Fixed a bug, added an example, and start of tests for Rectangular grid.

README.md

@@ -96,6 +96,12 @@ To run a shell to do dev work:
 pixi shell -e dev
 ```
 
+If you want to run the examples (notebooks and al that):
+
+```bash
+pixi shell -e all
+```
+
 That will set up a conda environment with all the develop dependencies.
 
 To run a shell in which you can run the examples:
@@ -131,4 +137,4 @@ If you need the development tools, you can also install:
 conda install --file conda_requirements_dev.txt
 ```
 
-(requirements should all be on the conda-forge channel)
+(requirements should all be on the conda-forge channel)

docs/examples/.gitignore

@@ -0,0 +1 @@
+AMSEAS-subset.nc

docs/examples/RegularGridTHREDDS.ipynb

@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e601719e-1253-4140-aa64-274c5a36f724",
+   "metadata": {},
+   "source": [
+    "# subsetting a regular grid from an TDS source\n",
+    "\n",
+    "In this case, the Navy American Seas (AMSEAS) model, as provided by NCEI:\n",
+    "\n",
+    "https://www.ncei.noaa.gov/thredds-coastal/catalog/ncom_amseas_agg/catalog.html\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "caa476fd-2375-4a22-8981-5869bbff3eb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import xarray_subset_grid\n",
+    "from xarray_subset_grid.utils import format_bytes\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f01dd841-7c89-47ba-8c16-a154bdee1b80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# EXAMPLE (very small) subset:\n",
+    "bbox = (268.0, 29.0, 269, 29.75)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f22007b0-f540-45a8-b974-e41c321cad0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create an xarray dataset from the OpenDAP url\n",
+    "ds = xr.open_dataset('https://www.ncei.noaa.gov/thredds-coastal/dodsC/ncom_amseas_agg/AmSeas_Dec_17_2020_to_Current_best.ncd')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4f93c32b-bc4d-4e1e-af4c-dfa890729a20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset is: 10.0 TB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Dataset is: {format_bytes(ds.nbytes)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5f4fde4a-39bf-491c-a57c-acc2f3216ffa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# downscale in time (would be nice to make this smart, but for now:\n",
+    "# 3 hour timesteps, last 12 timesteps is 3 days\n",
+    "ds = ds.isel(time=slice(-12, None))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "ce852bd3-0485-4d59-9310-e35c2d8e0ce5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset is: 9.9 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Dataset is: {format_bytes(ds.nbytes)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "215d1a33-7bb6-47b5-9343-1e8ff6267103",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_ss = ds.xsg.subset_bbox(bbox)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ee346b9f-8b5c-4f57-8c63-944c3a11febf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset is: 6.4 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Dataset is: {format_bytes(ds_ss.nbytes)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f6487d58-5479-4c86-a927-0accc7f11209",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save out the subset as a netcdf file.\n",
+    "\n",
+    "ds_ss.to_netcdf(\"AMSEAS-subset.nc\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2536e8c5-5531-499a-a799-873be65fcd2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "549bda29-a996-49d0-be1d-89e8a30b18e2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66aa16b1-832e-4882-b974-526553f36bd9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "240d4dc1-8a2b-4696-8cd1-eb1ee78c6ef8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}