From ed75a9d5396344e317f4d7a1672b06fccb954cae Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Thu, 30 May 2024 15:20:08 -0400 Subject: [PATCH 01/19] Initial paper commit --- .../IpyoverlayLayoutDesign.png | Bin 0 -> 175341 bytes papers/nathan_martindale/banner.png | Bin 0 -> 506825 bytes papers/nathan_martindale/context_menu.png | Bin 0 -> 190723 bytes papers/nathan_martindale/detailsondemand.png | Bin 0 -> 16999 bytes papers/nathan_martindale/ipw_ipv.png | Bin 0 -> 31608 bytes .../ipyvuetify_example.drawio.png | Bin 0 -> 197448 bytes .../nathan_martindale/ipywidgets_example.png | Bin 0 -> 39605 bytes papers/nathan_martindale/ipywidgets_state.png | Bin 0 -> 163570 bytes papers/nathan_martindale/main.md | 755 ++++++++++++++++++ papers/nathan_martindale/myst.yml | 55 ++ .../overlay_figures_over_multiple.png | Bin 0 -> 494414 bytes papers/nathan_martindale/overlays.png | Bin 0 -> 73621 bytes .../overlays_connections.png | Bin 0 -> 150366 bytes 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z@v8F!{1FYOL+$fl&HtZ6h4Yhsd!35mq7r|iQizW6FKgtdP5D_Sd48V#a`eyS{mz{K uOy2MI!aqCjw+`-~o%cIO_W$lS_xS#ZJI!Iss>gi<_-Uv@RElm|1pgmR2?_21 literal 0 HcmV?d00001 diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md new file mode 100644 index 0000000000..7baf9b79a0 --- /dev/null +++ b/papers/nathan_martindale/main.md @@ -0,0 +1,755 @@ +--- +title: Supporting Greater Interactivity in the IPython Visualization Ecosystem +abstract: | + + Interactive visualizations are invaluable tools for building intuition and + supporting rapid exploration of datasets and models. Numerous libraries in + Python support interactivity, and workflows that combine Jupyter and + IPyWidgets in particular make it straightforward to build data analysis + tools on the fly. However, the field is missing the ability to arbitrarily + overlay widgets and plots on top of others to support more flexible + details-on-demand techniques. This work discusses some limitations of the + base IPyWidgets library, explains the benefits of IPyVuetify and how it + addresses these limitations, and finally presents a new open-source solution + that builds on IPyVuetify to provide easily integrated widget overlays in + Jupyter. + +--- + +## Introduction + +As the tech industry and computation-based research continues to gravitate +toward using more and more data to solve problems, the capability to interact +with and understand the underlying data that is being used becomes increasingly +important. As part of this growing need, development of effective +visualization tools and frameworks has turned into an ecosystem where components +from different sources all work in unison and developers can piece together what +they need for their specific goals. For many reasons, including the ease of +organization and allowing documentation, development, and analysis to all exist +in the same place, Jupyter notebooks [@jupyter] have become a common platform +for data science workflows, and by extension have become the common ground where these +different tools can coexist. This ability to interact with and visualize data +plays a crucial role in many data science/analysis workflows by allowing users +to manipulate and explore, thus enhancing comprehension, improving user +engagement, and often providing a faster route to an intuitive understanding of +the data. + +IPyWidgets [@ipywidgets], a framework for bridging between the IPython kernel +and HTML widgets within Jupyter, has been particularly influential for enabling +interactive visualization. Because of its ease of use and extendibility, many +libraries such as Panel [@10.5281/zenodo.11046027], Solara [@solara], and Streamlit +[@streamlit] integrate closely with IPyWidgets and thus contribute to +the greater visualization ecosystem within Python. Although a great deal of +progress has been made in this community, a missing element is a more +generalized and flexible approach to aspects of the details-on-demand paradigm. +To support this approach, the authors have developed a library that +provides wrapper components that can render any widget as an overlay on top of +other widgets. These components simplify designing layouts and visualizations +that would be difficult to implement from scratch. + +In this work, we first discuss IPyWidgets and what makes it such an important +piece of the overall system, as well as some of the limitations encountered when +building only with the base IPyWidgets framework. We then briefly cover some of +the available libraries that build on top of IPyWidgets, highlighting +IPyVuetify, which addresses most of the limitations of base IPyWidgets. Finally, +we discuss a library we have open sourced called IPyOverlay and an example +use case that motivated its design. + +### Details on Demand + +Details on demand is one piece of the "visual information-Seeking mantra" +[@shneidermanEyesHaveIt1996], a paper describing an overall design approach to +building interactive tooling. The general flow of interaction proposed by this +mantra is "overview first, zoom and filter, then details-on-demand." Overview +first ideally starts with broader aggregate visualizations: something to gain a sense +of the general scope, complexity, or shape of the data. From there a user can +identify their own areas of interest to hone in on with whatever zoom and filter +tools are applicable for their data's modality. Finally, details on demand +means that the user can see or interact with more in-depth and specific +information for pieces of their data, often in the form of a pop-up window or +similar. This selective detail capability provides a flexible user experience +that keeps the initial interface uncluttered without sacrificing the ability to +organically explore the data. + + +A simple and widespread example of details on demand are tooltips. Descriptions +of interface components or values of data points in visualizations would often +create an unusably noisy user interface if they are always visible. Instead, a +more concise view is maintained while providing the necessary information in a +spatially relevant view when the user specifically requests it. Readers viewing +the web version of these proceedings can explore a simple example of details on +demand live by hovering over any of the figure references, such as this one: +@fig:detailsondemand, which shows how hovering over a reference (the demand) +opens a window at the cursor that displays the target of the reference (the +details.) + + +:::{figure} detailsondemand.png +:label: fig:detailsondemand +A figure demonstrating details on demand in action when a user hovers over the figure +reference for @fig:detailsondemand. +::: + + + +## IPyWidgets + +IPyWidgets is a library that enables implementing user interactivity inside of a +Jupyter Notebook/Jupyter Lab. A key challenge this framework has to overcome is +the separation between the Python kernel and the browser-based Jupyter frontend, +which operates through HTML, CSS, and JavaScript as a web application. Web +application development is often a challenging software engineering problem in +its own right, and many Python developers and scientists may not have either the +experience or the desire to write custom frontend code in JavaScript. IPyWidgets +addresses this problem by supporting mechanisms for automatic state +synchronization between a Python model and a JavaScript model, expanded on +below, and providing a set of predefined components that a developer +can initialize and use in their Python code. Although there are limitations to +using IPyWidgets alone, this library brings a lot of value to the scientific +Python community and the data science Jupyter workflow with its ability to marry +the two language ecosystems, each of which include many visualization tools +and capabilities. + +To highlight the simplicity of IPyWidgets, a couple of minimal +examples are provided here. In the simplest UI cases, IPyWidgets allows wrapping a +function call that produces a visual output based on some set of input +parameters with `ipywidgets.interactive()`, which automatically +constructs an appropriate form widget for each parameter. In this case, the +developer need not manually initialize any Python widgets, as seen in the below +code sample @code:interactive and @fig:ipywidgets_example. + + +```{code-block} python +:label: code:interactive +:linenos: true +:caption: Minimal IPyWidget usage for a plot of a sine wave based on one parameter/coefficient. The `interactive()` call turns the passed tuple for `coefficient` into a slider widget with the range set to this tuple. See @fig:ipywidgets_example for output. + +import matplotlib.pyplot as plt +import numpy as np +import ipywidgets as ipw + +def graph(coefficient): + x = np.arange(0, 10, .1) + y = np.sin(coefficient * x) + plt.plot(x, y) + +ipw.interactive(graph, coefficient=(0.1, 10.1)) +``` + +:::{figure} ipywidgets_example.png +:label: fig:ipywidgets_example +A simple IPyWidgets example using `interactive()`. Every time the slider is +moved, the `graph(coefficient)` function is called with the updated value and +regenerates/displays the new plot. +::: + +More complex interfaces often require displaying multiple visualizations and +outputs and may have controls that must interact with each other. Thus, in +practice a more common setup might follow a pattern shown in +@code:ipywidgets_dashboard, where several user input and output widgets are +defined and placed into a layout widget. UI code is generally event driven, so +functionality is added by attaching event handlers to input widgets with +`.observe()`, which will then call the associated event functions when the user +interacts with the input widget. IPyWidget's generic `Output` widget supports +IPython's set of display functions, and anything that can be rendered as the +output to a Jupyter cell can be placed into a specified output widget. + + + +```{code-block} python +:label: code:ipywidgets_dashboard +:linenos: true +:caption: An example of a more typical structure for a dashboard with several inputs and outputs that might have more complicated interdependencies than can be handled with `interactive()`. + +import ipywidgets as ipw + +# initialize the widgets +graph1_out = ipw.Output() +graph2_out = ipw.Output() + +parameter1 = ipw.FloatSlider(...) +parameter2 = ipw.IntText(...) +parameter3 = ipw.IntSlider(...) + +# create the dashboard layout, combining all the widgets +dashboard = ipw.VBox([ + ipw.HBox([graph1_out, graph2_out]), + ipw.HBox([parameter1, parameter2, parameter3]), +]) + +# event handler functions +def on_parameter1_change(change): + with graph1_out: + graph1_render_function(parameter1.value, parameter2.value) + +def on_parameter2_change(change): + with graph1_out: + graph1_render_function(parameter1.value, parameter2.value) + with graph2_out: + graph2_render_function(parameter2.value, parameter3.value) + +def on_parameter3_change(change): + with graph2_out: + graph2_render_function(parameter2.value, parameter3.value) + +# attach event handlers +parameter1.observe(on_parameter1_change, names=["value"]) +parameter2.observe(on_parameter2_change, names=["value"]) +parameter3.observe(on_parameter3_change, names=["value"]) +``` + + +### State Synchronization + +One of the primary challenges that IPyWidgets helps solve is the +problem of state synchronization, which amounts to monitoring for changes to +variables made on either side of the Python-JavaScript link and communicating those +changes across when they occur. Thus, changes to variables in Python are +reflected in the browser's JavaScript and vice versa. Under the hood, +IPyWidgets makes use of backbone.js [@LowLevelWidget], a model-view-* (MV*) framework for +JavaScript that implements synchronization between a JavaScript _model_ +(where data/attributes/values for a widget are stored) and a JavaScript +_view_ (the code that creates the visual UI elements the user sees and +interacts with in their browser). IPyWidgets connects to this backbone model via +WebSockets, through which model changes and events are sent as serialized JSON. + + +:::{figure} ipywidgets_state.png +:label: fig:ipywidgets_state +State mirroring between models and view interaction for an example slider widget. +::: + + + +@fig:ipywidgets_state shows a simplified view of the communication channels +between the Python model and JavaScript model/view for an example slider widget. +Modifying an attribute on the Python model sends the modification to the +JavaScript model, where any changes that would modify the visual aspects of the +widget are communicated to the JavaScript view (e.g., a change in the slider +value would correspondingly