https://hackmd.io/@holoviz/minutes/edit HoloViz meetings are usually for general planning and status discussions. See https://hackmd.io/@holoviz/BygVgIC6L for the triaging checklist, and https://status.holoviz.org for the list of projects to triage. 2025-03-28 HoloViz Steering Committee Attendees: Jim, Philipp, Dharhas, Andy, Simon, Isaiah, Mateusz, Richard, Andrew, Demetris Notes: Lumen AI status updateAnnounced with blog post (both Anaconda and HoloViz)Anaconda's blog post had language that implied some ownership over lumen They did fund a lot of the work, but they don't control/own it, so language was updated

See https://hackmd.io/@holoviz/minutes for current minutes. HoloViz Meeting June 27th Attendees: Philipp, Simon, Maxime, Demetris, Andrew, Jean-Luc (ML) Panel/HoloViews/GeoViews/hvPlot extension UX and documentationhvPlot PR: https://github.com/holoviz/hvplot/pull/1359have to check polars does not embed each time! UX is not ideal: User has no way to know that extension is embedded and is required

See https://hackmd.io/@holoviz/minutes for current minutes. 2023-12-20 HoloViz Docs meeting Attendees: Philipp, Sophia, Andrew, Demetris, Simon, Maxime (ML): Experiment new workflow with Jupytext and more integrated pyodide directive nbsite #295markdown that support the pyodide directive that are also understood by jupytext playground/pyodide/jupytext.mdwhen you install jupytext, you can open a markdown file as a notebook Myst extension allows you to use the fancy admonition directive special ':pyodide: null' markers bypasses the parsing by mystnb

See https://hackmd.io/@holoviz/minutes for current minutes. HoloViz meetings https://hackmd.io/@holoviz/minutes/edit HoloViz meetings are usually either General (planning and status discussions) or Triaging (dealing with open issues on HoloViz repos.) See https://hackmd.io/@holoviz/BygVgIC6L for the triaging checklist, and https://status.holoviz.org for the list of projects to triage. 2023-01-20 HoloViz Steering Committee meeting Agenda: Welcome, Intros

See https://hackmd.io/@holoviz/minutes for current minutes. 2020-12-14 Triaging meeting Jim still working on Datashader bugfix/release Jean-Luc and Kim to triage HoloViews for a bugfix release Chris and Tony to triage Panel, which looks like it still has a pretty big backlog 2020-12-11 HoloViz meeting Agenda

First Meeting - 01/11/22 Introduction & History Original vision for Datashader as described by Peter: Statistical computation aggregating kernels, e.g. of uncertainty per datapoint Simpler vision that actually got implemented: "2D histograms" Original version written in Java, then briefly implemented as "abstract rendering" in Bokeh in early days of Anaconda/Continuum, then finally implemented from scratch in Python/Numba with some basic Bokeh support Originally created using funding from government grants, then funded through various contracts with primarily government agencies

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High-level themes Views of datasets vs. derived source Dynamic transformations vs. Pangeo-forge aims to produce resampled/regridded/rechunked datasets which are persisted Where do the transforms go? Intake derived sources Lumen derived sources (i.e. filters and transforms)

Monitoring applications Key for where information can be obtained: (K)ubernetes, (5) ae5_tools, (S)ystem/OS, (A)pplication (Ac: canned/automatic, Au: user/custom functionality), A, (W)ebserver, [P]roxy in front or your apps, (?) Ensure they stay up (Ac) Currently up (K) Uptime (duration and percentage) (P/Ac/?) User failure rate (number and percentage) Resource utilization (under/over-utilization)

2020-06-16 Drilldown Bokeh is based on events, and if you wanted to link your various plots in Bokeh, you'd write callbacks that subscribe to Bokeh events. This is very general and can handle linked brushing, drilldown, and just about any other GUI interactions you might come up with. But it's difficult to reason about and not compositional; complex dashboards end up tightly bound together by their callback graph so that each new dashboard has to be built up from scratch. HoloViews abstracts the Bokeh event system into streams, which can have a semantic content (being "about the data"). HoloViews streams are compositional and reactive, which makes it much simpler to connect them up as you build a complex system, each bit taking care of its own business. The streams are Python (Parameterized) objects that can be accessed and queried independently of the event/callback system, allowing you to develop and maintain separate parts of your application separately, and port bits of functionality from one dashboard to another. But it still requires reasoning about streams of data and how they are connected up, using a mental model that's far from where most data-science users are starting. Ideally, interactions between plots would be expressed as relationships between datasets and dimensions, not connecting streams of values from sources to sinks. link_selections now lets a user set up various streams automatically, traversing a set of plots and finding all related dimensions and shared datasets, then creating streams that connect all of them to a shared selection object and also adding extra selected plots overlaid on the main plots. For the special case of a shared selection object, this approach allows users to achieve quite sophisticated ways of exploring multidimensional datasets too large to be conveyed in a single plot, without having to learn about events or even streams. However, at present only a single "cross-filter" configuration is supported: each plot visualizes all the data it covers, and then also has an overlaid selection of a subset of what is in the main plot. Can we do the same for drilldown, where there is no underlying main plot, but which in many ways otherwise similar to link_selections? In a drilldown, the user interacts in some way with one plot, selecting one (or sometimes multiple) item(s) that determine what is shown in another separate plot. A canonical example is to select some aggregated or reduced value in a main plot (e.g. the current value of X for a county or a company or some other entity), then the drilldown plot shows a non-aggregated or unreduced plot (e.g. a timeseries of value X, a distribution of value X, or the same for some other different value Y). GUI tools do provide some support for drilldowns, but it is not clear how relevant they are to what we are trying to do here: https://docs.devexpress.com/Dashboard/117061/web-dashboard/create-dashboards-on-the-web/interactivity/drill-down https://community.tableau.com/thread/153220, https://help.tableau.com/current/pro/desktop/en-us/actions_filter.htm

Author: Philipp Rudiger What is a data pipeline? Effectively it is a DAG which starts with some dataset and applies various transforms on the data. In the case of HoloViews each node in the DAG may also have semantic meaning, e.g. an element type, or add additional metadata, e.g. labels or options. Each transform can have parameters which can either be static or be driven by some dynamic value. What implementations of data pipelines exist in HoloViews?