amphi-ncdata

amphi-ncdata is a Python client library for loading and sampling Amphi NetCDF datasets.

It exposes these main entry points:

• AmphiNcData: the easiest high-level API for clients using Amphi-style dataset folders.
• MultiAmphiNcData: an AmphiNcData-style env spanning several bounding boxes, routing each query to the owning box in O(1).
• NcData: a lower-level API for loading arbitrary NetCDF files or basepaths.

This README is for clients using an already built wheel. Repository maintainers should use README_DEV.md.

All timestamps in the public API are Unix timestamps in seconds.

Install From The Wheel

Ask for the wheel that matches:

• your operating system
• your Python version
• your machine architecture

Install it with:

python3 -m pip install /path/to/amphi_ncdata-0.1.0-<platform>.whl

Quick import check:

python3 -c "import amphi_ncdata; print(amphi_ncdata.__version__)"

If installation succeeds but import fails with a missing shared-library error, ask us for a wheel built for your environment or install the required NetCDF runtime libraries for your platform.

Quick Start

The fastest way to use the library is with AmphiNcData.

Example script:

from amphi_ncdata import AmphiNcData

env = AmphiNcData(
    {
        "currentsBasepaths": ["/data/data_api/FORECAST-CURRENT/"],
        "windBasepaths": ["/data/data_api/FORECAST/wind/"],
        "waveBasepaths": ["/data/data_api/FORECAST/wave/"],
        "climWindBasepaths": ["/data/data_api/HINDCAST-3Y/"],
        "climWaveBasepaths": ["/data/data_api/HINDCAST-3Y-WAVE/"],
    },
    start=1781478000,
    end=1781650800,
    south=35.0,
    west=-73.0,
    north=43.0,
    east=-30.0,
)

lat, lon = 40.0, -50.0
timestamp = 1781524800

currents = env.currents()
wind = env.wind()
waves = env.waves()

currents.set_interpolation_method("linear")
wind.set_interpolation_method("linear")
waves.set_interpolation_method("linear")

u_current, v_current = currents.at(lat, lon, timestamp)
u_wind, v_wind = wind.at(lat, lon, timestamp)
wave_height, wave_period, wave_direction = waves.at(lat, lon, timestamp)

print("Currents:", u_current, v_current)
print("Wind:", u_wind, v_wind)
print("Waves:", wave_height, wave_period, wave_direction)

A ready-to-copy version also lives in examples/client_quickstart.py.

Main Concepts

• AmphiNcData is the convenience layer for Amphi folder layouts, masks, and tides.
• NcData is the generic layer when you want to choose the files or variables yourself.
• load() registers which variables you want available.
• set_interpolation_method(method, skip_nan=...) sets the dataset interpolation mode once. skip_nan=True lets "linear" interpolate from the corners that hold data instead of returning NaN whenever one of the four is a coastal no-data cell.
• set_no_data_value(value, when=...) gives the product's no-data mask a value instead of NaN, per variable, either when="before_interpolation" or when="after_interpolation".
• MultiAmphiNcData(..., inclusive=..., snap_to_grid=..., cell_size=...) tune how boxes claim the routing grid; all three are optional and every default matches earlier behaviour.
• set_out_of_window_behaviour(behaviour, value, dim=...) sets what a query outside the loaded window returns, per dimension (dim="time" / "space" / "both"): "nearest", "nan", "value", or "fallback" (another NcData). Defaults: time→nearest, space→nan.
• sample() returns one variable.
• at() returns all loaded variables for the dataset.
• at() / sample() are fast for sequential, point-by-point access (e.g. routing); sample_batch() / at_batch() are best when you already have many independent points (roughly a few dozen or more) to sample at once. Pass out= (a buffer from empty_batch()) to sample in place with no per-call allocation.
• env.wind() / env.waves() auto-fall-back to climatology past the forecast horizon (when climatology basepaths are set); env.climWind() / env.climWave() are the underlying climatology datasets.

Using AmphiNcData

AmphiNcData is the recommended API for most client code.

