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generate_synthetic_measurements — how to use generate_synthetic_measurements how to use generate_synthetic_measurements, generate_synthetic_measurements alternative, generate_synthetic_measurements setup guide, generate_synthetic_measurements vs simulation tools, what is generate_synthetic_measurements, generate_synthetic_measurements install, microscope measurement simulation, 3D ground-truth volume processing

v1.0.0
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About this Skill

Perfect for Scientific Computing Agents needing advanced microscope measurement simulation capabilities. generate_synthetic_measurements is a skill that generates synthetic measurements from 3D ground-truth volumes using precomputed system matrices

Features

Simulates microscope measurements using precomputed system matrices (the forward operator $A$)
Supports 3D ground-truth (GT) volumes as input ($x$) in reconstruction.pt format
Generates output measurements ($b = Ax$) as .tif images
Utilizes Interp_Vol_ID_*.pt files in a directory for system matrices
Enables solver development and benchmarking

# Core Topics

Winfred666 Winfred666
[0]
[0]
Updated: 3/7/2026

Quality Score

Top 5%
51
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add Winfred666/lightfield_linearsys/generate_synthetic_measurements

Agent Capability Analysis

The generate_synthetic_measurements MCP Server by Winfred666 is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use generate_synthetic_measurements, generate_synthetic_measurements alternative, generate_synthetic_measurements setup guide.

Ideal Agent Persona

Perfect for Scientific Computing Agents needing advanced microscope measurement simulation capabilities.

Core Value

Empowers agents to simulate microscope measurements from 3D ground-truth volumes using precomputed system matrices, facilitating solver development and benchmarking with file formats like `.pt` and `.tif`.

Capabilities Granted for generate_synthetic_measurements MCP Server

Simulating microscope measurements for solver development
Generating synthetic data for benchmarking
Creating realistic measurement simulations using precomputed system matrices

! Prerequisites & Limits

  • Requires precomputed system matrices
  • Limited to 3D ground-truth volumes
  • Output measurements saved as `.tif` images
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SKILL.md
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Generate Measurements from Synthetic Data

Overview

Use this skill when you have a 3D ground-truth (GT) volume and want to simulate what the microscope/system would measure, using precomputed system matrices (the forward operator $A$).

This is typically a prerequisite for solver development and benchmarking:

  • GT volume ($x$): reconstruction.pt (or similar)
  • system matrices ($A$): Interp_Vol_ID_*.pt files in a directory
  • output measurements ($b = Ax$): saved as .tif images

Instructions

  1. Prepare the ground-truth volume.
  • Ensure the GT volume is a .pt file (for example reconstruction.pt).
  • If your GT is stored in H5 format, convert it using scripts/convert_h5_to_pt.py.
  1. Locate the forward model (A matrices).
  • Identify the directory containing Interp_Vol_ID_*.pt.
  • Confirm this directory corresponds to the same optical/system setup you want to simulate.
  1. Pick/verify the crop (if needed).
  • If the A volume extent is larger than your GT volume (or mismatched), set --crop-box-A.
  • Format is x0 y0 z0 x1 y1 z1
  • User would give the crop, or check crop in most related config/*.yaml. If you do not know how to crop, stop and ask user for help.
  1. (Optional) Decide on noise injection.
  • Use --noise-index to add noise to a specific measurement index.
  • Use --noise-index -1 for clean measurement without noise.
  1. Generate the measurements.

Run scripts/generate_bunny_measurements.py with:

  • --gt-vol-path: path to GT .pt
  • --raw-A-dir: path to the system matrices
  • --output-dir: output folder for .tif measurements

What success looks like

You should consider this skill successful when:

  • the script finishes without error, and
  • output_dir contains one or more .tif measurement images, and
  • (recommended) the images look plausible (non-empty, not all zeros/NaNs) when opened or visualized.

Examples

Example: generate noise-free measurements with A-cropping

Generate measurements from a GT volume using a specified system-matrix directory, and crop A to match the GT:

bash
1python scripts/generate_bunny_measurements.py \ 2 --gt-vol-path data/synthetic/balls/case_1/reconstruction.pt \ 3 --raw-A-dir data/raw/lightsheet_vol_6.9 \ 4 --output-dir data/synthetic/balls/case_1/measurements \ 5 --crop-box-A 5 268 18 69 332 82 \ 6 --noise-index -1

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