From 136069e6650e3777636e6b60a6f1459298ad4cc2 Mon Sep 17 00:00:00 2001
From: Cora Schneck <22159116+cyschneck@users.noreply.github.com>
Date: Fri, 26 Jul 2024 13:59:54 -0600
Subject: [PATCH] Fix Merge (#3)

---
 _toc.yml                                      |   2 +-
 .../example-workflows/jingle-bells.ipynb      |  11 +-
 notebooks/example-workflows/nino3.ipynb       | 145 +++++++-----------
 notebooks/example-workflows/spy-keypad.ipynb  |  37 ++---
 .../wavelet-introduction/wavelet-basics.ipynb |  83 ++--------
 5 files changed, 104 insertions(+), 174 deletions(-)

diff --git a/_toc.yml b/_toc.yml
index 4725260..8659918 100644
--- a/_toc.yml
+++ b/_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
diff --git a/notebooks/example-workflows/jingle-bells.ipynb b/notebooks/example-workflows/jingle-bells.ipynb
index c88afb8..a858c4b 100644
--- a/notebooks/example-workflows/jingle-bells.ipynb
+++ b/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]"
    ]
   },
   {
@@ -66,6 +66,13 @@
     "---"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Background"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -76,6 +83,7 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,6 +110,7 @@
    "metadata": {},
    "source": [
     "### Input Values\n",
+    "- data: input data as a array_like\n",
     "- scales: array_like collection of the scales to use (np.arange(s0, jtot, dj))\n",
     "- wavelet: name of Mother wavelet\n",
     "- sampling_period: optional sampling period for frequencies output"
diff --git a/notebooks/example-workflows/nino3.ipynb b/notebooks/example-workflows/nino3.ipynb
index ea7e440..803f1a4 100644
--- a/notebooks/example-workflows/nino3.ipynb
+++ b/notebooks/example-workflows/nino3.ipynb
@@ -4,6 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "Weekly Sea Surface Temperature Patterns from NOAA\n",
     "<p align=\"center\">\n",
     "  <img src=\"https://www.climate.gov/sites/default/files/2023-06/ENSOblog_animation_SSTA_anom_weekly_2023_Jun.gif\" alt=\"Weekly Sea Surface Temperature Patterns from NOAA\" />\n",
     "</p>"
@@ -89,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -116,27 +117,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ValueError",
-     "evalue": "did not find a match in any of xarray's currently installed IO backends ['scipy']. Consider explicitly selecting one of the installed engines via the ``engine`` parameter, or installing additional IO dependencies, see:\nhttps://docs.xarray.dev/en/stable/getting-started-guide/installing.html\nhttps://docs.xarray.dev/en/stable/user-guide/io.html",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[7], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#nino_url = 'https://paos.colorado.edu/research/wavelets/wave_idl/nino3sst.txt'\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#nino3_data = np.genfromtxt(nino_url, skip_header=19)\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m nino3_data \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_dataset(gcd\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mascii_files/sst_nino3.dat\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(nino3_data)\n",
-      "File \u001b[0;32m~/miniconda3/envs/wavelet_tutorial/lib/python3.11/site-packages/xarray/backends/api.py:552\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m    549\u001b[0m     kwargs\u001b[38;5;241m.\u001b[39mupdate(backend_kwargs)\n\u001b[1;32m    551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m engine \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 552\u001b[0m     engine \u001b[38;5;241m=\u001b[39m plugins\u001b[38;5;241m.\u001b[39mguess_engine(filename_or_obj)\n\u001b[1;32m    554\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m from_array_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    555\u001b[0m     from_array_kwargs \u001b[38;5;241m=\u001b[39m {}\n",
