# aqua-blue
Lightweight and basic reservoir computing library
[](https://chicago-club-management-company.github.io/aqua-blue/)
[](https://pypi.python.org/pypi/aqua-blue/)
[](https://pypi.python.org/pypi/aqua-blue/)
## 🌊 What is aqua-blue?
`aqua-blue` is a lightweight `python` library for reservoir computing (specifically [echo state networks](https://en.wikipedia.org/wiki/Echo_state_network)) depending only on `numpy`. `aqua-blue`'s namesake comes from:
- A blue ocean of data, aka a reservoir 💧
- A very fancy cat named Blue 🐾
## 🐜 Found a bug?
Please open an issue [here](https://github.com/Chicago-Club-Management-Company/aqua-blue/issues) if you found a bug!
The easier it is to reproduce the bug, the faster we will find a solution to the problem. Please consider including the
following info in your issue:
- Steps to reproduce
- Expected and actual behavior
- Version info, OS, etc.
## 🔧 Contributing
Please see [CONTRIBUTING.md](https://github.com/Chicago-Club-Management-Company/aqua-blue/blob/main/CONTRIBUTING.md)
for instructions on how to contribute to `aqua-blue` ☺
## 📥 Installation
`aqua-blue` is on PyPI, and can therefore be installed with `pip`:
```bash
pip install aqua-blue
```
## 📝 Quickstart
```py
import numpy as np
import aqua_blue
# generate arbitrary two-dimensional time series
# y_1(t) = cos(t), y_2(t) = sin(t)
# resulting dependent variable has shape (number of timesteps, 2)
t = np.linspace(0, 4.0 * np.pi, 10_000)
y = np.vstack((2.0 * np.cos(t) + 1, 5.0 * np.sin(t) - 1)).T
# create time series object to feed into echo state network
time_series = aqua_blue.time_series.TimeSeries(dependent_variable=y, times=t)
# normalize
normalizer = aqua_blue.utilities.Normalizer()
time_series = normalizer.normalize(time_series)
# make model and train
model = aqua_blue.models.Model(
reservoir=aqua_blue.reservoirs.DynamicalReservoir(
reservoir_dimensionality=100,
input_dimensionality=2
),
readout=aqua_blue.readouts.LinearReadout()
)
model.train(time_series)
# predict and denormalize
prediction = model.predict(horizon=1_000)
prediction = normalizer.denormalize(prediction)
```
## 📃 License
`aqua-blue` is released under the MIT License.
---
*Blue, the cat behind `aqua-blue`.*
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"description": "# aqua-blue\nLightweight and basic reservoir computing library\n\n[](https://chicago-club-management-company.github.io/aqua-blue/)\n\n\n[](https://pypi.python.org/pypi/aqua-blue/)\n[](https://pypi.python.org/pypi/aqua-blue/)\n\n## \ud83c\udf0a What is aqua-blue?\n\n`aqua-blue` is a lightweight `python` library for reservoir computing (specifically [echo state networks](https://en.wikipedia.org/wiki/Echo_state_network)) depending only on `numpy`. `aqua-blue`'s namesake comes from:\n\n- A blue ocean of data, aka a reservoir \ud83d\udca7\n- A very fancy cat named Blue \ud83d\udc3e\n\n## \ud83d\udc1c Found a bug?\n\nPlease open an issue [here](https://github.com/Chicago-Club-Management-Company/aqua-blue/issues) if you found a bug!\nThe easier it is to reproduce the bug, the faster we will find a solution to the problem. Please consider including the\nfollowing info in your issue:\n\n- Steps to reproduce\n- Expected and actual behavior\n- Version info, OS, etc.\n\n## \ud83d\udd27 Contributing\n\nPlease see [CONTRIBUTING.md](https://github.com/Chicago-Club-Management-Company/aqua-blue/blob/main/CONTRIBUTING.md)\nfor instructions on how to contribute to `aqua-blue` \u263a\n\n## \ud83d\udce5 Installation\n\n`aqua-blue` is on PyPI, and can therefore be installed with `pip`:\n\n```bash\npip install aqua-blue\n```\n\n## \ud83d\udcdd Quickstart\n\n```py\nimport numpy as np\nimport aqua_blue\n\n# generate arbitrary two-dimensional time series\n# y_1(t) = cos(t), y_2(t) = sin(t)\n# resulting dependent variable has shape (number of timesteps, 2)\nt = np.linspace(0, 4.0 * np.pi, 10_000)\ny = np.vstack((2.0 * np.cos(t) + 1, 5.0 * np.sin(t) - 1)).T\n\n# create time series object to feed into echo state network\ntime_series = aqua_blue.time_series.TimeSeries(dependent_variable=y, times=t)\n\n# normalize\nnormalizer = aqua_blue.utilities.Normalizer()\ntime_series = normalizer.normalize(time_series)\n\n# make model and train\nmodel = aqua_blue.models.Model(\n reservoir=aqua_blue.reservoirs.DynamicalReservoir(\n reservoir_dimensionality=100,\n input_dimensionality=2\n ),\n readout=aqua_blue.readouts.LinearReadout()\n)\nmodel.train(time_series)\n\n# predict and denormalize\nprediction = model.predict(horizon=1_000)\nprediction = normalizer.denormalize(prediction)\n```\n\n## \ud83d\udcc3 License\n\n`aqua-blue` is released under the MIT License.\n\n---\n\n\n\n*Blue, the cat behind `aqua-blue`.*",
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