How to get the calibrated value for differential attenuation (dalpha)?

[jennifer-frederick]

I have DTS data from a single-ended set-up using a Silixa machine, with two sections where I had a portion of the cable in an ice bath of a known temperature (measured with probe 1 and probe 2, and reported in the xml file). I ran a calibration and the results looked great! However, how do I access the value of dalpha, the differential attenuation, that the calibration routine solved for? I guessed a value when using the Silixa GUI as it was collecting the data, but of course I just eye-balled it and don't trust it 100%. I'd like to get the value that the calibration routine solved for. What variable do I print out? THANKS!

[BSchilperoort]

Hi Jennifer,

The calibrated Dataset object should have a "dalpha" variable. You can access this by doing;

ds = ds.dts.calibrate_single_ended(...)
ds["dalpha"]

The ds object is an xarray Dataset, more info on this data structure is available e.g. here; https://docs.xarray.dev/en/v2024.02.0/user-guide/data-structures.html#dataset

[BSchilperoort]

Also note that it's better to re-calibrate the value of differential attenuation for any new data, as the attenuation can vary somewhat over time, especially with varying amounts of strain.

[jennifer-frederick]

Bart,
Thank you so much! In fact, now that I know this is an xarray Dataset object, I figured out how to display all the available variables stored inside. For the benefit of future discussion readers, print(ds.data_vars) will give you the following list after performing the calibration:

Data variables:
st (x, time) float64 6.528e+03 6.525e+03 ... 1.248e+03
ast (x, time) float64 5.888e+03 5.883e+03 ... 881.0 880.5
tmp (x, time) float64 22.95 22.89 23.18 ... 25.05 25.05
acquisitionTime (time) float32 1.8e+03
referenceTemperature (time) float32 27.76
probe1Temperature (time) float32 0.7815
probe2Temperature (time) float32 0.7976
referenceProbeVoltage (time) float32 0.0
probe1Voltage (time) float32 0.0
probe2Voltage (time) float32 0.0
userAcquisitionTimeFW (time) float32 1.8e+03
gamma float64 485.8
gamma_var float64 0.0
talpha_fw (trans_att, time) float64
talpha_fw_var (trans_att, time) float64
c (time) float64 1.546
c_var (time) float64 2.493e-08
dalpha float64 -7.812e-05
dalpha_var float64 8.856e-15
alpha (x) float64 -0.007843 -0.007883 ... -0.2569 -0.257
alpha_var (x) float64 8.928e-11 9.018e-11 ... 9.584e-08
talpha_fw_full (x, time) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
talpha_fw_full_var (x, time) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0
tmpf (x, time) float64 22.77 22.71 22.99 ... 23.42 23.42
var_fw_da (comp_fw, x, time) float64 dask.array<chunksize=(1, 6274, 1), meta=np.ndarray>
tmpf_var (x, time) float64 dask.array<chunksize=(6274, 1), meta=np.ndarray>
p_val (params1) float64 485.8 -7.812e-05 1.546
p_cov (params1, params2) float64 0.0 0.0 ... 2.493e-08

Thanks again!
Jenn