semvec/pypi-packages huggingface dataset:
modcs-mercupy 0.0.2
A package that abstracts the Mercury's script language in Python.
Requires Python: >=3.8

# modcs-mercupy

modcs-mercupy is a project that abstracts Mercury's script language, providing a simple interface for creating and evaluating stochastic models using the Mercury software. Developed by the **MoDCS** research group at the Center of Informatics (**CIN**) of the Federal University of Pernambuco, Brazil, MercuPy aims to simplify the modeling process and improve user experience.

## Features

- **Simplified Scripting**: Abstracts the complexities of Mercury's script language.
- **Seamless Integration**: Works smoothly with Mercury for model evaluation.
- **Extensible Architecture**: Easily extendable to incorporate new features and functionalities.
- **User-Friendly Interface**: Intuitive Python interface for model creation and analysis.

## Installation

To get started with modcs-mercupy, you can install it directly via `pip`:

```bash
pip install modcs-mercupy
hydra-lsf-launcher 0.1.2
Lsf Hydra Launcher plugin

# hydra-lsf-launcher
This is a simple launcher for launching Hydra jobs on LSF clusters. It is a simple wrapper around the `bsub` command that allows you to launch Hydra jobs with a single command.

### **WARNING: expect bugs and run at your own risk. This is not an official Hydra project.**

## Installation
Install the package with pip:
```bash
pip install hydra-lsf-launcher
```

## Usage

Here is an example using `hydra-zen` to launch a job on an LSF cluster:

```python
from hydra_zen import store, zen
import time


def main_func(sentence):
    time.sleep(120)
    print(sentence)


store(
    main_func,
    hydra_defaults=[
            "_self_",
    ]
)


if __name__ == "__main__":
    store.add_to_hydra_store()
    zen(main_func).hydra_main(
        config_name="main_func", version_base="1.1", config_path=None
    )
```

Then you can run it with:

```bash
python myscript.py hydra/launcher=lsf sentence="Hello World!" -m 
```

## Job groups

To limit the number of concurrent jobs, as for now job arrays are not supported, the user can set job groups:

```bash
bgadd /myJobGroup
bgmod -L 4 /myJobGroup  # limit to 4 concurrent jobs
```



Then you can run it with:

```bash
python myscript.py hydra/launcher=lsf hydra.launcher.bsub_args="-g /myJobGroup" sentence="Hello World!" -m 
```

Some useful commands.
To change the limit of a job group:
```bash
bgmod -L <new_limit> /myJobGroup
```

To delete a job group:
```bash
bgdel /myJobGroup
```


## More complex examples
Run 5 jobs (4 parallel), each with 2GB of memory, and 4 cores from 2 different nodes.

```bash
python myscript.py hydra/launcher=lsf hydra.launcher.verbose=False hydra.launcher.n=4 'hydra.launcher.bsub_args= -g /myJobGroup -R span\[ptile\=2\]' hydra.launcher.M="2GB" sentence="Hello World!,Hello World!,Hello World!,Hello World!,Hello World!" -m
```
horiba-raman-ccoverstreet 0.0.9
Package for processing Horiba LabRAM HR data
Requires Python: >3.8

# Horiba LabRAM Processing Scripts

Cale Overstreet

This package contains scripts meant for processing data files generated by the LabSpec6 software, particularly for use with the mapping feature

## Installation

Installing python package from PyPI

```
python -m pip install horiba_raman_ccoverstreet
```


Installing from downloaded repository

```
git clone https://github.com/ccoverstreet/horiba-raman
cd horiba-raman
python -m pip install -e .
```

or with a zip file

