diff options
Diffstat (limited to 'bin/mim-vs-keysight.py')
| -rwxr-xr-x | bin/mim-vs-keysight.py | 205 |
1 files changed, 141 insertions, 64 deletions
diff --git a/bin/mim-vs-keysight.py b/bin/mim-vs-keysight.py index d1ff4e6..c214f2f 100755 --- a/bin/mim-vs-keysight.py +++ b/bin/mim-vs-keysight.py @@ -18,6 +18,7 @@ ks = KeysightCSV() charges, triggers = mim.load_data(mimfile) timestamps, currents = ks.load_data(csvfile) + def calfunc330(charge): if charge < 140.210488888889: return 0 @@ -26,6 +27,7 @@ def calfunc330(charge): else: return float(charge) * 0.0897304193584184 + -47.2437278033012 + 36.358862 + def calfunc82(charge): if charge < 126.993600: return 0 @@ -43,24 +45,25 @@ def calfunc33(charge): else: return charge * 0.884357 + -112.500777 + 36.358862 + calfuncs = { - 33 : calfunc33, - 82 : calfunc82, - 330 : calfunc330, + 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) +# 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_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_x = timestamps[: len(timestamps) - 9] ks_y = running_mean(currents, 10) # look for synchronization opportunity in first 5 seconds @@ -97,12 +100,12 @@ 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]') +(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 = [] @@ -112,11 +115,21 @@ 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: + 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]: + 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 @@ -141,21 +154,31 @@ 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)) + 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.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) @@ -163,60 +186,114 @@ 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.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.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) +(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.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) +(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.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') +(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.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) +(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.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') +(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.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() +# 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() |