Optimizing if statement check over a numpy value

Heli Nix hemla21 at gmail.com
Thu Jul 23 11:21:33 CEST 2015


Dear all, 

I have the following piece of code. I am reading a numpy dataset from an hdf5 file and I am changing values to a new value if they equal 1. 

 There is 90 percent chance that (if id not in myList:) is true and in 10 percent of time is false. 

with h5py.File(inputFile, 'r') as f1:
    with h5py.File(inputFile2, 'w') as f2:
        ds=f1["MyDataset"].value
        myList=[list of Indices that must not be given the new_value]

        new_value=1e-20
        for index,val in     np.ndenumerate(ds):
            if val==1.0 :
                id=index[0]+1
                if id not in myList:
                    ds[index]=new_value
           
        dset1 = f2.create_dataset("Cell Ids", data=cellID_ds)  
        dset2 = f2.create_dataset("Porosity", data=poros_ds) 

My numpy array has 16M data and it takes 9 hrs to run. If I comment my if statement (if id not in myList:) it only takes 5 minutes to run. 

Is there any way that I can optimize this if statement. 

Thank you very much in Advance for your help. 

Best Regards, 


More information about the Python-list mailing list