semvec/pypi-packages huggingface dataset:
testrail-interface 0.0.3
A small testrail interface package
Requires Python: >=3.10

# TestRail Interface


## Pypi
https://pypi.org/project/testrail-interface/
### Install
```bash
pip install testrail-interface
```
### Usage
Set environment variables:
1. TR_DOMAIN
2. TR_USERNAME
3. TR_API_KEY
4. TR_PROJECT_ID


To open help:
```bash
python3 -m testrail_interface -h
```
### Build
Make sure you have the latest version of PyPA’s build installed:
```bash
python3 -m pip install --upgrade build
```
Now run this command from the same directory where pyproject.toml is located:
```bash
python3 -m build
```
### Upload to Pypi
Install twine
```bash
python3 -m pip install --upgrade twine
```
Upload:
```bash
python3 -m twine upload dist/*
```


## Docker
### Build
```bash
docker build -t nexus.start-i.ru:8083/testrail_interface --rm .
```
### Nexus container registry
```bash
# Login
docker login nexus.start-i.ru:8083 --username $USERNAME --password $PASSWORD
# Push
docker push nexus.start-i.ru:8083/testrail_interface
```

### Usage
```bash
# Start
docker run -dit --name testrail_reporter --rm \
        -e TR_DOMAIN \
        -e TR_USERNAME \
        -e TR_API_KEY \
        -e TR_PROJECT_ID \
        --mount type=bind,source="$(pwd)"/$TESTS_DIR,target=/home/app_user/$TESTS_DIR \
        nexus.start-i.ru:8083/testrail_interface
# Open help
docker exec -it testrail_reporter python testrail_interface.py -h
# Send report
docker exec -it testrail_reporter python testrail_interface.py send_report -f $TESTS_DIR/report_.json
# Stop
docker stop testrail_reporter
```
torchmix 0.1.0rc8
Flexible components for transformers 🧩
Requires Python: >=3.8

<h1 align="center">torchmix</h1>

<h3 align="center">The missing component library for PyTorch</h3>

<br />

`torchmix` is a collection of pre-made PyTorch components designed to simplify model development process, primarily for **transformers**. Our goal is to enable easy adoption of cutting-edge components with minimal code and maximum scalability.

**Note: `torchmix` is a prototype that is currently in development and has not been tested for production use. The API may change at any time.**

## Install

To use `torchmix`, you will need to have `torch` already installed on your environment.

```sh
pip install torchmix
```

## Documentation

To learn more, check out our [documentation](https://torchmix.vercel.app).

## Contributing

If you have ideas for new components or ways to enhance the library, feel free to open an issue or start a discussion. We welcome all forms of feedback, including criticism and suggestions for significant design changes.

## License

`torchmix` is licensed under the MIT License.
online-mean 2023.1.1
online algorithm for mean, variance, and covariance
Requires Python: >=3.6
Keywords: online algorithm,online mean,online variance,online covariance,online statistics

online_mean
===========

Online algorithm for the mean, variance, and covariance.

The `online_mean` package contains a single function `add_sample` which updates
the array for the sample mean, as well as optionally the sample variance and the
sample covariance matrix.

The package has no dependencies, as the `add_sample` function works with any
input data type that supports in-place addition and fancy slicing.

Usage
-----

```py
import numpy as np

# the only function in the package
from online_mean import add_sample

# online algorithm for the mean
# start from zero
mu = np.zeros(4)

# generate samples and compute their mean
for i in range(1000):
    x = np.random.normal([0.1, 0.3, 0.5, 0.7])
    add_sample(i, x, mu)

# the mean is computed in place
print(mu)  # [0.08804402 0.25896929 0.44891264 0.73418769]

# compute the variance
mu = np.zeros(4)
var = np.zeros(4)
for i in range(1000):
    x = np.random.normal([0.1, 0.3, 0.5, 0.7], [0.2, 0.4, 0.6, 0.8])
    add_sample(i, x, mu, var=var)

print(mu)  # [0.09854301 0.29509305 0.4777673  0.70008311]
print(var**0.5)  # [0.19900518 0.4012857  0.59267129 0.81856542]

# compute the covariance matrix
mu = np.zeros(4)
cov = np.zeros((4, 4))
for i in range(100_000):
    x = np.random.multivariate_normal([0.1, 0.3, 0.5, 0.7],
            [[0.2, 0.02, 0.04, 0.06],
             [0.02, 0.4, 0.06, 0.08],
             [0.04, 0.06, 0.6, 0.10],
             [0.06, 0.08, 0.10, 0.8]])
    add_sample(i, x, mu, cov=cov)

print(mu)  # [0.10095607 0.30486108 0.50113141 0.69912377]
print(cov)
# [[0.20101406 0.02105503 0.0382198  0.06220174]
#  [0.02105503 0.39909545 0.06192678 0.0791239 ]
#  [0.0382198  0.06192678 0.59960537 0.1082596 ]
#  [0.06220174 0.0791239  0.1082596  0.80071002]]

```
green-energy-api 0.1.0
A python library to access the Awattar Energy Price API.
Requires Python: >=3.7
Keywords: green_energy,kfw,api,cloud

# green_energy_api

A python library to access the Awattar Api. Refer also to the [Awattar Energy Price API](https://www.awattar.de/). This repo provides functionality for:

- Getting Energy Price forecast information.

## Installing the library locally

Python 3 is recommended for this project.

```bash
python -m pip install -e .
```

> **This is needed for the first time when working with the library/examples/tests.**

## Example usage

```bash
ENERGY_DATA_API_URL="https://api.awattar.de" python3 examples/simple.py
```

or

```python
from green_energy_api.green_energy_api import GreenEnergyApi

green_energy_api = GreenEnergyApi('provide_api_url')
# or you can define additional optional parameters
# green_energy_api = GreenEnergyApi('provide_api_url', timeout=10)

print(green_energy_api.get_electricity_price())
```

## Development

### Installing required pip packages

```bash
python pip install -r requirements.txt
pre-commit install -t pre-push
```

### Linting

```bash
pylint green_energy_api/*.py tests/*.py examples/*.py
```

### Unit testing

```bash
pytest tests/*.py

# show logs
pytest -o log_cli=true

# code coverage
pytest --durations=10 --cov-report term-missing --cov=green_energy_api tests
```