semvec/pypi-packages huggingface dataset:
grdwindinversion 1.0.2
Package to perform Wind inversion from GRD Level-1 SAR images
License: MIT
Requires Python: >=3.9
Keywords: xarray, earth-observation, remote-sensing, satellite-imagery, Sentinel-1, RCM, RadarSat2, sar, synthetic-aperture-radar


[![Python Version](https://img.shields.io/pypi/pyversions/grdwindinversion.svg)](https://pypi.org/project/grdwindinversion/)
[![Dependencies Status](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg)](https://github.com/umr-lops/grdwindinversion/pulls?utf8=%E2%9C%93&q=is%3Apr%20author%3Aapp%2Fdependabot)

Package to perform Wind inversion from GRD Level-1 SAR images

-   Free software: MIT license
-   Documentation: https://cerweb.ifremer.fr/datarmor/doc_sphinx/grdwindinversion/index.html.

## Usage

```python

   SAR_L1-to-L2_wind_processor -h
   usage: SAR_L1-to-L2_wind_processor [-h] --input_file INPUT_FILE [--config_file CONFIG_FILE] --outputdir OUTPUTDIR [--verbose] [--overwrite]

   Perform inversion from S1(L1-GRD) SAFE, L1-RCM, L1-RS2 ; using xsar/xsarsea tools

   options:
     -h, --help            show this help message and exit
     --input_file INPUT_FILE
                           input file path
     --config_file CONFIG_FILE
                           config file path [if not provided will take config file based on input file]
     --outputdir OUTPUTDIR
     --verbose
     --overwrite           overwrite existing .nc files [default is False]
```

## Features

This Python library (based on `xarray`) allows to perform wind inversion from level-1 GRD (projected magnitude image).
Mission supported:

-   Sentinel-1
-   RCM
-   RadarSat-2
denonavr 1.3.2
Automation Library for Denon AVR receivers
License: MIT
Requires Python: >=3.8

# denonavr
[![Release](https://img.shields.io/github/v/release/ol-iver/denonavr?sort=semver)](https://github.com/ol-iver/denonavr/releases/latest)
[![Build Status](https://github.com/ol-iver/denonavr/actions/workflows/python-tests.yml/badge.svg)](https://github.com/ol-iver/denonavr/actions/workflows/python-tests.yml)
[![PyPi](https://img.shields.io/pypi/v/denonavr.svg)](https://pypi.org/project/denonavr)
[![License](https://img.shields.io/github/license/ol-iver/denonavr.svg)](LICENSE)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

Automation Library for Denon AVR receivers

## Installation

Use pip:

```$ pip install denonavr```

or 

```$ pip install --use-wheel denonavr```
  
## Usage with `async`

Writing `async` and `await` methods are outside the scope of the documentation.  You can test `async` usage from the Python REPL.  In a terminal run:

`python3 -m asyncio`

The `asyncio` library should automatically be imported in the REPL.  Import the `denonavr` library and set up your receiver.  If you know the  IP address, enter it below replacing `192.168.1.119`.

```
>>> import asyncio
>>> import denonavr
>>> d = denonavr.DenonAVR("192.168.1.119")
>>> await d.async_setup()
>>> await d.async_update()
>>> print(d.volume)
-36.5
```

### Monitoring with telnet
In addition to retrieving the current device status via HTTP calls, `denonavr` library also has the ability to setup a task that will connect to the receiver via telnet on TCP port 23 and listen for real-time events to notify of status changes.
This provides instant updates via a callback when the device status changes. Receivers support only one active telnet connection.

```
>>> import asyncio
>>> import denonavr
>>> d = denonavr.DenonAVR("192.168.1.119")
>>> await d.async_setup()
>>> await d.async_telnet_connect()
>>> await d.async_update()
>>> async def update_callback(zone, event, parameter):
>>>>>> print("Zone: " + zone + " Event: " + event + " Parameter: " + parameter)
>>> d.register_callback("ALL", update_callback)
```

### Power & Input
```
>>> await d.async_power_on()
>>> await d.async_update()
>>> d.power
'ON'

>>> await d.async_power_off()
>>> await d.async_update()
>>> d.power
'OFF'

>>> d.input_func
'Tuner'
>>> await d.async_set_input_func("Phono")
>>> d.input_func
'Phono'
```
### Sound
```
>>> await d.async_mute(True)
>>> await d.async_mute(False)
```

### Other methods

Other `async` methods available include:

* `d.async_bass_down`
* `d.async_bass_up`
* `d.async_treble_down`
* `d.async_treble_up`
* `d.async_volume_down`
* `d.async_volume_up`
* `d.async_set_volume(50)`

## Collection of HTTP calls
For a collection of HTTP calls for Denon receivers please have a look at the [doc folder](doc/XML_data_dump.txt).

## License
MIT

## Users
Home Assistant: https://github.com/home-assistant/home-assistant/  
denonavr-cli: https://pypi.org/project/denonavr-cli/
immucellai2 2.1.34
A tool for immune cell type deconvolution
License: MIT
Requires Python: >=3.7

# ImmuCellAI2.0

ImmuCellAI2.0
ImmuCellAI2 is a novel computational framework to accurately infer the abundance of 9 major and 53 minor types of immune cells from bulk RNA-seq.

ImmuCellAI Portal Website
The ImmuCellAI Portal serves as a comprehensive companion platform for ImmuCellAI2. Furthermore, the ImmuCellAI Portal offers an intuitive online service for ImmuCellAI2, allowing users to perform immune cell infiltration analysis without local installation. To access this online service, simply click here (https://guolab.wchscu.cn/ImmuCellAI2/#/).

PyPI Page
ImmuCellAI 2.0 homepage on PyPI: https://pypi.org/project/immucellai2/

Install ImmuCellAI2

```bash
pip install immucellai2

Usage
ImmuCellAI 2.0 expects a TPM matrix as input and can be implemented with only two lines of code in Python.

Basic Usage Code
import immucellai2
reference_data = immucellai2.load_tumor_reference_data()

result = immucellai2.run_ImmuCellAI2(
    reference_file=reference_data,
    sample_file=<file_path>,    #  User-defined
    output_file=<file_path>,    #  User-defined
    thread_num=8
)

Citation
If you use ImmuCellAI2 in your research, please cite the following publication: