JetBrains/lets-plot
Dec 2025 – Apr 2026
5 Months active
Languages Used
JavaScriptKotlinPythonMarkdownJSONCSSHTML
Technical Skills
Kotlindata visualizationfrontend developmentnotebook developmentplotting librariesstatistical analysis
Ivan Seleznev
PROFILE
Ivan Seleznev
Over five months, Horis Iseleznev contributed to JetBrains/lets-plot by engineering robust enhancements to the plotting and statistical analysis workflows. He developed new features such as advanced regression statistics, interactive plotting elements, and improved missing-value handling, focusing on API clarity and user experience. Using Kotlin, Python, and JavaScript, Horis refactored core components to standardize parameter behavior, streamline data cleaning, and extend time formatting utilities. His work addressed both feature expansion and technical debt, with careful attention to documentation and test coverage. The resulting improvements increased reliability, maintainability, and usability for analysts and developers working with complex data visualizations.
Overall Statistics
Feature vs Bugs
79%Features
Repository Contributions
56Total
Bugs
5
Commits
56
Features
19
Lines of code
81,023
Activity Months5
Work History
April 2026
15 Commits • 3 Features
Apr 1, 2026Monthly summary for 2026-04 (JetBrains/lets-plot) Overview: Focused on stabilizing API behavior around missing values, enhancing plotting UX, and extending time/date utilities. Delivered across the lets-plot repository with a clear emphasis on business value: predictable data cleanliness semantics, improved interactivity, and richer time formatting for dashboards. Key features delivered: - Missing value handling and messaging improvements across statistical and plotting APIs: introduced na_rm across geoms and statistical functions, standardized default behavior to False, consolidated non-finite value removal messaging, and removed obsolete messages. This unifies behavior for users with cleaner, more predictable plots and stats. - Commits illustrating this work include: 3562e6ed2a41539ae8c0bf46cd261906515021b5 (Add na_rm parameter to expected_y_stat for point statistics in marginal layer tests), c2db340c6c14f87ef6aa05dd7686a2e73d11ae83 (Add non-finite value removal messages to various statistical computations), 539f5a1b9d9ba123262ece3213edef8ba578fb77 (Add na_rm parameter to geoms), 0180711b8095f0a9556c176a40bb82c582a7c90b (Add na_rm parameter to statistical functions), b68d8876d185f6dff63bae07618e795a77f8ac2f (Add na_rm option to suppress computation messages in statistical layers), c158147c985175dec5e6e9149e2eadebf2db1c5d (Set default value of na_rm parameter to False), cf028ed01c3e606161ff7ccf3da8e5ab3a18a29f (Remove na messages from SmoothStatSummary), 2697f11c7e8f57266572a3eb403e690a29e1e5bc (Remove unused parameters from smooth_summary function in geom.py), b07a129013ea60e5fa1880f7aa97414c66946823 (Refactor dropped points messages to use "row" instead of "data point" for consistency). - Plotting enhancements: dropped points reporting, interactivity, and plot clarity: improved interpretability and user engagement for plots. - Commits include: 36343ba682af5c52adf0166767e87a7e2f8e917d (Dropped points (#1475)), fa74bc79345a5c77f17c69b23a246a060b928c01 (Add interactive to geom_livemap()), 7c9e2c00ba39109935b07a6a82ba26ab8a9f78a5 (Hide legend for smooth_summary). - Time/date utilities: milliseconds formatting support: added patterns and tests/docs to support formatting milliseconds in DateTime formats. - Commits include: 7b48332a5361bd2e3384e82afde0274e30bf09aa (Add milliseconds support to DateTimeFormat (#1482)), d73cfe664a6a4dcc3d06aea16a59a20a2d24b309 (Update future_changes.md), 48a7b67574225faa7b88df8d73b4b0f51e5f6d3a (Update future_changes.md). Major bugs fixed (stability and clarity): - Removed legacy/obsolete messaging in SmoothStatSummary and standardized messaging to reference "row" instead of "data point" for consistency, reducing user confusion. - Introduced non-finite value removal messages across statistical computations to improve visibility into data-cleaning decisions. - Fixed API surface by setting default na_rm to False and removing unused parameters from smooth_summary, reducing potential misconfiguration and confusion. Overall impact and accomplishments: - Increased reliability and predictability in missing-value handling and data cleaning flows, enabling analysts to trust plotting and statistics when dealing with incomplete data. - Enhanced user experience through clearer messaging, improved plot interactivity, and better dashboard-ready time formatting. - Stabilized APIs and documentation, lowering maintenance costs and accelerating onboarding for new users. Technologies and skills demonstrated: - API design and stabilization: parameterization of na_rm, default value choices, and messaging consistency. - UX-focused plotting improvements: dropped-points reporting, interactive livemap integration, and legend controls. - Time/date utilities extension: millisecond formatting support with tests/docs. - Test coverage and documentation updates: future_changes.md updates and broader test scenarios.