update the position of the slider indicator within +the track). Similarly, a user interacting with the widget (e.g., moving the +slider within the track) updates the JavaScript state, which communicates this +change to the Python model and calls any event handlers listening for +changes to the value. + + +### Common Framework + +One of the most important benefits of IPyWidgets is that it provides a low-level +framework abstracting away many of the specifics for how communication between a +kernel, server, and frontend takes place. This framework can be used to design +new widgets by providing implementations for the models and view code, without +needing to reinvent the various messaging system mechanisms that solve the state +synchronization problem discussed above. All of this has allowed many libraries +to emerge, providing novel functionality, a great diversity of new types of +widgets, and other frameworks that can support rendering IPyWidgets within other +contexts. + + +### Limitations + +Despite the flexibility of IPyWidgets as a framework and its value as a +core-enabling technology for user interaction, we explicitly highlight some of +the limitations of using IPyWidgets as a library of components alone. These +limitations motivate the use of some of the many existing libraries that extend +and build on top of the IPyWidgets framework, as well as our own work in the +IPyOverlay implementation. + +#### Components + +The set of default components included in the IPyWidgets library cover all +standard form elements such as buttons, drop-down menus, and text fields, as well as a +generic `Output` widget. The `Output` widget can render any of the MIME-types +supported by normal Jupyter cell outputs, including Markdown, images, +plots, and even raw HTML. Additionally, there are basic layout components to +stack other widgets vertically, horizontally, or via tabs, all of which can be +nested to allow grouping widgets. +Although these components are often more than sufficient for simple visualizations, +more complex interfaces with richer interaction paradigms can be hard to achieve. +Some examples of dashboard elements that cannot be created +with IPyWidgets' default components include interactive data frames, dialog +windows, and floating buttons. + +#### Events + +Another limiting factor for creating more complex interactivity is the type of +events that can be observed on the components. UIs are inherently +event-driven; the user interacts with widgets/visualizations, and these events +run code that update the interface. This is most often +implemented by registering "observers" or event-handlers, which tells IPyWidgets to +run a specified function every time a particular event is raised. In practice, +for the default components provided by the IPyWidgets library, this is mostly limited to +value changes (e.g., a slider is moved, the text in a textbox is changed, or an +item in a drop-down menu is selected). + +These types of events are sufficient for basic interfaces, but often more complex types of +interactions or responsiveness can greatly enhance the capabilities of a tool. +The ability to detect mouse events can be crucial, enabling possibilities +such as highlighting parts of a visualization when the mouse hovers over a row +in a dataframe, or running an analysis when a user has highlighted a piece of +text from a document. These types of user actions are not feasible +with the default widgets. + + +#### Custom Styling and Layout + +Web pages and dashboards that need more complicated layouts than conventional +linear stacks and grids of elements generally need to use CSS to control positioning. More +than just a mechanism for changing aesthetic and colors, CSS provides rules +for structuring the interface. However, this often comes at the cost of greater code complexity and +excessive layers of nested elements and rule sets. IPyWidgets components have +some capability to modify their CSS but are limited to a +subset of rules available through Python attributes. This makes common +style changes easier to work with, but the lack of any direct access to arbitrary CSS +rules limits more complex layouts, namely the ability to float +one widget on top of another. + + + +#### Custom IPyWidget Complexity + +One of the key benefits of IPyWidgets as a framework is that it allows +developers to design, build, and publish new components that other people can +use in place of or alongside the default widgets. However, the complexity of the +infrastructure required to do this with the base IPyWidgets library still makes +it challenging to prototype or put together a one-off custom component in a +project. Several libraries we discuss in the next section have improved this +process substantially, but traditionally a custom component library was +developed by starting from a Jupyter-provided cookie-cutter template +[@ipywidgets_cookiecutter] in a separate project. + +One of the difficulties that comes with a project created in this way is the +need to include the development infrastructure of both a Python and a JavaScript +project. JavaScript toolchains and dependency management are somewhat +notoriously complicated. Prototyping the component can be a slow process as +well, requiring a rebuild on every JavaScript change. Setting up Jupyter to +use these unpublished components requires additional environment management +to work correctly. +Finally, custom widgets built through this method often require +fairly significant boilerplate code because the developer is directly in +charge of defining all pieces of the Python model, equivalent JavaScript model, +and JavaScript view. + +## Beyond Base IPyWidgets + +IPyWidgets as a low-level widget framework has allowed an ecosystem of other +libraries and tools to develop on top of it, dramatically pushing the boundary +of what can be constructed and incorporated into interactive dashboards with +relative ease. From general plotting tools like interactive Matplotlib +[@10.5281/zenodo.10974323]), plotly [@plotly], mpld3 [@mpld3], altair +[@altair], and bqplot [@bqplot], to more complex or specialized visualization +widgets like pythreejs [@pythreejs] and ipyvolume [@10.5281/zenodo.1286976] for +3D rendering, ipyleaflet [@ipyleaflet] for interactive maps, and ipycytoscape +[@ipycytoscape] for interactive networks, there are dozens of packages providing +specific components and bridges to JavaScript visualization libraries. Other +libraries such as Panel, Solara, and Streamlit provide more holistic +integrations between IPyWidgets and other visualization techniques, +which can support production-style dashboards for +use outside of Jupyter alone. Although these libraries address the limited component +set included with IPyWidgets, another class of Python packages exists in this ecosystem +that completely changes and improves the workflow for defining new widgets. These +workflow improvements eliminate the need for JavaScript toolchains, and in some cases, they also +provide the capabilities from various JavaScript web frameworks. A few +we briefly touch on here are AnyWidget, IPyReact, and IPyVuetify. + +AnyWidget [@10.5281/zenodo.11182005] is a recent library that has become +influential in the past year or so. New widgets can be created by simply +extending the Python `anywidget.AnyWidget` class, defining the class attributes +that are to be synced between the Python and JavaScript models using the +`traitlets` library [@traitlets], and providing a string of +JavaScript code that specifies the view rendering logic. Much of the JavaScript +MV* boilerplate code is abstracted away, and component development can be done without +any JavaScript project infrastructure, specialized cookie cutter, or separate +environment setup. Fundamentally, this type of approach allows even developing +widgets directly within a notebook, rather than doing so in a separate project. +AnyWidget is used in another project called IPyReact [@ipyreact], which brings +the ReactJS library to support component-based design and allows wrapping +many existing React components. + + + +A similar library we focus on in this work that addresses all of the [main +limitations of IPyWidgets](#limitations) is IPyVuetify [@ipyvuetify], which +brings many of the capabilities of Vue.js into IPyWidgets. Vue.js is a web +component framework similar to React and emphasises a component-driven design, +where a single Vue file can contain an entire component: the template HTML to +render, the CSS to apply, and the JavaScript to provide any interactivity +and mechanisms to connect it to the rest of the application. Attributes within +the HTML can be bound to JavaScript variables, which makes it possible to easily +automate the JavaScript model $\rightarrow$ view updates. IPyVuetify brings two main +advantages: a large library of flexible premade components and the ability to +easily define new components on the fly using Vue to simplify the resulting +JavaScript code. + +### Vuetify Component Library + +IPyVuetify is an IPyWidgets port of the Vuetify component library, a set of more +than 70 premade material design UI widgets. Most of these components support a +fairly extensive set of attributes and properties to change both appearance and +functionality, and more importantly each generally has multiple types of events +that can be observed. A simple example would be a textbox form input component. +In IPyWidgets, the `Text` widget can mostly only observe a value change when the +user changes the text inside. The IPyVuetify `TextField` can additionally raise +events for key presses, mouse clicks, the element gaining and losing focus, as +well as any HTML event. This level of control can be useful, for example, when a +text value change triggers a heavy processing task. The interaction is more +effective when the computation is delayed until the field loses focus or the +user has pressed enter, rather than restarting the computation on every letter +the user adds or removes. + +All of the default components in IPyWidgets similarly have more flexible +versions in IPyVuetify, and many additional components. @fig:ipw_ipv +shows a small sample of default IPyWidgets on the left with their corresponding +IPyVuetify versions on the right. Also pictured at the bottom is the Vuetify +DataTable, which is an example of a component that is not in the default IPyWidgets. The DataTable allows +interactivity to be added to a table supporting filtering, searching, and both client/server +side pagination. Other examples of useful UI elements IPyVuetify adds +are various layout widgets like navigation drawers, dialog windows, and +steppers. + + +:::{figure} ipw_ipv.png +:label: fig:ipw_ipv +A comparison of a few IPyWidget components on the upper left and their corresponding +IPyVuetify components on the upper right. The table on the bottom is a +DataTable component, which does not have a default IPyWidgets equivalent. +::: + +Finally, the Python API for every IPyVuetify component includes the ability to set CSS +class and style attributes. The former of these provides access to a long list of +predefined classes for easy and consistent control of colors, spacing, and other +attributes. +For anything not covered by these classes or separate attributes already part of the +Vuetify components, the style attribute controls the raw in-line CSS applied to +the element. + + +### Custom Vue Components + +IPyVuetify provides clean abstractions and mechanisms for creating custom +components. Similar to AnyWidget, IPyVuetify allows defining a Python model +class with synced `traitlets`, all of which are automatically available within +the JavaScript code. In IPyVuetify's case, the frontend code is expected to be a +string of Vue code or a path to a Vue file, which allows for fairly +self-contained HTML, JavaScript, and styling. The design of Vue.js lends itself +well to reducing boilerplate by allowing bindings in the HTML to directly refer +to Python attribute traitlets, removing both the need to specify the attribute +in the JavaScript model as well as the function to update the view from the +model. We discuss this further in the section on [implementing IPyOverlay in +IPyWidgets](#implementing-in-ipywidgets). @code:ipyvuetify exemplifies this, +defining a custom button with a color change on toggle, where the colors can be +modified from Python as well, shown in @fig:ipyvuetify_example. In this +particular example, both the `@click` event and `:color` property directly bind +to the Python attributes, and no explicit JavaScript is necessary. Finally, +IPyVuetify components make it easy to directly call functions across