I have a netcdf file with data as a function of lon,lat and time. I would like to calculate the total number of missing entries in each grid cell summed over the time dimension, preferably with CDO or NCO so I do not need to invoke R, python etc.
I know how to get the total number of missing values
ncap2 -s "nmiss=var.number_miss()" in.nc out.nc
as I answered to this related question:
count number of missing values in netcdf file - R
and CDO can tell me the total summed over space with
cdo info in.nc
but I can't work out how to sum over time. Is there a way for example of specifying the dimension to sum over with number_miss in ncap2?
We added the missing() function to ncap2 to solve this problem elegantly as of NCO 4.6.7 (May, 2017). To count missing values through time:
ncap2 -s 'mss_val=three_dmn_var_dbl.missing().ttl($time)' in.nc out.nc
Here ncap2 chains two methods together, missing(), followed by a total over the time dimension. The 2D variable mss_val is in out.nc. The response below does the same but averages over space and reports through time (because I misinterpreted the OP).
Old/obsolete answer:
There are two ways to do this with NCO/ncap2, though neither is as elegant as I would like. Either call assemble the answer one record at a time by calling num_miss() with one record at a time, or (my preference) use the boolean comparison function followed by the total operator along the axes of choice:
zender#aerosol:~$ ncap2 -O -s 'tmp=three_dmn_var_dbl;mss_val=tmp.get_miss();tmp.delete_miss();tmp_bool=(tmp==mss_val);tmp_bool_ttl=tmp_bool.ttl($lon,$lat);print(tmp_bool_ttl);' ~/nco/data/in.nc ~/foo.nc
tmp_bool_ttl[0]=0
tmp_bool_ttl[1]=0
tmp_bool_ttl[2]=0
tmp_bool_ttl[3]=8
tmp_bool_ttl[4]=0
tmp_bool_ttl[5]=0
tmp_bool_ttl[6]=0
tmp_bool_ttl[7]=1
tmp_bool_ttl[8]=0
tmp_bool_ttl[9]=2
or
zender#aerosol:~$ ncap2 -O -s 'for(rec=0;rec<time.size();rec++){nmiss=three_dmn_var_int(rec,:,:).number_miss();print(nmiss);}' ~/nco/data/in.nc ~/foo.nc
nmiss = 0
nmiss = 0
nmiss = 8
nmiss = 0
nmiss = 0
nmiss = 1
nmiss = 0
nmiss = 2
nmiss = 1
nmiss = 2
Even though you are asking for another solution, I would like to show you that it takes only one very short line to find the answer with the help of Python. The variable m_data has exactly the same shape as a variable with missing values read using the netCDF4 package. With the execution of only one np.sum command with the correct axis specified, you have your answer.
import numpy as np
import matplotlib.pyplot as plt
import netCDF4 as nc4
# Generate random data for this experiment.
data = np.random.rand(365, 64, 128)
# Masked data, this is how the data is read from NetCDF by the netCDF4 package.
# For this example, I mask all values less than 0.1.
m_data = np.ma.masked_array(data, mask=data<0.1)
# It only takes one operation to find the answer.
n_values_missing = np.sum(m_data.mask, axis=0)
# Just a plot of the result.
plt.figure()
plt.pcolormesh(n_values_missing)
plt.colorbar()
plt.xlabel('lon')
plt.ylabel('lat')
plt.show()
# Save a netCDF file of the results.
f = nc4.Dataset('test.nc', 'w', format='NETCDF4')
f.createDimension('lon', 128)
f.createDimension('lat', 64 )
n_values_missing_nc = f.createVariable('n_values_missing', 'i4', ('lat', 'lon'))
n_values_missing_nc[:,:] = n_values_missing[:,:]
f.close()
I would like to convert all values in a text file to the correspondent log2 values. I have a huge text file and would be interesting to avoid R.
Nevertheless, the below R code exemplify what I want to implement in a more efficient way in bash.
df <- 'sam1 sam2 sam3
2000 3000 4000
2000 1500 1200
2000 7000 6000'
df <- read.table(text=df, header=T)
dflog <- log(df)
My expected output:
dfout <- 'sam1 sam2 sam3
7.600902 8.006368 8.294050
7.600902 7.313220 7.090077
7.600902 8.853665 8.699515'
dfout <- read.table(text=dfout, header=T)
I will be grateful for any help to perform it in bash.
awk to the rescue!
