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Pure Data: Language features, Similarities to Max & Overview
Pure Data (Pd) is a visual programming language developed by Miller Puckette in the 1990s for creating interactive computer music and multimedia works. While Puckette is the main author of the program, Pd is an open-source project with a large developer base working on new extensions. It is released under BSD-3-Clause. It runs on Linux, MacOS, iOS…
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Pure Data topic overview
The analysis highlights Language features, Similarities to Max and Overview as prominent areas in the source structure around Pure Data.
Related topics
61
Source areas
5
Connected nodes
66
Extracted relationships
93
Concept neighborhoods
26
Bridge connections
66
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Overview · 22 topics
Language features · 17 topics
Similarities to Max · 16 topics
Projects using Pure Data · 4 topics
Code examples · 2 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
License
BSD-3-Clause
Original author
Miller Puckette
Repository
github.com/pure-data/pure-data
Stable release
0.55-2 / November 18, 2024; 21 months ago (2024-11-18)
Type
Visual programming language
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Pure Data connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Pure Data shows recurring relationship patterns in the source. For example, Pure Data → Android, Andy, Barkl, Brinkmann, Composition, Compositions Volume, Designing Sound, Electronic Music, Farnell, Farsi, Habibdoost, Hofheim, ISBN, Johannes, Kreidler, Loadbang, Making Musical Apps, Mansoor, Matsumura, Meta-Compositional Instrument Another extracted example is Pure Data → API, Ariel ISPW, CPU, Dataflow, David Zicarelli's MSP, DSP, However, In Pure Data, Like Max, Lua, Max, Max/FTS, Modular, Pd, Python, Scheme, Tcl, This, Unlike. Use these groups to spot repeated connection types before inspecting the individual relationships.
Pure Data
Top relations
related to Further reading· 39
Pure Data → Android, Andy, Barkl, Brinkmann, Composition, Compositions Volume, Designing Sound, Electronic Music, Farnell, Farsi, Habibdoost, Hofheim, ISBN, Johannes, Kreidler, Loadbang, Making Musical Apps, Mansoor, Matsumura, Meta-Compositional Instrument
related to Similarities to Max· 19
Pure Data → API, Ariel ISPW, CPU, Dataflow, David Zicarelli's MSP, DSP, However, In Pure Data, Like Max, Lua, Max, Max/FTS, Modular, Pd, Python, Scheme, Tcl, This, Unlike
related to References· 11
Pure Data → Danks, Gem, In, International Computer Music Association, International Computer Music Conference, Proceedings, Puckette, Pure, Real-time, San Francisco, The
related to Projects using Pure Data· 10
Pure Data → EA, EA Spore, EAPd, Electronic Arts, For, It, Pd, Reactable, RjDj, The
related to Code examples· 8
Pure Data → Filters, Hello, Hz, In Pd, Patch, Pd, Reverberation, The
License· 1
Pure Data → BSD-3-Clause
Original author· 1
Pure Data → Miller Puckette
Repository· 1
Pure Data → github.com/pure-data/pure-data
Stable release· 1
Pure Data → 0.55-2 / November 18, 2024; 21 months ago (2024-11-18)
Type· 1
Pure Data → Visual programming language
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Pure Data relationships Subject–Predicate–Object triples
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 93 structured relationships around Pure Data. Examples in this analysis include Pure Data → License → BSD-3-Clause and Pure Data → Original author → Miller Puckette. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Pure Data
License
BSD-3-Clause
1.00
infobox
Pure Data
Original author
Miller Puckette
1.00
infobox
Pure Data
Repository
github.com/pure-data/pure-data
1.00
infobox
Pure Data
Stable release
0.55-2 / November 18, 2024; 21 months ago (2024-11-18)
1.00
infobox
Pure Data
Type
Visual programming language
1.00
infobox
Pure Data
Website
puredata.info
1.00
infobox
Pure Data
related to Code examples
Hello
0.60
section
Pure Data
related to Code examples
Pd
0.60
section
Pure Data
related to Code examples
Patch
0.60
section
Pure Data
related to Code examples
Reverberation
0.60
section
Pure Data
related to Code examples
Filters
0.60
section
Pure Data
related to Code examples
The
0.60
section
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Pure Data bring nearby vocabulary together. In this analysis, examples include Pure, Pd and Visual. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Pure Data
Pure
Pd
Visual
Graphical
Programming
Control
Language
Like
Multimedia
Miller
Used
Code
pure data
Pure
Pd
Visual
Graphical
Language
Structures
Audio
Programming
Control
Like
Multimedia
Miller
visual programming language
Language
Programming
Miller
Code
Patches
Max
Puckette
Bsd-3-clause
Patch
Pd
Pure
Also
max
Original
Software
Code
Environment
Audio
Processing
Like
Objects
Pd
Programming
Used
Bsd-3-clause
pure data packet
Pure
Pd
Visual
Graphical
Language
Structures
Audio
Programming
Control
Like
Multimedia
Miller
audio processing
Control
Max
Processing
Software
Environment
Used
Windows
Multimedia
Data
Designed
Flow
Messages
sample (audio) rate
Control
Max
Processing
Software
Environment
Used
Multimedia
Data
Designed
Flow
Messages
Sound
data structures
Graphical
Pure
Pd
Language
Structures
Visual
Audio
Control
Like
Programming
Used
Flow
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Pure Data, one of the stronger structural bridges in this analysis connects Pure Data with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Min side: 3
Pure Data — Overview· splits 44 ⟂ 23Pure Data — Language features· splits 49 ⟂ 18Pure Data — Similarities to Max· splits 50 ⟂ 17Pure Data — Projects using Pure Data· splits 62 ⟂ 5Pure Data — Code examples· splits 64 ⟂ 3
Map overview Semantic statistics
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Pure Data
Nodes67
Edges66
Triples93
Avg. degree1.97
Density0.029851
Components1
Source & methodology
TTTA analyzes the structure around Pure Data to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Language features, Similarities to Max & Overview, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
