Text processing/2
You are encouraged to solve this task according to the task description, using any language you may know.
The following data shows a few lines from the file readings.txt (as used in in the Data Munging task).
The data comes from a pollution monitoring station with twenty four instruments monitoring twenty four aspects of pollution in the air. periodically a record is added to the file constituting a line of 49 white-space separated fields, where white-space can be one or more space or tab characters.
The fields (from the left) are:
DATESTAMP [ VALUEn FLAGn ] * 24
i.e. a datestamp followed by twenty four repetitions of a floating point instrument value and that instruments associated integer flag. Flag values are >= 1 if the instruments is working and < 1 if their is some problem with that instrument in which case that instruments value should be ignored.
A sample from the full data file readings.txt is:
1991-03-30 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 1991-03-31 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 10.000 1 20.000 1 20.000 1 20.000 1 35.000 1 50.000 1 60.000 1 40.000 1 30.000 1 30.000 1 30.000 1 25.000 1 20.000 1 20.000 1 20.000 1 20.000 1 20.000 1 35.000 1 1991-03-31 40.000 1 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 0.000 -2 1991-04-01 0.000 -2 13.000 1 16.000 1 21.000 1 24.000 1 22.000 1 20.000 1 18.000 1 29.000 1 44.000 1 50.000 1 43.000 1 38.000 1 27.000 1 27.000 1 24.000 1 23.000 1 18.000 1 12.000 1 13.000 1 14.000 1 15.000 1 13.000 1 10.000 1 1991-04-02 8.000 1 9.000 1 11.000 1 12.000 1 12.000 1 12.000 1 27.000 1 26.000 1 27.000 1 33.000 1 32.000 1 31.000 1 29.000 1 31.000 1 25.000 1 25.000 1 24.000 1 21.000 1 17.000 1 14.000 1 15.000 1 12.000 1 12.000 1 10.000 1 1991-04-03 10.000 1 9.000 1 10.000 1 10.000 1 9.000 1 10.000 1 15.000 1 24.000 1 28.000 1 24.000 1 18.000 1 14.000 1 12.000 1 13.000 1 14.000 1 15.000 1 14.000 1 15.000 1 13.000 1 13.000 1 13.000 1 12.000 1 10.000 1 10.000 1
The task:
- Confirm the general field format of the file
- Identify any DATASTAMPs that are duplicated.
- What number of records have good readings for all instruments.
AWK
A series of AWK one-liners are shown as this is often what is done. If this information were needed repeatedly, (and this is not known), a more permanent shell script might be created that combined multi-line versions of the scripts below.
Gradually tie down the format.
(In each case offending lines will be printed)
If their are any scientific notation fields then their will be an e in the file:
bash$ awk '/[eE]/' readings.txt bash$
Quick check on the number of fields:
bash$ awk 'NF != 49' readings.txt bash$
Full check on the file format using a regular expression:
bash$ awk '!(/^[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]([ \t]+[-]?[0-9]+\.[0-9]+[\t ]+[-]?[0-9]+)+$/ && NF==49)' readings.txt bash$
Full check on the file format as above but using regular expressions allowing intervals (gnu awk):
bash$ awk --re-interval '!(/^[0-9]{4}-[0-9]{2}-[0-9]{2}([ \t]+[-]?[0-9]+\.[0-9]+[\t ]+[-]?[0-9]+){24}+$/ )' readings.txt
bash$
Identify any DATASTAMPs that are duplicated.
Accomplished by counting how many times the first field occurs and noting any second occurrences.
bash$ awk '++count[$1]==2{print $1}' readings.txt
1990-03-25
1991-03-31
1992-03-29
1993-03-28
1995-03-26
bash$
What number of records have good readings for all instruments.
bash$ awk '{rec++;ok=1; for(i=0;i<24;i++){if($(i+3)<1){ok=0}}; recordok += ok} END {print "Total records",rec,"OK records", recordok, "or", recordok/rec*100,"%"}' readings.txt
Total records 5471 OK records 4728 or 86.4193 %
bash$
Python
<Python>import re import zipfile import StringIO
def munge2(readings):
datePat = re.compile(r'\d{4}-\d{2}-\d{2}')
valuPat = re.compile(r'[-+]?\d+\.\d+')
statPat = re.compile(r'-?\d+')
allOk, totalLines = 0, 0
datestamps = set([])
for line in readings:
totalLines += 1
fields = line.split('\t')
date = fields[0]
pairs = [(fields[i],fields[i+1]) for i in range(1,len(fields),2)]
lineFormatOk = datePat.match(date) and \
all( valuPat.match(p[0]) for p in pairs ) and \
all( statPat.match(p[1]) for p in pairs )
if not lineFormatOk:
print 'Bad formatting', line
continue
if len(pairs)!=24 or any( int(p[1]) < 1 for p in pairs ):
print 'Missing values', line
continue
if date in datestamps:
print 'Duplicate datestamp', line
continue
datestamps.add(date)
allOk += 1
print 'Lines with all readings: ', allOk print 'Total records: ', totalLines
- zfs = zipfile.ZipFile('readings.zip','r')
- readings = StringIO.StringIO(zfs.read('readings.txt'))
readings = open('readings.txt','r') munge2(readings)</Python> The results indicate 5013 good records, which differs from the Awk implementation. The final few lines of the output are as follows
Missing values 2004-12-29 2.900 1 2.700 1 2.800 1 3.300 1 2.900 1 2.300 1 0.000 0 1.700 1 1.900 1 2.300 1 2.600 1 2.900 1 2.600 1 2.600 1 2.600 1 2.700 1 2.300 1 2.200 1 2.100 1 2.000 1 2.100 1 2.100 1 2.300 1 2.400 1 Missing values 2004-12-30 2.400 1 2.600 1 2.600 1 2.600 1 3.000 1 0.000 0 3.300 1 2.600 1 2.900 1 2.400 1 2.300 1 2.900 1 3.500 1 3.700 1 3.600 1 4.000 1 3.400 1 2.400 1 2.500 1 2.600 1 2.600 1 2.800 1 2.400 1 2.200 1 Missing values 2004-12-31 2.400 1 2.500 1 2.500 1 2.400 1 0.000 0 2.400 1 2.400 1 2.400 1 2.200 1 2.400 1 2.500 1 2.000 1 1.700 1 1.400 1 1.500 1 1.900 1 1.700 1 2.000 1 2.000 1 2.200 1 1.700 1 1.500 1 1.800 1 1.800 1 Lines with all readings: 5013 Total records: 5471