dot stuff

- Fixed HEIGHT from 480 to 640 to match actual videotestsrc output
- Added DEBUG flag to control debug output visibility
- Added cv2.namedWindow() for proper window initialization
- Updated all Python script references in markdown files to scripts/ folder
- Updated network_guide.md with correct frame dimensions and Python receiver option

.gitignore

@@ -38,3 +38,6 @@ ipch/
 
 *.mkv
 *.raw
+*.dot
+gst_plugs/
+results/

DESIGN_ROLLINGSUM.md

@@ -1,290 +0,0 @@
-# Rolling Sum Filter - Design Document
-
-## Overview
-
-A GStreamer element that drops frames based on rolling mean analysis of a single column of pixels. Inspired by the detection algorithm in `cli.py`, simplified for real-time streaming.
-
-## Element Name
-
-**`rollingsum`** - Transform element that analyzes pixel values and selectively drops frames
-
-## Purpose
-
-Monitor a vertical column of pixels in video frames, calculate the rolling mean over a time window, and drop frames when the current frame's column mean deviates significantly from the rolling mean baseline.
-
-## Architecture
-
-### Base Class
-- Inherits from `GstBaseTransform` (similar to [`select`](gst/select/gstselect.c))
-- In-place transform (analysis only, no frame modification)
-- Returns `GST_BASE_TRANSFORM_FLOW_DROPPED` to drop frames
-- Returns `GST_FLOW_OK` to pass frames
-
-### Element Structure
-
-```c
-struct _GstRollingSum
-{
-  GstBaseTransform element;
-  
-  /* Properties */
-  gint window_size;      // Number of frames in rolling window (default: 1000)
-  gint column_index;     // Which column to analyze (default: 1, second column)
-  gint stride;           // Row sampling stride (default: 1, every row)
-  gdouble threshold;     // Deviation threshold for dropping (default: 0.5)
-  
-  /* State */
-  gdouble *ring_buffer;  // Circular buffer of column means
-  gint ring_index;       // Current position in ring buffer
-  gint frame_count;      // Total frames processed
-  gdouble rolling_mean;  // Current rolling mean
-};
-```
-
-## Algorithm (Simplified from cli.py)
-
-### Per Frame Processing
-
-```
-1. Extract column data:
-   - Select column at column_index
-   - Sample every stride rows
-   - Calculate mean of sampled pixels: frame_mean
-   
-2. Update ring buffer:
-   - Store frame_mean in ring_buffer[ring_index]
-   - Increment ring_index (wrap around)
-   
-3. Calculate rolling mean:
-   - Sum values in ring buffer (up to window_size or frame_count)
-   - Divide by actual window size
-   
-4. Calculate deviation:
-   - deviation = abs(frame_mean - rolling_mean)
-   
-5. Decision:
-   - If deviation > threshold: DROP frame
-   - Else: PASS frame
-```
-
-### Key Simplifications from cli.py
-
-- **No EMA tracking**: Use simple rolling mean instead of exponential moving average
-- **No variance tracking**: Use fixed threshold instead of dynamic variance-based detection
-- **No recording logic**: Just drop/pass, no buffering for output segments
-- **No patience mechanism**: Immediate decision per frame
-
-## Properties
-
-| Property | Type | Default | Range | Description |
-|----------|------|---------|-------|-------------|
-| `window-size` | int | 1000 | 1-100000 | Number of frames in rolling window |
-| `column-index` | int | 1 | 0-width | Which vertical column to analyze |
-| `stride` | int | 1 | 1-height | Row sampling stride (1=all rows) |
-| `threshold` | double | 0.5 | 0.0-1.0 | Normalized deviation threshold for dropping |
-
-## Data Structures
-
-### Ring Buffer
-- Array of doubles sized to `window_size`
-- Stores column mean for each processed frame
-- Circular wrapping via modulo: `ring_index % window_size`
-
-### Frame Analysis
-```
-For column_index=1, stride=1, frame height=H:
-- Extract pixels: frame[:, column_index]
-- Sample: frame[::stride, column_index]
-- Count: H / stride samples
-- Mean: sum(samples) / count
-```
-
-## Implementation Files
-
-### Directory Structure
-```
-gst/rollingsum/
-├── CMakeLists.txt
-├── gstrollingsum.c
-└── gstrollingsum.h
-```
-
-### gstrollingsum.h
-- Element type definitions
-- Structure declarations
-- Property enums
-- Function prototypes
-
-### gstrollingsum.c
-- GObject methods (init, dispose, get/set properties)
-- GstBaseTransform methods (transform_ip)
-- Helper functions (extract_column_mean, update_rolling_mean)
-- Plugin registration
