2026-03-05 v0.0.17 — HTML dashboard generator, viz module polish

Author: eddmpython

API_SPEC.md

@@ -731,6 +731,28 @@ analysisReport(
 ) -> go.Figure               # 2x2: DNA radar (top-left) + feature bars (top-right) + difficulty indicator (bottom)
 ```
 
+### HTML Dashboard
+
+```python
+from vectrix.viz import dashboard
+
+dashboard(
+    forecast=None,           # EasyForecastResult | None
+    analysis=None,           # EasyAnalysisResult | None
+    comparison=None,         # pd.DataFrame | None — from compare()
+    historical=None,         # pd.DataFrame | None
+    title="Vectrix Report",  # str — custom report title
+    theme="dark"             # str — 'dark' or 'light'
+) -> DashboardResult         # Self-contained HTML report
+```
+
+**DashboardResult methods:**
+- `.show()` — display inline (Jupyter) or open browser (terminal)
+- `.save(path)` — save to HTML file
+- `.html` — raw HTML string
+
+**Report sections:** Overview KPIs → Data Profile (DNA) → Forecast Results (metrics + model comparison) → Visualizations (forecast chart + DNA radar)
+
 ### Theme Utilities
 
 ```python

CHANGELOG.md

@@ -5,6 +5,35 @@ All notable changes to Vectrix will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.0.17] - 2026-03-05
+
+HTML dashboard generator and visualization module polish.
+
+### Added
+
+**HTML Dashboard (`vectrix.viz.dashboard`)**
+- `dashboard()` function generates self-contained HTML reports with embedded Plotly charts
+- shadcn/ui dark theme design tokens — 3-layer brightness, transparency borders, monochrome palette
+- Report narrative flow: Overview → Data Profile → Forecast Results → Visualizations
+- Vectrix brand icon (base64 embedded) and gradient wordmark in header
+- Custom title support via `title` parameter
+- `DashboardResult` class with `.show()` (Jupyter inline / browser), `.save(path)`, `.html`
+- Terminal progress feedback via stderr during dashboard generation
+- Data Profile section with DNA feature bars, descriptive statistics, key insights, recommended models
+- Forecast Results section with accuracy KPIs and integrated model comparison table
+- Partial report support — any combination of forecast/analysis/comparison/historical
+
+### Changed
+
+- `theme.py`: HEATMAP_COLORSCALE updated to 5-step monochrome (dark green → teal → cyan → violet → dark purple)
+- Removed unused imports across viz module (COLORS in charts.py, report.py, dashboard.py)
+- Fixed extraneous f-string prefixes in report.py
+
+### Documentation
+
+- `07_visualization.md`: Added HTML Dashboard section with basic usage, custom title, save, partial reports, terminal progress
+- `API_SPEC.md`: Added `dashboard()` function signature, `DashboardResult` methods, report structure
+
 ## [0.0.16] - 2026-03-05
 
 Code quality and architecture cleanup release — eliminates MODEL_INFO indirection layer, enforces registry as single source of truth for all model metadata, fixes ETSModel refit bug, and adds refit contract tests.

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectrix"
-version = "0.0.16"
+version = "0.0.17"
 edition = "2021"
 license-file = "LICENSE"
 

constants.json

@@ -1,5 +1,5 @@
 {
-  "version": "0.0.16",
+  "version": "0.0.17",
   "rustFunctions": 29,
   "testCount": 603,
   "modelCount": "30+",

docs/overrides/home.html

@@ -11,7 +11,7 @@
   <section class="vx-hero">
     <div class="vx-hero-badge">
       <svg width="14" height="14" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2"><polygon points="13 2 3 14 12 14 11 22 21 10 12 10 13 2"></polygon></svg>
-      v0.0.16 — Built-in Rust Engine
+      v0.0.17 — Built-in Rust Engine
     </div>
     <h1 class="vx-hero-title">
       Time Series Forecasting<br>

docs/tutorials/07_visualization.md

@@ -340,9 +340,92 @@ fig.write_json("forecast.json")
 
