diff --git a/Frequency Analysis/fft_testing.py b/Frequency Analysis/fft_testing.py
new file mode 100755
index 0000000..699c524
--- /dev/null
+++ b/Frequency Analysis/fft_testing.py	
@@ -0,0 +1,85 @@
+#! /usr/bin/env python
+
+###############################################################################
+# fft_testing.py
+#
+# Script to test updated NumPy FFT algorithms
+#
+# NOTE: Any plotting is set up for output, not viewing on screen.
+#       So, it will likely be ugly on screen. The saved PDFs should look
+#       better.
+#
+# Created: 08/05/20
+#   - Joshua Vaughan
+#   - joshua.vaughan@louisiana.edu
+#   - @doc_vaughan
+#   - http://www.ucs.louisiana.edu/~jev9637
+#
+# Modified:
+#   * 
+#
+# TODO:
+#   * 
+###############################################################################
+
+import numpy as np
+import matplotlib.pyplot as plt
+from numpy.fft import rfft, rfftfreq
+
+# Set up the test data
+dt = 0.01                       # Sample time (s)
+t = np.arange(0,10 + dt, dt)
+
+f1 = 1.0 * 2.0 * np.pi  # 1 Hz
+f2 = 3.4 * 2.0 * np.pi  # 3.4 Hz
+f3 = 30.0 * 2.0 * np.pi
+
+data =  np.sin(f1 * t) + np.sin(f2 * t) + np.sin(f3 * t)
+num_samples = len(data)
+
+fft_mag = np.abs(rfft(data))
+fft_freq = rfftfreq(num_samples, dt)
+
+# Set the plot size - 3x2 aspect ratio is best
+fig = plt.figure(figsize=(6,4))
+ax = plt.gca()
+plt.subplots_adjust(bottom=0.17, left=0.17, top=0.96, right=0.96)
+
+# Change the axis units font
+plt.setp(ax.get_ymajorticklabels(),fontsize=18)
+plt.setp(ax.get_xmajorticklabels(),fontsize=18)
+
+ax.spines['right'].set_color('none')
+ax.spines['top'].set_color('none')
+
+ax.xaxis.set_ticks_position('bottom')
+ax.yaxis.set_ticks_position('left')
+
+# Turn on the plot grid and set appropriate linestyle and color
+ax.grid(True,linestyle=':', color='0.75')
+ax.set_axisbelow(True)
+
+# Define the X and Y axis labels
+plt.xlabel('Frequency (Hz)', fontsize=22, weight='bold', labelpad=5)
+plt.ylabel('FFT Magnitude', fontsize=22, weight='bold', labelpad=10)
+ 
+plt.plot(fft_freq, fft_mag, linewidth=2, linestyle='-', label=r'Data 1')
+
+# uncomment below and set limits if needed
+plt.xlim(0, 50)
+# plt.ylim(0,10)
+
+# Create the legend, then fix the fontsize
+# leg = plt.legend(loc='upper right', ncol = 1, fancybox=True)
+# ltext  = leg.get_texts()
+# plt.setp(ltext,fontsize=18)
+
+# Adjust the page layout filling the page using the new tight_layout command
+plt.tight_layout(pad=0.5)
+
+# save the figure as a high-res pdf in the current folder
+# plt.savefig('plot_filename.pdf')
+
+# show the figure
+plt.show()
+
diff --git a/Frequency Analysis/spectrogram_testing.py b/Frequency Analysis/spectrogram_testing.py
new file mode 100755
index 0000000..d3fec82
--- /dev/null
+++ b/Frequency Analysis/spectrogram_testing.py	
@@ -0,0 +1,252 @@
+#! /usr/bin/env python
+
+###############################################################################
+# spectrogram_testing.py
+#
+# Script to test updated Python spectrogram algorithms
+#
+# NOTE: Any plotting is set up for output, not viewing on screen.
+#       So, it will likely be ugly on screen. The saved PDFs should look
+#       better.