-      "File \u001b[0;32m~/miniconda3/envs/wavelet_tutorial/lib/python3.11/site-packages/xarray/backends/plugins.py:197\u001b[0m, in \u001b[0;36mguess_engine\u001b[0;34m(store_spec)\u001b[0m\n\u001b[1;32m    189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    190\u001b[0m     error_msg \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    191\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfound the following matches with the input file in xarray\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms IO \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    192\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbackends: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcompatible_engines\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. But their dependencies may not be installed, see:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    193\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://docs.xarray.dev/en/stable/user-guide/io.html \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    194\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://docs.xarray.dev/en/stable/getting-started-guide/installing.html\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    195\u001b[0m     )\n\u001b[0;32m--> 197\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(error_msg)\n",
-      "\u001b[0;31mValueError\u001b[0m: did not find a match in any of xarray's currently installed IO backends ['scipy']. Consider explicitly selecting one of the installed engines via the ``engine`` parameter, or installing additional IO dependencies, see:\nhttps://docs.xarray.dev/en/stable/getting-started-guide/installing.html\nhttps://docs.xarray.dev/en/stable/user-guide/io.html"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-0.15 -0.3  -0.14 -0.41 -0.46 -0.66 -0.5  -0.8  -0.95 -0.72 -0.31 -0.71\n",
+      " -1.04 -0.77 -0.86 -0.84 -0.41 -0.49 -0.48 -0.72 -1.21 -0.8   0.16  0.46\n",
+      "  0.4   1.    2.17  2.5   2.34  0.8   0.14 -0.06 -0.34 -0.71 -0.34 -0.73\n",
+      " -0.48 -0.11  0.22  0.51  0.51  0.25 -0.1  -0.33 -0.42 -0.23 -0.53 -0.44\n",
+      " -0.3   0.15  0.09  0.19 -0.06  0.25  0.3   0.81  0.26  0.1   0.34  1.01\n",
+      " -0.31 -0.9  -0.73 -0.92 -0.73 -0.31 -0.03  0.12  0.37  0.82  1.22  1.83\n",
+      "  1.6   0.34 -0.72 -0.87 -0.85 -0.4  -0.39 -0.65  0.07  0.67  0.39  0.03\n",
+      " -0.17 -0.76 -0.87 -1.36 -1.1  -0.99 -0.78 -0.93 -0.87 -0.44 -0.34 -0.5\n",
+      " -0.39 -0.04  0.42  0.62  0.17  0.23  1.03  1.54  1.09  0.01  0.12 -0.27\n",
+      " -0.47 -0.41 -0.37 -0.36 -0.39  0.43  1.05  1.58  1.25  0.86  0.6   0.21\n",
+      "  0.19 -0.23 -0.29  0.18  0.12  0.71  1.42  1.59  0.93 -0.25 -0.66 -0.95\n",
+      " -0.47  0.06  0.7   0.81  0.78  1.43  1.22  1.05  0.44 -0.35 -0.67 -0.84\n",
+      " -0.66 -0.45 -0.12 -0.2  -0.16 -0.47 -0.52 -0.79 -0.8  -0.62 -0.86 -1.29\n",
+      " -1.04 -1.05 -0.75 -0.81 -0.9  -0.25  0.62  1.22  0.96  0.21 -0.11 -0.25\n",
+      " -0.24 -0.43  0.23  0.67  0.78  0.41  0.98  1.28  1.45  1.02  0.03 -0.59\n",
+      " -1.34 -0.99 -1.49 -1.74 -1.33 -0.55 -0.51 -0.36 -0.99  0.32  1.04  1.41\n",
+      "  0.99  0.66  0.5   0.22  0.71 -0.16  0.38  0.   -1.11 -1.04  0.05 -0.64\n",
+      " -0.34 -0.5  -1.85 -0.94 -0.78  0.29  0.27  0.69 -0.06 -0.83 -0.8  -1.02\n",
+      " -0.96 -0.09  0.62  0.87  1.03  0.7  -0.1  -0.31  0.04 -0.46  0.04  0.24\n",
+      " -0.08 -0.28  0.06  0.05 -0.31  0.11  0.27  0.26  0.04  0.12  1.11  1.53\n",
+      "  1.23  0.17 -0.18 -0.56  0.05  0.41  0.22  0.04 -0.19 -0.46 -0.65 -1.06\n",
+      " -0.54  0.14  0.25 -0.21 -0.73 -0.43  0.48  0.26  0.05  0.11 -0.27 -0.08\n",
+      " -0.1   0.29 -0.15 -0.28 -0.55 -0.44 -1.4  -0.55 -0.69  0.58  0.37  0.42\n",
+      "  1.83  1.23  0.65  0.41  1.03  0.64 -0.07  0.98  0.36 -0.3  -1.33 -1.39\n",
+      " -0.94  0.34 -0.   -0.15  0.06  0.39  0.36 -0.49 -0.53  0.35  0.07 -0.24\n",
+      "  0.2  -0.22 -0.68 -0.44  0.02 -0.22 -0.3  -0.59  0.1  -0.02 -0.27 -0.6\n",
+      " -0.48 -0.37 -0.53 -1.35 -1.22 -0.99 -0.34 -0.79 -0.24  0.02  0.69  0.78\n",
+      "  0.17 -0.17 -0.29 -0.27  0.31  0.44  0.38  0.24 -0.13 -0.89 -0.76 -0.71\n",
+      " -0.37 -0.59 -0.63 -1.47 -0.4  -0.18 -0.37 -0.43 -0.06  0.61  1.33  1.19\n",
+      "  1.13  0.31  0.14  0.03  0.21  0.15 -0.22 -0.02  0.03 -0.17  0.12 -0.35\n",
+      " -0.06  0.38 -0.45 -0.32 -0.33 -0.49 -0.14 -0.56 -0.18  0.46  1.09  1.04\n",
+      "  0.23 -0.99 -0.59 -0.92 -0.28  0.52  1.31  1.45  0.61 -0.11 -0.18 -0.39\n",
+      " -0.39 -0.36 -0.5  -0.81 -1.1  -0.29  0.57  0.68  0.78  0.78  0.63  0.98\n",
+      "  0.49 -0.42 -1.34 -1.2  -1.18 -0.65 -0.42 -0.97 -0.28  0.77  1.77  2.22\n",
+      "  1.05 -0.67 -0.99 -1.52 -1.17 -0.22 -0.04 -0.45 -0.46 -0.75 -0.7  -1.38\n",
