filename stringclasses 4
values | method stringclasses 4
values | shape stringclasses 1
value | dtype stringclasses 1
value | n_time int64 200 200 | n_space int64 100 100 | n_realizations int64 5.12k 5.12k | dt_saved float64 0 0 | dx float64 0.01 0.01 |
|---|---|---|---|---|---|---|---|---|
Dean-Kawasaki.npy | Dean-Kawasaki | (200, 100, 5120) | float64 | 200 | 100 | 5,120 | 0.00015 | 0.01 |
Markovian-ML.npy | Markovian-ML | (200, 100, 5120) | float64 | 200 | 100 | 5,120 | 0.00015 | 0.01 |
Non-Markovian-ML.npy | Non-Markovian-ML | (200, 100, 5120) | float64 | 200 | 100 | 5,120 | 0.00015 | 0.01 |
Random-Walkers.npy | Random-Walkers | (200, 100, 5120) | float64 | 200 | 100 | 5,120 | 0.00015 | 0.01 |
Dataset Card
Dataset Description
This dataset consists of number density for 5120 realizations of a 100 cell one-dimensional system subjected to an external potential.
The system is modeled with 4 different methods, namely, random-walker particles (Random-Walkers.npy), Dean-Kawasaki model (Dean-Kawasaki.npy),
Markovian ML model (Markovian-ML.npy), and non-Markovian ML model (Non-Markovian-ML.npy). Simulation time step is 3.0e-6 and cell size is 1.0e-2.
The data is saved at 50 time step intervals.
Each NumPy binary file (.npy) contains multidimensional arrays of shape (200, 100, 5120).
The ordering of data is time, spatial positions, and independent realizations.
The Dataset Viewer is configured against preview.csv, which provides one metadata row per .npy file. The .npy files remain the canonical dataset artifacts.
Array Structure
| Axis | Dimension | Description |
|---|---|---|
| 0 | 200 | Time steps |
| 1 | 100 | System (spatial) direction |
| 2 | 5120 | Realizations (ensemble members) |
Usage
import numpy as np
data = np.load("file_name.npy")
print(data.shape) # (200, 100, 5120)
n_time, n_space, n_realizations = data.shape
dt_saved = 1.5e-4
dx = 0.01
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