the +Python--JavaScript link by making any Python functions prefixed with `vue_` +available within JavaScript and any JavaScript functions +prefixed with `jupyter_` callable from Python. + +```{code-block} python +:label: code:ipyvuetify +:linenos: true +:caption: An example of a custom IPyVuetify component. `color1`, `color2`, and `state` are all part of the automatically synced models. We can refer to them in the Vue bindings as shown on lines 15 and 16, where line 16 is an inline event handler that modifies the `state` property when the button is clicked. +import traitlets +import ipyvuetify as v + +class ColoredToggleButton(v.VuetifyTemplate): + color1 = traitlets.Unicode("var(--md-light-blue-500)").tag(sync=True) + color2 = traitlets.Unicode("#AA6688").tag(sync=True) + state = traitlets.Bool(True).tag(sync=True) + + @traitlets.default("template") + def _template(self): + return """ + + """ +``` + +:::{figure} ipyvuetify_example.drawio.png +:label: fig:ipyvuetify_example +:width: 500px +An instance of the `ColoredToggleButton` from @code:ipyvuetify. Clicking the button switches to color2, and modifying the Python property `color2` immediately updates the button to the new second color. +::: + + +## IPyOverlay + +IPyOverlay is a Python library the authors have implemented and +open-sourced[^footnote-1] that uses IPyVuetify to provide several wrapper +components for other IPyWidgets. These components add novel UI capabilities +within the IPython ecosystem designed to enable [details-on-demand interaction +paradigms](#details-on-demand). Namely, we implement support for rendering +widgets on top of other widgets with arbitrary and controllable positioning. +This capability enables techniques such as click-and-draggable overlay windows +containing any other widget. An example of IPyOverlay in action is shown in +@fig:example_dashboard. Throughout this section we will discuss an example use +case where we worked with one of the authors, a seismologist, +to construct a dashboard for exploring models trained on seismic data. This +use case was what led to the development of IPyOverlay as a library. + +[^footnote-1]: https://github.com/ORNL/ipyoverlay + + + +:::{figure} overlay_figures_over_multiple.png +:label: fig:example_dashboard +A dashboard for exploring seismic station data embedded with a self-supervised +encoder model. The background layout is a Matplotlib UMAP and a plotly +geographic map. Pictured are three draggable overlay ("windowed") Matplotlib +plots (with colored bars on top) that provide additional details for +user-selected clusters found within the UMAP. +::: + + +The application in this use case is a tool to assist in evaluating how a model +internally represents data from seismic events. +Determining properties about a seismic event's source is a critical part of +analyzing and interpreting observed seismic signals, and large deep neural +networks are increasingly useful for this task. Qualitatively assessing how well +trained models perform on these types of problems, both as a model developer as +well as an end user, requires hypothesis testing that visualization can help +make substantially easier. + +In this use case, the dashboard is constructed with IPyWidgets and IPyOverlay, +taking advantage of the techniques described in this section, to determine the +success of training a model to meet our domain-specific criteria. More +specifically, we use Barlow Twins [@barlowtwins] as a self-supervised learning objective +to try to force a model’s learned representations to reflect source properties +rather than other observational characteristics. For example, different +geographic locations will observe slightly different signals of the same source +event based on geological differences in the paths to those locations from the +source. An ideal model would exhibit path invariance and represent these signals similarly. We use the +constructed visualization tool along with metadata (information other than the +timeseries the model was trained on, e.g. event location, depth, distance) to +understand how well the training objective succeeds in learning invariance to +the metadata attributes that are unrelated to the event source properties. +Additionally, we use the dashboard to help end users understand +how well the model generalizes to new sources and to explore uncertainty. + +The dashboard includes a UMAP plot of signals embedded by the model under +exploration. Each datapoint is a single observed seismic signal, and these +data points are then clustered with DBSCAN. +Metadata attributes aggregated +by cluster can be explored by clicking within the UMAP ipympl plot, which aids +in determining how much correlation exists for those attributes within that +cluster. Invariant properties would tend to show as spread out distributions, +while attributes that heavily inform a models' embedding would tend to cause +clustering and produce narrow distributions. + + +### Implementing in IPyWidgets + +Within base IPyWidgets, there is no straightforward way of constructing a +layout in which one widget is overlaid on top of another. Certain other libraries +have limited functionality for this within their own widget instances, for +example Matplotlib can display an axes within another axes, as can plotly, and +many libraries support basic tooltips with arbitrary textual content. Others +such as IPyVuetify support certain types of limited "dialog" overlays, which can +contain inner widgets and display overtop of the rest of the webpage, but do not +support the same type of arbitrary positioning necessary for some +details-on-demand approaches. + +This limitation seems to arise primarily from the conventional layout mechanisms +employed by many widget libraries, which consists of nesting rows and columns +(`HBox` and `VBox` from IPyWidgets) of CSS-flexbox style layouts, or even +stricter grid layouts. +These approaches are generally easier to design and work +with, and abstract away or entirely avoid some of the messier CSS layout rules +required to render HTML elements that do not follow an ordered top-to-bottom/left-to-right flow. +Fundamentally, to enable this behavior, we implement +custom IPyWidget containers that use specific CSS rules to control for +nonlinear layouts, namely relying on absolute positioning (by setting +`position: absolute`), and each child widget setting `left` and `top` CSS +properties to have pixel-level control of its location relative to the parent +container. A diagram demonstrating this design principle is shown in +@fig:ipyoverlay_layout_design. + +:::{figure} IpyoverlayLayoutDesign.png +:label: fig:ipyoverlay_layout_design +Component wrappers provided by IPyOverlay (discussed in the +following section) demonstrating how the positioning works for absolutely +positioned child widgets. +::: + + +### Embedding Widgets in Container Widgets + +A key enabler is IPyVuetify's ability to embed other IPyWidgets inside +of the template HTML, allowing us to construct custom layouts of one or more +child widgets, similar to `HBox` and `VBox` in IPyWidgets. In effect, we can +wrap any other widget with custom HTML, CSS, and JavaScript. The Vue framework +is particularly well suited to concise implementation with this approach. +Vue-flavored template HTML supports binding reactive properties on attributes +(e.g., `

      `), meaning we can reference any +model variables in the template syntax, and any time those attributes update, +the corresponding HTML is automatically updated without other code having to monitor and +orchestrate the JavaScript model $\rightarrow$ view update. Additionally, Vue's +template syntax supports several programmatic control structures such as looping +(`v-for`) and conditionals (`v-if`), all of which combined allows +declaratively constructing a bound list of child widgets each wrapped in custom +HTML that reactively responds to list updates and other model property changes. +A simplified example is shown in @code:container. + + + +```{code-block} html +:label: code:container +:linenos: true +:caption: A simplified version of the Vue template for `OverlayContainer`. The `
      tag with `v-for` reactively produces a `
      ` tag for each widget in the `children` list attribute within the Python model. Note, the style attribute is bound and references the position arrays on lines 21--22. Changing the position of each overlay widget is done by modifying the corresponding values in these arrays. We keep these arrays only on the JavaScript side to prevent undue traffic between the Python and JavaScript models from high-frequency events (e.g., mouse movements) that can cause significant lag in the interface. + + + + + + +``` + +Our primary contribution with IPyOverlay is an `OverlayContainer` widget, which creates a +container `
      ` tag embedding a background (nonoverlay) widget, which can +consist of a more traditional grid-like layout of nested rows/columns of other +IPyWidgets, and absolutely positioned floating `
      ` tags for each +child widget. To support additional functionalities in any of the child +overlay widgets, such as the ability to click and drag to move them, we +provide a `WidgetWrapper` (clicking anywhere within begins the drag) and a +`DecoratedWidgetWrapper` (a bar is rendered above the widget which can be +clicked and dragged to move the entire widget.) An example is shown in +@fig:overlays, where the background widget is an ipympl Matplotlib plot and +three `DecoratedWidgetWrapper`'s that embed additional ipympl Matplotlib plots +are floating freely above the background. + + +:::{figure} overlays.png +:label: fig:overlays +Three `DecoratedWidgetWrapper` widgets, each wrapping an ipympl widget with more +in-depth plots detailing distributions of attributes within selected clusters. +::: + + +### Connection Lines + +A potential concern with free-floating details-on-demand overlays is the +dissociation from the underlying data: if a user opens more than a couple +overlays that correspond to specific pieces of an underlying visualization, it +is easy to lose track of the overlay widgets and the pieces they are associated with. +In the example use case, clicking on a cluster within the UMAP opens +distribution plots for data within that cluster. Because of the number of clusters, +it can be difficult to determine which distribution plot belongs to which +cluster. IPyOverlay adds the ability to render lines connecting overlay widgets +to specific pieces of the background widget (@fig:overlays_connections). + +:::{figure} overlays_connections.png +:label: fig:overlays_connections +The same detail plots as in @fig:overlays. The overlay widgets have been dragged +further out to show the connection lines to the clusters in the UMAP each is based +on. +::: + +One end point of the line is always associated with a specified overlay widget +and is automatically updated to the widget's center whenever it is moved. The +other side of the line can be "attached" in a few different ways, including +simply specifying pixel coordinates within the background, but a significant +challenge is locations within dynamic content such as interactive plots that can +be panned and scaled. The conceptual approach to solving this for many plotting +libraries is to store the x/y data point the connection should point to, the +initial ranges of any plot axes, and current pixel boundaries of the plot within +the container. Then, listening for any events that indicate an axis range has +changed, handler functions can recalculate pixel locations based on the new +relative position of the x/y data point within the new range. + +Unfortunately, in practice the functions necessary to do this for different +plotting libraries can vary substantially as a result of implementation differences, and +at the time of this writing, IPyOverlay only supports autoupdating connections for +dynamic content inside of ipympl (Matplotlib) and plotly plots. The authors +intend to continue expanding the set of supported visualization libraries for +this particular feature. + + + +### Context Menus + +A common feature in UI design across most platforms is the context menu. +Generally initiated from a right-click in desktop operating systems and +applications, or tapping and holding on a mobile device, context menus provide a +localized and easy to access palette of possible commands to run at the +current location. Complex dashboards can often have many different possible +actions for the user to take, and simply displaying all of these as buttons may +overwhelm or clutter the interface. Alternately, the actions may be too component/position specific +to work well