1 files changed, 40 insertions, 0 deletions
diff --git a/bin/analyze.py b/bin/analyze.py
new file mode 100755
index 0000000..57803fe
--- /dev/null
+++ b/bin/analyze.py
@@ -0,0 +1,40 @@
+#!/usr/bin/env python3
+
+import json
+import numpy as np
+import os
+from scipy.cluster.vq import kmeans2
+import struct
+import sys
+import tarfile
+from dfatool import running_mean, MIMOSA
+
+voltage = float(sys.argv[1])
+shunt = float(sys.argv[2])
+filename = sys.argv[3]
+
+mim = MIMOSA(voltage, shunt)
+
+charges, triggers = mim.load_data(filename)
+trigidx = mim.trigger_edges(triggers)
+triggers = []
+cal_edges = mim.calibration_edges(running_mean(mim.currents_nocal(charges[0:trigidx[0]]), 10))
+calfunc, caldata = mim.calibration_function(charges, cal_edges)
+vcalfunc = np.vectorize(calfunc, otypes=[np.float64])
+
+json_out = {
+    'triggers' : len(trigidx),
+    'first_trig' : trigidx[0] * 10,
+    'calibration' : caldata,
+    'trace' : mim.analyze_states(charges, trigidx, vcalfunc)
+}
+
+basename, _ = os.path.splitext(filename)
+
+# TODO also look for interesting gradients inside each state
+
+with open(basename + ".json", "w") as f:
+    json.dump(json_out, f)
+    f.close()
+
+#print(kmeans2(charges[:firstidx], np.array([130 * ua_step, 3.6 / 987 * 1000000, 3.6 / 99300 * 1000000])))