semvec/pypi-packages huggingface dataset:
cft-zarr 0.0.6
CFT Zarr codecs for 12-bit fluorescence and RGB compression
Requires Python: <=3.14,>=3.11

# CFT Zarr Custom Codecs

Custom codecs for Zarr v3 optimized for CFT (Cryo-Fluorescence Tomography) data storage.

## Codecs

This package provides custom Zarr v3 codecs:

- **`cft_zarr.jpeg_compressor`**: JPEG compressor for RGB images (BytesBytesCodec - supports incremental updates)
- **`cft_zarr.shift12jls_compressor`**: JPEG-LS compressor for 12-bit fluorescent data (BytesBytesCodec - supports incremental updates)
- **`cft_zarr.jpeg`**: JPEG codec for RGB images (ArrayBytesCodec)
- **`cft_zarr.shift12jls`**: JPEG-LS codec for 12-bit fluorescent data (ArrayBytesCodec)
- **`cft_zarr.jpegxl`**: JPEG XL codec (ArrayBytesCodec)

## Installation

```bash
pip install cft-zarr
```

## Usage

### Reading Zarr Files

**Important**: You must import `cft_zarr` before opening Zarr files that use these codecs. This registers the codecs with Zarr.

```python
import cft_zarr  # This registers the codecs
import zarr

# Now you can open Zarr files that use custom codecs
arr = zarr.open('rgb.zarr', mode='r')
```

### Using with napari

When opening Zarr files in napari, `cft_zarr` can be auto-registered via the
napari plugin system (installed in the same environment as napari). If you
still have trouble, import `cft_zarr` first:

```python
import cft_zarr  # Register codecs before opening files
import napari

# Now napari can read Zarr files with custom codecs
viewer = napari.Viewer()
viewer.open('path/to/file.zarr')  # Will work with custom codecs
```

Or in a Python script before launching napari:

```python
import cft_zarr  # Must import before opening Zarr files
import napari

viewer = napari.Viewer()
viewer.open('rgb.zarr')
napari.run()
```

### Creating Zarr Arrays with Custom Codecs

```python
import cft_zarr
from cft_zarr import JPEGCompressor, Shift12JLSCompressor
import zarr

# Create RGB array with JPEG compression
rgb_array = zarr.create(
    shape=(100, 512, 512, 3),
    chunks=(4, 512, 512, 3),
    dtype='uint8',
    compressors=[JPEGCompressor(level=85)]
)

# Create fluorescent array with Shift12JLS compression
fl_array = zarr.create(
    shape=(100, 512, 512),
    chunks=(4, 512, 512),
    dtype='uint16',
    compressors=[Shift12JLSCompressor()]
)
```
trajpy-ui 0.2.0
Trajpy UI - empowering feature engineering for trajectory analysis across domains.
Requires Python: <4.0,>=3.11
Keywords: python, repeated-mesurements, time-series, trajectory

# TrajPy User Interface

## Overview
TrajPy GUI is a graphical interface for analyzing particle trajectories using the TrajPy library. It allows you to upload trajectory files, visualize them, compute various physical features, and export results.
Getting Started

1. **Upload Trajectory Files**
- Click "Upload one or several files (CSV or YAML)"
- Select one or multiple files:
    - CSV files: Should contain trajectory coordinates (skip first line as header)
    - YAML files: LAMMPS dump format supported
- Multiple files can be uploaded sequentially (they accumulate)
- Status shows total files and trajectories loaded

2. **Visualize Trajectories**

- Click "Plot Trajectories" to display all loaded trajectories
- The plot shows:
    - Trajectory paths in different colors
    - Start points (circles)
    - End points (squares)
    - Legend identifying each trajectory
  
3. **Select Features to Compute**
- Choose from the following analysis features:
    - Anomalous Exponent: Characterizes diffusion type (α)
    - MSD Ratio: Mean Square Displacement ratio
    - Fractal dimension: Self-similarity measure 
    - Anisotropy & Kurtosis: Shape and directional properties 
    - Straightness: Path linearity 
    - Efficiency: Direct vs. actual path ratio 
    - Gaussianity: Displacement distribution normality 
    - Diffusivity: Diffusion coefficient 
    - Confinement Prob.: Probability of confined motion

4. **Compute Results**
- Select desired features using checkboxes
- Click "Compute!"
- Results for the first trajectory appear in the status area
- Warnings/errors are displayed if computation fails for any trajectory

5. **Save Results**
Click "Save results (CSV)" after computation
A CSV file (trajpy_results.csv) downloads automatically
Contains computed features for all trajectories

6. **About**
Click "About" for version information and contact details

## Tips
- Upload files incrementally to analyze multiple datasets together
- NaN/Error values indicate computation issues for specific features
- Plot trajectories before computing to verify data loaded correctly