 **Note:** `write_image()` requires the `kaleido` package: `pip install kaleido`.
 
-## Complete Dashboard Example
+## HTML Dashboard
 
-Build a full analysis dashboard by combining multiple charts:
+The `dashboard()` function generates a self-contained HTML report that combines all analysis into a single page — data profile, forecast results, model comparison, and interactive charts. No Plotly CDN dependency required in the output.
+
+### Basic Usage
+
+```python
+from vectrix import forecast, analyze, compare, loadSample
+from vectrix.viz import dashboard
+
+df = loadSample("airline")
+
+result = forecast(df, steps=12)
+analysis = analyze(df)
+comparison = compare(df, steps=12)
+
+report = dashboard(
+    forecast=result,
+    analysis=analysis,
+    comparison=comparison,
+    historical=df,
+)
+report.show()  # Opens in browser (terminal) or displays inline (Jupyter)
+```
+
+### Custom Title
+
+```python
+report = dashboard(
+    forecast=result,
+    analysis=analysis,
+    comparison=comparison,
+    historical=df,
+    title="Airline Passengers — Monthly Forecast",
+)
+report.show()
+```
+
+### Save to File
+
+```python
+report.save("forecast_report.html")
+```
+
+The HTML file is fully self-contained (embedded Plotly JS + inline CSS). Share it by email, upload to a web server, or open locally in any browser.
+
+### Report Sections
+
+The dashboard follows a narrative flow:
+
+1. **Overview** — observation count, frequency, difficulty score, forecast horizon
+2. **Data Profile** — DNA feature bars, descriptive statistics, key insights, recommended models
+3. **Forecast Results** — accuracy KPIs (MAPE, RMSE, MAE, sMAPE), forecast details, model comparison table
+4. **Visualizations** — interactive forecast chart with confidence intervals, DNA radar chart
+
+### Partial Reports
+
+Every parameter is optional. Generate a profile-only report (no forecast):
+
+```python
+report = dashboard(analysis=analysis, historical=df, title="Data Profile Report")
+report.save("profile.html")
+```
+
+Or a forecast-only report (no DNA analysis):
+
+```python
+report = dashboard(forecast=result, historical=df, title="Forecast Report")
+report.save("forecast.html")
+```
+
+### Terminal Progress
+
+When running from a terminal (not Jupyter), the dashboard builder prints progress to stderr:
+
+```
+[vectrix] Building header... (0.0s)
+[vectrix] Analyzing data profile... (0.0s)
+[vectrix] Computing performance metrics... (0.0s)
+[vectrix] Rendering charts... (0.2s)
+[vectrix] Dashboard ready (0.3s, 48,244 bytes)
+```
+
+## Combining Individual Charts
+
+For maximum flexibility, build your own dashboards by combining individual chart functions. Each returns a standard Plotly `go.Figure`:
 
 ```python
 from vectrix import forecast, analyze, compare, loadSample

landing/src/lib/components/sections/Hero.svelte

@@ -15,7 +15,7 @@
 	<div class="relative z-10 max-w-4xl mx-auto">
 		<Badge class="mb-6">
 			<Zap class="w-3.5 h-3.5" />
-			v0.0.16 — Built-in Rust Engine
+			v0.0.17 — Built-in Rust Engine
 		</Badge>
 
 		<h1 class="text-5xl md:text-7xl font-extrabold tracking-tight leading-[1.1] mb-4">

landing/src/routes/+page.svelte

@@ -41,7 +41,7 @@
 		"description": "Zero-config time series forecasting for Python. 30+ models, one line of code, built-in Rust engine.",
 		"url": "https://eddmpython.github.io/vectrix/",
 		"downloadUrl": "https://pypi.org/project/vectrix/",
-		"softwareVersion": "0.0.16",
+		"softwareVersion": "0.0.17",
 		"author": { "@type": "Person", "name": "eddmpython", "url": "https://github.com/eddmpython" },
 		"offers": { "@type": "Offer", "price": "0", "priceCurrency": "USD" },
 		"license": "https://opensource.org/licenses/MIT",

landing/static/llms-full.txt

@@ -4,7 +4,7 @@
 
 Vectrix is a Python library for time series forecasting. It evaluates 30+ statistical models (ETS, ARIMA, Theta, CES, DOT, MSTL, TBATS, GARCH, and more), validates each on a holdout set, and returns the best one with 95% confidence intervals — in a single function call.
 
-- Version: 0.0.16
+- Version: 0.0.17
 - License: MIT
 - Python: 3.10+
 - Dependencies: NumPy, pandas, SciPy (only 3)

llms-full.txt

@@ -2,7 +2,7 @@
 
 > Zero-config time series forecasting library for Python. Automatic model selection with built-in Rust engine (29 accelerated functions), adaptive intelligence, full regression suite, and business analytics.
 
-- Version: 0.0.16
+- Version: 0.0.17
 - Python: >=3.10
 - Core deps: numpy>=1.24, pandas>=2.0, scipy>=1.10
 - Install: `pip install vectrix`