+#
+# Created: 08/05/20
+#   - Joshua Vaughan
+#   - joshua.vaughan@louisiana.edu
+#   - @doc_vaughan
+#   - http://www.ucs.louisiana.edu/~jev9637
+#
+# Modified:
+#   * 
+#
+# TODO:
+#   * 
+###############################################################################
+
+import numpy as np
+import matplotlib.pyplot as plt
+from numpy.fft import rfft, rfftfreq
+from scipy import signal
+
+# Set up the test data
+dt = 0.01                       # Sample time (s)
+t = np.arange(0, 10 + dt, dt)
+
+f1 = 1.0 * 2.0 * np.pi  # 1 Hz
+f2 = 30.0 * 2.0 * np.pi
+
+data =  np.sin(f1 * t) + np.sin(f2 * (t[-1] - t[0]) / len(t) * t**2)
+
+noise_power = 0.01 * 0.5 * 1/dt
+noise = np.random.normal(scale=np.sqrt(noise_power), size=t.shape)
+
+data = data + noise
+
+num_samples = len(data)
+
+
+# First, plot the signal
+# Set the plot size - 3x2 aspect ratio is best
+fig = plt.figure(figsize=(6,4))
+ax = plt.gca()
+plt.subplots_adjust(bottom=0.17, left=0.17, top=0.96, right=0.96)
+
+# Change the axis units font
+plt.setp(ax.get_ymajorticklabels(),fontsize=18)
+plt.setp(ax.get_xmajorticklabels(),fontsize=18)
+
+ax.spines['right'].set_color('none')
+ax.spines['top'].set_color('none')
+
+ax.xaxis.set_ticks_position('bottom')
+ax.yaxis.set_ticks_position('left')
+
+# Turn on the plot grid and set appropriate linestyle and color
+ax.grid(True,linestyle=':', color='0.75')
+ax.set_axisbelow(True)
+
+# Define the X and Y axis labels
+plt.xlabel('Time (s)', fontsize=22, weight='bold', labelpad=5)
+plt.ylabel('Acceleration (m/s$^2$)', fontsize=22, weight='bold', labelpad=10)
+ 
+plt.plot(t, data, linewidth=2, linestyle='-', label=r'Acceleration')
+
+# uncomment below and set limits if needed
+# plt.xlim(0,5)
+# plt.ylim(0,10)
+
+# Create the legend, then fix the fontsize
+# leg = plt.legend(loc='upper right', ncol = 1, fancybox=True)
+# ltext  = leg.get_texts()
+# plt.setp(ltext,fontsize=18)
+
+# Adjust the page layout filling the page using the new tight_layout command
+plt.tight_layout(pad=0.5)
+
+# save the figure as a high-res pdf in the current folder
+# plt.savefig('plot_filename.pdf')
+
+# show the figure
+# plt.show()
+
+
+
+# Plot the FFT of the entire signal
+fft_mag = np.abs(rfft(data))
+fft_freq = rfftfreq(num_samples, dt)
+
+# Set the plot size - 3x2 aspect ratio is best
+fig = plt.figure(figsize=(6,4))
+ax = plt.gca()
+plt.subplots_adjust(bottom=0.17, left=0.17, top=0.96, right=0.96)
+
+# Change the axis units font
+plt.setp(ax.get_ymajorticklabels(),fontsize=18)
+plt.setp(ax.get_xmajorticklabels(),fontsize=18)
+
+ax.spines['right'].set_color('none')
+ax.spines['top'].set_color('none')
+
+ax.xaxis.set_ticks_position('bottom')
+ax.yaxis.set_ticks_position('left')
+
+# Turn on the plot grid and set appropriate linestyle and color
+ax.grid(True,linestyle=':', color='0.75')
+ax.set_axisbelow(True)
+
+# Define the X and Y axis labels
+plt.xlabel('Frequency (Hz)', fontsize=22, weight='bold', labelpad=5)
+plt.ylabel('FFT Magnitude', fontsize=22, weight='bold', labelpad=10)
+ 
+plt.plot(fft_freq, fft_mag, linewidth=2, linestyle='-', label=r'Data 1')
+
+# uncomment below and set limits if needed
+plt.xlim(0, 50)
+# plt.ylim(0,10)
+
+# Create the legend, then fix the fontsize
+# leg = plt.legend(loc='upper right', ncol = 1, fancybox=True)
+# ltext  = leg.get_texts()
+# plt.setp(ltext,fontsize=18)
+
+# Adjust the page layout filling the page using the new tight_layout command
+plt.tight_layout(pad=0.5)
+
+# save the figure as a high-res pdf in the current folder
+# plt.savefig('plot_filename.pdf')
+
+# show the figure
+# plt.show()
+
+
+# Now, let's look at the SciPy implementation of spectrogram
