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geninst.py
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geninst.py
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import numpy as np
import numpy.ma as ma
from aopliab.aopliab_common import within_limits, nearest_index
from time import sleep
class PreAmp():
"""
Generic base class for preamplifiers
"""
inst = None
freqs = np.array([])
senss = np.array([])
phases = np.array([])
max_output = 1.
current_freq = 1e3
detects_overload = False
overload_delay = 0.1
adc = None
ovrld = None
def __init__(self, inst):
pass
def close(self):
pass
@property
def sensitivity_index(self):
pass
@property
def sensitivity(self):
pass
@sensitivity.setter
def sensitivity(self):
pass
def freq_cutoff(self, sens):
pass
@property
def inc_sensitivity(self):
"""Returns next higher incremental sensitivity"""
if self.adc is not None:
curlev = np.abs(self.adc()*self.sensitivity)
else:
curlev = -1.
nidx = self.sensitivity_index - 1
if nidx < 0 or self.max_output < curlev/self.senss[nidx]:
#print(f"nidx {nidx} curlev/self.senss[nidx] {curlev/self.senss[nidx]} self.max_output {self.max_output}")
return np.nan
if self.freq_cutoff(self.senss[nidx]) <= self.current_freq:
#print(f"freq cutoff")
return np.nan
return self.senss[nidx]
@property
def dec_sensitivity(self):
"""Returns next lower incremental sensitivity"""
nidx = self.sensitivity_index + 1
if nidx >= self.senss.size:
return np.nan
nsens = self.senss[nidx]
return nsens
@property
def phase_shift(self):
"""Phase shift for current sensitivity"""
return self.phases[self.sensitivity_index]
@phase_shift.setter
def phase_shift(self, value):
self.phases[self.sensitivity_index] = value
@property
def overload(self):
"""Returns True if currently overloaded"""
if self.ovrld is not None:
if self.ovrld():
if self.adc is None:
return True
else:
return False
if self.adc is not None:
ms0 = self.adc()
if (self.max_output < ms0):
sleep(0.02)
ms1 = self.adc()
while ms1 < ms0:
ms0 = ms1
sleep(0.02)
ms1 = self.adc()
return (self.max_output < np.abs(self.adc()))
else:
return False
def fix_overload(self):
"""Attempts to fix overload error and returns final overload state"""
while (self.overload and not np.isnan(self.dec_sensitivity)):
self.sensitivity = self.dec_sensitivity
return self.overload
@property
def noise_base(self):
"""Returns baseline noise level of current settings"""
pass
class LockInAmplifier():
"""Generic base class for lockin-amplifiers"""
tcons = np.array([])
slopes = np.array([])
noise_base = np.array([])
noise_ratio = np.array([])
noise_slope = np.array([])
tol_maxsettle = 20.
tol_rel = np.array([])
tol_abs = np.array([])
preamps = np.array([])
auto_scale = False
auto_dewll = False
auto_phase = False
auto_wait = False
auto_phase_tc = 5.
