[skip ci] docs build of 63ca94e

Author: ci-doc-deploy-bot

…-learning-with-pong-from-pixels_29_2.png …-learning-with-pong-from-pixels_14_2.png_images/tutorial-deep-reinforcement-learning-with-pong-from-pixels_29_2.png renamed to _images/tutorial-deep-reinforcement-learning-with-pong-from-pixels_14_2.png _images/tutorial-deep-reinforcement-learning-with-pong-from-pixels_29_2.png renamed to _images/tutorial-deep-reinforcement-learning-with-pong-from-pixels_14_2.png

@@ -12,7 +12,7 @@ kernelspec:
 ---
 
 # Determining Moore's Law with real data in NumPy
-![image.png](01-mooreslaw-tutorial-intro.png)
+![Plot of Moore's Law data](_static/01-mooreslaw-tutorial-intro.png)
 
 _The number of transistors reported on a given chip plotted on a log scale in the y axis with the date of introduction on the linear scale x-axis. The blue data points are from a [transistor count table](https://en.wikipedia.org/wiki/Transistor_count#Microprocessors). The red line is an ordinary least squares prediction and the orange line is Moore's law._
 

…-learning-with-pong-from-pixels_33_2.png …-learning-with-pong-from-pixels_18_1.png_images/tutorial-deep-reinforcement-learning-with-pong-from-pixels_33_2.png renamed to _images/tutorial-deep-reinforcement-learning-with-pong-from-pixels_18_1.png _images/tutorial-deep-reinforcement-learning-with-pong-from-pixels_33_2.png renamed to _images/tutorial-deep-reinforcement-learning-with-pong-from-pixels_18_1.png

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5585ada0",
+   "id": "23324181",
    "metadata": {},
    "source": [
     "# Pairing Jupyter notebooks and MyST-NB\n",
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "a21bd74e",
+   "id": "c9775d2b",
    "metadata": {},
    "outputs": [
     {
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7932d553",
+   "id": "17a1b3b9",
    "metadata": {},
    "source": [
     "---\n",
@@ -224,14 +224,14 @@
     "    <summary>\n",
     "        <b>1. Classic Jupyter Jupytext pairing</b>.\n",
     "    </summary>\n",
-    "    <img src=\"01-classic.gif\" width=80% height=80%>\n",
+    "    <img src=\"../_static/01-classic.gif\" width=80% height=80%>\n",
     "</details>\n",
     "    \n",
     "<details>\n",
     "    <summary>\n",
     "        <b>2. Jupyter Lab Jupytext pairing</b>\n",
     "    </summary>\n",
-    "    <img src=\"02-jupyterlab.gif\" width=80% height=80%>\n",
+    "\t<img src=\"../_static/02-jupyterlab.gif\" width=80% height=80%>\n",
     "</details>\n",
     "\n",
     "<details>\n",

_sources/content/mooreslaw-tutorial.ipynb

@@ -209,14 +209,14 @@ or the command line:
     <summary>
         <b>1. Classic Jupyter Jupytext pairing</b>.
     </summary>
-    <img src="01-classic.gif" width=80% height=80%>
+    <img src="../_static/01-classic.gif" width=80% height=80%>
 </details>
 
 <details>
     <summary>
         <b>2. Jupyter Lab Jupytext pairing</b>
     </summary>
-    <img src="02-jupyterlab.gif" width=80% height=80%>
+	<img src="../_static/02-jupyterlab.gif" width=80% height=80%>
 </details>
 
 <details>

_sources/content/mooreslaw-tutorial.md.txt

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "dd98a600",
+   "id": "2764c4d0",
    "metadata": {},
    "source": [
     "# Saving and sharing your NumPy arrays\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "35d4cf08",
+   "id": "dc5ab629",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98cb4c4e",
+   "id": "c41598fd",
    "metadata": {},
    "source": [
     "In this tutorial, you will use the following Python, IPython magic, and NumPy functions:\n",
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f8f71ce",
+   "id": "5d5da535",
    "metadata": {},
    "source": [
     "---\n",
@@ -81,7 +81,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "1f8f5927",
+   "id": "4670ae09",
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd7e5182",
+   "id": "4af2deeb",
    "metadata": {},
    "source": [
     "## Save your arrays with NumPy's [`savez`](https://numpy.org/doc/stable/reference/generated/numpy.savez.html?highlight=savez#numpy.savez)\n",
@@ -125,7 +125,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d4a0b108",
+   "id": "ca4c932c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3f896f6",
+   "id": "25f411ea",
    "metadata": {},
    "source": [
     "## Remove the saved arrays and load them back with NumPy's [`load`](https://numpy.org/doc/stable/reference/generated/numpy.load.html#numpy.load)\n",
@@ -159,7 +159,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "d2d6cdba",
+   "id": "c5cbba94",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "424d63bd",
+   "id": "463b653f",
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "5394ae50",
+   "id": "6f8a7238",
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "938b8cfb",
+   "id": "53227bac",
    "metadata": {},
    "outputs": [
     {
@@ -218,7 +218,7 @@
      "text": [
       "Variable   Type       Data/Info\n",
       "-------------------------------\n",
-      "load_xy    NpzFile    <numpy.lib.npyio.NpzFile <...>object at 0x7f514c7cd6a0>\n",
+      "load_xy    NpzFile    <numpy.lib.npyio.NpzFile <...>object at 0x7fa0b2ce0af0>\n",
       "np         module     <module 'numpy' from '/ho<...>kages/numpy/__init__.py'>\n"
      ]
     }
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2124f17a",
+   "id": "709e6e8f",
    "metadata": {},
    "source": [
     "## Reassign the NpzFile arrays to `x` and `y`\n",
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "68748164",
+   "id": "a7d245bf",
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fcd4c68d",
+   "id": "e4ba85f8",
    "metadata": {},
    "source": [
     "## Success\n",
@@ -294,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "937db58a",
+   "id": "32f394d0",
    "metadata": {},
    "outputs": [
     {
@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7de5819a",
+   "id": "ae2fcb83",
    "metadata": {},
    "source": [
     "## Save the data to csv file using [`savetxt`](https://numpy.org/doc/stable/reference/generated/numpy.savetxt.html#numpy.savetxt)\n",
@@ -338,7 +338,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "b7d0ade6",
+   "id": "7114f700",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29367127",
+   "id": "66c0208c",
    "metadata": {},
    "source": [
     "Open the file, `x_y-squared.csv`, and you'll see the following:\n",
@@ -387,7 +387,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "e8b235de",
+   "id": "09d0d00b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -397,7 +397,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "b9983a38",
+   "id": "27c60774",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -409,7 +409,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "99ef069f",
+   "id": "e2617aa4",
    "metadata": {},
    "outputs": [
     {
@@ -430,7 +430,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "5c1000e9",
+   "id": "66245fab",
    "metadata": {},
    "outputs": [
     {
@@ -451,7 +451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0fe8c262",
+   "id": "822af148",
    "metadata": {},
    "source": [
     "## Success, but remember your types\n",
@@ -462,7 +462,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a763846c",
+   "id": "13862047",
    "metadata": {},
    "source": [
     "## Wrapping up\n",