semvec/pypi-packages huggingface dataset:
stannum 0.9.1
Fusing Taichi into PyTorch
Requires Python: >=3.6


<div align="center">
  <img width="300px" src="https://github.com/ifsheldon/stannum/raw/main/logo.PNG"/>
</div>


# Stannum

![Gradient Tests](https://github.com/ifsheldon/stannum/actions/workflows/run_tests.yaml/badge.svg)

Fusing Taichi into PyTorch

**PRs are always welcomed, please see TODOs and issues.**

## Why Stannum?

In differentiable rendering including neural rendering, rendering algorithms are transferred to the field of computer vision, but some rendering operations (e.g., ray tracing and direct volume rendering) are not easy to be expressed in tensor operations but in kernels. Differentiable kernels of Taichi enables fast, efficient and differentiable implementation of rendering algorithms while tensor operators provides math expressiveness. 

Stannum bridges Taichi and PyTorch to have advantage of both kernel-based and operator-based parallelism.

## Documentation and Usage

Please see [documentation](https://fengliang.io/stannum/).

Code sample of `Tube`:

```python
import taichi as ti
import torch

@ti.kernel
def mul(arr: ti.template(), out: ti.template()):
    for i in arr:
        out[i] = arr[i] * 2.0


if __name__ == "__main__":
    ti.init(ti.cpu)
    a = torch.ones(10, requires_grad=True)
    tube = Tube() \
        .register_input_tensor((10,), torch.float32, "arr") \
        .register_output_tensor((10,), torch.float32, "out", True) \
        .register_kernel(mul, ["arr", "out"]) \
        .finish()
    out = tube(a)
    loss = out.sum()
    loss.backward()
    assert torch.allclose(out, torch.ones_like(out) * 2)
    assert torch.allclose(a.grad, torch.ones_like(a) * 2)
```



## Installation & Dependencies

Install `stannum` with `pip` by

`python -m pip install stannum`

Make sure you have the following installed:

* PyTorch
* latest Taichi
    * For performance concerns, we strongly recommend to use Taichi >= 1.1.3 (see Issue #9 for more information)

## Bugs & Issues

Please feel free to file issues. If a runtime error occurs from the dependencies of `stannum`, you may also want to check the [upstream breaking change tracker](https://github.com/ifsheldon/stannum/issues/11).
spectra2rgb 1.0.0
Converts multi-spectrum cube to RGB image

# spectra2rgb
Converting multi-spectra numpy array to RGB format

## Usage

```python
from spectra2rgb import SpectralData
spectral_data = SpectralData(multi_spectra_data, axis=0)
rgb_data = spectral_data.to_rgb()
```

Where:
* `multi_spectra_data` is of shape (48, 100, 250) and first dimension represents the spectral bands (in this case there are 48 spectra bands)
* `rgb_data` will be of shape (3, 100, 250) and will have values between 0 and 255
* 0th index in spectral dimension will be considered as the lowest wavelength (violet) and last index will be considered as longest wavelength (red)
exoclasma-note 0.9.0
Exo-C Data Quality Check, Mapping, and Variant Calling, part of ExoClasma Suite
Requires Python: >=3.8
Keywords: annotation,bioinformatics,variant

# exoclasma-note

Annotation of genomic variants.

## Dependencies

Perl:

```bash
sudo apt install perl
```

Annovar:

```bash
GENOME="hg19" # hg38 if you prefer
curl --output annovar.latest.tar.gz www.openbioinformatics.org/annovar/download/0wgxR2rIVP/annovar.latest.tar.gz
tar -xzf annovar.latest.tar.gz
cd annovar
for database in refGene knownGene ensGene dbnsfp35c dbscsnv11 intervar_20180118 gnomad211_genome gene4denovo201907 kaviar_20150923 hrcr1 abraom 1000g2015aug gme esp6500siv2_all avsnp150 clinvar_20200316 regsnpintron revel gwava;
do {
	perl annotate_variation.pl -downdb -buildver ${GENOME} -webfrom annovar ${database} humandb/
} done
cd ..
rm annovar.latest.tar.gz
```

## Install

```bash
sudo python3 -m pip install exoclasma-note
```

## Usage

```bash
exoclasma-note -u ${unit_json} -a ${annovar_folder} -g ${genome} [--nofilter]
```

* `unit_json`: Unit JSON file which was created by exoclasma-pipe
* `annovar_folder`: Path to ANNOVAR folder where perl scripts are located
* `genome`: Genome assembly which use ANNOVAR (i.e., hg19, hg38, etc.)
samesyslib 0.0.42
Common libs used by SameSystem Data Science team.
Requires Python: >=3.7

## Common functions for Data Science

[![Build Status](https://app.travis-ci.com/samesystem-ds/samesyslib.svg?branch=master)](https://app.travis-ci.com/github/samesystem-ds/samesyslib) 
[![codecov](https://codecov.io/gh/samesystem-ds/samesyslib/branch/master/graph/badge.svg?token=W6fJRyzkU2)](https://codecov.io/gh/samesystem-ds/samesyslib)
[![PyPI](https://img.shields.io/pypi/v/samesyslib)](https://pypi.org/project/samesyslib/)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/samesyslib)](https://pypi.org/project/samesyslib/)
[![GitHub](https://img.shields.io/github/license/samesystem-ds/samesyslib)](https://github.com/samesystem-ds/samesyslib/blob/master/LICENSE)

[![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)
[![Python 3.8](https://img.shields.io/badge/python-3.8-blue.svg)](https://www.python.org/downloads/release/python-380/)
[![Python 3.9](https://img.shields.io/badge/python-3.9-blue.svg)](https://www.python.org/downloads/release/python-390/)

Common libs used by SameSystem Data Science team.

