semvec/pypi-packages huggingface dataset:
usdrate 0.2.6
usdrate is a lightweight CLI tool that retrieves and caches USD/JPY mid rate exchange data from 77bank's website. It automatically fetches data for the current and past 7 years, stores the results in a package-local cache, and allows you to easily query the mid rate for any specific date.
Requires Python: >=3.10

# usdrate

`usdrate`は、七十七銀行の為替情報サイトからUSD/JPYの仲値データを取得・キャッシュし、指定した日付の為替レートを簡単に問い合わせるためのCLIツールです。RSUの申告の際に過去にVestされた際の仲値を一々調べるのが辛くなって作りました。

## 動作概要

- キャッシュデータの確認をし、なかった場合は現在の西暦および過去7年分（合計8年分）の為替データを七十七銀行から取得します。
- キャッシュデータはパッケージ内のキャッシュディレクトリに保存され、以降の実行で再利用されます。
- 指定した日付のUSD/JPY仲値をコマンドラインから問い合わせができます。

## インストール

以下のコマンドでインストールできます:

    pip install usdrate

**ChromeDriverについて**

`-p`オプションを利用する場合はChromeDriverが必要になります。

ChromeDriverのインストール方法については、以下のURLを参照し、手動でインストールしてください:

https://developer.chrome.com/docs/chromedriver/downloads?hl=ja

## 使い方

### 基本的な使い方

指定した日付の為替仲値を取得するには、以下のようにコマンドを実行します:

    usdrate 2024/08/27
    The USD/JPY mid rate on 2024-08-27 is 144.86.

このコマンドは、2024年8月27日のUSD/JPY仲値を表示します。

### キャッシュの更新

キャッシュを更新したい場合は、`--update`または`-u`オプションを使用します:

    usdrate 2024/01/01 --update

### PDF出力機能

`-p`（または`--pdfoutput`）オプションを指定すると、確定申告時の根拠資料となるPDFもダウンロードします。
- 過去の年度のPDFは、既に存在する場合はダウンロードされません。
- 今年のデータに関しては毎日更新されるため、`-p`が指定された場合は既存のPDFがあっても最新のPDFに更新されます。

### 一括問い合わせ機能

`-b`（または`--bulk`）オプションを使用すると、Enter区切りで日付が記載されたファイルを指定できます。
この場合、ファイル内の各日付に対してUSD/JPYの仲値が順次表示されます。

#### 一括問い合わせファイルの例

以下は、一括問い合わせ用のファイル例です。
各行に問い合わせたい日付を記入します:

    2024/08/27
    2024/08/28
    2024/09/01

このようなファイルを用意し、`-b`オプションで指定してください。

## Release情報

### 0.2.3リリース

以下の機能拡張が追加されています:
- **Retry処理の追加:** 一時的に七十七銀行へのアクセスができなかったとしても3回retryするように変更。

### 0.2.2リリース

以下の機能拡張が追加されています:
- **loggerの導入:** 一部print文をloggerに置き換えました。

### 0.2.0リリース

以下の機能拡張が追加されています:

- **PDF出力:** HTMLからPDFを生成し、キャッシュディレクトリ内の`pdfs`フォルダに保存します。
  特に、今年のデータは常に最新の状態に更新されます。

- **一括問い合わせ:** 複数の日付を1つのファイルに記載することで、一度に複数の日付の仲値を問い合わせることができます。

## ライセンス

このプロジェクトはMITライセンスの下で公開されています。

## 作者

kon0n0 (kono@ms1.kononet.jp)
astro-mcp 0.0.0a5
Model Context Protocol (MCP) server for interacting with the Astro and Airflow APIs
Requires Python: >=3.12

# astro-mcp

Model Context Protocol (MCP) server for interacting with the Astro and Airflow APIs.

