semvec/pypi-packages huggingface dataset:
datasoup 0.0.1
A Python package to manage data storage, hosting, collection and processing.
Requires Python: >=3.7

```python
import datasoup as ds

dsdb = ds.db.connect("<connection_string>", options={})

dsdb.project.list()

dsdb.project.set("<project_name>", in_place=True)

ds_project = dsdb.project.set("<project_name>", in_place=False)

dsdb.project["<project_name>"].some_method()
```


---

### thoughts

use symlinks to distribute data
or docker-compose to mount projects
tracelogdetdiag 0.0.1
Some implementations of randomized, matrix-free algorithms for estimating matrix traces, log determinants, diagonals, and diagonals of inverses.
Requires Python: >=3.7
Keywords: linear algebra,log determinant,trace,randomized estimators

# randomized-trace-logdet-diag-diaginv
Some Python implementations of randomized, matrix-free algorithms for estimating $\text{tr}(A)$, $\log \det(A)$, $\text{diag}(A)$, and $\text{diag}(A^{-1})$. Here $A$ is SPD or SPSD. We also provide implementations of non-randomized methods requiring access to matrices for convenience.

This package is a work-in-progress. The goal is to implement some of the algorithms detailed in the references below. 


## References

<a id="1">[1]</a>  M.F. Hutchinson (1989). A Stochastic Estimator of the Trace of the Influence Matrix for Laplacian Smoothing Splines. Communications in Statistics - Simulation and Computation, 18(3), 1059-1076.

<a id="2">[2]</a> Avron, H., & Toledo, S. (2011). Randomized Algorithms for Estimating the Trace of an Implicit Symmetric Positive Semi-Definite Matrix. J. ACM, 58(2).

<a id="3">[3]</a> Roosta-Khorasani, F., & Ascher, U. (2015). Improved Bounds on Sample Size for Implicit Matrix Trace Estimators. Foundations of Computational Mathematics, 15(5), 1187–1212.

<a id="4">[4]</a> Saibaba, A., Alexanderian, A., & Ipsen, I. (2017). Randomized matrix-free trace and log-determinant estimators. Numerische Mathematik, 137(2), 353–395.

<a id="5">[5]</a> C. Bekas, E. Kokiopoulou, & Y. Saad (2007). An estimator for the diagonal of a matrix. Applied Numerical Mathematics, 57(11), 1214-1229.

<a id="6">[6]</a> Christos Boutsidis, Petros Drineas, Prabhanjan Kambadur, Eugenia-Maria Kontopoulou, & Anastasios Zouzias (2017). A randomized algorithm for approximating the log determinant of a symmetric positive definite matrix. Linear Algebra and its Applications, 533, 95-117.

<a id="7">[7]</a> Han, I., Malioutov, D.M., & Shin, J. (2015). Large-scale log-determinant computation through stochastic Chebyshev expansions. International Conference on Machine Learning.

<a id="8">[8]</a> Chen, J. (2016). How Accurately Should I Compute Implicit Matrix-Vector Products When Applying the Hutchinson Trace Estimator? SIAM J. Sci. Comput., 38.

<a id="9">[9]</a> Meyer, R.A., Musco, C., Musco, C., & Woodruff, D.P. (2020). Hutch++: Optimal Stochastic Trace Estimation. Proceedings of the SIAM Symposium on Simplicity in Algorithms, 2021, 142-155 .

<a id="10">[10]</a> Persson, D., Cortinovis, A., & Kressner, D. (2021). Improved variants of the Hutch++ algorithm for trace estimation. SIAM J. Matrix Anal. Appl., 43, 1162-1185.
whistleaio 0.1.2
Asynchronous Python library for Whistle's API
Requires Python: >=3.7
Keywords: whistle,whistle fit,whistle api,whistle client,whistle gps,whistle pet

# Whistleaio

Asynchronous Python library for Whistle's API using Whistle account email and password.

This package provides an API client for the Whistle API to be used with a Whistle account email and password.
This API is not published, so there is a chance it may break in the future.