Using an input.json-style config

from amphi_ncdata import AmphiNcData
import json

with open("input.json", "r", encoding="utf-8") as f:
    cfg = json.load(f)

env = AmphiNcData(
    cfg,
    start=1781478000,
    end=1781650800,
    south=35.0,
    west=-73.0,
    north=43.0,
    east=-30.0,
)

Using a flat config

env = AmphiNcData(
    {
        "currentsBasepaths": ["/data/data_api/FORECAST-CURRENT/"],
        "tidesBasepaths": ["/data/data_api/FORECAST-TIDE/"],
        "windBasepaths": ["/data/data_api/FORECAST/wind/"],
        "waveBasepaths": ["/data/data_api/FORECAST/wave/"],
        "climWindBasepaths": ["/data/data_api/HINDCAST-3Y/"],
        "climWaveBasepaths": ["/data/data_api/HINDCAST-3Y-WAVE/"],
        "bathymetryPath": "/data/data_save/ETOPO/bathy.nc",
        "tidesMaskPath": "/data/data_api/FORECAST-TIDE/masks_tide.nc",
        "nogoPath": "/data/data_user/nogo.nc",
        "coastalZonePath": "/data/data_save/coastal_zone_mask.nc",
    },
    start=1781478000,
    end=1781650800,
    south=35.0,
    west=-73.0,
    north=43.0,
    east=-30.0,
)

High-level helpers

currents = env.currents()
wind = env.wind()
waves = env.waves()
tidal = env.tidal_currents()
bathy = env.bathymetry()
nogo = env.nogo()
coastal = env.coastal_zone()
tides_mask = env.tides_mask()

Return shapes:

• env.currents().at(...) -> (ucos, vcos)
• env.tidal_currents().at(...) -> (utotal, vtotal)
• env.wind().at(...) -> (u10, v10)
• env.waves().at(...) -> (swh, mwp, mwd)
• env.bathymetry().at(...) -> bathymetry
• env.nogo().at(...) -> mask
• env.coastal_zone().at(...) -> mask
• env.tides_mask().at(...) -> mask_TIDE

Tidal currents: the fused tidal_currents()

Tides only matter near coasts (the global tide mask is ~9% active), so env.tidal_currents() does not load the whole bounding box. With a tidesMaskPath configured it loads eager 1-degree masked tiles: every 1-degree tile that the mask marks active is pulled into RAM up front, in the raw NetCDF dtype, so the routing hot path never touches NetCDF I/O. Open-ocean and land tiles allocate nothing.

tidal_currents() is fused: at() returns the hourly tidal current where the mask is active (coastal) and the open-ocean current where it is not — so a single call gives the right water velocity everywhere, no manual branch:

from datetime import datetime, timezone
from amphi_ncdata import AmphiNcData

start = int(datetime(2026, 6, 23, tzinfo=timezone.utc).timestamp())
env = AmphiNcData(
    {
        "tidesBasepaths":    ["/data/data_api/FORECAST-TIDE/"],
        # The mask is what turns tides into eager 1-degree masked tiles:
        "tidesMaskPath":     "/data/data_api/FORECAST-TIDE/masks_tide.nc",
        # "tidesTileSizeDegrees": 1.0,   # optional; 1.0 is the default
    },
    start=start, end=start + 24 * 3600,
    south=48.0, west=-6.0, north=52.5, east=4.5,    # English Channel
)

tides = env.tidal_currents()        # eager 1° masked tiles + a daily open-ocean step
u, v = tides.at(lat, lon, timestamp)   # tide near the coast, current offshore — one call

How the open ocean is served. Where the tide product is not tidal (mask 0), its values are constant over the day and equal the surface currents (verified: p99 = 0, mean diff 3.7e-5 m/s over thousands of open-ocean points). So tidal_currents() loads one daily tide step over the full bbox as the open-ocean field — self-contained (no separate currents product needed), same variable names, and roughly half the RAM of loading the multi-step currents product. Coastal cells are overridden by the hourly tide tiles.

You normally never call the gate yourself, but it is exposed for inspection and for fused=False:

tides.mask(lat, lon)                 # compiled gate (~0.2 us): is this point tidal?
tides.mask_batch(lats, lons)         # vectorised

mask() is the nearest high-res mask_TIDE cell (active = non-zero); it is spatial, so timestamp is accepted for symmetry but ignored. A masked-in cell can still be land / fill (NaN) — mask() selects the data source, not its presence.

Knobs: tidesTileSizeDegrees (default 1.0) sets the tile size; TidalCurrents(..., eager=False) keeps tiles lazy with an LRU cache (smaller resident footprint, per-call I/O); TidalCurrents(..., fused=False) makes at() tide-only again (NaN in the open ocean) if you want to gate manually.

What if you omit the tide paths?

configuration	env.tidal_currents() behaviour
tidesBasepaths and tidesMaskPath set	Eager 1° masked tiles + fused open ocean (the recommended setup). at() is tide-where-coastal, current-where-not; mask() reflects the high-res tide mask.
tidesBasepaths set, tidesMaskPath omitted	Falls back to the full-bbox tidal view (lazy, no tiling, no fusion). at() samples tides everywhere in the box, and mask() returns True for any in-box point (no mask to gate on). Memory is the full bbox × all tide steps — fine for a small box, large for a wide one.
tidesBasepaths omitted	env.tidal_currents() raises AmphiDiscoveryError ("No valid NetCDF files were found …"). Only call it when tides are configured; currents, wind, and waves are independent and still work — env.currents() stays the currents-only, memory-light path.