as buttons. Context menus fill this role by hiding these commands until +the user requests them by right-clicking (another form of details on demand) and supporting different menus when +over different components. +Displaying a context menu similarly requires +rendering some form of widget over another -- IPyOverlay includes a +`ContextMenuArea` component that can wrap any widget within an +`OverlayContainer` and define a context menu with options and event handlers for +that specific component. In the example use case, we add a context menu to +the scatter plot to allow a user to optionally choose between displaying a +violin plot with the distributions versus the density plots (@fig:context_menu). + + +:::{figure} context_menu.png +:width: 800px +:label: fig:context_menu +An activated context menu providing a selection of commands, which can be +expanded well beyond the single possible action that can be taken from a left-click. +::: + +## Conclusion + +The prevalence and utility of Jupyter notebooks in data analysis and scientific +workflows has prompted the development of a community of tools for interactive +visualization. IPyWidgets, one of the main drivers and the backbone that +supports much of this ecosystem, provides the mechanisms for the kernel to +mediate communication between Python and JavaScript. A multitude of libraries +have built on top of these mechanisms and IPyVuetify in particular offers a +collection of well designed and flexible components to use in dashboards and a +straightforward abstraction for building custom components in projects based on +Vue.js. A missing piece of this ecosystem is the ability to flexibly render +localized pop-up windows in support of details on demand. We developed +IPyOverlay to provide this capability through wrapper components that can +arbitrarily position widgets on top of other widgets, opening the door to more +types of visualization and dashboard layouts. + + +## Acknowledgements + +Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/doe-public-access-plan). diff --git a/papers/nathan_martindale/myst.yml b/papers/nathan_martindale/myst.yml new file mode 100644 index 0000000000..402b7efeb6 --- /dev/null +++ b/papers/nathan_martindale/myst.yml @@ -0,0 +1,55 @@ +version: 1 +project: + # Update this to match `scipy-2024-` the folder should be `` + id: scipy-2024-nathan_martindale + # Ensure your title is the same as in your `main.md` + title: Supporting Greater Interactivity in the IPython Visualization Ecosystem + subtitle: IPyWidgets, IPyVuetify, and beyond + # Authors should have affiliations, emails and ORCIDs if available + authors: + - name: Nathan Martindale + email: martindalena@ornl.gov + corresponding: true + orcid: 0000-0002-5036-5433 + affiliations: + - Oak Ridge National Laboratory + - name: Jacob Smith + email: smithjw1@ornl.gov + orcid: 0009-0000-4565-2692 + affiliations: + - Oak Ridge National Laboratory + - name: Lisa Linville + email: llinvil@sandia.gov + orcid: 0000-0003-1319-1056 + affiliations: + - Sandia National Laboratories + keywords: + - interactive visualization + - UX + - python + - javascript + - web development + - custom visualizations + # Add the abbreviations that you use in your paper here + abbreviations: + MyST: Markedly Structured Text + # It is possible to explicitly ignore the `doi-exists` check for certain citation keys + error_rules: + - rule: doi-exists + severity: ignore + keys: + - Atr03 + - terradesert + - jupyter + - sklearn1 + - sklearn2 + # A banner will be generated for you on publication, this is a placeholder + banner: banner.png + # The rest of the information shouldn't be modified + subject: Research Article + open_access: true + license: CC-BY-4.0 + venue: Scipy 2024 + date: 2024-07-10 +site: + template: article-theme diff --git a/papers/nathan_martindale/overlay_figures_over_multiple.png 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research results, it is important that this code is published. We present Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable. We discuss various tools and use cases for notebook documents.}, + author = {Kluyver, Thomas and Ragan-Kelley, Benjamin and Pérez, Fernando and Granger, Brian and Bussonnier, Matthias and Frederic, Jonathan and Kelley, Kyle and Hamrick, Jessica and Grout, Jason and Corlay, Sylvain and Ivanov, Paul and Avila, Damián and Abdalla, Safia and Willing, Carol and {Jupyter Development Team}}, + editor = {Loizides, Fernando and Scmidt, Birgit}, + location = {Netherlands}, + publisher = {IOS Press}, + url = {https://eprints.soton.ac.uk/403913/}, + booktitle = {Positioning and Power in Academic Publishing: Players, Agents and Agendas}, + year = {2016}, + pages = {87--90}, + title = {Jupyter Notebooks - A Publishing Format for Reproducible Computational Workflows}, +} + + +@inproceedings{shneidermanEyesHaveIt1996, + title = {The eyes have it: A task by data type taxonomy for information visualizations}, + shorttitle = {The eyes have it}, + url = {https://ieeexplore.ieee.org/abstract/document/545307}, + doi = {10.1109/VL.1996.545307}, + abstract = {A useful starting point for designing advanced graphical user interfaces is the visual information seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. The paper offers a task by data type taxonomy with seven data types (one, two, three dimensional data, temporal and multi dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts).}, + urldate = {2023-12-04}, + booktitle = {Proceedings 1996 {IEEE} {Symposium} on {Visual} {Languages}}, + author = {Shneiderman, B.}, + month = sep, + year = {1996}, + note = {ISSN: 1049-2615}, + keywords = {ToRead}, + pages = {336--343}, + file = {IEEE Xplore Abstract Record:/home/81n/Zotero/storage/46XEVFA4/545307.html:text/html;IEEE Xplore Full Text PDF:/home/81n/Zotero/storage/CEWS7NQL/Shneiderman - 1996 - The eyes have it a task by data type taxonomy for.pdf:application/pdf}, +} + +@article{lucianiDetailsFirstShowContext2019, + title = {Details-{First}, {Show} {Context}, {Overview} {Last}: {Supporting} {Exploration} of {Viscous} {Fingers} in {Large}-{Scale} {Ensemble} {Simulations}}, + volume = {25}, + issn = {1941-0506}, + shorttitle = {Details-{First}, {Show} {Context}, {Overview} {Last}}, + url = {https://ieeexplore.ieee.org/abstract/document/8440850}, + doi = {10.1109/TVCG.2018.2864849}, + abstract = {Visualization research often seeks designs that first establish an overview of the data, in accordance to the information seeking mantra: “Overview first, zoom and filter, then details on demand”. However, in computational fluid dynamics (CFD), as well as in other domains, there are many situations where such a spatial overview is not relevant or practical for users, for example when the experts already have a good mental overview of the data, or when an analysis of a large overall structure may not be related to the specific, information-driven tasks of users. Using scientific workflow theory and, as a vehicle, the problem of viscous finger evolution, we advocate an alternative model that allows domain experts to explore features of interest first, then explore the context around those features, and finally move to a potentially unfamiliar summarization overview. In a model instantiation, we show how a computational back-end can identify and track over time low-level, small features, then be used to filter the context of those features while controlling the complexity of the visualization, and finally to summarize and compare simulations. We demonstrate the effectiveness of this approach with an online web-based exploration of a total volume of data approaching half a billion seven-dimensional data points, and report supportive feedback provided by domain experts with respect to both the instantiation and the theoretical model.}, + number = {1}, + urldate = {2023-12-04}, + journal = {IEEE Transactions on Visualization and Computer Graphics}, + author = {Luciani, Timothy and Burks, Andrew and Sugiyama, Cassiano and Komperda, Jonathan and Marai, G. Elisabeta}, + month = jan, + year = {2019}, + note = {Conference Name: IEEE Transactions on Visualization and Computer Graphics}, + keywords = {ToRead}, + pages = {1225--1235}, + file = {IEEE Xplore Abstract Record:/home/81n/Zotero/storage/BAJ97DJQ/8440850.html:text/html;IEEE Xplore Full Text PDF:/home/81n/Zotero/storage/4D3XCSLA/Luciani et al. - 2019 - Details-First, Show Context, Overview Last Suppor.pdf:application/pdf}, +} + +@inproceedings{barlowtwins, + title = {Barlow {Twins}: {Self}-{supervised} {learning} via {redundancy} {reduction}}, + shorttitle = {Barlow {Twins}}, + url = {https://proceedings.mlr.press/v139/zbontar21a.html}, + abstract = {Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are invariant to distortions of the input sample. However, a recurring issue with this approach is the existence of trivial constant solutions. Most current methods avoid such solutions by careful implementation details. We propose an objective function that naturally avoids collapse by measuring the cross-correlation matrix between the outputs of two identical networks fed with distorted versions of a sample, and making it as close to the identity matrix as possible. This causes the embedding vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors. The method is called Barlow Twins, owing to neuroscientist H. Barlow’s redundancy-reduction principle applied to a pair of identical networks. Barlow Twins does not require large batches nor asymmetry between the network twins such as a predictor network, gradient stopping, or a moving average on the weight updates. Intriguingly it benefits from very high-dimensional output vectors. Barlow Twins outperforms previous methods on ImageNet for semi-supervised classification in the low-data regime, and is on par with current state of the art for ImageNet classification with a linear classifier head, and for transfer tasks of classification and object detection.}, + language = {en}, + urldate = {2024-05-14}, + booktitle = {Proceedings of the 38th {International} {Conference} on {Machine} {Learning}}, + publisher = {PMLR}, + author = {Zbontar, Jure and Jing, Li and Misra, Ishan and LeCun, Yann and Deny, Stephane}, + month = jul, + year = {2021}, + note = {ISSN: 2640-3498}, + pages = {12310--12320}, +} + +@misc{ipywidgets, + title = {jupyter-widgets/ipywidgets}, + copyright = {BSD-3-Clause}, + url = {https://github.com/jupyter-widgets/ipywidgets}, + abstract = {Interactive Widgets for the Jupyter Notebook}, + urldate = {2024-05-14}, + author = {{Jupyter Widgets}}, + year = {2015}, + note = {Accessed 14 May. 2024}, +} + +@misc{LowLevelWidget, + author = {{Jupyter Widgets}}, + title = {Low {Level} {Widget} {Explanation} — {Jupyter} {Widgets} 8.1.2 documentation}, + url = {https://ipywidgets.readthedocs.io/en/latest/examples/Widget%20Low%20Level.html}, + urldate = {2024-05-14}, + year = {2024}, + note = {Accessed 14 May. 2024}, +} + +@misc{ipywidgets_cookiecutter, + author = {{Jupyter Widgets}}, + title = {jupyter-widgets/widget-cookiecutter: {A} cookiecutter template for creating a custom {Jupyter} widget project.}, + url = {https://github.com/jupyter-widgets/widget-cookiecutter}, + urldate = {2024-05-14}, + year = {2024}, + note = {Accessed 14 May. 2024}, +} + +@misc{plotly, + author = {{Plotly, Inc.}}, + title = {plotly/plotly.py: {The} interactive graphing library for {Python}}, + url = {https://github.com/plotly/plotly.py}, + urldate = {2024-05-14}, + year = {2019}, + NOTE: = {Accessed 14 May. 2024}, +} + +@misc{mpld3, + title = {mpld3/mpld3}, + copyright = {BSD-3-Clause}, + url = {https://github.com/mpld3/mpld3}, + abstract = {An interactive data visualization tool which brings matplotlib graphics to the browser using D3.