Truncate / quantize numpy array of float32 to given number of signifiant bits

truncate_digits.py

def strip_bits(array, significant_bits):
  
    """
    
    This function truncates the mantiss of float32 values in an ndarray to a given number of significant bits.
   
    It is useful to drop non significant data before compressing the output, to achieve better compression ratios.
    
    This method is already available in NetCDF (https://unidata.github.io/netcdf4-python/#efficient-compression-of-netcdf-variables) 
    with the parmeter `significant_digits` of `createVariable` but might not be compatible with older versions.
    
    Note that one digit (in decimal format) corresponds roughly to 3.3 bits = 10 bits for 3 digits of precision, which might be sufficient  
    for many use case.
    
    """
  
    array = array.astype(np.float32)
    
    # Create a mask keeping expient + significant bits of the mantiss
    nbits = 9 + significant_bits
    mask = (1 << nbits) - 1 << (32 - nbits) # (N x 1 bits) + 0000000

    int_array = array.view(np.uint32)
    int_array = int_array & np.uint32(mask)
    
    return int_array.view(np.float32)