-
-### CMakeLists.txt
-- Build configuration (copy from [`gst/select/CMakeLists.txt`](gst/select/CMakeLists.txt))
-- Link GStreamer base and video libraries
-
-## Video Format Support
-
-### Initial Implementation
-- **Primary target**: Grayscale (GRAY8, GRAY16)
-- **Secondary**: Bayer formats (common in machine vision)
-
-### Caps Filter
-```c
-static GstStaticPadTemplate sink_template =
-  GST_STATIC_PAD_TEMPLATE ("sink",
-    GST_PAD_SINK,
-    GST_PAD_ALWAYS,
-    GST_STATIC_CAPS (
-      "video/x-raw, format=(string){GRAY8,GRAY16_LE,GRAY16_BE}; "
-      "video/x-bayer, format=(string){bggr,grbg,gbrg,rggb}"
-    )
-  );
-```
-
-## Pipeline Usage Examples
-
-### Basic Usage
-```bash
-gst-launch-1.0 idsueyesrc config-file=config.ini ! \
-    rollingsum window-size=1000 column-index=1 threshold=0.5 ! \
-    autovideosink
-```
-
-### Custom Configuration
-```bash
-gst-launch-1.0 idsueyesrc config-file=config.ini ! \
-    rollingsum window-size=5000 column-index=320 stride=2 threshold=0.3 ! \
-    queue ! \
-    autovideosink
-```
-
-### With Format Conversion
-```bash
-gst-launch-1.0 idsueyesrc ! \
-    videoconvert ! \
-    video/x-raw,format=GRAY8 ! \
-    rollingsum ! \
-    autovideosink
-```
-
-## Performance Considerations
-
-### Memory Usage
-- Ring buffer: `window_size * sizeof(double)` = ~8KB for default 1000 frames
-- Minimal per-frame allocation
-
-### CPU Usage
-- Column extraction: O(height/stride)
-- Rolling mean update: O(1) using incremental sum
-- Very lightweight compared to full-frame processing
-
-### Optimization Opportunities
-1. **Incremental mean**: Track sum instead of recalculating
-2. **SIMD**: Vectorize column summation
-3. **Skip calculation**: Only recalc every N frames if baseline is stable
-
-## Testing Strategy
-
-### Unit Tests
-- Ring buffer wrapping
-- Mean calculation accuracy
-- Threshold comparison logic
-
-### Integration Tests
-- Pipeline with videotestsrc
-- Pipeline with idsueyesrc
-- Frame drop verification
-- Property changes during playback
-
-### Test Cases
-1. Static video (all frames similar) → all pass
-2. Single bright frame → that frame drops
-3. Gradual change → frames pass
-4. Periodic pattern → pattern frames drop
-
-## Future Enhancements
-
-### Phase 2 (If Needed)
-- Add EMA baseline tracking (like cli.py)
-- Add variance-based thresholds
-- Support multiple columns or regions
-- Add metadata output (tag frames with deviation values)
-- RGB format support (analyze specific channel)
-
-### Phase 3 (Advanced)
-- Full cli.py recording logic
-- Buffer and output segments
-- Integration with probe detection systems
-
-## Integration with Existing Project
-
-### Build System
-Update [`gst/CMakeLists.txt`](gst/CMakeLists.txt):
-```cmake
-add_subdirectory (rollingsum)
-```
-
-### Documentation
-Update [`README.md`](README.md):
-- Add rollingsum to "Other elements" section
-- Add pipeline example
-
-## Mermaid Diagram: Data Flow
-
-```mermaid
-graph TD
-    A[Video Frame] --> B[Extract Column]
-    B --> C[Calculate Column Mean]
-    C --> D[Store in Ring Buffer]
-    D --> E[Update Rolling Mean]
-    E --> F{Deviation > Threshold?}
-    F -->|Yes| G[DROP Frame]
-    F -->|No| H[PASS Frame]
-    C --> E
-    
-    style G fill:#ff6b6b
-    style H fill:#51cf66
-```
-
-## Mermaid Diagram: Ring Buffer Operation
-
-```mermaid
-graph LR
-    subgraph Ring Buffer
-        A[0] --> B[1]
-        B --> C[2]
-        C --> D[...]
-        D --> E[N-1]
-        E ---|wrap| A
-    end
-    
-    F[New Frame Mean] --> G[ring_index]
-    G --> A
-    
-    H[Rolling Mean] --> I[Sum all values]
-    I --> J[Divide by count]
-    
-    style G fill:#ffd43b
-```
-
-## Implementation Checklist
-
-- [ ] Create gst/rollingsum directory
-- [ ] Implement gstrollingsum.h
-- [ ] Implement gstrollingsum.c
-- [ ] Create CMakeLists.txt
-- [ ] Update gst/CMakeLists.txt
-- [ ] Build and test basic functionality
-- [ ] Test with idsueyesrc
-- [ ] Update README.md
-- [ ] Create feature branch
-- [ ] Commit and document
-
-## References
-
-- Original algorithm: `cli.py` lines 64-79 (column extraction and mean comparison)
-- Template element: [`gst/select/gstselect.c`](gst/select/gstselect.c)
-- GStreamer base transform: [GstBaseTransform documentation](https://gstreamer.freedesktop.org/documentation/base/gstbasetransform.html)

