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Update Code/HoseiPlot.py

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xans 2024-09-28 12:48:47 +02:00
parent f2d92bd6ae
commit f37d292be6
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Code/HoseiPlot.py
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@ -11,111 +11,130 @@ import shutil
import csv import csv
basePath = os.getcwd() basePath = os.getcwd()
postprocjump = 0 ## Amount of INL averages
nrepeats = 2
## Initialize GPIB adapter ## Initialize GPIB adapter
GPIB = GPIBPrologix.ResourceManager("/dev/ttyACM0") GPIB = GPIBPrologix.ResourceManager("/dev/ttyACM0")
# Connect equipment # Connect equipment
#inst2 = GPIB.open_resource(30) # 4805 inst2 = GPIB.open_resource(30) # 4805
inst3 = GPIB.open_resource(22) # 3458A inst3 = GPIB.open_resource(22) # 3458A
inst4 = GPIB.open_resource(16) # R6581T inst4 = GPIB.open_resource(16) # R6581T
# Initialize BME280 temperature/humidity sensor try:
os.remove("data_post.csv")
except:
print('no datafile found to remove')
try:
os.remove("data.csv")
except:
print('no datafile found to remove')
# Initialize BME280 temperature/humidity sensor
bus = smbus2.SMBus(1) bus = smbus2.SMBus(1)
## Configure equipment ## Configure equipment
# BME280 temperature/humidity sensor # BME280 temperature/humidity sensor
calibration_params = bme280.load_calibration_params(bus, 0x76) calibration_params = bme280.load_calibration_params(bus, 0x76)
if postprocjump == 1: # Datron 4805 Calibrator
# Datron 4805 Calibrator inst2.query("F0=") #DCV
inst2.query("F0=") #DCV inst2.query("R6=") #10/100K Range
inst2.query("R6=") #10/100K Range inst2.write("G0=") #Local guard
inst2.query("S1=") #RemoteSense inst2.write("S0=") #Local sense
inst2.query("M0.0069=") #6.9mV Out, sanity check inst2.query("M-11.5=") #6.9mV Out, sanity check
inst2.query("O1=") #OutputON inst2.query("O1=") #OutputON
#Setup 3458A #Setup 3458A
#inst3.write("PRESET NORM") #inst3.write("PRESET NORM")
inst3.write("MEM OFF") inst3.write("MEM OFF")
inst3.write("OFORMAT ASCII") inst3.write("OFORMAT ASCII")
inst3.write("END ALWAYS") inst3.write("END ALWAYS")
inst3.write("TARM HOLD") inst3.write("TARM HOLD")
inst3.write("TRIG AUTO") inst3.write("TRIG AUTO")
inst3.write("DCV 10") inst3.write("DCV 10")
inst3.write("NRDGS 1,AUTO") inst3.write("NRDGS 1,AUTO")
inst3.write("NPLC 100") inst3.write("NPLC 100")
inst3.write("NDIG 9") inst3.write("NDIG 9")
inst3.write("AZERO ON") inst3.write("AZERO ON")
# Advantest R6581T # Advantest R6581T
inst4.write(":BEEP:STAT OFF") inst4.write(":BEEP:STAT OFF")
inst4.write(":CONF:VOLT:DC") inst4.write(":CONF:VOLT:DC")
inst4.write(":SENS:VOLT:DC:RANG 10") inst4.write(":SENS:VOLT:DC:RANG 10")
inst4.write(":SENS:VOLT:DC:NPLC 100") inst4.write(":SENS:VOLT:DC:NPLC 100")
inst4.write(":SENS:VOLT:DC:DIG MAX") inst4.write(":SENS:VOLT:DC:DIG MAX")
inst4.write(":ZERO:AUTO ON") inst4.write(":ZERO:AUTO ON")
# Measurement functions
def readValue(instObj,handle):
out = ""
if handle == "3458A":
instObj.write("TARM SGL,1")
for i in range(10):
out = instObj.read()
if out:
break
elif handle == "6581T":
out = instObj.query("FETch?")
