start adding [new] scipy2022 tutorial content

_toc.yml

@@ -2,30 +2,67 @@
 root: intro
 format: jb-book
 parts:
+  - caption: Overview
+    chapters:
+      - file: overview/xarray-in-45-min
   - caption: Fundamentals
     chapters:
-      - file: scipy-tutorial/00_overview
-      - file: scipy-tutorial/01_datastructures_and_io
-      - file: scipy-tutorial/02_working_with_labeled_data
-      - file: scipy-tutorial/03_computation_with_xarray
-      - file: scipy-tutorial/04_plotting_and_visualization
-      - file: scipy-tutorial/05_intro_to_dask
-      - file: scipy-tutorial/06_xarray_and_dask
+      - file: fundamentals/00_overview
+      - file: fundamentals/01_datastructures_and_io
+      - file: fundamentals/02.1_working_with_labeled_data
+        sections:
+          - file: fundamentals/02.2_manipulating_dimensions
+          - file: fundamentals/02.3_aligning_data_objects
+      - file: fundamentals/03.1_computation_with_xarray
+        sections:
+          - file: fundamentals/03.2_groupby_with_xarray
+      - file: fundamentals/04_plotting_and_visualization
+
+  - caption: Intermediate
+    chapters:
+      - file: intermediate/01-high-level-computation-patterns
+
+  - caption: Advanced
+    chapters:
+      - file: advanced/xarray_and_dask
 
   - caption: Workshops
     chapters:
-      - file: workshops/oceanhackweek-2020/README
+      - file: workshops/oceanhackweek2020/README
         sections:
-          - file: workshops/oceanhackweek-2020/xarray-oceanhackweek20
+          - url: https://tutorial.xarray.dev/overview/xarray-in-45-min
+            title: Xarray in 45 minutes
       - file: workshops/thinking-like-xarray/README
         sections:
-          - file: workshops/thinking-like-xarray/01-high-level-computation-patterns
+          - url: https://tutorial.xarray.dev/intermediate/01-high-level-computation-patterns
+            title: High-level computation patterns
       - file: workshops/online-tutorial-series/README
         sections:
           - file: workshops/online-tutorial-series/01_xarray_fundamentals
           - file: workshops/online-tutorial-series/02_indexing
           - file: workshops/online-tutorial-series/03_computation
       - file: workshops/scipy2022/README
+        sections:
+          - url: https://tutorial.xarray.dev/fundamentals/00_overview
+            title: Introduction
+          - url: https://tutorial.xarray.dev/fundamentals/01_datastructures_and_io
+            title: Data Structures
+          - file: workshops/scipy2022/cleaning_real_data
+          - url: https://tutorial.xarray.dev/fundamentals/02.1_working_with_labeled_data
+            title: Labeled Data
+          - url: https://tutorial.xarray.dev/fundamentals/02.2_manipulating_dimensions
+            title: Manipulating Dimensions
+          - url: https://tutorial.xarray.dev/fundamentals/02.3_aligning_data_objects
+            title: Data Alignment
+          - url: https://tutorial.xarray.dev/fundamentals/03.1_computation_with_xarray
+            title: Computation
+          - url: https://tutorial.xarray.dev/fundamentals/03.2_groupby_with_xarray
+            title: Groupby
+          - url: https://tutorial.xarray.dev/fundamentals/04_plotting_and_visualization.html
+            title: Plotting and Visualization
+          - file: workshops/scipy2022/xarray_ecosystem
+          - url: https://tutorial.xarray.dev/fundamentals/06_xarray_and_dask
+            title: Xarray and Dask
 