last_mags = np.array([])
last_meas = np.array([])
min_sleep = 0.001
enbws = ma.array([])
waittimes = ma.array([])
lock_time_constant = True
auto_phase_slope = 12
def __init__(self, inst):
pass
@property
def senss(self):
"""List of available sensitivities"""
pass
@property
def sensitivity_index(self):
pass
@property
def sensitivity(self):
"""Current sensitivity (full scale range)"""
pass
@sensitivity.setter
def sensitivity(self, value):
pass
@property
def inc_sensitivity(self):
"""Next higher sensitivity (more sensitive)"""
nidx = self.sensitivity_index - 1
rtn = np.ones(nidx.size)
for k, n in enumerate(nidx):
if n < 0:
rtn[k] = np.nan
else:
rtn[k] = self.senss[n, k]
return rtn
@property
def dec_sensitivity(self):
"""Next lower sensitivity (less sensitive)"""
nidx = self.sensitivity_index + 1
rtn = np.ones(nidx.size)
for k, n in enumerate(nidx):
if n >= self.senss.shape[0]:
rtn[k] = np.nan
else:
rtn[k] = self.senss[n, k]
return rtn
def noise_measure(self, tc_noise, tc_mag, slope_noise,
slope_mag):
"""
Perform noise measurement
Parameters
----------
tc_noise : Filter time constant for noise measurement
tc_mag : Filter time constant for magnitude measurement
slope_noise: Filter slope for noise measurement
slope_mag: Filter slope for magnitude measurement
"""
pass
@property
def time_constant(self):
pass
@time_constant.setter
def time_constant(self, value):
pass
@property
def wait_time(self):
"""Dwell time needed to reach 99 percent of value"""
pass
@property
def slope(self):
pass
@slope.setter
def slope(self):
pass
@property
def enbw(self):
"""Equivalent noise bandwidth for current settings"""
pass
@property
def freq(self):
pass
@property
def phaseoff(self):
pass
@phaseoff.setter
def phaseoff(self, value):
pass
def system_auto_phase(self):
pass
@property
def cmeas(self):
"""Current measurement values (accounting for preamp)"""
pass
@property
def cmags(self):
"""Current magnitude values (accounting for preamp)"""
pass
def adc(self, index):
"""Reading of analog to digital converter"""
pass
@property
def jointSensitivity(self):
"""Product of sensitivity with preamp sensitivity"""
sens = self.sensitivity
for k, slia, pre in zip(range(sens.size), sens, self.preamps):
if pre is not None:
sens[k] = slia*pre.sensitivity
return sens
@property
def meas(self):
"""Measurement function with automatic functions"""
if np.any(np.isnan(self.last_mags)):
self.last_mags = self.cmags
scaled = False
dwelled = False
waited = False
while ((self.auto_scale and not scaled) or
(self.auto_dewll and not dwelled) or
(self.auto_wait and not waited)):
if self.auto_scale:
tmags = self.last_mags
tnse = 2.*self.approx_noise(tmags)*np.sqrt(self.enbw)
scaled = not self.update_scale(np.vstack((tmags, tnse)).max(axis=0))
if self.auto_dwell:
dwelled = not self.update_timeconstant(self.last_mags)
if self.auto_dewll or self.auto_wait:
sleep(self.wait_time)
waited = True
tmp = self.cmags
if self.auto_scale and np.any(np.abs(1.-tmp/self.last_mags) > 0.5):
scaled = False
self.last_mags = tmp
return self.cmeas
def update_scale(self, mags):
"""
Update sensitivities from last measurement magnitudes
"""
remeas = False
js = self.jointSensitivity
for k, m in enumerate(mags):
change = False
if self.preamps[k] is not None:
cps = self.preamps[k].sensitivity
if self.preamps[k].overload:
self.preamps[k].fix_overload()
m = m*self.preamps[k].sensitivity/cps
js[k] = js[k]*self.preamps[k].sensitivity/cps
cps = self.preamps[k].sensitivity
change = True
elif m <= 0.05*js[k]:
nps = self.preamps[k].inc_sensitivity
if not np.isnan(nps):
self.preamps[k].sensitivity = nps
cps = nps
change = True
sleep(0.1)
if self.preamps[k].overload:
print("GAHH FUCK")
print(f"adc() {self.preamps[k].adc()}, max output {self.preamps[k].max_output}, tia.sens {cps}")
self.preamps[k].fix_overload()
m = m*self.preamps[k].sensitivity/cps
js[k] = js[k]*self.preamps[k].sensitivity/cps
