chore(pre-commit): add nbstripout to strip notebook outputs on commit

Stacks on #293 (which switched pre-commit to ruff-only and cleaned the
text-file housekeeping). The one piece #293 didn't cover is the
checked-in notebook outputs — they balloon the demo notebook diffs on
every re-run with timestamps, quota counters, and HTML/plot blobs, even
when the source cells are unchanged.

Adds the kynan/nbstripout pre-commit hook (rev 0.8.1) and runs it once
across the existing tree, stripping cell outputs + execution_count from
the 15 checked-in waterdata notebooks. Demos still execute cleanly
locally; commits now diff the source rather than the rendered run.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Author: thodson-usgs

.pre-commit-config.yaml

@@ -26,3 +26,12 @@ repos:
       - id: ruff-check
         args: [--fix]
       - id: ruff-format
+
+  # Strip cell outputs + execution_count from notebooks on commit so the
+  # diff is the source, not the rendered run. Demos still execute fine
+  # locally; clean commits keep PRs reviewable and avoid quota/timestamp
+  # churn on every re-run.
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.8.1
+    hooks:
+      - id: nbstripout

demos/USGS_WaterData_ContinuousData_Examples.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d664492b",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Continuous Data\n",
@@ -21,7 +21,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7e06e81",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0136bd1",
+   "id": "2",
    "metadata": {},
    "source": [
     "## What continuous data are available?\n",
@@ -46,7 +46,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6f8a9d87",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fdaa8150",
+   "id": "4",
    "metadata": {},
    "source": [
     "## Large requests are chunked for you\n",
@@ -79,7 +79,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6bc05102",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -93,7 +93,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "353ad4ec",
+   "id": "6",
    "metadata": {},
    "source": [
     "## Resilient pulls: resume after an interruption\n",
@@ -119,7 +119,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e2e9ddff",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "397e87b5",
+   "id": "8",
    "metadata": {},
    "source": [
     "## The 3-year window: the one axis you split yourself\n",
@@ -170,7 +170,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bd26d199",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3bc4f40f",
+   "id": "10",
    "metadata": {},
    "source": [
     "Then request each window and concatenate. (We use a short two-window span here so\n",
@@ -201,7 +201,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "01ebb4a0",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -225,7 +225,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2487bf4",
+   "id": "12",
    "metadata": {},
    "source": [
     "Wrap each window's call in the resume pattern above for an unattended,\n",

demos/USGS_WaterData_DailyStatistics_Examples.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "fe73969b",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Daily statistics: `get_stats_por` and `get_stats_date_range`\n",
@@ -29,7 +29,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "d6ab1ce4",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cf8868ae",
+   "id": "2",
    "metadata": {},
    "source": [
     "## Fetching day-of-year and month-of-year statistics\n",
@@ -61,7 +61,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f0ab13bb",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3dc2b04f",
+   "id": "4",
    "metadata": {},
    "source": [
     "The first two rows are the day-of-year averages. What's the third row? Its\n",
@@ -93,7 +93,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4d561aba",
+   "id": "5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +103,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43fe1eef",
+   "id": "6",
    "metadata": {},
    "source": [
     "### Percentile band plot\n",
@@ -121,7 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "18bd842c",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c8fc4a28",
+   "id": "8",
    "metadata": {},
    "source": [
     "Pivoting the day-of-year rows so each percentile is a column lets us draw the\n",
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "aaa72823",
+   "id": "9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b4075bd",
+   "id": "10",
    "metadata": {},
    "source": [
     "Finally, overlay the actual daily mean discharge so we can see where recent\n",
@@ -205,7 +205,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "961eea3a",
+   "id": "11",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e31f1726",
+   "id": "12",
    "metadata": {},
    "source": [
     "## Fetching monthly and annual statistics within a date range\n",
@@ -254,7 +254,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "0bc8cd83",
+   "id": "13",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -270,7 +270,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d915aed",
+   "id": "14",
    "metadata": {},
    "source": [
     "Instead of `time_of_year`, the output has `start_date`, `end_date`, and\n",
@@ -282,7 +282,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cfe28029",
+   "id": "15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,7 +298,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c30978f",
+   "id": "16",
    "metadata": {},
    "source": [
     "Filter the `interval_type` column (values `month`, `calendar_year`,\n",
@@ -308,7 +308,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7ff90e81",
+   "id": "17",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -319,7 +319,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "061b9cbe",
+   "id": "18",
    "metadata": {},
    "source": [
     "### Monthly mean table\n",
@@ -333,7 +333,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "1c705056",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -383,7 +383,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31cd8b14",
+   "id": "20",
    "metadata": {},
    "source": [
     "## Statistics API tips\n",