#### Example Usage

```python
# /opt/settings/config.yml
database:
  user: 'root'
# ----

from pathlib import Path
from samesyslib.utils import load_config

config_path = Path("/opt/settings/config.yml")
conf = load_config(config_path)

conf['database']['user']

# or 

from samesyslib.db import DB
from samesyslib.db_config import DBConfig


db = DB(DBConfig().get_config())
```

#### Install

The latest stable version can always be installed or updated via pip:

```python
pip install samesyslib
```

#### Test Coverage

```bash
pip install pytest-cov
python -m pytest --cov=samesyslib tests
```

#### Updating

After editing the functions, increment package version number in `setup.py` before pushing to master, so that pypi package can be automatically build. To update installed package:

```python
pip install samesyslib --upgrade
```

#### Development Version

The latest development version can be installed directly from GitHub:

```python
pip install git+https://github.com/samesystem-ds/samesyslib.git
```
 
#### License

Code and documentation are available according to the MIT License
(see [LICENSE](https://github.com/samesystem-ds/samesyslib/blob/master/LICENSE)).
mosaik-pandapower 0.2.2
An adapter to use pandapower with mosaik.

mosaik-pandapower
=================

This package contains an adapter to connect *pandapower* to *mosaik*.


Input
-----
Grid import can be done from Json or Excel files, and the file
should exist in the working directory of mosaik scenario::

    sim_config = {
       'Grid': {
            'python': 'mosaik_pandapower.simulator:Pandapower'
            }
    }
     
    GRID_FILE = 'path to the file.json' or  'path to the file.xlsx'
    gridsim = world.start('Grid', step_size=15*60)
    grid = gridsim.Grid(gridfile=GRID_FILE).children
    
* In pandapower library there exist a list of standard grids that 
  can be directly imported and simulated. An overview is given
  in the `documentation`__

__ https://pandapower.readthedocs.io/en/develop/networks.html
 
  
The following list of grid clusters that could be simulated by 
this model are:

* Simbench::

          GRID_FILE = 'name of the simbench grid'

To use Simbench grid, simbench has to be installed with 'pip install simbench'
          
* Cigre networks::

          GRID_FILE = 'cigre_(voltage level: hv,mv,lv)'
          
* Cigre network with DER::

          GRID_FILE = 'cigre_mv_all' or 'cigre_mv_pv_wind'
          
* Power system Cases :
  these networks exist as Json files in
  ~/pandapower/networks/power_system_test_case_jsons

  these files should be copied in the working directory and 
  imported as json file
          
          
Examples
--------

Examples of the mosaik-pandapower adapter can be found in a seperated `repository`__.

__ https://gitlab.com/mosaik/examples/mosaik-pandapower-examples


Changelog
=========

0.2.2 - 2023-01-10
------------------

- Fix indexing for grids with non-sequential element indices

0.2.1 - 2022-07-13
------------------

- Fix activation of connected loads and generators in setup_done

0.2.0 - 2022-02-28
------------------

- Added storage attribute
- Adapted handling of input values for in_service and controllable
- Fix load cigre network via gridfile

0.1.0 - 2021-08-31
------------------

- Initial release of mosaik-pandapower adapter


Authors
=======

The author of the mosaik-pandapower adapter is Rami Elshinawy.

Additional contributors: Annika Ofenloch, Fernando Penaherrera, Jan Sören Schwarz,
Eike Schulte
simplay 1.0.1
A library to generate an EventQueue to visualize with simplay-web
Requires Python: >=3.9

# SimPlay

*Hint: What you probably want to do, is install [simplay-jupyter](https://pypi.org/project/simplay-jupyter/) which bundles simplay with simplay-web, and allows you to run this project within JupyterLab.*

Enhance [SimPy](https://simpy.readthedocs.io/) simulations with animation details.
More Information can be found in the [SimPlay Documentation](https://simplay.readthedocs.io/).

## Install

To install the package, execute:

```bash
pip install simplay
```
nothingapi 2.0.0
The Official Python API Wrapper for the Nothing Currency!
Requires Python: >=3.7

# NothingAPI - The Official Python API Wrapper for the Nothing Currency!
## Basic Syntax
### Installing the API using pip:
```
pip install nothingapi
```

### Fetching a User's object and printing it's balance:
```
import nothingapi as api
user = api.get_user(user_id)
print(user.balance)
```

### Fetching a Guild's object and printing it's total value:
```
import nothingapi as api
user = api.get_guild(guild_id)
print(guild.total_value)
```