LICENSE

@@ -1,17 +0,0 @@
-Copyright (C) <2003> David Schleef <ds@schleef.org>
-Copyright (C) 2016-2019 Tim-Philipp Müller <tim@centricular.com>
-Copyright (C) 2016-2017 PlayGineering Ltd.
-
-This library is free software; you can redistribute it and/or
-modify it under the terms of the GNU Lesser General Public
-License as published by the Free Software Foundation; either
-version 2.1 of the License, or (at your option) any later version.
-
-This library is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-Lesser General Public License for more details.
-
-You should have received a copy of the GNU Lesser General Public
-License along with this library; if not, write to the Free Software
-Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA

README.md

@@ -19,13 +19,13 @@ GStreamer plugins for IDS uEye cameras with frame analysis capabilities.
 ## Usage Examples
 
 ### Basic capture from IDS uEye camera
-```bash
+```powershell
 gst-launch-1.0 idsueyesrc config-file=ini/whole-presacler64_autoexp-binningx2.ini exposure=0.5 ! queue ! autovideosink
 ```
 
 ### Frame filtering based on column analysis
 Drop frames when column mean deviates from rolling baseline by more than 0.5:
-```bash
+```powershell
 gst-launch-1.0 idsueyesrc config-file=ini/whole-presacler64_autoexp-binningx2.ini exposure=0.5 ! videoconvert ! video/x-raw,format=GRAY8 ! rollingsum window-size=1000 column-index=1 threshold=0.5 ! queue ! autovideosink
 ```
 
@@ -34,17 +34,17 @@ gst-launch-1.0 idsueyesrc config-file=ini/whole-presacler64_autoexp-binningx2.in
 ### Additional rollingsum examples
 
 Analyze column 320 with larger window:
-```bash
+```powershell
 gst-launch-1.0 idsueyesrc config-file=ini/whole-presacler64_autoexp-binningx2.ini exposure=0.5 ! videoconvert ! video/x-raw,format=GRAY8 ! rollingsum window-size=5000 column-index=320 threshold=0.3 ! queue ! autovideosink
 ```
 
 Use stride for faster processing (sample every 2 rows):
-```bash
+```powershell
 gst-launch-1.0 idsueyesrc config-file=ini/whole-presacler64_autoexp-binningx2.ini exposure=0.5 ! videoconvert ! video/x-raw,format=GRAY8 ! rollingsum window-size=1000 column-index=1 stride=2 threshold=0.5 ! queue ! autovideosink
 ```
 
 Lower threshold for more sensitive detection:
-```bash
+```powershell
 gst-launch-1.0 idsueyesrc config-file=ini/whole-presacler64_autoexp-binningx2.ini exposure=0.5 ! videoconvert ! video/x-raw,format=GRAY8 ! rollingsum threshold=0.2 ! queue ! autovideosink
 ```
 
@@ -81,7 +81,7 @@ If you prefer to build manually:
 3. Install [IDS uEye SDK](https://www.ids-imaging.com) (default path: `C:\Program Files\IDS\uEye\Develop`)
 4. Run:
 
-```bash
+```powershell
 git clone https://github.com/joshdoe/gst-plugins-vision.git
 cd gst-plugins-vision
 mkdir build
@@ -101,7 +101,7 @@ The `build.ps1` script automatically copies plugins to `$env:GST_PLUGIN_PATH` if
 
 ### Verify Installation
 
-```bash
+```powershell
 gst-inspect-1.0 idsueyesrc
 gst-inspect-1.0 rollingsum
 ```
@@ -109,3 +109,13 @@ gst-inspect-1.0 rollingsum
 ## Documentation
 
 - [Rolling Sum Filter Design](DESIGN_ROLLINGSUM.md)
+
+## Debugging
+add `$env:GST_DEBUG_DUMP_DOT_DIR='.'` 
+
+![](dot_pause-play.svg)
+
+to get dotfile, and view using https://dreampuf.github.io/GraphvizOnline/, or
+```pwsh
+dot -Tsvg C:\dev\gst-plugins-vision\0.00.02.922833100-gst-launch.PAUSED_PLAYING.dot -o same.svg
+```

ROLLINGSUM_GUIDE.md

@@ -1,9 +1,28 @@
-# GStreamer Rolling Sum Plugin Guide
+# GStreamer Rolling Sum Plugin - Complete Documentation
+
+## Table of Contents
+- [Overview](#overview)
+- [How It Works](#how-it-works)
+- [Architecture & Design](#architecture--design)
+- [Plugin Properties](#plugin-properties)
+- [Basic Usage](#basic-usage)
+- [Debugging](#debugging)
+- [CSV Analysis](#csv-analysis)
+- [Recommended Thresholds](#recommended-thresholds)
+- [Troubleshooting](#troubleshooting)
+- [Performance](#performance)
+- [Integration Examples](#integration-examples)
+- [Developer Guide](#developer-guide)
+- [References](#references)
 
 ## Overview
 
 The `rollingsum` plugin analyzes video frames in real-time by tracking the mean pixel intensity of a specific column across frames. It maintains a rolling window of these values and can drop frames that deviate significantly from the rolling mean, useful for detecting and filtering unstable or anomalous frames.
 
