36 lines
1.5 KiB
Org Mode
36 lines
1.5 KiB
Org Mode
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#+TITLE: Tensorflow experiments
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#+AUTHOR: Amin Kasrou Aouam
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#+PROPERTY: header-args :session poetry-session
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* Experiments
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#+begin_src elisp :results silent
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(pyvenv-activate "~/.cache/pypoetry/virtualenvs/locimend-hM_4JND0-py3.8/")
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#+end_src
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In this notebook we'll extract knowledge from our generated dataset. First, let's import our dependencies:
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#+begin_src python
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from tensorflow_io import genome
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#+end_src
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#+RESULTS:
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: 2021-05-06 20:19:46.143707: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /nix/store/9ilyrqidrjbqvmnn8ykjc7lygdd86g7q-gcc-10.2.0-lib/lib:
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: 2021-05-06 20:19:46.143750: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
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Tensorflow I/O is an extension that contains a module for genome parsing, we'll use it to import the sequences contained in our FASTQ files:
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#+begin_src python :results silent
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def parse_data(filepath):
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HVR = genome.read_fastq(filename=filepath)
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return HVR.sequences, HVR.raw_quality
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#+end_src
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Let's import both the immuneSIM generated HVR dataset and the CuReSim processed one, which contains sequencing errors (mostly indels):
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#+begin_src python
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original_HVR, _ = parse_data("data/HVR.fastq")
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processed_HVR, _ = parse_data("data/CuReSim_HVR.fastq")
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print(original_HVR)
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#+end_src
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