prcpy 0.1.0

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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. You can install PRCpy using Poetry by adding it to your project's dependencies:

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.Pipeline_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": "BG_450mT_10to60GHz_4K.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]