semvec/pypi-packages huggingface dataset:
llm-tools-simpleeval 0.1.1
Make simple_eval available as an LLM tool
Requires Python: >=3.9

# llm-tools-simpleeval

[![PyPI](https://img.shields.io/pypi/v/llm-tools-simpleeval.svg)](https://pypi.org/project/llm-tools-simpleeval/)
[![Changelog](https://img.shields.io/github/v/release/simonw/llm-tools-simpleeval?include_prereleases&label=changelog)](https://github.com/simonw/llm-tools-simpleeval/releases)
[![Tests](https://github.com/simonw/llm-tools-simpleeval/actions/workflows/test.yml/badge.svg)](https://github.com/simonw/llm-tools-simpleeval/actions/workflows/test.yml)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/simonw/llm-tools-simpleeval/blob/main/LICENSE)

Make simple_eval available as an LLM tool

## Installation

Install this plugin in the same environment as [LLM](https://llm.datasette.io/).
```bash
llm install llm-tools-simpleeval
```
## Usage

```bash
llm -T simple_eval "4444 * 233423" --td
```

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd llm-tools-simpleeval
python -m venv venv
source venv/bin/activate
```
Now install the dependencies and test dependencies:
```bash
python -m pip install -e '.[test]'
```
To run the tests:
```bash
python -m pytest
```
coremstools 0.0.8.4
A package for processing CoreMS assignments of ultrahigh mass resolution LC-ESI-MS data
Requires Python: >=3.10

# CoreMS LC Modules
A Python package for processing CoreMS assignments of ultrahigh mass resolution LC-ESI-MS data. 

### Functionality 
##### Functions that do not require CoreMS:
- Alignment of assigned features across a dataset
- Calculation of average assignment & feature parameters across a dataset:
    1. measured m/z
    2. calibrated m/z
    3. resolving power
    4. m/z error
    5. feature S/N
    6. confidence score 
- Gap filling of ambiguous assignments 
- Stoichiometric classifications 
- NOSC calucations 
- O/C, H/C, N/C calculations 
- Identification of significant assignment errors in a dataset, based on rolling average and standard deviation

##### Functions that require CoreMS:
- Determination of a feature's chromatographic dispersity
- Generation of calibrant list(s) for data calibration 
- QC checks of retention and intensity of an internal standard across a dataset 

### Installation

##### Local editable installation
    git clone https://github.com/deweycw/corems-tools.git corems-tools
    cd corems-tools/src
    python -m pip install -e . # from root directory of project  

##### Install pkg from TestPyPi
    python -m pip install --index-url https://test.pypi.org/simple/ --no-deps coremstools

### Build and upload to TestPyPi
See: https://packaging.python.org/en/latest/tutorials/packaging-projects/

##### Build prior to uploading to archives
    python -m pip install -U build
    python -m build

##### Upload to TestPyPi with Twine
    python -m pip install -U twine
    python -m twine upload --repository testpypi dist/*

### Example Workflow

##### Phase 1: Assignments & initial QC
1. Generate calibrants using calibrant generator function (requires CoreMS)
2. Perform assignments (requires CoreMS)
3. Calculate dispersity (requires CoreMS)
4. Evaluate retention and intensity of internal standard (requires CoreMS)
5. Generate assignment error plots (error v. m/z; error dist in each time window)

##### Phase 2: Alignment, gapfilling, blank correction, error flags
6. Align features across dataset
7. Gapfill across dataset 
8. Perform blank correction 
9. Flag features with potentially signficant assignment error (i.e., feature is flagged if difference between rolling average of assignment error (across dataset) and error of individual assignment exceeds 4x the standard deviation of the assignment error for the specific feature) 

##### Phase 3: Additional classifications 
10. Determine O/C, H/C, N/C & NOSC for features 
11. Determine stoichiometric classifcations
tkSDLImg 0.0.3
Tk widget for showing an image using SDL.
Requires Python: >=3.10

# tkSDLImg

Display an image inside your tkinter application using SDL for fast rendering.
This also allows for frequent updates to allow video playbacks, animation, etc.

## Installation

```sh
pip install tkSDLImg
```

## Usage

A usage example can be found in the modules `_main` function.