I am using np.absolute to try to find datasets that have meaningful data. The data includes negative numbers and I am trying to use the mean to decide wether it is meaningful. However, I don't want the negative values to cancel out the positive ones and give me a mean close to zero. Hence I use np.absolute, and take the mean of that. My code wasn't finding some datasets to be meaningful that I knew were, so I did some troublshooting, and found that np.absolute() was returning negative values! How can this be? Here is a shortened version of the program. I tested it and it has the same problem as the full version. import h5py import numpy as np with h5py.File('<filename>.h5', 'r') as hdf: timestream_data = hdf.get('timestream') timestream = np.array(timestream_data) for x in timestream: timestream_list = timestream.tolist() dataset_as_list = x.tolist() print("Index: %d" % timestream_list.index(dataset_as_list), x, np.absolute(x)) And the output (I'll only include one of the lines that shows the error): Index: 32 [ 127 -128 -128 ... -128 -128 127] [ 127 -128 -128 ... -128 -128 127] Notice the last thing it should be printing on that line is np.absolute(x)... but there are negative values in the array... Continue reading...