cps = self.preamps[k].sensitivity
change = True
if m >= 0.9*js[k]:
if not np.isnan(self.dec_sensitivity[k]):
self.sensitivity = (k, self.dec_sensitivity[k])
change = True
else:
nps = self.preamps[k].dec_sensitivity
if not np.isnan(nps):
self.preamps[k].sensitivity = nps
cps = nps
change = True
if self.auto_phase and change:
if np.isnan(self.preamps[k].phase_shift):
ctc = self.time_constant
csl = self.slope
self.time_constant = self.auto_phase_tc
self.slope = self.auto_phase_slope
sleep(self.wait_time)
self.system_auto_phase(k)
self.time_constant = ctc
self.slope = csl
self.preamps[k].phase_shift = self.phaseoff[k]
else:
self.phaseoff = (k, self.preamps[k].phase_shift)
remeas = (remeas or (change and (m > js[k] or m < 0.1*js[k])))
if (not change and m <= 0.3*js[k] and
not np.isnan(self.inc_sensitivity[k])):
self.sensitivity = (k, self.inc_sensitivity[k])
remeas = (remeas or m < 0.1*js[k])
elif (not change and m >= 0.9*js[k] and
not np.isnan(self.dec_sensitivity[k])):
self.sensitivity = (k, self.dec_sensitivity[k])
remeas = (remeas or m > js[k])
return remeas
def update_timeconstant(self, mags):
"""
Update time constant from last magnitudes
Uses noise estimates and tolerance settings
"""
remeas = np.any(
self.tolerance(mags) <
(self.approx_noise(mags)*np.sqrt(self.enbw)))
dfs = np.power(self.tolerance(mags)/self.approx_noise(mags), 2)
tcs = np.zeros(mags.size)
slopes = np.zeros(mags.size)
wts = np.zeros(mags.size)
for k, df in enumerate(dfs):
if np.any(self.enbws <= df):
k0, k1 = np.where(
self.waittimes == self.waittimes[
(self.enbws <= df)].min())
else:
k0, k1 = np.where(
self.waittimes == self.waittimes.max())
if k0.size > 1 and k1.size > 1:
k2 = np.argmin(np.array([
self.enbws[n0, n1] for n0, n1 in zip(k0, k1)]))
k0 = k0[k2]
k1 = k1[k2]
else:
k0 = k0[0]
k1 = k1[0]
tcs[k] = self.tcons[k0]
slopes[k] = self.slopes[k1]
wts[k] = self.waittimes[k0, k1]
k = np.argmax(wts)
tc = tcs[k]
slope = slopes[k]
wait = wts[k]
if wait > self.tol_maxsettle:
tc, slope, wait = self.best_tc_for_wait(self.tol_maxsettle)
if self.wait_time >= wait:
remeas = False
return False
if wait == self.wait_time:
remeas = False
return False
remeas = (remeas or self.wait_time < wait)
self.time_constant = tc
self.slope = slope
return remeas
def best_tc_for_wait(self, wait):
"""Time constant that will reach at least 99 percent within time"""
k0, k1 = np.where(self.enbws == self.enbws[self.waittimes <= wait].min())
return (self.tcons[k0[0]], self.slopes[k1[0]], self.waittimes[k0[0], k1[0]])
def tolerance(self, mags):
"""Tolerance based on last measured values"""
return np.vstack((
mags*self.tol_rel,
self.tol_abs)).max(axis=0)
def approx_noise(self, mags):
"""Noise approximation based on slope and baseline"""
if self.noise_slope.size > 0:
mags = np.power(mags, self.noise_slope)
if self.noise_ratio.size > 0:
mags = self.noise_ratio*mags
if self.noise_base.size > 0:
mags = np.sqrt(np.power(mags, 2)+np.power(self.noise_base, 2))
return mags
class ParameterAnalyzer():
_use_channels = []
def __init__(self, inst):
pass
@property
def use_channels(self):
pass
@use_channels.setter
def use_channels(self, value):
pass
class SMU():
use_ascii = True
def __init__(self, inst=None, parent=None, number=None):
pass
@property
def output(self):
"""
Output state of the SMU (True = on)
"""
pass
@output.setter
def output(self, value):
"""
Set the output state of the SMU (True = on)
"""
pass
@property
def source_range(self):
"""
Source range (None=Auto)
"""
@source_range.setter
def source_range(self, value):
pass
@property
def source_voltage(self):
"""
Source in voltage mode (True=Voltage, False=Current)
"""
pass
@source_voltage.setter
def source_voltage(self, value):
"""
Source in voltage mode (True=Voltage, False=Current)
"""
pass
@property
def source_mode(self):
"""
Source mode ('LINear' / 'LOGarithmic' / 'LIST' / 'FIXed')
"""
pass
@source_mode.setter
