semvec/pypi-packages huggingface dataset:
mojxml 0.5.4
A tool for fast conversion of Japanese "MOJ Map XML" (land registration polygons) into geospatial formats.
Requires Python: >=3.10.0

# mojxml-py

[![Test](https://github.com/MIERUNE/mojxml-py/actions/workflows/test.yml/badge.svg)](https://github.com/MIERUNE/mojxml-py/actions/workflows/test.yml) [![PyPI Package](https://img.shields.io/pypi/v/mojxml?color=%2334D058&label=PyPI%20package)](https://pypi.org/project/mojxml) [![codecov](https://codecov.io/gh/MIERUNE/mojxml-py/branch/main/graph/badge.svg?token=mkeysxV2xy)](https://codecov.io/gh/MIERUNE/mojxml-py)

法務省登記所備付地図データ（地図XML）を各種GISデータ形式 (GeoJSON, GeoPackage, FlatGeobuf, etc.) に変換するコマンドラインツールです。Pythonライブラリとしても使用できます。

A tool for fast conversion of Japanese "MOJ Map XML" (land registration polygons) into geospatial format, written in Python.

特徴：

- デジタル庁のリファレンス実装 ([mojxml2geojson](https://github.com/JDA-DM/mojxml2geojson)) よりも高速に変換できます（XMLパーサとして [lxml](https://github.com/lxml/lxml) (libxml2) を使用しています）。
- XMLファイルだけなく、配布されているZIPファイルをそのまま入力することができます。
- GeoJSON以外の効率的かつコンパクトな形式 (GeoPackage, FlatGeobuf, etc.) でも出力できます。

## インストール

Ubuntu/Debian:

```bash
apt install libgdal-dev
pip3 install mojxml
```

macOS (Homebrew):

```bash
brew install gdal
pip3 install mojxml
```

## コマンドラインインタフェース

```
Usage: mojxml2ogr [OPTIONS] DST_FILE SRC_FILES...

  Convert MoJ XMLs to GeoJSON/GeoPackage/FlatGeobuf/etc.

  DST_FILE: output filename (.geojson, .gpkg, .fgb, etc.)

  SRC_FILES: one or more .xml/.zip files

Options:
  --worker [multiprocess|thread|single]
                                  [default: multiprocess]
  -a, --arbitrary                 Include 任意座標系
  -c, --chikugai                  Include 地区外 and 別図
```

- 出力フォーマットは、出力ファイル名の拡張子から自動で判断されます。
- `-a` オプションを指定すると、任意座標系のXMLファイルも変換されます。
- `-c` オプションを指定すると、地番が「地区外」「別図」の地物も出力されます。

### 使用例

```bash
# XMLファイルをGeoJSONに変換する
❯ mojxml2ogr output.geojson 15222-1107-1553.xml

# 複数のXMLファイルを1つのGeoJSONに変換する
❯ mojxml2ogr output.geojson 15222-1107-1553.xml 15222-1107-1554.xml

# 配布用ZIPファイルに含まれる全XMLをFlatGeobufに変換する
❯ mojxml2ogr output.fgb 15222-1107.zip

# 3つのZIPファイルをまとめて1つのFlatGeobufに変換する
❯ mojxml2ogr output.fgb 01202-4400.zip 01236-4400.zip 01337-4400.zip

# ZIPファイルを1段階展開して出てくるZIPファイルも入力できる
❯ mojxml2ogr output.fgb 15222-1107-15*.zip
```

## License

MIT License

### テスト用データについて

[`./testdata/`](./testdata/) に含まれるZIPファイルおよびXMLファイルは、「登記所備付地図データ」（法務省）を、登記所備付地図データ利用規約に基づいて複製したものです。

## Authors

- Taku Fukada ([@ciscorn](https://github.com/ciscorn)) - Original Author
- MIERUNE Inc.
TPHATE 1.2.1
Temporal PHATE (TPHATE) is a python package for learning robust manifold representations of timeseries data with high temporal autocorrelation.
Requires Python: >=3.8

[![PyPI version](https://badge.fury.io/py/TPHATE.svg)](https://badge.fury.io/py/TPHATE)[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7637522.svg)](https://doi.org/10.5281/zenodo.7637522)



