-dataset")
+
+
+def test_grid_only_dataset_open_dataset_error():
+ with pytest.raises(ValueError, match="does not define a data file"):
+ ux.tutorial.open_dataset("outCSne30")
+
+
+def test_open_mfdataset():
+ uxds = ux.tutorial.open_mfdataset("quad-hexagon-random")
+ assert uxds.uxgrid is not None
+
+
+def test_open_dataset_rejects_multifile():
+ with pytest.raises(ValueError, match="multiple data files"):
+ ux.tutorial.open_dataset("quad-hexagon-random")
+
+
+def test_file_paths():
+ paths = ux.tutorial.file_paths("quad-hexagon-random")
+ assert len(paths) == 3
+ assert all(path.exists() for path in paths)
diff --git a/uxarray/__init__.py b/uxarray/__init__.py
index 80b0dcc9e..c6cb8ec3d 100644
--- a/uxarray/__init__.py
+++ b/uxarray/__init__.py
@@ -1,3 +1,4 @@
+from . import tutorial
from .constants import INT_DTYPE, INT_FILL_VALUE
from .core.api import (
concat,
@@ -30,6 +31,7 @@
"open_multigrid",
"list_grid_names",
"concat",
+ "tutorial",
"UxDataset",
"UxDataArray",
"INT_DTYPE",
diff --git a/uxarray/tutorial/__init__.py b/uxarray/tutorial/__init__.py
new file mode 100644
index 000000000..9c2a5f0ce
--- /dev/null
+++ b/uxarray/tutorial/__init__.py
@@ -0,0 +1,173 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+from uxarray.tutorial.registry import DATASETS, Component, TutorialDataset
+
+
+def available_datasets() -> tuple[str, ...]:
+ """Return the names of available tutorial datasets."""
+ return tuple(sorted(DATASETS))
+
+
+def describe_dataset(name: str) -> str:
+ """Return a short description for a tutorial dataset."""
+ return _get_dataset_entry(name).description
+
+
+def file_path(name: str, component: Component | None = None) -> Path:
+ """Return the local path for a tutorial dataset file."""
+ dataset = _get_dataset_entry(name)
+
+ if component is None:
+ components = _available_components(dataset)
+ if len(components) != 1:
+ raise ValueError(
+ f"Dataset {name!r} has multiple files. Please specify one of: "
+ f"{', '.join(components)}."
+ )
+ component = components[0]
+
+ path_parts = _get_component_path(dataset, name, component)
+ path = _meshfiles_path().joinpath(*path_parts)
+
+ if not path.exists():
+ raise FileNotFoundError(
+ f"Tutorial dataset file for {name!r} was not found at {path}."
+ )
+
+ return path
+
+
+def file_paths(name: str) -> tuple[Path, ...]:
+ """Return the local paths for a multi-file tutorial dataset."""
+ dataset = _get_dataset_entry(name)
+
+ if dataset.data_files is None:
+ raise ValueError(
+ f"Dataset {name!r} does not define multiple data files. "
+ "Use uxarray.tutorial.file_path(...) instead."
+ )
+
+ meshfiles_path = _meshfiles_path()
+ paths = tuple(meshfiles_path.joinpath(*parts) for parts in dataset.data_files)
+
+ for path in paths:
+ if not path.exists():
+ raise FileNotFoundError(
+ f"Tutorial dataset file for {name!r} was not found at {path}."
+ )
+
+ return paths
+
+
+def open_grid(name: str, **kwargs):
+ """Open the grid file for a tutorial dataset."""
+ from uxarray import open_grid as ux_open_grid
+
+ return ux_open_grid(file_path(name, component="grid"), **kwargs)
+
+
+def open_dataset(name: str, **kwargs):
+ """Open a tutorial dataset with its associated grid."""
+ dataset = _get_dataset_entry(name)
+
+ if dataset.data_files is not None:
+ raise ValueError(
+ f"Dataset {name!r} defines multiple data files. "
+ "Use uxarray.tutorial.open_mfdataset(...) instead."
+ )
+
+ if dataset.data is None:
+ raise ValueError(
+ f"Dataset {name!r} does not define a data file. "
+ "Use uxarray.tutorial.open_grid(...) instead."
