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Right now I can measure a simple array and plot using either MatPlot or QtPlot. But when I start processing this data, there is no good option to plot the results with the same axis and labels.
An example:
importmatplotlib.pyplotaspltimportcopyimportnumpyasnpimportqcodesasqc#%% Make toy modelfromtoymodelimportAModel, MockGates, MockSource, MockMeter, AverageGetter, AverageAndRawmodel=AModel()
gates=MockGates('gates', model=model)
source=MockSource('source', model=model)
meter=MockMeter('meter', model=model)
station=qc.Station(gates, source, meter)
station.set_measurement(meter.amplitude)
c0, c1, c2, vsd=gates.chan0, gates.chan1, gates.chan2, source.amplitude#%% 1D loop to make datadata=qc.Loop(c0[-20:20:0.2], 0.01).run(location='data/testsweep', overwrite=True)
data.complete()
Plotting the data is fine!
plotQ=qc.QtPlot(data.amplitude)
Now suppose we want to calculate the derivative and show results. We can convert to Numpy and show results, but then we have no axis labels...
Right now I can measure a simple array and plot using either
MatPlot
orQtPlot
. But when I start processing this data, there is no good option to plot the results with the same axis and labels.An example:
Plotting the data is fine!
Now suppose we want to calculate the derivative and show results. We can convert to Numpy and show results, but then we have no axis labels...
Trying to make a copy of the DataArray and setting values fails...
Finally: apply the derivative to the DataArray itself. No labels again...
Options I see:
DataArray
accepting anotherDataArray
and a plain numpy arrayBasePlot
so we can do a.add([DataSet], [numpy array])
add_array
option to the DataSet with a Numpy array as argument (not aDataArray
)showImage(data_set, image)
function that duplicates part of the functionality ofBasePlot
Any suggestions on which way to go?
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