Resolve commits (#4)

notebooks/example-workflows/nino3.ipynb

@@ -112,12 +112,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Download Nino 3 data and skip the header information"
+    "#### Download Nino 3 data from geocat-datafiles"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -170,9 +170,11 @@
     }
    ],
    "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",
+    "# Download nino3 data\n",
+    "import geocat.datafiles as gcd\n",
+    "\n",
+    "nino3_data = gcd.get('ascii_files/sst_nino3.dat')\n",
+    "nino3_data = np.loadtxt(nino3_data)\n",
     "print(nino3_data)"
    ]
   },

notebooks/wavelet-introduction/wavelet-basics.ipynb

@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -101,7 +101,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Background"
+    "## Load Wav File for Audio"
    ]
   },
   {
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -221,7 +221,7 @@
        "4          0.0004      -8540"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -266,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -326,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -370,12 +370,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Important note: Only plot positive Frequencies (hz) in range of notes (200-500)"
+    "### Only plot positive Frequencies (hz) in range of notes (200-500)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -408,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -449,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -497,7 +497,13 @@
     "\n",
     "### What is a Wavelet\n",
     "\n",
-    "TODO"
+    "A wavelet is a short wave-like oscillation that averages out to zero.\n",
+    "\n",
+    "> Many signals and images of interest exhibit piecewise smooth behavior punctuated by transients. Speech signals are characterized by short bursts encoding consonants followed by steady-state oscillations indicative of vowels. Natural images have edges. Financial time series exhibit transient behavior, which characterize rapid upturns and downturns in economic conditions. Unlike the Fourier basis, wavelet bases are adept at sparsely representing piecewise regular signals and images, which include transient behavior.\n",
+    "> \n",
+    "> [Mathworks](https://www.mathworks.com/help/wavelet/gs/what-is-a-wavelet.html): \"What is a Wavelet\"\n",
+    "\n",
+    "Fourier transforms is made up of sine waves of different and various frequencies to best match a signal. However, while Fourier transforms can be used to match frequency, information about when each frequency occurs in the signal is lost. This can be overcome with wavelet analysis. A wavelet scales (expanded or shrunk) different shaped wavelets and is shifted along the signal. The scaled wavelet is shifted along the signal, which allows for a signal's frequency at each time step to be determined. "
    ]
   },
   {
@@ -527,7 +533,7 @@
    "source": [
     "### Time-Series Data\n",
     "\n",
-    "TODO"
+    "Time-series data is data recorded over known intervals of time. For example, time-series data for weather might track temperature every hour or every month."
    ]
   },
   {
@@ -544,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -662,6 +668,7 @@
    "source": [
     "## Resources and references\n",
     " - [MathWorks Wavelet Transforms](https://www.mathworks.com/discovery/wavelet-transforms.html)\n",
+    " - [MathWorks: \"What is a Wavelet\"](https://www.mathworks.com/help/wavelet/gs/what-is-a-wavelet.html)\n",
     " "
    ]
   }