$ awk 'NR>1{for(i=1;i<=NF;i++) $i=log($i)}1' sams
sam1 sam2 sam3
7.6009 8.00637 8.29405
7.6009 7.31322 7.09008
7.6009 8.85367 8.69951
this is the quick solution, for additional decimal points you can format the output with printf, but I'm not sure it's needed.
I have two large files. One of them is an info file(about 270MB and 16,000,000 lines) like this:
1101:10003:17729
1101:10003:19979
1101:10003:23319
1101:10003:24972
1101:10003:2539
1101:10003:28242
1101:10003:28804
The other is a standard FASTQ format(about 27G and 280,000,000 lines) like this:
#ST-E00126:65:H3VJ2CCXX:7:1101:1416:1801 1:N:0:5
NTGCCTGACCGTACCGAGGCTAACCCTAATGAGCTTAATCAAGATGATGCTCGTTATGG
+
AAAFFKKKKKKKKKFKKKKKKKFKKKKAFKKKKKAF7AAFFKFAAFFFKKF7FF<FKK
#ST-E00126:65:H3VJ2CCXX:7:1101:10003:75641:N:0:5
TAAGATAGATAGCCGAGGCTAACCCTAATGAGCTTAATCAAGATGATGCTCGTTATGG
+
AAAFFKKKKKKKKKFKKKKKKKFKKKKAFKKKKKAF7AAFFKFAAFFFKKF7FF<FKK
The FASTQ file uses four lines per sequence. Line 1 begins with a '#' character and is followed by a sequence identifie. For each sequence,this part of the Line 1 is unique.
1101:1416:1801 and 1101:10003:75641
And I want to grab the Line 1 and the next three lines from the FASTQ file according to the info file. Here is my code:
import gzip
import re
count = 0
with open('info_path') as info, open('grab_path','w') as grab:
for i in info:
sample = i.strip()
with gzip.open('fq_path') as fq:
for j in fq:
count += 1
if count%4 == 1:
line = j.strip()
m = re.search(sample,j)
if m != None:
grab.writelines(line+'\n'+fq.next()+fq.next()+fq.next())
count = 0
break
And it works, but because both of these two files have millions of lines, it's inefficient(running one day only get 20,000 lines).
UPDATE at July 6th:
I find that the info file can be read into the memory(thank #tobias_k for reminding me), so I creat a dictionary that the keys are info lines and the values are all 0. After that, I read the FASTQ file every 4 line, use the identifier part as the key,if the value is 0 then return the 4 lines. Here is my code:
import gzip
dic = {}
with open('info_path') as info:
for i in info:
sample = i.strip()
dic[sample] = 0
with gzip.open('fq_path') as fq, open('grap_path',"w") as grab:
for j in fq:
if j[:10] == '#ST-E00126':
line = j.split(':')
match = line[4] +':'+line[5]+':'+line[6][:-2]
if dic.get(match) == 0:
grab.writelines(j+fq.next()+fq.next()+fq.next())
This way is much faster, it takes 20mins to get all the matched lines(about 64,000,000 lines). And I have thought about sorting the FASTQ file first by external sort. Splitting the file that can be read into the memory is ok, my trouble is how to keep the next three lines following the indentifier line while sorting. The Google's answer is to linear these four lines first, but it will take 40mins to do so.
Anyway thanks for your help.
You can sort both files by the identifier (the 1101:1416:1801) part. Even if files do not fit into memory, you can use external sorting.
After this, you can apply a simple merge-like strategy: read both files together and do the matching in the meantime. Something like this (pseudocode):
entry1 = readFromFile1()
entry2 = readFromFile2()
while (none of the files ended)
if (entry1.id == entry2.id)
record match
else if (entry1.id < entry2.id)
entry1 = readFromFile1()
else
entry2 = readFromFile2()
This way entry1.id and entry2.id are always close to each other and you will not miss any matches. At the same time, this approach requires iterating over each file once.
I would like to use fortran to read ultraviolet radiation data that has been produced by the Japan Aerospace Exploration Agency. This data is at a daily and monthly temporal resolution from 2000-2010 at a ~5 km spatial resolution. This question is worth answering as the data could be useful for a number of environment/health projects and is freely available, with proper acknowledgement of source and sharing of preprint of any subsequent publications, from:
ftp://suzaku.eorc.jaxa.jp/pub/GLI/glical/Global_05km/monthly/uvb/
There is a readme file available, which provides instructions on how to read data using fortran as follows:
Instructions for _le files
Header
Read header (size= pixel size *2byte):
character head*14400
read(10,rec=1) head
read(head,'(2i6,2f8.2,f8.4,2e12.5,a1,a8,a1,a40)')
& npixel,nline,lon_min,lat_max,reso,slope,offset,',',
& para,',',outfile
Read data (e.g., fortran77)
parameter(nl=7200, ml=3601)
... open file by "unformatted", "recl=nl*2(byte)" (,"bytereclen")
integer*2 i2buf(nl,ml)
do m=1,ml
read(10,rec=1+m) (i2buf(n,m), n=1,nl)
do n=1,nl
par=i2buf(n,m)*slope+offset
write(6,*) 'PAR[Ein/m^2/day]=',par
enddo
enddo
slope values
par__le : daily PAR [Ein/m^2/day] = DN * 0.01
dpar_le : direct PAR = DN * 0.01
swr__le : daily mean shortwave radiation [W/m^2] = DN * 0.01
tip__le : transmittance of instantaneous PAR at noon = DN * 0.0001
uva__le : daily mean UVA [W/m^2] = DN * 0.001
uvb__le : daily mean UVB [W/m^2] = DN * 0.0001
rpar_le : PAR-range surface reflectance (TOP of canopy/solid surfaces) = DN * 0.0001 (monthly data only)
error values
-1 as signed short integer (int16)
65535 as unsigned short integer (uint16)
Progress so far
I have downloaded and installed gfortran successfully on mac OSX. I have downloaded a test file (MOD02SSH_A20000224Av6_v601_7200_3601_uvb__le.gz) and decompressed it. I have created a program file:
PROGRAM readuvr
IMPLICIT NONE
!some code
END PROGRAM
I will then type the following into the command line to create an executable and run it to extract the data.
gfortran -o executable
./executable
As a complete beginner to fortran, my question is: how can I use the instructions provided to build a program that can read the data and output it into a text file?
Well, that file expands to 51,868,800 bytes. The comments imply the header is 14,400 bytes, which leaves 51,854,400 bytes of actual data payload.
There seem to be 7200 lines of data, so that means there are 7202 bytes per line. There seem to be 2 bytes (16-bit samples) so if we assume 2 bytes/sample, that means there are 3601 samples per line, which matches the ml=3601.
So basically, you need to read 14,400 bytes of header, then 7200 lines of data, each line consisting of 3601 values, each of those being 2 bytes wide...
Actually, if you are that unfamiliar with FORTRAN, you may like to extract the data with Perl which is already installed and available on OS X anyway. I have started a VERY SIMPLISTIC Perl program that reads the dat and prints the first 2 values on each line:
#!/usr/bin/perl
use strict;
use warnings;
# Read 14,400 bytes of header
my $buffer;
my $nBytes = 14400;
my $bytesRead = read (STDIN, $buffer, $nBytes) ;
my ($npixel,$nline,$lon_min,$lat_max,$reso,$slope,$offset,$junk)=split(' ',$buffer);
print "npixel:$npixel\n";
print "nline:$nline\n";
print "lon_min:$lon_min\n";
print "lat_max:$lat_max\n";
print "reso:$reso\n";
print "slope:$slope\n";
$offset =~ s/,.*//; # strip trailing comma and junk
print "offset:$offset\n";
# Read actual lines of data
my $line;
for(my $m=1;$m<=$nline;$m++){
read(STDIN,$line,$npixel*2);
my $x=$npixel*2;
my #values=unpack("S$x",$line);
printf "Line: %d",$m;
for(my $j=0;$j<2;$j++){
printf ",%f",$values[$j]*$slope+$offset;
}
printf "\n"; # newline
}
Save it as go.pl and then in the Terminal, type the following once to make it executable
chmod +x go.pl
and then run it like this
./go.pl < MOD02SSH_A20000224Av6_v601_7200_3601_uvb__le
Sample output extract:
npixel:7200
nline:3601
lon_min:0.00
lat_max:90.00
reso:0.0500
slope:0.10000E-03
offset:0.00000E+00
...
...
Line: 3306,0.099800,0.099800
Line: 3307,0.099900,0.099900
Line: 3308,0.099400,0.074200
Line: 3309,0.098900,0.098900
Line: 3310,0.098400,0.098400
Line: 3311,0.074300,0.074200
Line: 3312,0.071300,0.071200
fortran (f2003 or so) solution. (The linked instructions are awful by the way )
implicit none
character*80 para,outfile
character(len=:),allocatable::header,infile
integer npixel,nline,blen,i
c note kind=2 is not standard. This needs to be a 2-byte integer.
integer(kind=2),allocatable :: data(:,:)
real lon_min,lat_max,reso,slope,off
c header is plain text, so first open formatted and
c directly read header data
infile='MOD02SSH_A20000224Av6_v601_7200_3601_uvb__le'
open(10,file=infile)
read(10,*)npixel,nline,lon_min,lat_max,reso,slope,off,
$ para,outfile
close(10)
write(*,*)npixel,nline,lon_min,lat_max,reso,slope,off,
$ trim(para),' ',trim(outfile)
blen=2*npixel
allocate(character(len=blen)::header)
allocate(data(npixel,nline))
if( sizeof(data(1,1)).ne.2 )then
write(*,*)'error kind=2 did not give a 2 byte integer'
stop
endif
c now close and reopen for binary read.
c direct access approach:
open(20,file=infile,access='direct',recl=blen/4)
c note the granularity of the recl= specifier is not standard.
c ifort uses 4 bytes. (note this will break if npixel is not even )
read(20,rec=1)header
write(*,*)trim(header)
do i=1,nline
read(20,rec=i+1)data(:,i)
enddo
c note streams if available is simpler: (we don't need to know rec len )
c open(20,file=infile,access='stream')
c read(20)header,data
end
This is not actually validated because I don't have known file content to compare against.
I have written a python script that calls unix sort using subprocess module. I am trying to sort a table based on two columns(2 and 6). Here is what I have done
sort_bt=open("sort_blast.txt",'w+')
sort_file_cmd="sort -k2,2 -k6,6n {0}".format(tab.name)
subprocess.call(sort_file_cmd,stdout=sort_bt,shell=True)
The output file however contains an incomplete line which produces an error when I parse the table but when I checked the entry in the input file given to sort the line looks perfect. I guess there is some problem when sort tries to write the result to the file specified but I am not sure how to solve it though.
The line looks like this in the input file
gi|191252805|ref|NM_001128633.1| Homo sapiens RIMS binding protein 3C (RIMBP3C), mRNA gnl|BL_ORD_ID|4614 gi|124487059|ref|NP_001074857.1| RIMS-binding protein 2 [Mus musculus] 103 2877 3176 846 941 1.0102e-07 138.0
In output file however only gi|19125 is printed. How do I solve this?
Any help will be appreciated.
Ram
Using subprocess to call an external sorting tool seems quite silly considering that python has a built in method for sorting items.
Looking at your sample data, it appears to be structured data, with a | delimiter. Here's how you could open that file, and iterate over the results in python in a sorted manner:
def custom_sorter(first, second):
""" A Custom Sort function which compares items
based on the value in the 2nd and 6th columns. """
# First, we break the line into a list
first_items, second_items = first.split(u'|'), second.split(u'|') # Split on the pipe character.
if len(first_items) >= 6 and len(second_items) >= 6:
# We have enough items to compare
if (first_items[1], first_items[5]) > (second_items[1], second_items[5]):
return 1
elif (first_items[1], first_items[5]) < (second_items[1], second_items[5]):
return -1
else: # They are the same
return 0 # Order doesn't matter then
else:
return 0
with open(src_file_path, 'r') as src_file:
data = src_file.read() # Read in the src file all at once. Hope the file isn't too big!
with open(dst_sorted_file_path, 'w+') as dst_sorted_file:
for line in sorted(data.splitlines(), cmp = custom_sorter): # Sort the data on the fly
dst_sorted_file.write(line) # Write the line to the dst_file.
FYI, this code may need some jiggling. I didn't test it too well.
What you see is probably the result of trying to write to the file from multiple processes simultaneously.
To emulate: sort -k2,2 -k6,6n ${tabname} > sort_blast.txt command in Python:
from subprocess import check_call
with open("sort_blast.txt",'wb') as output_file:
check_call("sort -k2,2 -k6,6n".split() + [tab.name], stdout=output_file)
You can write it in pure Python e.g., for a small input file:
def custom_key(line):
fields = line.split() # split line on any whitespace
return fields[1], float(fields[5]) # Python uses zero-based indexing
with open(tab.name) as input_file, open("sort_blast.txt", 'w') as output_file:
L = input_file.read().splitlines() # read from the input file
L.sort(key=custom_key) # sort it
output_file.write("\n".join(L)) # write to the output file
If you need to sort a file that does not fit in memory; see Sorting text file by using Python