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hdf5 support

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This commit is contained in:
matei jordache
2026-03-30 20:33:28 -07:00
parent 53e43e8761
commit 7b309a8b23
15 changed files with 1079 additions and 206 deletions
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161
backend/nodes/image.py
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@@ -1,14 +1,12 @@
from __future__ import annotations
from functools import lru_cache
import numpy as np
from pathlib import Path
import nanonispy as nap
import gwyfile
from backend.node_registry import register_node
from backend.execution_context import emit_warning
from backend.data_types import COLORMAPS, DataField, resolve_colormap_input
from backend.nodes.helpers import _resolve_path, _SPM_EXTENSIONS, _import_ibw_loader
from backend.nodes.helpers import _resolve_path
from backend.importers import get_importer, calibrated_extensions
@register_node(display_name="Image")
@@ -34,7 +32,7 @@ class Image:
DESCRIPTION = (
"Load any supported file. "
"SPM formats (.gwy, .sxm, .ibw) provide calibrated dimensions; "
"SPM formats (.gwy, .sxm, .ibw) and HDF5 (.h5, .hdf5) provide calibrated dimensions; "
"each channel gets its own output. "
"Images (.png, .tiff, .jpg) and arrays (.npy, .npz) are loaded as uncalibrated fields."
)
@@ -65,158 +63,17 @@ class Image:
for field in fields:
field.colormap = resolved_colormap
if ext not in _SPM_EXTENSIONS:
self._send_warning("Uncalibrated data — no physical dimensions.")
if ext not in calibrated_extensions():
emit_warning("Uncalibrated data — no physical dimensions.")
return (str(path_obj.resolve()),) + fields
def _send_warning(self, message: str):
emit_warning(message)
@staticmethod
@lru_cache(maxsize=32)
def _load_fields_cached(path_str: str, mtime_ns: int, size_bytes: int) -> tuple[DataField, ...]:
path = Path(path_str)
ext = path.suffix.lower()
if ext in _SPM_EXTENSIONS:
return tuple(Image._load_spm_all(path, ext))
return (Image._load_image_or_array(path, ext),)
@staticmethod
def _load_spm_all(path: Path, ext: str) -> list[DataField]:
if ext == ".gwy":
return Image._load_gwy_all(path)
elif ext == ".sxm":
return Image._load_sxm_all(path)
elif ext == ".ibw":
return Image._load_ibw_all(path)
else:
raise ValueError(f"Unsupported SPM format: {ext}")
@staticmethod
def _load_gwy_all(path: Path) -> list[DataField]:
obj = gwyfile.load(str(path))
channels = gwyfile.util.get_datafields(obj)
if not channels:
raise ValueError(f"No data channels found in {path.name}")
fields = []
for ch in channels.values():
data = np.array(ch.data, dtype=np.float64).reshape(ch.yres, ch.xres)
fields.append(DataField(
data=data,
xreal=float(ch.xreal),
yreal=float(ch.yreal),
xoff=float(getattr(ch, "xoff", 0.0)),
yoff=float(getattr(ch, "yoff", 0.0)),
si_unit_xy="m",
si_unit_z="m",
))
return fields
@staticmethod
def _load_sxm_all(path: Path) -> list[DataField]:
sxm = nap.read.Scan(str(path))
signals = sxm.signals
if not signals:
raise ValueError(f"No signals found in {path.name}")
header = sxm.header
scan_range = header.get("scan_range", [1e-6, 1e-6])
fields = []
for sig in signals.values():
data = sig.get("forward", list(sig.values())[0])
data = np.asarray(data, dtype=np.float64)
if data.ndim != 2:
data = data.reshape(data.shape[-2], data.shape[-1])
fields.append(DataField(
data=data,
xreal=float(scan_range[0]),
yreal=float(scan_range[1]),
si_unit_xy="m",
si_unit_z="m",
))
return fields
@staticmethod
def _load_ibw_all(path: Path) -> list[DataField]:
load_ibw = _import_ibw_loader()
wave = load_ibw(str(path))
wdata = wave["wave"]
header = wdata["wave_header"]
raw = wdata["wData"]
n_channels = raw.shape[2] if raw.ndim >= 3 else 1
sfA = header.get("sfA", None)
def _decode_unit(raw_unit):
if raw_unit is None:
return "m"
if isinstance(raw_unit, bytes):
return raw_unit.split(b"\x00", 1)[0].decode("ascii", errors="replace").strip() or "m"
if isinstance(raw_unit, np.ndarray):
return bytes(raw_unit).split(b"\x00", 1)[0].decode("ascii", errors="replace").strip() or "m"
return str(raw_unit).strip() or "m"
dim_units_raw = header.get("dimUnits", None)
data_units_raw = header.get("dataUnits", None)
if isinstance(dim_units_raw, np.ndarray) and dim_units_raw.ndim == 2:
si_unit_xy = _decode_unit(dim_units_raw[0])
elif isinstance(dim_units_raw, (list, np.ndarray)) and len(dim_units_raw) > 0:
si_unit_xy = _decode_unit(dim_units_raw[0])
else:
si_unit_xy = _decode_unit(dim_units_raw)
si_unit_z = _decode_unit(data_units_raw)
fields = []
for ch_idx in range(n_channels):
if raw.ndim >= 3:
ch_data = raw[:, :, ch_idx]
elif raw.ndim == 1:
ch_data = raw.reshape(-1, 1)
else:
ch_data = raw
data = np.flipud(ch_data.T).astype(np.float64)
yres, xres = data.shape
if sfA is not None and len(sfA) >= 2:
xreal = abs(float(sfA[0]) * xres) or 1e-6
yreal = abs(float(sfA[1]) * yres) or 1e-6
else:
hsA = header.get("hsA", 0.0)
xreal = abs(float(hsA) * xres) or 1e-6
yreal = xreal * (yres / xres) if xres else 1e-6
fields.append(DataField(
data=data, xreal=xreal, yreal=yreal,
si_unit_xy=si_unit_xy, si_unit_z=si_unit_z,
))
return fields
@staticmethod
def _load_image_or_array(path: Path, ext: str) -> DataField:
if ext == ".npy":
arr = np.load(str(path)).astype(np.float64)
elif ext == ".npz":
npz = np.load(str(path))
key = list(npz.files)[0]
arr = npz[key].astype(np.float64)
else:
from PIL import Image as PILImage
img = PILImage.open(str(path))
arr = np.array(img)
if arr.dtype != np.uint8:
arr = arr.astype(np.float64)
if arr.ndim == 3:
gray = np.mean(arr.astype(np.float64), axis=2)
else:
gray = arr.astype(np.float64)
return DataField(data=gray)
importer = get_importer(ext)
if importer is None:
raise ValueError(f"Unsupported file format: {ext}")
return tuple(importer.load(path))