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Empty HPO does not generate OMIM phenotype file #2

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arine opened this issue Feb 20, 2024 · 1 comment
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Empty HPO does not generate OMIM phenotype file #2

arine opened this issue Feb 20, 2024 · 1 comment
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@arine
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arine commented Feb 20, 2024

When hpo.txt is an empty file, the pipeline fails because phenoSim.R does not generate OMIM HPO file {{Job ID}}-dx, so main.py fails.
Log:

Feature engineering                                                                                                               
input file: /out/jobid-vep-1.txt                                                                               
type of input file: vepAnnotTab                                                                                                   
outPrefix: /out/rami-test/r1                                                                                                      
modules: curate,conserve                                                                                                          
modules list: ['curate', 'conserve']                                                                                              
Traceback (most recent call last):                                                                                                
  File "/run/annotation/main.py", line 486, in <module>                                                                           
    main()                                                                                                                        
  File "/run/annotation/main.py", line 174, in main                                                                               
    omimHPOScoreDf = pd.read_csv(fileName, sep='\t')                                                                              
  File "/usr/local/lib/python3.8/dist-packages/pandas/util/_decorators.py", line 311, in wrapper                                  
    return func(*args, **kwargs)                                                                                                  
  File "/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py", line 680, in read_csv                               
    return _read(filepath_or_buffer, kwds)                                                                                        
  File "/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py", line 575, in _read                                  
    parser = TextFileReader(filepath_or_buffer, **kwds)          
  File "/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py", line 934, in __init__                               
    self._engine = self._make_engine(f, self.engine)                                                                              
  File "/usr/local/lib/python3.8/dist-packages/pandas/io/parsers/readers.py", line 1218, in _make_engine                          
    self.handles = get_handle(  # type: ignore[call-overload]                                                                     
  File "/usr/local/lib/python3.8/dist-packages/pandas/io/common.py", line 786, in get_handle
    handle = open(                                                                                                                
FileNotFoundError: [Errno 2] No such file or directory: '/out/jobid-dx'  
@arine arine added the bug Something isn't working label Feb 20, 2024
@arine arine self-assigned this Feb 20, 2024
@arine
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arine commented Feb 20, 2024

Resolved at commit 5d7e32a

@arine arine closed this as completed Feb 20, 2024
jylee-bcm added a commit that referenced this issue Jun 27, 2024
Previously, multiprocessing was utilized in the prediction step, resulting in increased memory consumption proportional
to the number of worker processes.
By refactoring the code to replace multiprocessing with a simple for loop, memory usage has been significantly reduced.
This change not only enhances efficiency by eliminating the overhead of virtual memory but also accelerates runtime performance.
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