1 files changed, 225 insertions, 0 deletions
diff --git a/bin/mim-vs-keysight.py b/bin/mim-vs-keysight.py
new file mode 100755
index 0000000..2cfa976
--- /dev/null
+++ b/bin/mim-vs-keysight.py
@@ -0,0 +1,225 @@
+#!/usr/bin/env python3
+
+import csv
+import numpy as np
+import os
+import struct
+import sys
+import tarfile
+import matplotlib.pyplot as plt
+from dfatool import running_mean, MIMOSA, Keysight
+
+voltage = float(sys.argv[1])
+shunt = float(sys.argv[2])
+
+mimfile = "../data/20161114_arb_%d.mim" % shunt
+csvfile = "../data/20161114_%d_arb.csv" % shunt
+
+mim = MIMOSA(voltage, shunt)
+ks = Keysight()
+
+charges, triggers = mim.load_data(mimfile)
+timestamps, currents = ks.load_data(csvfile)
+
+def calfunc330(charge):
+    if charge < 140.210488888889:
+        return 0
+    if charge <= 526.507377777778:
+        return float(charge) * 0.0941215500652876 + -13.196828549634
+    else:
+        return float(charge) * 0.0897304193584184 + -47.2437278033012 + 36.358862
+
+def calfunc82(charge):
+    if charge < 126.993600:
+        return 0
+    if charge <= 245.464889:
+        return charge * 0.306900 + -38.974361
+    else:
+        return charge * 0.356383 + -87.479495 + 36.358862
+
+
+def calfunc33(charge):
+    if charge < 127.000000:
+        return 0
+    if charge <= 127.211911:
+        return charge * 171.576006 + -21790.152700
+    else:
+        return charge * 0.884357 + -112.500777 + 36.358862
+
+calfuncs = {
+    33 : calfunc33,
+    82 : calfunc82,
+    330 : calfunc330,
+}
+
+vcalfunc = np.vectorize(calfuncs[int(shunt)], otypes=[np.float64])
+
+#plt.plot(np.arange(0, 1000, 0.01), vcalfunc(np.arange(0, 1000, 0.01)))
+#plt.xlabel('Rohdatenwert')
+#plt.ylabel('Strom [µA]')
+#plt.show()
+#sys.exit(0)
+
+mim_x = np.arange(len(charges)-199) * 1e-5
+mim_y = running_mean(mim.charge_to_current_nocal(charges), 200) * 1e-6
+cal_y = running_mean(vcalfunc(charges), 200) * 1e-6
+ks_x = timestamps[:len(timestamps)-9]
+ks_y = running_mean(currents, 10)
+
+# look for synchronization opportunity in first 5 seconds
+mim_sync_idx = 0
+ks_sync_idx = 0
+for i in range(0, 500000):
+    if mim_sync_idx == 0 and mim_y[i] > 0.001:
+        mim_sync_idx = i
+    if ks_sync_idx == 0 and ks_y[i] > 0.001:
+        ks_sync_idx = i
+
+mim_x = mim_x - mim_x[mim_sync_idx]
+ks_x = ks_x - ks_x[ks_sync_idx]
+
+mim_max_start = int(len(mim_y) * 0.4)
+mim_max_end = int(len(mim_y) * 0.6)
+mim_start_end = int(len(mim_y) * 0.1)
+mim_end_start = int(len(mim_y) * 0.9)
+mim_max = np.max(mim_y[mim_max_start:mim_max_end])
+mim_min1 = np.min(mim_y[:mim_start_end])
+mim_min2 = np.min(mim_y[mim_end_start:])
+mim_center = 0
+mim_start = 0
+mim_end = 0
+for i, y in enumerate(mim_y):
+    if y == mim_max and i / len(mim_y) > 0.4 and i / len(mim_y) < 0.6:
+        mim_center = i
+    elif y == mim_min1 and i / len(mim_y) < 0.1:
+        mim_start = i
+    elif y == mim_min2 and i / len(mim_y) > 0.9:
+        mim_end = i
+
+plt.plot([mim_x[mim_center]], [mim_y[mim_center]], "yo")
+plt.plot([mim_x[mim_start]], [mim_y[mim_start]], "yo")
+plt.plot([mim_x[mim_end]], [mim_y[mim_end]], "yo")
+#
+mimhandle, = plt.plot(mim_x, mim_y, "r-", label='MIMOSA')
+#calhandle, = plt.plot(mim_x, cal_y, "g-", label='MIMOSA (autocal)')
+kshandle, = plt.plot(ks_x, ks_y, "b-", label='Keysight')
+#plt.legend(handles=[mimhandle, calhandle, kshandle])
+plt.xlabel('Zeit [s]')
+plt.ylabel('Strom [A]')
+plt.grid(True)
+
+ks_steps_up = []
+ks_steps_down = []
+mim_steps_up = []
+mim_steps_down = []
+
+skip = 0
+for i, gradient in enumerate(np.gradient(ks_y, 10000)):
+    if gradient > 0.5e-9 and i - skip > 200 and ks_x[i] < mim_x[mim_center] and ks_x[i] > 5:
+        plt.plot([ks_x[i]], [ks_y[i]], "go")
+        ks_steps_up.append(i)
+        skip = i
+    elif gradient < -0.5e-9 and i - skip > 200 and ks_x[i] > mim_x[mim_center] and ks_x[i] < mim_x[mim_end]:
+        plt.plot([ks_x[i]], [ks_y[i]], "g*")
+        ks_steps_down.append(i)
+        skip = i
+
+j = 0
