semvec/pypi-packages huggingface dataset:
RRaster 0.0.3
Rasterio function wrappers for simple raster processing in Python that mimics the R Raster syntax.
Requires Python: >=3.6, <4
Keywords: raster,rasterio,R,simplified

# RRaster
### Simple and readable raster manipulation with Python.

This package is an attempt to create a set of user-friendly functions for reprojecting and manipulating rasters with 
Python. The syntax is meant to mirror that of the R Raster library. 

Dependencies
------------
- `Python` 3.8+
- `numpy` 1.3+
- `rasterio` 1.2.6+
- `matplotlib` 3.4.1+

Installation
------------
It is recommended to use this package within a `virtualenv`.

```sh
$ python3 -m venv venv
$ source venv/bin/activate

# The RRaster package can be installed with pip
$ pip install rraster

# Alternatively, the development version of the package can be installed from 
$ pip install -r requirements.txt
$ pip install .
````

Examples
--------
The current operations supported by RRaster are reprojection, reduction, writing to disk, and basic raster calculations
(addition, subtraction, etc.). Below, a [gridMet](http://www.climatologylab.org/gridmet.html) precipitation raster is 
projected to the [EASE-2 grid](https://nsidc.org/data/ease) and some basic manipulations are done. Although this syntax
may be less Pythonic, I find it much easier to read and remember than the typical rasterio syntax.

```python
import numpy as np
from pathlib import Path
import pkg_resources
from rraster.Raster import Raster, RasterStack

# Find path to data within the package
pth = Path(pkg_resources.resource_filename('rraster', 'data'))

# Read in rasters
r1 = Raster(pth / '19990601_pr.tif')
r1 = Raster(pth / 'template.tif')

# Reproject with one line of code
reproj = r1.reproject(r2, method='bilinear')

# Do some stuff to the new raster
reproj = reproj * r2 # Possible because the two rasters now share the same projection and resolution.
reproj /= 18

# Make a RasterStack 
stk = RasterStack(reproj, r2)
# Calculate the pixel-wise mean
new_raster = stk.reduce(np.mean, axis=0)

# Save the new raster to disk.
new_raster.write('./example.tif')
```
NNMDKit 0.1.0
Automated generation of LAMMPS data and input files for polymer molecular dynamics simulations
Keywords: LAMMPS,polymer,SMILES

# Neural Network Molecular Dynamics Kit
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](http://opensource.org/licenses/MIT)

NNMDKit generates polymer topology from SMILES strings and automatically builds LAMMPS data and input files for molecular dynamics simulations

## Example
Below is an example python script where we use NNMDKit to generate LAMMPS data and input files for Tg measurement with a list of SMILES strings.
```python
import nnmdkit

smiles = ['*CC*', '*CC(*)C', '*CC(*)CC', '*CC(*)CCC', '*CC(*)CCCC','*CC(*)c1ccccc1']

for s in smiles:
    sys = nnmdkit.System(smiles=s, mw=10000, ntotal=3000, density=0.5, builtby='emc')
    data = sys.write_data(output_dir=s)

    lmp = nnmdkit.Lammps(data, NN_POTENTIAL='~/p-rramprasad3-0/NNLMP/potential_saved')
    lmp.add_procedure('minimization', min_style='cg')
    lmp.add_procedure('equilibration', Tfinal=600, Pfinal=1, Tmax=800, Pmax=49346.163)
    lmp.add_procedure('Tg_measurement', Tinit=600, Tfinal=100, Tinterval=25, step=1000000)
    lmp.write_input(output_dir=s)
                       
    job = nnmdkit.Job(jobname=s, project='GT-rramprasad3-CODA20', nodes=2, ppn=24, 
                      walltime='48:00:00', LAMMPS_EXEC='~/p-rramprasad3-0/NNLMP/lmp')
    job.write_pbs(output_dir=s)
```
<p align="center">
    <img src='./tutorial/img/temp_vs_density.png' width="70%"/>
</p>

A tutorial on using NNMDKit to create simulations of hydrocarbon polymers can be found [here](https://github.com/Ramprasad-Group/NNMDKit/tree/master/tutorial/CaseStudies.Hydrobarbons.ipynb).

## Installation

```bash
pip install .
```

### Requirements
* [EMC](http://montecarlo.sourceforge.net/emc/Welcome.html) or [PSP](https://github.com/Ramprasad-Group/PSP)
* [RDKit](https://www.rdkit.org/)

Note that, both [EMC](http://montecarlo.sourceforge.net/emc/Welcome.html)/[PSP](https://github.com/Ramprasad-Group/PSP) and [RDKit](https://www.rdkit.org/) are required to be installed manually. NNMDKit requires EMC or PSP to create polymer structures. To configure the integration with EMC, two environment variables are required to be addded to locate your EMC executable (`emc_linux64` for Linux, `emc_macos` for MacOS, or `emc_win32` for Windows) and setup tool (`emc_setup.pl`). Add the paths of the EMC executable and setup tool as environment variables "EMC_EXEC" and "EMC_SETUP", respectively.