15 Commits • 3 Features
Apr 1, 2026Monthly summary for 2026-04 (JetBrains/lets-plot) Overview: Focused on stabilizing API behavior around missing values, enhancing plotting UX, and extending time/date utilities. Delivered across the lets-plot repository with a clear emphasis on business value: predictable data cleanliness semantics, improved interactivity, and richer time formatting for dashboards. Key features delivered: - Missing value handling and messaging improvements across statistical and plotting APIs: introduced na_rm across geoms and statistical functions, standardized default behavior to False, consolidated non-finite value removal messaging, and removed obsolete messages. This unifies behavior for users with cleaner, more predictable plots and stats. - Commits illustrating this work include: 3562e6ed2a41539ae8c0bf46cd261906515021b5 (Add na_rm parameter to expected_y_stat for point statistics in marginal layer tests), c2db340c6c14f87ef6aa05dd7686a2e73d11ae83 (Add non-finite value removal messages to various statistical computations), 539f5a1b9d9ba123262ece3213edef8ba578fb77 (Add na_rm parameter to geoms), 0180711b8095f0a9556c176a40bb82c582a7c90b (Add na_rm parameter to statistical functions), b68d8876d185f6dff63bae07618e795a77f8ac2f (Add na_rm option to suppress computation messages in statistical layers), c158147c985175dec5e6e9149e2eadebf2db1c5d (Set default value of na_rm parameter to False), cf028ed01c3e606161ff7ccf3da8e5ab3a18a29f (Remove na messages from SmoothStatSummary), 2697f11c7e8f57266572a3eb403e690a29e1e5bc (Remove unused parameters from smooth_summary function in geom.py), b07a129013ea60e5fa1880f7aa97414c66946823 (Refactor dropped points messages to use "row" instead of "data point" for consistency). - Plotting enhancements: dropped points reporting, interactivity, and plot clarity: improved interpretability and user engagement for plots. - Commits include: 36343ba682af5c52adf0166767e87a7e2f8e917d (Dropped points (#1475)), fa74bc79345a5c77f17c69b23a246a060b928c01 (Add interactive to geom_livemap()), 7c9e2c00ba39109935b07a6a82ba26ab8a9f78a5 (Hide legend for smooth_summary). - Time/date utilities: milliseconds formatting support: added patterns and tests/docs to support formatting milliseconds in DateTime formats. - Commits include: 7b48332a5361bd2e3384e82afde0274e30bf09aa (Add milliseconds support to DateTimeFormat (#1482)), d73cfe664a6a4dcc3d06aea16a59a20a2d24b309 (Update future_changes.md), 48a7b67574225faa7b88df8d73b4b0f51e5f6d3a (Update future_changes.md). Major bugs fixed (stability and clarity): - Removed legacy/obsolete messaging in SmoothStatSummary and standardized messaging to reference "row" instead of "data point" for consistency, reducing user confusion. - Introduced non-finite value removal messages across statistical computations to improve visibility into data-cleaning decisions. - Fixed API surface by setting default na_rm to False and removing unused parameters from smooth_summary, reducing potential misconfiguration and confusion. Overall impact and accomplishments: - Increased reliability and predictability in missing-value handling and data cleaning flows, enabling analysts to trust plotting and statistics when dealing with incomplete data. - Enhanced user experience through clearer messaging, improved plot interactivity, and better dashboard-ready time formatting. - Stabilized APIs and documentation, lowering maintenance costs and accelerating onboarding for new users. Technologies and skills demonstrated: - API design and stabilization: parameterization of na_rm, default value choices, and messaging consistency. - UX-focused plotting improvements: dropped-points reporting, interactive livemap integration, and legend controls. - Time/date utilities extension: millisecond formatting support with tests/docs. - Test coverage and documentation updates: future_changes.md updates and broader test scenarios.