## Installation

```
uv pip install mcp[cli]
python -m mcp.server install git+https://github.com/astronomer/astro-mcp.git
```

## Usage

To use the Astro MCP server with the MCP CLI:

```
# Set your API key
export ASTRO_API_KEY=your_api_key

# Start a chat with the server
mcp chat --server astro

# Or specify the server directly
mcp chat --server astro --server-args api_key=your_api_key
```

## Development

```
# Clone the repo
git clone https://github.com/astronomer/astro-mcp.git
cd astro-mcp

# Install dependencies
uv pip install -e "."

# Download the API specs
python scripts/download_specs.py

# Run the server directly
python -m src
```

## Build and Distribute

To build the package with the API specs bundled:

```
# First make sure the API specs are downloaded
python scripts/download_specs.py

# Build the package
uv pip install build
python -m build

# The built package will be in the dist/ directory
# Install it directly
uv pip install dist/*.whl

# Or publish to PyPI
uv pip install twine
twine upload dist/*
```
tssynth 0.2.2
A library for generating synthetic time series data
Requires Python: >=3.9

# tssynth

[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

Generate realistic synthetic time series data with customizable patterns, seasonality, and noise components. Perfect for testing time series algorithms, machine learning models, or generating sample datasets.

## Features

✨ **Flexible Generation**: Create time series with various components:
- Linear, exponential, or custom trends
- Multiple seasonality patterns
- Configurable noise distributions
- Anomaly injection

🚀 **Easy to Use**: Simple, intuitive API built on numpy and pandas
```python
import tssynth as ts

# Generate a time series with multiple components
data = ts.generate(
    length=365,                    # One year of daily data
    trend="exponential",           # Exponential growth
    seasonality=["weekly", "yearly"],
    noise_type="gaussian",
    noise_level=0.05
)
```

## Installation

```bash
pip install tssynth
```

## Documentation

For detailed usage examples and API reference, visit our [documentation](https://tssynth.readthedocs.io).

### Quick Examples

```python
# Generate a simple trend
basic_trend = ts.generate(length=100, trend="linear")

# Add seasonality and anomalies
complex_ts = ts.generate(
    length=1000,
    trend="exponential",
    seasonality="monthly",
    anomalies={"frequency": 0.01, "magnitude": 3.0}
)

# Export to pandas DataFrame
df = complex_ts.to_dataframe()
```

## Development

```bash
# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black .
isort .
```

## License

MIT License - See LICENSE file for details.

## Author

Will Judge (williamjudge94@gmail.com)
mcp-tree-explorer 0.1.4
MCP tool that provides directory tree visualization for Cursor
Requires Python: >=3.8
Keywords: mcp, tree, directory, visualization, cursor

# MCP Tree Explorer

A lightweight MCP tool for directory tree visualization in Cursor.

## Features

- Visualize directory structures with the `tree` command
- Smart filtering of common large directories (node_modules, .git, etc.)
- Customizable ignore and keep patterns
- Automatic installation of the `tree` command if not available
- Works on Windows, macOS, and Linux

## Installation

```bash
# Using pip
pip install mcp-tree-explorer

# Using uv
uv pip install mcp-tree-explorer
```

## Usage with Cursor

Configure Cursor to use this tool by editing your Cursor configuration file:

```json
{
  "tools": {
    "mcp-tree-explorer": {
      "command": "mcp-tree-explorer"
    }
  }
}
```

## Tool Parameters

The `project_tree` tool accepts these parameters:

- `directory`: The directory to examine (default: current directory)
- `depth`: Maximum depth of the tree (optional, unlimited if not specified)
- `ignore`: Additional patterns to ignore, comma-separated (optional)
- `keep`: Patterns to keep even if they match auto-ignore patterns, comma-separated (optional)

Example usage in Cursor:
- "Show me the directory structure of this project"
- "Run tree in the src directory but include node_modules"
- "Show me a tree of the project, ignoring test files but keeping the dist folder"

## Development

```bash
# Clone the repository
git clone https://github.com/yourusername/mcp-tree-explorer.git
cd mcp-tree-explorer

# Install in development mode
uv pip install -e .
```

## License

MIT
econometrics-python 1.0.2
Package for econometric.
Requires Python: >=3.6

# Econometrics 

## Author : Tran Minh Tam

## Installation

You can install the package via pip: 
```
pip install econometrics_python
```

## Linear Regression

A simple econometrics package for linear regression and plotting.


