semvec/pypi-packages huggingface dataset:
HP-C1Pyr-PK 0.0.4
Scripts and functions for the pharmacokinetic modeling of hyperpolarized [1-13C]-pyruvate.
Requires Python: ~=3.10

<ul>
Python code for pharmacokinetic modeling of hyperpolarized [1-13C]-pyruvate.
</ul>

<ul>
These scripts calculate the relationship between [1-13C]-pyruvate and [1-13C]-lactate, but can easily be extended to include other metabolites.
</ul>

<ul>
There are three base models, of differing complexity, which include a different number of physical compartments:
</ul>

<ul>
The scripts titled P2L3*.m describe signal evolution between two chemical pools and three physical compartments. The three-compartment model is the most physiologically accurate among these models, but it is also the most computationally intensive, requiring the largest number of descriptive parameters.
</ul>

<ul>
The scripts titled P2L2*.m are for the PK model with two chemical pools and two physical compartments. Here, HP pyruvate and lactate in the extravascular/extracellular space is assumed to be very well mixed with HP pyruvate in the intracellular space - and separate from pyr/lac in the vascular supply.
</ul>

<ul>
The scripts titled P2L1*.m represent a simple precursor-product model, which does not consider physical compartmentalization of imaging agents. Equivalently, this assumes that all compartments (vascular; extravascular/extracellular; and cellular) are well mixed.
</ul>

<ul>
Each of these groups of scripts are also accompanied by a test script (TestP2L#.m) that shows how to work with these models - from setup and generation of synthetic data to PK analysis by fitting.
</ul>

<ul>
Please do not hesitate to contact with any questions, comments, concerns, or suggestions for improvement: Ryan Boyce ryty.boyce@gmail.com
</ul>
crossout 1.0.0
Python package using the API of CrossoutDB
Requires Python: >=3.10

# Crossout Python package

The `crossout` Python package is a Python library for interacting with the Crossout API provided by the [CrossoutDB website](https://crossoutdb.com). It is designed to be simple to use and gives general information about the game items. As it is based on a third party API, data may be outdated or missing.

> **Note :** At the moment, the package does not provide information about the market state and item prices.

> **Note :** This package is not affiliated with CrossoutDB, Targem Games or Gaijin Enternaiment in any way.

## How to use

### Requirements

The package was made with Python `3.10.4`. Although its dependencies should be automatically installed by `pip`, here is a list of the required packages :

- [requests>=2.30.0](https://pypi.org/project/requests/) for HTTP requests
- [urllib3>=2.0.2](https://pypi.org/project/urllib3/) to create GET parameters of requests

### Installation

The package is available on PyPI and can be installed using `pip`:

```bash
pip install crossout
```

### Usage

The package provides a core class called `CrossoutDB`. It wraps the lower level API calls and provides a simple interface to get information about the game items. Here the sample code retrieve the name of the item with the ID `1050`, which is the [ZS-33 Hulk](https://crossoutdb.com/item/1050).

```python
import crossout

# Create a CrossoutDB instance
db = crossout.CrossoutDB()
name = db.item(1050).name
# name = "ZS-33 Hulk"
```

## Roadmap

In order of priority :

- Make documentation
- Consider adding option to scrap additionnal data from the website if possible
- Add support for item prices
- Add support for market data

## Resources

- [CrossoutDB repository](https://github.com/Zicore/CrossoutMarket)
cmdsave 1.0.3
💾 CMDs
Keywords: save,cli,console,terminal,prductivity

# CMDSave - Command Line Application for Saving and Managing Terminal Commands for Later Use

> **This application is only supported on Windows.**

This application allows you to save and manage terminal commands for easy reference and execution. It provides a command-line interface (CLI) to interact with the application and perform various operations such as saving commands, listing saved commands, deleting commands, and editing the command file.

## Usage Commands

To use the application, you can execute the following commands:

### `cmds save`

This command allows you to save a command for easy reference. It prompts you to enter the command you want to save and a name to associate with it. The command and its corresponding name are then stored in a CSV file.

### `cmds list`

This command lists all the saved commands stored in the CSV file. You can navigate through the list using the arrow keys and select a command by pressing Enter. The selected command is copied to the clipboard for easy access.

### `cmds delete`

This command enables you to delete a command from the list. It displays the saved commands and allows you to navigate through them using the arrow keys. Pressing Enter on a command prompts you for confirmation to delete it. If confirmed, the selected command is removed from the CSV file.

### `cmds edit`

This command opens the CSV file in the default text editor (in this case, Notepad). You can use this command to manually edit the CSV file and make changes to the saved commands. Advised to not use `cmds edit` to add new commands. Instead, use the `cmds save` command.

---

Please make sure you have the required dependencies installed before running the application.
jupysec 0.1.17
Jupyter Security Utilities
Requires Python: >=3.7

# jupysec

![logo](jupysec.png)

_JupyterLab Security Utilities_

Security utilities for Jupyter environments. This set of rules and JupyerLab extension evaluates the security posture of the environment by comparing configuration values with best practices. Read more [here](https://developer.nvidia.com/blog/evaluating-the-security-of-jupyter-environments/).

## Function

![demo](demo2.gif)

Run the extension to generate an HTML report of the security configuration of your Jupyter instance and other Jupyter instances on your host.

Configurations will be compared against [these rules](https://github.com/JosephTLucas/jupysec/blob/main/jupysec/rules.py).

These rules currently evaluate:

- Whether there are any executables in your ipython startup directories
- What lines of your configuration are nonstandard with known malicious uses
- Whether your servers require tokens for authentication
- Whether your server and client are communicating over HTTPS
- Whether you are serving Jupyter to a broader domain than just localhost
- If silent commands have been run against your kernels

Some of these categories may have false-positives depending on your environment and use-case. However, users should monitor their environments and be aware of their security posture and any changes.

Matches against [the rules](https://github.com/JosephTLucas/jupysec/blob/main/jupysec/rules.py) are referred to as "Findings" and displayed in the Report Card.

![report card](report.png)

## Requirements

- JupyterLab >= 3.0

## Getting Started

To install just the rules, install with:

```bash
pip install jupysec
```

Then in your python environment:

```python
from jupysec.rules import Rules

Rules().get_findings()
```

Or to also install the JupyterLab extension:

```bash
pip install 'jupysec[jupyterlab]'
```

After starting jupyterlab, your launcher window should now have a "Security" section with a widget for generating your findings. This will launch and index page with a list of all findings, color-coded by category. Click into findings for more details.