}, + urldate = {2024-05-14}, + publisher = {mpld3}, + author = {VanderPlas, Jake}, + year = {2013}, + note = {Accessed 14 May. 2024}, +} + +@article{altair, + title = {Altair: {Interactive} {Statistical} {Visualizations} for {Python}}, + volume = {3}, + issn = {2475-9066}, + shorttitle = {Altair}, + url = {https://joss.theoj.org/papers/10.21105/joss.01057}, + doi = {10.21105/joss.01057}, + abstract = {VanderPlas et al., (2018). Altair: Interactive statistical visualizations for Python. Journal of Open Source Software, 3(32), 1057, https://doi.org/10.21105/joss.01057}, + language = {en}, + number = {32}, + urldate = {2024-05-14}, + journal = {Journal of Open Source Software}, + author = {VanderPlas, Jacob and Granger, Brian E. and Heer, Jeffrey and Moritz, Dominik and Wongsuphasawat, Kanit and Satyanarayan, Arvind and Lees, Eitan and Timofeev, Ilia and Welsh, Ben and Sievert, Scott}, + month = dec, + year = {2018}, + pages = {1057}, +} + +@misc{bqplot, + author = {{The BQplot Project}}, + title = {bqplot/bqplot}, + copyright = {Apache-2.0}, + url = {https://github.com/bqplot/bqplot}, + abstract = {Plotting library for IPython/Jupyter notebooks}, + urldate = {2024-05-14}, + year = {2015}, + note = {Accessed 14 May. 2024}, +} + +@misc{pythreejs, + author = {{Jupyter Widgets}}, + title = {jupyter-widgets/pythreejs}, + url = {https://github.com/jupyter-widgets/pythreejs}, + abstract = {A Jupyter - Three.js bridge}, + urldate = {2024-05-14}, + publisher = {Jupyter Widgets}, + year = {2024}, + note = {Accessed 14 May. 2024}, +} + +@misc{ipyleaflet, + author = {{Jupyter Widgets}}, + title = {jupyter-widgets/ipyleaflet}, + copyright = {MIT}, + url = {https://github.com/jupyter-widgets/ipyleaflet}, + abstract = {A Jupyter - Leaflet.js bridge}, + urldate = {2024-05-14}, + year = {2024}, + note = {Accessed 14 May. 2024}, +} + +@misc{ipycytoscape, + title = {cytoscape/ipycytoscape}, + copyright = {BSD-3-Clause}, + url = {https://github.com/cytoscape/ipycytoscape}, + abstract = {A Cytoscape Jupyter widget}, + urldate = {2024-05-14}, + publisher = {Cytoscape Consortium}, + author = {Meireles, Mariana}, + year = {2020}, + note = {Accessed 14 May. 2024}, +} + +@misc{solara, + author = {Maarten A. Breddels}, + title = {widgetti/solara}, + copyright = {MIT}, + url = {https://github.com/widgetti/solara}, + abstract = {A Pure Python, React-style Framework for Scaling Your Jupyter and Web Apps}, + urldate = {2024-05-14}, + year = {2022}, + note = {Accessed: 14 May. 2024}, +} + +@misc{streamlit, + author = {{Snowflake Inc.}}, + title = {streamlit/streamlit: {Streamlit} — {A} faster way to build and share data apps.}, + url = {https://github.com/streamlit/streamlit}, + urldate = {2024-05-14}, + year = {2020}, + note = {Accessed: 14 May. 2024}, +} + +@misc{ipyreact, + author = {Maarten A. Breddels}, + title = {widgetti/ipyreact}, + copyright = {BSD-3-Clause}, + url = {https://github.com/widgetti/ipyreact}, + abstract = {React for ipywidgets that just works. No webpack, no npm, no hassle}, + urldate = {2024-05-14}, + year = {2023}, + note = {Accessed 14 May. 2024}, +} + +@misc{ipyvuetify, + author = {Mario Buikhuizen}, + title = {widgetti/ipyvuetify: {Jupyter} widgets based on vuetify {UI} components}, + url = {https://github.com/widgetti/ipyvuetify}, + urldate = {2024-05-14}, + year = {2019}, + note = {Accessed 14 May. 2024}, +} + +@misc{traitlets, + author = {{IPython Development Team}}, + title = {ipython/traitlets}, + copyright = {BSD-3-Clause}, + url = {https://github.com/ipython/traitlets}, + abstract = {A lightweight Traits like module}, + urldate = {2024-05-14}, + year = {2001}, + note = {Accessed 14 May. 2024}, +} From b9f287465eb90c107a1bc96d64686d50de1ee390 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Thu, 30 May 2024 16:02:26 -0400 Subject: [PATCH 02/19] Ignore citations that don't have DOI --- papers/nathan_martindale/myst.yml | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/papers/nathan_martindale/myst.yml b/papers/nathan_martindale/myst.yml index 402b7efeb6..21b31db317 100644 --- a/papers/nathan_martindale/myst.yml +++ b/papers/nathan_martindale/myst.yml @@ -38,11 +38,22 @@ project: - rule: doi-exists severity: ignore keys: - - Atr03 - - terradesert + - ipywidgets + - solara + - streamlit + - LowLevelWidget + - ipywidgets_cookiecutter + - plotly + - mpld3 + - bqplot + - pythreejs + - ipyleaflet + - ipycytoscape + - traitlets + - ipyreact + - ipyvuetify + - barlowtwins - jupyter - - sklearn1 - - sklearn2 # A banner will be generated for you on publication, this is a placeholder banner: banner.png # The rest of the information shouldn't be modified From ae0e421f6f1c4c618719f8b9d3b029cf871ee19b Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Thu, 30 May 2024 16:06:23 -0400 Subject: [PATCH 03/19] Ignore 403 link resolve (works fine in browser?) --- papers/nathan_martindale/myst.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/papers/nathan_martindale/myst.yml b/papers/nathan_martindale/myst.yml index 21b31db317..65a3c0a27c 100644 --- a/papers/nathan_martindale/myst.yml +++ b/papers/nathan_martindale/myst.yml @@ -54,6 +54,10 @@ project: - ipyvuetify - barlowtwins - jupyter + - rule: links-resolve + severity: ignore + keys: + - https://www.energy.gov/doe-public-access-plan # A banner will be generated for you on publication, this is a placeholder banner: banner.png # The rest of the information shouldn't be modified From ce3c1aa9b6d1749d7c1f5d6625b45222c7a27199 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Thu, 30 May 2024 16:08:53 -0400 Subject: [PATCH 04/19] Fix typo --- papers/nathan_martindale/myst.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/papers/nathan_martindale/myst.yml b/papers/nathan_martindale/myst.yml index 65a3c0a27c..26e176c6e4 100644 --- a/papers/nathan_martindale/myst.yml +++ b/papers/nathan_martindale/myst.yml @@ -54,7 +54,7 @@ project: - ipyvuetify - barlowtwins - jupyter - - rule: links-resolve + - rule: link-resolves severity: ignore keys: - https://www.energy.gov/doe-public-access-plan From 95635d663ac5591db8c3f45d7de10378a1b2f02d Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Fri, 31 May 2024 17:05:49 -0400 Subject: [PATCH 05/19] Some minor wording tweaks --- papers/nathan_martindale/main.md | 89 ++++++++++++++++---------------- 1 file changed, 45 insertions(+), 44 deletions(-) diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md index 7baf9b79a0..daa0a148e5 100644 --- a/papers/nathan_martindale/main.md +++ b/papers/nathan_martindale/main.md @@ -20,14 +20,14 @@ abstract: | As the tech industry and computation-based research continues to gravitate toward using more and more data to solve problems, the capability to interact -with and understand the underlying data that is being used becomes increasingly -important. As part of this growing need, development of effective -visualization tools and frameworks has turned into an ecosystem where components -from different sources all work in unison and developers can piece together what -they need for their specific goals. For many reasons, including the ease of -organization and allowing documentation, development, and analysis to all exist -in the same place, Jupyter notebooks [@jupyter] have become a common platform -for data science workflows, and by extension have become the common ground where these +with and understand that underlying data becomes increasingly +important. Development of effective visualization tools and frameworks to +address this need has turned into an ecosystem where components from different +sources all work in unison and developers can piece together what they need for +their specific goals. For many reasons, including the ease of organization and +allowing documentation, development, and analysis to all exist in the same +place, Jupyter notebooks [@jupyter] have become a common platform for data +science workflows, and by extension have become the common ground where these different tools can coexist. This ability to interact with and visualize data plays a crucial role in many data science/analysis workflows by allowing users to manipulate and explore, thus enhancing comprehension, improving user @@ -36,13 +36,13 @@ the data. IPyWidgets [@ipywidgets], a framework for bridging between the IPython kernel and HTML widgets within Jupyter, has been particularly influential for enabling -interactive visualization. Because of its ease of use and extendibility, many -libraries such as Panel [@10.5281/zenodo.11046027], Solara [@solara], and Streamlit -[@streamlit] integrate closely with IPyWidgets and thus contribute to -the greater visualization ecosystem within Python. Although a great deal of -progress has been made in this community, a missing element is a more -generalized and flexible approach to aspects of the details-on-demand paradigm. -To support this approach, the authors have developed a library that +interactive visualization. Many libraries such as Panel +[@10.5281/zenodo.11046027], Solara [@solara], and Streamlit [@streamlit] +integrate closely with IPyWidgets and thus contribute to the greater +visualization ecosystem within Python. Although a great deal of progress has +been made in this community, a missing element is a more generalized and +flexible approach to aspects of the [details-on-demand](#details-on-demand) +paradigm. To support this approach, the authors have developed a library that provides wrapper components that can render any widget as an overlay on top of other widgets. These components simplify designing layouts and visualizations that would be difficult to implement from scratch. @@ -57,10 +57,11 @@ use case that motivated its design. ### Details on Demand -Details on demand is one piece of the "visual information-Seeking mantra" +Details on demand is one piece of the "visual information-seeking mantra" [@shneidermanEyesHaveIt1996], a paper describing an overall design approach to building interactive tooling. The general flow of interaction proposed by this -mantra is "overview first, zoom and filter, then details-on-demand." Overview +mantra is "overview first, zoom and filter, then details-on-demand" +[@shneidermanEyesHaveIt1996]. Overview first ideally starts with broader aggregate visualizations: something to gain a sense of the general scope, complexity, or shape of the data. From there a user can identify their own areas of interest to hone in on with whatever zoom and filter @@ -73,15 +74,15 @@ organically explore the data. A simple and widespread example of details on demand are tooltips. Descriptions -of interface components or values of data points in visualizations would often -create an unusably noisy user interface if they are always visible. Instead, a -more concise view is maintained while providing the necessary information in a -spatially relevant view when the user specifically requests it. Readers viewing -the web version of these proceedings can explore a simple example of details on -demand live by hovering over any of the figure references, such as this one: -@fig:detailsondemand, which shows how hovering over a reference (the demand) +of components or data point values in visualizations would often create an +unusably noisy interface if they were always visible. Instead, a more concise +view is maintained while providing the necessary information in a spatially +relevant view when the user specifically requests it. Readers viewing the web +version of these proceedings can explore a simple example of details on demand +live by hovering over any of the figure references. +@fig:detailsondemand shows how hovering over a reference (the demand) opens a window at the cursor that displays the target of the reference (the -details.) +details). :::{figure} detailsondemand.png @@ -110,11 +111,11 @@ Python community and the data science Jupyter workflow with its ability to marry the two language ecosystems, each of which include many visualization tools and capabilities. -To highlight the simplicity of IPyWidgets, a couple of minimal +To highlight the simplicity of IPyWidgets, some minimal examples are provided here. In the simplest UI cases, IPyWidgets allows wrapping a function call that produces a visual output based on some set of input parameters with `ipywidgets.interactive()`, which automatically -constructs an appropriate form widget for each parameter. In this case, the +constructs an appropriate input widget for each parameter. In this case, the developer need not manually initialize any Python widgets, as seen in the below code sample @code:interactive and @fig:ipywidgets_example. @@ -201,17 +202,17 @@ parameter3.observe(on_parameter3_change, names=["value"]) ### State Synchronization -One of the primary challenges that IPyWidgets helps solve is the -problem of state synchronization, which amounts to monitoring for changes to -variables made on either side of the Python-JavaScript link and communicating those -changes across when they occur. Thus, changes to variables in Python are -reflected in the browser's JavaScript and vice versa. Under the hood, -IPyWidgets makes use of backbone.js [@LowLevelWidget], a model-view-* (MV*) framework for -JavaScript that implements synchronization between a JavaScript _model_ -(where data/attributes/values for a widget are stored) and a JavaScript -_view_ (the code that creates the visual UI elements the user sees and -interacts with in their browser). IPyWidgets connects to this backbone model via -WebSockets, through which model changes and events are sent as serialized JSON. +One of the primary challenges that IPyWidgets helps solve is the problem of +state synchronization, where updates