semvec/pypi-packages huggingface dataset:
bbo-labelgui 0.17.1
GUI for guided data labeling

# BBO-labelgui

GUI for guided data labeling

## Installation
(You do not need to clone this repository.)
1. [Install Anaconda](https://docs.anaconda.com/anaconda/install/)
2. Start Anaconda Prompt (Windows) / terminal (linux) and navigate into repository directory
3. Create conda environment `conda env create -f https://raw.githubusercontent.com/bbo-lab/ACM-traingui/master/environment.yml`

## Update 
1. Start Anaconda Prompt (Windows) / terminal (linux) and navigate into repository directory
2. Update with `conda env update -f https://raw.githubusercontent.com/bbo-lab/ACM-traingui/master/environment.yml --prune`.

## Running
1. Start Anaconda Prompt (Windows) / terminal (linux) and navigate into repository directory
2. Switch to environment `conda activate bbo_acm-traingui`
3. Run with `python -m labelgui [options ...]`

## Options
### Labeling mode
Run with `python -m labelgui [base data directory]`.
This starts a GUI in drone mode, for the use by assistants with limited options to influence how the program runs and were it saves. This expects the following file structure:
```
[base data directory]/pose/data/users/{user1,user2,...}/labeling_gui_cfg.py
[base data directory]/pose/users/
```
{user1,user2,...} will be presented in a selection dialog on startup. Marking results will be placed in `[base data directory]/users/`

### Check mode
Run with 
```
python -m labelgui [directory of labels.npz] --check [bbo_calibcam calibration npy] # to use specified npy or
python -m labelgui [directory of labels.npz] --check [labeling_gui_cfg.py folder] # to use standardCalibrationFile from labeling_gui_cfg.py or
python -m labelgui [directory of labels.npz] --check '-' # or
python -m labelgui [directory of labels.npz] --check # to use backup labeling_gui_cfg.py in labels.npy folder. (Will often fail due to different paths between checker und labeler, as relative pathes are resolved, here).
```

This gives sorted text output of 3d and reprojections errors. Reporjection errors above 5-10px usually indicate errors in labeling and respective frames have to be checked.

### Join mode
Run with `python -m labelgui [configdir of ACM-dlcdetect] --check [multiple directories containing labels.npz files] [--strict]`.
This joins all marked labels in the labels.npz files into the labels.npz file in the dlcdetect configuration. Marked labels overwrite existing labels framewise.

```--strict``` only merges frames where all cameras have marked points.

### Compile to exe
1. `conda activate bbo_acm-traingui`.
2. Install pyinstaller: `pip install pyinstaller.
3. If present, empty dist/ dirctory.
4. `pyinstaller --onefile traingui.py`.
5. Distribute exe file in dist/ folder.

Note: It might be necessary to remove mkl: `conda install -c conda-forge nomkl`.


## TODO
- Document config
- Document sketch file (2d sketch of animal. If not presented, 3d wireframe is shown instead)
- Document [model file](https://github.com/bbo-lab/ACM/blob/main/INPUTS.md#modelnpy)
teklib 1.0.11
Library of tools for the analysis of marine technology
Requires Python: >=3.6

# TEKLIB Package

A "dumping ground" for python functions and classes used in the analysis of marine systems.

## Installing TEKLIB

To install TEKLIB, first run the command

```sh
python -m pip install teklib # To install latest version
# OR
python -m pip install teklib==<version nr> # To install a specific version
```
zscore-anomaly-detector 0.1.2
A Python package to detect anomalies using Z-Scores
Requires Python: >=3.6

# Zscore Anomaly Detector

** Zscore Anomaly Detector** is a Python package designed to detect anomalies in numerical datasets using Z-Score analysis. This package identifies outliers by calculating the Z-Score for each numerical column and flags data points that deviate significantly from the mean. The package can handle mixed datasets containing numerical, categorical, and object types.

## Installation

You can install the package using pip. Run the following command:

```bash
pip install zscore-anomaly-detector


## Usage
Here is an example of how to use the ZScore Anomaly Detector package with a realistic dataset containing both numerical and categorical columns:

import pandas as pd

from zscore_anomaly.zscore_anomaly_detector import ZScoreAnomalyDetector

## Sample Dataset

data = pd.DataFrame({
    'Age': [25, 32, 47, 51, 62, 35, 27, 100, 29, 38],  # Numeric
    'Salary': [50000, 54000, 58000, 62000, 65000, 52000, 51000, 200000, 53000, 56000],  # Numeric
    'Department': ['HR', 'IT', 'Finance', 'HR', 'IT', 'Finance', 'HR', 'IT', 'Finance', 'HR'],  # Categorical
    'Has_Debt': [True, False, True, True, False, False, True, True, False, True],  # Boolean
    'City': ['New York', 'San Francisco', 'Los Angeles', 'New York', 'San Francisco', 'Los Angeles', 'New York', 'San Francisco', 'Los Angeles', 'New York'],  # Object
})


#### Initialize the ZscoreAnomalyDetector

detector = ZScoreAnomalyDetector(threshold=2)  # The user can specify the threshold value for detecting anomalies. By default, the threshold is set to 3 if not provided.



#### Create a DataFrame that Includes Anomalies Marked

df_with_anomalies = detector.create_dataframe_with_anomalies(data)


#### Style the DataFrame to Highlight Anomalies in Red

styled_df = detector.style_dataframe(df_with_anomalies)


#### Display the Styled DataFrame

styled_df


Explanation
Age and Salary are numeric columns where Z-Scores will be calculated to detect anomalies.

Department is a categorical column, and City is an object column. These will not be included in Z-Score calculations, but they remain in the dataset.

Has_Debt is a boolean column.

This example shows how to detect anomalies in the numeric columns (Age and Salary) while leaving the non-numeric columns intact.

Output

After running the above code, the DataFrame will display anomalies detected in the numeric columns. Anomalies will be highlighted in red if used in a Jupyter notebook or a similar environment that supports DataFrame styling.