Out-of-window behaviour (per dimension, with climatology fallback)

A query can fall outside the loaded window in time (past/before the forecast) or in space (outside the bounding box). Each is handled independently:

wind.set_out_of_window_behaviour(behaviour, value=0.0, dim="both")

• dim: "time", "space", or "both" (default).
• behaviour:
  • "nearest" — clamp to the nearest timestep / edge cell.
  • "nan" — return NaN.
  • "value" — return the constant value (a float).
  • "fallback" (alias "data") — sample value instead, which must be another NcData that covers the query (e.g. a climatology). The fallback dataset carries its own out-of-window behaviour.
• Defaults: time → nearest, space → nan.

Precedence when out of window: a fallback dimension delegates the whole sample to its dataset (time is checked before space); otherwise a nan/value on space applies before time; nearest simply clamps that dimension. This is independent of NaN coming from the data itself (land / fill), which is always NaN. (set_out_of_window_behaviour("nan") / ("value", -999) still set both dimensions, for backward compatibility.)

Climatology fallback (automatic for wind / waves)

env.wind() and env.waves() automatically fall back to climatology past the forecast horizon when climatology basepaths are configured — equivalent to:

wind.set_out_of_window_behaviour("fallback", env.climWind(), dim="time")

So a long voyage just keeps sampling, with no manual gap handling:

wind = env.wind()                      # forecast + climatology time-fallback
u, v = wind.at(40.0, -50.0, ts)        # forecast in-window, climatology past the horizon

The climatology (env.climWind() -> (u10, v10), env.climWave() -> (swh, mwp, mwd)) is still its own dataset and is configured as the safety net: nearest in both time and space and linear interpolation, so it always returns a value. Pass no climatology basepaths, or call wind.set_out_of_window_behaviour("nan", dim="time"), to opt out (e.g. to detect the gap yourself).

This holds for a window lying wholly past the horizon too, not just one that straddles it. With no forecast file for the window at all the dataset is empty rather than absent, and an empty dataset is out of window everywhere — so every query goes to climatology:

# Window 15 days out; wind forecast only reaches ~today+14, so nothing covers it.
env = AmphiNcData(config, start=far_future, end=far_future + 86400, **bbox)
wind = env.wind()
wind.is_empty            # True  -- no file backs it
wind.warnings            # WARN_NO_FILES
wind.at(lat, lon, ts)    # the climatology value, not an exception
wind.time_range          # None -- it holds no timestep; guard before indexing

Missing data is reported, not raised: warnings

A forecast archive routinely has gaps, so ordinary discovery problems do not raise. They set a bitmask on the dataset and the accessor still returns something usable:

code	meaning
WARN_NONE (0)	nothing was wrong
WARN_NO_FILES (1)	no file at all for the window; the dataset is empty, so queries return the out-of-window value or a wired fallback
WARN_PARTIAL_COVERAGE (2)	some of the requested days resolved, some did not — including a day whose file has no time axis and was skipped
WARN_LAST_AVAILABLE (4)	nothing covered the window, so the newest file on disk was served — the values are stale
WARN_CLIMATOLOGY_MISSING (8)	a climatology basepath was configured but its MM.nc is absent, so there is no safety net
WARN_HETEROGENEOUS_FILES (16)	the window's files change format partway (grid, resolution or packing); each format is sampled on its own grid, so values are right, but the resolution changes at the switch — see below

It is a bitmask, not a sequential code, because these co-occur — a window can be both partially covered and served from a stale run:

from amphi_ncdata import WARN_LAST_AVAILABLE, WARN_NO_FILES, warning_names

currents = env.currents()
if currents.warnings & WARN_LAST_AVAILABLE:
    print("stale:", currents.warning_messages)

env.warnings                # OR across every dataset built so far; 0 == clean
warning_names(env.warnings) # ('no_files', 'last_available')
env.warning_messages        # ('wind: no NetCDF files were found for 2026-08-22..2026-08-23', ...)

MultiAmphiNcData aggregates the same way, per box: routed.warnings ORs its members and routed.warning_messages prefixes each line with box <i>.

Turning them back into exceptions: on_warning

AmphiNcData(config, ..., on_warning="raise")                      # every code raises
AmphiNcData(config, ..., on_warning={"no_files": "raise"})         # just this one
AmphiNcData(config, ..., on_warning={"default": "raise",
                                     "partial_coverage": "silent"})

Genuine caller errors are never downgraded and always raise, whatever on_warning says: an unknown variable name, a bounding box that does not overlap the grid, an unconfigured mask or climatology path. The distinction is whether the data is missing (a fact about the archive) or the request is wrong.

Windows that span a change of file format

An archive folder can switch product from one day to the next: a wave hindcast built from WAVERYS (0.2°, packed int16, latitude ascending, longitude −180..180, 3-hourly) up to one day and from ERA5 (0.5°, float32, latitude descending, longitude 0..360, hourly) after it, or a forecast whose resolution is upgraded. A window that straddles such a switch is split into one part per format, and each part is sampled on its own native grid, exactly as that product would be on its own. Nothing changes in how you query it:

from amphi_ncdata import WARN_HETEROGENEOUS_FILES

waves = env.waves()                       # window 2026-04-30 .. 2026-05-01
waves.warnings & WARN_HETEROGENEOUS_FILES  # set: the files change format
waves.warning_messages
# ('the files change format at .../2026/05/20260501.nc (grid 899x1800 (0.2 deg, lat -89.8..89.8,
#   lon -180..179.8) -> 361x720 (0.5 deg, lat 90..-90, lon 0..359.5); mwd int16 x0.01 +180
#   fill -32767 -> float32 fill nan); each format is sampled on its own grid',)

for part in waves.segments:               # one per format, in time order
    print(part.time_range, [round(step, 3) for step in part.spatial_resolution])
# (1777507200, 1777582800) [0.2, 0.2]
# (1777593600, 1777593600) [0.5, 0.5]

wave_height, wave_period, wave_direction = waves.at(lat, lon, timestamp)

• Inside a part, every value is exactly what that part's files return on their own.
• Across the switch — between one part's last timestep and the next part's first, 21:00 on the 0.2° grid and 00:00 on the 0.5° one above — each side is interpolated on its own grid and the two are blended in time, with the same rules as any two timesteps (skip_nan, set_no_data_value, out-of-window behaviour, climatology fallback).
• No resampling and no decoding. Each part keeps its native grid and dtype, so memory is the sum of the parts, and the compiled sampler serves every query — MultiAmphiNcData keeps its compiled dispatch over such a box too.
• latitudes, longitudes and spatial_resolution describe the finest part; read segments for each part's own. A single-format dataset is its own single segment.
• A point at the very edge of the box can fall inside one part's grid and outside the other's. Across the switch, where both are sampled, it counts as outside the box and follows the space out-of-window behaviour.
• A variable that one of the formats lacks raises AmphiVariableError on load(), naming the file, as it does for any file missing it.

A file with no time axis at all cannot be placed in the window. It is skipped and reported with WARN_PARTIAL_COVERAGE, instead of failing the whole window.

load_defaults()

env.load_defaults(
    include_tides=True,
    include_bathymetry=True,
    include_nogo=True,
    include_coastal_zone=True,
)

This preloads:

• currents
• wind
• waves
• optionally tidal currents and tides mask (tides eager-load their active 1° tiles up front — fast for a route corridor, heavier for a continental box; see the gate section)
• optionally bathymetry
• optionally no-go mask
• optionally coastal zone mask

Overriding default variable names

If your files use different names:

custom_currents = env.currents(u_var="u_current", v_var="v_current")
custom_wind = env.wind(u_var="u_wind", v_var="v_wind")
custom_waves = env.waves(height_var="hs", period_var="tp", direction_var="dir")

Multiple bounding boxes: MultiAmphiNcData

Use MultiAmphiNcData when one env should cover several bounding boxes / time windows at once — e.g. tiling a large region, or layering a smaller box on top of a bigger one. All boxes share the same config; each carries its own start, end, south, west, north, east. Every accessor mirrors AmphiNcData (currents(), wind(), waves(), tidal_currents(), bathymetry(), …) but returns a routed dataset whose at/at_batch/sample/sample_batch dispatch each point to the box that owns it.

from amphi_ncdata import MultiAmphiNcData   # or AmphiNcData.from_bounding_boxes(...)

env = MultiAmphiNcData(
    {"currentsBasepaths": ["/data/data_api/FORECAST-CURRENT/"]},   # shared by all boxes
    boxes=[
        dict(start=1781478000, end=1781650800, south=35.0, west=-73.0, north=43.0, east=-30.0),
        dict(start=1781478000, end=1781650800, south=43.0, west=-30.0, north=60.0, east=10.0),
    ],
    # out_of_box_value=float("nan"),   # what a point in no box returns (default NaN)
)

currents = env.currents()
currents.set_interpolation_method("linear")

u, v = currents.at(40.0, -50.0, 1781524800)         # routed to the owning box in C, O(1)
uv   = currents.at_batch(lats, lons, timestamps)    # routed + sampled in C, one call

buf = currents.empty_batch(len(lats))               # reusable (n, 2) float32 buffer
currents.at_batch(lats, lons, timestamps, out=buf)  # fill in place, returns buf