to variables made on either side of the +Python-JavaScript link are monitored and communicated across when they occur. +Based on this communication, changes to variables in Python are reflected in the +browser's JavaScript and vice versa. Under the hood, IPyWidgets makes use of +backbone.js [@LowLevelWidget], a model-view-* (MV*) framework for JavaScript +that implements synchronization between a JavaScript _model_ (where +data/attributes/values for a widget are stored) and a JavaScript _view_ (the +code that creates the visual UI elements the user sees and interacts with in +their browser). IPyWidgets connects to this backbone model via WebSockets, +through which model changes and events are sent as serialized JSON. :::{figure} ipywidgets_state.png @@ -222,7 +223,7 @@ State mirroring between models and view interaction for an example slider widget @fig:ipywidgets_state shows a simplified view of the communication channels -between the Python model and JavaScript model/view for an example slider widget. +between the Python model and the JavaScript model/view for an example slider widget. Modifying an attribute on the Python model sends the modification to the JavaScript model, where any changes that would modify the visual aspects of the widget are communicated to the JavaScript view (e.g., a change in the slider @@ -308,7 +309,7 @@ one widget on top of another. #### Custom IPyWidget Complexity -One of the key benefits of IPyWidgets as a framework is that it allows +A key benefit of IPyWidgets as a framework is that it allows developers to design, build, and publish new components that other people can use in place of or alongside the default widgets. However, the complexity of the infrastructure required to do this with the base IPyWidgets library still makes @@ -353,7 +354,7 @@ provide the capabilities from various JavaScript web frameworks. A few we briefly touch on here are AnyWidget, IPyReact, and IPyVuetify. AnyWidget [@10.5281/zenodo.11182005] is a recent library that has become -influential in the past year or so. New widgets can be created by simply +influential in the past year. New widgets can be created by simply extending the Python `anywidget.AnyWidget` class, defining the class attributes that are to be synced between the Python and JavaScript models using the `traitlets` library [@traitlets], and providing a string of @@ -398,7 +399,7 @@ user has pressed enter, rather than restarting the computation on every letter the user adds or removes. All of the default components in IPyWidgets similarly have more flexible -versions in IPyVuetify, and many additional components. @fig:ipw_ipv +versions and many additional components in IPyVuetify. @fig:ipw_ipv shows a small sample of default IPyWidgets on the left with their corresponding IPyVuetify versions on the right. Also pictured at the bottom is the Vuetify DataTable, which is an example of a component that is not in the default IPyWidgets. The DataTable allows @@ -491,7 +492,7 @@ containing any other widget. An example of IPyOverlay in action is shown in @fig:example_dashboard. Throughout this section we will discuss an example use case where we worked with one of the authors, a seismologist, to construct a dashboard for exploring models trained on seismic data. This -use case was what led to the development of IPyOverlay as a library. +use case led to the development of IPyOverlay as a library. [^footnote-1]: https://github.com/ORNL/ipyoverlay @@ -580,7 +581,7 @@ positioned child widgets. ::: -### Embedding Widgets in Container Widgets +### Embedding IPyWidgets in IPyOverlay Containers A key enabler is IPyVuetify's ability to embed other IPyWidgets inside of the template HTML, allowing us to construct custom layouts of one or more From c988bcb41ea834e37c827f95077df4c461fe0003 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Tue, 16 Jul 2024 14:17:10 -0400 Subject: [PATCH 06/19] Fix missing endquote in program 4 caption --- papers/nathan_martindale/main.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md index daa0a148e5..a353ee6870 100644 --- a/papers/nathan_martindale/main.md +++ b/papers/nathan_martindale/main.md @@ -604,7 +604,7 @@ A simplified example is shown in @code:container. ```{code-block} html :label: code:container :linenos: true -:caption: A simplified version of the Vue template for `OverlayContainer`. The `

      tag with `v-for` reactively produces a `
      ` tag for each widget in the `children` list attribute within the Python model. Note, the style attribute is bound and references the position arrays on lines 21--22. Changing the position of each overlay widget is done by modifying the corresponding values in these arrays. We keep these arrays only on the JavaScript side to prevent undue traffic between the Python and JavaScript models from high-frequency events (e.g., mouse movements) that can cause significant lag in the interface. +:caption: A simplified version of the Vue template for `OverlayContainer`. The `
      ` tag with `v-for` reactively produces a `
      ` tag for each widget in the `children` list attribute within the Python model. Note, the style attribute is bound and references the position arrays on lines 21--22. Changing the position of each overlay widget is done by modifying the corresponding values in these arrays. We keep these arrays only on the JavaScript side to prevent undue traffic between the Python and JavaScript models from high-frequency events (e.g., mouse movements) that can cause significant lag in the interface. """ + +button = ColoredToggleButton() + +# render the button +button ``` :::{figure} ipyvuetify_example.drawio.png From 81985cc7b0d71c832e66a1af04c632123e9885e3 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Fri, 26 Jul 2024 10:03:53 -0400 Subject: [PATCH 08/19] Fix Python capitalization in figure 3 --- papers/nathan_martindale/ipywidgets_state.png | Bin 163570 -> 163259 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/papers/nathan_martindale/ipywidgets_state.png b/papers/nathan_martindale/ipywidgets_state.png index 7e02bc5717ca0969bfe7f82c954011d67d28d7d2..1ccce98a24d3c274cbe647b1eb0fd9505baec248 100644 GIT binary patch delta 135004 zcmc$_2{_bm`!^iv7m_S3)?{C@&DhH}gISE-*elCm?1M3OzLK5nkwhXTl0?>!WQ#&c zDm$g@m3@7$>G$04{XWnCeg4P)e;@brz8#Z;xxUwRo!fPupYuGg@2@?E#vO)K7>#%u zTHnLwS^mj;o0tCBWCn)=x#j$uA>}2F9+h z?23n(nHy11UgjQf63Q(&D5I6;G6eU6!7=ilCPwB86z3p46MKIh>%d@yqlKxqlN%+- z!QH}K!Jn)m3`bHdkd|65!OnUJGhJg#9d93^yOXT8im?_JZS3ozqfY?Oz>uz9D4eVr 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      Ah4Fp0+DC9(;< zUeNHag45+xaH;foOCqQ`UcC&nr;F?Enr-XPDk7PK*i59)YXE5+<0)#m?b)Ny8%!)c z-lW&Hnz{RT{w2^?ao+V+(4SV4w*UZ54cWt~8!}-5)w9lx?9gUO^XW8>m3+deoe9S` zUC^Ap9Vqh50Yc4``v!|7%biZPOcGyi*?Xp(!YL0Woz|>kLqc&3we|uyNUgTQX8V+y zwosgkLZrRPtjGz0s$Ee8|w3efpA8992)A02oPPU2*F$PcZJfFo#TT z>DlJ-R6XPD1mCW-fmLH6G=~6J)*BY&O0Y2)%{42VQyjY1S85+&1uIqe~@P*b`~%DkGa zV%hRu#R4r@blzfs^&(KR)Q`q@sc>SCEscPG`q&^Mu_6P6={R5BGUz)0qY>|Tgfk$ew+IV4( zxPXO`Y;Pfs%B$jTZEiY}5S~JkUlnjOd12wh+Ej#w0>T4fqBGzPhfxvQC^}LR`59cB zh_IA5%v?ezA>io4;5$+iqv2HmnkRngjf!bB_VX=-7$b+LBf7uU}YskXH1UsQSTkgWY^S!3lb5! j|G)76lIoQRrFnyu*`Mh|Y5J{N0zMvFeOxbXAu;|7vH-h2 From ba0e849c0aafa3fb5006ba774b9b45a5386434fd Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Fri, 26 Jul 2024 10:27:35 -0400 Subject: [PATCH 09/19] Incorporate several wording suggestions from @JuanCab --- papers/nathan_martindale/main.md | 46 ++++++++++++------------- papers/nathan_martindale/references.bib | 9 +++++ 2 files changed, 32 insertions(+), 23 deletions(-) diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md index 80ae0b4f59..d4d69bfb44 100644 --- a/papers/nathan_martindale/main.md +++ b/papers/nathan_martindale/main.md @@ -57,20 +57,19 @@ use case that motivated its design. ### Details on Demand -Details on demand is one piece of the "visual information-seeking mantra" -[@shneidermanEyesHaveIt1996], a paper describing an overall design approach to -building interactive tooling. The general flow of interaction proposed by this -mantra is "overview first, zoom and filter, then details-on-demand" -[@shneidermanEyesHaveIt1996]. Overview -first ideally starts with broader aggregate visualizations: something to gain a sense -of the general scope, complexity, or shape of the data. From there a user can -identify their own areas of interest to hone in on with whatever zoom and filter -tools are applicable for their data's modality. Finally, details on demand -means that the user can see or interact with more in-depth and specific -information for pieces of their data, often in the form of a pop-up window or -similar. This selective detail capability provides a flexible user experience -that keeps the initial interface uncluttered without sacrificing the ability to -organically explore the data. +Details on demand is one piece of the "visual information-seeking mantra" of +Schneiderman (1996) describing an overall design approach to building +interactive tooling. The general flow of interaction proposed by this mantra is +"overview first, zoom and filter, then details-on-demand" +[@shneidermanEyesHaveIt1996]. Overview ideally starts with broader aggregate +visualizations: something to gain a sense of the general scope, complexity, or +shape of the data. From there a user can identify areas of interest to +hone in on with whatever zoom and filter tools are applicable for their data's +modality. Finally, details on demand means that the user can see or interact +with more in-depth and specific information for pieces of their data, often in +the form of a pop-up window or inset graph. This selective detail capability +provides a flexible user experience that keeps the initial interface uncluttered +without sacrificing the ability to organically explore the data. A simple and widespread example of details on demand are tooltips. Descriptions @@ -96,7 +95,7 @@ reference for @fig:detailsondemand. ## IPyWidgets IPyWidgets is a library that enables implementing user interactivity inside of a -Jupyter Notebook/Jupyter Lab. A key challenge this framework has to overcome is +Jupyter Notebook. A key challenge this framework has to overcome is the separation between the Python kernel and the browser-based Jupyter frontend, which operates through HTML, CSS, and JavaScript as a web application. Web application development is often a challenging software engineering problem in @@ -244,7 +243,7 @@ framework abstracting away many of the specifics for how communication between a kernel, server, and frontend takes place. This framework can be used to design new widgets by providing implementations for the models and view code, without needing to reinvent the various messaging system mechanisms that solve the state -synchronization problem discussed above. All of this has allowed many libraries +synchronization problem discussed previously. All of this has allowed many libraries to emerge, providing novel functionality, a great diversity of new types of widgets, and other frameworks that can support rendering IPyWidgets within other contexts. @@ -287,7 +286,7 @@ value changes (e.g., a slider is moved, the text in a textbox is changed, or an item in a drop-down menu is selected). These types of events are sufficient for basic interfaces, but often more complex types of -interactions or responsiveness can greatly enhance the capabilities of a tool. +interactions can greatly enhance the capabilities of a tool. The ability to detect mouse events can be crucial, enabling possibilities such as highlighting parts of a visualization when the mouse hovers over a row in a dataframe, or running an analysis when a user has highlighted a piece of @@ -324,8 +323,9 @@ developed by starting from a Jupyter-provided cookie-cutter template One of the difficulties that comes with a project created in this way is the need to include the development infrastructure of both a Python and a JavaScript -project. JavaScript toolchains and dependency management are somewhat -notoriously complicated. Prototyping the component can be a slow process as +project. JavaScript toolchains and dependency management can quickly become +complicated with the number of packages, build system tools, and config files often +necessary. Prototyping the component can be a slow process as well, requiring a rebuild on every JavaScript change. Setting up Jupyter to use these unpublished components requires additional environment management to work correctly. @@ -388,16 +388,16 @@ JavaScript code. ### Vuetify Component Library IPyVuetify is an IPyWidgets port of the Vuetify component library, a set of more -than 70 premade material design UI widgets. Most of these components support a +than 70 premade material