semvec/pypi-packages huggingface dataset:
flowercare-exporter 0.3.1
Export Miflora Bluetooth LE sensor to Graphite and Prometheus

Xiaomi MiFlora JSON/Prometheus/Graphite Exporter
================================================

# Export targets

  * JSON
  * [Grafana Graphite metrics](https://grafana.com/docs/grafana-cloud/send-data/metrics/metrics-graphite/)
  * [Prometheus pushgateway](https://github.com/prometheus/pushgateway)

# Tested Devices

  * Xiaomi Mi Flora plant sensor

# Example Usage

Push to Graphite

```bash
flowercare_exporter   -l DEBUG -t 120 \
	-a "C4:7C:8D:6C:10:9B=Alyssum" \
	-a "C4:7C:8D:6C:10:C2=Acacia" \
	-a "C4:7C:8D:6C:10:4D=Alcea" \
	-g "https://graphite-prod-13-prod-us-east-0.grafana.net/graphite/metrics"  metrics
```
Push to Prometheus pushgateway

```bash
flowercare_exporter   -l DEBUG  -t 120  \
	-a "C4:7C:8D:6C:10:9B=Alyssum" \
	-a "C4:7C:8D:6C:10:C2=Acacia" \
	-a "C4:7C:8D:6C:10:4D=Alcea" \
	-p http://herakles:9091/metrics/job/some_job  metrics
```


Blink LED to identify device
```bash
flowercare_exporter   -l DEBUG  -t 120 \
	-a "C4:7C:8D:6C:10:9B=Alyssum" \
	-a "C4:7C:8D:6C:10:C2=Acacia" \
	-a "C4:7C:8D:6C:10:4D=Alcea" \
	blink
```
kkconfig 0.0.2
Package to help with configuration of local workspaces.

# Karol Krizka's Project Configuration
Module to help with configuration of local workspaces.

## Local Configuration
The goal of the local configuration is to provide a mechanism for providing ]
setting specific to the local workspaces (ie: paths to local data).

The project should provide a module `config` that contains the following
snipplet:

```python
from kkconfig import local

mydatapath = '/default/path/to/data'

local.load_settings('.myproject.yaml', globals())
```

The `load_settings` function will load the settings from the specified file (if
it exists) and update the global namespace (aka `config` module globals) with
the contents. The variables should also be provided this module to provide
default values and show available settings.
Human-Data-Analytics 1.0.2
A brief description of your package
Requires Python: >=3.6

# Human Data Analytics Project

The repository contains, under the folder HDA, the functions for the definition of models, the generator used for training, augmentation functions and finally the functions used to display the images. 
The structure of the folder is the following:

```plaintext
HDA/
├── __init__.py
├── __pycache__/
│   ├── __init__.cpython-38.pyc
│   ├── cbam.cpython-38.pyc
│   ├── Inception.cpython-38.pyc
│   ├── resnet18.cpython-38.pyc
├── models/
│   ├── __init__.py
│   ├── __pycache__/
│   │   ├── __init__.cpython-38.pyc
│   │   ├── cbam.cpython-38.pyc
│   │   ├── Inception.cpython-38.pyc
│   │   ├── resnet18.cpython-38.pyc
│   ├── cbam.py
│   ├── Inception.py
│   ├── resnet18.py
├── preprocessing/
│   ├── __init__.py
│   ├── __pycache__/
│   │   ├── __init__.cpython-38.pyc
│   │   ├── data_augmentation.cpython-38.pyc
│   │   ├── image_preprocessing.cpython-38.pyc
│   │   ├── imageutils.cpython-38.pyc
│   ├── data_augmentation.py
│   ├── image_preprocessing.py
│   ├── imageutils.py
```

## Description of Files and Folders

### **Models Folder (`HDA/models/`)**
Definition of the models and the CBAM module in tensor flow:
- `cbam.py`: Contains the definition of the CBAM model.
- `Inception.py`: Contains the definition of the Inception model.
- `resnet18.py`: Contains the definition of the ResNet18 model.

### **Preprocessing Folder (`HDA/preprocessing/`)**
- `data_augmentation.py`: Defines the functions used for data augmentation.
- `image_preprocessing.py`: Contains functions to extract patches and the generator for training.
- `imageutils.py`: Includes helper functions for data preprocessing used in `image_preprocessing.py`.