avidaq

controls for napari and micromanger

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation

Standard installation

You can install avidaq via pip:

pip install napari[all] avidaq

Install from plugin menu

Alternatively you can install avidaq via the napari plugin menu:

Running

First start micromanager. Make sure the server port checkbox is activated.

Then to start napari with the avidaq plugin active run: napari -w avidaq

Updating presets

MDA presets are stored in a json file in the user's home directory.

`C:\\Users\YourName\.avidaq\mda_presets.json`

This file should exist after plugin installation with some defaults. You do not need to create the file yourself.

Add or modify the values and reload napari to see the changes.

All parameter entries are optional, if not provided the default value will be used.

The parameter names and their descriptions can be found [here] (https://github.com/micro-manager/pycro-manager/blob/main/pycromanager/acq_util.py#L102-L115)

The format is as follows:

{
    "gui_display_name": {
        "parameter_name": value,
        "parameter_name": value,
        ...
    },
    "gui_display_name": {
        "parameter_name": value,
        "parameter_name": value,
        ...
    },
    ...
}

defaults:

{
  "Basic": {
    "num_time_points": 5,
    "z_start": 0,
    "z_end": 6,
    "z_step": 0.4
  },
  "Simple": {
    "num_time_points": 2,
    "z_start": 0,
    "z_end": 2,
    "z_step": 0.1
  },
  "Detailed": {
    "num_time_points": 10,
    "z_start": 0,
    "z_end": 12,
    "z_step": 0.2
  }
}

Issues

If you encounter any problems, please [file an issue] along with a detailed description.

Development

You should have python3.8 or higher installed.

1. clone this repo
2. create a virtual environment python -m venv .venv && source .venv/bin/activate
3. run pip install -e '.[testing,build]'
4. run pre-commit install

To run unit tests

pytest

typical workflow

1. edit code in /src
2. run napari -w avidaq
3. repeat

Releasing to pypi

Project is automically built and deployed to pypi upon