statescale 0.3.0

snapshot-driven state upscaling

statescale - Snapshot-driven state upscaling

statescale is a Python package for snapshot-driven upscaling of high-dimensional simulation states along arbitrary parameter or signal paths.

✨ Overview

statescale provides a lightweight API to manage time-dependent point- and cell-data and time-independent field-data across snapshots and to interpolate that data on new signals. The central class is SnapshotModel.

Highlights

• ✅ Snapshot-driven state upscaling
• ✅ Upscale simulation data to continuous signals from sparse snapshots
• ✅ Efficient handling of high-dimensional data
• ✅ Easy-to-use API

📦 Installation

Install from PyPI:

pip install statescale

Development install (from source):

1. Clone the repository:

git clone https://github.com/adtzlr/statescale.git
cd statescale

2. Install in editable mode:

pip install --editable .

Dependencies: numpy and scipy (and pytest for running tests).

🚀 Quickstart

A minimal example. Snapshots must have shapes (n_snapshots, n_dim), point- and cell- data (n_snapshots, ...) and the dimension of the signal must be compatible with snapshots, i.e. (n_steps, n_dim). The second dimension of snapshots and the signal are optional, 1d-arrays are also supported. The model result will be of shape (n_steps, ...).

Array-based input data

import numpy as np
import statescale

snapshots = np.linspace(0, 1, num=3).reshape(-1, 1)  # 3 snapshots, 1 parameter
point_data = {"displacement": np.random.rand(3, 9, 3)}  # 3 snapshots, 9 points, 3 dim
cell_data = {"strain": np.random.rand(3, 4, 6)}  # 3 snapshots, 4 cells, 6 dim
field_data = {"id": 1001}  # time-independent data

model = statescale.SnapshotModel(
    snapshots=snapshots,
    point_data=point_data,
    cell_data=cell_data,
    field_data=field_data,
    # kernel="surrogate",  # use a POD "surrogate" model or simple "griddata"
    # modes=(2, 10),  # min- and max no. of modes for surrogate model
)

signal = np.linspace(0, 1, num=20).reshape(-1, 1)  # 20 items, 1 parameter

# a `ModelResult` object with `point_data`, `cell_data` and `field_data`.
res = model.evaluate(signal, method="griddata")  # method "griddata" or "rbf"

List-based input data

If the data is list-based, the model can also import lists of dicts, with per-snapshot list items. Model results also support indexing and a conversion to lists of dicts.

import numpy as np
import statescale

point_data = [
    {"displacement": np.random.rand(6, 2)},  # 1. snapshot, 6 points, 2 dim
    {"displacement": np.random.rand(6, 2)},  # 2. snapshot, 6 points, 2 dim
    {"displacement": np.random.rand(6, 2)},  # 3. snapshot, 6 points, 2 dim
]
cell_data = [
    {"strain": np.random.rand(4, 2, 2)},  # 1. snapshot, 4 cells, (2, 2) dim
    {"strain": np.random.rand(4, 2, 2)},  # 2. snapshot, 4 cells, (2, 2) dim
    {"strain": np.random.rand(4, 2, 2)},  # 3. snapshot, 4 cells, (2, 2) dim
]

model = statescale.SnapshotModel(
    snapshots=snapshots,
    point_data=point_data,
    cell_data=cell_data,
    field_data=field_data,
)

# `point_data`, `cell_data` and `field_data` for step 5 of the signal.
res_5 = model.evaluate(signal)[5]

Any NumPy-function may be applied to the model result data on all time-dependent arrays. E.g., the mean over all cells (here, the first axis) of the cell-data is evaluated by:

res_5_mean = res_5.apply(np.mean, on_point_data=False, on_cell_data=True)(axis=0)

More details can be found in the documentation.

🛠️ Tests

The tests are located in tests. Run them locally with:

tox

➕ Contributing

Bug reports and pull requests are welcome. Please open an issue or PR in the repository. The package is in early development, expect breaking API changes until version 1.0.0.

📄 Changelog

All notable changes to this project will be documented in this file. The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

🔓 License

See the LICENSE file.