return out
def setValue(instObj, inputVar):
instObj.query("M"+str(inputVar)+"=")
return inputVar
def getEnvironment(instObj, i2cbus):
value = instObj.sample(i2cbus, 0x76, calibration_params)
return value
setValue(inst2,-1)
#inst3.write("BEEP")
print('3458A reading: ',readValue(inst3,"3458A"))
#inst4.write("BEEP")
print('R6581T reading: ',readValue(inst4,"6581T"))
## Have gitpython pull in the repository # save original INL constants
import csv with open(basePath+"/original_hosei.txt", 'a') as f:
import time f.write(inst4.query("CAL:INT:DCV:HOSEI?"))
import datetime # write Advantest correction to zero, in theory not needed as at the end coefficients get summed.
import numpy as np inst4.write("CAL:EXT:EEPROM:PROTECTION 1")
import pandas as pd for i in range(15):
from math import sin print("CAL:INT:DCV:HOSEI {},{}".format(i,0))
## Create logfile inst4.write("CAL:INT:DCV:HOSEI {},{}".format(i,0))
with open(basePath+"/data.csv", 'a') as f: inst4.write("CAL:INT:DCV:HOSEI {},{}".format(15,1))
writer = csv.writer(f) print("CAL:INT:DCV:HOSEI {},{}".format(15,1))
writer.writerow(["DateTime","SetVolts","RefVolts","DutVolts","Env Pressure","Env Temperature", "Env Humidity"])
print(basePath+"/data.csv")
sweep = np.append(np.linspace(-11.5,11.5,int(11.5/0.5*2)+1), np.linspace(-0.1,0,int(0.1/0.01)+1)) # Measurement functions
sweep = sorted(sweep) def readValue(instObj,handle):
out = ""
if handle == "3458A":
instObj.write("TARM SGL,1")
for i in range(10):
out = instObj.read()
if out:
break
elif handle == "6581T":
out = instObj.query("FETch?")
return out
def setValue(instObj, inputVar):
instObj.query("M"+str(inputVar)+"=")
time.sleep(40)
return inputVar
def getEnvironment(instObj, i2cbus):
value = instObj.sample(i2cbus, 0x76, calibration_params)
return value
#inst3.write("BEEP")
print('3458A reading: ',readValue(inst3,"3458A"))
#inst4.write("BEEP")
print('R6581T reading: ',readValue(inst4,"6581T"))
cntr = 0 ## Have gitpython pull in the repository
d = datetime.datetime.now() import csv
dlast = datetime.datetime.now() import time
## Collect data import datetime
time.sleep(3) import numpy as np
import pandas as pd
from math import sin
## Create logfile
with open(basePath+"/data.csv", 'a') as f:
writer = csv.writer(f)
writer.writerow(["DateTime","SetVolts","RefVolts","DutVolts","Env Pressure","Env Temperature", "Env Humidity"])
print(basePath+"/data.csv")
cntr = 0
d = datetime.datetime.now()
dlast = datetime.datetime.now()
## Collect data
time.sleep(3)
for n in range(nrepeats):
sweep = np.append(np.linspace(-10,10,int(10/2*2)+1), np.linspace(-0.1,-0.02,int((0.1-0.02)/0.02)+1))
np.random.shuffle(sweep)
for x in sweep: for x in sweep:
cntr = cntr + 1 cntr = cntr + 1
try: try:
#Set volt and let accimatize #Set volt and let accimatize
setPoint = setValue(inst2,round(x,5)) setPoint = setValue(inst2,round(x,5))
timebetween = (d-dlast) * (len(sweep)-cntr) timebetween = (d-dlast) * (len(sweep)*nrepeats-cntr)
dlast = d dlast = d
print(str(cntr)+'/'+str(len(sweep))+' Estimated Time Left: '+str(timebetween)) print(str(cntr)+'/'+str(len(sweep)*nrepeats)+' Estimated Time Left: '+str(timebetween))
time.sleep(60)
for i in range(3): for i in range(3):
try: try:
time.sleep(5)
#Get DUT value #Get DUT value
readoutRef = readValue(inst3,"3458A") readoutRef = readValue(inst3,"3458A")
readoutDut = readValue(inst4,"6581T") readoutDut = readValue(inst4,"6581T")
#Get envirnmental values #Get envirnmental values
data = getEnvironment(bme280, bus) data = getEnvironment(bme280, bus)
#Write to file #Write to file
d = datetime.datetime.now() d = datetime.datetime.now()
