<jupyter_start><jupyter_text>New date new wafer- Nb28/Niw60s for Tc measurements. New Nb target, bulk Tc = 8.6 K.- Thermometer read with KT2001 multimeter at 200 kOhm range.- Bias: 10 mV ac over 100 kOhm resistor, 83 Hz, 300 ms tc- Input: 1 kOhm - 10 nF LPF, ac float, low noise, max filtering.  Process new datafiles<jupyter_code>%matplotlib inline
%load_ext autoreload
%autoreload 1
%aimport nb_cmds, mylib

import glob

datafiles = glob.glob("data/*.zip")
ignore    = glob.glob("data/RSJ_*") + glob.glob("data/004*")
nb_cmds.process_new_datafiles(datafiles, ignore)<jupyter_output>N of datafiles: 12
Datafile: data/001_VItrace-HI_B160617_chip23_A01_3500OeP.zip<jupyter_text>Show all Ic(B)<jupyter_code>%matplotlib inline
%load_ext autoreload
%autoreload 1
%aimport nb_cmds, mylib

nb_cmds.show_BIc()<jupyter_output>The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload
Loaded existing db: results.txt<jupyter_text>Thickness dependence<jupyter_code>%matplotlib inline
%load_ext autoreload
%autoreload 1
%aimport nb_cmds, mylib

devspecs = [{"wafer":"B160617", "cols": ["2","3","4"], 'xoffset': 0, 'duration': 29, 'calfile': "wedge_Ni_B160224b.dat",
            'reticle': 'SF1', 'angle': 0}]
nb_cmds.show_thickness_dependence(dbs=['results.txt'], devspecs=devspecs)
# fitted thickness distribution
import cryomem.fab.wedge as wedge

w = wedge.Wedge()
for chip in ["36", "35", "34", "33", "32", "31", "41"]:
    kwargs = {'calfile': 'wedge_Ni_B180329.dat', 'reticle': 'SF1', 'chip': chip, 'device': "A10", 'angle': 0,
                 "duration": 60}
    d = w.get_thickness(**kwargs)
    print("Chip {}: {:.2f} nm".format(chip, d))
# plot wedge rate calibration
#%matplotlib notebook
import cryomem.fab.wedge as wedge
import matplotlib.pyplot as plt
w = wedge.Wedge()
w.plot(calfile='wedge_Ni_B180329.dat', ncontour=20)
#plt.savefig("wedge.png")<jupyter_output><empty_output>
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