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numpy · Oct 8, 2024 · 613b768 · 613b768
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diff --git a/_sources/content/mooreslaw-tutorial.ipynb b/_sources/content/mooreslaw-tutorial.ipynb
diff --git a/_sources/content/mooreslaw-tutorial.md b/_sources/content/mooreslaw-tutorial.md
@@ -21,8 +21,7 @@ _The number of transistors reported per a given chip plotted on a log scale in t
 In 1965, engineer Gordon Moore
 [predicted](https://en.wikipedia.org/wiki/Moore%27s_law) that
 transistors on a chip would double every two years in the coming decade
-[[1](https://en.wikipedia.org/wiki/Moore%27s_law),
-[2](https://newsroom.intel.com/wp-content/uploads/sites/11/2018/05/moores-law-electronics.pdf)].
+[[1](https://en.wikipedia.org/wiki/Moore%27s_law)].
 You'll compare Moore's prediction against actual transistor counts in
 the 53 years following his prediction. You will determine the best-fit constants to describe the exponential growth of transistors on semiconductors compared to Moore's Law.
 
@@ -130,7 +129,7 @@ print("This is x{:.2f} more transistors than 1971".format(ML_1973 / ML_1971))
 
 Now, make a prediction based upon the historical data for
 semiconductors per chip. The [Transistor Count
-\[4\]](https://en.wikipedia.org/wiki/Transistor_count#Microprocessors)
+\[3\]](https://en.wikipedia.org/wiki/Transistor_count#Microprocessors)
 each year is in the `transistor_data.csv` file. Before loading a \*.csv
 file into a NumPy array, its a good idea to inspect the structure of the
 file first. Then, locate the columns of interest and save them to a
@@ -520,7 +519,7 @@ double every two years from 1965 through 1975, but the average growth
 has maintained a consistent increase of $\times 1.98 \pm 0.01$ every two
 years from 1971 through 2019.  In 2015, Moore revised his prediction to
 say Moore's law should hold until 2025.
-[[3](https://spectrum.ieee.org/computing/hardware/gordon-moore-the-man-whose-name-means-progress)].
+[[2](https://spectrum.ieee.org/computing/hardware/gordon-moore-the-man-whose-name-means-progress)].
 You can share these results as a zipped NumPy array file,
 `mooreslaw_regression.npz`, or as another csv,
 `mooreslaw_regression.csv`.  The amazing progress in semiconductor
@@ -533,6 +532,5 @@ has been over the last half-century.
 ## References
 
 1. ["Moore's Law." Wikipedia article. Accessed Oct. 1, 2020.](https://en.wikipedia.org/wiki/Moore%27s_law)
-2. [Moore, Gordon E. (1965-04-19). "Cramming more components onto integrated circuits". intel.com. Electronics Magazine. Retrieved April 1, 2020.](https://newsroom.intel.com/wp-content/uploads/sites/11/2018/05/moores-law-electronics.pdf)
-3. [Courtland, Rachel. "Gordon Moore: The Man Whose Name Means Progress." IEEE Spectrum. 30 Mar. 2015.](https://spectrum.ieee.org/computing/hardware/gordon-moore-the-man-whose-name-means-progress).
-4. ["Transistor Count." Wikipedia article. Accessed Oct. 1, 2020.](https://en.wikipedia.org/wiki/Transistor_count#Microprocessors)
+2. [Courtland, Rachel. "Gordon Moore: The Man Whose Name Means Progress." IEEE Spectrum. 30 Mar. 2015.](https://spectrum.ieee.org/computing/hardware/gordon-moore-the-man-whose-name-means-progress).
+3. ["Transistor Count." Wikipedia article. Accessed Oct. 1, 2020.](https://en.wikipedia.org/wiki/Transistor_count#Microprocessors)
diff --git a/_sources/content/pairing.ipynb b/_sources/content/pairing.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f6566618",
+   "id": "b38e01c0",
    "metadata": {},
    "source": [
     "# Pairing Jupyter notebooks and MyST-NB\n",
@@ -76,7 +76,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "dd24bcf5",
+   "id": "4210e19a",
    "metadata": {},
    "outputs": [
     {
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d06b50c",
+   "id": "1afc2be2",
    "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": "d6f729bb",
+   "id": "7938a1eb",
    "metadata": {},
    "source": [
     "# Saving and sharing your NumPy arrays\n",
@@ -37,7 +37,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "a8f7a9c5",
+   "id": "a6a7597c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f171f883",
+   "id": "df686392",
    "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": "3ead3fd4",
+   "id": "eb9c62c6",
    "metadata": {},
    "source": [
     "---\n",
@@ -81,7 +81,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "b6064099",
+   "id": "901e41b4",
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8da62b9a",
+   "id": "0ba51951",
    "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": "5ea83c78",
+   "id": "f9faff24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "367e06e8",
+   "id": "4f31b6e6",
    "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": "7632d3da",
+   "id": "8477249b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,7 +169,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "157e725c",
+   "id": "323f65bb",
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "74541a9b",
+   "id": "a3d17fce",
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +209,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "fd7b6e47",
+   "id": "929154f7",
    "metadata": {},
    "outputs": [
     {
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f7a92dce",
+   "id": "235bc03d",
    "metadata": {},
    "source": [
     "## Reassign the NpzFile arrays to `x` and `y`\n",
@@ -242,7 +242,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "59c8c685",
+   "id": "e5df78bf",
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d28eccb",
+   "id": "a7283513",
    "metadata": {},
    "source": [
     "## Success\n",
@@ -294,7 +294,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "4479e644",
+   "id": "6588dd2b",
    "metadata": {},
    "outputs": [
     {
@@ -323,7 +323,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b901a9d0",
+   "id": "49b3c824",
    "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": "980b6399",
+   "id": "f645c2e9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "829f31c7",
+   "id": "2f80ba88",
    "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": "b0137fe8",
+   "id": "e3d2e293",
    "metadata": {},
    "outputs": [
     {
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dabee94f",
+   "id": "37923b2a",
    "metadata": {},
    "source": [
     "## Our arrays as a csv file\n",
@@ -406,7 +406,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "5f5f29de",
+   "id": "511fec8c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -416,7 +416,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "a220a3fc",
+   "id": "4b06bb56",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -426,7 +426,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "22bc3617",
+   "id": "de92af9c",
    "metadata": {},
    "outputs": [
     {
@@ -447,7 +447,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "06b8eb4d",
+   "id": "e8923b34",
    "metadata": {},
    "outputs": [
     {
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c669ab5d",
+   "id": "c031900b",
    "metadata": {},
    "source": [
     "## Success, but remember your types\n",
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3683cab3",
+   "id": "e2cfe81a",
    "metadata": {},
    "source": [
     "## Wrapping up\n",