[skip ci] docs build of 5984063

numpy · Mar 11, 2024 · d8033e3 · d8033e3
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diff --git a/_sources/content/mooreslaw-tutorial.ipynb b/_sources/content/mooreslaw-tutorial.ipynb
diff --git a/_sources/content/pairing.ipynb b/_sources/content/pairing.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "1b8ba7b5",
+   "id": "54d363c3",
    "metadata": {},
    "source": [
     "# Pairing Jupyter notebooks and MyST-NB\n",
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "dedd373f",
+   "id": "504f0df0",
    "metadata": {},
    "outputs": [
     {
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd88ff99",
+   "id": "cdee8ed9",
    "metadata": {},
    "source": [
     "---\n",

diff --git a/_sources/content/save-load-arrays.ipynb b/_sources/content/save-load-arrays.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "08d25bc4",
+   "id": "09cc1035",
    "metadata": {},
    "source": [
     "# Saving and sharing your NumPy arrays\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "539560d8",
+   "id": "bde32133",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9fa2bd31",
+   "id": "0f2f26e7",
    "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": "2b73d4de",
+   "id": "f1caeec1",
    "metadata": {},
    "source": [
     "---\n",
@@ -81,7 +81,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "8c64a13f",
+   "id": "db77e040",
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df92b324",
+   "id": "295b1614",
    "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": "94e29d9c",
+   "id": "81cb0583",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3ce05075",
+   "id": "8b4d48c5",
    "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": "3c250ec2",
+   "id": "5182c47b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "617ca504",
+   "id": "459c063b",
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d63fd187",
+   "id": "b31c8895",
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "964048b7",
+   "id": "7c731943",
    "metadata": {},
    "outputs": [
     {
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3d52251",
+   "id": "442ba1a5",
    "metadata": {},
    "source": [
     "## Reassign the NpzFile arrays to `x` and `y`\n",
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "2c525ad1",
+   "id": "1e0cd711",
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0134b8a",
+   "id": "b613715c",
    "metadata": {},
    "source": [
     "## Success\n",
@@ -294,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "aaeec3f3",
+   "id": "ab75b660",
    "metadata": {},
    "outputs": [
     {
@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d411f283",
+   "id": "c887200e",
    "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": "d3acf89d",
+   "id": "be6b5696",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,25 +347,44 @@
   },
   {
    "cell_type": "markdown",
-   "id": "274fcaf0",
+   "id": "0ea4febb",
+   "metadata": {},
+   "source": [
+    "Open the file, `x_y-squared.csv`, and you'll see the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f049f973",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# x, y\r\n",
+      "0.000000000000000000e+00,0.000000000000000000e+00\r\n",
+      "1.000000000000000000e+00,1.000000000000000000e+00\r\n",
+      "2.000000000000000000e+00,4.000000000000000000e+00\r\n",
+      "3.000000000000000000e+00,9.000000000000000000e+00\r\n",
+      "4.000000000000000000e+00,1.600000000000000000e+01\r\n",
+      "5.000000000000000000e+00,2.500000000000000000e+01\r\n",
+      "6.000000000000000000e+00,3.600000000000000000e+01\r\n",
+      "7.000000000000000000e+00,4.900000000000000000e+01\r\n",
+      "8.000000000000000000e+00,6.400000000000000000e+01\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!head x_y-squared.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a3b5fc6",
    "metadata": {},
    "source": [
-    "Open the file, `x_y-squared.csv`, and you'll see the following:\n",
-    "\n",
-    "```\n",
-    "# x, y\n",
-    "0.000000000000000000e+00,0.000000000000000000e+00\n",
-    "1.000000000000000000e+00,1.000000000000000000e+00\n",
-    "2.000000000000000000e+00,4.000000000000000000e+00\n",
-    "3.000000000000000000e+00,9.000000000000000000e+00\n",
-    "4.000000000000000000e+00,1.600000000000000000e+01\n",
-    "5.000000000000000000e+00,2.500000000000000000e+01\n",
-    "6.000000000000000000e+00,3.600000000000000000e+01\n",
-    "7.000000000000000000e+00,4.900000000000000000e+01\n",
-    "8.000000000000000000e+00,6.400000000000000000e+01\n",
-    "9.000000000000000000e+00,8.100000000000000000e+01\n",
-    "```\n",
-    "\n",
     "## Our arrays as a csv file\n",
     "\n",
     "There are two features that you shoud notice here:\n",
@@ -386,8 +405,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "07fcb9c3",
+   "execution_count": 12,
+   "id": "25c9b534",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -396,8 +415,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "c3aaceac",
+   "execution_count": 13,
+   "id": "7463b252",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -406,8 +425,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "d0bd177e",
+   "execution_count": 14,
+   "id": "a869c858",
    "metadata": {},
    "outputs": [
     {
@@ -416,7 +435,7 @@
        "(10, 2)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -427,8 +446,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "50ae6d0f",
+   "execution_count": 15,
+   "id": "42821431",
    "metadata": {},
    "outputs": [
     {
@@ -449,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1dbb437",
+   "id": "d5d53a6e",
    "metadata": {},
    "source": [
     "## Success, but remember your types\n",
@@ -460,7 +479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b478e283",
+   "id": "21036c9c",
    "metadata": {},
    "source": [
     "## Wrapping up\n",
@@ -517,12 +536,14 @@
    174,
    184,
    186,
-   221,
-   225,
-   229,
-   233,
-   238,
-   245
+   190,
+   192,
+   211,
+   215,
+   219,
+   223,
+   228,
+   235
   ]
  },
  "nbformat": 4,

diff --git a/_sources/content/save-load-arrays.md b/_sources/content/save-load-arrays.md
@@ -187,18 +187,8 @@ np.savetxt("x_y-squared.csv", X=array_out, header="x, y", delimiter=",")
 
 Open the file, `x_y-squared.csv`, and you'll see the following:
 
-```
-# x, y
-0.000000000000000000e+00,0.000000000000000000e+00
-1.000000000000000000e+00,1.000000000000000000e+00
-2.000000000000000000e+00,4.000000000000000000e+00
-3.000000000000000000e+00,9.000000000000000000e+00
-4.000000000000000000e+00,1.600000000000000000e+01
-5.000000000000000000e+00,2.500000000000000000e+01
-6.000000000000000000e+00,3.600000000000000000e+01
-7.000000000000000000e+00,4.900000000000000000e+01
-8.000000000000000000e+00,6.400000000000000000e+01
-9.000000000000000000e+00,8.100000000000000000e+01
+```{code-cell}
+!head x_y-squared.csv
 ```
 
 ## Our arrays as a csv file