diff --git a/notebooks/example-workflows/nino3.ipynb b/notebooks/example-workflows/nino3.ipynb
index e831b56..33ca71a 100644
--- a/notebooks/example-workflows/nino3.ipynb
+++ b/notebooks/example-workflows/nino3.ipynb
@@ -4,7 +4,6 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Weekly Sea Surface Temperature Patterns from NOAA\n",
"\n",
" ![\"Weekly](\"https://www.climate.gov/sites/default/files/2023-06/ENSOblog_animation_SSTA_anom_weekly_2023_Jun.gif\")
\n",
"
"
@@ -251,8 +250,10 @@
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": 59,
"metadata": {},
+ "outputs": [],
"source": [
"TODO: Choosing a Complex Morlet"
]
diff --git a/notebooks/wavelet-introduction/jingle_bells.wav b/notebooks/wavelet-introduction/jingle_bells.wav
deleted file mode 100644
index d76ee96..0000000
Binary files a/notebooks/wavelet-introduction/jingle_bells.wav and /dev/null differ
diff --git a/notebooks/wavelet-introduction/wavelet-basics.ipynb b/notebooks/wavelet-introduction/wavelet-basics.ipynb
index eb613a4..a99c76e 100644
--- a/notebooks/wavelet-introduction/wavelet-basics.ipynb
+++ b/notebooks/wavelet-introduction/wavelet-basics.ipynb
@@ -33,8 +33,8 @@
"2. Background\n",
"3. Load Wav File for Audio\n",
"4. Fourier Transform - Frequency, but not Time\n",
- "6. Wavelet Transform - Frequency and Time\n",
- "7. Wavelet Terminology"
+ "5. Wavelet Transform - Frequency and Time\n",
+ "6. Wavelet Terminology"
]
},
{
@@ -111,11 +111,352 @@
"### Load .wav file data"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load .wav file data"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Sample Rate: 10000\n",
+ "duration = 15.6991 seconds (is the ratio of sample rate and data = 156991 / 10000)\n",
+ "length of audio file = 156991 time steps\n"
+ ]
+ }
+ ],
+ "source": [
+ "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": {},
+ "source": [
+ "### Convert .wav file to pandas dataframe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " time (seconds) | \n",
+ " amplitude | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 0.0000 | \n",
+ " -417 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 0.0001 | \n",
+ " -2660 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 0.0002 | \n",
+ " -2491 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 0.0003 | \n",
+ " 6441 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 0.0004 | \n",
+ " -8540 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "