semvec/pypi-packages huggingface dataset:
led-calibration-plugin 2.0.0
Calibrate your LEDs using an external light probe.
License: MIT


## Pioreactor LED calibration plugin

![CI tests](https://github.com/pioreactor/pioreactor-led-calibration-plugin/actions/workflows/ci.yaml/badge.svg)

The LED automations available on the Pioreactor are limited: light intensity percentages are based on the power supplied to the LED wires. For specific research and for comparing results between Pioreactors, this plugin can be used to determine the exact LED intensity values.

This plugin offers the ability to calibrate your LEDs using an **external light probe**. It functions in two parts:
1) a command line calibration that creates a line-of-best-fit and
2) a calibrated light/dark cycle automation available on the Pioreactor web interface.

## Installation instructions

Install from the command line.

```
pio plugins install led-calibration-plugin  ## to install on a single Pioreactor

## OR, on the command line of the leader Pioreactor

pios plugins install led-calibration-plugin ## to install on all Pioreactors in a cluster
```

This plugin is also available on the Pioreactor web interface, in the _Plugins_ tab. Downloading from the web interface will install on all Pioreactors in a cluster.

## Run your calibration

Type into your command line:

```
pio calibrations run --device led_C --protocol-name led_calibration
pio calibrations run --device led_D --protocol-name led_calibration
```

To perform this calibration, insert your vial containing media into the Pioreactor and submerge your light probe. Follow the prompts on the command line. The plugin will increase the light intensity, and prompt you to record the readings from your light probe. A calibration line of best fit will be generated based on your light probe readings.

## Use the calibration on the UI

An automation will become available on the web interface. To use this automation, use two LED cables in each of channels C and D, and insert the bulbs into the X2 and X3 pockets on the Pioreactor vial holder. **Calibrations for LEDs in channels "C" and "D" must exist.**

In the _Pioreactors_ tab, under _Manage_, you can _Start_ an _LED automation_. A new option becomes available in the drop-down menu called "Calibrated Light/Dark Cycle". Input your desired light intensity in AU (ex. 1000 AU). The automation will set the percent light intensity such that an output of 1000 AU occurs on **both** LEDs.

## When to perform an LED calibration

Calibrations should be performed on a case-by-case basis. A new calibration must be performed per channel, and/or for new LED cables, and with any change in media that can alter the light intensity within the vial.

## Plugin documentation

Documentation for plugins can be found on the [Pioreactor wiki](https://docs.pioreactor.com/developer-guide/intro-plugins).
YALTAi 2.0.5
You Actually Look Twice At it, YOLOv5-Kraken adapter for region detection
License: MIT
Requires Python: >=3.8.0


# YALTAi
You Actually Look Twice At it

This provides an adapter for Kraken to use YOLOv8 (1.0.0 update; use previous version to reuse YOLOv5 models) Object Detection routine.

This tool can be used for both segmenting and conversion of models.

# Routine

## Instal

```bash
pip install YALTAi
```

## Training

Convert (and split optionally) your data

```bash
# Keeps .1 data in the validation set and convert all alto into YOLOv5 format
#  Keeps the segmonto information up to the regions
yaltai convert alto-to-yolo PATH/TO/ALTOorPAGE/*.xml my-dataset --shuffle .1 --segmonto region
```

And then [train YOLO](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 

```bash
yolo task=detect mode=train model=yolov8n.pt data=my-dataset/config.yml epochs=100 plots=True device=0 batch=8 imgsz=960
```

## Predicting

YALTAi has the same CLI interface as Kraken, so:

- You can use base BLLA model for line or provide yours with `-i model.mlmodel`
- Use a GPU (`--device cuda:0`) or a CPU (`--device cpu`)
- Apply on batch (`*.jpg`)

```bash
# Retrieve the best.pt after the training
# It should be in runs/train/exp[NUMBER]/weights/best.pt
# And then annotate your new data with the same CLI API as Kraken !
yaltai kraken --device cuda:0 -I "*.jpg" --suffix ".xml" segment --yolo runs/train/exp5/weights/best.pt
```

## Metrics

The metrics produced from various libraries never gives the same mAP or Precision. I tried

- `object-detection-metrics==0.4`
- `mapCalc`
- `mean-average-precision` which ended up being the chosen one (cleanest in terms of how I can access info) 

and of course I compared with YOLOv5 raw results. Nothing worked the same. And the library YOLOv5 derives its metrics from is uninstallable through pip.
SubTrack 0.1.0
A library for tracking subscribers and managing unsubscribed profiles.
License: MIT
Requires Python: >=3.7

# SubTrack

SubTrack is a Python library for tracking and managing profiles, identifying new subscribers and unsubscribed profiles, and storing data in Excel.

## Features
- Parse HTML to extract profile names.
- Maintain a list of subscribers and unsubscribed profiles in an Excel file.
- Automatically update the subscription status with timestamps.

## Installation
Install SubTrack using pip:

```bash
pip install SubTrack
pylint-junit 0.3.5
pylint reporter for junit format.
License: MIT


pylint_junit
============

Pylint plugin that generates JUnit based report that can be consumed
by CI environments like Jenkins, Azure Devops and such.

Why ?
=====

At the time of writing, few tools existed that where able to generate
JUnit format from pylint output. Both of these projects where not
handling "empty" results scenario well, eg both cases generated an empty
file and that caused issues in ci as the resulting document was not
proper xml file. Another issue was that issues found by pylint where
marked under single testcase failure and this caused atleast Azure to
report a single item per file even thought there could have been multiple.

Thus, pylint_junit was written.

Installation
============

Install the package with

.. code-block::

   pip install pylint_junit

or add it to your requirements.

Usage
=====

After the plugin has been installed there are few options to take it into use.

Add following lines into your .pylintrc:

.. code-block::

   [MASTER]
   load-plugins=pylint_junit
   [REPORTS]
   output-format=junit

This does set the default output to junit, so if still want to run pylint and get
normal output, you might want to consider omitting the "output-format=junit"
portion and only when junit format is needed, provide it via command line like this:

.. code-block::

   pylint --output-format=junit src/



Or, without making any changes to your pylint configuration, you can just pass the class
as report formatter like this:

.. code-block::

   pylint --output-format=pylint_junit.JUnitReporter src/

Dependencies
============


* junit_xml__

__ https://github.com/kyrus/python-junit-xml

Bugs / Feedback
===============

Use Github Issues Tracker @ https://github.com/rasjani/pylint_junit/issues