+**Element Name:** `rollingsum` - Transform element that analyzes pixel values and selectively drops frames
+
+**Purpose:** Monitor a vertical column of pixels in video frames, calculate the rolling mean over a time window, and drop frames when the current frame's column mean deviates significantly from the rolling mean baseline.
+
 ## How It Works
 
 1. **Column Analysis**: Extracts mean pixel intensity from a specified vertical column
@@ -12,15 +31,134 @@ The `rollingsum` plugin analyzes video frames in real-time by tracking the mean
 4. **Frame Filtering**: Optionally drops frames exceeding the deviation threshold
 5. **CSV Logging**: Records all frame statistics for analysis
 
+### Data Flow
+
+```mermaid
+graph TD
+    A[Video Frame] --> B[Extract Column]
+    B --> C[Calculate Column Mean]
+    C --> D[Store in Ring Buffer]
+    D --> E[Update Rolling Mean]
+    E --> F{Deviation > Threshold?}
+    F -->|Yes| G[DROP Frame]
+    F -->|No| H[PASS Frame]
+    C --> E
+    
+    style G fill:#ff6b6b
+    style H fill:#51cf66
+```
+
+### Ring Buffer Operation
+
+```mermaid
+graph LR
+    subgraph Ring Buffer
+        A[0] --> B[1]
+        B --> C[2]
+        C --> D[...]
+        D --> E[N-1]
+        E ---|wrap| A
+    end
+    
+    F[New Frame Mean] --> G[ring_index]
+    G --> A
+    
+    H[Rolling Mean] --> I[Sum all values]
+    I --> J[Divide by count]
+    
+    style G fill:#ffd43b
+```
+
+## Architecture & Design
+
+### Base Class
+- Inherits from `GstBaseTransform` (similar to [`select`](gst/select/gstselect.c))
+- In-place transform (analysis only, no frame modification)
+- Returns `GST_BASE_TRANSFORM_FLOW_DROPPED` to drop frames
+- Returns `GST_FLOW_OK` to pass frames
+
+### Element Structure
+
+```c
+struct _GstRollingSum
+{
+  GstBaseTransform element;
+  
+  /* Properties */
+  gint window_size;      // Number of frames in rolling window (default: 1000)
+  gint column_index;     // Which column to analyze (default: 1, second column)
+  gint stride;           // Row sampling stride (default: 1, every row)
+  gdouble threshold;     // Deviation threshold for dropping (default: 0.5)
+  gchar *csv_file;       // CSV output file path (default: NULL)
+  
+  /* State */
+  gdouble *ring_buffer;  // Circular buffer of column means
+  gint ring_index;       // Current position in ring buffer
+  gint frame_count;      // Total frames processed
+  gdouble rolling_mean;  // Current rolling mean
+  FILE *csv_fp;          // CSV file pointer
+};
+```
+
+### Algorithm (Simplified from cli.py)
+
+**Per Frame Processing:**
+
+1. **Extract column data:**
+   - Select column at column_index
+   - Sample every stride rows
+   - Calculate mean of sampled pixels: frame_mean
+   
+2. **Update ring buffer:**
+   - Store frame_mean in ring_buffer[ring_index]
+   - Increment ring_index (wrap around)
+   
+3. **Calculate rolling mean:**
+   - Sum values in ring buffer (up to window_size or frame_count)
+   - Divide by actual window size
+   
+4. **Calculate deviation:**
+   - deviation = abs(frame_mean - rolling_mean)
+   - normalized_deviation = deviation / 255.0 (for 8-bit video)
+   
+5. **Decision:**
+   - If normalized_deviation > threshold: DROP frame
+   - Else: PASS frame
+
+**Key Simplifications from cli.py:**
+- **No EMA tracking**: Use simple rolling mean instead of exponential moving average
+- **No variance tracking**: Use fixed threshold instead of dynamic variance-based detection
+- **No recording logic**: Just drop/pass, no buffering for output segments
+- **No patience mechanism**: Immediate decision per frame
+
+### Video Format Support
+
+**Initial Implementation:**
+- **Primary target**: Grayscale (GRAY8, GRAY16)
+- **Secondary**: Bayer formats (common in machine vision)
+
+**Caps Filter:**
+```c
+static GstStaticPadTemplate sink_template =
+  GST_STATIC_PAD_TEMPLATE ("sink",
+    GST_PAD_SINK,
+    GST_PAD_ALWAYS,
+    GST_STATIC_CAPS (
+      "video/x-raw, format=(string){GRAY8,GRAY16_LE,GRAY16_BE}; "
+      "video/x-bayer, format=(string){bggr,grbg,gbrg,rggb}"
+    )
+  );
+```
+
 ## Plugin Properties
 
-| Property | Type | Default | Description |
+| Property | Type | Default | Range | Description |
-|----------|------|---------|-------------|
+|----------|------|---------|-------|-------------|
-| `window-size` | int | 1000 | Number of frames in rolling window (1-100000) |
+| `window-size` | int | 1000 | 1-100000 | Number of frames in rolling window |
-| `column-index` | int | 1 | Which vertical column to analyze (0-based) |
+| `column-index` | int | 1 | 0-width | Which vertical column to analyze (0-based) |
-| `stride` | int | 1 | Row sampling stride (1 = every row) |
+| `stride` | int | 1 | 1-height | Row sampling stride (1 = every row) |
-| `threshold` | double | 0.5 | Normalized deviation threshold for dropping frames (0.0-1.0) |
+| `threshold` | double | 0.5 | 0.0-1.0 | Normalized deviation threshold for dropping frames |
-| `csv-file` | string | NULL | Path to CSV file for logging (NULL = no logging) |
+| `csv-file` | string | NULL | - | Path to CSV file for logging (NULL = no logging) |
 