+SEG_LENGTH = 32  # Number of samples per segment, scipy defaults to overlap of 1/8 this
+spec_freq, spec_times, Sxx = signal.spectrogram(data, 1/dt, nperseg=SEG_LENGTH)#, scaling='spectrum')
+
+# Set the plot size - 3x2 aspect ratio is best
+fig = plt.figure(figsize=(6,4))
+ax = plt.gca()
+plt.subplots_adjust(bottom=0.17, left=0.17, top=0.96, right=0.96)
+
+# Change the axis units font
+plt.setp(ax.get_ymajorticklabels(),fontsize=18)
+plt.setp(ax.get_xmajorticklabels(),fontsize=18)
+
+ax.spines['right'].set_color('none')
+ax.spines['top'].set_color('none')
+
+ax.xaxis.set_ticks_position('bottom')
+ax.yaxis.set_ticks_position('left')
+
+# Turn on the plot grid and set appropriate linestyle and color
+ax.grid(True,linestyle=':', color='0.75')
+ax.set_axisbelow(True)
+
+# Define the X and Y axis labels
+plt.xlabel('Time (s)', fontsize=22, weight='bold', labelpad=5)
+plt.ylabel('Frequency (Hz)', fontsize=22, weight='bold', labelpad=10)
+ 
+plt.pcolormesh(spec_times, spec_freq, Sxx)#, shading='gouraud')
+
+# uncomment below and set limits if needed
+# plt.xlim(0,5)
+# plt.ylim(0,10)
+
+# Create the legend, then fix the fontsize
+# leg = plt.legend(loc='upper right', ncol = 1, fancybox=True)
+# ltext  = leg.get_texts()
+# plt.setp(ltext,fontsize=18)
+
+# Adjust the page layout filling the page using the new tight_layout command
+plt.tight_layout(pad=0.5)
+
+# save the figure as a high-res pdf in the current folder
+# plt.savefig('plot_filename.pdf')
+
+# show the figure
+# plt.show()
+
+
+# Finally, let's see if we can roll our own, which could be used for streaming
+OVERLAP = SEG_LENGTH // 8 
+window_start_indices = np.arange(0, num_samples, SEG_LENGTH-OVERLAP)
+
+# Don't allow the final window to be smaller than the SEG_LENGTH
+window_start_indices = window_start_indices[window_start_indices + SEG_LENGTH < len(t)]
+
+spect_freq = rfftfreq(SEG_LENGTH, dt)
+
+spect_times = t[np.arange(SEG_LENGTH // 2, num_samples, SEG_LENGTH-OVERLAP)]
+
+# Don't allow the final window to be smaller than the SEG_LENGTH
+spect_times = spect_times[:len(window_start_indices)]
+
+spect_mags = np.zeros((len(spec_freq), len(spect_times)))
+
+for index, start_index in enumerate(window_start_indices):
+    spect_mags[:, index] =  10 * np.log10(np.abs(rfft(data[start_index:start_index + SEG_LENGTH]))**2)
+    
+
+# Set the plot size - 3x2 aspect ratio is best
+fig = plt.figure(figsize=(6,4))
+ax = plt.gca()
+plt.subplots_adjust(bottom=0.17, left=0.17, top=0.96, right=0.96)
+
+# Change the axis units font
+plt.setp(ax.get_ymajorticklabels(),fontsize=18)
+plt.setp(ax.get_xmajorticklabels(),fontsize=18)
+
+ax.spines['right'].set_color('none')
+ax.spines['top'].set_color('none')
+
+ax.xaxis.set_ticks_position('bottom')
+ax.yaxis.set_ticks_position('left')
+
+# Turn on the plot grid and set appropriate linestyle and color
+ax.grid(True,linestyle=':', color='0.75')
+ax.set_axisbelow(True)
+
+# Define the X and Y axis labels
+plt.xlabel('Time (s)', fontsize=22, weight='bold', labelpad=5)
+plt.ylabel('Frequency (Hz)', fontsize=22, weight='bold', labelpad=10)
+ 
+plt.pcolormesh(spect_times, spect_freq, spect_mags)#, shading='gouraud')
+
+# uncomment below and set limits if needed
+# plt.xlim(0,5)
+# plt.ylim(0,10)
+
+# Create the legend, then fix the fontsize
+# leg = plt.legend(loc='upper right', ncol = 1, fancybox=True)
+# ltext  = leg.get_texts()
+# plt.setp(ltext,fontsize=18)
+
+# Adjust the page layout filling the page using the new tight_layout command
+plt.tight_layout(pad=0.5)
+
+# save the figure as a high-res pdf in the current folder
+# plt.savefig('plot_filename.pdf')
+
+# show the figure
+plt.show()
+
+