Here you go B

data.py

@@ -0,0 +1,58 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy import signal
+
+from signalprocess import SignalProcess
+
+# Read data from the video processing pipeline
+sig = SignalProcess(path="out.tsv")
+# Uncomment to see fft plot
+# sig.fft()
+
+# Applies the butter filter from the Signal Process class
+data = sig.butter_filter()
+
+
+plt.plot(sig.times, data)
+plt.plot(sig.times, sig.ave_counts)
+# Uncomment to see raw waveform
+# plt.plot(sig.times,sig.counts)
+
+# Peak finding algo provided by scipy package
+peaks, descript = signal.find_peaks((-1) * data, prominence=0.2, distance=30)
+
+# Plot the calculated peaks
+plt.scatter(
+    sig.times[peaks],
+    data[peaks],
+)
+
+# Array of peak to peak distances
+distances = []
+# Array of the peak heights as calculated in the plot
+heights = []
+for i in range(len(peaks)):
+    heights.append(data[peaks[i]])
+
+for i in range(1, len(peaks) - 1):
+    distance = (peaks[i] - peaks[i - 1]) / 30.0
+    distances.append(distance)
+
+# Basic stats
+print("distances: median={}, std={}".format(np.median(distances), np.std(distances)))
+print("heights: median={}, std={}".format(np.median(heights), np.std(heights)))
+
+# Standard deviation rejection of outliers
+d = np.abs(heights - np.median(heights))
+mdev = np.median(d)
+s = d / mdev if mdev else 0.0
+peaks = peaks[s < 3.0]
+
+# plot the non-rejected data on top of the old peak data
+plt.scatter(
+    sig.times[peaks],
+    data[peaks],
+)
+
+# Draw that bitch
+plt.show()