semvec/pypi-packages huggingface dataset:
orange3-autogluon-timeseries 1.5.0
AutoGluon Time Series forecasting widget for Orange3 with local Chronos support
License: MIT
Requires Python: >=3.9
Keywords: orange3 add-on, time series, forecasting, autogluon, chronos, local models, offline

# AutoGluon TimeSeries Widget for Orange3

Прогнозирование временных рядов с помощью [AutoGluon TimeSeries](https://auto.gluon.ai/stable/) через интерфейс [Orange3](https://orange.biolab.si/).

## 🧠 Возможности

- Поддержка пользовательских столбцов и частоты
- Автоопределение частоты временного ряда
- Настройка метрики и длины прогноза
- Учет праздничных дней
- Очистка от отрицательных и некорректных значений
- Удобный лог и вывод модели

## 🧪 Зависимости

- Orange3 >= 3.38
- AutoGluon >= 1.3.1
- pandas == 2.2.3
- Python 3.9+
- numpy >= 1.25
- PyQt5 >= 5.15
- matplotlib >= 3.5
- holidays >= 0.20

## 🚀 Установка

```bash
git clone https://github.com/KordyakIM/autogluon-timeseries-widget.git
cd autogluon-timeseries-widget
pip install .
```
or
```bash
pip install orange3-autogluon-timeseries
```

## 📦 Добавление виджета в Orange если устанавливаешь вручную

Скопируйте папку `widget` в директорию пользовательских виджетов Orange:
```bash
mkdir -p ~/orange3-widgets/autogluon_timeseries
cp -r widget/* ~/orange3-widgets/autogluon_timeseries/
```

## 📝 Лицензия

MIT License
terna-py 0.5.5
Python API wrapper for Terna transparency data
License: MIT
Keywords: Terna data api energy

# terna-py
Python client for the Transparency API of Terna, the Italian electricity transmission system operator

[![PyPI Latest Release](https://img.shields.io/pypi/v/terna-py.svg)](https://pypi.org/project/terna-py/)
[![Downloads](https://static.pepy.tech/badge/terna-py)](https://pypi.org/project/terna-py/)

Documentation of the API: https://developer.terna.it/docs/read/APIs_catalog#en

## Installation
`python3 -m pip install terna-py`

## Usage
```python
import terna as trn
import pandas as pd

# Please follow the API documentation to register an account and create credentials
key = '<YOUR API KEY>'
secret = '<YOUR API SECRET>'
client = trn.TernaPandasClient(api_key=key,api_secret=secret)

# Note: you specifically need to set a start= and end= parameter which should be a pandas timestamp with timezone
start = pd.Timestamp("20210101", tz='Europe/Rome')
end = pd.Timestamp("20210131", tz='Europe/Rome')
bzone = ["Centre-North", "Centre-South", "North", "Sardinia", "Sicily", "South", "Calabria", "Italy"]
gen_type = ['Thermal', 'Wind', 'Geothermal', 'Photovoltaic', 'Self-consumption', 'Hydro']
res_gen_type = ['Wind', 'Geothermal', 'Photovoltaic', 'Hydro']
type = ['Thermal', 'Wind', 'Geothermal', 'Photovoltaic', 'Self-consumption', 'Hydro', 'Pumping-consumption', 'Net Foreign Exchange']
year = 2022

# Note: all methods return Pandas DataFrames
df_tload = client.get_total_load(start=start, end=end, bzone=bzone)
df_mload = client.get_market_load(start=start, end=end, bzone=bzone)

df_act_gen = client.get_actual_generation(start=start, end=end, gen_type=gen_type)
df_res_gen = client.get_renewable_generation(start=start, end=end, res_gen_type=res_gen_type)
df_ener_bal = client.get_energy_balance(start=start, end=end, type=type)
df_cap = client.get_installed_capacity(year=year, gen_type=gen_type)

df_xborderschedule = client.get_scheduled_foreign_exchange(start=start, end=end)
df_xborderflow = client.get_physical_foreign_flow(start=start, end=end)

df_internalschedule = client.get_scheduled_internal_exchange(start=start, end=end)
df_internalflow = client.get_physical_internal_flow(start=start, end=end)
```