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xarray dataset dependency is incorrect when adding an independent parameter to the measurement

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Assessment

Difficulty
3/5
Estimated time
1-2 days
Newbie friendliness
52/100
Issue type
Bug
Clarity
Mostly clear
Activity status
Stale
Tech stack
numpy, python
Domain
data

Research direction

Start with the Measurement, datasaver.dataset, and to_xarray_dataset entry points from the reproduction, then trace how ParamSpec depends_on metadata is converted into xarray variables. Reproduce the example with deterministic setpoints and compare the resulting dependencies for X, Y, and independent Z. Done means Y depends on X while Z depends on the index, with regression coverage for this case.

Written by the indexing model from the issue text.

Description

I created a parameter with setpoints Y which depends on setpoint X.
Then I created another parameter Z which is independent from the other two.
I measured Y and Z in the measurement context, then convert the result dataset to an xarray.

Expected behaviour

I expect that the Y would be a data variable that depends on X, and Z on index, as inferred from the paramspecs.

{'foo_instrument_X': ParamSpec('foo_instrument_X', 'array', 'X', '', inferred_from=[], depends_on=[]),
 'foo_instrument_Y': ParamSpec('foo_instrument_Y', 'array', 'Y', '', inferred_from=[], depends_on=['foo_instrument_X']),
 'foo_instrument_Z': ParamSpec('foo_instrument_Z', 'numeric', 'Z', '', inferred_from=[], depends_on=[])}
Actual behaviour

All parameters became data variables that depended on index.
image

Steps to reproduce

Run the following snippet

import numpy as np
import qcodes as qc
from qcodes import Measurement
from qcodes.instrument import Instrument
from qcodes.parameters import Parameter, ParameterWithSetpoints
from qcodes.validators import Arrays, Numbers
class FooGeneratedSetpoints(Parameter):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
    def get_raw(self):
        return np.array(range(4,8))
class FooArray(ParameterWithSetpoints):
    def get_raw(self):
        return np.array(range(4))
class FooInstrument(Instrument):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.add_parameter(
            "X",
            parameter_class=FooGeneratedSetpoints,
            vals=Arrays(shape=(4,))
        )
        self.add_parameter(
            "Y",
            parameter_class=FooArray,
            setpoints=(self.X,),
            vals=Arrays(shape=(4,))
        )
foo = FooInstrument("foo_instrument")

foo.add_parameter("Z", set_cmd=None)

meas = Measurement()
meas.register_parameter(foo.Y)
meas.register_parameter(foo.Z)
with meas.run() as datasaver:
    for z in range(8,12):
        datasaver.add_result(
            (foo.Y, foo.Y()), (foo.Z, z)
        )
    dataset = datasaver.dataset
dataset.to_xarray_dataset()

If I modify the setpoint to the following, the issue disappeared and the result dataset is as expected.

from random import random
class FooGeneratedSetpoints(Parameter):
    ...
    def get_raw(self):
        return np.array([random() + i for i in range(4,8)])
...
image
Dominant language
Python
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Forks
359
Avg merge
2d 10h
Merged PRs (30d)
79

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