Variance stokes linear vs constant

[LiduinBos]

Hi,

Currently I'm trying to calibrate a double ended setup (merged measurements from 2 single ended channels) of about 800 m. One step is to determine the stokes variance. Given the description in the documentation I should take the "variance_stokes_linear" function. This function has an output of 6 variables, namely slope, offset, sort_mean, sort_var, resid, var_fun. From the documentation (and from the code) it is not clear to me which variables I have to use in the calibrate_double_ended function. All output variables from the variance stokes function are not multi-dimensional while the calibrate_double_ended function seems to expect this for the st_var, ast_var, etc. variables.

I'm currently stuck on the error message: "ValueError: operands could not be broadcast together with shapes (3139,9) (39,) " when trying to run:

slope, offset, st_sort_mean, st_sort_var, resid, var_fun = variance_stokes_linear(ds.dts.st,sections=sections,acquisitiontime=ds.userAcquisitionTimeFW)
slope, offset, ast_sort_mean, ast_sort_var, resid, var_fun = variance_stokes_linear(ds.dts.ast,sections=sections,acquisitiontime=ds.userAcquisitionTimeFW)
slope, offset, rst_sort_mean, rst_sort_var, resid, var_fun = variance_stokes_linear(ds.dts.rst,sections=sections,acquisitiontime=ds.userAcquisitionTimeBW)
slope, offset, rast_sort_mean, rast_sort_var, resid, var_fun = variance_stokes_linear(ds.dts.rast,sections=sections,acquisitiontime=ds.userAcquisitionTimeBW)

ds_calibrated = ds.dts.calibrate_double_ended(
sections = sections,
st_var=st_sort_var,
ast_var=ast_sort_var,
rst_var=rst_sort_var,
rast_var=rast_sort_var,
method='wls',
)

I probably am doing something wrong, but I cannot figure out what. Do you have any ideas on how to correctly apply both the variance_stokes_linear function as well as the calibrate_double_ended?

note: I am using the dtscalibration version 3.1.0.

Best regards,

Liduin

[BSchilperoort]

Hi Liduin, the st_var keyword argument in ds.dts.calibrate_double_ended can be a function (also called 'callable' when it's an input argument). This is the var_fun returned by the variance_stokes_linear.

The function is defined as:

    def var_fun(stokes):
        return slope * stokes + offset

Try this;

_, _, _, _, _, st_var = variance_stokes_linear(ds.dts.st,sections=sections,acquisitiontime=ds.userAcquisitionTimeFW)
_, _, _, _, _, ast_var = variance_stokes_linear(ds.dts.ast,sections=sections,acquisitiontime=ds.userAcquisitionTimeFW)
_, _, _, _, _, rst_var = variance_stokes_linear(ds.dts.rst,sections=sections,acquisitiontime=ds.userAcquisitionTimeBW)
_, _, _, _, _, rast_var = variance_stokes_linear(ds.dts.rast,sections=sections,acquisitiontime=ds.userAcquisitionTimeBW)

ds_calibrated = ds.dts.calibrate_double_ended(
    sections = sections,
    st_var=st_var,
    ast_var=ast_var,
    rst_var=rst_var,
    rast_var=rast_var,
    method='wls',
)

Do be careful when using this variance relationship, and verify the linear fit performed by the variance_stokes_linear function. It can also plot the data for you to analyze.

I assume you read this document: https://github.com/dtscalibration/python-dts-calibration/blob/main/examples/temperature_variance_from_stokes.pdf ?

[LiduinBos]

Hi Bart,

Thanks for the quick response and answer! I better understand what's going on now.

Yes I did, thanks for double checking.

[LiduinBos]

This works, thanks!