Malformed column reporting and joining in BASH by paste or awk

I’ve spent some hours trying to figure out, why the heck my scripts using awk and paste are returning malformed output. Simply, lines were wrongly pasted together, some columns were missing, while some were malformed… and in case of awk, trying to print columns in unsorted order (ie. column #3 before column #2 awk '{print $3,$2}') was producing malformed output.
After some time, I have realised it was due to windows-like new line escape \r\n, instead of standard Linux-like \n (of course I got this file from third party using Windows…).

Below, you can find more details.

# first, let's create dummy files containing 4 lines and 5 columns, each line ending with \r\n
python -c "with open('wrong.tsv','w') as out: out.write(''.join('line%s\t%s\r\n'%(i, '\t'.join('column%s'%j for j in range(1,5))) for i in range(1,4)))"
# and ending just with \n
python -c "with open('correct.tsv','w') as out: out.write(''.join('line%s\t%s\n'%(i, '\t'.join('column%s'%j for j in range(1,5))) for i in range(1,4)))"

# now let's paste wrong and correct files
paste wrong.tsv wrong.tsv
line1	line1n1	column1	column2	column3	column4
line2	line2n1	column1	column2	column3	column4
line3	line3n1	column1	column2	column3	column4

paste correct.tsv correct.tsv
line1	column1	column2	column3	column4	line1	column1	column2	column3	column4
line2	column1	column2	column3	column4	line2	column1	column2	column3	column4
line3	column1	column2	column3	column4	line3	column1	column2	column3	column4

# can you see the difference?

Simply, \r is interpreted as return to the beginning of the line in Unix, thus pasting lines containing such character will fail.
In order to convert files containing \r\n into Unix style \n, simply execute:

# replaces file and creates backup: inputfile.bak
sed -i.bak 's/\r$//' inputfile

# creates outputfile with correct formatting
tr -d '\r' < inputfile > outputfile

You can read more on new-line escape characters at Wikipedia.

Running Jupyter as public service

Some time ago, I’ve written about setting up IPython as a public service. Today, I’ll write about setting up Jupyter, IPython descendant, that beside Python supports tons of other languages and frameworks.

Jupyter notebook will be running in separate user, so your personal files are safe, but not as system service. Therefore, you will need to restart it upon system reboot. I recommend running it in SCREEN session, so you can easily login into the server and check the Jupyter state.

  1. Install & setup Jupyter
  2. #
    sudo apt-get install build-essential python-dev
    sudo pip install jupyter
    # create new user
    sudo adduser jupyter
    # login as new user
    su jupyter
    # make sure to add `unset XDG_RUNTIME_DIR` to ~/.bashrc
    # otherwise you'll encounter: OSError: [Errno 13] Permission denied: '/run/user/1003/jupyter'
    echo 'unset XDG_RUNTIME_DIR' >> ~/.bashrc
    source ~/.bashrc
    # generate ssl certificates
    mkdir ~/.ssl
    openssl req -x509 -nodes -days 999 -newkey rsa:1024 -keyout ~/.ssl/mykey.key -out ~/.ssl/mycert.pem
    # generate config
    jupyter notebook --generate-config
    # generate pass and checksum
    ipython -c "from IPython.lib import passwd; passwd()"
    # enter your password twice, save it and copy password hash
    ## Out[1]: 'sha1:[your hashed password here]'
    # add to ~/.jupyter/
    c.NotebookApp.ip = '*'
    c.NotebookApp.open_browser = False
    c.NotebookApp.port = 8881
    c.NotebookApp.password = u'sha1:[your hashed password here]'
    c.NotebookApp.certfile = u'/home/jupyter/.ssl/mycert.pem'
    c.NotebookApp.keyfile = u'/home/jupyter/.ssl/mykey.key'
    # create some directory for notebook files ie. ~/Public/jupyter
    mkdir -p ~/Public/jupyter && cd ~/Public/jupyter
    # start notebook server
    jupyter notebook
  3. Add kernels
  4. You can add multiple kernels to Jupyter. Here I’ll cover installation of some:

    • Python
    • sudo pip install ipykernel
      # if you wish to use matplotlib, make sure to add to 
      # ~/.ipython/profile_default/
      c.InteractiveShellApp.matplotlib = 'inline'
    • BASH kernel
    • sudo pip install bash_kernel
      sudo python -m bash_kernel.install
    • Perl
    • This didn’t worked for me:/

      sudo cpan Devel::IPerl
    • IRkernel
    • Follow this tutorial.

    • Haskell
    • sudo apt-get install cabal-install
      git clone
      cd IHaskell

Then, just navigate to https://YOURDOMAIN.COM:8881/, accept self-signed certificate and enjoy!
Alternatively, you can obtain certificate from Let’s encrypt.

Using existing domain encryption aka Apache proxy
If your domain is already HTTPS, you may consider setting up Jupyter on localhost and redirect all incoming traffic (already encrypted) to particular port on localhost (as suggested by @shebang).

# enable Apache mods
sudo a2enmod proxy proxy_http proxy_wstunnel && sudo service apache2 restart

# add to your Apache config
    <Location "/jupyter" >
        ProxyPass http://localhost:8881/jupyter
        ProxyPassReverse http://localhost:8881/jupyter
    <Location "/jupyter/api/kernels/" >
        ProxyPass        ws://localhost:8881/jupyter/api/kernels/
        ProxyPassReverse ws://localhost:8881/jupyter/api/kernels/
    <Location "/jupyter/api/kernels/">
        ProxyPass        ws://localhost:8881/jupyter/api/kernels/
        ProxyPassReverse ws://localhost:8881/jupyter/api/kernels/

# update you Jupyter config (~/.jupyter/
c.NotebookApp.ip = 'localhost'
c.NotebookApp.open_browser = False
c.NotebookApp.port = 8881
c.NotebookApp.base_url = '/jupyter'
c.NotebookApp.password = u'sha1:[your hashed password here]'
c.NotebookApp.allow_origin = '*'

Note, it’s crucial to add Apache proxy for kernels (/jupyter/api/kernels/), otherwise you won’t be able to use terminals due to failed: Error during WebSocket handshake: Unexpected response code: 400 error.

On handy docker images

Motivated by successful stripping problematic dependencies from Redundans, I have decided to generate smaller Docker image, starting with Alpine Linux image (2Mb / 5Mb after downloading) instead of Ubuntu (49Mb / 122Mb). Previously, I couldn’t really rely on Alpine Linux, because it was impossible to make these problematic dependencies running… But now it’s whole new world of possibilities ๐Ÿ˜‰

There are very few dependencies left, so I have started… (You can find all the commands below).

  1. First, I have check what can be installed from package manager.
    Only Python and Perl.

  2. Then I have checked if any of binaries are working.
    For example, GapCloser is provided as binary. It took me some time to find source code…
    Anyway, none of the binaries worked out of the box. It was expected, as Alpine Linux is super stripped…

  3. I have installed build-base in order to be able to build things.
    Additionally, BWA need zlib-dev.

  4. Alpine Linux doesn’t use standard glibc library, but musl-libc (you can read more about differences between the two), so some programmes (ie. BWA) may be quite reluctant to compile.
    After some hours of trying & thanks to the help of mp15, I have found a solution, not so complicated ๐Ÿ™‚

  5. I have realised, that Dockerfile doesn’t like standard BASH brace expansion, that is working otherwise in Docker Alpine console…
    so ls *.{c,h} should be ls *.c *.h

  6. After that, LAST and GapCloser compilation were easy, relatively ๐Ÿ˜‰

Below, you can find the code from Docker file (without RUN commands).

apk add --update --no-cache python perl bash wget build-base zlib-dev
mkdir -p /root/src && cd /root/src && wget && tar xpfj bwa-0.7.15.tar.bz2 && ln -s bwa-0.7.15 bwa && cd bwa && \
cp kthread.c && echo "#include <stdint.h>" > kthread.c && cat >> kthread.c && \
sed -ibak 's/u_int32_t/uint32_t/g' `grep -l u_int32_t *.c *.h` && make && cp bwa /bin/ && \
cd /root/src && wget && tar xpfz GapCloser-src-v1.12-r6.tgz && ln -s v1.12-r6/ GapCloser && cd GapCloser && make && cp bin/GapCloser /bin/ && \
cd /root/src && wget && unzip && ln -s last-744 last && cd last && make && make install && \
cd /root/src && rm -r last* bwa* GapCloser* v* 

# SSPACE && redundans in /root/srt
cd /root/src && wget -q && tar xpfz 41SSPACE-STANDARD-3.0_linux-x86_64.tar.gz && ln -s SSPACE-STANDARD-3.0_linux-x86_64 SSPACE && wget -O- -q > SSPACE/dotlib/ && \
wget --no-check-certificate -q -O redundans.tgz && tar xpfz redundans.tgz && mv redundans-master redundans && ln -s /root/src/redundans /redundans && rm *gz

apk del wget build-base zlib-dev 
apk add libstdc++

After building & pushing, I have noticed that Alpine-based image is slightly smaller (99Mb), than the one based on Ubuntu (127Mb). Surprisingly, Alpine-based image is larger (273Mb) than Ubuntu-based (244Mb) after downloading. So, I’m afraid all of these hours didn’t really bring any substantial reduction in the image size.

I was very motivated to build my application on Alpine Linux and expected substantial size reduction. But I’d say that relying on Alpine Linux image doesn’t always pay off in terms of smaller image size, forget about production time… And this I know from my own experience.
But maybe I didn’t something wrong? I’d be really glad for some advices/comments!

Nevertheless, stripping a few dependencies from my application (namely Biopython, numpy & scipy), resulted in much more compact image even using Ubuntu-based image (127Mb vs 191Mb; and 244Mb vs 440Mb after downloading). So I think this is the way to go ๐Ÿ™‚

On simplifying dependencies

Lately, to make Redundans more user friendly, I have simplified it’s dependencies, by replacing Biopython, numpy, scipy and SQLite with some (relatively) simple functions or modules.

Here, I will just focus on replacing Biopython, particularly SeqIO.index_db with FastaIndex. You may ask yourself, why I have invested time in reinventing the wheel. I’m big fan of Biopython, yet it’s huge project and some solutions are not optimal or require problematic dependencies. This is the case with SeqIO.db_index, that relies on SQLite3. Here again, I’m a big fan of SQLite, yet building Biopython with SQLite enabled proved not to be very straightforward for non-standard systems or less experience users. Beside, on some NFS settings, the SQLite3 db cannot be created at all.

Ok, let’s start from the basics. SeqIO.index_db allows random access to sequence files, so for example you can rapidly retrieve any entry from very large file. This is achieved by storing the ID and position of each entry from particular file in database, SQLite3 db. Then, if you want to retrieve particular record, SeqIO.index_db looks up if this record is present in SQLite3 db, retrieves record position in the file and reads only small chunk of this file instead of parsing entire file every time you want to get some record(s).
Similar feature is offered by samtools faidx, but in this case, the coordinates of each entry are stored in tab-delimited file .fai (more info about .fai). This format can be easily read & write by any programme, so I have decided to use it. In addition, I have realised, that samtools faidx is flexible enough, so you can add additional columns to the .fai without interrupting its functionality, but about that later…

In Redundans, I’ve been using SeqIO.index_db during assembly reduction ( Additionally, beside storing index, I’ve been also generating statistics for every FastA file, like number of contigs, cumulative size, N50, N90, GC and so on. I have realised, that these two can be easily combined, by extending .fai with four additional columns, storing number of occurencies for A, C, G & T in every sequence. Such .fai is compatible with samtools faidx and provides very easy way of calculating bunch of statistics about this file.
All of these, I’ve implemented in FastaIndex. Beside being dependency-free & very handy indexer, it can be used also as alternative to samtools faidx to retrieve sequences from large FastA files.

# retrieve bases from 20 to 60 from NODE_2
./ -i test/run1/contigs.fa -r NODE_2_length_7674_cov_46.7841_ID_3:20-60
#Time elapsed: 0:00:00.014243

samtools faidx test/run1/contigs.fa NODE_2_length_7674_cov_46.7841_ID_3:20-60

Using docker for application development

I found Docker super useful, but going through a manual is quite time consuming. Here, very stripped manual to create your first image and push it online ๐Ÿ™‚

# install docker
wget -qO- | sh
# add your user to docker group
sudo usermod -aG docker $USER
# check if it's working
docker run docker/whalesay cowsay "hello world!"
# create an account on
# and login
docker login -u $USER --email=EMAIL
# run image
docker run -it ubuntu
# make some changes ie. create user, install needed software etc
# finally open new terminal & commit changes (SESSIONID=HOSTNAME)
docker commit SESSIONID $USER/image:version
# mount local directory `pwd`/test as /test in read/write mode
docker run -it -v `pwd`/test:/test:rw $USER/image:version some command with arguments
# push image
docker push $USER/image:version

From now, you can get your image from any other machine connected to Internet by executing:

docker run -it $USER/image:version
# ie. redundans image
docker run -it -w /root/src/redundans lpryszcz/redundans:v0.11b ./ -v -i test/{600,5000}_{1,2}.fq.gz -f test/contigs.fa -o test/run1
# you can create alias latest, then version can be skipped on running
docker tag lpryszcz/redundans:v0.11b lpryszcz/redundans:latest
docker push lpryszcz/redundans:latest
docker run -it lpryszcz/redundans

You can add info about your repository at$USER/image/

Working efficiently with millions of files

Working with millions of intermediate files can be very challenging, especially if you need to store them in distributed / network file system (NFS). This will make listing / navigating the directories to take agesโ€ฆ and removing of these files very time-consuming.
During building metaPhOrs DB, I needed to store some ~7.5 million of intermediate files that were subsequently processed in HPC. Saving these amount of files in the NFS would seriously affect not only myself, but also overall system performance.
One could store files in an archive, but then if you want to retrieve the data you would need to parse rather huge archives (tens-to-hundreds of GB) in order to retrieve rather small portions of data.
I have realised that TAR archives are natively supported in Python and can be indexed (see `tar_indexer`), which provide easy integration into existing code and random-access. If you work with text data, you can even zlib.compress the data stored inside you archives!
Below, Iโ€™m providing relevant parts of my code:

# index content of multiple tar archives -v -i db_*/*.tar -d archives.db3
# search for some_file in mutliple archives -v -f some_file -d archives.db3


import sqlite3, time
import tarfile, zlib, cStringIO
# lookup function
def tar_lookup(dbpath, file_name):
    """Return file name inside tar, tar file name, offset and file size."""
    cur = sqlite3.connect(dbpath).cursor()
    cur.execute("""SELECT o.file_name, f.file_name, offset, file_size
                FROM offset_data as o JOIN file_data as f ON o.file_id=f.file_id
                WHERE o.file_name like ?""", (file_name,))
    return cur.fetchall()
# saving to archive
    # open tarfile
    tar =, "w")
    # save files to tar
    for fname, txt in files_generator:
        # compress file content (optionally)
        gztxt = zlib.compress(txt)
        # get tarinfo
        ti = tarfile.TarInfo(fname)
        ti.size  = len(gztxt)
        ti.mtime = time.time()
        # add to tar
        tar.addfile(ti, cStringIO.StringIO(gztxt))
# reading from indexed archive(s)
# NOTE: before you need to run on your archives
    tarfnames = tar_lookup(index_path, file_name)
    for i, (name, tarfn, offset, file_size) in enumerate(tarfnames, 1):
        tarf = open(tarfn)
        # move pointer to right archive place
        # read tar fragment & uncompress
        txt = zlib.decompress(

Tracing exceptions in multiprocessing in Python

I had problems with debugging my programme using multiprocessing.Pool.

Traceback (most recent call last):
  File "src/", line 294, in <module>
  File "src/", line 289, in main
    o.noupload, o.verbose)
  File "src/", line 242, in homologies2mysql
    for i, data in enumerate(p.imap_unordered(worker, pairs), 1):
  File "/usr/lib64/python2.6/multiprocessing/", line 520, in next
    raise value
ValueError: need more than 1 value to unpack

I could run it without multiprocessing, but then Iโ€™d have to wait some days for the program to reach the point where it crashes.
Luckily, Python is equipped with traceback, that allows handy tracing of exceptions.
Then, you can add a decorator to problematic function, that will report nice error message:

import traceback, functools, multiprocessing
def trace_unhandled_exceptions(func):
    def wrapped_func(*args, **kwargs):
            return func(*args, **kwargs)
            print 'Exception in '+func.__name__
    return wrapped_func
def go():
    raise Exception()
p = multiprocessing.Pool(1)

The error message will look like:

Exception in go
Traceback (most recent call last):
  File "<stdin>", line 5, in wrapped_func
  File "<stdin>", line 4, in go

Solution found on StackOverflow.

Connecting to MySQL without passwd prompt

If you are (like me) annoyed by providing password at every mysql login, you can skip it. Also it makes easier programmatic access to any MySQL db, as not passwd prompting is necessary ๐Ÿ™‚
Create `~/.my.cnf` file:


And login without `-p` parameter:

mysql -h host -u username dbname

If you want to use `~/.my.cnf` file in MySQLdb, just connect using this:

import MySQLdb
cnx = MySQLdb.connect(host=host, port=port, read_default_file="~/.my.cnf")

Batch convert of .xlsx (Microsoft Office) to .tsv (tab-delimited) files

I had to retrieve data from multiple .xlsx files with multiple sheets. This can be done manually, but it will be rather time-consuming tasks, plus Office quotes text fields, which is not very convenient for downstream analysisโ€ฆ
I have found handy script,, that does the job, but it reports only one sheet at the time. Thus, I have rewritten a little to save all sheets from given .xlsx file into separate folder. In addition, multiple .xlsx files can be process at once. My version can be found on github. *.xlsx