prcpy 0.1.5

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PRCpy: A Python Library for Physical Reservoir Computing

PRCpy is a Python package designed to ease experimental data processing for physical reservoir computing.

Features

• Data handling and preprocessing.
• Customizable data processing pipelines for various research needs.

Installation

PRCpy requires Python 3.9 or later.

Using pip

pip install prcpy

Using Poetry

poetry add prcpy

General usage overview

1. Define data path
2. Define pre-processing parameters
3. Create RC pipeline
4. Define target and add to pipeline
5. Define model for training
6. Define RC parameters
7. Run RC

Example:

import PRCpy

from prcpy.RC import Pipeline
from prcpy.TrainingModels.RegressionModels import define_Ridge
from prcpy.Maths.Target_functions import get_mackey_glass, get_square_waves

Define data directory and processing parameters

Note: Data files must contain "scan" in their file names. See examples/data for example data files.

data_dir_path = "your/data/path"
process_params = {
    "Xs": "Frequency",
    "Readouts": "Spectra",
    "remove_bg": True,
    "bg_fname": "background_data.txt",
    "smooth": False,
    "smooth_win": 51,
    "smooth_rank": 4,
    "cut_xs": False,
    "x1": 2,
    "x2": 5,
    "normalize": False,
    "sample": True,
    "sample_rate": 13
}

Create RC pipeline

rc_pipeline = Pipeline(data_dir_path, process_params)

Target generation

Transformation

period = 10
sample_spacing = rc_pipeline.get_sample_spacing(period)
target_values = get_square_waves(sample_spacing, period, norm=True)

Forecasting

target_values = get_mackey_glass(norm=True)

Add target to pipeline

rc_pipeline.define_target(target_values)

Define model

model_params = {
        "alpha": 1e-3,
        "fit_intercept": True,
        "copy_X": True,
        "max_iter": None,
        "tol": 0.0001,
        "solver": "auto",
        "positive": False,
        "random_state": None,
    }
model = define_Ridge(model_params)

Define RC parameters

Set "tau": 0 for transformation.

rc_params = {
        "model": model,
        "tau": 10,
        "test_size": 0.3,
        "error_type": "MSE"
    }

Run RC

rc_pipeline.run()

Get results

results = rc_pipeline.get_rc_results()

Contributing

Any community contributions are welcome. Please refer to the project's GitHub repository for contribution guidelines.

Authors

• Oscar Lee [e-mail]