semvec/pypi-packages huggingface dataset:
aioqsw 0.4.2
Library to control QNAP QSW devices
Requires Python: >=3.12
Keywords: qnap, network, switch, home

# aioqsw
[![Latest Version][mdversion-button]][md-pypi]
[![Python Versions][pyversion-button]][md-pypi]
[![License: Apache 2.0][apache-button]](LICENSE)

[apache-button]: https://img.shields.io/badge/License-Apache%202.0-blue.svg
[md-pypi]: https://pypi.org/project/aioqsw
[mdversion-button]: https://img.shields.io/pypi/v/aioqsw.svg
[pyversion-button]: https://img.shields.io/pypi/pyversions/aioqsw.svg

Python library to control QNAP QSW devices.

## Requirements
- Python >= 3.12

## Install
```bash
pip install aioqsw
```

## Install from Source
Run the following command inside this folder
```bash
pip install --upgrade .
```

## Examples
Examples can be found in the `examples` folder
nicescholia 0.0.4
nicegui based Scholia
Requires Python: >=3.11

# nicescholia
nicegui based scholia

| | |
| :--- | :--- |
| **PyPi** | [![PyPI Status](https://img.shields.io/pypi/v/nicescholia.svg)](https://pypi.python.org/pypi/nicescholia/) [![License](https://img.shields.io/github/license/WolfgangFahl/nicescholia.svg)](https://www.apache.org/licenses/LICENSE-2.0) [![pypi](https://img.shields.io/pypi/pyversions/nicescholia)](https://pypi.org/project/nicescholia/) [![format](https://img.shields.io/pypi/format/nicescholia)](https://pypi.org/project/nicescholia/) [![downloads](https://img.shields.io/pypi/dd/nicescholia)](https://pypi.org/project/nicescholia/) |
| **GitHub** | [![Github Actions Build](https://github.com/WolfgangFahl/nicescholia/actions/workflows/build.yml/badge.svg)](https://github.com/WolfgangFahl/nicescholia/actions/workflows/build.yml) [![Release](https://img.shields.io/github/v/release/WolfgangFahl/nicescholia)](https://github.com/WolfgangFahl/nicescholia/releases) [![Contributors](https://img.shields.io/github/contributors/WolfgangFahl/nicescholia)](https://github.com/WolfgangFahl/nicescholia/graphs/contributors) [![Last Commit](https://img.shields.io/github/last-commit/WolfgangFahl/nicescholia)](https://github.com/WolfgangFahl/nicescholia/commits/) [![GitHub issues](https://img.shields.io/github/issues/WolfgangFahl/nicescholia.svg)](https://github.com/WolfgangFahl/nicescholia/issues) [![GitHub closed issues](https://img.shields.io/github/issues-closed/WolfgangFahl/nicescholia.svg)](https://github.com/WolfgangFahl/nicescholia/issues/?q=is%3Aissue+is%3Aclosed) |
| **Code** | [![style-black](https://img.shields.io/badge/%20style-black-000000.svg)](https://github.com/psf/black) [![imports-isort](https://img.shields.io/badge/%20imports-isort-%231674b1)](https://pycqa.github.io/isort/) |
| **Docs** | [![API Docs](https://img.shields.io/badge/API-Documentation-blue)](https://WolfgangFahl.github.io/nicescholia/) [![formatter-docformatter](https://img.shields.io/badge/%20formatter-docformatter-fedcba.svg)](https://github.com/PyCQA/docformatter) [![style-google](https://img.shields.io/badge/%20style-google-3666d6.svg)](https://google.github.io/styleguide/pyguide.html#s3.8-comments-and-docstrings) |
[![DOI](https://zenodo.org/badge/1118183488.svg)](https://doi.org/10.5281/zenodo.17967779)

# Demo
https://nicescholia.wikidata.dbis.rwth-aachen.de/
macroframe-forecast 0.1.6
Macroframework forecasting with accounting identities
Requires Python: >=3.11
Keywords: econometrics, forecasting, high-dimensional forecasting, macroeconomic identities, macroframework

# `macroframe-forecast`: a Python package to assist with macroframework forecasting


[![!pypi](https://img.shields.io/pypi/v/macroframe-forecast?color=green)](https://pypi.org/project/macroframe-forecast/) [![Downloads](https://static.pepy.tech/personalized-badge/macroframe-forecast?period=total&units=international_system&left_color=grey&right_color=blue&left_text=cumulative%20(pypi))](https://pepy.tech/project/macroframe-forecast)

This package is based on the following papers:
* [A Python Package to Assist Macroframework Forecasting: Concepts and Examples (2025)](https://www.imf.org/en/Publications/WP/Issues/2025/08/29/A-Python-Package-to-Assist-Macroframework-Forecasting-Concepts-and-Examples-570041).
* [Smooth Forecast Reconciliation (2024)](https://www.imf.org/en/Publications/WP/Issues/2024/03/22/Smooth-Forecast-Reconciliation-546654)
* [Systematizing Macroframework Forecasting: High-Dimensional Conditional Forecasting with Accounting Identities (2023)](https://link.springer.com/article/10.1057/s41308-023-00225-8)

# Documentation

Please refer to [this link](https://sakaiando.github.io/macroframe-forecast/) for documentation.

# Installation

To install the `macroframe-forecast` package, run the following from the repository root:

```shell
pip install macroframe-forecast
```

# Quick start

```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from macroframe_forecast import MFF

# true data
df_true = pd.DataFrame({
    'var1': np.random.randn(30),  # 100 random values from normal distribution
    'var2': np.random.randn(30)
})
df_true['sum'] = df_true['var1'] + df_true['var2']

# input dataframe
df = df_true.copy()
fh = 5
df.iloc[-fh:, 1:] = np.nan

# apply MFF
m = MFF(df, equality_constraints=['var1_? + var2_? - sum_?'])
df2 = m.fit()

# plots results
fig,axes = plt.subplots(3,1,sharey=True, figsize=(9,9))

axes[0].plot(df2['var2'], label='forecasted var2')
axes[0].plot(df_true['var2'], label='true var2')
axes[0].legend()

axes[1].plot(df2['sum'], label='forecasted sum')
axes[1].plot(df_true['sum'], label='true sum')
axes[1].legend()

axes[2].plot( df2['var1'] + df2['var2'] - df2['sum'], label='summation error')
axes[2].legend()
```

# Disclaimer

Reuse of this tool and IMF information does not imply any endorsement of the research and/or product. Any research presented should not be reported as representing the views of the IMF, its Executive Board, or member governments.