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#!/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, KeysightCSV
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 = KeysightCSV()
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()
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