## Installation

### PyPI
```
pip3 install whistleaio
```


This package depends on [aiohttp](https://docs.aiohttp.org/en/stable/), and requires `Python 3.7` or greater.
righty 0.1.0
Code to interact with the "righty" fly CNS GridTape-TEM dataset
Requires Python: >=3.6

### Python code for the "righty" GridTape-TEM dataset

#### Installation
Install this package into your python environment using pip:

    # Install from Python Package Index
    pip install righty
    # Install from GitHub
    pip install git+https://github.com/jasper-tms/righty
    
To later pull updates, use:

    # If you installed from PyPI
    pip install --upgrade righty
    # If you installed from GitHub
    pip install --force-reinstall git+https://github.com/jasper-tms/righty

#### Get started
Launch python, import this package, and get started by looking at the dataset's description:

    import righty
    print(righty.info.dataset_description)

See what other information is available:

    print(righty.info.descriptions)
    # Or if you want to print these descriptions formatted nicely:
    import json
    print(json.dumps(righty.info.descriptions, indent=2))
    
Then access the info you are interested in, e.g.:

    voxel_size = righty.info.voxel_size
    shape = righty.info.shape
aiopaystack 2.0.0
Asynchronous PayStack library
Requires Python: >=3.10
Keywords: paystack,asynchronous

# aiopaystack


Asynchronous Python library for [Paystack](https://paystack.com/)

![GitHub](https://img.shields.io/github/license/ichinga-samuel/aiopaystack)
![PyPI](https://img.shields.io/pypi/v/aiopaystack)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/aiopaystack)
![GitHub issues](https://img.shields.io/github/issues/ichinga-samuel/aiopaystack)
![GitHub Workflow Status (branch)](https://img.shields.io/github/workflow/status/ichinga-samuel/aiopaystack/master/master)
## Installation
```bash
pip install aiopaystack
```

## Usage
Add your paystack secret key as an environment variable as PAY_STACK_SECRET_KEY
```python
from paystack import Transactions

trans = Transactions()

# All parameters must be passed in as keywords. For both required and optional arguments.
res = await trans.initialize(email="sam@gmail.com", amount='5000')

# Passing secret key as an argument
# This replaces any key set in the environment
from paystack import Paystack
paystack = Paystack(secret_key="paystack_secret_key")

# to use one session for multiple request use the class as a context manager
async with Transactions() as trans:
    res= await trans.verify(reference="ref")
    
# The response type for every request is a typed dict.
from typing import TypedDict, Any
Response = TypedDict('Response', {'status_code': int, 'status': bool, 'message': str, 'data': dict | Any})

# Sample response
{'status': True,
 'message': 'Authorization URL created',
 'data': 
     {'authorization_url': 'https://checkout.paystack.com/3521i62zf1i0ljl',
      'access_code': '3521i62zf1i0ljl', 'reference': '2q16btxglw'
      },
 'status_code': 200
 }
## DOC Reference: <https://developers.paystack.co/v2.0/reference>
### Static Use
```
Don't forget to get your API key from [Paystack](https://paystack.com/) and assign to the variable `PAYSTACK_SECRET_KEY`
Please reference the **docs** folder for usage,
ivt 0.1.5
Inverse-Rendering Toolkit
Requires Python: >=3.8

# ivt
`ivt` stands for Inverse-Rendering Toolkit. It is a simple framework for conducting inverse rendering experiments. Since there are a lot differentiable renderers in development, mastering them all and migrating between them can be a headache. `ivt` provides a simple scene representation based on `pytorch` that can be rendered by **connectors** that connect to different differentiable renderers and get the graident of the scene parameters. Migrating between renderers is just a matter of changing the connector. 

## Installation
`ivt` depends on `pytorch`. Since it is widely used and some projects might have specific requirements to it, we leave its installation to the user. 

After installing `pytorch`, you can install `ivt` via
```
pip install ivt
```
nkt-tools 0.0.8
Python package for accessing NKT photonics project through NKTPDLL
Requires Python: >=3.7

This repository contains tools for accessing NKT photonics devices via python through the NKT SDK

nkt_tools turns this:

.. code-block:: python

    import NKTP_DLL as nkt
    nkt.registerWriteU8('COM27', 15, 0x30, 0x03, -1)

into this:

.. code-block:: python

    from nkt_tools.extreme import Extreme
    laser = Extreme()
    laser.set_emission(True)

nkt_tools is a wrapper around NKT's DLL to provide object oriented interaction with NKT products. NKT Extreme/Fianium lasers and the Varia system are currently supported. Additional systems can be accessed through the DLL by using the registerRead/Write functions within nkt_tools.NKTP_DLL or interested developers can write new modules by following the development notes provided.

**ReadtheDocs Page:**
https://nkt-tools.readthedocs.io/en/latest/

**PyPI Page:**
https://pypi.org/project/nkt-tools/

Please also check out my related package, CataLight, which automates data collection and processing for photocatalysis research!

**ReadtheDocs Page:**
https://catalight.readthedocs.io/en/latest/

**PyPI Page:**
https://pypi.org/project/catalight/