[skip ci] docs build of c8cb6e0

Author: ci-doc-deploy-bot

_sources/content/mooreslaw-tutorial.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "a64b24be",
+   "id": "962da2b2",
    "metadata": {},
    "source": [
     "# Pairing Jupyter notebooks and MyST-NB\n",
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9824cbbc",
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    "metadata": {},
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     {
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
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+   "id": "5193dd4c",
    "metadata": {},
    "source": [
     "---\n",
@@ -286,7 +286,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
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+   "version": "3.10.17"
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    13,

_sources/content/pairing.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2ecb2a28",
+   "id": "7c4603f9",
    "metadata": {},
    "source": [
     "# Saving and sharing your NumPy arrays\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "26882291",
+   "id": "ba10cff7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "66052311",
+   "id": "1660f2a0",
    "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": "7e7cd9b8",
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    "source": [
     "---\n",
@@ -81,7 +81,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "386cc1ce",
+   "id": "3206676a",
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f23a1ef",
+   "id": "1430dfed",
    "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": "c83199a7",
+   "id": "54a36aef",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6094a790",
+   "id": "38481893",
    "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": "3aacf955",
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    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "2fa3a79f",
+   "id": "87be6419",
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "5141c546",
+   "id": "9538636d",
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "eeca9679",
+   "id": "7f1369ba",
    "metadata": {},
    "outputs": [
     {
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af513d3a",
+   "id": "f2f0b724",
    "metadata": {},
    "source": [
     "## Reassign the NpzFile arrays to `x` and `y`\n",
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "49590569",
+   "id": "014d0414",
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "29ad99e9",
+   "id": "84c6b39c",
    "metadata": {},
    "source": [
     "## Success\n",
@@ -294,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "f2b2197c",
+   "id": "4b6d9afc",
    "metadata": {},
    "outputs": [
     {
@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dffdc024",
+   "id": "ab019c17",
    "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": "9e105acd",
+   "id": "c66bc250",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3ecc6c9",
+   "id": "64f76bc3",
    "metadata": {},
    "source": [
     "Open the file, `x_y-squared.csv`, and you'll see the following:"
@@ -356,7 +356,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "417fa21f",
+   "id": "6179343b",
    "metadata": {},
    "outputs": [
     {
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94e0a766",
+   "id": "67c474ec",
    "metadata": {},
    "source": [
     "## Our arrays as a csv file\n",
@@ -406,7 +406,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "5f07aa60",
+   "id": "f69d1b7e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -416,7 +416,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "a1c95007",
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    "metadata": {},
    "outputs": [],
    "source": [
@@ -426,7 +426,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "d5d4597e",
+   "id": "ba971f83",
    "metadata": {},
    "outputs": [
     {
@@ -447,7 +447,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "52b5b560",
+   "id": "0d060d9d",
    "metadata": {},
    "outputs": [
     {
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64e31673",
+   "id": "de302462",
    "metadata": {},
    "source": [
     "## Success, but remember your types\n",
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54e4f71d",
+   "id": "2a12634c",
    "metadata": {},
    "source": [
     "## Wrapping up\n",
@@ -514,7 +514,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "version": "3.10.17"
   },
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    12,