# TdVisu
[](https://pypi.python.org/pypi/tdvisu/)
[](https://codecov.io/gh/VaeterchenFrost/tdvisu)
[](https://pypi.python.org/pypi/tdvisu/)
[](https://pypi.python.org/pypi/tdvisu/)
[](https://pypi.python.org/pypi/tdvisu/)
---
Visualization for [dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming) on [tree decompositions](https://en.wikipedia.org/wiki/Tree_decomposition).
Create a graph object for each given graph that is of interest for the dynamic programming.
The visualization generates highlights and adds solution-tables for user defined time steps.
These snapshot of the graphs will be written in a graphviz-supported file-format to a folder of your choosing.
For the portable and light weight '.svg' format, all graphs for a timestep can be joined together to provide a thoroughly view on the process of dynamic programming.
With the '.svg' format the images are highly customizable, and even combining several timesteps together using svg [animate](https://developer.mozilla.org/en-US/docs/Web/SVG/Element/animate) would be an option in the future.
---
# Using
> Note: see also the steps prepared in the CI/CD [.github/workflows/python-app.yml](https://github.com/VaeterchenFrost/tdvisu/actions/workflows/python-app.yml):
[Graphviz (>=2.38)](https://graphviz.gitlab.io/download/). Be aware of changes in default layouts over different major versions of Graphviz. The project currently tests with `graphviz-version: "12.2.1"`.
[python-benedict[xml]](https://pypi.org/project/python-benedict/)
PostgreSQL adapter for Python: [psycopg (3)](https://www.psycopg.org/docs/index.html)
---
# To register the graphviz plugins
https://gitlab.com/graphviz/graphviz/-/issues/1352
```shell
dot.exe -c
```
# To install
In a command prompt with pip (to get _pip_ see: https://pip.pypa.io/en/stable/) installed:
Just run
```shell
pip install -h (for more information on install options)
pip install tdvisu
```
To download the latest version from the default branch:
```shell
git clone --depth 1 --single https://github.com/VaeterchenFrost/tdvisu
```
# To isolate the dependencies
With [virtualenv](https://virtualenv.pypa.io/en/latest/installation.html) on the system installed you can isolate the environment, for example
```shell
virtualenv tdvisu_dir -p 3.12
cd tdvisu_dir/bin/
source activate
# Windows: ./tdvisu_dir/Scripts/activate
```
With [Conda](https://docs.conda.io/en/latest/) on the system installed the dependencies for this project can be automatically installed in a new environment:
Go to the projects base directory.
Open a _conda-command-prompt_ with admin privileges and run the commands from the project folder
- to create a new environment with basic dependencies:
```shell
conda env create -f ./environment.yml
```
- to activate the environment:
```shell
conda activate tdvisu
```
# Install from source
To clone the complete repository:
```shell
git clone https://github.com/VaeterchenFrost/tdvisu
```
To download only the latest version from the default branch:
```shell
git clone --depth 1 --single https://github.com/VaeterchenFrost/tdvisu
```
To install the project from the source folder:
```shell
pip install -h (for more information on install options)
pip install .
```
to confirm that the visualization finds all dependencies:
```shell
python ./tdvisu/visualization.py -h
```
to run all tests:
```shell
pip install .[test]
pytest ./test/
```
---
# How to use
The visualization needs input in the form of the [Json API](https://github.com/VaeterchenFrost/gpusat-VISU/blob/main/JsonAPI_v1.3.md).
The creation of this file is exemplary implemented in _construct_dpdb_visu.py_ or the fork [GPUSAT](https://github.com/VaeterchenFrost/GPUSAT) and _--visufile filename_ (optionally disabling preprocessing with _-p_).
Run the python file with the above dependencies installed:
[visualization.py](https://github.com/VaeterchenFrost/gpusat-VISU/blob/main/satvisualization_repo/satvisu/visualization.py)