 ### Understanding Normalized Deviation
 
@@ -35,24 +173,43 @@ The `rollingsum` plugin analyzes video frames in real-time by tracking the mean
 
 ### Simple Pipeline
 
-```bash
+```powershell
-gst-launch-1.0 idsueyesrc config-file=config.ini ! \
+gst-launch-1.0 idsueyesrc config-file=config.ini ! `
-  videoconvert ! \
+  videoconvert ! `
-  video/x-raw,format=GRAY8 ! \
+  video/x-raw,format=GRAY8 ! `
-  rollingsum window-size=1000 column-index=1 threshold=0.0002 ! \
+  rollingsum window-size=1000 column-index=1 threshold=0.0002 ! `
   autovideosink
 ```
 
 ### With CSV Logging
 
-```bash
+```powershell
-gst-launch-1.0 idsueyesrc config-file=config.ini exposure=0.5 ! \
+gst-launch-1.0 idsueyesrc config-file=config.ini exposure=0.5 ! `
-  videoconvert ! \
+  videoconvert ! `
-  video/x-raw,format=GRAY8 ! \
+  video/x-raw,format=GRAY8 ! `
-  rollingsum window-size=1000 column-index=1 threshold=0.0002 csv-file=output.csv ! \
+  rollingsum window-size=1000 column-index=1 threshold=0.0002 csv-file=output.csv ! `
   fakesink
 ```
 
+### Custom Configuration
+
+```powershell
+gst-launch-1.0 idsueyesrc config-file=config.ini ! `
+  rollingsum window-size=5000 column-index=320 stride=2 threshold=0.3 ! `
+  queue ! `
+  autovideosink
+```
+
+### With Format Conversion
+
+```powershell
+gst-launch-1.0 idsueyesrc ! `
+  videoconvert ! `
+  video/x-raw,format=GRAY8 ! `
+  rollingsum ! `
+  autovideosink
+```
+
 ## Debugging
 
 ### Enable Debug Output
@@ -105,7 +262,7 @@ GST_DEBUG=rollingsum:5 gst-launch-1.0 [pipeline...]
 
 #### 1. Verify Plugin Loaded
 
-```bash
+```powershell
 gst-inspect-1.0 rollingsum
 ```
 
@@ -149,15 +306,22 @@ frame,column_mean,rolling_mean,deviation,normalized_deviation,dropped
 
 Use the included analysis script:
 
-```bash
+```powershell
-uv run analyze_sma.py output.csv
+uv run scripts/analyze_sma.py output.csv
 ```
 
 **Output includes:**
 - Statistical summary (min/max/mean/std)
 - Threshold recommendations based on percentiles
 - Standard deviation-based suggestions
-- Visualization plots (`output_analysis.png`)
+- Visualization plots saved to `results/debug/`
+- Archived CSV with timestamp in `results/debug/`
+
+**Output files are automatically organized:**
+- `results/debug/output_YYYYMMDD_HHMMSS.csv` - Archived CSV
+- `results/debug/output_analysis_YYYYMMDD_HHMMSS.png` - Analysis plots
+
+The `results/` directory is gitignored to keep your repository clean.
 
 ### Interpreting Results
 
@@ -176,7 +340,7 @@ Based on analysis of stable camera footage:
 
 ### For General Use
 
-```bash
+```powershell
 # Conservative (1-2% frame drop)
 threshold=0.0003
 
@@ -190,17 +354,17 @@ threshold=0.0001
 ### For Specific Scenarios
 
 **High-speed acquisition** (minimal processing):
-```bash
+```powershell
 window-size=100 threshold=0.0005
 ```
 
 **Quality-focused** (stable scenes):
-```bash
+```powershell
 window-size=1000 threshold=0.0001
 ```
 
 **Real-time monitoring** (fast response):
-```bash
+```powershell
 window-size=50 threshold=0.0002
 ```
 
@@ -212,7 +376,7 @@ window-size=50 threshold=0.0002
 
 **Solution**:
 1. Run with CSV logging
-2. Analyze with `uv run analyze_sma.py output.csv`
+2. Analyze with `uv run scripts/analyze_sma.py output.csv`
 3. Use recommended threshold from 90th-99th percentile
 
 ### Too many frames dropped (threshold too low)
@@ -252,13 +416,32 @@ WARNING rollingsum: Column index 1000 >= width 1224, using column 0
 - Choose different `column-index` (avoid edges)
 - Use `stride=2` or higher for faster processing
 
-## Performance Tips
+## Performance
+
+### Performance Tips
 
 1. **Larger window = more stable** but slower to adapt to scene changes
 2. **Stride > 1** reduces computation but less accurate column mean
 3. **CSV logging** has minimal performance impact
 4. **Debug level 5** can produce massive logs, use only when needed
 
+### Memory Usage
+
+- Ring buffer: `window_size * sizeof(double)` = ~8KB for default 1000 frames
+- Minimal per-frame allocation
+
+### CPU Usage
+
+- Column extraction: O(height/stride)
+- Rolling mean update: O(1) using incremental sum
+- Very lightweight compared to full-frame processing
+
+### Optimization Opportunities
+
+1. **Incremental mean**: Track sum instead of recalculating
+2. **SIMD**: Vectorize column summation
+3. **Skip calculation**: Only recalc every N frames if baseline is stable
+
 ## Integration Examples
 
 ### Python Script Control
@@ -281,7 +464,7 @@ subprocess.run([
 ])
 
 # Analyze results
-subprocess.run(['uv', 'run', 'analyze_sma.py', 'output.csv'])
+subprocess.run(['uv', 'run', 'scripts/analyze_sma.py', 'output.csv'])
 ```
 
