semvec/pypi-packages huggingface dataset:
pv-power-forecast 0.0.6
photovoltaic, forecast, power, PV, weather, DWD, machine, learning
Requires Python: >=3.8

# photovoltaic power forecast

pv_forecast provides a set of classes to obtain PV power forecast. Internally this library uses machine learning to perform PV power forecasts.
To get appropriated results, real measured PV power values must be provided, periodically. Additionally, the library uses weather forecast data of [DWD](https://dwd-geoportal.de/products/G_FJM/) to perform accurate PV power forecasts.

To install this software you may use [PIP](https://realpython.com/what-is-pip/) package manager such as shown below

**PIP approach**
```
sudo pip install pv_forecast
```

After this installation you should configure the library with your environment parameters.
You have to set the closest DWD station id of the location of our PV system. To find the proper station id refer [DWD station list](https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/mosmix_stationskatalog.cfg?view=nasPublication&nn=16102)     
```
dwd_station_id = 'L160'
pv_power_forecast = PvPowerForecast(dwd_station_id)
```

It is essential that the PvPowerForecast library will be provided with real measured PV values of our PV system. 
The provided real data is used as train data to adapt the internal model on your environment. 
Providing additional technical parameters of your PV system such as installed power or cardinal direction is not required. This libray is self-learning. Technical parameters scuh as technical parameters of your PV system such as installed power or cardinal direction will be considered implicitly.
```
# please provide the real pv value periodically. The period should be between 1 minute and 15 minutes.
while True:
    real_pv_power_watt = ...read real PV power ...
    pv_power_forecast.add_train_sample(real_pv_power_watt)
    time.sleep(60)
```
The provided train data will be store internally on disc and be used to update the internal prediction model. Please consider, that more accurate forecast predictions require collecting real PV power data for at least 2 weeks. Do not stop providing real PV power data, even though the prediction becomes better and better. You may use a periodic job to provide the real PV values

To get the power forecast the power method has to be called 
```
tomorrow = datetime.now() + timedelta(days=1)
predicted_pv_power_watt_tomorrow = forecast.predict(tomorrow)
```
pv-forecast 0.0.5
photovoltaic, forecast, power, PV, weather, DWD, machine, learning
Requires Python: >=3.8

# photovoltaic power forecast

pv_forecast provides a set of classes to obtain PV power forecast. Internally this library uses machine learning to perform PV power forecasts.
To get appropriated results, real measured PV power values must be provided, periodically. Additionally, the library uses weather forecast data of [DWD](https://dwd-geoportal.de/products/G_FJM/) to perform accurate PV power forecasts.

To install this software you may use [PIP](https://realpython.com/what-is-pip/) package manager such as shown below

**PIP approach**
```
sudo pip install pv_forecast
```

After this installation you should configure the library with your environment parameters.
You have to set the closest DWD station id of the location of our PV system. To find the proper station id refer [DWD station list](https://www.dwd.de/DE/leistungen/met_verfahren_mosmix/mosmix_stationskatalog.cfg?view=nasPublication&nn=16102)     
```
dwd_station_id = 'L160'
pv_power_forecast = PvPowerForecast(dwd_station_id)
```

It is essential that the PvPowerForecast library will be provided with real measured PV values of our PV system. 
The provided real data is used as train data to adapt the internal model on your environment. 
Providing additional technical parameters of your PV system such as installed power or cardinal direction is not required. This libray is self-learning. Technical parameters scuh as technical parameters of your PV system such as installed power or cardinal direction will be considered implicitly.
```
# please provide the real pv value periodically. The period should be between 1 minute and 15 minutes.
while True:
    real_pv_power_watt = ...read real PV power ...
    pv_power_forecast.add_train_sample(real_pv_power_watt)
    time.sleep(60)
```
The provided train data will be store internally on disc and be used to update the internal prediction model. Please consider, that more accurate forecast predictions require collecting real PV power data for at least 2 weeks. Do not stop providing real PV power data, even though the prediction becomes better and better. You may use a periodic job to provide the real PV values

To get the power forecast the power method has to be called 
```
tomorrow = datetime.now() + timedelta(days=1)
predicted_pv_power_watt_tomorrow = forecast.predict(tomorrow)
```
Odoo-API-Library 1.1.4
Store user access token for one-time-login

# REST API Library

### Version : 1.1.4
### Description : Store user access token for one-time-login
### Package : rest_api_library 
###### package link : https://pypi.org/project/Odoo-API-Library
###### github link : https://github.com/HanZawNyein/odoo_rest_api_library.git
###### Author_email: hanzawnyineonline@gmail.com
###### Author : Han Zaw Nyein
avrzero 0.1.dev1
A simple AVR instruction set simulator for educational purposes.
Requires Python: >=3.7

# AVR Zero
A simple AVR instruction set IDE and simulator for educational purposes.

> **Warning**
>
> This is in no way an accurate representation of the AVR instruction set and
> the AVR assembly. Programs that work in this simulator are not guaranteed to
> work on an actual AVR chip.

![Screenshot](image/screenshot.png)

## Starting Guide

The project is available on PyPI, simply install it with `pip`.

```
python -m pip install avrzero
```

Then, you can run the graphics user interface with

```
python -m avrzero.gui
```

Or you can run the command line tools with

```
python -m avrzero
```

## Install from Source

First, you need to clone the GitHub repository.

At the project root, run

```
python -m build
```

The artifacts will be in the `dist` directory. Install with

```
python -m pip install --no-deps dist/avrsim-*-py3-none-any.whl
```