Structured Grid Reader

README.md

@@ -46,6 +46,7 @@ recognizing unstructured grid model outputs. We picked the name "UXarray"
 
 * ``Grid`` class for storing grid information and providing grid-specific functionality
   * Support for reading UGRID, MPAS, ESMF, ICON, GEOS-CS, SCRIP, and EXODUS grid formats
+  * Support for reader structured (i.e. latitude longitude) grids
 * Extension of xarray's ``DataArray`` and ``Dataset`` classe to support unstructured grid operations
   * ``uxarray.UxDataArray`` inherits ``xarray.DataArray`` and is attached to a ``Grid`` instance through the ``.uxgrid`` accessor
   * ``uxarray.UxDataset`` inherits ``xarray.Dataset`` and is attached to a ``Grid`` instance through the ``.uxgrid`` accessor
@@ -71,10 +72,6 @@ Raijin, and on several community platforms such as [Xarray GitHub
 Repository](https://github.com/pydata/xarray/issues/4222). The UXarray team
 is receptive to additional functionality requests.
 
-
-* Support for arbitrary structured and unstructured grids on the sphere,
-  including latitude-longitude grids, grids with only partial coverage of
-  the sphere, and grids with concave faces.
 * Support for finite volume and finite element outputs.
 * Triangular decompositions.
 * Calculation of supermeshes (consisting of grid lines from two input grids).

ci/environment.yml

@@ -30,6 +30,7 @@ dependencies:
   - scipy
   - shapely
   - spatialpandas
+  - pooch
   - geopandas
   - xarray
   - asv

docs/api.rst

@@ -41,6 +41,7 @@ I/O & Conversion
    Grid.from_dataset
    Grid.from_file
    Grid.from_topology
+   Grid.from_structured
    Grid.to_xarray
    Grid.to_geodataframe
    Grid.to_polycollection
@@ -205,6 +206,13 @@ Grid Accessor
 
    UxDataset.uxgrid
 
+I/O & Conversion
+~~~~~~~~~~~~~~~~
+
+.. autosummary::
+   :toctree: generated/
+
+   UxDataset.from_structured
 
 Plotting
 --------

docs/user-guide/structured.ipynb

@@ -0,0 +1,256 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4201b69329447989",
+   "metadata": {},
+   "source": [
+    "# Reading Structured Grids\n",
+    "\n",
+    "UXarray supports reading structured grids and representing them as unstructured grids. This user-guide section will discuss how to load in structured grids using UXarray."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "149f175e67dc1a07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uxarray as ux\n",
+    "import xarray as xr\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a6a3b7f5930ab21",
+   "metadata": {},
+   "source": [
+    "## Data\n",
+    "\n",
+    "For this notebook, we will be using datasets from the [Xarray tutorial](https://docs.xarray.dev/en/stable/generated/xarray.tutorial.open_dataset.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c63337e3083b6ca5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_air_temp = xr.tutorial.open_dataset(\"air_temperature\")\n",
+    "ds_air_temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc0fd470a7f64504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ersstv5 = xr.tutorial.open_dataset(\"ersstv5\")\n",
+    "ds_ersstv5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3696e3df89a57d96",
+   "metadata": {},
+   "source": [
+    "## Grid\n",
+    "\n",
+    "A structured grid can be converted into an unstructured grid by using the ``ux.Grid.from_structured()`` class method. An ``xarray.Dataset`` can be passed in, with CF-compliant longitude and latitude coordinates parsed and converted to an unstructured grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d16555e6ec8dee74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uxgrid = ux.Grid.from_structured(ds_air_temp)\n",
+    "uxgrid"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bbd88b0954b53f8",
+   "metadata": {},
+   "source": "You can also manually pass in longitude and latitude coordinates."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d121ef33c3aaed90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uxgrid = ux.Grid.from_structured(lon=ds_air_temp.lon, lat=ds_air_temp.lat)\n",
+    "uxgrid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab3c5ca428b99bdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uxgrid.plot(\n",
+    "    title=\"Structured Grid loaded using UXarray\",\n",
+    "    backend=\"matplotlib\",\n",
+    "    width=1000,\n",
+    "    coastline=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ddf203ac8e9a0c2",
+   "metadata": {},
+   "source": [
+    "## Dataset\n",
+    "\n",
+    "If you have a dataset that contains data variables, you can convert the entire ``xarray.Dataset`` into a ``uxarray.UxDataset`` using the ``ux.UxDataset.from_structured()`` class method. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ed9a15c25a14c3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uxds = ux.UxDataset.from_structured(ds_air_temp)\n",
+    "uxds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8037fd0309774356",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uxds[\"air\"][0].plot(\n",
+    "    title=\"Structured Grid with Data loaded using UXarray\",\n",
+    "    backend=\"matplotlib\",\n",
+    "    width=500,\n",
+    "    coastline=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3badcc09659920b5",
+   "metadata": {},
+   "source": [
+    "## Limitations of Structured Grids\n",
+    "\n",
+    "Structured grids are a common choice in climate modeling due to their ease of implementation. However, they come with many limitations, which have been a driving factor in the adoption of unstructured grids.\n",
+    "\n",
+    "\n",
+    "\n",
+    "#### Limited Flexibility in Handling Complex Geometries\n",
+    "\n",
+    "Structured grids, with their regular and grid-aligned cells, struggle to capture complex geometries. This limitation forces modelers to use a higher number of grid points to approximate complex features, which can lead to increased computational costs and reduced simulation efficiency. Additionally, the inability to precisely represent intricate boundaries can result in inaccuracies in modeling climate processes that are influenced by these geographical features, potentially affecting the reliability of the simulation outcomes.\n",
+    "\n",
+    "#### Difficulty in Local Grid Refinement\n",
+    "\n",
+    "Climate phenomena like tropical cyclones, atmospheric fronts, and localized convection require high-resolution grids to be accurately modeled. Structured grids make it challenging to refine the grid locally in regions where such detailed resolution is needed without uniformly increasing the grid resolution across the entire model domain. This uniform refinement results in inefficient use of computational resources, as large areas of the model may have unnecessarily high resolution where it is not required.\n",
+    "\n",
+    "#### Pole Point Singularities\n",
+    "\n",
+    "Pole point singularities are a significant challenge in climate models that utilize structured latitude-longitude grids. In such grid systems, the lines of longitude converge as they approach the Earth's poles, causing the grid cells to become increasingly small near these regions. This convergence leads to several issues:\n",
+    "\n",
+    "- **Numerical Instability:** The drastically reduced cell size near the poles can cause numerical methods to become unstable, requiring smaller time steps to maintain accuracy and stability in simulations.\n",
+    "\n",
+    "- **Accuracy Problems:** The distortion of grid cell shapes near the poles can lead to inaccuracies in representing physical processes, such as atmospheric circulation and ocean currents, which are critical for realistic climate simulations.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6b872c3341c3eba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uxds = ux.UxDataset.from_structured(ds_ersstv5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "556a04ca5c799cbb",
+   "metadata": {},
+   "source": "Below is a plot of Sea Surface Temperature on a structured grid."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd07e0e5848d5095",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uxds[\"sst\"][0].plot(\n",
+    "    projection=ccrs.Orthographic(central_latitude=60),\n",
+    "    periodic_elements=\"split\",\n",
+    "    coastline=True,\n",
+    "    grid=True,\n",
+    "    title=\"SST Near North Pole\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd7779c434574e45",
+   "metadata": {},
+   "source": "If we expose the grid structure, we can observe the singularity at the poles."
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9ded6909c6adc18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "uxds.uxgrid.plot(\n",
+    "    projection=ccrs.Orthographic(central_latitude=60),\n",
+    "    periodic_elements=\"split\",\n",
+    "    width=600,\n",
+    "    linewidth=1,\n",
+    "    coastline=True,\n",
+    "    title=\"Grid Near NorthPole\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/userguide.rst