 ### Adaptive Threshold
@@ -298,7 +481,33 @@ recommended_threshold = df['normalized_deviation'].quantile(0.95)
 print(f"Recommended threshold: {recommended_threshold:.6f}")
 ```
 
-## Developer Notes
+## Developer Guide
+
+### Implementation Files
+
+**Directory Structure:**
+```
+gst/rollingsum/
+├── CMakeLists.txt
+├── gstrollingsum.c
+└── gstrollingsum.h
+```
+
+**gstrollingsum.h:**
+- Element type definitions
+- Structure declarations
+- Property enums
+- Function prototypes
+
+**gstrollingsum.c:**
+- GObject methods (init, dispose, get/set properties)
+- GstBaseTransform methods (transform_ip)
+- Helper functions (extract_column_mean, update_rolling_mean)
+- Plugin registration
+
+**CMakeLists.txt:**
+- Build configuration (copy from [`gst/select/CMakeLists.txt`](gst/select/CMakeLists.txt))
+- Link GStreamer base and video libraries
 
 ### Adding New Features
 
@@ -309,14 +518,17 @@ Key files:
 
 ### Rebuild After Changes
 
-```bash
+```powershell
 .\build.ps1  # Windows
+```
+
+```bash
 ./build.sh   # Linux
 ```
 
