Migrating to the v3 paradigm¶

The v3 paradigm introduces a small set of composable primitives – DataSource, Pipeline, Container, Storage – and a library of pure ops. This guide shows how to adopt them. The disk-first v2 pressure entry points have been removed: post-processing now runs through the v3 recipes and the cfdmod run <template.yaml> CLI.

What changed at the import surface¶

New top-level symbols¶

from cfdmod import (
    # value objects
    DataSource, SurfaceDataSource, VolumeDataSource,
    PointsDataSource, GroupsDataSource, ModesDataSource,
    TimeAxis, Topology, ElementMeta, Grouping, FieldMeta,
    # composition
    Pipeline, compose, Container,
    # adapters (storage seam)
    MemoryStorage, MemoryFieldStore,
    XdmfH5Storage, H5FieldStore,
    # ops + recipes namespaces
    core_ops, recipes,
)

Removed and relocated symbols¶

The cfdmod.pressure package has been removed. run_cp, run_cf, run_cm, run_ce, apply_filters, and MovingAverageFilter no longer exist; use the v3 recipes (build_cp, cf_pipeline, cm_pipeline, ce_pipeline) or a YAML template run with cfdmod run <template.yaml>. The functional filtering that apply_filters / MovingAverageFilter provided is now the moving_average field op, composable into any pipeline.

Everything else is unchanged: InflowData, Profile, every *Config model, and every LoftParams / RadialParams / WindProfile_* are still exported from cfdmod.

A Cp pipeline (small-data, no I/O)¶

import numpy as np
from cfdmod import (
    SurfaceDataSource, TimeAxis, Topology, ElementMeta,
    MemoryFieldStore,
)
from cfdmod.recipes import CpRecipeConfig, build_cp

body = SurfaceDataSource(
    time=TimeAxis(initial_time=0.0, timestep_size=0.001, n_timesteps=10_000),
    topology=Topology.triangles(connectivity, vertices),
    elements=ElementMeta(area=area_array),
    fields=MemoryFieldStore({"pressure": pressure_array}),
)
cp = build_cp(
    body,
    p_ref=ref_data_source_or_scalar,
    cfg=CpRecipeConfig(dynamic_pressure=0.5 * 1.225 * 12.0**2,
                       statistics=["mean", "rms", "peak_max"]),
)
# cp is a SurfaceDataSource with time-aggregated stat fields.

The same recipe runs against XdmfH5Storage for production-size data without changing the pipeline – only the Storage adapter swaps.

When to reach for v3¶

New consulting notebooks: prefer the recipes. The data flow is explicit and small-data is fast.
Batch post-processing: author a YAML template and run it with cfdmod run <template.yaml>. The template declares its own inputs, pipeline steps, and outputs, and produces the XDMF + H5 outputs via XdmfH5Storage.
Any code that wants a custom pipeline (e.g. extra filtering step, alternative aggregation): build a Pipeline from core_ops.

Recipe reference¶

Recipe	Inputs	Output	Module
`build_cp`	body + reference data sources	surface (with stats)	`cfdmod.recipes.cp`
`cf_pipeline`	grouped Cp data source	groups (per-body Cf)	`cfdmod.recipes.cf`
`cm_pipeline`	grouped moment-contribution data source	groups (per-body Cm)	`cfdmod.recipes.cm`
`ce_pipeline`	zoned Cp data source	groups (per-zone Ce)	`cfdmod.recipes.ce`
`build_s1`	CFD profile + reference profile	points (S1 vs height)	`cfdmod.recipes.s1`
`build_pedestrian_comfort`	velocity field + probes	points (probe stats)	`cfdmod.recipes.pedestrian_comfort`
`build_dynamic_response`	force field + mode shapes + solver	points (response)	`cfdmod.recipes.dynamic`

Op reference¶

Every recipe is compose(...) of these ops; you can build your own.

Family	Ops
Time	`window_selection`, `translate`, `rescale`
Field	`add`, `sub`, `mul`, `div`, `scale`, `moving_average`
Geometric	`attach_grouping`
Source-create	`compute_statistics`, `field_series_for_groups`, `filter_by_grouping`, `probe_extraction`, `profile_interpolation`, `modal_projection`, `modal_recomposition`

All ops are pure functions: op(ds, params) -> DataSource.

Where the old entry points went¶

The v2 pressure entry points (run_cp, apply_filters, and the cfdmod.pressure package internals) have been removed. Migrate to the recipes above, or express the workflow as a YAML template and run it with cfdmod run <template.yaml>. The filtering step that apply_filters / MovingAverageFilter provided is now the moving_average field op.