How dispatch works:

• A dense (lat, lon, hour) lookup grid (1° × 1° × 1 h) stores, per cell, the index of the winning box. A query computes three integer indices and reads one cell — the heavy bilinear + time interpolation still runs in each box's compiled sampler.
• Both .at() and .at_batch() run compiled, matching a plain dataset: at/sample route each point in C, and at_batch/sample_batch route and sample every point in C (no Python bucketing). They accept the same out= buffer and empty_batch() helper (see Reusing an output buffer with out= under Using NcData below), so batching is fast from small sizes up.
• Overlap → first listed box wins. When boxes overlap in both space and time, the earliest box in boxes that covers the point is chosen (so you can layer a high-res box on top of a coarse one by listing it first).
• A point in no box returns out_of_box_value (default NaN).
• Cells cut by a box edge are resolved per query against the real box bounds, so routing is exact wherever the edges fall — including a point in a shared edge cell, which goes to the box that actually holds it rather than to whichever claimed the cell. inclusive and snap_to_grid below remove that per-query step for layouts that do not need it.
• If every box shares the same [start, end], the time axis collapses and the grid stays tiny (~130 KB); otherwise it grows with the total time span (~22 MB for a 7-day window).

inclusive: say which edges each box owns

Box bounds are closed by default, so [0°, 10°] and [10°, 20°] both contain latitude 10.0 — they overlap on their shared edge, and the cell holding it has to be resolved per query.

inclusive lets you declare, per box, which of the six edges are inclusive:

MultiAmphiNcData(config, boxes, inclusive="half_open")   # default for every box

"half_open" keeps south/west inclusive and makes north/east exclusive, so abutting boxes partition space instead of overlapping. Nothing is left to resolve, and the box edges stay exactly where you put them.

What changes, for boxes [0°, 10°] and [10°, 20°] listed in that order:

query latitude	closed (default)	half_open
9.999	box 0	box 0
10.0	box 0 — both hold it, first listed wins	box 1 — the box that starts there
10.001	box 1	box 1
19.999	box 1	box 1
20.0	box 1 — it owns its north edge	out of box — nothing above claims it

Only the shared edge moves; everything else routes identically.

Accepted forms — a string of the edges that are inclusive, a {edge: bool} dict patching the default, a set of names, or an alias:

inclusive="south west start"     # the named edges are inclusive; the rest exclusive
inclusive={"north": False}       # patch one edge, keep the rest
inclusive={"south", "west"}      # set form
inclusive="closed"               # all six (the default — unchanged behaviour)
inclusive="half_open"            # south/west inclusive, north/east exclusive
inclusive="half_open_time"       # ...and `end` exclusive too

Any box may override the env-wide default with its own inclusive key. That matters at the outer rim: with north exclusive nothing claims the topmost box's north edge, so a query at exactly that latitude is out of box. Close the last box in each direction to get it back:

boxes = [dict(..., south=0, north=10), dict(..., south=10, north=20, inclusive="closed")]

Time edges

start and end work the same way, and matter when your windows abut rather than sharing one instant. Three consecutive one-hour boxes:

boxes = [dict(start=i * 3600, end=(i + 1) * 3600, south=0, west=0, north=10, east=10)
         for i in range(3)]

timestamp	closed (default)	end exclusive
0	box 0	box 0
3599	box 0	box 0
3600	box 0 — both windows hold it, first listed wins	box 1 — the window that starts there
3601	box 1	box 1
7200	box 1	box 2
10800	box 2	box 2
12000 (past every window)	box 2	box 2

Two things to note. Unlike latitude, a timestamp past the last window is not out of box: nothing holds that instant, so routing falls back to a box that holds the position and lets that box's own set_out_of_window_behaviour() decide — which is what keeps a climatology fallback reachable past the forecast horizon. And an exclusive end is exact to the second: [start, end) is [start, end-1], because timestamps are whole seconds.

Ask for it with "half_open_time" (spatial half-open plus an exclusive end), or by naming the edges:

MultiAmphiNcData(config, boxes, inclusive="half_open_time")
MultiAmphiNcData(config, boxes, inclusive={"end": False})   # only the time edge

"half_open" on its own is deliberately spatial only — most box sets pin one instant (start == end), and an exclusive end would make those windows empty, which raises rather than routing to nothing.

snap_to_grid: a cheaper dispatch, for a coarser edge

Routing buckets space into 1° cells, and box edges rarely land on cell edges. By default a cell cut by an edge is resolved per query against the real box bounds — exact, and a point outside every box reliably returns out_of_box_value.

