semvec/pypi-packages huggingface dataset:
racp 0.1.2
A tool for building academic paper retriever.
License: MIT
Requires Python: >=3.8.0


# README

## Setup

To start using this project, first you should run the setup script. You can either run
```shell
pip install racp
```

or

```shell
python setup.py install
```

at the project root directory. This allows you to import `racp` in python code.

Our docs can be viewed on https://racp.readthedocs.io/en/latest/.

If you want to build the docs locally, you need to install `mkdocs`. Run these commands:
```shell
pip install mkdocs mkdocs-include-markdown-plugin mkdocstrings mkdocstrings-python
```

If you want to view the docs, run `mkdocs build` at the project root directory. It will create `./site` directory which contains `index.html` file. Open the file with your browser and read the docs.

You can run the scripts in `example` to test installation.
ocf-ml-metrics 0.0.11
Common metrics and baselines for OCF's energy forecasting work
License: MIT

# ocf-ml-metrics
Collection of simple baseline models and metrics for standardized evaluation of OCF forecasting models.

This package computes a variety of baselines and error metrics
including persistence of both last value and last day of generation,
comparison to PVLive, and max and zero baselines.

## Installation

Install with `pip install ocf-ml-metrics`

## Usage

The easiest way to use this package is to use the
convenience function `ocf_ml_metrics.metrics.errors.compute_metrics` that
computes all the basic error metrics overall, with and without night time,
for different parts of the year, different times of day, and all forecast
horizons by default.

There is also `ocf_ml_metrics.evaluation.evaluation.evaluation` that computes metrics after taking in a pandas
dataframe. These metrics are computed for the raw values, normalized values, against simple baseline models,
and per ID in the input dataframe. The input dataframe required data can be found in the docstring

And example usage would be

```python
from ocf_ml_metrics.evaluation.evaluation import evaluation
import pandas as pd

results_df = pd.read_csv('<path to csv>')

metrics: dict = evaluation(results_df=results_df, model_name='<model name>')
```
librarykelompok-2 0.1
Kelompok 2
License: MIT
Keywords: Hello

# import awal
from librarykelompok2.mylibrary import deskripsi


# tambahan pada kode program
class AttendanceApp:
    def _init_(self, root):
        self.btn_tampilkan_deskripsi = tk.Button(root, text='Tampilkan Deskripsi', command=self.tampilkan_deskripsi, font=("Arial", 15))
        self.btn_tampilkan_deskripsi.place(x=1100, y=590)
        self.btn_tampilkan_deskripsi.configure(width=20, height=3)
        
    def tampilkan_deskripsi(self):
        # Memanggil fungsi untuk menampilkan deskripsi dari mylibrary
        deskripsi()