**visualization.py** takes two parameters, the json-**infile** to read from, and optionally one **outputfolder**.
With both arguments a run might look like this:
```shell
python tdvisu/visualization.py visugpusat.json examplefolder
```
For #SAT it produces for example three different graphs suffixed with a running integer to represent timesteps:
- _TDStep_ the tree decomposition with solved nodes
- _PrimalGraphStep_ the primal graph with currently active variables highlighted
- _IncidenceGraphStep_ the bipartite incidence graph with active clauses/variables highlighted
The graphs are images encoded in resolution independent **.svg files** (see https://www.lifewire.com/svg-file-4120603)
<p align="center"><img src="https://raw.githubusercontent.com/VaeterchenFrost/tdvisu/main/doc/images/combined6DA4.svg?sanitize=true" width="70%"/></p>
## How to use construct_dpdb_visu.py
After installing the project [dp_on_dbs](https://github.com/hmarkus/dp_on_dbs) with the there listed [requirements](https://github.com/hmarkus/dp_on_dbs#requirements), we need to
- edit the [database.ini](https://github.com/VaeterchenFrost/tdvisu/blob/main/tdvisu/database.ini) with our password to [postgresql](https://www.postgresql.org/)
- Solve a problem with `python dpdb.py [GENERAL-OPTIONS] -f <INPUT-FILE> <PROBLEM> [PROBLEM-SPECIFIC-OPTIONS]`
- for the problem **VertexCover**
- with flag `--gr-file` to store the htd Input (if the input was in a different format)
- for the problem **SharpSat**
- with flag `--store-formula` to store the formula in the database
- Run
- **Sat** / **SharpSat**: `python construct_dpdb_visu.py [PROBLEMNUMBER]`
- **VertexCover**: `python construct_dpdb_visu.py [PROBLEMNUMBER] --twfile [TWFILE]`
with the file in DIMACS tw-format containing the edges of the graph.
# Installation of the psycopg package
See https://www.psycopg.org/psycopg3/docs/basic/install.html
**Note** Whatever version of `libpq` psycopg is compiled with, it will be possible to connect to PostgreSQL servers of any [supported version](https://www.psycopg.org/docs/install.html#prerequisites): just install the most recent libpq version or the most practical, without trying to match it to the version of the PostgreSQL server you will have to connect to.
---
# New Release
## Version
- Bump `/version.py` according to the changes made
- Change date to the release day, keep format
## Requirements
In case dependencies have changed, or just to update some, check
- _requirements.txt_
- _stable-requirements.txt_ (using `pip freeze`)
- _setup.py_
## Write Changelog.md
- Add tag with link (see bottom for linking examples)
- Add changes, maybe some are already in _Unreleased_
- Update _Unreleased_ with **(No) unreleased changes**
## Review code
- Run tests (pytest)
- Check codestyle (pylint)
## Push
- Push changes to main
- Wait for all automated checks! (All checks have passed...)
## Create Release
- On the GitHub page go to: Release, **[Draft a new release](https://github.com/VaeterchenFrost/tdvisu/releases/new)**
- Enter v'YOUR VERSION NUMBER' as the tag.
- Add a **Release Title** (could be just the version)
- Add some description (like in the CHANGELOG.md)
- Click on **Publish release** on the bottom
## Should automatically release to [PyPI](https://pypi.org/project/tdvisu/)
- For details see: [Upload Python Package](https://github.com/VaeterchenFrost/tdvisu/blob/main/.github/workflows/python-publish.yml)
**Now you are set for the new release :tada:**
---
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"description": "# TdVisu\n\n\n[](https://pypi.python.org/pypi/tdvisu/)\n\n[](https://codecov.io/gh/VaeterchenFrost/tdvisu)\n\n\n[](https://pypi.python.org/pypi/tdvisu/)\n\n[](https://pypi.python.org/pypi/tdvisu/)\n[](https://pypi.python.org/pypi/tdvisu/)\n\n\n\n---\n\nVisualization for [dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming) on [tree decompositions](https://en.wikipedia.org/wiki/Tree_decomposition).\n\nCreate a graph object for each given graph that is of interest for the dynamic programming.\n\nThe visualization generates highlights and adds solution-tables for user defined time steps.\n\nThese snapshot of the graphs will be written in a graphviz-supported file-format to a folder of your choosing.\n\nFor the portable and light weight '.svg' format, all graphs for a timestep can be joined together to provide a thoroughly view on the process of dynamic programming.\n\nWith the '.svg' format the images are highly customizable, and even combining several timesteps together using svg [animate](https://developer.mozilla.org/en-US/docs/Web/SVG/Element/animate) would be an option in the future.\n\n---\n\n# Using\n\n> Note: see also the steps prepared in the CI/CD [.github/workflows/python-app.yml](https://github.com/VaeterchenFrost/tdvisu/actions/workflows/python-app.yml):\n\n[Graphviz (>=2.38)](https://graphviz.gitlab.io/download/). Be aware of changes in default layouts over different major versions of Graphviz. The project currently tests with `graphviz-version: \"12.2.1\"`.\n\n[python-benedict[xml]](https://pypi.org/project/python-benedict/)\n\nPostgreSQL adapter for Python: [psycopg (3)](https://www.psycopg.org/docs/index.html)\n\n---\n\n# To register the graphviz plugins\n\nhttps://gitlab.com/graphviz/graphviz/-/issues/1352\n\n```shell\ndot.exe -c\n```\n\n# To install\n\nIn a command prompt with pip (to get _pip_ see: https://pip.pypa.io/en/stable/) installed:\nJust run\n\n```shell\npip install -h (for more information on install options)\npip install tdvisu\n```\n\nTo download the latest version from the default branch:\n\n```shell\ngit clone --depth 1 --single https://github.com/VaeterchenFrost/tdvisu\n```\n\n# To isolate the dependencies\n\nWith [virtualenv](https://virtualenv.pypa.io/en/latest/installation.html) on the system installed you can isolate the environment, for example\n\n```shell\nvirtualenv tdvisu_dir -p 3.12\ncd tdvisu_dir/bin/\nsource activate\n# Windows: ./tdvisu_dir/Scripts/activate\n```\n\nWith [Conda](https://docs.conda.io/en/latest/) on the system installed the dependencies for this project can be automatically installed in a new environment:\n\nGo to the projects base directory.\n\nOpen a _conda-command-prompt_ with admin privileges and run the commands from the project folder\n\n- to create a new environment with basic dependencies:\n\n```shell\nconda env create -f ./environment.yml\n```\n\n- to activate the environment:\n\n```shell\nconda activate tdvisu\n```\n\n# Install from source\n\nTo clone the complete repository:\n\n```shell\ngit clone https://github.com/VaeterchenFrost/tdvisu\n```\n\nTo download only the latest version from the default branch:\n\n```shell\ngit clone --depth 1 --single https://github.com/VaeterchenFrost/tdvisu\n```\n\nTo install the project from the source folder:\n\n```shell\npip install -h (for more information on install options)\npip install .\n```\n\nto confirm that the visualization finds all dependencies:\n\n```shell\npython ./tdvisu/visualization.py -h\n```\n\nto run all tests:\n\n```shell\npip install .[test]\npytest ./test/\n```\n\n---\n\n# How to use\n\nThe visualization needs input in the form of the [Json API](https://github.com/VaeterchenFrost/gpusat-VISU/blob/main/JsonAPI_v1.3.md).\nThe creation of this file is exemplary implemented in _construct_dpdb_visu.py_ or the fork [GPUSAT](https://github.com/VaeterchenFrost/GPUSAT) and _--visufile filename_ (optionally disabling preprocessing with _-p_).\n\nRun the python file with the above dependencies installed:\n[visualization.py](https://github.com/VaeterchenFrost/gpusat-VISU/blob/main/satvisualization_repo/satvisu/visualization.py)\n\n**visualization.py** takes two parameters, the json-**infile** to read from, and optionally one **outputfolder**.\nWith both arguments a run might look like this:\n\n```shell\npython tdvisu/visualization.py visugpusat.json examplefolder\n```\n\nFor #SAT it produces for example three different graphs suffixed with a running integer to represent timesteps:\n\n- _TDStep_ the tree decomposition with solved nodes\n- _PrimalGraphStep_ the primal graph with currently active variables highlighted\n- _IncidenceGraphStep_ the bipartite incidence graph with active clauses/variables highlighted\n\nThe graphs are images encoded in resolution independent **.svg files** (see https://www.lifewire.com/svg-file-4120603)\n\n<p align=\"center\"><img src=\"https://raw.githubusercontent.com/VaeterchenFrost/tdvisu/main/doc/images/combined6DA4.svg?sanitize=true\" width=\"70%\"/></p>\n\n## How to use construct_dpdb_visu.py\n\nAfter installing the project [dp_on_dbs](https://github.com/hmarkus/dp_on_dbs) with the there listed [requirements](https://github.com/hmarkus/dp_on_dbs#requirements), we need to\n\n- edit the [database.ini](https://github.com/VaeterchenFrost/tdvisu/blob/main/tdvisu/database.ini) with our password to [postgresql](https://www.postgresql.org/)\n- Solve a problem with `python dpdb.py [GENERAL-OPTIONS] -f <INPUT-FILE> <PROBLEM> [PROBLEM-SPECIFIC-OPTIONS]`\n - 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