semvec/pypi-packages huggingface dataset:
cyber-labrat 0.1.1
Contains the classes used for generix data analysis
Requires Python: >=3.10

# phd

Notebook is used to do the actual data processing
Module contains all necessary functions

getter (ie all functions strating with 'get') is used to get data. The getter will either generate the data or retrieve it from the cache if it exists

the cache

REMI: QUESTIONS FOR JASMINE JAS: i think this convention is ok - discuss

- I'm wondering what to do with figure naming because potentially many figure for the grouping will we be built. Im currently using an automatic naming convention
  {"histogram": 'f"{experiment}_for_{compound}_in_{region}"',
  "correlogram": 'f"{experiment}_{correlogram_type}_{buildCorrelogramFilenmae(to*correlate, columns)}"',
  "head_twitch_histogram": 'f"head_twitch_histogram*{experiment}_for_{to_plot}"',}
  but maybe the user would want to pick the name themself? probably better for them to remember what is what in the case of multiple stats choices

##### TO USE:

add csv with columns : mouse_id , group_id , COMPOUND_REGION... or BEHAVIOR_TIME (e.g. HT_20) to input folder

fill info in cell 1 of notebook (compound_ratio_mapping, ect)

perform outlier selection for experiment (including ratios chiosen in first cell)

generate quantitative histograms and aggregated stats table functions : REMI?

generate correlograms (use case for all three in functions) : REMI?
clasical_corellogram : getAndPlotSingleCorrelogram(filename, experiment='agonist_antagonist', correlogram_type='compound',  
 to_correlate='GLU', p_value_threshold=0.05, n_minimum=5, from_scratch= True)

    square_correlogram       :      getAndPlotSingleCorrelogram(filename, experiment='agonist_antagonist', correlogram_type='compound',
                                                                to_correlate='GLU-GABA', p_value_threshold=0.05, n_minimum=5, from_scratch= True)


    bar_corellogram
                                                    #see whatsapp image 3/5/23
        within BR       /       within compound
bedrock-a-bot 1.0.4
A chatbot tool using AWS Bedrock and Streamlit
Requires Python: >=3.12

# Bedrock-A-Bot

Bedrock-A-Bot is a chatbot tool using AWS Bedrock and Streamlit. It provides a user-friendly interface for interacting with various language models and includes features like file upload, session management, and customizable model parameters.

https://github.com/user-attachments/assets/b09b64d8-2901-4c02-8b74-b8ac304453e1

## Installation

You can install Bedrock-A-Bot using pip:

```
pip install bedrock-a-bot
```

## Usage

To run Bedrock-A-Bot, use the following command:

```
bedrock-a-bot [--port PORT]
```

The `--port` argument is optional and defaults to 8501 if not specified.

## Features

- Interactive chat interface
- Support for multiple language models
- File upload functionality
- Session management
- Customizable model parameters (temperature, top_p)
- Integration with AWS Bedrock

## Requirements

- Python 3.12 or higher
- AWS account with Bedrock access
- AWS Credential profile [aws configure --profile ziya](https://docs.aws.amazon.com/cli/v1/userguide/cli-authentication-user.html)
- Required Python packages (automatically installed with pip)

## Development

To set up the development environment:

1. Clone the repository
2. Create a virtual environment:
   ```
   conda create -n BedRockABot python=3.12
   conda activate BedRockABot
   ```
3. Install the package in editable mode:
   ```
   pip install -e .
   ```

## Uploading to PyPI

To upload a new version of Bedrock-A-Bot to PyPI:

1. Update the version number in `setup.py`.
2. Run the upload command:
   ```
   upload-bedrock-a-bot
   ```
3. Confirm that you have updated the version when prompted.

Note: Ensure you have `twine` installed and configured with your PyPI credentials before uploading.

## License

This project is licensed under the MIT License.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
streamlit-mdm-table 0.0.8
Streamlit component that allows for easy management of task mapping. Specific to a project at Eigen X.
Requires Python: >=3.7

# streamlit-mdm-table

Streamlit component that allows for easy mapping of tasks and IDs to a table.

## Installation instructions

Import the wheel file in your project.

## Usage instructions

```python
import streamlit as st

from streamlit_mdm_table import streamlit_mdm_table

value = streamlit_mdm_table(
    tasks, 
    linkedin_content, 
    youtube_content,
    google_analytics_content,
    poppulo_harmony_content,
    poppulo_email_content,
    sharepoint_content,
    viva_engage_content
)

if value is not None:
    st.dataframe(value)
```