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Hidden Markov Model profile tools (reader/writer/data structures)

Project description


License: MIT Actions Status Wheel Status Supported Python versions PyPI - Status Latest version

Hidden Markov Model profile toolkit.

Written in the base of HMMER User's Guide p.107.


With my package you can read and write hmm profile files. It's easy to use and easy to read - the best documentation is a well-written code itself, so don't be scared about reading source code.


Read all hmm from file

The read_all function returns generator to optimise memory usage - it's a common pattern that one file contains many profiles.

from hmm_profile import reader

with open('/your/hmm/profile/file.hmm') as f:
    model_generator = reader.read_all(f)  # IMPORTANT: returns generator

profiles = list(model_generator)

Read single model

If you have only single model files, you can use this method. It will return models.HMM ready to use.

from hmm_profile import reader

with open('/your/hmm/profile/file.hmm') as f:
    model = reader.read_single(f) 


Write multiple profiles to single file

from hmm_profile import writer

profiles = [...]
path = '/your/hmm/profile/file.hmm'

writer.save_many_to_file(hmms=profiles, output=path)

Write single model to file

from hmm_profile import writer

model = ...
path = '/your/hmm/profile/file.hmm'

writer.save_to_file(hmm=model, output=path)

Get file content without saving

from hmm_profile import writer

model = ...

lines = writer.get_lines(model)  # IMPORTANT: returns generator
content = ''.join(lines)


If you have a file that is not readable or has some glitches on save, please crate the issue and attach this file. Bug reports without files (or good examples if you can't provide full file) will be ignored.


Full database test

Above you can see if all hmm profiles from Pfam works. Test are running every day.

Test flow:

  1. Download all hmm profiles from Pfam.
  2. Load profiles sequentially.
  3. Write model to file.
  4. Load saved model from file.
  5. Check if both loaded profiles are equals.

For this test the latest version of hmm_profile from pypi is used.

Full DB test also runs before each release, but badge above shows only periodic tests results.


Whole package is written in pure Python, without C extensions.

You can treat full DB test as benchmark.

Benchmark should be depended mainly on single core of CPU and secondarily on storage and eventually on RAM. Storage is used only for read from then files will be saved to "in-memory file" (StringIO).

Remember: Results may vary when CPU is under load. Also, hmm profiles in db can be modified in future or some profiles may be added/removed from DB.

Processor Storage Time [s] Profiles Date Version Python
Intel Core i7-4702MQ Crucial MX500 500 GB 342 17928 2020.02.22 0.0.9 3.7
Intel Core i7-4702MQ Crucial MX500 500 GB 322 17928 2020.02.22 0.0.9 3.6
Intel Core i7-4702MQ GoodRAM Iridium Pro240 GB TBA TBA TBA TBA 3.6

To run benchmark:

pip install .
python test --addopts -s

Run test at least 3 times if you want to share results (last line) and close as much process as possible. Important: do not run tests inside so-called terminal in IDE - it will do much more job with output and benchmark result will be affected.

As you can see python 3.6 is a little faster, probably due to different implementation of backported dataclasses, but I'm not sure.



  1. Change version in to x.y.z.dev0 (or leave if minor version bump) and ensure changelog is up to date. (Nothing changed yet. is not ok, CI will fail)
  2. Tag head of master branch with x.y.z without .dev0

Important: release ALWAYS is from master branch! So keep master untouched when you want to release.

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