plotID for Python

This is the python plotID project.
plotID is a program connected to Research Data Management (RDM). It has two main functionalities:

1. Tag your plot with an identifier.
2. Export the resulting file to a specified directory along the corresponding research data, the plot is based on. Additionally, the script that created the plot will also be copied to the directory.

Note: To run plotID python version ≥ 3.10 is required.

Installation

Currently there are two options to run plotID. Either install it via pip from the Python Package Index (PyPi) or install plotID from the source code.

1. [Optional] Create a virtual environment and activate it:

pip install venv
mkdir venv  
python3 -m venv venv

source venv/bin/activate  # Unix

venv\Scripts\activate.bat # Windows Command Prompt
venv\Scripts\Activate.ps1 # Windows PowerShell

From PyPi with pip

2. Install plotID
   pip install plotid --extra-index-url=https://test.pypi.org/simple/
   If you also want to run the unittests use plotid[test] instead of plotid.

From source

2. Download the source code from Gitlab:
   git clone https://git.rwth-aachen.de/plotid/plotid_python.git
cd plotid_python
3. Install dependencies
   pip install -r requirements.txt
4. Install plotID
   pip install .

Usage

plotID has two main functionalities:

1. Tag your plot with an identifier.
2. Export the resulting file to a specified directory along the corresponding research data, the plot is based on. Additionally, the script that created the plot will also be copied to the directory.

tagplot()

Tag your figure/plot with an ID. It is possible to tag multiple figures at once.
tagplot(figures, plot_engine)
The variable "figures" can be a single figure or a list of multiple figures.
The argument "plot_engine" defines which plot engine was used to create the figures. It also determines which plot engine plotID uses to place the ID on the plot. Currently supported plot engines are:

• 'matplotlib', which processes figures created by matplotlib.
• 'image', which processes pictures with common extensions (jpg, png, etc.)

tagplot returns a list that contains two lists each with as many entries as figures were given. The first list contains the tagged figures. The second list contains the corresponding IDs as strings

Optional parameters can be used to customize the tag process.

• prefix : str, optional Will be added as prefix to the ID.
• id_method : str, optional id_method for creating the ID. Create an ID by Unix time is referenced as 'time', create a random ID with id_method='random'. The default is 'time'.
• location : string, optional Location for ID to be displayed on the plot. Default is 'east'.

Example:

FIG1 = plt.figure()  
FIG2 = plt.figure()   
FIGS_AS_LIST = [FIG1, FIG2]  
[TAGGED_FIGS, ID] = tagplot(FIGS_AS_LIST, 'matplotlib', prefix='XY23_', id_method='random', location='west')

publish()

Save plot, data and measuring script. It is possible to export multiple figures at once. publish(src_datapath, dst_path, figure, plot_name)

• "src_datapath" specifies the path to (raw) data that should be published. It can be a string or a list of strings that specifies all files and directories which will be published.
• "dst_path" is the path to the destination directory, where all the data should be copied/exported to.
• "figure" expects the figure or a list of figures that were tagged and now should be saved as pictures. If image files were tagged, all of them need to have the same file extension.
• "plot_names" will be the file names for the exported plots. If you give only one plot name but several figures, plotID will name the exported plots with an appended number, e.g. example_fig1.png, example_fig2.png, ...

Optional parameters can be used to customize the publish process.

• data_storage: str, optional
  Method how the data should be stored. Available options:
  - centralized: The raw data will copied only once. All other plots will reference this data via sym link. - individual: The complete raw data will be copied to a folder for every plot, respectively. Example: `publish('/home/user/Documents/research_data', '/home/user/Documents/exported_data', FIG1, 'EnergyOverTime-Plot')

Build

If you want to build plotID yourself, follow these steps:

1. Download the source code from Gitlab:
   git clone https://git.rwth-aachen.de/plotid/plotid_python.git
cd plotid_python
2. [Optional] Create a virtual environment (see Installation).
3. [Optional] Run unittests and coverage:
   python3 tests/runner_tests.py
4. Build the package python3 -m build

Documentation

If you have more questions about plotID, please have a look at the documentation.
Also have a look at the example.py that is shipped with plotID.