 ### Testing
 
-```bash
+```powershell
 # Quick test
 gst-inspect-1.0 rollingsum
 
@@ -325,16 +537,77 @@ $env:GST_DEBUG="rollingsum:5"
 gst-launch-1.0 videotestsrc ! rollingsum ! fakesink
 ```
 
+### Testing Strategy
+
+**Unit Tests:**
+- Ring buffer wrapping
+- Mean calculation accuracy
+- Threshold comparison logic
+
+**Integration Tests:**
+- Pipeline with videotestsrc
+- Pipeline with idsueyesrc
+- Frame drop verification
+- Property changes during playback
+
+**Test Cases:**
+1. Static video (all frames similar) → all pass
+2. Single bright frame → that frame drops
+3. Gradual change → frames pass
+4. Periodic pattern → pattern frames drop
+
+### Integration with Existing Project
+
+**Build System:**
+Update [`gst/CMakeLists.txt`](gst/CMakeLists.txt):
+```cmake
+add_subdirectory (rollingsum)
+```
+
+**Documentation:**
+Update [`README.md`](README.md):
+- Add rollingsum to "Other elements" section
+- Add pipeline example
+
+### Future Enhancements
+
+**Phase 2 (If Needed):**
+- Add EMA baseline tracking (like cli.py)
+- Add variance-based thresholds
+- Support multiple columns or regions
+- Add metadata output (tag frames with deviation values)
+- RGB format support (analyze specific channel)
+
+**Phase 3 (Advanced):**
+- Full cli.py recording logic
+- Buffer and output segments
+- Integration with probe detection systems
+
+### Implementation Checklist
+
+- [x] Create gst/rollingsum directory
+- [x] Implement gstrollingsum.h
+- [x] Implement gstrollingsum.c
+- [x] Create CMakeLists.txt
+- [x] Update gst/CMakeLists.txt
+- [x] Build and test basic functionality
+- [x] Test with idsueyesrc
+- [x] Update README.md
+- [x] Create feature branch
+- [x] Commit and document
+
 ## References
 
-- [DESIGN_ROLLINGSUM.md](DESIGN_ROLLINGSUM.md) - Design document
+- Original algorithm: `cli.py` lines 64-79 (column extraction and mean comparison)
-- [analyze_sma.py](analyze_sma.py) - Analysis tool
+- Template element: [`gst/select/gstselect.c`](gst/select/gstselect.c)
+- GStreamer base transform: [GstBaseTransform documentation](https://gstreamer.freedesktop.org/documentation/base/gstbasetransform.html)
+- [scripts/analyze_sma.py](scripts/analyze_sma.py) - Analysis tool
 - GStreamer documentation: https://gstreamer.freedesktop.org/documentation/
 
 ## Support
 
 For issues or questions:
-1. Enable debug output (`GST_DEBUG=rollingsum:5`)
+1. Enable debug output (`$env:GST_DEBUG="rollingsum:5"` in PowerShell)
 2. Generate CSV log and analyze
 3. Check this guide's troubleshooting section
 4. Review debug output for errors/warnings

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+AutoImageControlPeakWhiteGranularity=0
+Auto WB control=0
+Auto WB type=2
+Auto WB RGB color model=1
+Auto WB RGB color temperature=5000
+Auto WB offsetR=0
+Auto WB offsetB=0
+Auto WB gainMin=0
+Auto WB gainMax=100
+Auto WB speed=50
+Auto WB Aoi Left=0
+Auto WB Aoi Top=0
+Auto WB Aoi Width=2456
+Auto WB Aoi Height=4
+Auto WB Once=0
+Auto WB Hysteresis=2
+Brightness Skip Frames Trigger Mode=4
+Brightness Skip Frames Freerun Mode=4
+Auto WB Skip Frames Trigger Mode=4
+Auto WB Skip Frames Freerun Mode=4
+
+
+[Trigger and Flash]
+Trigger mode=0
+Trigger timeout=200
+Trigger delay=0
+Trigger debounce mode=0
+Trigger debounce delay time=1
+Trigger burst size=1
+Trigger prescaler frame=1
+Trigger prescaler line=1
+Trigger input=1
+Flash strobe=0
+Flash delay=0
+Flash duration=0
+Flash auto freerun=0
+PWM mode=0
+PWM frequency=20000000
+PWM dutycycle=20000000
+GPIO state=3
+GPIO direction=0
+GPIO1 Config=1
+GPIO2 Config=1
+
+
+[Vertical AOI Merge Mode]
+Mode=0
+Position=0
+Additional Position=0
+Height=2
+
+
+[Level Controlled Trigger Mode]
+Mode=0
+
+
+[Memory]
+Camera memory mode=1

network_guide.md

@@ -0,0 +1,39 @@
+# how to send a line
+
+real data 
+```powershell
+gst-launch-1.0 idsueyesrc config-file=ini/200fps-2456x4pix-cw.ini exposure=5 framerate=300  `
+    ! queue `   
+    ! udpsink host=127.0.0.1 port=5000
+```
+
+note: 5ms is bit too fast for us
+
+### Python/OpenCV Receiver
+```pwsh
+uv run scripts/recv_raw_column.py
+```
+or rolling like 
+```pwsh
+uv run .\scripts\recv_raw_rolling.py
+```
+See [`scripts/recv_raw_column.py`](scripts/recv_raw_column.py) for the Python implementation with debug options.
+
+
+# demo data
+## Sender (crop to first column, send raw over UDP)
+```pwsh
+gst-launch-1.0 -v `
+    videotestsrc pattern=smpte ! `
+    videocrop left=0 right=639 top=0 bottom=0 ! `
+    video/x-raw,format=RGB,width=1,height=640,framerate=30/1 ! `
+    udpsink host=127.0.0.1 port=5000
+```
+
+### GStreamer Receiver (raw UDP → display)
+```pwsh
+gst-launch-1.0 -v `
+    udpsrc port=5000 caps="video/x-raw,format=RGB,width=1,height=640,framerate=30/1" ! `
+    videoconvert ! `
+    autovideosink
+```

scripts/analyze_sma.py

@@ -18,11 +18,17 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
 from pathlib import Path
+from datetime import datetime
+import shutil
 
 
 def analyze_csv(csv_file: str = "output.csv"):
     """Analyze the rolling sum CSV data and generate insights."""
     
+    # Create output directory
+    output_dir = Path("results/debug")
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
     # Read the CSV
     try:
         df = pd.read_csv(csv_file)
@@ -30,6 +36,12 @@ def analyze_csv(csv_file: str = "output.csv"):
         print(f"Error: CSV file '{csv_file}' not found.")
         sys.exit(1)
     
+    # Copy input CSV to results directory with timestamp
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    csv_name = Path(csv_file).stem
+    archived_csv = output_dir / f"{csv_name}_{timestamp}.csv"
+    shutil.copy(csv_file, archived_csv)
+    
     print("=" * 80)