def source_mode(self, value):
"""
Source mode ('LINear' / 'LOGarithmic' / 'LIST' / 'FIXed')
"""
pass
@property
def sweep_up(self):
"""
bool sweep up == True
"""
pass
@sweep_up.setter
def sweep_up(self):
"""
bool sweep up == True
"""
pass
@property
def sweep_points(self):
"""
int number of points
"""
pass
@sweep_points.setter
def sweep_points(self, value):
"""
int number of points
"""
pass
@property
def sweep_bidirectional(self):
"""
bool bi-directional sweep (double)
"""
pass
@sweep_bidirectional.setter
def sweep_bidirectional(self, value):
"""
bool bi-directional sweep (double)
"""
pass
@property
def bias(self):
"""
Source value/range/values
"""
pass
@bias.setter
def bias(self, value):
"""
Source value/range/values
"""
pass
@property
def compliance(self):
"""
Compliance value
"""
pass
@compliance.setter
def compliance(self, value):
"""
Compliance value
"""
pass
@property
def kelvin(self):
"""
bool Kelvin or 4 point probe mode
"""
pass
@kelvin.setter
def kelvin(self, value):
"""
bool Kelvin or 4 point probe mode
"""
pass
@property
def integration_time(self):
"""
Integration time in seconds
"""
pass
@integration_time.setter
def integration_time(self, value):
"""
Integration time in seconds
"""
pass
@property
def integration_time_NPLC(self):
"""
Integration time in power line cycles
"""
pass
@integration_time_NPLC.setter
def integration_time_NPLC(self, value):
"""
Integration time in power line cycles
"""
pass
@property
def sense_range(self):
"""
Measurement range None = Auto
"""
pass
@sense_range.setter
def sense_range(self, value):
"""
Measurement range
"""
pass
@property
def sense_threshold(self):
"""
Threshold to auto change range in %
"""
pass
@sense_threshold.setter
def sense_threshold(self, value):
"""
Threshold to auto change range in %
"""
pass
@property
def sense_range_auto_llim(self):
"""
Minimum range to switch to for auto-ranging
"""
pass
@sense_range_auto_llim.setter
def sense_range_auto_llim(self, value):
"""
Minimum range to switch to for auto-ranging
"""
pass
@property
def sense_range_auto_ulim(self):
"""
Maximum range to switch to for auto-ranging
"""
pass
@sense_range_auto_ulim.setter
def sense_range_auto_ulim(self, value):
"""
Maximum range to switch to for auto-ranging
"""
pass
@property
def pulse(self):
"""
bool Pulsed mode
"""
pass
@pulse.setter
def pulse(self, value):
"""
bool Pulsed mode
"""
pass
@property
def trigger_source(self):
"""
source to trigger off of
"""
pass
@trigger_source.setter
def trigger_source(self, value):
"""
source to trigger off of
"""
pass
@property
def timer_interval(self):
"""
interval in seconds between internal triggers
"""
pass
@timer_interval.setter
def timer_interval(self):
"""
interval in seconds between internal triggers
"""
pass
@property
def trigger_transition_delay(self):
"""
delay time for value transition in seconds
"""
pass
@trigger_transition_delay.setter
def trigger_transition_delay(self, value):
"""
delay time for value transition in seconds
"""
pass
@property
def trigger_aquire_delay(self):
"""
delay time for measurement start in seconds
"""
pass
@trigger_aquire_delay.setter
def trigger_aquire_delay(self, value):
"""
delay time for measurement start in seconds
"""
pass
@property
def source_wait(self):
"""
wait time for source in seconds
"""
pass
@source_wait.setter
def source_wait(self, value):
"""
wait time for source in seconds
"""
pass
@property
def sense_wait(self):
"""
wait time for sense in seconds
"""
pass
@sense_wait.setter
def sense_wait(self, value):
"""
wait time for sense in seconds
"""
pass
@property
def pulse_delay(self):
"""
delay time before pulse in seconds
"""
pass
@pulse_delay.setter
def pulse_delay(self):
"""
delay time before pulse in seconds
"""
pass
@property
def pulse_width(self):
"""
pulse width in seconds
"""
pass
@pulse_width.setter
def pulse_width(self, value):
"""
pulse width in seconds
"""
pass