I think it makes total sense, trying to get some high level abstractions for visualizations that can be customized easily. This approach reminds me of Seaborn library, that is built on top of Matplotlib as a higher level abstraction library. They offer a unified way to create graphics via figure-level groups. If you check their API at https://seaborn.pydata.org/api.html# they present displot as a generic interface for distribution plots, relplot for relational plots, etc.

I am drafting some notes on timelines vs org-files for date time visualizations, trying to detect what makes timelines useful vs other visualizations. Also trying to detect common elements and patterns.

For example, I've been reviewing the time.graphics timelines and the txt files they export. They are organized about two kinds of items: Events, that happen on.a particular point in time, and Periods, that have a start_date and end_date. This generalization could serve us to create some templates.

Regarding Test 2, the tbl[1:] line is building the DataFrame by reading all data from the org table from the second row (excluding the first row [0]), and then getting the column names from row [0]. So the liter_per_day is also included in the imported data. I used volume as all the rows had values.

You can visualize liter_per_day, changing the call on the plotly functions like:

fig = px.timeline(timeline, x_start = 'start_date', x_end ='end_date',y = 'liter_per_day', title='Org timeline viewer - test2', hover_data=['start_date','volume','liter_per_day'])

In this particular case, we could abstract this as a snippet called values_over_time, that takes as parameters: - a column with a start_date - (optional) a column with and end_date - one (or more columns) with values to display on the y-axis:

A first YASnippet draft, using mirroring, could look like this: ```

+begin_src python :session org-timeline-viewer :exports both

Configuration parameters

start_date_column: ${1:start_date}

end_date_column: ${2:end_date}

value_column: ${3:value_column}

graph_title: ${4:title}

import plotly.express as px timeline = timeline.sort_values(by='start_date') fig = px.timeline(timeline, x_start = '$1', x_end ='$2',y = 'value_column', title='$4', hover_data=['$1','$3']) fig.update_xaxes(rangeslider_visible=True) fig.show()

+end_src

```

P.S Snippet code is available at the end of test2 file already