semvec/pypi-packages huggingface dataset:
model-evaluator 1.0.13
Library for beautiful training loop for PyTorch
Requires Python: >=3.10.0

# Model Evaluator


## Description

A package for training and evaluating PyTorch models.

Now it supports only visualization of training of models, but in future will support training 
and evaluation of many models which can be very useful for students.

If you have any bugs/improvement ideas, create an issue thread.

[GitHub repository](https://github.com/artemgalyan/model-evaluator)

[PyPi](https://pypi.org/project/model-evaluator/)

## Installation

For installation you need to execute following command:

    pip install model-evaluator

Then you can import library and use it in your code:

    import model_evaluator

## Example usage

    from model_evaluator import Trainer, PlottingOptions, Accuracy, F1Score

    train_data_loader = ...
    test_data_loader = ...
    model = ...
    optimizer = ...
    criterion = ... # those are yours

    trainer = Trainer(model, optimizer, criterion, [Accuracy(), F1Score()],
                      plotting_options=PlottingOptions.PLOT_BOTH, plot_interval=8)
    trainer.train(NUM_EPOCHS, train_data_loader, test_data_loader, device)

## Authors

Artsiom Halian - [GitHub](https://github.com/artyomgalyan)

## Versions

1.0.13 - Added cpu loss for weighted losses\
1.0.12 - Fixes\
1.0.11 - Fixes\
1.0.10 - Fixes\
1.0.9 - Fixes\
1.0.8 - Fixes\
1.0.7 - Added HuggingFace support\
1.0.6 - Explicit conversion to tensor\
1.0.5 - Fixed plotting\
1.0.4 - Fixed binary recognition\
1.0.3 - Fixed multiclass backward error\
1.0.2 - Fixed history error\
1.0.1 - Updated readme\
1.0.0 - Initial version
streamlit-charts 0.1.1
Render Ant Design Charts in Streamlit
Requires Python: >=3.8
Keywords: antv,charts,Ant Design Charts,streamlit-component,streamlit-charts

<h1 align="center">
  <b>Streamlit Charts</b>
</h1>

<div align="center">

[Ant Design Charts](https://ant-design-charts.antgroup.com/) is AntV React component library, Simple and easy to use React chart library.

This project was created to allow us to render charts in streamlit, [live demo](https://ant-design-charts.streamlit.app/).

![examples](https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*11uNQrnKdFoAAAAAAAAAAAAADmJ7AQ/original)
![npm Version](https://img.shields.io/npm/v/@ant-design/charts)
![npm Download](https://img.shields.io/npm/dm/@ant-design/charts)
[![GitHub stars](https://img.shields.io/github/stars/ant-design/ant-design-charts)](https://github.com/ant-design/ant-design-charts/stargazers)
[![npm License](https://img.shields.io/npm/l/@ant-design/charts.svg)](https://www.npmjs.com/package/@ant-design/charts)

</div>


## Installation

```
pip3 install streamlit-charts
```


## Usage

```py
import streamlit as st
from streamlit_charts import charts

 options = {
    "height": 400,
    "data": [
        { "genre": "Sports", "sold": 275 },
        { "genre": "Strategy", "sold": 115 },
        { "genre": "Action", "sold": 120 },
        { "genre": "Shooter", "sold": 350 },
        { "genre": "Other", "sold": 150 },
    ],
    "xField": 'genre',
    "yField": 'sold',
    "colorField": "genre",
    "style": {
      "radiusTopLeft": 10,
      "radiusTopRight": 10,
    },
  }

  charts(type="Column", options=options, key="streamlit-charts")
```

<img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*aPxqSpQcsUQAAAAAAAAAAAAADmJ7AQ/original" width="640" alt="example">


## API

Now, There is only one API for `streamlit-charts`, named `charts`, see the `type | options` in [Ant Design Charts](https://ant-design-charts.antgroup.com/).

| Property | Description                                                                                                     | Type                  | Default |
| -------- | --------------------------------------------------------------------------------------------------------------- | --------------------- | ------- |
| type    | the chart type of the charts, eg: `Line、Column、Bar、Pie...`                                                                                    | `Str`       | `Column`      |
| options  | the options for the visualization | `Options` | -       |


## Development

- Building frontend code by running `npm run start` in fold `streamlit_charts/frontend`.
- Run the example by running `streamlit run streamlit_charts/__init__.py` with `_RELEASE = False`.


## License

MIT@[lxfu1](https://github.com/lxfu1).
example-pkg-adj-dataparrots 0.0.2
A small example package
Requires Python: >=3.6


Copyright (c) 2021 The Python Packaging Authority
 
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
 
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
 
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
pyNUIT-RSAG 0.101
Wrapper for source term alaysis code NUIT

# Contents
- [Summary](#summary)
- [Install](#install)
- [Get Started](#get-started)
- [Reference](#reference)

# Summary
pyNUIT is a package in Python which wrappers the source term analysis code NUIT, and integrates various util tools for its control and management.

# Install
1. Using git clone: `git clone https://github.com/thu-inet/pyNUIT.git`
2. Download the zip and unzip to destination path

Only a few basic Python packages are required, including:
- Numpy
- Matplotlib
- Pandas
*pyNUIT is platform-independent, yet NUIT is in C++ and you need to have the right complied version to use it. Currrently, pyNUIT uses `wine` and NUIT for windows to make it usable on Linux, so make sure you get `wine` installed correctly if you are working with Linux.

# Get Started
The most basic module is nt.Model and nt.Output, for management of NUIT input model and its output.
```
# import pyNUIT
import pyNUIT as nt

# define the model
model = nt.Model()
model.add_nuclide("U235", 0.85)
model.add_nuclide("U238", 4.15)

# define power history
for i in range(10):
    model.add_burnup(time=10, unit='day', val=800E-6)

# define the output
model.set_output("isotope", print_all_step=0)

# library configuration
model.set_library(r"D:\\NUIT\\NUITLib_HTGR_900k")

# run the model to get output
out = model("model.xml", nuitpath="D:\\NUIT\\NUITx.exe")

# read calculation results
burnup = out.burnups[-1]
density = out.get_nuclide_mass("Cs137")[-1]
print(f"Nuclide density of Cs137 at {burnup} MWd/kgU is {density} n/cm/barn")
```
Histogram class is used to represent the power history, and can transfer its power history into nt.Model to form a complete model.

```

# define two histogram instances
histo1 = nt.Histogram(["2019-01-15 00:00", "2019-02-26 00:00", "2019-03-26 00:00"], [(0,0), (3.7E6,3.7E6), (3.7E6, 0)])
histo2 = nt.Histogram(["2019-05-28 00:00", "2019-07-02 00:00"], [(0, 0), (3E6, 3E6)])

# concatenate two histograms
histo = histo1 + histo2

# pass the power history to model
histo.model = model
model = histo.to_model(step_length=10*86400)
```

Classes in nt.data are used to interact with data libraries.
```
# read the multi-group xs lib
mgxslib = nt.data.MGXSlib.from_datfile("D:\\NUIT\\NUITLib_HTGR_900k\\NuitMgXsLib.dat")

# use existing flux data to collapse into one-group xslib
xslib = mgxslib.to_xslib(flux)

# check the xs of Cs134, MT=102 reaction
print(f"Cross section of Cs-134 Reaction(102) is {xslib("Cs134")(102)}")

# export xslib into .dat file
xslib.to_datfile("xslib.dat")
```
# Reference