tono/docs/library.md

Using tono as a Python library

tono's processing nodes can be used as a standalone Python library for scripting, batch processing, and integration into other tools — no web server required.

Installation

pip install -e .

This installs the core library with all signal processing dependencies (numpy, scipy, scikit-image, etc.) but not the web server (aiohttp). To install the full app with the server, use pip install -e ".[server]".

Quick start

import tono

# Load an SPM data file
fields = tono.load("scan.gwy")
height = fields[0]

# Every registered node is available as a top-level callable
leveled = tono.PlaneLevelField(height)
filtered = tono.GaussianFilter(leveled, sigma=2.0)

# Access the raw numpy array
print(filtered.data.shape)   # (256, 256)
print(filtered.data.mean())  # height in metres

Discovering node signatures

Every node carries a real inspect.Signature synthesised from its input declarations, so help(), Jupyter's ?, and IPython tab-completion all show the correct parameters, types, defaults, and enum choices:

>>> help(tono.EdgeDetect)
EdgeDetect(
    field: DataField,
    method: Literal['sobel', 'prewitt', 'laplacian', 'log'],
    sigma: float = 1.0,
) -> DataField
    Detect edges using Sobel, Prewitt, Laplacian, or LoG operators.
    ...

dir(tono) lists every registered node — useful for tab-completion and programmatic discovery.

API reference

Loading data

tono.load(path) -> list[DataField]

Load an SPM data file. Returns one DataField per channel.

fields = tono.load("scan.hdf5")  

tono.channel_names(path) -> list[str]

Get channel names without loading the full data.

names = tono.channel_names("scan.gwy")
# ['Height', 'Phase', 'Amplitude']

tono.supported_formats() -> frozenset[str]

List all supported file extensions.

Processing

There are two equivalent ways to invoke a node. Both return a single value when the node has one output, or a tuple when it has multiple.

tono.NodeName(*args, **kwargs) — typed call syntax

Recommended for scripts and notebooks. Each node is exposed as a top-level callable with a real inspect.Signature, so your editor, help(), and Jupyter all know the parameters and defaults:

# Positional arguments map to required inputs in declaration order
result = tono.GaussianFilter(my_field, sigma=3.0)

# Fully keyword — order-independent
result = tono.GaussianFilter(field=my_field, sigma=3.0)

# Defaults declared in the node's INPUT_TYPES metadata are auto-filled
result = tono.GaussianFilter(my_field)  # uses sigma=1.0 from metadata

# Multi-output nodes return a tuple
log_mag, mag, phase, psdf = tono.FFT2D(my_field, windowing="hann", level="mean")

tono.apply(node_name, *args, **kwargs) — string-based dispatch

Use this when the node name is only known at runtime (e.g. a user-selected pipeline). Same arg conventions, same default-filling behaviour.

name = choose_node_from_config()
result = tono.apply(name, my_field, sigma=3.0)

tono.describe(name) -> dict

Return a dict describing a node's inputs, outputs, description, keywords, and category. Thin wrapper around the registry metadata used by the web UI.

info = tono.describe("EdgeDetect")
print(info["input"]["required"].keys())  # dict_keys(['field', 'method', 'sigma'])
print(info["output_name"])               # ['edges']

tono.get_node(name) -> node_instance

Get a node instance for direct use. This gives full control over the node's process() method.

gauss = tono.get_node("GaussianFilter")
(result,) = gauss.process(field=my_field, sigma=3.0)

tono.nodes() -> list[str]

List all available node class names.

for name in tono.nodes():
    print(name)

Creating data

tono.field(data, xreal=1e-6, yreal=1e-6, ...) -> DataField

Create a DataField from a numpy array.

import numpy as np

# From a numpy array
f = tono.field(np.random.randn(256, 256))

# With physical dimensions (10 um x 10 um scan)
f = tono.field(data, xreal=10e-6, yreal=10e-6, si_unit_z="V")

Data types

DataField

The core 2D data container, analogous to Gwyddion's GwyDataField.

Attribute	Type	Description
data	np.ndarray	2D float64 array (yres x xres)
xres, yres	int	Pixel dimensions
xreal, yreal	float	Physical dimensions in metres
dx, dy	float	Pixel size in metres (property)
si_unit_xy	str	Lateral unit (e.g. "m")
si_unit_z	str	Value unit (e.g. "m", "V", "A")
domain	str	"spatial" or "frequency"

Key methods:

• field.copy() — deep copy
• field.replace(data=..., si_unit_z=...) — copy with selected fields replaced

Other types

• LineData — 1D data with optional x-axis and units
• RecordTable — list of {quantity, value, unit} dicts (scalar measurements)
• DataTable — list of row dicts (tabular data like grain statistics)

Batch processing example

import tono
from pathlib import Path

input_dir = Path("scans/")
results = {}

for path in input_dir.glob("*.gwy"):
    fields = tono.load(path)
    height = fields[0]

    # Standard processing pipeline
    leveled = tono.PlaneLevelField(height)
    filtered = tono.GaussianFilter(leveled, sigma=1.5)

    # Scalar measurements
    table = tono.Statistics(filtered)
    results[path.name] = table

    print(f"{path.name}: processed {height.xres}x{height.yres} "
          f"({height.xreal*1e6:.1f} x {height.yreal*1e6:.1f} um)")

Integration with matplotlib

import tono
import matplotlib.pyplot as plt
import numpy as np

fields = tono.load("scan.gwy")
field = fields[0]

# Convert physical coordinates for axis labels
extent = [0, field.xreal * 1e6, 0, field.yreal * 1e6]  # in micrometres

fig, axes = plt.subplots(1, 3, figsize=(15, 4))

axes[0].imshow(field.data * 1e9, extent=extent, cmap="afmhot")
axes[0].set_title("Raw")
axes[0].set_xlabel("x (um)")
axes[0].set_ylabel("y (um)")

leveled = tono.PlaneLevelField(field)
axes[1].imshow(leveled.data * 1e9, extent=extent, cmap="afmhot")
axes[1].set_title("Leveled")

filtered = tono.GaussianFilter(leveled, sigma=2.0)
axes[2].imshow(filtered.data * 1e9, extent=extent, cmap="afmhot")
axes[2].set_title("Filtered")

for ax in axes:
    ax.set_xlabel("x (um)")

plt.tight_layout()
plt.savefig("pipeline.png", dpi=150)

Available nodes

Use tono.nodes() to list all nodes. Major categories include:

Category	Example nodes
Level & Correct	PlaneLevelField, PolyLevelField, FacetLevelField, LineCorrection, DriftCorrection
Filter	GaussianFilter, MedianFilter, KuwaharaFilter, WaveletDenoise, EdgeDetect
Spectral	FFT2D, FFT2DInverse, FFTFilter, PSDF, ACF2D, CrossCorrelate
Measure	Statistics, Histogram, CrossSection, Curvature, FractalDimension
Detect	FeatureDetection, HoughTransform, TemplateMatch, PixelClassification
Mask	ThresholdMask, GrainMark, DrawMask, MaskMorphology
Grains	GrainAnalysis, WatershedSegmentation, GrainDistributions
Geometry	CropResizeField, RotateField, Resample, AffineCorrection
Tip	TipModel, TipDeconvolution, BlindTipEstimate