[Requirement] Slicing dataset based on a certain log.

[YooSunYoung]

Executive summary

Dataset should be split based on the timestamp of the certain logs.

Context and background knowledge

During the measurement, there will be some changes and they will be logged into the files.
They will have timestamps and the dataset should be binned(sliced) based on thaose timestamps.

Inputs

File or dataset that contains NXlog or other time-dependent fields.

Methodology

I think it'll be nice if there is a binning helper that uses foreign key for binning.
For example, we need to use the timestamp of the rotation_angle to slice the data, and the timestamp will be the foreign key.

Otherwise, we can just assume the foreign key will always be the timestamp...?

Outputs

Sliced dataset as....

@scipp/ess-maintainers I'm not sure if it should be a DataGroup or DataArray...

Which interfaces are required?

Python module / function

Test cases

There is a timepix dataset that recorded rotation angle of the sample.
The dataset should be split based on the rotation angle.

Comments

No response

[SimonHeybrock]

The description here is unclear, I am not sure what is required, aside from existing Scipp functionality.

[YooSunYoung]

We just need a helper that derives timestamp slicing based on the f144 values.

We could just do it case-by-case with existing scipp functionality but I thought it's a very common thing that every instrument needs.
So I think it's worth wrapping it as a common tool...?

[SimonHeybrock]

Can you clarify what you have in mind, beyond the built-in functionality like https://scipp.github.io/user-guide/binned-data/filtering.html#Compute-derived-event-parameters-from-time-series-or-other-metadata? Aside from that, is this a duplicate of #153?

[jokasimr]

@YooSunYoung

Is this the functionality that is required here?

import h5py
import scippnexus as snx
import scipp as sc

with h5py.File('dummy.nxs', mode='w', driver="core", backing_store=False) as f:
    # setup, create a NXlog...
    da = sc.DataArray(   
        sc.array(dims=['time'], values=[1.1, 2.2, 3.3]),
        coords={
            'time': sc.epoch(unit='ns')
            + sc.array(dims=['time'], unit='s', values=[4.4, 5.5, 6.6]).to(
                unit='ns', dtype='int64'
            )
        },    
    )
    log = snx.create_class(f, 'log', NXlog)    
    snx.create_field(log, 'value', da.data)
    snx.create_field(log, 'time', da.coords['time'] - sc.epoch(unit='ns'))
    log = snx.Group(log, definitions=snx.base_definitions())

    # Slice part of it based on the `time` column
    print(log['time', sc.scalar(5.5, unit='s').to(unit='ns'):])

Output:

<scipp.DataArray>
Dimensions: Sizes[time:2, ]
Coordinates:
* time                    datetime64             [ns]  (time)  [1970-01-01T00:00:05.500000000, 1970-01-01T00:00:06.600000000]
Data:
                            float64  [dimensionless]  (time)  [2.2, 3.3]