dx = d - datetime.timedelta(microseconds=d.microsecond) dx = d - datetime.timedelta(microseconds=d.microsecond)
fields=[dx.strftime("%d-%m-%y %H:%M:%S"),setPoint,float(readoutRef),float(readoutDut),round(data.humidity,2),round(data.temperature,2),round(data.pressure,2)] fields=[dx.strftime("%d-%m-%y %H:%M:%S"),setPoint,float(readoutRef),float(readoutDut),round(data.pressure,2),round(data.temperature,2),round(data.humidity,2)]
print(fields) print(fields)
with open(basePath+"/data.csv", 'a') as f: with open(basePath+"/data.csv", 'a') as f:
writer = csv.writer(f) writer = csv.writer(f)
@ -128,28 +147,36 @@ if postprocjump == 1:
time.sleep(1) time.sleep(1)
# initialize the image to plot as correction goes # initialize the image to plot as correction goes
fig = make_subplots(rows=4, cols=1) fig = make_subplots(rows=3, cols=1)
## Calculate high order polynomial ## Calculate high order polynomial
df = pd.read_csv(basePath+"/data.csv") df = pd.read_csv(basePath+"/data.csv")
df = df.groupby(["SetVolts"], as_index=False).mean(numeric_only=True) df = df.groupby(["SetVolts"], as_index=False).mean(numeric_only=True)
tmpa = df.loc[df['SetVolts'] == 0]["RefVolts"].iloc[0]
tmpb = df.loc[df['SetVolts'] == 0]["DutVolts"].iloc[0]
# compensate for offset error around 0 -> this is cal error instead of INL # compensate for offset error around 0 -> this is cal error instead of INL
df["RefVolts"] = df["RefVolts"] - df.loc[df['SetVolts'] == 0]["RefVolts"].iloc[0] df["RefVolts"] = df["RefVolts"] - tmpa
df["DutVolts"] = df["DutVolts"] - df.loc[df['SetVolts'] == 0]["DutVolts"].iloc[0] df["DutVolts"] = df["DutVolts"] - tmpb
#df["OptVolts"] = df["OptVolts"] - df.loc[df['SetVolts'] == 0]["OptVolts"].iloc[0]
# get 1st order to 10V -> 10V gain error, this is cal error instead of INL, -10 could be due to INL. # get 1st order to 10V -> 10V gain error, this is cal error instead of INL, -10 could be due to INL.
# this is heavily dependant on cal procedure, say 7V is the reference calibration point # this is heavily dependant on cal procedure, say 7V is the reference calibration point
alphaRef = df.loc[df['SetVolts'] == 10]["RefVolts"].iloc[0]/10 alphaRef = df.loc[df['SetVolts'] == 10]["RefVolts"].iloc[0]/10
alphaDut = df.loc[df['SetVolts'] == 10]["DutVolts"].iloc[0]/10 alphaDut = df.loc[df['SetVolts'] == 10]["DutVolts"].iloc[0]/10
#alphaOpt = df.loc[df['SetVolts'] == 10]["OptVolts"].iloc[0]/10
# remove 1st order to get the INL # remove 1st order to get the INL
df["RefVolts"] = df["RefVolts"] - alphaRef*df['SetVolts'] df["RefVolts"] = df["RefVolts"] - alphaRef*df['SetVolts']
df["DutVolts"] = df["DutVolts"] - alphaDut*df['SetVolts'] df["DutVolts"] = df["DutVolts"] - alphaDut*df['SetVolts']
#df["OptVolts"] = df["OptVolts"] - alphaOpt*df['SetVolts']
df["DutToRefVolts"] = df['DutVolts'] - df["RefVolts"] df["DutToRefVolts"] = df['DutVolts'] - df["RefVolts"]
# plot INL of calibrator # plot INL of calibrator
fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["RefVolts"],mode='lines+markers',name='Original REF INL'),row=1, col=1) fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["RefVolts"],mode='lines+markers',name='Original REF INL'),row=1, col=1)
fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["DutVolts"],mode='lines+markers',name='Original DUT INL'),row=1, col=1) fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["DutVolts"],mode='lines+markers',name='Original DUT INL'),row=1, col=1)
fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["DutToRefVolts"],mode='lines+markers',name='DUT vs REF'),row=2, col=1) #fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["OptVolts"],mode='lines+markers',name='Original OPT INL'),row=1, col=1)
fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["DutToRefVolts"],mode='lines+markers',name='Original DUT vs REF'),row=2, col=1)
## Make plot and save
fig.write_html('inl_evaluation_plots.html')
fig.write_image('inl_evaluation_plots.png',width=720, height=1280)
# generate correction parameters # generate correction parameters
# default correction assumed unless minMaxEqual is set, this will equalize error above and under ideal correction # default correction assumed unless minMaxEqual is set, this will equalize error above and under ideal correction
@ -161,7 +188,7 @@ if minMaxEqual:
# not implemented as of yet # not implemented as of yet
print("not implemented") print("not implemented")
else: else:
# postive part of INL # postive part of INL
correctionfactors[0] = (df.loc[df['SetVolts'] == 0]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 2]["DutToRefVolts"].iloc[0])/2 correctionfactors[0] = (df.loc[df['SetVolts'] == 0]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 2]["DutToRefVolts"].iloc[0])/2
correctionfactors[1] = (df.loc[df['SetVolts'] == 2]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 4]["DutToRefVolts"].iloc[0])/2 correctionfactors[1] = (df.loc[df['SetVolts'] == 2]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 4]["DutToRefVolts"].iloc[0])/2
correctionfactors[2] = (df.loc[df['SetVolts'] == 4]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 6]["DutToRefVolts"].iloc[0])/2 correctionfactors[2] = (df.loc[df['SetVolts'] == 4]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 6]["DutToRefVolts"].iloc[0])/2
@ -181,14 +208,93 @@ else:
correctionfactors[13] = (df.loc[df['SetVolts'] == -8]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -6]["DutToRefVolts"].iloc[0])/2 correctionfactors[13] = (df.loc[df['SetVolts'] == -8]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -6]["DutToRefVolts"].iloc[0])/2
correctionfactors[14] = (df.loc[df['SetVolts'] == -10]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -8]["DutToRefVolts"].iloc[0])/2 correctionfactors[14] = (df.loc[df['SetVolts'] == -10]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -8]["DutToRefVolts"].iloc[0])/2
# negative 10v voltage reversal alpha error for faster measurements # negative 10v voltage reversal alpha error for faster measurements
correctionfactors[15] = 1+minusRangeScaling correctionfactors[15] = round(1+minusRangeScaling,8)
# negative 10v voltage reversal alpha error for faster measurements
## Make plot and save
fig.write_image('inl_evaluation_plots.png',width=720, height=1280)
fig.write_html('inl_evaluation_plots.html')
## Read the HOSEI parameters and save the original constants ## Read the HOSEI parameters and save the original constants
## add constants together in case of nonzero sweep constants
inst4.write("CAL:EXT:EEPROM:PROTECTION 1") inst4.write("CAL:EXT:EEPROM:PROTECTION 1")
print(inst4.query("CAL:INT:DCV:HOSEI?")) print(inst4.query("CAL:INT:DCV:HOSEI?"))
with open(basePath+"/original_hosei.txt", 'a') as f: tmp = inst4.query("CAL:INT:DCV:HOSEI?")
tmp = tmp.replace(',',' ').split()[1::2]
tmp = [float(i) for i in tmp[:-2]]
correctionfactors = [round(tmp[i]+correctionfactors[i],8) for i in range(0,len(correctionfactors)-1)]
correctionfactors.append(1+minusRangeScaling+1-tmp[15])
for idx in range(0,len(correctionfactors)):
print("CAL:INT:DCV:HOSEI {},{}".format(idx,correctionfactors[idx]))
inst4.write("CAL:INT:DCV:HOSEI {},{}".format(idx,correctionfactors[idx]))
with open(basePath+"/new_hosei.txt", 'a') as f:
f.write(inst4.query("CAL:INT:DCV:HOSEI?")) f.write(inst4.query("CAL:INT:DCV:HOSEI?"))
print(inst4.query("CAL:INT:DCV:HOSEI?"))
inst4.write("CAL:EXT:EEPROM:PROTECTION 0")
## Create logfile
with open(basePath+"/data_post.csv", 'a') as f:
writer = csv.writer(f)
writer.writerow(["DateTime","SetVolts","RefVolts","DutVolts","Env Pressure","Env Temperature", "Env Humidity"])
print(basePath+"/data_post.csv")
cntr = 0
d = datetime.datetime.now()
dlast = datetime.datetime.now()
## Collect data
time.sleep(3)
for n in range(nrepeats):
sweep = np.append(np.linspace(-10,10,int(10/2*2)+1), np.linspace(-0.1,-0.02,int((0.1-0.02)/0.02)+1))
np.random.shuffle(sweep)
for x in sweep:
cntr = cntr + 1
try:
#Set volt and let accimatize
setPoint = setValue(inst2,round(x,5))
timebetween = (d-dlast) * (len(sweep)*nrepeats-cntr)
dlast = d
print(str(cntr)+'/'+str(len(sweep)*nrepeats)+' Estimated Time Left: '+str(timebetween))
for i in range(3):
try:
#Get DUT value
readoutRef = readValue(inst3,"3458A")
readoutDut = readValue(inst4,"6581T")
#Get envirnmental values
data = getEnvironment(bme280, bus)
#Write to file
d = datetime.datetime.now()
dx = d - datetime.timedelta(microseconds=d.microsecond)
fields=[dx.strftime("%d-%m-%y %H:%M:%S"),setPoint,float(readoutRef),float(readoutDut),round(data.pressure,2),round(data.temperature,2),round(data.humidity,2)]
print(fields)
with open(basePath+"/data_post.csv", 'a') as f:
writer = csv.writer(f)
writer.writerow(fields)
except Exception as e:
time.sleep(15)
i = i-1
except Exception as e:
print(e)
time.sleep(1)
# Calculate high order polynomial
df = pd.read_csv(basePath+"/data_post.csv")
df = df.groupby(["SetVolts"], as_index=False).mean(numeric_only=True)
# compensate for offset error around 0 -> this is cal error instead of INL
df["RefVolts"] = df["RefVolts"] - df.loc[df['SetVolts'] == 0]["RefVolts"].iloc[0]
df["DutVolts"] = df["DutVolts"] - df.loc[df['SetVolts'] == 0]["DutVolts"].iloc[0]
#df["OptVolts"] = df["OptVolts"] - df.loc[df['SetVolts'] == 0]["OptVolts"].iloc[0]
# get 1st order to 10V -> 10V gain error, this is cal error instead of INL, -10 could be due to INL.
# this is heavily dependant on cal procedure, say 7V is the reference calibration point
alphaRef = df.loc[df['SetVolts'] == 10]["RefVolts"].iloc[0]/10
alphaDut = df.loc[df['SetVolts'] == 10]["DutVolts"].iloc[0]/10
#alphaOpt = df.loc[df['SetVolts'] == 10]["OptVolts"].iloc[0]/10
# remove 1st order to get the INL
df["RefVolts"] = df["RefVolts"] - alphaRef*df['SetVolts']
df["DutVolts"] = df["DutVolts"] - alphaDut*df['SetVolts']
#df["OptVolts"] = df["OptVolts"] - alphaOpt*df['SetVolts']
df["DutToRefVolts"] = df['DutVolts'] - df["RefVolts"]
# plot INL of calibrator
fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["RefVolts"],mode='lines+markers',name='Post REF INL'),row=3, col=1)
fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["DutVolts"],mode='lines+markers',name='Post DUT INL'),row=3, col=1)
#fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["OptVolts"],mode='lines+markers',name='Original OPT INL'),row=1, col=1)
fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["DutToRefVolts"],mode='lines+markers',name='Post DUT vs REF'),row=2, col=1)
## Make plot and save
fig.write_html('inl_evaluation_plots.html')
fig.write_image('inl_evaluation_plots.png',width=720, height=1280)