Metadata-Version: 2.1
Name: suehtyp
Version: 1.2.3
Summary: Theseus, a highly-efficient inverse-design algorithm for quantum optical experiments
Home-page: https://github.com/artificial-scientist-lab/Pytheus
Author: artificial-scientist-lab
Author-email: cruizgo@proton.me, soeren.arlt@mpl.mpg.de, mario.krenn@mpl.mpg.de
License: MIT
Platform: ALL
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: sphinx
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: termcolor
Requires-Dist: Click

# Theseus
Theseus, a highly-efficient inverse-design algorithm for quantum optical experiments

## Installation

When published it will be possible to install via `pip install`. Untill then, install from source (after cloning the repository):

```
python setup.py install
```

## Running Theseus

To list the included examples, type

```
theseus list
```

To run one of the included examples, type e.g.

```
theseus run --example ghz_346
```

To run your own input file, type

```
theseus run PATH_TO_YOUR_INPUT_FILE
```

Output of optimization is saved to a directory called `output`. Names of the subdirectorie are specified by the name
and content of the config file.

To plot the graph corresponding to one result saved as a json file, execute 

```
theseus plot PATH_TO_RESULT_FILE
```

To analyze a subdirectory corresponding to one run, type

```
theseus analyze -d outputs/ghz_346/ghz_346 
```
or just 
```
theseus analyze  
```
then an overview of all available folders that can be selected is given.
After that one can choose which run (if there exists different run-folders having different
summary files) one wants to analyze. When you have decided on a state, an overview plot is created
that shows the graph, the development of the loss function and various properties 
that can be declared via ```-i```. With ```-pm``` a pdf can be created that shows all perfect matchings. 
When one wants to set all weights to plus minus one one can choose the option ```-one```.
Everthing together:
```
theseus analyze -d your/directory -one -pm -i 'norm' -i 'ent' -i 'k'  
```
```norm``` shows the normalization of the state, ```ent``` gives information about entanglement for 
the different bipartitions and ```k``` gives information if there is a k-uniform state and which
 bipartitions are maximally entangled (=1) or separable (=0).

To get help, add the `--help` option to any command. For instance

```
> theseus run --help

Usage: theseus run [OPTIONS] FILENAME

  Run an input file.

Options:
  --example  Load input file from examples directory.
  --help     Show this message and exit.
```

## Development

### Clone repository

```
git clone https://github.com/artificial-scientist-lab/Theseus.git
```

### Create virtual environment

From the project root directory, submit

```
python -m venv venv
```

This will create a subfolder with your virtual environment.

To activate, type

```
. venv/bin/activate
```

Note the leading point!

### Local development installation

Submit

```
python setup.py develop
```

from the project root directory (where `setup.py` is located).
Any changes in the code will now automatically be reflected
in your local package installation.


## Tests

### Run test suite

#### Running all tests

```
python -m unittest discover tests
```

#### Running only the fast tests

```
python -m unittest discover -s tests/fast
```

### Test coverage

Install `coverage`, if you have not yet done so:

```
pip install coverage
```

Then run coverage scan:

```
coverage run --source=theseus -m unittest discover tests 
```

After that, create the coverage report:

```
coverage report -m
```



