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Single Molecule Imaging Toolbox Extraordinaire

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# ***smite***: Single Molecule Imaging Toolbox Extraordinaire

This work was supported by the following [grants](doc/grants.md).

This MATLAB-based toolbox provides analysis tools for fluorescence
single molecule imaging with an emphasis on single molecule
localization microscopy (SMLM) and single particle tracking (SPT).

---

## Overview

### Workflow concept
***smite*** is designed around the concept that a parameter structure,
the Single Molecule Fitting (SMF) structure, uniquely and completely
defines the data analysis.  The results are completely contained
in a Single Molecule Data (SMD) structure.  ***smite*** is designed
to make lowest-level tools just as easy to use as the higher-level
application-specific classes.  All tools make use of the SMF and
SMD structures.

### Code organization
***smite*** is organized into a set of namespaces that group similar
tools and concepts.  The namespace  `+smi`  contains the highest
level tools that will be the most common entry point for processing
SMLM and SPT data sets.  The file [SMITEclasses.md](doc/SMITEclasses.md)
provides a short 1-line description of each class in the distribution.

### Image and Detector Model
Image arrays follow MATLAB's column-major format.  An image coordinate
of (1,1) means the center of the top-left pixel, whereas (2,1) would
indicate the center of the pixel that is one down from the top, but
in the left-most column.

---

## 🤖 LLM-Assisted Development

This repository includes **comprehensive LLM-friendly documentation** designed for interactive assistance with smite.

> **Note:** Documentation generated by Claude Sonnet 4.5. Not extensively human-reviewed. [Report issues](https://github.com/LidkeLab/smite/issues).

### Setup (5 minutes)

1. **Clone smite locally** (skip if you already have it)
   ```bash
   cd ~/Documents/MATLAB  # or your preferred location
   git clone https://github.com/LidkeLab/smite.git
   ```

2. **Install Claude Desktop**

Download from [claude.ai/download](https://claude.ai/download)

3. **Enable filesystem access**

In Claude Desktop: **File → Settings → Extensions → Enable "Filesystem"**

4. **Configure access to smite**

In the Filesystem extension settings, add the path where you cloned smite:
   - Example (Linux/MacOS): `~/Documents/MATLAB/smite`
   - Example (Windows): `C:\Users\YourName\Documents\MATLAB\smite`

5. **Start using**

In any conversation, tell Claude:
   ```
   Read the file: ~/Documents/MATLAB/smite/LLM_DOCS.md
   ```
   (Use your actual path to smite)

Then ask questions naturally:
   - "How do I analyze SMLM data?"
   - "Write a script to process my dataset"
   - "Explain the SMF parameters"

**Optional: Create a reusable project**

For convenience, create a Claude Desktop project for smite to avoid repeating the bootstrap prompt:

1. In Claude Desktop, create new project named "smite"
2. Add this project description (copy-paste ready):
   ```
   Interactive assistant for smite (Single Molecule Imaging Toolbox Extraordinaire).

smite is a MATLAB toolbox for fluorescence single molecule localization microscopy
   (SMLM) and single particle tracking (SPT). This project provides access to comprehensive
   LLM documentation including API references, workflows, code examples, and troubleshooting
   guides.

Read LLM_DOCS.md on startup to load documentation and provide interactive assistance
   with analysis workflows, code generation, parameter tuning, and problem-solving.
   ```
3. In project settings, add custom instructions to automatically load documentation:
   ```
   At the start of each conversation, read the file: /path/to/smite/LLM_DOCS.md
   ```
   (Replace with your actual smite path)
4. Open this project whenever working with smite

### What's Available

The LLM documentation includes:
- Complete API reference for all classes
- Installation and troubleshooting guides
- Code examples for common workflows
- SMF/SMD structure details
- GPU/CUDA setup guidance

See **[LLM_DOCS.md](LLM_DOCS.md)** for complete details.

---

## Installation
Clone (Linux/MacOS example; similar for Windows) into `~/Documents/MATLAB`
the ***smite*** GitHub distribution (https://github.com/LidkeLab/smite.git)
to obtain the development version.
Otherwise, choose the latest release
(https://github.com/LidkeLab/smite/releases) for the most recent frozen
version.
Add to `~/Documents/MATLAB/startup.m` the following:
```
   addpath '~/Documents/MATLAB/smite/MATLAB'
   setupSMITE
```
Now, ***smite*** contains some mex and CUDA files.  Precompiled files for
64-bit Linux, MacOS and Windows (mex extensions: mexa64, mexmaci64, mexw64,
respectively; CUDA extension: ptx) come with the repository, so often no
further installation will be needed.  Note that the GPU compute capability
is assumed to be 5.0 or greater.
However, if for some reason (for example, the user modifies a mex or CUDA
source file), recompilation of the appropriate file is necessary, then see
[mex+CUDA](doc/mex+CUDA.md) for details.

To verify that ***smite*** is running properly, see the Testing
subsection below.