## Quick Start
If you would like to get started using T-PHATE, check out our example below.

If you have loaded a data matrix `data` in python (with samples on rows, features on columns, where you believe the samples are non-independent), you can run TPHATE as follows:

```
import tphate

tphate_op = tphate.TPHATE()
data_tphate = tphate_op.fit_transform(data)
```


## Temporal PHATE

Temporal PHATE (T-PHATE) is a python package for learning robust manifold representations of timeseries data with high temporal autocorrelation. TPHATE does so with a dual-kernel approach, estimating the first view as an affinity matrix based on PHATE manifold geometry, and the second view as summarizing the transitional probability between two timepoints based on the autocorrelation of the signal. For more information, see our [publication in Nature Computational Science](https://www.nature.com/articles/s43588-023-00419-0).

Busch, et al. **Multi-view manifold learning of human brain-state trajectories**. 2023. *Nature Computational Science.*


## Installation

`pip install tphate`
vocalizr 0.1.0
Voice Generator part of the Chatacter Backend
Requires Python: <3.13,>=3.12
Keywords: audio, kokoro, voice

---
title: Vocalizr
emoji: 🔊
colorFrom: purple
colorTo: yellow
sdk: docker
app_port: 7860
---

## Vocalizr: Voice Generator part of the Chatacter Backend

[![Build](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/build.yaml/badge.svg)](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/build.yaml)
[![CI Tools](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/ci_tools.yaml/badge.svg)](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/ci_tools.yaml)
[![CodeQL](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/github-code-scanning/codeql)
[![Copilot](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/copilot-swe-agent/copilot/badge.svg)](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/copilot-swe-agent/copilot)
[![Copilot code review](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/copilot-pull-request-reviewer/copilot-pull-request-reviewer/badge.svg)](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/copilot-pull-request-reviewer/copilot-pull-request-reviewer)
[![Dependabot Updates](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/dependabot/dependabot-updates/badge.svg)](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/dependabot/dependabot-updates)
[![Release](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/release.yaml/badge.svg)](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/release.yaml)
[![Test](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/test.yaml/badge.svg)](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/test.yaml)
[![Version](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/version.yaml/badge.svg)](https://github.com/AlphaSphereDotAI/vocalizr/actions/workflows/version.yaml)
ringity 0.4a4
Calculates ring scores for various data structures
Requires Python: >=3.8
Keywords: network, ring-structure, tda

.. |PyPI| image:: https://img.shields.io/pypi/v/ringity.svg
    :target: https://pypi.org/project/ringity/
.. |Python| image:: https://img.shields.io/pypi/pyversions/ringity.svg
    :target: https://pypi.org/project/ringity/
.. |License| image:: https://img.shields.io/pypi/l/ringity.svg
    :target: https://github.com/ClusterDuck123/ringity/blob/main/LICENSE.txt
.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4908927.svg
    :target: https://doi.org/10.5281/zenodo.4908927
.. |version| image:: https://img.shields.io/github/v/tag/ClusterDuck123/ringity?style=social
    :target: https://github.com/ClusterDuck123/ringity/tags

💍 ringity
===========

|DOI| |license| |version|

ringity is a Python package to analyze various data structures with respect to their ring structure.

- **Source:** https://github.com/ClusterDuck123/ringity
- **Issues:** https://github.com/ClusterDuck123/ringity/issues
- **Docs:** “coming soon”

🚀 Quick Examples
--------------------------

Ring Score of Networks
^^^^^^^^^^^^^^^^^^^^^^

Calculate ring score as described in [1]:

.. code-block:: python

    import ringity as rng
    import networkx as nx
    G = nx.Graph()
    G.add_edges_from([(i%100,(i+1)%100) for i in range(100)])
    dgm = rng.pdiagram(G)   # constructs a persistence diagram from G
    dgm.ring_score()        # -> 1

[1]: Paper not available yet.

