semvec/pypi-packages huggingface dataset:
wiscopy 0.1.5
A Python API wrapper for the Wisconsin environmental mesonet API: Wisconet
Requires Python: >=3.10

# Wiscopy
Python wrapper for [Wisconet](https://wisconet.wisc.edu/). Currently supporting [API v1](https://wisconet.wisc.edu/docs).

## Main Features
1. Easy to use interface: simply specify the stations, datetime range, and fields you want
2. Data automatically formatted into a pandas DataFrame
3. Fetch large amounts of data with transparent concurrency

## Install

### From PyPI

#### base install

To install `wiscopy` from [PyPI](https://pypi.org/project/wiscopy/) run

```
python -m pip install wiscopy
```

#### install with plotting library dependencies

```
python -m pip install 'wiscopy[plot]'
```

### From conda-forge

To install and add `wiscopy` to a project from [conda-forge](https://github.com/conda-forge/wiscopy-feedstock) with [Pixi](https://pixi.sh/), from the project directory run

```
pixi add wiscopy
```

and to install into a particular conda environment with [`conda`](https://docs.conda.io/projects/conda/), in the activated environment run

```
conda install --channel conda-forge wiscopy
```

## Usage

### Fetch data from multiple stations, create a Dataframe, and plot.
```python
import nest_asyncio  # needed to run wiscopy in a notebook
import hvplot.pandas  # needed for df.hvplot()
import holoviews as hv
from datetime import datetime

from wiscopy.interface import Wisconet

hv.extension('bokeh')
hv.plotting.bokeh.element.ElementPlot.active_tools = ["box_zoom"]
nest_asyncio.apply()  # needed to run in notebook

w = Wisconet()
df = w.get_data(
    station_ids=["maple", "arlington"],
    start_time="2025-01-01",
    end_time="2025-02-01",
    fields=["60min_air_temp_f_avg"]
)
df.hvplot(
    y="value",
    by="station_id",
    title="60min_air_temp_f_avg",
    ylabel=df.final_units.iloc[0],
    grid=True,
    rot=90,
)

```
![Specific data over a specific time period](./notebooks/specific_data_specific_time.png)

### More examples
see more examples in [notebooks/examples.ipynb](https://github.com/UW-Madison-DSI/wiscopy/blob/main/notebooks/examples.ipynb), or run

```
pixi run start
```

## Wisconet

### Current stations
Wisconet's [list of current stations](https://wisconet.wisc.edu/stations.html) shows all active station names. You can get also that list of strings in wiscopy with:
```python
from wiscopy.interface import Wisconet

w = Wisconet()
station_names = w.all_station_names()
```

Wisconet also provides a [map](https://wisconet.wisc.edu/maps.html) of those stations with a dropdown menu including some of the currently available fields/variables. You can determine the fields available per station in wiscopy with:

```python
from wiscopy.interface import Wisconet

w = Wisconet()
station_names = w.all_station_names()
this_station = w.get_station(station_names[0])
fields = this_station.get_field_names()
```


## dev install (contribute!)
### 1. install pixi
See [pixi install guide](https://pixi.sh/latest/advanced/installation/).

### 2. check out from repo
```bash
git clone git@github.com:UW-Madison-DSI/wiscopy.git
```

### 3. install local editable version
```bash
cd wiscopy
pixi install
```
a-texam 1.1
A-TExam is a local testing platform designed for creating and conducting tests with extensive test management capabilities
Requires Python: >=3.6

# A-TExam 😃

**A-TExam** is a local testing platform designed for creating and conducting tests with extensive management capabilities.

## Overview ✨

A-TExam is a next-generation testing system developed by Toshmatov—a web developer and UX/UI designer passionate about building user-friendly and efficient IT solutions. The platform is designed to simplify the testing process and make it accessible to everyone. 🚀

## Core Features 🔑

### Test Creation and Editing 📝
- **Customizable Tests**: Easily create and edit tests with full control over questions, answer options, and passing conditions. 🎯
- **Real-Time Updates**: Instantly modify test content via the admin panel, including adding questions and updating correct answers. ⚡

### Test Taking 🎓
- **User-Friendly Interface**: Designed to ensure an intuitive testing experience. 😊
- **Copy Protection**: Copy functionality is disabled to maintain test integrity. 🔒
- **Automatic Termination**: The test automatically ends if the user switches to another tab or page, while preserving test timing, attempt count, and handling session termination properly. ⏱️

### Administration and Management 🛠️
- **Result Monitoring**: Administrators can view detailed test results. 📊
- **User Management**: Manage users effectively with features to view details, delete accounts, and control registrations. 👥
- **Registration with Approval**: New user registrations require admin approval before activation. ✅
- **User Blocking (Ban)**: Administrators can block users to ensure system security. 🚫
- **Comprehensive Data Control**: The admin panel provides detailed statistics and allows deletion of test history. 📋

## Additional Features 🌟

- **File Attachments**: Attach videos, audio, and images to questions to enhance the test content. 🎥🎵🖼️
- **MathJax Integration**: Supports the display and formatting of mathematical expressions for more technical tests. ➗
- **Flexible Display Modes**: Choose to display questions one at a time or all at once, depending on the test format. 🔄
- **Robust Security**: Ensures test integrity by automatically ending the test upon any tab or window change, while securely tracking time and attempts. 🔐

## Installation

Install the package using pip:

```bash
pip install a-texam

## Usage

```bash
a-texam


## License

This project is licensed under the MIT License - see the LICENSE file for details.
refrot 1.4.3
Verify website links

# Refrot

A [Python] script to check a website for broken links, also known as link rot or
reference rot.

## How to install it

Install using:

	pip3 install refrot

## How to use it

    refrot <url>

By default refrot checks all internal links, no matter how many levels, and a single level of external links. URLs with status codes other than OK (200) and Temporary Redirect (302) are displayed in the post-run summary.

### Options

    -h --help                   Show help.
    -i --ignore-external-links  Ignore external links.
    -u --user-agent AGENT       User agent.
    -v --version                Show version number.

## Links

- [Project page](https://seagrape.us/refrot.html)
- [Changelog](https://seagrape.us/refrot-history.html)

## Contact Me

Send bug reports and patches to <craig@seagrape.us>.

[Python]: https://www.python.org
sony-custom-layers-dev 0.4.0.dev6
Sony Custom Layers package

# Sony Custom Layers (SCL)

Sony Custom Layers (SCL) is an open-source project implementing detection post process NN layers not supported by the TensorFlow Keras API or Torch's torch.nn for the easy integration of those layers into pretrained models.

## Table of Contents

- [Getting Started](#getting-started)
  - [Installation](#installation)
  - [Supported Versions](#supported-versions)
- [API](#api)
  - [TensorFlow API](#tensorflow-api)
  - [PyTorch API](#pytorch-api)
- [License](#license)


## Getting Started

This section provides an installation and a quick starting guide.

### Installation

To install the latest stable release of SCL, run the following command:
```
pip install sony-custom-layers
```
By default, no framework dependencies are installed.
To install SCL including the latest tested dependencies (up to patch version) for TensorFlow:
```
pip install sony-custom-layers[tf]
```
To install SCL including the latest tested dependencies (up to patch version) for PyTorch/ONNX/OnnxRuntime:
```
pip install sony-custom-layers[torch]
```
### Supported Versions

#### TensorFlow

| **Tested FW versions** | **Tested Python version** | **Serialization** |
|------------------------|---------------------------|-------------------|
| 2.10                   | 3.9-3.10                  | .h5               |
| 2.11                   | 3.9-3.10                  | .h5               |
| 2.12                   | 3.9-3.11                  | .h5  .keras       |
| 2.13                   | 3.9-3.11                  | .keras            |
| 2.14                   | 3.9-3.11                  | .keras            |
| 2.15                   | 3.9-3.11                  | .keras            |

#### PyTorch

| **Tested FW versions**                                                                                                   | **Tested Python version** | **Serialization**              |
|--------------------------------------------------------------------------------------------------------------------------|---------------------------|--------------------------------|
| torch 2.0-2.4<br/>torchvision 0.15-0.19<br/>onnxruntime 1.15-1.19<br/>onnxruntime_extensions 0.8-0.12<br/>onnx 1.14-1.16 | 3.9-3.12                  | .onnx (via torch.onnx.export)  |

## API
For sony-custom-layers API see https://sony.github.io/custom_layers

### TensorFlow API
For TensorFlow layers see
[KerasAPI](https://sony.github.io/custom_layers/sony_custom_layers/keras.html)

To load a model with custom layers in TensorFlow, see [custom_layers_scope](https://sony.github.io/custom_layers/sony_custom_layers/keras.html#custom_layers_scope)

### PyTorch API
For PyTorch layers see
[PyTorchAPI](https://sony.github.io/custom_layers/sony_custom_layers/pytorch.html)

No special handling is required for torch.onnx.export and onnx.load.

For OnnxRuntime support see [load_custom_ops](https://sony.github.io/custom_layers/sony_custom_layers/pytorch.html#load_custom_ops) 

## License
[Apache License 2.0](LICENSE.md).