/
socket_baselines_v2bstd.py
363 lines (303 loc) · 16.6 KB
/
socket_baselines_v2bstd.py
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# this might read in a data file and calculate and/or make plots of baseline values
import subprocess,os,sys
import pandas as pd
import h5py
import plotly
import plotly.express as px
import chart_studio
import chart_studio.plotly as py
import plotly.graph_objs as go
import csv
import numpy as np
from collections import Counter
import time
mypid=os.getpid()
os.environ['PRINT_TIME_PID']=str(mypid)
print(mypid)
print('mypid in socket_baselines is ',mypid)
DateDirPath = time.strftime("%y%m%d")
if not os.path.exists(DateDirPath) : os.mkdir(DateDirPath)
NumASICchannels = 64
global TimeAnomaly
TimeAnomaly=0
mean = [0] * NumASICchannels
sdev = [0] * NumASICchannels
nentries = [0] * NumASICchannels
def getData(filename):
d = h5py.File(filename,mode='r')
date = list(d['_header'].attrs.values())[0]
d = d['packets']
d = pd.DataFrame(d[0:len(d)])
return date,d
def getMeanAndStd(adc_list, chan):
global mean, sdev
m = round(np.mean(adc_list),2)
sd = round(np.std(adc_list),2)
print("Chan {} Mean {} and Std {} and max {} and min {} for {} entries".format(chan,m,sd,np.max(adc_list),np.min(adc_list),len(adc_list)))
mean[chan] = m
sdev[chan] = sd
nentries[chan] = len(adc_list)
def BaselineLoop(data,firstChan=0,lastChan=NumASICchannels-1):
#print(data)
global TimeAnomaly
firstChipID=data['chip_id'].iloc[0]
print(firstChipID)
numnotchipid2 = data[data['chip_id']!=firstChipID].count()
numchipid2 = data[data['chip_id']==firstChipID].count()
ChipIDAnomaly=0
if numnotchipid2['chip_id'] != 0:
print("Found {} samples with the wrong chip_id !={}".format(numnotchipid2['chip_id'],firstChipID))
ChipIDAnomaly=ChipIDAnomaly+1
#print(numchipid2['chip_id'],numnotchipid2['chip_id'])
for chan in range(firstChan,lastChan+1):
BaselineMeanStd(data,chan)
BaselineTimeCheck(data,chan)
if TimeAnomaly!=0 or ChipIDAnomaly!=0 :
print("Bit problem found")
# copy h5 file to new location to be analyzed later
outfile=DateDirPath+"/bitswap-"+DateDirPath+"-"+ChipSN+".h5"
subprocess.run(["cp","testing.h5",outfile])
def BaselineMeanStd(data,chan):
#datachunk = getData(filename)
tempchunk = data[data['channel_id']==chan]
tempchunk = tempchunk['dataword'][tempchunk['packet_type']==0]
getMeanAndStd(tempchunk,chan)
def getTimeStd(timestamps,chan):
global TimeAnomaly
if len(timestamps)>0 :
maxTimestamp=np.max(timestamps)
minTimestamp=np.min(timestamps)
#print(timestamps)
firstTimestamp=timestamps.iloc[0]
lastTimestamp=timestamps.iloc[-1]
print("Chan {} has {} timestamps from {} to {} with min {} and max {}".
format(chan,len(timestamps),firstTimestamp,lastTimestamp,minTimestamp,maxTimestamp))
#if lastTimestamp==maxTimestamp : # no rollover
#avgDT=(lastTimestamp-firstTimestamp)/len(timestamps)
#avgDT=(maxTimestamp-minTimestamp)/len(timestamps)
avgDT=1001
prevtimestamp=firstTimestamp
index=1
for timestamp in timestamps.iloc[1:-1]:
dt=timestamp-prevtimestamp
if abs(dt) != avgDT and abs(dt)%1001 != 0 :
print("Time anomaly for chan {} dt={} and avgDT={} at index {} at timestamp {} after {}".
format(chan,dt,round(avgDT,0),index,timestamp,prevtimestamp))
TimeAnomaly=TimeAnomaly+1
prevtimestamp=timestamp
index=index+1
def BaselineTimeCheck(data,chan):
#datachunk = getData(filename)
tempchunk = data[data['channel_id']==chan]
tempchunk = tempchunk['timestamp'][tempchunk['packet_type']==0]
getTimeStd(tempchunk,chan)
def plot_interactive(data, filename):
'''plotting function'''
layout = go.Layout(title='Baseline Histo',
xaxis_title='ADC value',
yaxis_title='Frequency',
paper_bgcolor='rgb(233,233,233)',
plot_bgcolor='rgba(0,0,0,0)'
)
fig = go.Figure(data = [{ 'x': data[data[col].notnull()].index,
'y': data[data[col].notnull()][col],
'name': "{} - mean:{} , std:{}".format(col,mean[col],sdev[col]),
'mode':'lines+markers',
'line': dict(dash='dash')} for col in data.columns],
layout = layout
)
plotly.offline.plot(fig,
filename=filename,
auto_open=False)
#filename = 'datalog_2020_01_14_16_05_16_PST_.h5' # socket board chan 2-NumASICchannels-1
#filename = 'datalog_2020_01_15_11_55_06_PST_.h5' # 4 chip pixel board chan 0-NumASICchannels-1
#filename = 'datalog_2020_01_22_12_06_20_PST_.h5' # socket board chan 2-NumASICchannels-1 after clock modifications
#filename = 'datalog_2020_01_28_11_40_42_PST_.h5' # Boiling board - no mod's
#filename = 'datalog_2020_01_29_12_59_34_PST_.h5' # all NumASICchannels channels for 1 second
#filename = 'testing25.h5' # all NumASICchannels channels for 1 second
#filename = 'testing27.h5' # all NumASICchannels channels for 1 second
#filename = 'testing30.h5' # all NumASICchannels channels for 1 second
#filename = 'testing35.h5' # all NumASICchannels channels for 1 second
filename = 'testing.h5' # all NumASICchannels channels for 1 second
runtime, datachunk = getData(filename)
#datachunk.shape
#datachunk.to_csv("temp.csv")
#id(datachunk)
datachunk = datachunk[datachunk['packet_type']==0] # select only data packets
if len(datachunk)==0 : exit("No packet data in file")
#print(datachunk)
io_group=datachunk['io_group'].iloc[0]
io_channel=datachunk['io_channel'].iloc[0]
#print('data from io_group: ',io_group,' and io_channel: ',io_channel)
#datachunk.shape
#id(datachunk)
# Dump raw data to csv file
#datachunk.to_csv("temp.csv")
# grab user input for barcode SN
#ChipSN = mychipIDBox[0].get()
tempstatus = h5py.File("CurrentRun.tmp",mode='r')
dset = tempstatus['CurrentRun']
ChipSN = dset.attrs['ChipSN']
tempstatus.close()
fig = px.histogram(datachunk,x='dataword',color='channel_id',log_y=True,opacity=0.6)
fig.update_layout(barmode='overlay')
if os.getenv('socket_PlotBaselineChannels')=='1':
fig.show()
BaselineDirPath = DateDirPath+"/baselines/"
