Sample a field along a line

line(mesh, name, start, end, n_points, field_name) builds a PointDataSet with the requested field already interpolated onto evenly spaced points between start and end. This is the pyOFTools-side equivalent of OpenFOAM’s uniform set in system/sampleDict.

Open the case

import os
import subprocess

import pyOFTools.patch_pybfoam  # noqa: F401
from pyOFTools import clone_example

CASE = clone_example("damBreak")
subprocess.run(
    ["./Allrun"],
    cwd=CASE,
    check=True,
    env={**os.environ},
    capture_output=True,
    text=True,
)

from pybFoam import Time, fvMesh, volScalarField

time = Time(str(CASE.parent), CASE.name)
mesh = fvMesh(time)
p_field = volScalarField.read_field(mesh, "p")

Build the line

A vertical probe through the middle of the water column. create_uniform_set takes the field object directly (p_field), which avoids re-reading it — the line() builder in pyOFTools.builders would do a second read_field and double-register the field in OpenFOAM.

from pyOFTools.sets import create_uniform_set

dataset = create_uniform_set(
    mesh,
    name="vertical_p",
    start=(0.146, 0.0, 0.0073),
    end=(0.146, 0.584, 0.0073),
    n_points=100,
    field=p_field,
)

Plot it

import matplotlib.pyplot as plt
import numpy as np

distance = np.asarray(dataset.geometry.distance)
pressure = np.asarray(dataset.field)

fig, ax = plt.subplots(figsize=(6, 3.5))
ax.plot(distance, pressure)
ax.set_xlabel("distance along line [m]")
ax.set_ylabel("p [Pa]")
ax.set_title("Pressure along a vertical line (damBreak, t=0)")
fig.tight_layout()
plt.show()

Aggregate instead of plot

The same PointDataSet flows into any reducer node. Mean gives the arithmetic mean over the sampled points (not a length-weighted mean — use a Sum with a manual scaling factor for that).

from pyOFTools.aggregators import Max, Mean
from pyOFTools.workflow import WorkFlow

mean_p = (WorkFlow(initial_dataset=dataset) | Mean()).compute()
max_p = (WorkFlow(initial_dataset=dataset) | Max()).compute()
print(f"mean p along line = {mean_p.values[0].value:.4g} Pa")
print(f"max  p along line = {max_p.values[0].value:.4g} Pa")

mean p along line = 1e+05 Pa
max  p along line = 1e+05 Pa