For an abutting tiling, reach for inclusive="half_open" above first: it also leaves nothing to resolve, but keeps exact edges and loads no extra data. snap_to_grid earns its keep when box edges are ragged — not aligned to each other or to the cell grid — where no inclusivity rule can make them partition.

snap_to_grid=True widens every box outward until it swallows whole cells, then gives each cell to the first box listed that owns it. No cell is ever contested, so routing becomes a single array read with nothing to resolve:

multi = MultiAmphiNcData(config, boxes, snap_to_grid=True)
multi.requested_boxes[0]   # what you asked for:  north=10.0
multi.boxes[0]             # what you got:        north=11.0

The widening reaches the data, not just the routing — each box loads the wider area, otherwise it would own cells it cannot serve.

What it costs: each box loads up to one cell of extra data on every widened side, and returns data for up to a cell beyond what you asked for instead of out_of_box_value. The finer the tiling, the larger that overhead is relative to the boxes themselves.

Use it when box edges are ragged and you never treat out-of-box as a signal. Leave it off when out_of_box_value means something to you, or when RAM is the reason you are using multiple boxes at all.

cell_size: the resolution of the dispatch grid

Routing buckets space and time into cells; cell_size sets how big one is, as (lat_deg, lon_deg, seconds). The default is (1.0, 1.0, 3600).

MultiAmphiNcData(config, boxes, cell_size=(1.0, 1.0, 86400))

This is about the geometry of your box layout, not the resolution of your data. In particular the time cell has nothing to do with the product's timestep — tides at 1 h, wind and wave at 3 h then 6 h, currents at 24 h are all handled inside each box's sampler. The grid's time axis exists only to separate boxes that have different windows; if every box shares one [start, end] it collapses away entirely and the seconds you pass are irrelevant.

Two things follow.

Space: pick a cell that divides your tile pitch. A cell straddling a box edge has to be resolved per query, and the band of ground where that happens is one cell wide. Edges at 2.5° on a 1° grid put a full row in that band; a 0.5° grid makes every edge a cell edge. Against that, the grid is (180 / lat_deg) × (360 / lon_deg) × T cells of 2 bytes, so halving the cell quadruples it. Both sizes must divide 180° and 360° evenly, or the constructor raises.

Time: match your window granularity, not your files. One box per day wants seconds=86400 — at 3600 the grid carries 24× the layers to describe the same routing. Windows that abut want the opposite: a cell holding a window boundary is claimed by both neighbours, so coarse cells there create contention rather than removing it. Finer is safer when windows are adjacent, coarser when they are a day apart.

Routing itself is invariant: every cell size returns the same box for the same point. Only memory, build time, and how much resolving happens at the seams change. If you would rather not think about it, inclusive="half_open" removes the seam question at any resolution.

multi.cell_size          # (1.0, 1.0, 3600) -- what is actually in use

env.tidal_currents(), env.wind(), etc. work the same way and keep their per-box behaviour (tide tiling, climatology fallback). MultiAmphiNcData is a context manager and forwards close() to every box's env.

Using NcData

Use NcData when you want more manual control.

Load from basepaths

Use this when the library should discover the right NetCDF files for you.

from amphi_ncdata import NcData

currents = NcData.from_basepaths(
    basepaths=[
        "/data/data_api/FORECAST-CURRENT/",
        "/mnt/store/data_api/HIRES_v3/FORECAST/",
    ],
    start=1781478000,
    end=1781650800,
    south=35.0,
    west=-73.0,
    north=43.0,
    east=-30.0,
    allow_last_available=True,
)

currents.load("ucos")
currents.load("vcos")
currents.set_interpolation_method("linear")

print(currents.at(40.0, -50.0, 1781524800))

Load from explicit filepaths

Use this when you already know the exact files you want.

from amphi_ncdata import NcData

waves = NcData.from_filepaths(
    filepaths=[
        "/tmp/20260614.nc",
        "/tmp/20260615.nc",
    ],
    start=1781391600,
    end=1781478000,
    south=35.0,
    west=-73.0,
    north=43.0,
    east=-30.0,
)

waves.load("swh")
waves.load("mwp")
waves.load("mwd")
waves.set_interpolation_method("linear")

print(waves.at(40.0, -50.0, 1781434800))

Sampling

currents.set_interpolation_method("linear")
value = currents.sample("ucos", 40.0, -50.0, 1781524800)

Batch sampling

For large point sets, batch APIs avoid Python per-sample overhead:

lats = [39.5, 40.0, 40.5]
lons = [-51.0, -50.0, -49.0]
timestamps = [1781521200, 1781524800, 1781528400]

u_values = currents.sample_batch("ucos", lats, lons, timestamps)
uv_values = currents.at_batch(lats, lons, timestamps)