For Ramprasad Group users on Tyrion2, simply type in the command line:

```
echo "export EMC_EXEC=/data/kevin/EMC/bin/emc_linux64" >> ~/.bashrc
echo "export EMC_SETUP=/data/kevin/EMC/scripts/emc_setup.pl" >> ~/.bashrc
```

## Copyright
Ramprasad Group, Georgia Tech, USA
neverd 0.1.0
A 2D reverse engineering tool
Requires Python: >=3.6
Keywords: gui,tkinter,reverse engineering,2D dialog,geometry

2D REVerse ENginnering dialog
=============================


`neverd` is a 2D reverse engineering tool built on top of `tkinter`. It provides a set of geometric objects (points, lines and sliders) that can be used in conjunction with an image and a calibration rectangle to extract relevant geometric information.



## Installation

Install with

```bash
pip install neverd
```


## Usage

`neverd` can be used both as a normal package or as a CLI tool.


For CLI tool usage, do:

```bash
neverd --help
```
mCSSMapi 0.2
The mcssmapi library providing access to Cisco Smart Account licensing API with python.
Requires Python: >=3.6

# mCSSMAPI - Mini Cisco Smart Software Manager API
The mcssmapi library is providing access to Cisco Smart Account licensing API with python.

I needed to access licensing portal to get SLR licensing process a little bit easier.
I was inspired to create API by great app https://developer.cisco.com/codeexchange/github/repo/CiscoDevNet/smart-license-app/.

[![published](https://static.production.devnetcloud.com/codeexchange/assets/images/devnet-published.svg)](https://developer.cisco.com/codeexchange/github/repo/mmikulic212/mcssmapi)

## Version
(TODO)
- more API calls
- logging

0.2 (4.11.2021)
- init.py fix
- fixed request to swapi with json
- removed requirements.txt

0.1 (21.6.2021)
- initial release with only slr reserve call

## Prerequisite

For use the module you need to have:
- CCO/Cisco.com Account
- Valid Smart Account (SA) and Virtual Account in CSSM
- IOS-XE devices with Smart Licensing enabled image version (>16.10.1)
- Generated API access (Client ID and Client Secret)

## Installation

```
$ pip install mcssmapi
```
### API access
For accessing CSSM API you will need to request for API access on https://apidocs-prod.cisco.com/

How to request API access is described here: https://anypoint.mulesoft.com/apiplatform/apx#/portals/organizations/1c92147b-332d-4f44-8c0e-ad3997b5e06d/apis/5418104/versions/102456/pages/425744


## Usage

All code is only an example. Values are not valid.

```
import mcssmapi
import json

CLIENT_ID = "abcd1234"
CLIENT_SECRET = "secret1234"
SAD = "sadomain.com"
VA = "Test Account"
REQUEST_CODE = "CE-ZC9300-48P-4X:XDC240312LB5-A6rZG7jGd-75"

# init api instance
api = mcssmapi.Licensing(CLIENT_ID, CLIENT_SECRET)
# get oauth token
api.bearer_update()

# license definition

licenses = [
       {
           "quantity" : 1,
           "entitlementTag" : "regid.2018-05.com.cisco.C9300-DNA-E-48,1.0_e213fd3f-18f9-4940-a21c-617b92efdaae"
       },
       {
           "quantity" : 1,
           "entitlementTag" : "regid.2018-05.com.cisco.C9300-NW-E-48,1.0_52d2a0bd-de8a-480f-b70b-1d57e7738aec"
       },
   ]
result = api.reserve_license(SAD,VA,REQUEST_CODE, licenses)

print(json.dumps(result, indent=2)

```
## License

App is released under the MIT License. See LICENSE for details.

## Contact

Github: https://github.com/mmikulic212/mcssmapi
netqr 0.1.1
Rich Tong's Fine QR Utilities
Requires Python: >=3.8

# QR Code for NetDrones

Sample package that reads in QR code data, writes them into files for printing.
It also reads in a set of image files and looks for QR Codes.

## Python PIP Packaging Notes

The package layout has the source code in [src/netpy](src/netpy) and note the
critical files there are:

- py.typed. Must be an attribute in [setup.py](setup.py) which configures the
    python package.
- __init__.py. This must be there to denote the package and import all
    the modules.
- setup.py. The file that drives the packaging, it tells the build system where
    to find the components when you do a `python -m build`
- pip install -e. This is used for development and does an "editable"
    installation