A library for simulating EIS with equivalent circuits.
fasteis uses a few tricks to make EIS fitting faster and more reliable:
- Small convolutional neural networks are used to automatically get good initial fit parameters
- It is written in Rust with fast and numerically stable maths
Builds on the excellent work from
impedance.py and
PyEIS.
Quickstart:
pip install fasteis
import fasteis
circuit = fasteis.Circuit("R0-(CPE1,R1-W1)")
result = circuit.fit(f, Z)
See the docs for more details.
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Release files for fasteis 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | |
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| fasteis-0.1.0.tar.gz | 2.7 MB | Details |
Built distributions (wheels)
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Release files / fasteis-0.1.0.tar.gz
| Download URL | fasteis-0.1.0.tar.gz |
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| Size | 2.7 MB |
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Release files / fasteis-0.1.0-cp38-abi3-win_arm64.whl
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Release files / fasteis-0.1.0-cp38-abi3-win_amd64.whl
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Release files / fasteis-0.1.0-cp38-abi3-win32.whl
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Release files / fasteis-0.1.0-cp38-abi3-musllinux_1_2_x86_64.whl
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Release files / fasteis-0.1.0-cp38-abi3-musllinux_1_2_i686.whl
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Release files / fasteis-0.1.0-cp38-abi3-musllinux_1_2_armv7l.whl
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Release files / fasteis-0.1.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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Release files / fasteis-0.1.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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Release files / fasteis-0.1.0-cp38-abi3-manylinux_2_12_i686.manylinux2010_i686.whl
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