+ )
+
+ from uxarray import open_dataset as ux_open_dataset
+
+ return ux_open_dataset(
+ file_path(name, component="grid"),
+ file_path(name, component="data"),
+ **kwargs,
+ )
+
+
+def open_mfdataset(name: str, **kwargs):
+ """Open a multi-file tutorial dataset with its associated grid."""
+ dataset = _get_dataset_entry(name)
+
+ if dataset.data_files is None:
+ raise ValueError(
+ f"Dataset {name!r} does not define multiple data files. "
+ "Use uxarray.tutorial.open_dataset(...) instead."
+ )
+
+ from uxarray import open_mfdataset as ux_open_mfdataset
+
+ return ux_open_mfdataset(
+ file_path(name, component="grid"),
+ file_paths(name),
+ **kwargs,
+ )
+
+
+def _get_dataset_entry(name: str) -> TutorialDataset:
+ try:
+ dataset = DATASETS[name]
+ except KeyError as err:
+ available = ", ".join(available_datasets())
+ raise ValueError(
+ f"Unknown tutorial dataset {name!r}. Available datasets: {available}."
+ ) from err
+
+ _validate_dataset_entry(name, dataset)
+ return dataset
+
+
+def _meshfiles_path() -> Path:
+ """Locate the local ``test/meshfiles`` directory for tutorial datasets.
+ This supports both local development from a cloned
+ repo and cwd fallback supports docs builds that run from the root repo"""
+ candidates = [Path(__file__).resolve().parents[2] / "test" / "meshfiles"]
+
+ cwd = Path.cwd().resolve()
+ candidates.extend(parent / "test" / "meshfiles" for parent in (cwd, *cwd.parents))
+
+ for path in candidates:
+ if path.exists():
+ return path
+
+ checked = ", ".join(str(path) for path in candidates)
+ raise FileNotFoundError(
+ f"Could not locate the tutorial dataset directory. Checked: {checked}"
+ )
+
+
+def _available_components(dataset: TutorialDataset) -> tuple[Component, ...]:
+ if dataset.data is None:
+ return ("grid",)
+ return ("grid", "data")
+
+
+def _get_component_path(
+ dataset: TutorialDataset,
+ name: str,
+ component: Component,
+) -> tuple[str, ...]:
+ if component == "grid":
+ return dataset.grid
+
+ if component == "data":
+ if dataset.data is None:
+ raise ValueError(f"Dataset {name!r} does not define a data file.")
+ return dataset.data
+
+ raise ValueError("Invalid component. Expected one of: grid, data.")
+
+
+def _validate_dataset_entry(name: str, dataset: TutorialDataset) -> None:
+ if dataset.data is not None and dataset.data_files is not None:
+ raise ValueError(
+ f"Tutorial dataset {name!r} cannot define both 'data' and 'data_files'."
+ )
diff --git a/uxarray/tutorial/registry.py b/uxarray/tutorial/registry.py
new file mode 100644
index 000000000..c39e46ced
--- /dev/null
+++ b/uxarray/tutorial/registry.py
@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Literal
+
+Component = Literal["grid", "data"]
+
+
+@dataclass(frozen=True)
+class TutorialDataset:
+ """Metadata for a UXarray tutorial dataset."""
+
+ description: str
+ grid: tuple[str, ...]