design [@materialdesign] UI widgets. Most of these components support a fairly extensive set of attributes and properties to change both appearance and -functionality, and more importantly each generally has multiple types of events +functionality. More importantly, each IPyVuetify widget generally has multiple types of events that can be observed. A simple example would be a textbox form input component. In IPyWidgets, the `Text` widget can mostly only observe a value change when the user changes the text inside. The IPyVuetify `TextField` can additionally raise events for key presses, mouse clicks, the element gaining and losing focus, as well as any HTML event. This level of control can be useful, for example, when a text value change triggers a heavy processing task. The interaction is more -effective when the computation is delayed until the field loses focus or the +responsive when the computation is delayed until the field loses focus or the user has pressed enter, rather than restarting the computation on every letter the user adds or removes. @@ -445,7 +445,7 @@ defining a custom button with a color change on toggle, where the colors can be modified from Python as well, shown in @fig:ipyvuetify_example. In this particular example, both the `@click` event and `:color` property directly bind to the Python attributes, and no explicit JavaScript is necessary. Finally, -IPyVuetify components make it easy to directly call functions across the +IPyVuetify makes it easy to directly call functions across the Python--JavaScript link by making any Python functions prefixed with `vue_` available within JavaScript and any JavaScript functions prefixed with `jupyter_` callable from Python. diff --git a/papers/nathan_martindale/references.bib b/papers/nathan_martindale/references.bib index 61017f4d6c..30fa8215d7 100644 --- a/papers/nathan_martindale/references.bib +++ b/papers/nathan_martindale/references.bib @@ -228,3 +228,12 @@ @misc{traitlets year = {2001}, note = {Accessed 14 May. 2024}, } + +@misc{materialdesign, + author = {Google}, + title = {Material Design}, + year = {2014}, + url = {https://material.io}, + note = {Accessed 26 July, 2024}, +} + From f0270ddd3f8264e976e0302e93914665d3f9a3b8 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Fri, 26 Jul 2024 11:00:28 -0400 Subject: [PATCH 10/19] Add material design website reference to doi ignore list --- papers/nathan_martindale/myst.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/papers/nathan_martindale/myst.yml b/papers/nathan_martindale/myst.yml index 26e176c6e4..665705681a 100644 --- a/papers/nathan_martindale/myst.yml +++ b/papers/nathan_martindale/myst.yml @@ -54,6 +54,7 @@ project: - ipyvuetify - barlowtwins - jupyter + - materialdesign - rule: link-resolves severity: ignore keys: From f5d0f9bebe235fd00530d2d1d875df42f565d795 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Thu, 1 Aug 2024 10:48:30 -0400 Subject: [PATCH 11/19] Add ipyoverlay example snippet and remove streamlit reference --- .../nathan_martindale/ipyoverlay_example.png | Bin 0 -> 28672 bytes papers/nathan_martindale/main.md | 63 +++++++++++++++++- papers/nathan_martindale/myst.yml | 1 - papers/nathan_martindale/references.bib | 9 --- 4 files changed, 62 insertions(+), 11 deletions(-) create mode 100644 papers/nathan_martindale/ipyoverlay_example.png diff --git a/papers/nathan_martindale/ipyoverlay_example.png b/papers/nathan_martindale/ipyoverlay_example.png new file mode 100644 index 0000000000000000000000000000000000000000..9e8d38308110609520082c094e2b4d4bbce14b41 GIT binary patch literal 28672 zcmb5UbyOWe_bqsFcMI+w2=4AK!5sp@3GNQTHMqOG6WkqwySoKnW5!C!0jo%SquLK?52hw5^cjj`NHv+8Yb

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zU72?J(BBWjkp`%NyW8#f{s3fmNu5D3bO)8G(q}U*EuqC60-MLQL=?67BTqLJ+)sjDjWUWsKlh$X6EvB=MY#woIiD zx_kEp#yTJh&~PA8(l#VL_PxnkKirSYml1b1*@~~#Nr8o<3@JjqAskz`=$$}n{!6Sl zkgnsBrlzK<(fjTq$&_n>y7i>(EPcBuePuYC1#%|Pd#CY9u)e?XY*@{fAmxt)Zs_?! zh{(9vj0?O|&#c=gcslShY+TmvrouDHbaEZx|2DbN*=exk{MMfp6Bn+d%|a}UxJA+D zec&w&YuCEr&NI+r+lstbU~_h6Uwq6dc?_dwpvm}%4-qg#3>>OAE?+vQ_Z{i4+KEg( zoPQw!%DH^Gg@1LA4zpfii%(!9eFIWbQtPgoU{l~dupMBWW>%LLD@u_52oi&)CMGZw zP*|40P)QOw+q_TX-6FkVvK0yWN8gLI678nluaaydvet_%UpC+d6gtzQ^M#=T^U^XAwJ=*tCqnQRx4p;q*ZvA!3-Y2s3g&P9t(@q8H=U z7JyVFSM3^`@@U1G-s^NY=_j zXb@m+C`LdQW)GGY7!U5jB(#i!4-%Rsm6cW~XSe{hm6f<51@1(Y(L$8~i(ZiQ*eDO``4ZeT zGOX!rksdu`@yh>0`ES6+#tnO@WCt+)<#v#Zji2am8;qUc&nA=UIQR~8JU0UaA;r2= zU$_IFM@>P20nE1Y@ + +:::{figure} ipyoverlay_example.png +:label: fig:ipyoverlay_example +An `ipympl` scatter plot of random data with two inset plots showing the +additional data associated with each clicked point in a bar chart. +::: + + + ## Conclusion The prevalence and utility of Jupyter notebooks in data analysis and scientific diff --git a/papers/nathan_martindale/myst.yml b/papers/nathan_martindale/myst.yml index 665705681a..8bcc662a68 100644 --- a/papers/nathan_martindale/myst.yml +++ b/papers/nathan_martindale/myst.yml @@ -40,7 +40,6 @@ project: keys: - ipywidgets - solara - - streamlit - LowLevelWidget - ipywidgets_cookiecutter - plotly diff --git a/papers/nathan_martindale/references.bib b/papers/nathan_martindale/references.bib index 30fa8215d7..ef33504eb9 100644 --- a/papers/nathan_martindale/references.bib +++ b/papers/nathan_martindale/references.bib @@ -189,15 +189,6 @@ @misc{solara note = {Accessed: 14 May. 2024}, } -@misc{streamlit, - author = {{Snowflake Inc.}}, - title = {streamlit/streamlit: {Streamlit} — {A} faster way to build and share data apps.}, - url = {https://github.com/streamlit/streamlit}, - urldate = {2024-05-14}, - year = {2020}, - note = {Accessed: 14 May. 2024}, -} - @misc{ipyreact, author = {Maarten A. Breddels}, title = {widgetti/ipyreact}, From 3e61b4211f975b55696726b44af06408d3b24a13 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Thu, 1 Aug 2024 11:09:03 -0400 Subject: [PATCH 12/19] Address wording suggestions from @ktzhao --- papers/nathan_martindale/main.md | 37 ++++++++++++++++---------------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md index 8da2fd677c..4435a82c9a 100644 --- a/papers/nathan_martindale/main.md +++ b/papers/nathan_martindale/main.md @@ -20,19 +20,19 @@ abstract: | As the tech industry and computation-based research continues to gravitate toward using more and more data to solve problems, the capability to interact -with and understand that underlying data becomes increasingly -important. Development of effective visualization tools and frameworks to -address this need has turned into an ecosystem where components from different -sources all work in unison and developers can piece together what they need for -their specific goals. For many reasons, including the ease of organization and -allowing documentation, development, and analysis to all exist in the same -place, Jupyter notebooks [@jupyter] have become a common platform for data -science workflows, and by extension have become the common ground where these -different tools can coexist. This ability to interact with and visualize data -plays a crucial role in many data science/analysis workflows by allowing users -to manipulate and explore, thus enhancing comprehension, improving user -engagement, and often providing a faster route to an intuitive understanding of -the data. +with and understand that underlying data becomes increasingly important. +Development of effective visualization tools and frameworks to address this need +has turned into an ecosystem where components from different sources all work in +unison and developers can piece together what they need for their specific +goals. For many reasons, including the ease of organization and allowing +documentation, development, and analysis to all exist in the same place, Jupyter +notebooks [@jupyter] have become a common platform for data science workflows, +and by extension have become the common ground where these different tools can +coexist. An important piece to this platform is the ability to interact with and +visualize data, which is crucial in many data science/analysis workflows. +Interactive visualization allows users to manipulate and explore data, which +enhances understanding, improves engagement, and often leads to a quicker +intuitive grasp of the data. IPyWidgets [@ipywidgets], a framework for bridging between the IPython kernel and HTML widgets within Jupyter, has been particularly influential for enabling @@ -94,7 +94,7 @@ reference for @fig:detailsondemand. ## IPyWidgets -IPyWidgets is a library that enables implementing user interactivity inside of a +IPyWidgets is a library for implementing user interactivity inside of a Jupyter Notebook. A key challenge this framework has to overcome is the separation between the Python kernel and the browser-based Jupyter frontend, which operates through HTML, CSS, and JavaScript as a web application. Web @@ -104,11 +104,10 @@ experience or the desire to write custom frontend code in JavaScript. IPyWidgets addresses this problem by supporting mechanisms for automatic state synchronization between a Python model and a JavaScript model, expanded on below, and providing a set of predefined components that a developer -can initialize and use in their Python code. Although there are limitations to -using IPyWidgets alone, this library brings a lot of value to the scientific -Python community and the data science Jupyter workflow with its ability to marry -the two language ecosystems, each of which include many visualization tools -and capabilities. +can initialize and use in their Python code. While IPyWidgets has limitations, +it is a valuable tool for the scientific Python community by allowing the +combination of two language ecosystems with many visualization libraries and +capabilities. To highlight the simplicity of IPyWidgets, some minimal examples are provided here. In the simplest UI cases, IPyWidgets allows wrapping a From b49b9156a8ecf30a8a04a61141b6c6fe81d14b68 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Thu, 1 Aug 2024 12:07:26 -0400 Subject: [PATCH 13/19] Add abbreviations and references for UMAP and DBSCAN --- papers/nathan_martindale/main.md | 6 ++---- papers/nathan_martindale/myst.yml | 4 ++++ papers/nathan_martindale/references.bib | 7 +++++++ 3 files changed, 13 insertions(+), 4 deletions(-) diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md index 4435a82c9a..a37f8e6236 100644 --- a/papers/nathan_martindale/main.md +++ b/papers/nathan_martindale/main.md @@ -540,11 +540,9 @@ the metadata attributes that are unrelated to the event source properties. Additionally, we use the dashboard to help end users understand how well the model generalizes to new sources and to explore uncertainty. -The dashboard includes a UMAP plot of signals embedded by the model under +The dashboard includes a UMAP [@10.48550/arXiv.1802.03426] plot of signals embedded by the model under exploration. Each datapoint is a single observed seismic signal, and these -data points are then clustered with DBSCAN. -Metadata attributes aggregated +data points are then clustered with DBSCAN [@Ester1996ADA]. Metadata attributes aggregated by cluster can be explored by clicking within the UMAP ipympl plot, which aids in determining how much correlation exists for those attributes within that cluster. Invariant properties would tend to show as spread out distributions, diff --git a/papers/nathan_martindale/myst.yml b/papers/nathan_martindale/myst.yml index 8bcc662a68..0b11a2412c 100644 --- a/papers/nathan_martindale/myst.yml +++ b/papers/nathan_martindale/myst.yml @@ -33,6 +33,10 @@ project: # Add the abbreviations that you use in your paper here abbreviations: MyST: Markedly Structured Text + HTML: HyperText Markup Language + UMAP: Uniform Manifold Approximation and Projection + CSS: Cascading Style Sheets + DBSCAN: Density-Based Spatial Clustering of Applications with Noise # It is possible to explicitly ignore the `doi-exists` check for certain citation keys error_rules: - rule: doi-exists diff --git a/papers/nathan_martindale/references.bib b/papers/nathan_martindale/references.bib index ef33504eb9..402b5ec446 100644 --- a/papers/nathan_martindale/references.bib +++ b/papers/nathan_martindale/references.bib @@ -228,3 +228,10 @@ @misc{materialdesign note = {Accessed 26 July, 2024}, } +@inproceedings{Ester1996ADA, + title={A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise}, + author={Martin Ester and Hans-Peter Kriegel and J{\"o}rg Sander and Xiaowei Xu}, + booktitle={Knowledge Discovery and Data Mining}, + year={1996}, + url={https://api.semanticscholar.org/CorpusID:355163} +} From eddefda6f6f780c976e85a3d6d458c0401c79389 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Thu, 1 Aug 2024 12:14:54 -0400 Subject: [PATCH 14/19] Address other recommended wording changes from @JuanCab --- papers/nathan_martindale/main.