### Run with the package Linear Regression
```python
python LinearRegression/run_regression.py linear_regression.csv

hoặc python -m LinearRegression.run_regression linear_regression.csv      
```

### To get the values of the model's parameters
``` python 
import statsmodels.api as sm
import pandas as pd

# Giả sử bạn có một DataFrame data với cột 'X' và 'Y'
data = pd.read_csv("linear_regression.csv", sep=';')  # Đọc dữ liệu từ CSV
X = data['X']
y = data['Y']

# Thêm một cột intercept (hệ số tự do) vào X
X = sm.add_constant(X)

# Huấn luyện mô hình OLS
model = sm.OLS(y, X).fit()

# Trích xuất các giá trị từ kết quả mô hình
print(model.summary())  # Hiển thị tóm tắt kết quả

# Truy xuất các giá trị cụ thể
slope = model.params[1]  # Hệ số góc (slope)
intercept = model.params[0]  # Hệ số chặn (intercept)
r_squared = model.rsquared  # R-squared
p_values = model.pvalues  # P-values của các hệ số

# Hiển thị các giá trị trích xuất
print(f"Slope: {slope}")
print(f"Intercept: {intercept}")
print(f"R-squared: {r_squared}")
print(f"P-values: {p_values}")

```

## Multiple Linear Regression
### Run with the package Multiple Linear Regression
```python

python MultipleRegression/run_regression.py <data_file.csv> <target_column_name>

Example:
python MultipleRegression/run_regression.py du_lieu_2_bien_doc_lap.csv Giá
```

## Time Series Regression

### Run TSR with LSTM model
```python
Chạy câu lệnh : python TimeSeriesRegression\run_lstm.py time_series_data.csv (Bạn thay tên time_series_data.csv bằng tên file data của bạn nhé!)
```
tranqu 1.0.0
Tranqu: A one-stop framework for transpilers across multiple quantum programming libraries and formats for quantum circuits
Requires Python: >=3.10

![OQTOPUS logo](./docs/asset/oqtopus_logo.png)

# Tranqu

[![CI](https://github.com/oqtopus-team/tranqu/actions/workflows/ci.yaml/badge.svg)](https://github.com/oqtopus-team/tranqu/actions/workflows/ci.yaml)
[![codecov](https://codecov.io/gh/oqtopus-team/tranqu/graph/badge.svg?token=RCXTMMXOMV)](https://codecov.io/gh/oqtopus-team/tranqu)
[![pypi version](https://img.shields.io/pypi/v/tranqu.svg)](https://pypi.org/project/tranqu/)
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

Tranqu is a one-stop framework for transpilers across multiple quantum programming libraries and formats for quantum circuits.

## Overview

Since quantum circuit transpilation involves solving an NP-complete problem, developers typically rely on heuristic algorithms.
While many transpiler libraries exist, the optimal transpiler and its options often depend on the quantum circuit and device information.
Therefore, the ability to leverage various vendors' transpilers is highly desirable.
By using Tranqu, you can run multiple vendors' transpilers without being bothered by the conversion of quantum circuits or device information.

The name "Tranqu" is derived from "tranquility," reflecting the desire to enable transpilers' smooth and stress-free use.

## Features

- **Program Converter**: Converts quantum circuits into another library's format.
- **Device Converter**: Converts device information into another library's format.
- **Target Transpilation**: Converts quantum circuits and device information to run the target transpiler. Users familiar with the transpiler can easily specify its options.
- **Transpilation Statistics**: Outputs statistical data before and after transpilation.
- **Using Custom Transpilers and Converters**: Uses user-created Transpilers, Program Converters, and Device Converters in Tranqu.

## Documentation

- [Documentation Home](https://tranqu.readthedocs.io/)

## Citation

You can use the DOI to cite Tranqu in your research.

[![DOI](https://zenodo.org/badge/898082553.svg)](https://zenodo.org/badge/latestdoi/898082553)

Citation information is also available in the [CITATION](https://github.com/oqtopus-team/tranqu/blob/main/CITATION.cff) file.

## Contact

You can contact us by creating an issue in this repository or by email:

- [oqtopus-team[at]googlegroups.com](mailto:oqtopus-team[at]googlegroups.com)

## License

Tranqu is released under the [Apache License 2.0](https://github.com/oqtopus-team/tranqu/blob/main/LICENSE).