semvec/pypi-packages huggingface dataset:
cpf-utils-raul 1.1.1
Validação e formatação de documentos brasileiros (CPF, CNPJ, CEP, RG)
License: MIT
Requires Python: >=3.8

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tibetan-sanskrit-transliteration 0.1.3
Transliterate Tibetan-encoded Sanskrit mantras to IAST and phonetics
License: MIT
Requires Python: >=3.8
Keywords: tibetan, sanskrit, transliteration, iast, phonetics, mantra

# Tibetan Sanskrit Transliteration (Python)

A Python library for transliterating Tibetan-encoded Sanskrit mantras to IAST and phonetics.

## Installation

```bash
pip install tibetan-sanskrit-transliteration
```

Or install from source:

```bash
pip install -e .
```

## Usage

### Basic Usage

```python
from tibetan_sanskrit_transliteration import transliterate

# Transliterate to IAST (default)
tibetan = "ཨོཾ་མ་ཎི་པདྨེ་ཧཱུྃ།"
result = transliterate(tibetan)
print(result)  # "oṃ maṇi padmé hūṃ"

# Transliterate to phonetics
result = transliterate(tibetan, mode='phonetics')
print(result)  # "om mani padme hung"
```

### Options

```python
from tibetan_sanskrit_transliteration import transliterate

result = transliterate(
    tibetan,
    mode='iast',           # 'iast' or 'phonetics'
    capitalize=True,       # Capitalize first letter of each group
    anusvara_style='ṁ'     # 'ṃ' (default) or 'ṁ'
)
```

### Using the Class

For repeated transliterations, use the class to avoid reloading the CSV:

```python
from tibetan_sanskrit_transliteration import TibetanSanskritTransliterator

transliterator = TibetanSanskritTransliterator()

result1 = transliterator.transliterate("ཨོཾ་མ་ཎི་པདྨེ་ཧཱུྃ།")
result2 = transliterator.transliterate("ཨོཾ་ཨཱཿཧཱུྃ།")
```

### Custom Replacement Data

You can provide your own CSV file with replacement rules:

```python
from tibetan_sanskrit_transliteration import TibetanSanskritTransliterator

transliterator = TibetanSanskritTransliterator(csv_path='/path/to/custom.csv')
```

## Data Format

The replacement CSV file has three columns:

| Column            | Description                                 |
| ----------------- | ------------------------------------------- |
| `tibetan`         | Tibetan Unicode pattern (may include regex) |
| `transliteration` | IAST transliteration output                 |
| `phonetics`       | Phonetic output (optional)                  |

## License

MIT License - Copyright Padmakara, 2021-present.
tibetan-sanskrit-transliteration-data 0.1.3
Shared replacement map data for transliterating Tibetan-encoded Sanskrit mantras
License: MIT
Requires Python: >=3.8
Keywords: tibetan, sanskrit, transliteration, iast, phonetics, mantra, data

# Tibetan Sanskrit Transliteration Data

Shared replacement map data for transliterating Tibetan-encoded Sanskrit mantras to IAST and phonetics.

Available as both an **npm** and **Python** package.

## Installation

### JavaScript/Node.js

```bash
npm install tibetan-sanskrit-transliteration-data
```

### Python

```bash
pip install tibetan-sanskrit-transliteration-data
```

## Data Format

The `replacements.csv` file contains three columns:

| Column            | Description                                                        |
| ----------------- | ------------------------------------------------------------------ |
| `tibetan`         | Tibetan Unicode pattern (may include regex)                        |
| `transliteration` | IAST transliteration output                                        |
| `phonetics`       | Phonetic output (optional, defaults to normalized transliteration) |

## Usage

### JavaScript/Node.js

```javascript
import { loadReplacements } from "tibetan-sanskrit-transliteration-data";

// Get parsed data (works in both Node.js and browser)
const replacements = loadReplacements();
// Returns: [{ tibetan: "...", transliteration: "...", phonetics: "..." }, ...]

// You can also import the data directly
import { replacements } from "tibetan-sanskrit-transliteration-data";
```

**Note:** This package is compatible with both Node.js and browser environments (including Vite/Webpack builds).

### Python

```python
from tibetan_sanskrit_transliteration_data import load_replacements, get_replacements_path

# Get parsed data
replacements = load_replacements()

# Or get the path to the CSV file
csv_path = get_replacements_path()
```

## Contributing

To add or modify transliteration rules:

1. Edit `replacements.csv`
2. Test changes in both JS and Python implementations
3. Submit a pull request

## Order Matters

Entries are processed in order from top to bottom. More specific patterns should come before more general ones.

## License

MIT License - Copyright Padmakara, 2025-present.
evt3 0.1.0
High-performance EVT 3.0 decoder for Prophesee event cameras
License: MIT
Requires Python: >=3.8
Keywords: event-camera, evt3, prophesee, decoder, neuromorphic, dvs

# EVT3 Python Bindings

High-performance EVT 3.0 decoder for Prophesee event cameras with zero-copy numpy support.

## Installation

```bash
# From source (requires Rust toolchain)
cd evt3-python
pip install maturin
maturin develop

# Or build a wheel
maturin build --release
pip install target/wheels/evt3-*.whl
```

## Usage

```python
import evt3
import numpy as np

# Decode a raw file
events = evt3.decode_file("recording.raw")

# Access metadata
print(f"Decoded {len(events)} events")
print(f"Sensor: {events.sensor_width}x{events.sensor_height}")

# Access as numpy arrays
x = events.x  # np.ndarray[np.uint16]
y = events.y  # np.ndarray[np.uint16]
p = events.polarity  # np.ndarray[np.uint8] (0=OFF, 1=ON)
t = events.timestamp  # np.ndarray[np.uint64] (microseconds)

# Short aliases also work
p = events.p
t = events.t

# Get as dictionary (useful for pandas)
import pandas as pd
df = pd.DataFrame(events.to_dict())

# Decode with trigger events
events, triggers = evt3.decode_file_with_triggers("recording.raw")
trigger_times = triggers.timestamp
trigger_values = triggers.value

# Decode raw bytes (for streaming)
with open("recording.raw", "rb") as f:
    raw_bytes = f.read()
events = evt3.decode_bytes(raw_bytes, sensor_width=1280, sensor_height=720)
```

## Performance

The decoder is implemented in Rust with careful attention to performance:
- Streaming buffer decoding to handle large files
- Columnar data layout for cache-efficient numpy access
- Minimal memory allocations during decoding