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md index a37f8e6236..ad335e9625 100644 --- a/papers/nathan_martindale/main.md +++ b/papers/nathan_martindale/main.md @@ -554,9 +554,9 @@ clustering and produce narrow distributions. Within base IPyWidgets, there is no straightforward way of constructing a layout in which one widget is overlaid on top of another. Certain other libraries -have limited functionality for this within their own widget instances, for -example Matplotlib can display an axes within another axes, as can plotly, and -many libraries support basic tooltips with arbitrary textual content. Others +have limited functionality for this within their own widget instances. For +example Matplotlib and plotly can display an axes within another axes. +Furthermore, many libraries support basic tooltips with arbitrary textual content. Others such as IPyVuetify support certain types of limited "dialog" overlays, which can contain inner widgets and display overtop of the rest of the webpage, but do not support the same type of arbitrary positioning necessary for some @@ -570,7 +570,7 @@ here --> These approaches are generally easier to design and work with, and abstract away or entirely avoid some of the messier CSS layout rules required to render HTML elements that do not follow an ordered top-to-bottom/left-to-right flow. -Fundamentally, to enable this behavior, we implement +Fundamentally, to enable overlay layouts, we implement custom IPyWidget containers that use specific CSS rules to control for nonlinear layouts, namely relying on absolute positioning (by setting `position: absolute`), and each child widget setting `left` and `top` CSS @@ -701,9 +701,9 @@ the container. Then, listening for any events that indicate an axis range has changed, handler functions can recalculate pixel locations based on the new relative position of the x/y data point within the new range. -Unfortunately, in practice the functions necessary to do this for different -plotting libraries can vary substantially as a result of implementation differences, and -at the time of this writing, IPyOverlay only supports autoupdating connections for +Unfortunately, in practice, the functions necessary to do this for different +plotting libraries can vary substantially as a result of implementation differences. +At the time of this writing, IPyOverlay only supports autoupdating connections for dynamic content inside of ipympl (Matplotlib) and plotly plots. The authors intend to continue expanding the set of supported visualization libraries for this particular feature. @@ -713,7 +713,7 @@ this particular feature. ### Context Menus -A common feature in UI design across most platforms is the context menu. +A common form of details-on-demand feature in UI design across most platforms is the context menu. Generally initiated from a right-click in desktop operating systems and applications, or tapping and holding on a mobile device, context menus provide a localized and easy to access palette of possible commands to run at the @@ -721,7 +721,7 @@ current location. Complex dashboards can often have many different possible actions for the user to take, and simply displaying all of these as buttons may overwhelm or clutter the interface. Alternately, the actions may be too component/position specific to work well as buttons. Context menus fill this role by hiding these commands until -the user requests them by right-clicking (another form of details on demand) and supporting different menus when +the user requests them by right-clicking and supporting different menus when over different components. Displaying a context menu similarly requires rendering some form of widget over another -- IPyOverlay includes a @@ -804,7 +804,7 @@ additional data associated with each clicked point in a bar chart. The prevalence and utility of Jupyter notebooks in data analysis and scientific workflows has prompted the development of a community of tools for interactive -visualization. IPyWidgets, one of the main drivers and the backbone that +visualization. IPyWidgets, the backbone that supports much of this ecosystem, provides the mechanisms for the kernel to mediate communication between Python and JavaScript. A multitude of libraries have built on top of these mechanisms and IPyVuetify in particular offers a From ab29a7b403fcc2ddd4ccbb4e5d5a3d23af626680 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Fri, 2 Aug 2024 10:00:19 -0400 Subject: [PATCH 15/19] Ignore missing DOI --- papers/nathan_martindale/main.md | 4 ++-- papers/nathan_martindale/myst.yml | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md index ad335e9625..67da206920 100644 --- a/papers/nathan_martindale/main.md +++ b/papers/nathan_martindale/main.md @@ -338,8 +338,8 @@ and JavaScript view. IPyWidgets as a low-level widget framework has allowed an ecosystem of other libraries and tools to develop on top of it, dramatically pushing the boundary of what can be constructed and incorporated into interactive dashboards with -relative ease. From general plotting tools like interactive Matplotlib -[@10.5281/zenodo.10974323]), plotly [@plotly], mpld3 [@mpld3], altair +relative ease. From general plotting tools like interactive Matplotlib (ipympl) +[@10.5281/zenodo.10974323], plotly [@plotly], mpld3 [@mpld3], altair [@altair], and bqplot [@bqplot], to more complex or specialized visualization widgets like pythreejs [@pythreejs] and ipyvolume [@10.5281/zenodo.1286976] for 3D rendering, ipyleaflet [@ipyleaflet] for interactive maps, and ipycytoscape diff --git a/papers/nathan_martindale/myst.yml b/papers/nathan_martindale/myst.yml index 0b11a2412c..e690e91e9c 100644 --- a/papers/nathan_martindale/myst.yml +++ b/papers/nathan_martindale/myst.yml @@ -58,6 +58,7 @@ project: - barlowtwins - jupyter - materialdesign + - Ester1996ADA - rule: link-resolves severity: ignore keys: From ed7aa6db1afa403338fc1e645345a6dabe6dad0f Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Mon, 5 Aug 2024 13:31:17 -0400 Subject: [PATCH 16/19] Modify names in program 2 --- papers/nathan_martindale/main.md | 34 ++++++++++++++++---------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md index 67da206920..83d4fda7b9 100644 --- a/papers/nathan_martindale/main.md +++ b/papers/nathan_martindale/main.md @@ -166,35 +166,35 @@ import ipywidgets as ipw graph1_out = ipw.Output() graph2_out = ipw.Output() -parameter1 = ipw.FloatSlider(min=0, max=10, value=1) -parameter2 = ipw.IntText(value=1) -parameter3 = ipw.IntSlider(min=0, max=10, value=1) +graph1_param = ipw.FloatSlider(min=0, max=10, value=1) # a parameter only influencing graph 1 +graph2_param = ipw.IntSlider(min=0, max=10, value=1) # a parameter only influencing graph 2 +both_graphs_param = ipw.IntText(value=1) # a parameter that affects both graphs # create the dashboard layout, combining all the widgets dashboard = ipw.VBox([ ipw.HBox([graph1_out, graph2_out]), - ipw.HBox([parameter1, parameter2, parameter3]), + ipw.HBox([graph1_param, both_graphs_param, graph2_param]), ]) # event handler functions -def on_parameter1_change(change): +def on_graph1_param_change(change): with graph1_out: - print(f"parameter 1 is {change['new']} and parameter 2 is {parameter2.value}.") - -def on_parameter2_change(change): - with graph1_out: - print(f"parameter 1 is {parameter1.value} and parameter 2 is {change['new']}.") + print(f"Updating graph 1 based on: graph1_param={change['new']}, {both_graphs_param.value=}") + +def on_graph2_param_change(change): with graph2_out: - print(f"parameter 2 is {change['new']} and parameter 3 is {parameter3.value}.") - -def on_parameter3_change(change): + print(f"Updating graph 2 based on: graph2_param={change['new']}, {both_graphs_param.value=}") + +def on_both_graphs_param_change(change): + with graph1_out: + print(f"Updating graph 1 based on: {graph1_param.value=}, both_graphs_param={change['new']}") with graph2_out: - print(f"parameter 2 is {parameter2.value} and parameter 3 is {change['new']}.") + print(f"Updating graph 2 based on: {graph2_param.value=}, both_graphs_param={change['new']}") # attach event handlers -parameter1.observe(on_parameter1_change, names=["value"]) -parameter2.observe(on_parameter2_change, names=["value"]) -parameter3.observe(on_parameter3_change, names=["value"]) +graph1_param.observe(on_graph1_param_change, names=["value"]) +graph2_param.observe(on_graph2_param_change, names=["value"]) +both_graphs_param.observe(on_both_graphs_param_change, names=["value"]) # render the dashboard dashboard From a6cb1c227a48df86bab64f0d22f4ba4d9a72e9cc Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Mon, 5 Aug 2024 13:48:58 -0400 Subject: [PATCH 17/19] Manually fix ipympl citation --- papers/nathan_martindale/main.md | 2 +- papers/nathan_martindale/references.bib | 38 +++++++++++++++++++++++++ 2 files changed, 39 insertions(+), 1 deletion(-) diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md index 83d4fda7b9..8f349d97a2 100644 --- a/papers/nathan_martindale/main.md +++ b/papers/nathan_martindale/main.md @@ -339,7 +339,7 @@ IPyWidgets as a low-level widget framework has allowed an ecosystem of other libraries and tools to develop on top of it, dramatically pushing the boundary of what can be constructed and incorporated into interactive dashboards with relative ease. From general plotting tools like interactive Matplotlib (ipympl) -[@10.5281/zenodo.10974323], plotly [@plotly], mpld3 [@mpld3], altair +[@ipympl], plotly [@plotly], mpld3 [@mpld3], altair [@altair], and bqplot [@bqplot], to more complex or specialized visualization widgets like pythreejs [@pythreejs] and ipyvolume [@10.5281/zenodo.1286976] for 3D rendering, ipyleaflet [@ipyleaflet] for interactive maps, and ipycytoscape diff --git a/papers/nathan_martindale/references.bib b/papers/nathan_martindale/references.bib index 402b5ec446..a252e81fe7 100644 --- a/papers/nathan_martindale/references.bib +++ b/papers/nathan_martindale/references.bib @@ -235,3 +235,41 @@ @inproceedings{Ester1996ADA year={1996}, url={https://api.semanticscholar.org/CorpusID:355163} } + +@software{ipympl, + author = {Martin Renou and + Sylvain Corlay and + Ian Hunt-Isaak and + Steven Silvester and + Kyle Boone and + Ian Thomas and + Thomas A Caswell and + Elliott Sales de Andrade and + psteegstra and + Jens Hedegaard Nielsen and + Antony Lee and + Yvonne Fröhlich and + stonebig and + Todd and + Tim Paine and + Thomas Aarholt and + Rich Lewis and + Phil Elson and + Nathan Dunfield and + Mo-Gul and + Matt McCormick and + Lento Manickathan and + Jason Grout and + Guokai Liu and + Greg Lucas and + Fabian P. Schmidt and + Erik Sundell}, + title = {matplotlib/ipympl: Release 0.9.4}, + month = apr, + year = 2024, + publisher = {Zenodo}, + version = {0.9.4}, + doi = {10.5281/zenodo.10974323}, + url = {https://doi.org/10.5281/zenodo.10974323} +} + From b41f33ec448a2dd0a75d734be36f27bf2ed18cb9 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Mon, 5 Aug 2024 15:53:17 -0400 Subject: [PATCH 18/19] Update figure 4 caption --- papers/nathan_martindale/main.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/papers/nathan_martindale/main.md b/papers/nathan_martindale/main.md index 8f349d97a2..4332d8825e 100644 --- a/papers/nathan_martindale/main.md +++ b/papers/nathan_martindale/main.md @@ -413,9 +413,9 @@ steppers. :::{figure} ipw_ipv.png :label: fig:ipw_ipv -A comparison of a few IPyWidget components on the upper left and their corresponding +A visual comparison of a few IPyWidget components on the upper left and their corresponding IPyVuetify components on the upper right. The table on the bottom is a -DataTable component, which does not have a default IPyWidgets equivalent. +DataTable component, an example of a widget which does not have a default IPyWidgets equivalent. ::: Finally, the Python API for every IPyVuetify component includes the ability to set CSS From e814dabf3fdda2997577368359995abf1c8bae59 Mon Sep 17 00:00:00 2001 From: "Martindale, Nathan" Date: Wed, 7 Aug 2024 17:08:36 -0400 Subject: [PATCH 19/19] Update self-referential details on demand example (figure 1) to reflect final text --- papers/nathan_martindale/detailsondemand.png | Bin 16999 -> 15633 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/papers/nathan_martindale/detailsondemand.png b/papers/nathan_martindale/detailsondemand.png index db2a2fe466c36bcdc05d5356fa43e160f7b13d7e..193373bf77ee8409cc05e386ca795e6b738b2161 100644 GIT binary patch literal 15633 zcmb_@WmH^E(B@!4gA)ku3?X=McMER8A-KD{6WrYi?(PuWg1fuB>kK=2_uHR)&i>dr 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