```
unzip NAMEOFZIPFILE.zip
cd horiba-raman-main
python -m pip install -e .
```

## Required Data Files

- `*.txt` version of l6m: This can be saved either by saving from the `browser` tab or the mapping tab with the spectra sub-window highlighted and selected the `.txt` extension. This file contains all the collected spectra with corresponding spatial positions
- `*.txt` version of the video feed: This can be saved by going to the `video` tab and saving the current camera view with the `.txt` extension. This file contains a gray-scale image with spatial coordinates of pixels. **SHOULD BE TAKEN BEFORE ACQUISITION START**
- `*.bmp` version of the video feed: This can be saved by going to the `video` tab and saving the current camera view with the `.txt` extension. This file contains the color image of the camera feed. **SHOULD BE TAKEN BEFORE ACQUISITION START**
- `*l6m`: Binary default format used by the LabSpec software for storing mapping information. Useful for record-keeping.


## Example

An example is provided in the [src/example](src/example) directory
pyAnimals-ejh 0.2.1
A fun package that displays animals on the terminal with movement and messages.
Requires Python: >=3.6

![Build Status](https://github.com/software-students-fall2024/3-python-package-ejh/actions/workflows/event-logger.yml/badge.svg)

# pyAnimals-ejh

## Table of Contents
1. [Description](#description)
2. [PyPI Page](#pypi-page)
3. [Virtual Environment](#virtual-environment)
4. [Installation](#installation)
5. [Functions](#functions)
6. [Example](#example)
7. [Contributing](#contributing)
8. [Team Members](#team-members)

## Description
A fun package that displays animals on the terminal with movement and messages.

## PyPI Page
You can find pyAnimals_ejh on PyPI [HERE](https://pypi.org/project/pyAnimals-ejh/0.2.0/)

## Virtual Environment
Set up the virtual environment:
```
python3 -m venv venv
source venv/bin/activate
```

## Installation
Install `pyAnimals_ejh` with pip:
```
pip install pyAnimals_ejh
```
Check that the package is installed with pip:
```
pip list
```

## Functions
Parameter for all functions are: animal (str): name of animal.\
***Note: Valid animal names include "cat", "bunny", "elephant", "rabbit". Only these can be used with our package.

1. move(animal)
Moves specified animal across the terminal screen
```
move("cat")
```

2. race(animal)
Displays race between player and specified animal
```
race("rabbit")
```

3. randMessage(animal)
Prints a random message for the specified animal
```
randMessage("bunny")
```

4. print_fact(animal)
Prints a random fact for the specified animal.
Raises: ValueError if no facts are available for the specified animal.
```
print_fact("elephant")
```
## Example
To view an example implementation of `pyAnimals_ejh`, [see the example.py file](/example.py)

## Contributing
We welcome contributions to enhance and grow pyAnimals_ejh! Follow these steps to set up the development environment:
1. Clone the Repository:
```
git clone https://github.com/software-students-fall2024/3-python-package-ejh.git
cd 3-python-package-ejh
```
2. Set Up the Virtual Environment
```python -m venv venv
source venv/bin/activate
```
3. Build and Test the Package
```
python -m build
```
4. Run Tests
```
python -m pytest
```
5. Add New Tests\
If you add new functionality, include tests in the tests directory.

## Team members

[Haley Hobbs](https://github.com/haleyhobbs) \
[Emma Zhu](https://github.com/ez106) \
[Jason Tran](https://github.com/huyy422) \
[Jenna Han](https://github.com/jnahan)
DevOpsAPI 0.2.0a9
A simple object-oriented python interface to the DevOps REST API
Requires Python: >=3.7

# DevOpsAPI
> A simple object-oriented easy-to-use python inteface to the Azure DevOps REST API

## Features

-   Create, Delete and Modify WorkItems and Test Cases
-   Query WorkItems using the wsql


## Quickstart

-   Create an PAT on DevOps, give permissions for the scopes you want to
    access


        from DevOpsAPI import Api, Wit, Step

        api = Api(organisation="myorg",
                  projet="myproject",
                  user="your@email.com",
                  apikey="PAT")

-    Create a WorkItem

         wi = api.WorkItem.create(type=Wit.Task,
                                  title="First Task",
                                  area="my\\area")
         wi.Description = "Describe your Task"
         wi.Tags = "tag1,tag2"

-    Create a Test Case

         wi = api.TestCase.create(title="First TestCase",
                                 area="my\\area")
         wi.Description = "Describe your Test Case"
         wi.Tags = "tag1,tag2"
        
-    Add some Steps to the Test Case

         step1 = Step("Given I use DevOpsAPI")
         step2 = Step("Then I am happy")
         wi.Steps = [step1, step2]
regression-bias-corrector 0.0.2
Bias correction of regression ML models
Requires Python: >=3.7

# Regression bias corrector

Package is aimed to correct regression fit by any machine learning model that underestimate high values and overestimate low values of outcome variable (1D case). The problem usually arises in case when metrics like MSE and MAE shows better results in biased predictions.

# Algorithm

Now is implemeneted only linear correction for 1D case by the Linear Regression approach.

The idea is to linearly transform the `y_pred` to change the coefficient of linear regression between `y_true` and `y_pred` to 1. It is corresponds to the diagonal on plane `y_true` and `y_pred` that means equal pace of increasing `y_pred` with increasing `y_true`. 
- `y_true` - actual values
- `y_pred` - predicted values


# How to install
```bash
git clone https://github.com/mike-live/regression-bias-corrector.git
pip install .
```

# How to use
```python
# Import Linear Bias corrector
from regression-bias-corrector import LinearBiasCorrector

# Create instance of corrector
corrector = LinearBiasCorrector()

# Fit corrector on actual outcome variable and on predicted values of the model for train dataset
# y_train - actual values
# y_train_pred - predicted values
corrector.fit(y_train, y_train_pred)

# correct predicted values on test dataset
# y_test_pred - predicted values by model on test dataset
y_test_pred_unbiased = corrector.predict(y_test_pred)

# y_test_pred_unbiased - unbiased predicted values on test
```

# Authors
Lobachevsky University

1. Krivonosov Mikhail
2. Khabarova Tatiana
phippery 1.3.1
Tools for analyzing PhIP-Seq Data.
Requires Python: >=3.7
Keywords: phippery, phipseq, phage, modeling, phip

# Phippery

<p>
  <img src="data/cartoons/Xarray_function.png" width="250">
</p>

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Docker Repository on Quay](https://quay.io/repository/hdc-workflows/phippery/status "Docker Repository on Quay")](https://quay.io/repository/hdc-workflows/phippery)
[![build and test](https://github.com/matsengrp/phippery/workflows/build%20and%20test/badge.svg)](https://github.com/matsengrp/phippery/blob/master/.github/workflows/build-and-test.yaml)

A set of functions designed to query an 
[xarray DataSet](http://xarray.pydata.org/en/stable/user-guide/data-structures.html#dataset) 
object formatted to tie enrichment data with 
their respective row & column (peptide & sample) annotations. 

Please see the 
[documentation](https://matsengrp.github.io/phippery/) 
for further details.

## Developer Install

```
# using venv
python -m venv phippery_dev_env
source phippery_dev_env/bin/activate

# install
git clone https://github.com/matsengrp/phippery.git
(cd phippery && pip install -e ".[dev]")
```
pywatch-tracker 0.3.1
Coincidence Measurements with CosmicWatch detectors.

# Pywatch Tracker 
This Python Package is for making coincidence measurements 
with the 
<a href="http://www.cosmicwatch.lns.mit.edu/">CosmicWatch</a> 
Detectors. The measurements can be done with as many detectors as can 
be connected to the computer. 
The measurement can also be displayed live in a three-dimensional visualisation 
in the browser with any possible detector geometry.

## Installation 
Open the terminal and run the following command:
```commandline
pip install pywatch-tracker
```
which installs the `pywatch` package.