Column contexts can be used to organize columns inside a table. Taking the previous sciences example, column contexts enable us to organize the data like so.

import unify

with unify.Log(), unify.ColumnContext("Sciences"):
    with unify.ColumnContext("Maths"):
        unify.log(
            name="Zoe",
            question="what is 1 + 1?",
            region="US"
        )
    with unify.ColumnContext("Physics"):
        unify.log(
            name="John",
            question="what is the speed of light?",
            region="EU"
        )

Under the hood, column contexts are simply / separated field names, and so the below code is equivalent.

import unify

with unify.Log():
    unify.log(
        **{
            "Sciences/Maths/name": "Zoe",
            "Sciences/Maths/question": "what is 1 + 1?",
            "Sciences/Maths/region": "US",
        }
    )
    unify.log(
        **{
            "Sciences/Physics/name": "John",
            "Sciences/Physics/question": "what is the speed of light?",
            "Sciences/Physics/region": "EU",
        }
    )

Column contexts can be moved, hidden, and focused on in the table, making it easy to view and organize your structured data in bulk.

As with contexts, unify.ColumnContext("...") by default will also change the behaviour of get_logs, returning only the logs which are part of the column context.

import unify

with unify.ColumnContext("Sciences"):
    with unify.ColumnContext("Maths"):
        unify.log(
            name="Zoe",
            question="what is 1 + 1?",
            region="US"
        )
        assert len(unify.get_logs()) == 1
    with unify.ColumnContext("Physics"):
        unify.log(
            name="John",
            question="what is the speed of light?",
            region="EU"
        )
        assert len(unify.get_logs()) == 1
    assert len(unify.get_logs()) == 2
assert len(unify.get_logs()) == 2

However, note that column contexts are not disjoint. The column context "Sciences" does include both logs, whereas the context "Sciences" in the earlier example did not.

As before, this behaviour can be change by setting unify.ColumnContext("...", mode="read") which will only set the column context for log getting, and unify.ColumnContext("...", mode="write") which will only set the column context for log setting.

Note that the Log instances will still show the full field paths, even when inside a column context. The getting behaviour only impacts the logs which are retrieved, not the representation of their fields. This is in contrast to the setting behaviour, which does update the field pattern in the arguments.

import unify

with unify.ColumnContext("Sciences"):
    with unify.ColumnContext("Maths"):
        unify.log(
            name="Zoe",
            question="what is 1 + 1?",
            region="US"
        )
        entries = unify.get_logs()[0].entries
        assert "Sciences/Maths/name" in entries
        assert "Sciences/Maths/question" in entries
        assert "Sciences/Maths/region" in entries
entries = unify.get_logs()[0].entries
assert "Sciences/Maths/name" in entries
assert "Sciences/Maths/question" in entries
assert "Sciences/Maths/region" in entries

Overall, column contexts make it easy to organize data inside your tables. Try them out with more examples of your own!

Column contexts can be used to organize columns inside a table. Taking the previous sciences example, column contexts enable us to organize the data like so.

import unify

with unify.Log(), unify.ColumnContext("Sciences"):
    with unify.ColumnContext("Maths"):
        unify.log(
            name="Zoe",
            question="what is 1 + 1?",
            region="US"
        )
    with unify.ColumnContext("Physics"):
        unify.log(
            name="John",
            question="what is the speed of light?",
            region="EU"
        )

Under the hood, column contexts are simply / separated field names, and so the below code is equivalent.

import unify

with unify.Log():
    unify.log(
        **{
            "Sciences/Maths/name": "Zoe",
            "Sciences/Maths/question": "what is 1 + 1?",
            "Sciences/Maths/region": "US",
        }
    )
    unify.log(
        **{
            "Sciences/Physics/name": "John",
            "Sciences/Physics/question": "what is the speed of light?",
            "Sciences/Physics/region": "EU",
        }
    )

Column contexts can be moved, hidden, and focused on in the table, making it easy to view and organize your structured data in bulk.

As with contexts, unify.ColumnContext("...") by default will also change the behaviour of get_logs, returning only the logs which are part of the column context.

import unify

with unify.ColumnContext("Sciences"):
    with unify.ColumnContext("Maths"):
        unify.log(
            name="Zoe",
            question="what is 1 + 1?",
            region="US"
        )
        assert len(unify.get_logs()) == 1
    with unify.ColumnContext("Physics"):
        unify.log(
            name="John",
            question="what is the speed of light?",
            region="EU"
        )
        assert len(unify.get_logs()) == 1
    assert len(unify.get_logs()) == 2
assert len(unify.get_logs()) == 2

However, note that column contexts are not disjoint. The column context "Sciences" does include both logs, whereas the context "Sciences" in the earlier example did not.

As before, this behaviour can be change by setting unify.ColumnContext("...", mode="read") which will only set the column context for log getting, and unify.ColumnContext("...", mode="write") which will only set the column context for log setting.

Note that the Log instances will still show the full field paths, even when inside a column context. The getting behaviour only impacts the logs which are retrieved, not the representation of their fields. This is in contrast to the setting behaviour, which does update the field pattern in the arguments.

import unify

with unify.ColumnContext("Sciences"):
    with unify.ColumnContext("Maths"):
        unify.log(
            name="Zoe",
            question="what is 1 + 1?",
            region="US"
        )
        entries = unify.get_logs()[0].entries
        assert "Sciences/Maths/name" in entries
        assert "Sciences/Maths/question" in entries
        assert "Sciences/Maths/region" in entries
entries = unify.get_logs()[0].entries
assert "Sciences/Maths/name" in entries
assert "Sciences/Maths/question" in entries
assert "Sciences/Maths/region" in entries

Overall, column contexts make it easy to organize data inside your tables. Try them out with more examples of your own!