     print(f"ROLLING SUM ANALYSIS - {csv_file}")
     print("=" * 80)
@@ -98,16 +110,17 @@ def analyze_csv(csv_file: str = "output.csv"):
         print()
     
     # Create visualizations
-    create_plots(df, csv_file)
+    plot_file = create_plots(df, csv_file, output_dir, timestamp)
     
     print("=" * 80)
-    print("PLOTS SAVED:")
+    print("OUTPUT FILES:")
-    print(f"  - {csv_file.replace('.csv', '_analysis.png')}")
+    print(f"  CSV Archive: {archived_csv}")
+    print(f"  Analysis Plot: {plot_file}")
     print("=" * 80)
 
 
-def create_plots(df: pd.DataFrame, csv_file: str):
+def create_plots(df: pd.DataFrame, csv_file: str, output_dir: Path, timestamp: str) -> Path:
-    """Create analysis plots."""
+    """Create analysis plots and return the output file path."""
     
     fig, axes = plt.subplots(2, 2, figsize=(14, 10))
     fig.suptitle(f'Rolling Sum Analysis - {csv_file}', fontsize=16, fontweight='bold')
@@ -167,10 +180,13 @@ def create_plots(df: pd.DataFrame, csv_file: str):
     
     plt.tight_layout()
     
-    # Save the plot
+    # Save the plot to results/debug
-    output_file = csv_file.replace('.csv', '_analysis.png')
+    csv_name = Path(csv_file).stem
+    output_file = output_dir / f"{csv_name}_analysis_{timestamp}.png"
     plt.savefig(output_file, dpi=150, bbox_inches='tight')
     print(f"\n✓ Saved analysis plot to: {output_file}\n")
+    
+    return output_file
 
 
 if __name__ == "__main__":

scripts/recv_raw_column.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = "<=3.10"
+# dependencies = [
+#   "opencv-python",
+#   "numpy",
+# ]
+# ///
+
+import socket
+import numpy as np
+import cv2
+
+# Debug flag - set to True to see frame reception details
+DEBUG = False
+
+# Stream parameters (match your GStreamer sender)
+WIDTH = 1
+HEIGHT = 640  # Default videotestsrc height
+CHANNELS = 3
+FRAME_SIZE = WIDTH * HEIGHT * CHANNELS  # bytes
+
+UDP_IP = "0.0.0.0"
+UDP_PORT = 5000
+
+sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+sock.bind((UDP_IP, UDP_PORT))
+
+print(f"Receiving raw {WIDTH}x{HEIGHT} RGB frames on UDP port {UDP_PORT}")
+if DEBUG:
+    print(f"Expected frame size: {FRAME_SIZE} bytes")
+
+cv2.namedWindow("Raw Column Stream", cv2.WINDOW_NORMAL)
+
+frame_count = 0
+while True:
+    data, addr = sock.recvfrom(65536)
+    
+    if len(data) != FRAME_SIZE:
+        if DEBUG:
+            print(f"Received {len(data)} bytes (expected {FRAME_SIZE}), skipping...")
+        continue
+    
+    if DEBUG:
+        frame_count += 1
+        if frame_count % 30 == 0:
+            print(f"Received {frame_count} frames")
+
+    frame = np.frombuffer(data, dtype=np.uint8).reshape((HEIGHT, WIDTH, CHANNELS))
+
+    # scale for visibility
+    frame_large = cv2.resize(
+        frame, (200, HEIGHT), interpolation=cv2.INTER_NEAREST
+    )
+
+    cv2.imshow("Raw Column Stream", frame_large)
+
+    if cv2.waitKey(1) == 27:  # ESC to quit
+        break
+
+cv2.destroyAllWindows()

scripts/recv_raw_rolling.py

@@ -0,0 +1,86 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = "<=3.10"
+# dependencies = [
+#   "opencv-python",
+#   "numpy",
+# ]
+# ///
+
+import socket
+import numpy as np
+import cv2
+
+# Debug flag - set to True to see frame reception details
+DEBUG = False
+
+# Rotation mode - set to rotate incoming data before display
+# Options: None, cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE, cv2.ROTATE_180
+ROTATION = cv2.ROTATE_90_COUNTERCLOCKWISE  # Rotate rows to columns
+
+# Stream parameters (match your GStreamer sender)
+COLUMN_WIDTH = 4  # Width from 200fps-2456x4pix-cw.ini
+COLUMN_HEIGHT = 2456  # Height from 200fps-2456x4pix-cw.ini
+CHANNELS = 3
+FRAME_SIZE = COLUMN_WIDTH * COLUMN_HEIGHT * CHANNELS  # bytes (29472)
+
+# Display parameters
+DISPLAY_WIDTH = 800  # Width of rolling display in pixels
+DISPLAY_HEIGHT = COLUMN_HEIGHT
+
+UDP_IP = "0.0.0.0"
+UDP_PORT = 5000
+
+sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+sock.bind((UDP_IP, UDP_PORT))
+
+print(f"Receiving raw {COLUMN_WIDTH}x{COLUMN_HEIGHT} RGB columns on UDP port {UDP_PORT}")
+print(f"Display width: {DISPLAY_WIDTH} pixels (rolling)")
+if DEBUG:
+    print(f"Expected frame size: {FRAME_SIZE} bytes")
+
+cv2.namedWindow("Rolling Column Stream", cv2.WINDOW_NORMAL)
+
+# Initialize the rolling buffer
+rolling_buffer = np.zeros((DISPLAY_HEIGHT, DISPLAY_WIDTH, CHANNELS), dtype=np.uint8)
+current_column = 0
+frame_count = 0
+
+while True:
+    data, addr = sock.recvfrom(65536)
+    
+    if len(data) != FRAME_SIZE:
+        if DEBUG:
+            print(f"Received {len(data)} bytes (expected {FRAME_SIZE}), skipping...")
+        continue
+    
+    if DEBUG:
+        frame_count += 1
+        if frame_count % 30 == 0:
+            print(f"Received {frame_count} frames, current column: {current_column}")
+
+    # Parse the incoming data
+    frame = np.frombuffer(data, dtype=np.uint8).reshape((COLUMN_WIDTH, COLUMN_HEIGHT, CHANNELS))
+    
+    # Apply rotation if configured
+    if ROTATION is not None:
+        rotated = cv2.rotate(frame, ROTATION)
+    else:
+        rotated = frame
+    
+    # Extract only the first column for smoother rolling (1 pixel/frame)
+    column = rotated[:, 0:1, :]
+
+    # Insert the single column into the rolling buffer at the current position
+    rolling_buffer[:, current_column:current_column+1, :] = column
+
+    # Move to the next column position, wrapping around when reaching the end
+    current_column = (current_column + 1) % DISPLAY_WIDTH
+
+    # Display the rolling buffer
+    cv2.imshow("Rolling Column Stream", rolling_buffer)
+
+    if cv2.waitKey(1) == 27:  # ESC to quit
+        break
+
+cv2.destroyAllWindows()