.. image:: https://img.shields.io/pypi/l/Wilt.svg
:target: https://spdx.org/licenses/GPL-3.0-or-later.html
:alt: PyPI - License
.. image:: https://heptapod.host/saajns/wilt/badges/branch/default/pipeline.svg
:target: https://heptapod.host/saajns/wilt/-/commits/branch/default
:alt: Pipeline status
.. image:: https://heptapod.host/saajns/wilt/badges/branch/default/coverage.svg
:target: https://heptapod.host/saajns/wilt/-/commits/branch/default
:alt: Test code coverage
.. image:: https://badge.fury.io/py/Wilt.svg
:target: https://pypi.python.org/pypi/Wilt
:alt: PyPI
****
Wilt
****
Wilt is an architect's collection of codebase health probes. The name of the
package is inspired by the simplest possible code metric::
W Whitespace
I Integrated over
L Lines of
T Text
It comes from a great talk by Robert Smallshire called Confronting Complexity
[1]_ [2]_. In the talk there are a few other ideas for codebase health analysis
and visualisation that inspire development of this package.
Install with::
pipx install Wilt
Code quality
============
Wilt
----
An implementation of WILT itself. The metric can be calculated like::
$ wilt cq.wilt /usr/lib/python3.12/unittest/case.py
2677.75
$ wilt cq.wilt '/usr/lib/python3.12/**/*.py'
346219.0
$ echo " foo" | wilt cq.wilt -i 2 -
2.0
Continuous integration
======================
REST HTTP API resource synchronisation
--------------------------------------
This feature allows synchronising collections of CI HTTP API resources in a
SQLite database for analysis in SQL. The main use case is GitLab CI
pipelines. Create a file called ``resmapfile.py`` like the following:
.. code:: python
from wilt.resdb import resmap_api as rm
api = rm.Api(
base_url='https://gitlab.com/api/v4/projects/4961127',
default_headers={'PRIVATE-TOKEN': 'S3Cr3t'},
)
resources = [
rm.Resmap(
rm.SourceResourceCollection(
'/jobs',
request_params={'per_page': 100, 'sort': 'desc'},
resource_id_key='id',
resource_timestamp_key='created_at',
),
rm.TargetTable(
'job',
extract_columns=[
rm.ExtractColumn(
'id', rm.ColumnType.integer, name='job_id', pk=True
),
rm.ExtractColumn('name', rm.ColumnType.text),
rm.ExtractColumn('created_at', rm.ColumnType.datetime),
rm.ExtractColumn('duration', rm.ColumnType.numeric),
rm.ExtractColumn('status', rm.ColumnType.text),
rm.ExtractColumn('pipeline.id', rm.ColumnType.integer),
],
),
load_interval=rm.timedelta(days=2 * 365),
sync_lookbehind=rm.timedelta(hours=6),
),
]
Then run ``wilt -v ci.rest sync-db`` to create and then synchronise a SQLite
database with the HTTP API resources according to the mapping.
.. note::
Each table is expected to have ``{table_name}_id`` and ``created_at``
columns.
GitLab CI visualisation
-----------------------
Assuming the following pipeline synchronisation:
.. code:: python
resources = [
rm.Resmap(
rm.SourceResourceCollection(
'/pipelines',
request_params={'per_page': 10, 'sort': 'desc'},
page_size=10,
),
rm.TargetTable(
'pipeline_short',
extract_columns=[
rm.ExtractColumn(
'id', rm.ColumnType.integer, name='pipeline_short_id', pk=True
),
rm.ExtractColumn('created_at', rm.ColumnType.datetime),
],
),
subresources=[
rm.SubResmap(
rm.SourceSubresource('/pipelines/{id}', 'pipeline_id'),
rm.TargetTable(
'pipeline',
extract_columns=[
rm.ExtractColumn(
'id', rm.ColumnType.integer, name='pipeline_id', pk=True
),
rm.ExtractColumn('created_at', rm.ColumnType.datetime),
rm.ExtractColumn('duration', rm.ColumnType.numeric),
rm.ExtractColumn('ref', rm.ColumnType.text),
],
),
),
],
),
]
the pipeline runtime can be visualised with a command like::
wilt ci.gitlab pipeline-runtime --clamp-max 3600 --after 2023-01-01T00:00:00
This produces ``plot.html`` file with interactive Plotly visualisation. See
``wilt ci.gitlab pipeline-runtime --help`` for more details.
____
.. [1] https://www.youtube.com/watch?v=W44Ub5ykBY4
.. [2] https://web.archive.org/web/20170331000730/http://ticosa.org/output/Robert%20Smallshire-Confronting%20Complexity-%20TICOSA%202014.pdf
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