semvec/pypi-packages huggingface dataset:
pyPEAKO 0.0.2.post2
peak detection in cloud radar Doppler spectra
License: MIT
Requires Python: >=3.6

# pyPEAKO


**PEAKO** is a supervised radar Doppler spectrum peak finding algorithm. It finds the optimal 
parameters for detecting peaks in cloud radar Doppler spectra using user-generated training data. 


**PEAKO** is used to: 
- create labeled data (peaks marked by a user in cloud radar Doppler spectra), which can be used for training and testing the learned function
- train the algorithm using the labeled data to obtain the optimal parameter combination for peak detection. Optimization is done using a similarity measure based on the area below the peaks.
- test the performance of the learned function [TBD]
- detect peaks in cloud radar Doppler spectra using the learned function


Reference for PEAKO: [Kalesse et al. (2019), AMT](https://www.atmos-meas-tech.net/12/4591/2019/)

Documentation is available at: [https://pypeako.readthedocs.io/en/latest/](https://pypeako.readthedocs.io/en/latest/)

-------------------

## TBD : Installation
I want this package to be available via pip so that one can simply do :
``` 
$ pip install pyPEAKO
```

In the meantime, you will have to clone the repository, e.g. by
 ```
$ git clone https://github.com/ti-vo/pyPEAKO
```

Then navigate to the main folder (pyPEAKO):

```
$ pip install -e . 
```

## How PEAKO works
The current release is tailored to use cloud radar Doppler spectra netcdf files. The files are in a format which is 
currently under discussion in the Cloudnet community. Changes are likely to be made in the future, and Peako will have 
to be adjusted to work with the most current spectra file format. 
The cloudnet community will hopefully share their routines for bringing spectra files from different cloud radars into 
the desired format. Ongoing discussion is happening in the [Cloudnet forum](https://forum.cloudnet.fmi.fi/)  .


## Contributing
If you want to help develop peako, feel free to contact me, or open an issue on GitHub. If you want to become an active 
developer, that would be awesome! You will first have to install the "dev" dependencies specified in setup.py. 
To install PEAKO  along with the tools you need for developing and running tests, run:
```bash
$ pip install -e .[dev]
```
in the directory containing the setup.py file. Like this, you install pyPEAKO with the dev extras.
koverj 0.1.105
Test coverage for selenium based tests
License: MIT
Requires Python: >=3.7,<4.0

### Python (>=3.7) agent that allow to collect locators from Selenium based UI tests.
Inspired by SergeyPirogov koverj-java-agent.
Browser_driver instance (e.g. webdriver.Chrome()) has to be created and passed to a LocatorsWatchingDriver

#### - Selenium usage:
```
chrome_driver = webdriver.Chrome(executable_path=executable_path, options=chrome_options)
driver = LocatorsWatchingDriver(driver=chrome_driver)
```

#### - Selene usage:
```
driver = LocatorsWatchingDriver(driver=chrome_driver)
browser.set_driver(driver)
```

An web_driver may be created as pytest fixture, e.g.:

```
@pytest.fixture()    
def web_driver(chrome_driver):
    driver = LocatorsWatchingDrive(driver=chrome_web_driver)
    browser.set_driver(driver)
    yield browser.driver()
    browser.quit()
```

If pytest is used the koverj plugin may be registered in conftest.py:
```
pytest_plugins = [
    'koverj.common.plugin.pytest_koverj_plugin',
]
```

#### Supported integrations:
- Selene
- Selenium

#### Supported test runners:
- pytest

In order to collect data you have to run koverj server (thanks to):
```docker run -p 8086:8086 spirogov/koverj:0.1.0```

In order to see results in your browser add **koverj-browser-plugin** to your browser.