if not os.path.exists(BaselineDirPath) : os.mkdir(BaselineDirPath)
#plotly.offline.plot(fig,filename="baselines/Baseline_"+ChipSN+".html",auto_open=False )
fig.write_html(BaselineDirPath+"Baseline_"+ChipSN+".html",auto_open=False )
BaselineLoop(datachunk,0,NumASICchannels-1)
# Save raw .h5 baseline data
# copy h5 file to new location to be analyzed later
outfile=BaselineDirPath+"/bpsraw-"+DateDirPath+"-"+ChipSN+".h5"
subprocess.run(["cp","testing.h5",outfile])
#Output to csv files
#varlist = []
print('Processing data for chip ',ChipSN)
#tray = input("Enter the tray number: ")
#tRow = input("Enter the row number 0=bottom 14=top: ")
#tColumn = input("Enter the column number 0=left 5=right: ")
#summaryFrame = pd.DataFrame(columns = ['runtime','Mean','Std','Nent','ChanName','Chan','Tray','tRow','tColumn'])
summaryFrame = pd.DataFrame(columns = ['runtime','Mean','Std','Nent','ChanName','Chan','ChipSN','io_group','io_channel'])
for chan in range(NumASICchannels):
textchan = 'ch{:02d}'.format(chan)
summaryFrame = summaryFrame.append({'runtime':runtime,'Mean':mean[chan],'Std':sdev[chan],
'Nent':nentries[chan],'ChanName':textchan,'Chan':chan,
'ChipSN':ChipSN,'io_group':io_group,'io_channel':io_channel},ignore_index=True)
#summaryFrame.to_csv("t.csv",mode='a',header=False)
# New dated file paths and names
summaryFile=DateDirPath+"/bps-summary"+DateDirPath+".csv"
# If file exists, append with no header
if os.path.exists(summaryFile) : summaryFrame.to_csv(summaryFile,mode='a',header=False)
# else create file with header
else : summaryFrame.to_csv(summaryFile,mode='a',header=True)
# Load limits dataFrame Should do this from a file at some point.
v2std = False
if v2std:
MeanMeanbyChan=[14.07, 14.2, 14.27, 14.6, 15.34, 15.54, -999.0, -999.0, -999.0, -999.0, 42.58, 23.44, 19.02, 14.67, 14.19, 14.11, 13.18, 14.08, 14.2, 15.68, 15.45, 16.52, -999.0, -999.0, -999.0, -999.0, 11.48, 12.02, 10.47, 10.51, 13.3, 12.35, 12.34, 14.77, 15.06, 14.7, 18.25, 21.93, -999.0, -999.0, -999.0, 9.6, 11.35, 11.72, 9.15, 11.74, 11.8, 9.56, 12.02, 11.94, 9.9, 11.82, 11.92, 11.35, -999.0, -999.0, -999.0, -999.0, 12.2, 12.88, 12.98, 13.31, 13.55, 13.64]
StdMeanbyChan=[1.69, 1.87, 1.98, 11.15, 1.96, 2.21, -999.0, -999.0, -999.0, -999.0, 2.59, 2.6, 2.14, 1.88, 1.79, 1.75, 1.79, 1.82, 1.78, 2.06, 8.48, 2.53, -999.0, -999.0, -999.0, -999.0, 1.7, 1.8, 1.68, 1.83, 1.76, 1.89, 1.59, 1.84, 1.77, 1.62, 1.54, 1.92, -999.0, -999.0, -999.0, 1.69, 1.65, 1.69, 1.79, 1.68, 1.7, 1.65, 1.65, 1.7, 1.66, 1.82, 1.72, 1.85, -999.0, -999.0, -999.0, -999.0, 7.76, 2.06, 1.8, 1.89, 1.65, 1.94]
