resolve toc workflow

_toc.yml

@@ -10,5 +10,5 @@ parts:
   - caption: Example Workflows
     chapters:
       - file: notebooks/example-workflows/jingle-bells
-      - file: notebooks/example-workflows/spy-sounds
+      - file: notebooks/example-workflows/spy-keypad
       - file: notebooks/example-workflows/nino3

notebooks/example-workflows/jingle-bells.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<img src=\"images/ProjectPythia_Logo_Final-01-Blue.svg\" width=250 alt=\"Project Pythia Logo\"></img>"
+    "[IMAGE]"
    ]
   },
   {

notebooks/wavelet-introduction/wavelet-basics.ipynb

@@ -52,20 +52,6 @@
     "- **Time to learn**: 45 minutes"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Background\n",
-    "Time-series data refers to when data is collected over time, making the order of the data collection and not just the value important. For the purpose of an example, imagine a short piece of music. Each note in the piece can be any note from A to G. Each note varies based on frequency to produce different notes. A higher frequency is associated with a higher pitch, like an A note, while a lower frequency is associated with a lower pitch, like a C note.\n",
-    "\n",
-    "With tools like Fourier Transform, it will be obvious when a B and a D note appears in the piece of music. However, this does not encapulsate all the information. What is the order? BDDB is very different from DDDDBD. This is the importance of time and order in data that is lost in first passes of signal processing with tools like Fourier Transform. The power of wavelets is that it can return both information about the frequency and information about the time when the frequency occurred."
-    "| [Intro to Numpy]| Necessary | Familiarity with working with arrays |\n",
-    "| [Intro to SciPy] | Helpful | Familiarity with working with wave files and FFT |\n",
-    "\n",
-    "- **Time to learn**: 45 minutes"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -92,16 +78,6 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pywt                                 # PyWavelets\n",
-    "import numpy as np                          # working with arrays\n",
-    "import pandas as pd                         # working with dataframes\n",
-    "from scipy.io import wavfile                # loading in wav files\n",
-    "import matplotlib.pyplot as plt             # plot data (fourier transform and wav files)\n",
-    "from scipy.fftpack import fft, fftfreq      # working with Fourier Transforms"
    "execution_count": 2,
    "metadata": {},
    "outputs": [],
@@ -137,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -150,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -169,24 +145,6 @@
     "print(f\"length of audio file = {len(signal_data)} time steps\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Convert .wav file to pandas dataframe"
-    "# Load .wav file data\n",
-    "sample_rate, signal_data = wavfile.read('jingle_bells.wav')\n",
-    "\n",
-    "# Frequency determines the chord\n",
-    "\n",
-    "duration = len(signal_data) / sample_rate\n",
-    "time = np.arange(0, duration, 1/sample_rate) \n",
-    "\n",
-    "print(f\"Sample Rate: {sample_rate}\")\n",
-    "print(f\"duration = {duration} seconds (is the ratio of sample rate and data = {len(signal_data)} / {sample_rate})\")\n",
-    "print(f\"length of audio file = {len(signal_data)} time steps\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -196,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +221,7 @@
        "4          0.0004      -8540"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -282,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -308,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -368,24 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fourier_transform = abs(fft(signal_data))\n",
-    "freqs = fftfreq(len(fourier_transform), (1/sample_rate))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot Frequency Prevalence from Fast Fourier Transform"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -402,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -434,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -467,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -508,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -603,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {