semvec/pypi-packages huggingface dataset:
centaurminer 0.1.0
A selenium wrapper to help mining data from scientific literature.
Requires Python: >=3.4

Scientific Article (CENTAUR) Data Mining Package
================================================


This package is a wrapper on `selenium <https://pypi.org/project/selenium/>`_, a package that "automates web browser interaction from Python". In this package, we add the following features:

* Automatically determine and install the correct chromedriver version, using webdriver-manager.
* Add a class used to identify where elements are located on the DOM
* Add a class that specifies if any extra processing should be done to the elements in the previous class, rather than just extract an attribute.
* Create helper classes to simplify the process of identifying elements
* Helper functions are included to make processing and storing data easier.

Although this package is tailored to data mining scientific literature, is can be used in other applications in data mining.
daWUAP 0.3.0.dev0
Hydrologic Engine for daWUAP model

The data assimilation Water Use and Agricultural Productivity model (daWUAP) is a hydro-economic model that couples an economic model of agricultural production calibrated using positive mathematical programming (PMP) and a semidistributed rainfall-runoff-routing model that simulates water available to producers.

Features:

- Calibration of economic component can use standard the mathematical programming method or a new recursive stochastic filter.
- Stochastic calibration permits to obtain simulation results of agricultural outputs (land and water allocation, etc) as probability distributions that reflect the quality of the calibration.
- Recursive stochastic filter permits calibration of economic model with noisy but frequent remote sensing observations of agricultural activity.
- Model permits to trace the effect of producer choices on the hydrologic system and on other users.

[Contributions and comments are welcome at Github.](https://bitbucket.org/umthydromodeling/dawuap.git)
r2cloud 0.0.3
python SDK for R2CLOUD
Requires Python: >=3.0

## About
r2cloud python sdk makes it easier for you to work with your r2cloud server using the python programming language. The SDK uses an object-oriented design and tries to look like r2cloud API.

## Installation 

#### with pip3

```sh
pip install r2cloud
```

#### from repo

```sh
make install
make doc # for generate documentation
```

## Quick start

```python
import r2cloud.api
import r2cloud.tools.common

# init api
station = r2cloud.api('https://192.168.0.10')
# login to r2cloud
station.login("myemail@example.org", "my_password")

# get all observations
obs = station.observationList()

# filter observation of "NOAA 19", "NOAA 18" and "NOAA 15"
obs = r2cloud.tools.common.filterSat(obs, ["NOAA 19", "NOAA 18", "NOAA 15"])

# filter observation with data only
obs = r2cloud.tools.common.filterHasData(obs)

# print all filtred observations ids
for ob in obs:
    print("Observation " + str(ob.id) + " by " + ob.name)

```

more examples you can find in `/examples` dir on github

## Examples Screenshots

<img src="https://raw.githubusercontent.com/Lukas0025/r2cloud-python-sdk/master/examples_images/Figure_1.png" width="30%">&nbsp;<img src="https://raw.githubusercontent.com/Lukas0025/r2cloud-python-sdk/master/examples_images/Figure_2.png" width="30%">&nbsp;<img src="https://raw.githubusercontent.com/Lukas0025/r2cloud-python-sdk/master/examples_images/Figure_3.png" width="30%">&nbsp;<img src="https://raw.githubusercontent.com/Lukas0025/r2cloud-python-sdk/master/examples_images/Figure_4.png" width="30%">&nbsp;<img src="https://raw.githubusercontent.com/Lukas0025/r2cloud-python-sdk/master/examples_images/Figure_5.jpg" width="30%">
h2o-q 0.6.0
Python driver for H2O Q Realtime Apps
Requires Python: >=3.6.1

H2O Q
=====

H2O Q is a lightweight software stack for programming interactive web applications
entirely in Python (no HTML/Javascript/CSS) required.

It is designed to make it fast, fun and easy to build low-latency, realtime,
collaborative, web-based applications. It ships batteries-included with
a suite of form and data visualization components for rapidly prototyping
analytical and decision-support applications.

Q's components work in conjunction with the Q relay server that facilitates 
realtime state synchronization between Python and web browsers.


Installing
----------

Install and update using `pip`_:

.. code-block:: text

    pip install -U h2o-q


Hello world
----------------

``hello.py``:

.. code-block:: python

    from h2o_q import site, ui

    # Access the web page at http://localhost:55555/demo
    page = site['/demo']

    # Add some content.
    page['example'] = ui.markdown_card(
      box='1 1 2 2',
      title='Hello World!',
      content='And now for something completely different.',
    )

    # Save the page
    page.save()

Run ``hello.py``:

.. code-block:: text

    $ python hello.py


Links
-----

* Website: https://www.h2o.ai/h2o-q/
* Releases: https://pypi.org/project/h2o-q/
* Documentation: https://h2oai.github.io/qd/
* Code: https://github.com/h2oai/qd
* Issue tracker: https://github.com/h2oai/qd/issues

.. _pip: https://pip.pypa.io/en/stable/quickstart/
jupyter-tray-app 0.2
Run the Jupyter notebook server as an app in the system tray

# Jupyter Tray App

Run the Jupyter notebook server as a little app in the system tray. 

That way you don't have to keep a separate command window open just for the server.

## Installation

Make sure PyQt5 is installed:
```
conda install pyqt
```

Then run:
```
pip install jupyter-tray-app
```
This will get you the
[![latest version on PyPI](https://img.shields.io/pypi/v/jupyter-tray-app.svg?label=latest%20version%20on%20PyPI:)](https://pypi.python.org/pypi/jupyter-tray-app/)


## Auto-run at boot

How to start the tray server automatically at system boot.

These are instructions for Windows.\
Something similar should be possible on MacOS.

1. Find your windowless Python executable:
    - Type `pythonw.exe` in the start menu and choose "Open File Location".
2. Open your Startup folder:
    - Run `Run` and run `shell:startup`.
3. Make a shortcut to your `pythonw.exe` in this Startup folder.
4. In the shortcut's Properties, in the "Target" field, append `-m jupyter_tray_app`
    - So the total Target will read e.g. `C:\conda\pythonw.exe -m jupyter_tray_app`
5. Rename your shortcut to something like "Start Jupyter tray app"