Weighted concatenation fails due to negative integer powers not allowed

[joakimsk]

Hello,

While trying to concatenate all pings in an xtf, xtf_io.py line 228 raises a ValueError exception:

Traceback (most recent call last):
File "c:\dev\pyxtf\examples\plot_sonar_bathy.py", line 39, in
np_chan1 = concatenate_channel(p[XTFHeaderType.sonar], file_header=fh, channel=0, weighted=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "c:\dev\pyxtf\pyxtf\xtf_io.py", line 228, in concatenate_channel
out_array *= (2 ** -np.array(weight_factors)).astype(out_array.dtype)[:, np.newaxis]
~~^^~~~~~~~~~~~~~~~~~~~~~~~~~~
ValueError: Integers to negative integer powers are not allowed.

My xtf has positive integer weights defined for each ping.
The xtf_io.py:203 out_array datatype is uint16 by default
The xtf_io.py:228 weight_factors are also integers
The xtf_io.py:229 results involve element-wise negative power, but numpy does not allow this, as the result will be float

I "fixed" this by ensuring xtf_io.py:203 out_array was np.float32 and that xtf_io.py:228 weighted_factors cast the Weight to float as-well, but not sure this is the cleanest way to handle this. The output out_array will thus be float32, instead of uint16.

xtf_io.py:227:
if weighted:
weight_factors = [float(ping.ping_chan_headers[channel].Weight) for ping in pings[::-1]]
convertedArray = out_array.astype(np.float32)
convertedArray *= (2 ** -np.array(weight_factors)).astype(convertedArray.dtype)[:, np.newaxis]
return convertedArray

Anyone got a better suggestion?

[oysstu]

I believe this worked back in the day because numpy was more willing to do automatic type coercion. Negative powers are only permitted for floating point numbers. Try to change the type of the exponent to float32, that might work. Not sure if the output array needs to be float as well.

[joakimsk]

Thanks @oysstu, I used np.multiply instead of *= and also used np.power with 2.0 instead of 2; this seems to work ok. I also cast it to dtype of out_array, as original. I think it is as you suggest, the output does not need to be float. I hope this works with other xtf input as-well, do you have some to test it out on?

if weighted:
    weight_factors = [ping.ping_chan_headers[channel].Weight for ping in pings[::-1]]
    out_array = np.multiply(out_array, np.power(2.0, -np.array(weight_factors))[:, np.newaxis]).astype(out_array.dtype)

[oysstu]

Solved by #41