semvec/pypi-packages huggingface dataset:
nplocate 0.2.7
Python tools to locate nano particles from confocal microscope images

# Locate Nano Particles


## What is this

`nplocate` is a custom script I wrote to locate very tiny particles from a confocal image. These images often suffered from extreme influences of the [PSF](https://en.wikipedia.org/wiki/Point_spread_function), even after very detailed and completed deconvolution procedures.

To squeeze a bit more information out of these highly distorted data, I wrote this code to effectly "fit" the entire 3D image. This is done in a quite sloopy way. For a perfect fit, please take a look at the very well crafted [peri](https://github.com/peri-source/peri) project.


## The idea

This is not a fully functional particle tracking package like [trackpy](https://github.com/soft-matter/trackpy) or [colloids](https://github.com/MathieuLeocmach/colloids) or [peri](https://github.com/peri-source/peri). Instead, think of `nplocate` as an <big>extension</big> of current tracking packages.

The logic behind the code is quite simple. The arguments are,

1. It is *easy* to find *some* particles, even in a highly distorted image.
2. If we know the locations of some particles ({**r**}), we can measure their average shape (**S**).
3. With {**r**} and **S**, we can simulate a "fake image"
4. We can find previously unfound particles in the difference between the real image and fake image. 
5. The more particles we have, the merrier.

## Installing the code

The simplest way is

```
pip install nplocate
```

You can also download this repository, and use the following command to install the code

```
pip install .
```

## Using the code


There are some notebooks in the folder `example` that introduced how to use this package, along with [trackpy](https://github.com/soft-matter/trackpy). 

## Cite the code

~~Just tell people you used trackpy~~
RobustPolyfit 0.0.8.1
Outlier-robust regression for polynomials 1 <= degree <= 3

# outlier_robust_polyfit

- [Summary](#summary)
- [Installation](#installation)
- [Usage](#usage)

## Summary

This package fits linear, quadratic or cubic polynomials with Student's 
T-distributed errors using the EM algorithm. It's 2x as fast as
Scipy's siegelslopes for fitting robust linear models to small datasets
(10 - 20 datapoints) and an order of magnitude faster for larger 
datasets (>=100 datapoints). It yields a robust fit for datasets
where 25% of datapoints are outliers (use with caution for datasets
where the "outlier" population may be large).

![example](https://raw.githubusercontent.com/jlparki/outlier_robust_polyfit/main/resources/example_1.png)

![example](https://raw.githubusercontent.com/jlparki/outlier_robust_polyfit/main/resources/example_2.png)

## Installation

    pip install robustpolyfit

## Usage

For usage and further details see the [docs.](https://github.com/jlparkI/outlier_robust_polyfit)
nepal-stonks 0.421.69
Complete NepalStock solution in command line
Requires Python: >=3
Keywords: nepalstock stonk buddha python package buddha69

# STONK

## Installation
```console
pip install nepal_stonks
````
After the installation you can use the cli application with the priceof command

## Usage
```console
usage: priceof [-h] [-c [...]] [-f] [-n] [-m] [-ms] [-d] [-cu] [-ci]
               [--losers] [--gainers] [--supply] [--demand] [--turnover]
               [--volume] [--transactions]

STONK, complete NEPSE solution in command line

optional arguments:
  -h, --help            show this help message and exit
  -c [ ...], --company [ ...]
                        symbols of companies seperated by space
  -f, --floorsheet      get floorsheet
  -n, --nepse           get NEPSE index and subindices
  -m , --market_depth   get market depth of the company(enter symbol)
  -ms, --market_summary get market summary
  -d , --get_detail     get the non price related detail of the company
  -cu , --custom        custom list in data.py
  -ci , --change_index  change the value of id sent as a payload in the post
                        request
  --losers              get the top losers
  --gainers             get the top gainers
  --supply              get the scripts with maximum supply
  --demand              get the scripts with maximum demand
  --turnover            get the scripts with maximum turnover
  --volume              get the scripts with maximum volume
  --transactions        get the scripts with maximum transactions
  ```
pass-admin-pkg-Smarv 0.0.1
A simple password admin program
Requires Python: >=3.6

# Password Admin

This is a simple password admin program.
It is made to ask the user for username and password input data which
is then written and collected in a JSON file. There is also other functions
like editing created usernames and passwords, deleting information etc.

The main program has 3 modes:
- Main mode
- Admin mode
- User mode

In Main mode you have the options to login as admin, sign in a new user and login as user.

When you log in as an admin you enter in Admin mode, where you can view the users data, or
delete users data.
If there is no admin data for login you are asked to sign in as admin which admin data is then collected
in the JSON file. There can be only one admin.

When you sign in a new user it's data is also collected in the JSON file and you can log in after that.
There can be multiple users.
When you log in as a user you enter in User mode, where you can edit your username and password.
You can also add a nickname and have the option to view all your data.
avocado-price-predictor-model-shay-sirek 0.0.1
Avocado Price Prediction Model
Requires Python: >=3.7

# Avocado Price Predictor Model
Avocado Price Prediction Machine Learning Model

# What is about? 
- [Kaggle Dataset](https://www.kaggle.com/neuromusic/avocado-prices)
- [Task](https://www.kaggle.com/neuromusic/avocado-prices/tasks?taskId=3524)
gh-pr-update-description 0.2.0
Update Github PR description with top commit title + body

[![GitHub](https://img.shields.io/badge/GitHub-noahp%2Fgh--pr--update--description-8da0cb?style=for-the-badge&logo=github)](https://github.com/noahp/gh-pr-update-description)
[![PyPI
version](https://img.shields.io/pypi/v/gh-pr-update-description.svg?style=for-the-badge)](https://pypi.org/project/gh-pr-update-description/)
[![PyPI
pyversions](https://img.shields.io/pypi/pyversions/gh-pr-update-description.svg?style=for-the-badge)](https://pypi.python.org/pypi/gh-pr-update-description/)

# 🔃 GitHub PR Update Description

Python script to attempt to refresh the github PR description with the top
commit.

## Usage

Requires a github token available from git config, eg:

```bash
git config github.apitoken <your token>
```

```bash
❯ pip install gh-pr-update-description

❯ gh-pr-update-description
About to update description on https://github.com/foo/bar/pull/128, ready? [Y/n]:
PR successfully updated 🎉 !
```