+      " -1.15 -0.01  0.97  1.1   0.68 -0.02 -0.04  0.47  0.3  -0.55 -0.51 -0.09\n",
+      " -0.01  0.34  0.61  0.58  0.33  0.38  0.1   0.18 -0.3  -0.06 -0.28  0.12\n",
+      "  0.58  0.89  0.93  2.39  2.44  1.92  0.64 -0.24  0.27 -0.13 -0.16 -0.54\n",
+      " -0.13 -0.37 -0.78 -0.22  0.03  0.25  0.31  1.03  1.1   1.05  1.11  1.28\n",
+      "  0.57 -0.55 -1.16 -0.99 -0.38  0.01 -0.29  0.09  0.46  0.57  0.24  0.39\n",
+      "  0.49  0.86  0.51  0.95  1.25  1.33 -0.    0.34  0.66  1.11  0.34  0.48\n",
+      "  0.56  0.39 -0.17  1.04  0.77  0.12 -0.35 -0.22  0.08 -0.08 -0.18 -0.06]\n"
      ]
     }
    ],
    "source": [
-    "#nino_url = 'https://paos.colorado.edu/research/wavelets/wave_idl/nino3sst.txt'\n",
-    "#nino3_data = np.genfromtxt(nino_url, skip_header=19)\n",
-    "nino3_data = xr.open_dataset(gcd.get('ascii_files/sst_nino3.dat'))\n",
+    "nino_url = 'https://paos.colorado.edu/research/wavelets/wave_idl/nino3sst.txt'\n",
+    "nino3_data = np.genfromtxt(nino_url, skip_header=19)\n",
+    "#nino3_data = xr.open_dataset(gcd.get('ascii_files/sst_nino3.dat'))\n",
     "print(nino3_data)"
    ]
   },
@@ -149,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -195,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -248,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -335,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -345,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -388,69 +424,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Last Section\n",
-    "\n",
-    "If you're comfortable, and as we briefly used for our embedded logo up top, you can embed raw html into Jupyter Markdown cells (edit to see):"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-info\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Info</p>\n",
-    "    Your relevant information here!\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Feel free to copy this around and edit or play around with yourself. Some other `admonitions` you can put in:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-success\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Success</p>\n",
-    "    We got this done after all!\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-warning\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Warning</p>\n",
-    "    Be careful!\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"admonition alert alert-danger\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Danger</p>\n",
-    "    Scary stuff be here.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We also suggest checking out Jupyter Book's [brief demonstration](https://jupyterbook.org/content/metadata.html#jupyter-cell-tags) on adding cell tags to your cells in Jupyter Notebook, Lab, or manually. Using these cell tags can allow you to [customize](https://jupyterbook.org/interactive/hiding.html) how your code content is displayed and even [demonstrate errors](https://jupyterbook.org/content/execute.html#dealing-with-code-that-raises-errors) without altogether crashing our loyal army of machines!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/notebooks/example-workflows/spy-keypad.ipynb b/notebooks/example-workflows/spy-keypad.ipynb
index 25de384..0df3eef 100644
--- a/notebooks/example-workflows/spy-keypad.ipynb
+++ b/notebooks/example-workflows/spy-keypad.ipynb
@@ -4,9 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<p align=\"center\">\n",
-    "  <img src=\"images/ProjectPythia_Logo_Final-01-Blue.svg\" alt=\"Project Pythia Logo\" />\n",
-    "</p>"
+    "[IMAGE]"
    ]
   },
   {
@@ -86,11 +84,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import ipywidgets\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import scipy.io.wavfile\n",
@@ -102,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -136,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -169,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -236,7 +233,7 @@
        "4          0.0004      21618"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -248,12 +245,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -275,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -327,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -378,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -425,16 +422,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f73aff3f2d0>]"