MeanStdbyChan=[1.36, 1.4, 1.46, 1.58, 1.9, 5.14, -999.0, -999.0, -999.0, -999.0, 3.71, 2.68, 2.31, 2.16, 2.18, 2.28, 2.45, 2.71, 3.16, 3.92, 4.86, 7.96, -999.0, -999.0, -999.0, -999.0, 2.26, 2.03, 2.11, 1.95, 1.65, 1.87, 1.83, 1.64, 1.99, 2.18, 2.39, 3.75, -999.0, -999.0, -999.0, 3.72, 2.47, 1.72, 1.64, 1.44, 1.37, 1.53, 1.37, 1.39, 1.61, 1.55, 1.65, 1.99, -999.0, -999.0, -999.0, -999.0, 1.37, 1.35, 1.35, 1.33, 1.32, 1.34]
StdStdbyChan=[1.46, 4.34, 2.88, 2.83, 2.49, 2.45, -999.0, -999.0, -999.0, -999.0, 1.71, 4.55, 3.47, 2.85, 3.21, 3.17, 2.87, 3.44, 3.85, 3.71, 3.26, 4.39, -999.0, -999.0, -999.0, -999.0, 1.74, 1.88, 2.98, 2.74, 1.7, 2.6, 2.61, 1.66, 2.61, 2.77, 1.56, 1.43, -999.0, -999.0, -999.0, 1.87, 1.69, 1.6, 2.6, 1.49, 1.47, 2.77, 1.52, 1.88, 2.72, 1.38, 1.74, 2.19, -999.0, -999.0, -999.0, -999.0, 1.79, 1.65, 1.49, 1.86, 2.81, 4.21]
limitsdf=pd.DataFrame(columns=['Mean','errMean','Std','errStd','ChanName','Chan'])
for chan in range(NumASICchannels):
#if MeanMeanbyChan[chan]!=-999.:
textchan = 'ch{:02d}'.format(chan)
#original 38 used these
#errMean=3 * max(StdMeanbyChan[chan],4.0)
#errStd=5 * max(StdStdbyChan[chan],0.7)
errMean=1.0 * max(StdMeanbyChan[chan],10.0)
errStd=1.0 * max(StdStdbyChan[chan],3.0)
limitsdf=limitsdf.append({'Mean':MeanMeanbyChan[chan],'errMean':errMean,'Std':MeanStdbyChan[chan],
'errStd':errStd,'ChanName':textchan,'Chan':chan},ignore_index=True)
v2bstd=True
if v2bstd:
# standards measured from 177 v2b on the socket board.
MeanMeanbyChan=[16.56, 16.23, 16.7, 15.97, 16.49, 16.72, 16.64, 16.5, 11.82, 11.76, 12.34, 13.96, 14.73, 15.75, 15.87, 16.54, 16.75, 17.41, 17.41, 18.12, 17.69, 18.13, 18.08, 18.02, 17.16, 18.24, 18.06, 18.01, 17.38, 17.99, 17.18, 17.19, 17.12, 17.36, 16.82, 17.19, 16.94, 17.27, 17.04, 17.09, 18.21, 16.96, 16.99, 16.61, 16.66, 16.41, 17.09, 17.02, 16.93, 15.82, 16.34, 15.92, 16.34, 16.14, 16.83, 16.43, 16.8, 16.13, 16.78, 16.38, 16.34, 16.4, 16.69, 16.18]
StdMeanbyChan=[2.19, 2.44, 2.27, 2.05, 2.22, 2.41, 1.92, 2.15, 2.17, 1.94, 1.95, 2.13, 2.22, 2.03, 1.99, 2.07, 2.23, 2.36, 2.4, 2.15, 2.06, 2.18, 2.3, 2.44, 1.99, 2.13, 2.25, 2.18, 2.18, 2.05, 2.03, 2.01, 2.29, 2.08, 2.24, 2.15, 1.97, 2.03, 2.19, 2.44, 2.41, 2.06, 2.26, 2.03, 2.23, 2.26, 2.7, 2.64, 2.55, 2.39, 2.03, 2.26, 2.31, 2.15, 2.09, 2.01, 2.22, 2.28, 2.25, 2.26, 2.23, 2.18, 2.15, 2.07]
MeanStdbyChan=[1.13, 1.12, 1.12, 1.1, 1.11, 1.14, 1.16, 1.34, 1.0, 1.1, 1.12, 1.08, 1.1, 1.09, 1.09, 1.08, 1.09, 1.1, 1.1, 1.09, 1.09, 1.08, 1.08, 0.99, 1.33, 1.18, 1.14, 1.14, 1.13, 1.12, 1.14, 1.1, 1.12, 1.1, 1.16, 1.12, 1.12, 1.13, 1.16, 1.4, 2.07, 1.16, 1.14, 1.12, 1.12, 1.15, 1.14, 1.1, 1.16, 1.12, 1.12, 1.12, 1.12, 1.08, 1.11, 0.99, 1.6, 1.13, 1.11, 1.09, 1.09, 1.09, 1.11, 1.08]