+ data: tuple[str, ...] | None = None
+ data_files: tuple[tuple[str, ...], ...] | None = None
+
+
+DATASETS: dict[str, TutorialDataset] = {
+ "quad-hexagon": TutorialDataset(
+ description="Small UGRID grid with sample face, edge, and node data.",
+ grid=("ugrid", "quad-hexagon", "grid.nc"),
+ data=("ugrid", "quad-hexagon", "data.nc"),
+ ),
+ "outCSne30-vortex": TutorialDataset(
+ description="Cubed-sphere UGRID grid with sample vortex data.",
+ grid=("ugrid", "outCSne30", "outCSne30.ug"),
+ data=("ugrid", "outCSne30", "outCSne30_vortex.nc"),
+ ),
+ "outCSne30": TutorialDataset(
+ description="Cubed-sphere UGRID grid.",
+ grid=("ugrid", "outCSne30", "outCSne30.ug"),
+ ),
+ "quad-hexagon-random-node": TutorialDataset(
+ description="Small UGRID grid with random node-centered sample data.",
+ grid=("ugrid", "quad-hexagon", "grid.nc"),
+ data=("ugrid", "quad-hexagon", "random-node-data.nc"),
+ ),
+ "quad-hexagon-random-edge": TutorialDataset(
+ description="Small UGRID grid with random edge-centered sample data.",
+ grid=("ugrid", "quad-hexagon", "grid.nc"),
+ data=("ugrid", "quad-hexagon", "random-edge-data.nc"),
+ ),
+ "quad-hexagon-random-face": TutorialDataset(
+ description="Small UGRID grid with random face-centered sample data.",
+ grid=("ugrid", "quad-hexagon", "grid.nc"),
+ data=("ugrid", "quad-hexagon", "random-face-data.nc"),
+ ),
+ "outCSne30-timeseries": TutorialDataset(
+ description="Cubed-sphere UGRID grid with sample time series data.",
+ grid=("ugrid", "outCSne30", "outCSne30.ug"),
+ data=("ugrid", "outCSne30", "outCSne30_sel_timeseries.nc"),
+ ),
+ "ne120-tcsubset": TutorialDataset(
+ description="Regional NE120 tropical cyclone subset with sample data.",
+ grid=("ugrid", "ne120_TCsubset", "ne120_TCsubset.ug"),
+ data=("ugrid", "ne120_TCsubset", "ne120_TCsubset.nc"),
+ ),
+ "scrip-ne30pg2": TutorialDataset(
+ description="SCRIP NE30 grid with relative humidity sample data.",
+ grid=("scrip", "ne30pg2", "grid.nc"),
+ data=("scrip", "ne30pg2", "data.nc"),
+ ),
+ "mpas-QU-480": TutorialDataset(
+ description="MPAS QU 480 km grid with sample data.",
+ grid=("mpas", "QU", "480", "grid.nc"),
+ data=("mpas", "QU", "480", "data.nc"),
+ ),
+ "mpas-dyamond-30km-gradient": TutorialDataset(
+ description="MPAS DYAMOND 30 km subset used for gradient examples.",
+ grid=("mpas", "dyamond-30km", "gradient_grid_subset.nc"),
+ data=("mpas", "dyamond-30km", "gradient_data_subset.nc"),
+ ),
+ "mpas-QU-oQU480": TutorialDataset(
+ description="MPAS ocean QU 480 km mesh stored as a single file.",
+ grid=("mpas", "QU", "oQU480.231010.nc"),
+ data=("mpas", "QU", "oQU480.231010.nc"),
+ ),
+ "mpas-QU-1920": TutorialDataset(
+ description="MPAS QU 1920 km mesh stored as a single file.",
+ grid=("mpas", "QU", "mesh.QU.1920km.151026.nc"),
+ data=("mpas", "QU", "mesh.QU.1920km.151026.nc"),
+ ),
+ "geoflow-small-v1": TutorialDataset(
+ description="Small GeoFlow UGRID grid with v1 sample data.",
+ grid=("ugrid", "geoflow-small", "grid.nc"),
+ data=("ugrid", "geoflow-small", "v1.nc"),
+ ),
+ "scrip-outCSne8": TutorialDataset(
+ description="Small SCRIP cubed-sphere grid.",
+ grid=("scrip", "outCSne8", "outCSne8.nc"),
+ ),
+ "quad-hexagon-random": TutorialDataset(
+ description="Small UGRID grid with random node-, edge-, and face-centered sample data.",
+ grid=("ugrid", "quad-hexagon", "grid.nc"),
+ data_files=(
+ ("ugrid", "quad-hexagon", "random-node-data.nc"),
+ ("ugrid", "quad-hexagon", "random-edge-data.nc"),
+ ("ugrid", "quad-hexagon", "random-face-data.nc"),
+ ),
+ ),
+}