   - caption: Reference
     chapters:

scipy-tutorial/00_overview.ipynb → fundamentals/00_overview.ipynb

@@ -87,10 +87,13 @@
     "\n",
     "The layout of the tutorial will be as follows:\n",
     "\n",
-    "1. [Introduction + Data structures for multi-dimensional data](./01_datastructures_and_io.ipynb)\n",
-    "1. [Working with labeled data](02_working_with_labeled_data.ipynb)\n",
-    "1. [Computation with Xarray](03_computation_with_xarray.ipynb)\n",
-    "1. [Plotting and Visualization](04_plotting_and_visualization.ipynb)\n",
+    "1. [Introduction + data structures for multi-dimensional data](./01_datastructures_and_io.ipynb)\n",
+    "1. [Working with labeled data](02.1_working_with_labeled_data.ipynb)\n",
+    "1. [Manipulating dimensions](02.2_manipulating_dimensions.ipynb)\n",
+    "1. [Aligning data objects](02.3_aligning_data_objects.ipynb)\n",
+    "1. [Computation with Xarray](03.1_computation_with_xarray.ipynb)\n",
+    "1. [Groupby with Xarray](03.2_groupby_with_xarray.ipynb)\n",
+    "1. [Plotting and visualization](04_plotting_and_visualization.ipynb)\n",
     "1. [Introduction to Dask](05_intro_to_dask.ipynb)\n",
     "1. [Dask and Xarray](06_xarray_and_dask.ipynb)\n",
     "\n",

scipy-tutorial/02_working_with_labeled_data.ipynb → fundamentals/02.1_working_with_labeled_data.ipynb

@@ -301,190 +301,6 @@
    "source": [
     "# your code here"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Interpolation\n",
-    "\n",
-    "If we want to look at values between the current grid cells (interpolation), we\n",
-    "can do that with `interp` (requires `scipy`):\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr.interp(\n",
-    "    x=np.linspace(2, 6, 10),\n",
-    "    y=pd.date_range(\"2009-04-01\", \"2009-04-30\", freq=\"D\"),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "when trying to extrapolate, the resulting values will be `nan`.\n",
-    "\n",
-    "If we already have a object with the desired coordinates, we can use\n",
-    "`interp_like`:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "other = xr.DataArray(\n",
-    "    dims=(\"x\", \"y\"),\n",
-    "    coords={\n",
-    "        \"x\": np.linspace(2, 4, 10),\n",
-    "        \"y\": pd.date_range(\"2009-04-01\", \"2009-04-30\", freq=\"D\"),\n",
-    "    },\n",
-    ")\n",
-    "arr.interp_like(other)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Exercises\n",
-    "\n",
-    "Increase the step size along latitude and longitude from 2.5 degrees to 1\n",
-    "degree.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Broadcasting and automatic alignment\n",
-    "\n",
-    "Labels help with combining arrays with different coordinates:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = xr.DataArray(\n",
-    "    np.random.randn(3, 4),\n",
-    "    dims=(\"x\", \"y\"),\n",
-    "    coords={\"x\": [\"a\", \"b\", \"c\"], \"y\": np.arange(4)},\n",
-    ")\n",
-    "b = xr.DataArray(\n",
-    "    np.random.randn(2, 7),\n",
-    "    dims=(\"x\", \"y\"),\n",
-    "    coords={\"x\": [\"b\", \"d\"], \"y\": [-2, -1, 0, 1, 2, 3, 4]},\n",
-    ")\n",
-    "\n",
-    "a + b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This will automatically select only common labels from both arrays (a inner\n",
-    "join) and then perform the operation.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Broadcasting works similar:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr1 = xr.DataArray(\n",
-    "    np.random.randn(3),\n",
-    "    dims=\"x\",\n",
-    "    coords={\"x\": [\"a\", \"b\", \"c\"]},\n",
-    ")\n",
-    "arr2 = xr.DataArray(\n",
-    "    np.random.randn(4),\n",
-    "    dims=\"y\",\n",
-    "    coords={\"y\": np.arange(4)},\n",
-    ")\n",
-    "\n",
-    "arr1 + arr2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "where both arrays were automatically broadcasted against each other:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr1_, arr2_ = xr.broadcast(arr1, arr2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr1_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arr2_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "and then the operation (a sum) was executed.\n",
-    "\n",
-    "We can also call `align` speciically with different options.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a_al, b_al = xr.align(a, b, join=\"inner\")\n",
-    "b_al"
-   ]
   }
  ],
  "metadata": {