+for i, ts in enumerate(mim_x):
+    if j < len(ks_steps_up) and ts > ks_x[ks_steps_up[j]]:
+        mim_steps_up.append(i)
+        j += 1
+
+j = 0
+for i, ts in enumerate(mim_x):
+    if j < len(ks_steps_down) and ts > ks_x[ks_steps_down[j]]:
+        mim_steps_down.append(i)
+        j += 1
+
+print(ks_steps_up)
+print(mim_steps_up)
+
+mim_values = []
+cal_values = []
+ks_values = []
+
+for i in range(1, len(ks_steps_up)):
+    mim_values.append(np.mean(mim_y[mim_steps_up[i-1]:mim_steps_up[i]]))
+    cal_values.append(np.mean(cal_y[mim_steps_up[i-1]:mim_steps_up[i]]))
+    ks_values.append(np.mean(ks_y[ks_steps_up[i-1]:ks_steps_up[i]]))
+    print("step %d avg %5.3f vs %5.3f vs %5.3f mA" %
+        (i, np.mean(ks_y[ks_steps_up[i-1]:ks_steps_up[i]]) * 1e3,
+            np.mean(mim_y[mim_steps_up[i-1]:mim_steps_up[i]]) * 1e3,
+            np.mean(cal_y[mim_steps_up[i-1]:mim_steps_up[i]]) * 1e3))
+for i in range(1, len(ks_steps_down)):
+    mim_values.append(np.mean(mim_y[mim_steps_down[i-1]:mim_steps_down[i]]))
+    cal_values.append(np.mean(cal_y[mim_steps_down[i-1]:mim_steps_down[i]]))
+    ks_values.append(np.mean(ks_y[ks_steps_down[i-1]:ks_steps_down[i]]))
+    print("step %d avg %5.3f vs %5.3f vs %5.3f mA" %
+        (i, np.mean(ks_y[ks_steps_down[i-1]:ks_steps_down[i]]) * 1e3,
+            np.mean(mim_y[mim_steps_down[i-1]:mim_steps_down[i]]) * 1e3,
+            np.mean(cal_y[mim_steps_down[i-1]:mim_steps_down[i]]) * 1e3))
+
+mim_values = np.array(mim_values)
+cal_values = np.array(cal_values)
+ks_values = np.array(ks_values)
+
+plt.show()
+
+plt.hist(ks_y[ks_steps_up[48]:ks_steps_up[49]] * 1e3, 100, normed=0, facecolor='blue', alpha=0.8)
+plt.xlabel('mA Keysight')
+plt.ylabel('#')
+plt.grid(True)
+plt.show()
+plt.hist(mim_y[mim_steps_up[48]:mim_steps_up[49]] * 1e3, 100, normed=0, facecolor='blue', alpha=0.8)
+plt.xlabel('mA MimosaGUI')
+plt.ylabel('#')
+plt.grid(True)
+plt.show()
+
+mimhandle, = plt.plot(ks_values * 1e3, mim_values * 1e3, "ro", label='Unkalibriert', markersize=4)
+calhandle, = plt.plot(ks_values * 1e3, cal_values * 1e3, "bs", label='Kalibriert', markersize=4)
+plt.legend(handles=[mimhandle, calhandle])
+plt.xlabel('mA Keysight')
+plt.ylabel('mA MIMOSA')
+plt.grid(True)
+plt.show()
+
+mimhandle, = plt.plot(ks_values * 1e3, (mim_values - ks_values) * 1e3, "ro", label='Unkalibriert', markersize=4)
+calhandle, = plt.plot(ks_values * 1e3, (cal_values - ks_values) * 1e3, "bs", label='Kalibriert', markersize=4)
+plt.legend(handles=[mimhandle, calhandle])
+plt.xlabel('Sollstrom [mA]')
+plt.ylabel('Messfehler MIMOSA [mA]')
+plt.grid(True)
+plt.show()
+
+mimhandle, = plt.plot(ks_values * 1e3, (mim_values - ks_values) * 1e3, "r--", label='Unkalibriert')
+calhandle, = plt.plot(ks_values * 1e3, (cal_values - ks_values) * 1e3, "b-", label='Kalibriert')
+plt.legend(handles=[mimhandle, calhandle])
+plt.xlabel('Sollstrom [mA]')
+plt.ylabel('Messfehler MIMOSA [mA]')
+plt.grid(True)
+plt.show()
+
+mimhandle, = plt.plot(ks_values * 1e3, (mim_values - ks_values) / ks_values * 100, "ro", label='Unkalibriert', markersize=4)
+calhandle, = plt.plot(ks_values * 1e3, (cal_values - ks_values) / ks_values * 100, "bs", label='Kalibriert', markersize=4)
+plt.legend(handles=[mimhandle, calhandle])
+plt.xlabel('Sollstrom [mA]')
+plt.ylabel('Messfehler MIMOSA [%]')
+plt.grid(True)
+plt.show()
+
+mimhandle, = plt.plot(ks_values * 1e3, (mim_values - ks_values) / ks_values * 100, "r--", label='Unkalibriert')
+calhandle, = plt.plot(ks_values * 1e3, (cal_values - ks_values) / ks_values * 100, "b-", label='Kalibriert')
+plt.legend(handles=[mimhandle, calhandle])
+plt.xlabel('Sollstrom [mA]')
+plt.ylabel('Messfehler MIMOSA [%]')
+plt.grid(True)
+plt.show()
+
+#mimhandle, = plt.plot(mim_x, np.gradient(mim_y, 10000), "r-", label='MIMOSA')
+#kshandle, = plt.plot(ks_x, np.gradient(ks_y, 10000), "b-", label='Keysight')
+#plt.legend(handles=[mimhandle, kshandle])
+#plt.xlabel('Zeit [s]')
+#plt.ylabel('Strom [A]')
+#plt.show()