Notes:

• sample_batch() returns one NumPy array
• at_batch() returns one NumPy array shaped like (sample_count, variable_count) when multiple variables are loaded
• use batch APIs when you already hold many independent points; for sequential, point-by-point access (e.g. routing), at() / sample() are the right call

Reusing an output buffer with out=

at_batch() and sample_batch() accept an optional out= buffer: results are written into it in place and the same array is returned — no per-call allocation. Build the correctly-shaped, correctly-typed buffer once with empty_batch() and reuse it across calls (e.g. re-sampling the same points on every step of a loop):

import numpy as np

lats = [39.5, 40.0, 40.5]
lons = [-51.0, -50.0, -49.0]
timestamps = [1781521200, 1781524800, 1781528400]

buf = currents.empty_batch(len(lats))                 # (n, 2) float32 for a 2-variable dataset
currents.at_batch(lats, lons, timestamps, out=buf)    # returns buf, filled in place

sbuf = np.empty(len(lats), dtype=np.float32)          # (n,) float32 for a single variable
currents.sample_batch("ucos", lats, lons, timestamps, out=sbuf)

out must be a C-contiguous float32 array of shape (n, variable_count) for at_batch() — or (n,) when the dataset has a single loaded variable, and always (n,) for sample_batch(). empty_batch(n) returns exactly the shape at_batch() needs. Passing out= implies a NumPy-array return (the as_numpy flag is ignored).

When to batch: the batch APIs run the same compiled sampler as at() / sample(), but in one call. They carry a small fixed per-call cost, so for just a few points a plain at() loop is still faster; batching pays off from roughly a few dozen points upward and scales to millions.

Supported interpolation values for set_interpolation_method():

• "nearest"
• "linear"

No-data near the coast: skip_nan and set_no_data_value()

Coastal and land cells carry the product's _FillValue, which decodes to NaN. Bilinear interpolation reads four corners, so one no-data corner is enough to make the whole sample NaN — which is why a point near the coast can return a value under "nearest" and nothing under "linear".

There are two independent controls, and they compose.

1. Interpolate from the corners that do hold data:

currents.set_interpolation_method("linear", skip_nan=True)

The no-data corners drop out and the survivors are reweighted: two valid corners and two no-data corners interpolate from the two valid ones. All four no-data still gives NaN — there is nothing to interpolate from. The same rule extends to the time blend, so a timestep that is wholly no-data no longer poisons the one next to it. Water away from the coast is unaffected: a stencil with no no-data in it interpolates exactly as before.

skip_nan is remembered across calls, so pass it only when changing it. It has no effect under "nearest", which reads a single cell.

2. Give the no-data mask a value:

waves.set_no_data_value(0.0)                                   # every variable
waves.set_no_data_value((0.0, 0.0, float("nan")))              # swh, mwp, mwd
waves.set_no_data_value({"mwd": float("nan")}, when="after_interpolation")

The value is per variable — a scalar applies to all of them, a tuple/list is positional over loaded_variables (waves is height, period, direction; wind is u, v), and a dict sets only the variables it names. Each entry is a float, or NaN to leave that variable as it was.

when decides where the substitution happens, and the two are not equivalent:

when	What happens	Use it when
"before_interpolation"	No-data cells enter the stencil as value, and all four corners interpolate normally.	The mask means a real physical value (nil current inside a harbour), so it should bleed into nearby water the way the data would.
"after_interpolation"	Interpolate as usual, then replace the result if it came out NaN. Applies whatever skip_nan is set to.	You need a number everywhere — it is the backstop for what skip_nan cannot rescue.

after_interpolation never touches valid water, and it does not override set_out_of_window_behaviour(): it replaces no-data, not an out-of-window query, which stays with the out-of-window policy.

Two edges worth knowing. skip_nan reweights by interpolation weight, so a query sitting exactly on a no-data grid node still gives NaN — the valid corners carry zero weight there. And skip_nan never invents data. For a value in those cases, combine the two:

currents.set_interpolation_method("linear", skip_nan=True)
currents.set_no_data_value(0.0, when="after_interpolation")

no_data_values reports what is currently set, as {variable: (value, when)}. Both settings are available on NcData, on every AmphiNcData accessor (including tidal_currents()), and on MultiAmphiNcData routed datasets, where they apply to every box.