+       "[<matplotlib.lines.Line2D at 0x7f73ae7e7ad0>]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -459,7 +456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -486,10 +483,10 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f73b5a57290>]"
+       "[<matplotlib.lines.Line2D at 0x7f73afc7d1d0>]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
diff --git a/notebooks/wavelet-introduction/wavelet-basics.ipynb b/notebooks/wavelet-introduction/wavelet-basics.ipynb
index b54af25..feb0ef5 100644
--- a/notebooks/wavelet-introduction/wavelet-basics.ipynb
+++ b/notebooks/wavelet-introduction/wavelet-basics.ipynb
@@ -29,28 +29,12 @@
    "source": [
     "## Overview\n",
     "\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. Time-series data can include anything from atmospheric data over a year where the maximum and minimum values corresponds to specific days in a year.\n",
-    "\n",
-    "For the purpose of an example, imagine a short piece of music. Each note in the piece can be any note from A to F. Each note varies based on frequency to produce different pitches and notes. Frequency measures the amount of cycles over a single second. 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 notes.\n",
-    "\n",
-    "| Note   | Freq   |\n",
-    "|--------|--------|\n",
-    "| A note | 440 hz |\n",
-    "| B note | 494 hz |\n",
-    "| C note | 261 hz |\n",
-    "| D note | 293 hz |\n",
-    "| E note | 330 hz |\n",
-    "| F note | 350 hz |\n",
-    "| G note | 392 hz |\n",
-    "\n",
-    "However, just graphing that a B and a D note appear in the piece 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.\n",
-    "\n",
     "1. Prerequistites\n",
     "2. Background\n",
     "3. Load Wav File for Audio\n",
     "4. Fourier Transform - Frequency, but not Time\n",
-    "5. Wavelet Terminology\n",
-    "6. Wavelet Transform - Frequency and Time"
+    "6. Wavelet Transform - Frequency and Time\n",
+    "7. Wavelet Terminology"
    ]
   },
   {
@@ -76,14 +60,6 @@
     "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**: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.\n",
-    "- **System requirements**:\n",
-    "    - Populate with any system, version, or non-Python software requirements if necessary\n",
-    "    - Otherwise use the concepts table above and the Imports section below to describe required packages as necessary\n",
-    "    - If no extra requirements, remove the **System requirements** point altogether"
    ]
   },
   {
@@ -102,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,18 +88,6 @@
     "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": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "from scipy.fftpack import fft, fftfreq\n",
-    "from scipy.io import wavfile\n",
-    "import math\n",
-    "import pywt                                 # PyWavelets\n",
-    "plt.style.use('dark_background')"
    ]
   },
   {
@@ -149,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -170,9 +134,8 @@
      "output_type": "stream",
      "text": [
       "Sample Rate: 10000\n",
-      "duration = 15.6991 seconds (sample rate and audioBuffer = 156991 / 10000)\n",
-      "len of audio file = 156991\n",
-      "Total Length in time = 156991\n"
+      "duration = 15.6991 seconds (is the ratio of sample rate and data = 156991 / 10000)\n",
+      "length of audio file = 156991 time steps\n"
      ]
     }
    ],
@@ -187,23 +150,11 @@
    "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 (sample rate and audioBuffer = {len(signal_data)} / {sample_rate})\")\n",
-    "print(f\"len of audio file = {len(signal_data)}\")\n",
-    "print(f\"Total Length in time = {len(time)}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -270,7 +221,7 @@
        "4          0.0004      -8540"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -289,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -315,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -375,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -392,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -424,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -457,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -498,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -593,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {