194 lines
8.9 KiB
Python
194 lines
8.9 KiB
Python
import GPIBPrologix
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import bme280
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import smbus2
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from requests import post
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import time
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from plotly.subplots import make_subplots
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import plotly.graph_objects as go
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import pandas as pd
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import os
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import shutil
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import csv
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basePath = os.getcwd()
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postprocjump = 0
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## Initialize GPIB adapter
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GPIB = GPIBPrologix.ResourceManager("/dev/ttyACM0")
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# Connect equipment
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#inst2 = GPIB.open_resource(30) # 4805
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inst3 = GPIB.open_resource(22) # 3458A
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inst4 = GPIB.open_resource(16) # R6581T
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# Initialize BME280 temperature/humidity sensor
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bus = smbus2.SMBus(1)
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## Configure equipment
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# BME280 temperature/humidity sensor
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calibration_params = bme280.load_calibration_params(bus, 0x76)
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if postprocjump == 1:
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# Datron 4805 Calibrator
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inst2.query("F0=") #DCV
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inst2.query("R6=") #10/100K Range
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inst2.query("S1=") #RemoteSense
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inst2.query("M0.0069=") #6.9mV Out, sanity check
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inst2.query("O1=") #OutputON
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#Setup 3458A
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#inst3.write("PRESET NORM")
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inst3.write("MEM OFF")
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inst3.write("OFORMAT ASCII")
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inst3.write("END ALWAYS")
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inst3.write("TARM HOLD")
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inst3.write("TRIG AUTO")
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inst3.write("DCV 10")
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inst3.write("NRDGS 1,AUTO")
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inst3.write("NPLC 100")
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inst3.write("NDIG 9")
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inst3.write("AZERO ON")
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# Advantest R6581T
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inst4.write(":BEEP:STAT OFF")
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inst4.write(":CONF:VOLT:DC")
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inst4.write(":SENS:VOLT:DC:RANG 10")
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inst4.write(":SENS:VOLT:DC:NPLC 100")
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inst4.write(":SENS:VOLT:DC:DIG MAX")
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inst4.write(":ZERO:AUTO ON")
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# Measurement functions
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def readValue(instObj,handle):
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out = ""
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if handle == "3458A":
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instObj.write("TARM SGL,1")
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for i in range(10):
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out = instObj.read()
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if out:
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break
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elif handle == "6581T":
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out = instObj.query("FETch?")
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return out
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def setValue(instObj, inputVar):
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instObj.query("M"+str(inputVar)+"=")
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return inputVar
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def getEnvironment(instObj, i2cbus):
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value = instObj.sample(i2cbus, 0x76, calibration_params)
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return value
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setValue(inst2,-1)
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#inst3.write("BEEP")
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print('3458A reading: ',readValue(inst3,"3458A"))
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#inst4.write("BEEP")
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print('R6581T reading: ',readValue(inst4,"6581T"))
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## Have gitpython pull in the repository
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import csv
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import time
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import datetime
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import numpy as np
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import pandas as pd
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from math import sin
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## Create logfile
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with open(basePath+"/data.csv", 'a') as f:
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writer = csv.writer(f)
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writer.writerow(["DateTime","SetVolts","RefVolts","DutVolts","Env Pressure","Env Temperature", "Env Humidity"])
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print(basePath+"/data.csv")
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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))
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sweep = sorted(sweep)
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cntr = 0
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d = datetime.datetime.now()
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dlast = datetime.datetime.now()
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## Collect data
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time.sleep(3)
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for x in sweep:
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cntr = cntr + 1
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try:
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#Set volt and let accimatize
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setPoint = setValue(inst2,round(x,5))
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timebetween = (d-dlast) * (len(sweep)-cntr)
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dlast = d
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print(str(cntr)+'/'+str(len(sweep))+' Estimated Time Left: '+str(timebetween))
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time.sleep(60)
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for i in range(3):
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try:
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time.sleep(5)
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#Get DUT value
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readoutRef = readValue(inst3,"3458A")
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readoutDut = readValue(inst4,"6581T")
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#Get envirnmental values
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data = getEnvironment(bme280, bus)
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#Write to file
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d = datetime.datetime.now()
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dx = d - datetime.timedelta(microseconds=d.microsecond)
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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)]
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print(fields)
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with open(basePath+"/data.csv", 'a') as f:
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writer = csv.writer(f)
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writer.writerow(fields)
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except Exception as e:
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time.sleep(15)
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i = i-1
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except Exception as e:
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print(e)
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time.sleep(1)
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# initialize the image to plot as correction goes
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fig = make_subplots(rows=4, cols=1)
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## Calculate high order polynomial
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df = pd.read_csv(basePath+"/data.csv")
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df = df.groupby(["SetVolts"], as_index=False).mean(numeric_only=True)
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# compensate for offset error around 0 -> this is cal error instead of INL
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df["RefVolts"] = df["RefVolts"] - df.loc[df['SetVolts'] == 0]["RefVolts"].iloc[0]
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df["DutVolts"] = df["DutVolts"] - df.loc[df['SetVolts'] == 0]["DutVolts"].iloc[0]
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# get 1st order to 10V -> 10V gain error, this is cal error instead of INL, -10 could be due to INL.
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# this is heavily dependant on cal procedure, say 7V is the reference calibration point
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alphaRef = df.loc[df['SetVolts'] == 10]["RefVolts"].iloc[0]/10
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alphaDut = df.loc[df['SetVolts'] == 10]["DutVolts"].iloc[0]/10
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# remove 1st order to get the INL
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df["RefVolts"] = df["RefVolts"] - alphaRef*df['SetVolts']
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df["DutVolts"] = df["DutVolts"] - alphaDut*df['SetVolts']
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df["DutToRefVolts"] = df['DutVolts'] - df["RefVolts"]
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# plot INL of calibrator
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fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["RefVolts"],mode='lines+markers',name='Original REF INL'),row=1, col=1)
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fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["DutVolts"],mode='lines+markers',name='Original DUT INL'),row=1, col=1)
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fig.add_trace(go.Scatter(x=df['SetVolts'], y=df["DutToRefVolts"],mode='lines+markers',name='DUT vs REF'),row=2, col=1)
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# generate correction parameters
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# default correction assumed unless minMaxEqual is set, this will equalize error above and under ideal correction
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# folding the correction down and up to have a better fit, be sure that measurements were extremely stable to use this
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# this block output is to be specifically tailored towards your application, currently R6581T INL adjustment against 3458A
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minMaxEqual = 0
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correctionfactors = [None] * 16
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if minMaxEqual:
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# not implemented as of yet
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print("not implemented")
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else:
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# postive part of INL
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correctionfactors[0] = df.loc[df['SetVolts'] == 2]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 0]["DutToRefVolts"].iloc[0]
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correctionfactors[1] = df.loc[df['SetVolts'] == 4]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 2]["DutToRefVolts"].iloc[0]
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correctionfactors[2] = df.loc[df['SetVolts'] == 6]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 4]["DutToRefVolts"].iloc[0]
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correctionfactors[3] = df.loc[df['SetVolts'] == 8]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 6]["DutToRefVolts"].iloc[0]
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correctionfactors[4] = df.loc[df['SetVolts'] == 10]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 8]["DutToRefVolts"].iloc[0]
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# negative part of INL
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correctionfactors[5] = df.loc[df['SetVolts'] == -0.02]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == 0]["DutToRefVolts"].iloc[0]
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correctionfactors[6] = df.loc[df['SetVolts'] == -0.04]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -0.02]["DutToRefVolts"].iloc[0]
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correctionfactors[7] = df.loc[df['SetVolts'] == -0.06]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -0.04]["DutToRefVolts"].iloc[0]
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correctionfactors[8] = df.loc[df['SetVolts'] == -0.08]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -0.06]["DutToRefVolts"].iloc[0]
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correctionfactors[9] = df.loc[df['SetVolts'] == -0.1]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -0.08]["DutToRefVolts"].iloc[0]
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correctionfactors[10] = df.loc[df['SetVolts'] == -2]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -0.1]["DutToRefVolts"].iloc[0]
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correctionfactors[11] = df.loc[df['SetVolts'] == -4]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -2]["DutToRefVolts"].iloc[0]
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correctionfactors[12] = df.loc[df['SetVolts'] == -6]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -4]["DutToRefVolts"].iloc[0]
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correctionfactors[13] = df.loc[df['SetVolts'] == -8]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -6]["DutToRefVolts"].iloc[0]
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correctionfactors[14] = df.loc[df['SetVolts'] == -10]["DutToRefVolts"].iloc[0] - df.loc[df['SetVolts'] == -8]["DutToRefVolts"].iloc[0]
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# negative 10v voltage reversal alpha error for faster measurements
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df = pd.read_csv(basePath+"/data.csv")
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correctionfactors[15] = df.loc[df['SetVolts'] == -10]["DutVolts"].iloc[0]/df.loc[df['SetVolts'] == 10]["DutVolts"].iloc[0]
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print(correctionfactors)
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## Make plot and save
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fig.write_image('inl_evaluation_plots.png',width=720, height=1280)
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fig.write_html('inl_evaluation_plots.html')
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## Read the HOSEI parameters and save the original constants
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inst4.write("CAL:EXT:EEPROM:PROTECTION 1")
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print(inst4.query("CAL:INT:DCV:HOSEI?"))
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with open(basePath+"/original_hosei.txt", 'a') as f:
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f.write(inst4.query("CAL:INT:DCV:HOSEI?"))
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