Lazy loading and cache

cache = {"size": 128 * 1024 * 1024, "nelems": 20000, "preemption": 0.8}

tidal = NcData.from_basepaths(
    basepaths=["/data/data_api/FORECAST-TIDE/"],
    start=1781478000,
    end=1781650800,
    south=35.0,
    west=-73.0,
    north=43.0,
    east=-30.0,
    allow_last_available=True,
)

tidal.load("utotal", lazy=True, cache=cache)
tidal.load("vtotal", lazy=True, cache=cache)

Loaded variables keep their raw on-disk dtype in RAM (e.g. int16 for currents/tides/waves); scale_factor, add_offset, and _FillValue are applied inside the Cython sampler at lookup time. This halves resident memory for packed products versus decoding to float32 up front, with no loss of precision. (The decode= argument to load() is retained for API compatibility but is now a no-op — storage is always the raw dtype.)

Inspect loaded state

print(currents.resolved_files)
print(currents.loaded_variables)
print(currents.time_range)
print(currents.bounds)
print(currents.contains_timestamp(1781524800))

Context manager

from amphi_ncdata import NcData

with NcData.from_basepaths(
    basepaths=["/data/data_api/FORECAST-CURRENT/"],
    start=1781478000,
    end=1781650800,
    south=35.0,
    west=-73.0,
    north=43.0,
    east=-30.0,
) as data:
    data.load("ucos")
    data.set_interpolation_method("linear")
    print(data.sample("ucos", 40.0, -50.0, 1781524800))

Out-of-window behaviour

A query is "out of window" when its timestamp is outside the loaded time range, or its lat/lon is outside the loaded bounding box. Choose what at() / sample() (and the batch variants) return in that case:

wind.set_out_of_window_behaviour("nearest")   # default: clamp to the nearest
                                               # available timestep / edge cell
wind.set_out_of_window_behaviour("nan")        # return NaN (handy to detect gaps)
wind.set_out_of_window_behaviour("value", 0.0) # return a fixed fill value

This is independent of NaN coming from the data itself (land / fill gaps), which is always returned as NaN regardless of the mode. The setting applies equally to out-of-range time and out-of-box space.

Timestamps And Coordinates

Accepted timestamp format:

• Unix timestamp integer in seconds

Sampling coordinate format:

• lat and lon are passed as separate numeric arguments

Errors

The package raises:

• AmphiDiscoveryError: file discovery, open, or setup problems
• AmphiVariableError: variable loading or sampling problems
• AmphiNativeError: lower-level native errors

Missing data is not an error. A window with no files, partial coverage, or only a stale run on disk sets a code on dataset.warnings and still returns a usable dataset — see warnings. So this does not raise; it returns an empty dataset whose queries yield NaN:

from amphi_ncdata import NcData, WARN_NO_FILES

wind = NcData.from_basepaths(
    ["/tmp/does-not-exist"],
    start=0, end=1, south=0, west=0, north=1, east=1,
)
wind.is_empty                       # True
wind.warnings == WARN_NO_FILES      # True

# Pass on_warning="raise" for the old behaviour:
NcData.from_basepaths(["/tmp/does-not-exist"], start=0, end=1,
                      south=0, west=0, north=1, east=1,
                      on_warning="raise")   # -> AmphiDiscoveryError

What does raise, regardless of on_warning:

from amphi_ncdata import AmphiVariableError

try:
    env.wind(u_var="not_a_variable")     # request is wrong, not the archive
except AmphiVariableError as exc:
    print(exc)

One caveat: variable names are validated against the file, so on an empty dataset there is nothing to validate against — a typo'd name is accepted there and only surfaces once the window has files. Check warnings before concluding that a run of NaN means a bad variable name.

Performance

• at() and sample() are backed by a compiled sampler, so single-point lookups are fast. This is the intended path for routing algorithms (Dijkstra / A* / isochrone / DP) that sample one point at a time, where each step depends on the previous one.
• Eager-loaded variables (the default) stay in memory, so sampling never touches disk. Use lazy=True only for variables too large to keep resident; lazy sampling reads from disk and is much slower per point.
• at_batch() / sample_batch() run the same compiled sampler over many points in one call. They carry a small fixed per-call cost, so use them once you have roughly a few dozen points or more (below that, an at() loop wins). Pass out= (from empty_batch()) to avoid per-call allocation when sampling repeatedly.
• A window that spans a change of file format samples at the same speed as one that does not: each part keeps its own compiled sampler, and choosing the part costs a comparison or two. Only points in the hours across the switch itself read two grids, which costs a batch call there about 15% more.

Practical Notes

• A wheel is specific to an OS, Python version, and architecture.
• If you change Python versions, you may need a new wheel.
• AmphiNcData(...) accepts missing path keys, but a dataset helper such as env.wind() or env.waves() still raises immediately if that dataset cannot be resolved.
• AmphiNcData is the best starting point unless you specifically need file-level control.
• This package focuses on point sampling, not bulk NumPy-style array workflows.