StdStdbyChan=[0.23, 0.22, 0.2, 0.19, 0.2, 0.22, 0.21, 0.31, 0.1, 0.18, 0.2, 0.17, 0.18, 0.19, 0.19, 0.18, 0.2, 0.2, 0.2, 0.19, 0.18, 0.18, 0.16, 0.1, 0.28, 0.21, 0.2, 0.2, 0.21, 0.22, 0.21, 0.17, 0.2, 0.18, 0.21, 0.18, 0.19, 0.19, 0.2, 0.32, 0.24, 0.15, 0.19, 0.2, 0.2, 0.24, 0.22, 0.2, 0.24, 0.23, 0.2, 0.2, 0.21, 0.16, 0.17, 0.08, 0.3, 0.19, 0.2, 0.19, 0.19, 0.19, 0.2, 0.17]
limitsdf=pd.DataFrame(columns=['Mean','errMean','Std','errStd','ChanName','Chan'])
for chan in range(NumASICchannels):
textchan = 'ch{:02d}'.format(chan)
errMean=5.0 * max(StdMeanbyChan[chan],0.0)
errStd=4.0 * max(StdStdbyChan[chan],0.0)
limitsdf=limitsdf.append({'Mean':MeanMeanbyChan[chan],'errMean':errMean,'Std':MeanStdbyChan[chan],
'errStd':errStd,'ChanName':textchan,'Chan':chan},ignore_index=True)
# END Loading limits dataFrame
AllbadChan = summaryFrame[ (summaryFrame['Chan']==64) ] # gets an empty dataFrame
badChan = summaryFrame[ (summaryFrame['Chan']==64) ]
for chan in range(NumASICchannels):
#if MeanMeanbyChan[chan]!=-999:
if limitsdf['Mean'][limitsdf['Chan']==chan].values[0]!=-999.: # :MeanMeanbyChan[chan]!=-999:
theMean=limitsdf['Mean'][(limitsdf['Chan']==chan)].values[0]
theStd=limitsdf['Std'][(limitsdf['Chan']==chan)].values[0]
theErrMean=limitsdf['errMean'][(limitsdf['Chan']==chan)].values[0]
theErrStd=limitsdf['errStd'][(limitsdf['Chan']==chan)].values[0]
if v2std:
MaxMean = round(theMean + 1.0 * max(theErrMean,10.0),2)
MinMean = round(theMean - 1.0 * max(theErrMean,10.0),2)
MaxStd = round(theStd + 1.0 * max(theErrStd,3.0),2)
MinStd = round(theStd - 1.0 * max(theErrStd,3.0),2)
MaxMean = theMean + 1.0 * max(theErrMean,10.0)
MinMean = theMean - 1.0 * max(theErrMean,10.0)
MaxStd = theStd + 1.0 * max(theErrStd,3.0)
MinStd = theStd - 1.0 * max(theErrStd,3.0)
MaxMean = 50.0
MinMean = 3.0
MaxStd = 7.0
MinStd = 0.5
elif v2bstd:
#MaxMean = round(theMean + 1.0 * max(theErrMean,0.0),2)
#MinMean = round(theMean - 1.0 * max(theErrMean,0.0),2)
#MaxStd = round(theStd + 1.0 * max(theErrStd,0.0),2)
#MinStd = round(theStd - 1.0 * max(theErrStd,0.0),2)
MaxMean = 25.0
MinMean = 5.0
MaxStd = 4.0
MinStd = 0.8
# Not sure this part is right, try the version from v2astd.py
#badChan =summaryFrame[ (summaryFrame['Chan']==chan) & ( summaryFrame['Nent'] == 0 | ( (summaryFrame['Std']>MaxStd) | (summaryFrame['Std']<MinStd) )
# | ( (summaryFrame['Mean']<MinMean) | (summaryFrame['Mean']>MaxMean) ) ) ]
badChan = summaryFrame[ (summaryFrame['Chan']==64) ] # get empty badChan
thisChanSummaryFrame=summaryFrame[ (summaryFrame['Chan']==chan)]