April 2026
March 2026
8 Commits • 4 Features
Mar 1, 2026March 2026 monthly performance summary for JetBrains/lets-plot focused on delivering business value through robust regression analytics, plotting reliability, API clarity, and maintainability. The month centered on shipping high-impact features, addressing key plotting and API bugs, and reducing technical debt, with strong emphasis on measurable outcomes and cross-team learnings.
March 2026
8 Commits • 4 Features
Mar 1, 2026March 2026 monthly performance summary for JetBrains/lets-plot focused on delivering business value through robust regression analytics, plotting reliability, API clarity, and maintainability. The month centered on shipping high-impact features, addressing key plotting and API bugs, and reducing technical debt, with strong emphasis on measurable outcomes and cross-team learnings.
February 2026
24 Commits • 7 Features
Feb 1, 2026February 2026 performance summary for JetBrains/lets-plot: Delivered API refinements and stability improvements enabling more reliable and expressive plotting. Major work centered on smooth_labels refactor, Eq syntax overhaul, and integration polish with ggtb/gggrid, complemented by focused maintenance and documentation updates. Impact: more reliable smoothing workflows, fewer runtime/import issues, and clearer business value for downstream users.
24 Commits • 7 Features
Feb 1, 2026February 2026 performance summary for JetBrains/lets-plot: Delivered API refinements and stability improvements enabling more reliable and expressive plotting. Major work centered on smooth_labels refactor, Eq syntax overhaul, and integration polish with ggtb/gggrid, complemented by focused maintenance and documentation updates. Impact: more reliable smoothing workflows, fewer runtime/import issues, and clearer business value for downstream users.
February 2026
January 2026
7 Commits • 3 Features
Jan 1, 2026January 2026 monthly summary for JetBrains/lets-plot. Delivered significant plotting enhancements and reliability improvements that broaden API usability, improve data input, and enhance model evaluation visuals. Key outcomes include new smooth labels features, flexible coefficient input representations, enhanced regression statistics, and a fix to the smooth labels JS embedding to preserve visual integrity.
January 2026
7 Commits • 3 Features
Jan 1, 2026January 2026 monthly summary for JetBrains/lets-plot. Delivered significant plotting enhancements and reliability improvements that broaden API usability, improve data input, and enhance model evaluation visuals. Key outcomes include new smooth labels features, flexible coefficient input representations, enhanced regression statistics, and a fix to the smooth labels JS embedding to preserve visual integrity.
December 2025
2 Commits • 2 Features
Dec 1, 2025December 2025 monthly summary for JetBrains/lets-plot. Focused on delivering two high-impact features that enhance user experience and extend plotting capabilities, while maintaining stability and setting the stage for broader analytics tooling.
2 Commits • 2 Features
Dec 1, 2025December 2025 monthly summary for JetBrains/lets-plot. Focused on delivering two high-impact features that enhance user experience and extend plotting capabilities, while maintaining stability and setting the stage for broader analytics tooling.
December 2025
Activity
Loading activity data...
Quality Metrics
Correctness92.8%
Maintainability90.4%
Architecture90.8%
Performance90.8%
AI Usage27.4%
Skills & Technologies
Programming Languages
CSSHTMLJSONJavaScriptKotlinMarkdownPython
Technical Skills
API developmentCSSCode RefactoringCode refactoringData AnalysisData VisualizationData visualizationDocumentationGeometric Data HandlingHTMLIPythonJavaScriptJupyter NotebookJupyter NotebooksJupyter notebooks
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline