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docs/source/user-stories/custom-aggregations.ipynb

@@ -7,13 +7,16 @@
    "source": [
     "# Custom Aggregations\n",
     "\n",
-    "This notebook is motivated by a [post](https://discourse.pangeo.io/t/using-xhistogram-to-bin-measurements-at-particular-stations/2365/4) on the Pangeo discourse forum.\n",
+    "This notebook is motivated by a\n",
+    "[post](https://discourse.pangeo.io/t/using-xhistogram-to-bin-measurements-at-particular-stations/2365/4)\n",
+    "on the Pangeo discourse forum.\n",
     "\n",
     "> Even better would be a command that lets me simply do the following.\n",
     ">\n",
     ">     A = da.groupby(['lon_bins', 'lat_bins']).mode()\n",
     "\n",
-    "This notebook will describe how to accomplish this using a custom `Aggregation` since `mode` and `median` aren't supported by flox yet."
+    "This notebook will describe how to accomplish this using a custom `Aggregation`\n",
+    "since `mode` and `median` aren't supported by flox yet.\n"
    ]
   },
   {
@@ -439,7 +442,7 @@
    "source": [
     "## A built-in reduction\n",
     "\n",
-    "First a simple example of lat-lon binning using a built-in reduction: mean"
+    "First a simple example of lat-lon binning using a built-in reduction: mean\n"
    ]
   },
   {
@@ -494,9 +497,12 @@
    "source": [
     "## Aggregations\n",
     "\n",
-    "flox knows how to interperet `func=\"mean\"` because it's been implemented in `aggregations.py` as an [Aggregation](https://flox.readthedocs.io/en/latest/generated/flox.aggregations.Aggregation.html)\n",
+    "flox knows how to interperet `func=\"mean\"` because it's been implemented in\n",
+    "`aggregations.py` as an\n",
+    "[Aggregation](https://flox.readthedocs.io/en/latest/generated/flox.aggregations.Aggregation.html)\n",
     "\n",
-    "An `Aggregation` is a blueprint for computing an aggregation, with both numpy and dask data."
+    "An `Aggregation` is a blueprint for computing an aggregation, with both numpy\n",
+    "and dask data.\n"
    ]
   },
   {
@@ -545,19 +551,19 @@
     "```python\n",
     "mean = Aggregation(\n",
     "    name=\"mean\",\n",
-    "    \n",
-    "    # strings in the following are built-in grouped reductions \n",
+    "\n",
+    "    # strings in the following are built-in grouped reductions\n",
     "    # implemented by the underlying  \"engine\": flox or numpy_groupies or numbagg\n",
-    "    \n",
+    "\n",
     "    # for pure  numpy inputs\n",
-    "    numpy=\"mean\", \n",
-    "    \n",
+    "    numpy=\"mean\",\n",
+    "\n",
     "    # The next are for dask inputs and describe how to reduce\n",
     "    # the data in parallel\n",
     "    chunk=(\"sum\", \"nanlen\"), # first compute these blockwise : (grouped_sum, grouped_count)\n",
     "    combine=(\"sum\", \"sum\"), #  reduce intermediate reuslts (sum the sums, sum the counts)\n",
     "    finalize=lambda sum_, count: sum_ / count, # final mean value (divide sum by count)\n",
-    "    \n",
+    "\n",
     "    fill_value=(0, 0),  # fill value for intermediate  sums and counts when groups have no members\n",
     "    dtypes=(None, np.intp),  # optional dtypes for intermediates\n",
     "    final_dtype=np.floating,  # final dtype for output\n",
@@ -572,18 +578,21 @@
    "source": [
     "## Defining a custom aggregation\n",
     "\n",
-    "First we'll need a function that executes the grouped reduction given numpy inputs. \n",
+    "First we'll need a function that executes the grouped reduction given numpy\n",
+    "inputs.\n",
+    "\n",
+    "Custom functions are required to have this signature (copied form\n",
+    "numpy_groupies):\n",
     "\n",
-    "Custom functions are required to have this signature (copied form numpy_groupies):\n",
-    "``` python\n",
+    "```python\n",
     "\n",
     "def custom_grouped_reduction(\n",
     "    group_idx, array, *, axis=-1, size=None, fill_value=None, dtype=None\n",
     "):\n",
     "    \"\"\"\n",
     "    Parameters\n",
     "    ----------\n",
-    "    \n",
+    "\n",
     "    group_idx : np.ndarray, 1D\n",
     "        integer codes for group labels (1D)\n",
     "    array : np.ndarray, nD\n",
@@ -596,17 +605,19 @@
     "        fill_value for when number groups in group_idx is less than size\n",
     "    dtype : optional\n",
     "        dtype of output\n",
-    "     \n",
+    "\n",
     "    Returns\n",
     "    -------\n",
-    "    \n",
+    "\n",
     "    np.ndarray with array.shape[-1] == size, containing a single value per group\n",
     "    \"\"\"\n",
     "    pass\n",
     "```\n",
     "\n",
-    "\n",
-    "Since numpy_groupies does not implement a median, we'll do it ourselves by passing `np.median` to `numpy_groupies.aggregate_numpy.aggregate`. This will loop over all groups, and then execute `np.median` on the group members in serial. It is not fast, but quite convenient.\n"
+    "Since numpy_groupies does not implement a median, we'll do it ourselves by\n",
+    "passing `np.median` to `numpy_groupies.aggregate_numpy.aggregate`. This will\n",
+    "loop over all groups, and then execute `np.median` on the group members in\n",
+    "serial. It is not fast, but quite convenient.\n"
    ]
   },
   {
@@ -639,7 +650,7 @@
    "id": "b356f4f2-ae22-4f56-89ec-50646136e2eb",
    "metadata": {},
    "source": [
-    "Now we create the `Aggregation`"
+    "Now we create the `Aggregation`\n"
    ]
   },
   {
@@ -682,7 +693,7 @@
    "id": "899ece52-ebd4-47b4-8090-cbbb63f504a4",
    "metadata": {},
    "source": [
-    "And apply it!"
+    "And apply it!\n"
    ]
   },
   {