add running mean power line (yellow)

1 files changed, 22 insertions, 2 deletions

diff --git a/bin/msp430-etv b/bin/msp430-etv
index afe885f..4954304 100755
--- a/bin/msp430-etv
+++ b/bin/msp430-etv
@@ -15,6 +15,22 @@ import time
 
 opt = dict()
 
+def running_mean(x: np.ndarray, N: int) -> np.ndarray:
+    """
+    Compute `N` elements wide running average over `x`.
+
+    :param x: 1-Dimensional NumPy array
+    :param N: how many items to average. Should be even for optimal results.
+    """
+
+    # to ensure that output.shape == input.shape, we need to insert data
+    # at the boundaries
+    boundary_array = np.insert(x, 0, np.full((N // 2), x[0]))
+    boundary_array = np.append(boundary_array, np.full((N // 2 + N % 2), x[-1]))
+    print(boundary_array)
+    cumsum = np.cumsum(boundary_array)
+    return (cumsum[N:] - cumsum[:-N]) / N
+
 def measure_data(filename, duration):
     # libmsp430.so must be available
     if not 'LD_LIBRARY_PATH' in os.environ:
@@ -274,6 +290,9 @@ if __name__ == '__main__':
         print('Average per-peak energy (delta over baseline): {:.0f} µJ         : {:.9f}'.format(
             delta_energy * 1e6 / len(peaks), delta_energy / len(peaks)))
 
+    power_from_current = data[:, 1] * data[:, 2] * 1e-12
+    power_from_energy = ((data[1:, 3] - data[:-1, 3]) * 1e-9) / ((data[1:, 0] - data[:-1, 0]) * 1e-6)
+    mean_power = running_mean(power_from_energy, 10)
 
     if 'stat' in opt:
         mean_voltage = np.mean(data[:, 2])
@@ -286,8 +305,9 @@ if __name__ == '__main__':
 
     if 'plot' in opt:
         # nA * mV = pW
-        pwrhandle, = plt.plot(data[:, 0] * 1e-6, data[:, 1] * data[:, 2] * 1e-12, 'b-', label='P=U*I', markersize=1)
-        energyhandle, = plt.plot(data[1:, 0] * 1e-6, ((data[1:, 3] - data[:-1, 3]) * 1e-9) / ((data[1:, 0] - data[:-1, 0]) * 1e-6), 'r-', label='P=ΔE/Δt', markersize=1)
+        pwrhandle, = plt.plot(data[:, 0] * 1e-6, power_from_current, 'b-', label='P=U*I', markersize=1)
+        energyhandle, = plt.plot(data[1:, 0] * 1e-6, power_from_energy, 'r-', label='P=ΔE/Δt', markersize=1)
+        energyhandle2, = plt.plot(data[1:, 0] * 1e-6, mean_power, 'y-', label='E/Δt', markersize=1)
         plt.legend(handles=[pwrhandle, energyhandle])
         plt.xlabel('Time [s]')
         plt.ylabel('Power [W]')