Ring Score of Point Clouds
^^^^^^^^^^^^^^^^^^^^^^^^^^

Calculate ring score as described in [2]:

.. code-block:: python

    import numpy as np
    import ringity as rng
    t = np.linspace(0, 2*np.pi, 100)
    X = np.array((np.cos(t),np.sin(t))).T
    rng.ring_score(X)     # -> 1

[2]: Paper not available yet.

📦 Install
-----------

Install the latest version of ringity:

.. code-block:: console

   $ pip install ringity

🐞 Bugs
---------

Please report any bugs that you find `here <https://github.com/ClusterDuck123/ringity/issues>`_.
Or, even better, fork the repository on `GitHub <https://github.com/ClusterDuck123/ringity/>`_
and create a pull request. All inputs, suggestions and changes are more than welcome!

📄 License
-----------

MIT — see `LICENSE <./LICENSE.txt>`_.

📚 How to cite
---------------

DOI: `10.5281/zenodo.4908927 <https://doi.org/10.5281/zenodo.4908927>`_
aceti 1.0.2
A simple package to store and create maps for an experiment

aceti_maps
===========

* Python library to make an efficient use of data
* License: MIT
* Compatible With: python 3.0+

Develop API Usage
-----------------

available maps
- Alamillo
- Alamillo11x15
- Alamillo30x49

<pre>
<code>
>>>from aceti_maps import import_map
>>>base_matrix, lat_lon_map  = import_map("alamillo11x15")

</code>
</pre>
speechmatics-tts 0.1.2
Speechmatics TTS API Client
Requires Python: >=3.9
Keywords: speechmatics, speech-to-text, tts, transcription, api

# Speechmatics TTS API Client

[![PyPI](https://img.shields.io/pypi/v/speechmatics-tts)](https://pypi.org/project/speechmatics-tts/)
![PythonSupport](https://img.shields.io/badge/Python-3.9%2B-green)

Async Python client for Speechmatics TTS API.

## Features

- Async API client with comprehensive error handling
- Type hints throughout for better IDE support
- Environment variable support for credentials

## Installation

```bash
pip install speechmatics-tts
```

## Usage

### Quick Start

```python
import asyncio

from speechmatics.tts import AsyncClient, Voice, OutputFormat

# Generate speech data from text and save to WAV file
async def main():
    async with AsyncClient() as client:
        async with await client.generate(
            text="Welcome to the future of voice AI!",
            voice=Voice.SARAH,
            output_format=OutputFormat.WAV_16000
        ) as response:
            audio = b''.join([chunk async for chunk in response.content.iter_chunked(1024)])
            with open("output.wav", "wb") as f:
                f.write(audio)


# Run the async main function
if __name__ == "__main__":
    asyncio.run(main())

```

### Error Handling

```python
import asyncio
from speechmatics.tts import (
    AsyncClient,
    AuthenticationError,
    TimeoutError
)

async def main():
    try:
        async with AsyncClient() as client:
            response = await client.generate(text="Hello, this is the Speechmatics TTS API. We are excited to have you here!")

    except AuthenticationError:
        print("Invalid API key")
    except JobError as e:
        print(f"Job processing failed: {e}")
    except TimeoutError as e:
        print(f"Job timed out: {e}")
    except FileNotFoundError:
        print("Audio file not found")

asyncio.run(main())
```

### Connection Configuration

```python
import asyncio
from speechmatics.tts import AsyncClient, ConnectionConfig

async def main():
    # Custom connection settings
    config = ConnectionConfig(
        url="https://preview.tts.speechmatics.com",
        api_key="your-api-key",
        connect_timeout=30.0,
        operation_timeout=600.0
    )

    async with AsyncClient(conn_config=config) as client:
        response = await client.generate(text="Hello World")
   

asyncio.run(main())
```

## Logging

The client supports logging with job id tracing for debugging. To increase logging verbosity, set `DEBUG` level in your example code:

```python
import logging
import sys

logging.basicConfig(
    level=logging.DEBUG,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.StreamHandler(sys.stdout)
    ]
)
```

## Environment Variables

The client supports the following environment variables:

- `SPEECHMATICS_API_KEY`: Your Speechmatics API key
- `SPEECHMATICS_TTS_URL`: Custom API endpoint URL (optional)