@@ -43,6 +43,7 @@ These user guides provide detailed explanations of the core functionality in UXa
 `Advanced Plotting Techniques <user-guide/advanced-plotting.ipynb>`_
  Deeper dive into getting the most out of UXarray's plotting functionality
 
+
 `Subsetting <user-guide/subset.ipynb>`_
  Select specific regions of a grid
 
@@ -64,13 +65,16 @@ These user guides provide detailed explanations of the core functionality in UXa
 `Face Area Calculations <user-guide/area_calc.ipynb>`_
  Methods for computing the area of each face
 
+`Structured Grids <user-guide/structured.ipynb>`_
+ Loading structured (latitude-longitude) grids
+
 `Dual Mesh Construction <user-guide/dual-mesh.ipynb>`_
  Construct the Dual Mesh of an unstructured grid
 
 Supplementary Guides
 --------------------
 
-These user guides provide additional detail about specific features in UXarray.
+These user guides provide additional details about specific features in UXarray.
 
 `Compatibility with HoloViz Tools <user-guide/holoviz.ipynb>`_
  Use UXarray with HoloViz tools
@@ -96,4 +100,5 @@ These user guides provide additional detail about specific features in UXarray.
    user-guide/tree_structures.ipynb
    user-guide/area_calc.ipynb
    user-guide/dual-mesh.ipynb
+   user-guide/structured.ipynb
    user-guide/holoviz.ipynb

test/test_structured.py

@@ -0,0 +1,36 @@
+import uxarray as ux
+import xarray as xr
+import pytest
+
+
+@pytest.mark.parametrize("ds_name", ["air_temperature", "ersstv5"])
+def test_read_structured_grid_from_ds(ds_name):
+    ds = xr.tutorial.open_dataset(ds_name)
+    uxgrid = ux.Grid.from_structured(ds)
+
+    assert uxgrid.n_face == ds.sizes['lon'] * ds.sizes['lat']
+
+    assert uxgrid.validate()
+
+
+@pytest.mark.parametrize("ds_name", ["air_temperature", "ersstv5"])
+def test_read_structured_grid_from_latlon(ds_name):
+    ds = xr.tutorial.open_dataset(ds_name)
+    uxgrid = ux.Grid.from_structured(lon=ds.lon, lat=ds.lat)
+    assert uxgrid.n_face == ds.sizes['lon'] * ds.sizes['lat']
+    assert uxgrid.validate()
+
+@pytest.mark.parametrize("ds_name", ["air_temperature", "ersstv5"])
+def test_read_structured_uxds_from_ds(ds_name):
+    # Load the dataset using xarray's tutorial module
+    ds = xr.tutorial.open_dataset(ds_name)
+
+    # Create a uxarray Grid from the structured dataset
+    uxds = ux.UxDataset.from_structured(ds)
+
+    assert "n_face" in uxds.dims
+
+    assert "lon" not in uxds.dims
+    assert "lat" not in uxds.dims
+
+    assert uxds.uxgrid.validate()

uxarray/__init__.py

@@ -1,8 +1,4 @@
-# Sets the version of uxarray currently installeds
-# Attempt to import the needed modules
-
 import uxarray.constants
-
 from .core.api import open_grid, open_dataset, open_mfdataset
 
 from .core.dataset import UxDataset