### Dependencies
For full functionality, ***smite*** requires:
- Linux, MacOS or Windows
- MATLAB version R2021a or later
- NVIDIA GPU with CUDA compute capability (>= 5.0) [supported by your version of MATLAB](https://www.mathworks.com/help/parallel-computing/gpu-support-by-release.html)
- MATLAB Curve Fitting Toolbox [ONLY smi_cluster, smi_core.FRC,
  smi_stat.DiffusionEstimator]
- MATLAB Image Processing Toolbox
- MATLAB Optimization Toolbox [ONLY smi_cluster.PairCorrelation,
  smi_stat.DiffusionEstimator]
- MATLAB Parallel Computing Toolbox
- MATLAB Signal Processing Toolbox [ONLY smi_core.FRC]
- MATLAB Statistics and Machine Learning Toolbox
- ffmpeg installed for Linux (https://ffmpeg.org)
  [smi_core.LocalizeData.genLocalizations for obj.Verbose >= 3]

---

## Simple Examples
### Working with SMF
SMF is implemented as a class to enable a gui and to provide useful
helper methods.  However, the most common use will be as a structure
with fixed fields.

Create an SMF object:
```
  SMF = smi_core.SingleMoleculeFitting()
```
Get an SMF property:
```
  B = SMF.BoxFinding.BoxOverlap
```
Set an SMF property:
```
  SMF.BoxFinding.BoxOverlap = 0
```
Use the SMF GUI to interactively set values:
```
  SMF.gui()
```

### Finding coordinates from a stack of images containing blobs

Create a test dataset and make it noisy:
```
  B = smi_sim.GaussBlobs.genRandomBlobImage();
  B = poissrnd(B);
```
Create an `SMF` object with default values:
```
  SMF = smi_core.SingleMoleculeFitting()
```
Create a `LocalizeData` object with our `SMF`:
```
  LD = smi_core.LocalizeData(B, SMF)
```
Localize:
```
  [SMD] = LD.genLocalizations();
```

Localize again with `Verbose = 3` to show color overlay output:
```
  LD.Verbose = 3;
  [SMD] = LD.genLocalizations();
```

### High level SMLM analysis

Create an SMLM object.  When there are no input aruments, it will open the GUI:
```
  SMLMobj = smi.SMLM()  
```
Use the GUI to navigate to a test dataset such as available from

- Pallikkuth, S., Martin, C., Farzam, F., Edwards, J. S., Lakin,
  M. R., Lidke, D. S., & Lidke, K. A. (2018). Supporting data for
  Sequential Super-Resolution Imaging using DNA Strand Displacement
  [Data set]. University of New Mexico
  [https://doi.org/10.25827/CS2A-DH13](https://digitalrepository.unm.edu/physics_data/3/#attach_additional_files).
- Wester, Michael J., Mazloom-Farsibaf, Hanieh, Farzam, Farzin,
  Fazel, Mohamadreza, Meddens, Marjolein B. M., & Lidke, Keith A.
  (2020), Comparing Lifeact and Phalloidin for super-resolution imaging
  of actin in fixed cells, Dryad, Dataset,
  [https://doi.org/10.5061/dryad.xsj3tx9cn](https://datadryad.org/stash/dataset/doi:10.5061/dryad.xsj3tx9cn).

Set SMF values from within the GUI and run either a test dataset
or analyze all datasets.

---

## Getting Started
- Install ***smite*** as discussed above.
- Run the collection of unit tests as discussed in the Testing section
  below to verify that ***smite*** has been properly installed.
- Run some of the Code Examples linked to below which simulate data.
- Obtain or generate a dataset (see the citations above) and try out
  the core functionality (see Overview section below).  The user can
  also obtain a dataset by running
  [the SMLM unitTest](MATLAB/+smi/@SMLM/unitTest.m), which will produce
  the ~1 Gb `tempdir/smite/unitTest/SMLM/SMLM_testData.h5`.

### Testing
[run_tests](MATLAB/run_tests.m) run a series of unit tests that
cover major ***smite*** core functionality.  Much output will be
saved in tempdir/smite/unitTest/name_of_test.
Note that the first two tests (smi.SMLM.unitTest and
smi.SPT.unitTestFFGC) test a great deal of functionality all at once,
and so their success is a good indicator that ***smite*** is working.
[ExpectedResults](MATLAB/ExpectedResults/README.md) are provided
in the `smite/MATLAB` directory in which `run_tests.m` resides,
noting that very large files have been deleted so as to not bloat
up the the ***smite*** distribution (these files are listed in the
various `ExpectedResults` READMEs).  Also, the tests are frequently
stochastic in nature, so outputs from run to run will not necessarily
be identical, even on the same system, but the `ExpectedResults` will
provide a flavor of what to expect.

### [Code Examples](MATLAB/examples/README.md)
Additional ***smite*** examples can be found in the examples
subdirectory of MATLAB as well as the unitTests for some of the
classes (see [here](MATLAB/examples/README.md) for a summary).  Some
of the examples generate and analyze their own data, while others
provide a template for how to run the example given supplied data.
(The unit tests always generate their own data if any is needed.)

### [Overview of Core Functionality](doc/CoreOverview.md)
Additional details on the core functionality of ***smite*** can be
found [here](doc/CoreOverview.md), including both simple and
extended examples of usage.

### [UNM workflows](doc/UNMworkflows.md)
Typical Univerisity of New Mexico workflows for ***smite*** software.

---

## Contributions/Support
Issues or problems and people seeking support with the software
should be reported via the Issues tab of the ***smite*** GitHub
repository.  Contributions to ***smite*** should be performed on
new branches which are then requested to merge with the main branch
via Pull Requests.

---

## Related Software
Please note the related software: MATLAB Instrument Control
(***MIC***), a collection of MATLAB classes for automated data
collection on complex, multi-component custom built microscopes.
This software can be obtained from the ***MIC*** GitHub distribution
(https://github.com/LidkeLab/matlab-instrument-control.git).

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