ivscan Namespace Reference

Variables
	args = ky.parse_arguments()
	parse arguments
	outfiletagname = ky.build_tagname(args)
	output file run tag
	starttime = time.time()
	start
dict	Vbd_refV = {'SENSL' : 24.5 , 'BCOM' : 26.9 , 'FBK' : 32.0 }
	Vbd reference values from datasheet.
dict	Vbd_refT = {'SENSL' : 21.0 , 'BCOM' : 25.0 , 'FBK' : 24.0 }
dict	Vbd_coef = {'SENSL' : 0.021 , 'BCOM' : 0.025 , 'FBK' : 0.035 }
dict	NaverT = {293 : 1 , 283 : 1 , 273 : 2 , 263 : 2 , 253 : 4 , 243 : 4}
	temperature dependent optimisation
dict	Naver = NaverT[args.temperature]
dict	refV = Vbd_refV[args.board]
	calculate expected Vbd
dict	refT = Vbd_refT[args.board]
dict	coef = Vbd_coef[args.board]
dict	Vbd = refV + coef * (args.temperature - 273 - refT)
	fig
	add time tag timenow = datetime.datetime.now() timetag = timenow.strftime('YmdHMS') outfiletagname += '.
	ax
	add time tag timenow = datetime.datetime.now() timetag = timenow.strftime('YmdHMS') outfiletagname += '.
	figsize
dict	Vmin = Vbd - 2.
	connect and initialize
dict	Vmax = Vbd + 15.
float	Vstep = 0.200
	Vscan = np.arange(Vmin, Vmax, Vstep)
	configure source, measure and trigger model
list	Vsmart = [Vbd, Vbd + 5.]
	Ilim
	reverse
	True
	Twait
	Tstep
	Tmeas
	Nmeas
	measurements = ky.read_measurements()
	start trigger model and wait
	invertX
	plot measurements
	invertY
	endtime = time.time()
	write measurements

Variable Documentation

args

ivscan.args = ky.parse_arguments()

parse arguments

ax

ivscan.ax

add time tag timenow = datetime.datetime.now() timetag = timenow.strftime('YmdHMS') outfiletagname += '.

plot measurements

' + timetag

define plots

coef

dict ivscan.coef = Vbd_coef[args.board]

endtime

ivscan.endtime = time.time()

write measurements

write commands

close connection

done

fig

ivscan.fig

add time tag timenow = datetime.datetime.now() timetag = timenow.strftime('YmdHMS') outfiletagname += '.

' + timetag

define plots

figsize

ivscan.figsize

Ilim

ivscan.Ilim

invertX

ivscan.invertX

plot measurements

invertY

ivscan.invertY

measurements

ivscan.measurements = ky.read_measurements()

start trigger model and wait

read measurements

Naver

ivscan.Naver = NaverT[args.temperature]

NaverT

dict ivscan.NaverT = {293 : 1 , 283 : 1 , 273 : 2 , 263 : 2 , 253 : 4 , 243 : 4}

temperature dependent optimisation

Nmeas

ivscan.Nmeas

outfiletagname

ivscan.outfiletagname = ky.build_tagname(args)

output file run tag

refT

dict ivscan.refT = Vbd_refT[args.board]

refV

dict ivscan.refV = Vbd_refV[args.board]

calculate expected Vbd

reverse

ivscan.reverse

starttime

ivscan.starttime = time.time()

start

Tmeas

ivscan.Tmeas

True

ivscan.True

Tstep

ivscan.Tstep

Twait

ivscan.Twait

Vbd

ivscan.Vbd = refV + coef * (args.temperature - 273 - refT)

Vbd_coef

dict ivscan.Vbd_coef = {'SENSL' : 0.021 , 'BCOM' : 0.025 , 'FBK' : 0.035 }

Vbd_refT

dict ivscan.Vbd_refT = {'SENSL' : 21.0 , 'BCOM' : 25.0 , 'FBK' : 24.0 }

Vbd_refV

dict ivscan.Vbd_refV = {'SENSL' : 24.5 , 'BCOM' : 26.9 , 'FBK' : 32.0 }

Vbd reference values from datasheet.

Vmax

dict ivscan.Vmax = Vbd + 15.

Vmin

dict ivscan.Vmin = Vbd - 2.

connect and initialize

REVERSE BIAS SCAN ###

[Vbd - 2, Vbd + 10] in 200 mV steps

Vscan

ivscan.Vscan = np.arange(Vmin, Vmax, Vstep)

configure source, measure and trigger model

Vsmart

ivscan.Vsmart = [Vbd, Vbd + 5.]

Vstep

float ivscan.Vstep = 0.200