Add the analysis tool and Python script for drawing the results.

scripts/analysedriveresponse.py

@@ -0,0 +1,77 @@
+import numpy
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+
+TICK_FREQUENCY = 12e6
+TICKS_PER_US = TICK_FREQUENCY / 1e6
+print(TICKS_PER_US)
+
+# Load data.
+data = numpy.loadtxt(open("analysis.csv", "rb"), delimiter=",", skiprows=1)
+
+labels = data[:, 0]
+frequencies = data[:, 1:]
+
+# Scale the frequencies.
+def scaled(row):
+    m = row.mean()
+    if m != 0:
+        return row / m
+    else:
+        return row
+
+scaledfreq = numpy.array([scaled(row) for row in frequencies])
+
+# Create new Figure with black background
+fig = plt.figure(figsize=(8, 8), facecolor='#aaa')
+
+plt.imshow(scaledfreq, extent=[0, 512/TICKS_PER_US, labels[0], labels[-1]], cmap='jet',
+           vmin=0, vmax=1, origin='lower', aspect='auto')
+plt.colorbar()
+plt.ylabel("Interval period (us)")
+plt.xlabel("Response (us)")
+plt.show()
+
+## Add a subplot with no frame
+#ax = plt.subplot(111, frameon=False)
+#
+## Generate random data
+#X = numpy.linspace(-1, 1, frequencies.shape[-1])
+#G = 1.5 * numpy.exp(-4 * X ** 2)
+#
+#
+## Generate line plots
+#lines = []
+#for i in range(len(frequencies)):
+#    # Small reduction of the X extents to get a cheap perspective effect
+#    xscale = 1 - i / 300.
+#
+#    # Same for linewidth (thicker strokes on bottom)
+#    lw = 1.5 - i / 200.0
+#
+#    # Scale.
+#    row = frequencies[i]
+#    max = numpy.amax(row)
+#    if max == 0:
+#        Y = 1
+#    else:
+#        Y = 5 / max
+#    line, = ax.plot(xscale*X, i + Y*frequencies[i], color="w", lw=lw)
+#    lines.append(line)
+#
+## Set y limit (or first line is cropped because of thickness)
+##ax.set_ylim(-1, 100)
+#
+## No ticks
+##ax.set_xticks([])
+##ax.set_yticks([])
+#
+## 2 part titles to get different font weights
+##ax.text(0.5, 1.0, "MATPLOTLIB ", transform=ax.transAxes,
+##ha="right", va="bottom", color="w",
+##family="sans-serif", fontweight="light", fontsize=16)
+##ax.text(0.5, 1.0, "UNCHAINED", transform=ax.transAxes,
+##ha="left", va="bottom", color="w",
+##family="sans-serif", fontweight="bold", fontsize=16)
+
+plt.show()