semvec/pypi-packages huggingface dataset:
vizu 0.1.7
Visualization of data files
License: MIT
Requires Python: >=3.6

# Vizu - the simple CSV-viewer

Doing advanced analysis of data in CSV-format can be done with many tools - Excel and Pandas are two popular choises.
However, to quickly browse through the data for simple analysis and exploration, you will need more button-clicks than you should. 

`vizu` is meant to bridge the gap by providing a simple UI to browse a CSV file.

It uses the excellent libraries `dash` and `pandas` under the hood for GUI and data manipulation, respectively.

### Installation
`pip install vizu`

### Browse your first .csv file
Run the following command in a terminal

`vizu path/to/file.csv`

and go to your browser via the link it outputs.
django-rdtwt 3.0.0
Run Django tests with testcontainers.
License: MIT
Requires Python: >=3.10
Keywords: django, testing, testcontainers, docker, test automation

# django-rdtwt

[![Ruff](https://github.com/wonkybream/django-rdtwt/actions/workflows/ruff.yml/badge.svg)](https://github.com/wonkybream/django-rdtwt/actions/workflows/ruff.yml)
[![Tests](https://github.com/wonkybream/django-rdtwt/actions/workflows/tests.yml/badge.svg?branch=main)](https://github.com/wonkybream/django-rdtwt/actions/workflows/tests.yml)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![PyPI](https://img.shields.io/pypi/v/django-rdtwt)](https://pypi.org/project/django-rdtwt/)

*(Run Django Tests With Testcontainers)*

This focuses on users who wish to forget setting up a database for tests.
There's no need for manually starting up *Docker compose* or local database with this.

**Note:**

See: [Altering settings at runtime: Django documentation](https://docs.djangoproject.com/en/3.2/topics/settings/#altering-settings-at-runtime)

By default, this test runner changes _default_ database host and port dynamically,
because it's quite hard to know database host beforehand in dynamic environments, for example, some CI/CD runners.

Still, this just works and is quite simple, that's why I haven't spent that much time investigating alternative solutions. 

## Installation

1. Add rdtwt to your INSTALLED_APPS setting like this
    ```python
    INSTALLED_APPS = [
        ...
        'rdtwt',
    ]
    ```

2. Run tests with rdtwt runner,
    ```shell
    python manage.py test --testrunner=rdtwt.runner.PostgresDiscoverRunner
    ```

Though what I really suggest is to put the following in your test settings.

```python
# RDTWT SETTINGS
RDTWT_POSTGRESQL_IMAGE = 'postgres:14.1'
TEST_RUNNER = 'rdtwt.runner.PostgresDiscoverRunner'
```

This makes sure that tests run against the PostgreSQL version defined by you.
It also adds up to the test confidence; at least they aren't flaky because of a database version changing without your knowledge.

**Example:**

Defining all available options.

```python
# Database
DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'postgres',
        'USER': 'postgres',
        'PASSWORD': 'postgres',
        'HOST': '127.0.0.1',
        'PORT': '5432'
    }
}

# RDTWT SETTINGS
RDTWT_POSTGRESQL_IMAGE = 'postgres:14.1'
TEST_RUNNER = 'rdtwt.runner.PostgresDiscoverRunner'
RDTWT_DATABASES = ["default"]  # Add more if you have multiple databases
RDTWT_POSTGRESQL_USER = 'postgres'
RDTWT_POSTGRESQL_PASSWORD = 'postgres'
RDTWT_POSTGRESQL_NAME = 'postgres'
```
f9ml 0.1.6
JSI F9 machine learning framework.
License: MIT
Requires Python: <4.0,>=3.11

<div align="center">

# F9 Machine Learning Framework

<a href="https://www.python.org/"><img src="https://img.shields.io/badge/Python-3.11-blue.svg" /></a>
    <a href="https://f9ml.docs.cern.ch/"><img alt="Docs" src="https://img.shields.io/badge/docs-MkDocs-708FCC"></a>
    <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a>
    <a href="https://pytorch.org/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a>
    <a href="https://pytorchlightning.ai/"><img alt="Lightning" src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white">
</a>

</div>
MedReportEval 0.0.6
A brief description of your package
License: MIT
Requires Python: >=3.6

### Model Preparation

Our codebase integrates CheXbert [3] for report labeling and RadGraph [4] for evaluation. Download the required model checkpoints from the [repository here](https://github.com/rajpurkarlab/CXR-Report-Metric) and place them in the `./models/` directory.

### Environment Preparation

To set up the project environment, use the following command:

```
conda env create --name=<env_name> -f cxr-green.yml
```

If you're already on DSI cluster, use the following command to activate environment:

```
conda activate /net/projects/chai-lab/miniconda3/envs/cxr-green
```

### Test Example

Here is an example how to run evaluation:

```
python test.py \
--gt_csv /net/projects/chacha/cxr_eval/test/gt_imp_sample.csv \
--gen_csv /net/projects/chacha/cxr_eval/test/gen_imp_sample_3_1.csv \
--output_metrics /net/projects/chacha/cxr_eval/results/metrics.csv \
--output_combined /net/projects/chacha/cxr_eval/results/metrics_w_green.csv \
--output_dir /net/projects/chacha/cxr_eval/results
```

### Running Notes

1. Based on our testing, we can successfully run metrics without (GREEN, BertScore, RadGraph F1) on A40 GPU. We'll continue updating requirement for environment and gpu to run (GREEN, BertScore, RadGraph F1).
2. Each input csv should contain columns **['study_id', 'report']**. Each column needs to be str and 'study_id' should not include any other characters except numbers.
3. Ensure each input csv has no empty values.