teed 0.0.6

Telco engineering data library

teed

Telco engineering data library

Probe and transform raw telco files into CSV.

A simple BulkCm file parsing

(env) joaomg@mypc:~/teed$ python -m teed bulkcm parse data/bulkcm.xml data
Parsing data/bulkcm.xml
Created data/ManagedElement.csv
Created data/ManagementNode.csv
Created data/SubNetwork.csv
Time: 0:00:00.000856
(env) joaomg@mypc:~/teed$

Install from source

git clone https://github.com/joaomg/teed.git
cd teed
pip install -e .

Probe a file

python -m teed bulkcm probe data/bulkcm_with_vsdatacontainer.xml

Parse a file output it's content to CSV files

python -m teed bulkcm parse data/bulkcm_empty.xml data
python -m teed bulkcm parse data/bulkcm_with_header_footer.xml data
python -m teed bulkcm parse data/bulkcm_with_vsdatacontainer.xml data

Usage

Beside command-line teed can be used as a library:

from teed import bulkcm, meas

## bulkcm
stream = bulkcm.BulkCmParser.stream_to_csv("data")
bulkcm.parse("data/bulkcm.xml", "data", stream)

## meas 
meas.parse("data/mdc*xml", "data")

The bulkcm parser extracts content from a single file.

While the meas parser, in a single run, can process any number of XML files using wildcards and directory recursion.

The bulkcm and meas parsers also differ on CSV file creation:

• bulkcm deletes previously existing CSV files

• meas appends to existing CSV files

Take notice of these differences when calling the parsers from shell.

Or using them in data pipelines.

Documentation

Probe, split and extract configuration content from bulkcm XML files.

Extract performance data from meas XML files.

How to build teed.

License

The teed library is licensed under:

GNU Affero General Public License v3.0

On ETSI references and usage rights

https://www.etsi.org/intellectual-property-rights

https://www.etsi.org/images/files/IPR/etsi-ipr-policy.pdf

Background

The teed library aims to be a comprehensive parser toolkit for telecommunications engineering data.

It's inspired by frictionless-py. In fact, a production ready data pipeline can naturally glue together teed and frictionless-py.

The former extracting content from telco raw files to CSV.

And the later validating, cleaning and transforming data into a query ready system (parquet, RDBMS).

+---------------+
|Telco raw files|
|               |    
|  .xml  .asn1  |
+---------------+
        |
        |   teed (extract)
        V
+---------------+
| Tabular files |
|               |    
|      .csv     |
+---------------+
        |
        |   frictionless-py (clean, validate, transform, publish)
        V
+---------------+
|    Dataset    |
|    Parquet    |
|     RDBMs     |
+---------------+

Much alike to the work done by PUDL, with Frictionless Data itself, in Frictionless Public Utility Data - A Pilot Study.

Take a look at PUDL code.