semvec/pypi-packages huggingface dataset:
rshiny-server-cmd 0.0.2
Command line wrapper to run an R Shiny Server
Requires Python: >=3.6

# rshiny-server-cmd

Command line wrapper to run a named R Shiny Server script/folder.

This project is used in [ContainDS Dashboards](https://github.com/ideonate/cdsdashboards), which is a user-friendly 
way to launch Jupyter notebooks as shareable dashboards inside JupyterHub. Also works with Streamlit and other 
visualization frameworks.

## Install and Run

Install using pip.

```
pip install rshiny-server-cmd
```

The file to start is specified on the command line, for example:

```
rshiny-server-cmd ~/Dev/myrfile.R
```

By default the server will listen on port 8888

To specify a different port, use the --port flag.

```
rshiny-server-cmd --port=8888 ~/Dev/myrfile.R
```

To run directly in python: `python -m rshiny_server_cmd.main <rest of command line>`

## Other command line args

--debug

--ip
tiff2csv 0.0.4
A lightweight python package for processing .tif image data files.
Requires Python: >=3.6

# tiff2csv

This is a simple python package for processing ```.tif``` image data and outputting ```.csv``` files.
The intention is to operate on a sequence of ```.tif``` images and, based on a given discretization size, 
output a ```.csv``` file containing the voxel data. 
You can find the Github repository on https://github.com/alhermann/tiff2csv.git.

## Getting started 

You can use the Python package manager Pip for downloading the module from https://pypi.org/project/tiff2csv.

```pip3 install tiff2csv```

## Example

Suppose you have stored a stack of ```.tif``` image files in an upper subdirectory called ```../tiffs```. In order to run the main module, you have to provide this path as a mandatory argument. For the description of additional arguments, you can use the ```-h```or ```--help``` flag

```python3 -m tiff2csv --help```

which produces the following output:


```
usage: __main__.py [-h] [-n imgNum] [-r resolution] [-d dFile] [-p pooling] sFile [sFile ...]

Script that reads a sequence of .tif files and outputs .csv file of voxel data
to user specified directory

positional arguments:
  sFile          the source file of the .tif image sequence

optional arguments:
  -h, --help     show this help message and exit
  -n imgNum      number of .tif images
  -r resolution  voxel resolution in the .tif images
  -d dFile       the destination directory where the output should go to
  -p pooling     optionally activate 2D pooling on the input images
```

For an exemplary CT image resolution of 5mu the following command runs a max-pooling compression on the input image data files and outputs a ```voxelization.csv``` voxel data file to user specified (in this case parent) directory.

```
python3 -m tiff2csv ../tiffs/ -r 5e-6 -d ../ -p True
```

![VOX](./voxels.png "Voxelization in Paraview")