semvec/pypi-packages huggingface dataset:
ldif2json 1.0.8
A robust Python tool for converting LDAP Data Interchange Format (LDIF) files to JSON with support for hierarchical nesting and Base64 decoding.
Requires Python: >=3.9

# LDIF to JSON Converter

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A robust Python tool for converting LDAP Data Interchange Format (LDIF) files to JSON with support for hierarchical nesting and Base64 decoding.

## Features

- **LDIF Parsing**: Convert LDIF files to structured JSON
- **Base64 Handling**: Optional decoding of Base64-encoded attributes (`-d` flag)
- **Hierarchical Nesting**: Organize entries by DN structure (`-n` option)
- **Flexible I/O**: Works with files or stdin/stdout
- **Custom Formatting**: Control JSON indentation (`-i` option)

## Installation

```bash
pip install ldif2json
```

## Usage

Basic conversion:
```bash
ldif2json input.ldif -o output.json
```

With hierarchical nesting:
```bash
ldif2json input.ldif --nest -o output.json
```

With Base64 decoding:
```bash
ldif2json input.ldif --decode -o output.json
```

Using pipes:
```bash
ldapsearch -x -b "dc=example,dc=com" | ldif2json --nest children
```

## Options

```
usage: ldif2json.py [-h] [-o OUTPUT] [-i INDENT] [-n [ATTRIBUTE]] [-d] [input_file]

Convert LDIF to JSON with optional Base64 decoding and nesting

positional arguments:
  input_file            Input LDIF file (default: stdin)

options:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        Output JSON file (default: stdout)
  -i INDENT, --indent INDENT
                        JSON indentation spaces (0 for compact output) (default: 2)
  -n [ATTRIBUTE], --nest [ATTRIBUTE]
                        Enable hierarchical nesting under specified attribute (default: None)
  -d, --decode          Decode Base64-encoded attributes (marked with ::) (default: False)

```

## License

MIT License
quadfeather 2.3.0rc1
Quadtree tiling from CSV/Apache Arrow for use with deepscatter in the browser.
Requires Python: >=3.9

# Quadfeather

Quickly create deep quadtree structures from CSV or feather files to allow serving over HTTP or quicker access to random portions of spatial data.

Each node in the quadtree is a single feather file containing some number of rows (default 2**16) and arrow 
metadata indicating the existence of children.


From the command line (basic):

WORK IN PROGRESS BELOW.

```bash
python -m pip install git+https://github.com/bmschmidt/quadfeather
quadfeather-test-data 255000 # populates a csv at tmp.csv with 255000 items
quadfeather --files tmp.csv --destination tiles
```

From a python iterable of Arrow record batches (advanced)

```python


```

## Todo

1. Allow splitting off of some columns.
2. Explain JS integration.
3. Octtree for 3-d data.

### Optional todo

1. Other partition strategies? Quadtree is easy to understand.,
2. Linked-open data standards.