[skip ci] docs build of 17210ca

Author: ci-doc-deploy-bot

_sources/content/mooreslaw-tutorial.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b1b6c4f8",
+   "id": "cf600e9b",
    "metadata": {},
    "source": [
     "# Pairing Jupyter notebooks and MyST-NB\n",
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "85891e33",
+   "id": "6845360c",
    "metadata": {},
    "outputs": [
     {
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f871670",
+   "id": "af378a09",
    "metadata": {},
    "source": [
     "---\n",

_sources/content/pairing.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "acff8fea",
+   "id": "b3421abe",
    "metadata": {},
    "source": [
     "# Saving and sharing your NumPy arrays\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "5d73f02d",
+   "id": "0ffd8d9c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7281cd39",
+   "id": "8eeccb81",
    "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": "964551b8",
+   "id": "e0b0e675",
    "metadata": {},
    "source": [
     "---\n",
@@ -81,7 +81,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "d86d7de0",
+   "id": "3a720aa9",
    "metadata": {},
    "outputs": [
     {
@@ -94,15 +94,15 @@
     }
    ],
    "source": [
-    "x = np.arange(0, 10, 1)\n",
+    "x = np.arange(10)\n",
     "y = x ** 2\n",
     "print(x)\n",
     "print(y)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7de39d71",
+   "id": "87214cfc",
    "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": "db42484a",
+   "id": "dd3ca1fd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4ad0730",
+   "id": "addc8818",
    "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": "e1356e98",
+   "id": "730e5152",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "1922ed2c",
+   "id": "c7d4885f",
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "8768503d",
+   "id": "c479dca3",
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "7d141fc9",
+   "id": "21a5d8ea",
    "metadata": {},
    "outputs": [
     {
@@ -218,7 +218,7 @@
      "text": [
       "Variable   Type       Data/Info\n",
       "-------------------------------\n",
-      "load_xy    NpzFile    <numpy.lib.npyio.NpzFile <...>object at 0x7f8f1c03beb0>\n",
+      "load_xy    NpzFile    <numpy.lib.npyio.NpzFile <...>object at 0x7f9bd52d05e0>\n",
       "np         module     <module 'numpy' from '/ho<...>kages/numpy/__init__.py'>\n"
      ]
     }
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3cd2a8a1",
+   "id": "8ad9030d",
    "metadata": {},
    "source": [
     "## Reassign the NpzFile arrays to `x` and `y`\n",
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "d7d843dd",
+   "id": "faabe58b",
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "11a13cf3",
+   "id": "e6a1e692",
    "metadata": {},
    "source": [
     "## Success\n",
@@ -294,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "b82d3b26",
+   "id": "a8fb5162",
    "metadata": {},
    "outputs": [
     {
@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95dbd932",
+   "id": "b61cbdac",
    "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": "e6210daa",
+   "id": "f0c2b011",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "579baadf",
+   "id": "05f1ee9e",
    "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": "3c058934",
+   "id": "edf06984",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -397,7 +397,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "e75d9377",
+   "id": "9378d018",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -407,7 +407,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "c933aa67",
+   "id": "47780e6b",
    "metadata": {},
    "outputs": [
     {
@@ -428,7 +428,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "1a8e9334",
+   "id": "0c2ccd20",
    "metadata": {},
    "outputs": [
     {
@@ -449,7 +449,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16e38544",
+   "id": "0a689ad6",
    "metadata": {},
    "source": [
     "## Success, but remember your types\n",
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d921632",
+   "id": "b549d200",
    "metadata": {},
    "source": [
     "## Wrapping up\n",

_sources/content/save-load-arrays.ipynb

@@ -68,7 +68,7 @@ will assign `x` to the integers from 0 to 9 using
 [`np.arange`](https://numpy.org/doc/stable/reference/generated/numpy.arange.html).
 
 ```{code-cell}
-x = np.arange(0, 10, 1)
+x = np.arange(10)
 y = x ** 2
 print(x)
 print(y)