semvec/pypi-packages huggingface dataset:
aglio 0.2.0
Another Geodata Library for Input/Output (and more)
Requires Python: >=3.9

## Another Geodata Library for Input/Output (aglio :garlic:)

**aglio** focuses on data aggregation and regularization from a variety of sources, with an emphasis on geophysical applications but may be useful beyond. 

For now, check out the examples [here]() for an idea of what you can do! 

## Installation

For a base installation: 

```shell
pip install aglio
```

To install optional dependencies: 

```shell
pip install aglio[full]
```

To install extra dependencies that are not used by `aglio` but **are** used in 
some of the examples:

```shell
pip install aglio[extra]
```

## Development installation 

after forking and cloning: 

```shell
pip install -e .[dev]
```
erdio 0.0.3
CLI tool to convert DBML code to editable Draw.io diagram
Requires Python: >=3.8

# ERDio

This is CLI utility to generate editable Draw.io ERD from DBML or updating existing diagram.


## Installation

You can install ERDio using pip:

```bash
pip install erdio
```

## Quick start

Assuming you have already installed `dbt` and `dbterd` utility to generate artifacts.

1. Use `dbt` to generate docs.

```bash
$ dbt docs generate

16:15:02  Running with dbt=1.5.3
16:15:03  Registered adapter: postgres=1.5.3
16:15:03  Found 241 models, 492 tests, 4 snapshots, 0 analyses, 850 macros, 0 operations, 18 seed files, 70 sources, 0 exposures, 0 metrics, 0 groups
16:15:03
16:15:12  Building catalog
16:15:12  Catalog written to ./target/catalog.json
```

This will create two artifact files contains:

* `manifest.json` includes all tests and documentation
* `catalog.json` includes the set of all columns in the database



2. Use `dbterd` to generate DBML from dbt artifacts.

```bash
$ dbterd run

2023-07-25 16:16:45,768 - dbterd - INFO - Run with dbterd==1.2.4 (main.py:54)
2023-07-25 16:16:46,359 - dbterd - INFO - ./target/output.dbml (base.py:75)

```

This will create DBML manifest to be used for diagraming.



3. Create new Draw.io diagram.

```bash
erdio run
```
pipeline-anomaly-dimension-class 1.1
Python class for pipeline anomaly dimension classification

# Python class for pipeline anomaly dimension classification
[![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/vb64/pipeline.anomaly.dimension.class/pep257.yml?label=Pep257&style=plastic&branch=main)](https://github.com/vb64/pipeline.anomaly.dimension.class/actions?query=workflow%3Apep257)
[![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/vb64/pipeline.anomaly.dimension.class/py2.yml?label=Python%202.7&style=plastic&branch=main)](https://github.com/vb64/pipeline.anomaly.dimension.class/actions?query=workflow%3Apy2)
[![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/vb64/pipeline.anomaly.dimension.class/py3.yml?label=Python%203.7-3.11&style=plastic&branch=main)](https://github.com/vb64/pipeline.anomaly.dimension.class/actions?query=workflow%3Apy3)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/0fc99b84d0434a22b687bfdc1dd4281b)](https://app.codacy.com/gh/vb64/pipeline.anomaly.dimension.class/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
[![Codacy Badge](https://app.codacy.com/project/badge/Coverage/0fc99b84d0434a22b687bfdc1dd4281b)](https://app.codacy.com/gh/vb64/pipeline.anomaly.dimension.class/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_coverage)

This Python module implements the definition of the "defect dimension class" of a pipeline according to [Specifications and requirements for intelligent pig inspection of pipelines](https://www.researchgate.net/figure/a-Graphical-presentation-of-surface-dimensions-of-metal-loss-anomalies-per-dimension_fig1_370191037).

![Anomaly dimension classification](img/class_table.PNG)

![Graphical presentation of metal loss anomalies per dimension class](img/class_chart.PNG)

## Installation

```bash
pip install pipeline-anomaly-dimension-class
```

## Usage

```python
from pipeline_anomaly_dimension_class import size_class, FeatureClass

# a defect measuring 100x100 mm on a pipe wall 10 mm thick is a defect of the "GENE" class
assert size_class(100, 100, 10) == FeatureClass.GENE

# defects with zero dimensions are not allowed
size_class(0, 100, 10)
pipeline_anomaly_dimension_class.Error: Wrong FeatureClass params. l=0 w=100 t=10

```

## Development

```bash
git clone git@github.com:vb64/pipeline.anomaly.dimension.class.git
cd pipeline.anomaly.dimension.class
```

With Python 2
```bash
make setup2 PYTHON_BIN=/path/to/python27/executable
make tests2
```

With Python 3
```bash
make setup PYTHON_BIN=/path/to/python3/executable
make tests
```