#print(thisChanSummaryFrame['Mean'].values[0]) #[summaryFrame['Std']
thisChanMean=thisChanSummaryFrame['Mean'].values[0]
thisChanStd=thisChanSummaryFrame['Std'].values[0]
thisChanNent=thisChanSummaryFrame['Nent'].values[0]
#badChan =summaryFrame[ (summaryFrame['Chan']==chan) & ( summaryFrame['Nent'] == 0 |
# ( (summaryFrame['Std']>MaxStd) | (summaryFrame['Std']<MinStd) )
# | ( (summaryFrame['Mean']<MinMean) | (summaryFrame['Mean']>MaxMean) ) ) ]
#badChan =thisChanSummaryFrame[ ( thisChanSummaryFrame['Nent'] == 0 |
# ( (thisChanSummaryFrame['Std']>MaxStd) | (thisChanSummaryFrame['Std']<MinStd) )
# | ( (thisChanSummaryFrame['Mean']<MinMean) | (thisChanSummaryFrame['Mean'].values[0]>MaxMean) ) ) ]
if (thisChanNent == 0 or
thisChanMean > MaxMean or thisChanMean < MinMean or
thisChanStd>MaxStd or thisChanMean< MinStd ) :
badChan=thisChanSummaryFrame
if badChan.empty==False:
print(badChan)
print('Range for chan ',chan,' Mean and Std= [',MinMean,',',MaxMean,'][',MinStd,',',MaxStd,']')
#AllbadChan.append(badChan,ignore_index=True)
AllbadChan=pd.concat([AllbadChan,badChan],ignore_index=True)
global nBadBaselineChannels
if AllbadChan.empty==True:
print("No bad Channels found")
nBadBaselineChannels=0
else:
print(AllbadChan)
nBadBaselineChannels=len(AllbadChan)
os.environ['socket_BadBaselineChannels']=str(nBadBaselineChannels)
#meandf = pd.DataFrame(mean)
#stddf = pd.DataFrame(sdev)
#nentdf = pd.DataFrame(nentries)
#meandf=meandf.transpose()
#stddf=stddf.transpose()
#nentdf=nentdf.transpose()
#meandf.columns = varlist
#stddf.columns = varlist
#nentdf.columns = varlist
#meandf.insert(0,'runtime',runtime)
#stddf.insert(0,'runtime',runtime)
#nentdf.insert(0,'runtime',runtime)
#meandf.columns = varlist
#meandf.to_csv("means.csv",mode='a',header=True)
#stddf.to_csv("sdevs.csv",mode='a',header=True)
#nentdf.to_csv("nents.csv",mode='a',header=True)
#meandf.to_csv("means.csv",mode='a',header=False)
#stddf.to_csv("sdevs.csv",mode='a',header=False)
#nentdf.to_csv("nents.csv",mode='a',header=False)
#meandf = pd.read_csv("20200131_all78/means.csv")
#stddf = pd.read_csv("20200131_all78/sdevs.csv")
#nentdf = pd.read_csv("20200131_all78/nents.csv")
#x = ["Apples","Apples","Apples","Oranges", "Bananas"]
#y = ["5","10","3","10","5"]
#fig2 = go.Figure()
#fig2.add_trace(go.Histogram(histfunc="count", x=sdev, nbinsx=50, name="count"))
#fig.add_trace(go.Histogram(histfunc="sum", y=y, x=x, name="sum"))
#fig2.show()
# I don't really want to exit, just return the value
sys.exit(nBadBaselineChannels)
#def cleanup(nBadBaselineChannels=0):
# return nBadBaselineChannels
#cleanup(nBadBaselineChannels)