iivs-lib 0.2.0

A Python toolkit for multi-modal holographic systems and cellular analysis.

iivs-lib

A Python toolkit for multi-modal holographic systems and cellular analysis.

📦 Installation

Requires Python 3.13 or newer.

# With uv (recommended)
uv add iivs-lib

# With pip
pip install iivs-lib

Image I/O ships by default: imagecodecs (to decode the LZW-compressed Koala Image/*.tif uint8 previews) is a core dependency — handling image-like microscope data is this library's primary job. The only extra is [torch], which adds PyTorch for iivs.dhm.analysis.pytorch — tensor-in / tensor-out OPD / dry-mass twins with autograd (uv add "iivs-lib[torch]").

🚀 Quick start

from iivs.dhm.data.phase import PhaseBinFolder, PhaseUnit
from iivs.dhm.analysis import DryMassCalculator

# Lazily open a phase acquisition (a folder of numbered .bin frames).
phase = PhaseBinFolder("scan/Phase/Float/Bin", target_unit=PhaseUnit.RADIANS)
phase.frame_shape          # (H, W), shared across frames
img = phase[0]             # first frame as a float32 array (decoded on access)

# Per-frame dry mass over a segmented cell:
calc = DryMassCalculator(pixel_size=phase.header.pixel_size)
mass_pg = calc.calc_from_phase(img, mask=cell_mask)

🧩 Modules

Each package ships a detailed README in the source tree — the endpoints, examples, and the inherited sequence interface: hologram, phase, intensity, data (overview and timestamp), analysis.

iivs.dhm.data

Readers, writers, and lazy sequences for Lyncée Tec Koala acquisition data (validated end-to-end against a real Koala acquisition — every format, the sequences, and the round trips between them).

• hologram — uint8 holograms: .tif via load_hologram_tif / save_hologram_tif with HologramTifFolder / HologramTifList; a single multi-frame .raw via HologramRawFile (a lazy np.memmap), read_hologram_raw_header / save_hologram_raw; header-less .npy frames via HologramNpyFolder.
• phase — float32 .bin phase images: load_phase_bin / save_phase_bin / read_phase_bin_header, the typed PhaseBinHeader and PhaseUnit, and convert_phase_unit; folder/list sequences PhaseBinFolder / PhaseBinList. The same quantitative phase from Koala's Float/Txt export via load_phase_txt, PhaseTxtFolder / PhaseTxtList. The uint8 Image/*.tif display previews (not quantitative) via PhaseTifFolder / PhaseTifList. Header-less .npy frames via PhaseNpyFolder (pixel_size / unit / height_scale passed to the constructor; numpy.load, pickle disabled).
• intensity — float32 .bin intensity reconstructions (exported alongside phase): load_intensity_bin / save_intensity_bin / read_intensity_bin_header, the typed IntensityBinHeader, and folder/list sequences IntensityBinFolder / IntensityBinList; plus the Float/Txt twins load_intensity_txt, IntensityTxtFolder / IntensityTxtList, the uint8 Image/*.tif previews IntensityTifFolder / IntensityTifList, and header-less .npy frames via IntensityNpyFolder (pixel_size passed in). The phase and intensity .bin formats share the common.KoalaBinHeader base.
• timestamp — per-frame acquisition timing: the Timestamp record, TimestampsTxtFile (Koala timestamps.txt), and TimestampsFixedFPS (synthesized from a frame rate).

phase and intensity also expose suffix-dispatch entry points that pick the format by a path's extension — load_phase / read_phase_header / save_phase, phase_list / phase_folder (and the intensity twins) — plus save_phase_folder / save_intensity_folder to write any (e.g. a kaparoo- composed) image sequence to a numbered folder. load_phase / load_intensity take return_header (the header is None for the header-less .npy).

A timelapse module opens a whole Koala acquisition from its root folder: KoalaTimelapse composes the per-modality PhaseGroup / IntensityGroup (each picking its Float/{Bin,Txt} format), the holograms, timestamps.txt, and phbounds.txt display bounds, with lazy access, frame-count consistency checks (is_consistent), and content validate. search_timelapses finds every acquisition under a root; KOALA_TIMELAPSE_TREE describes the layout for hierarchy.validate.

Every sequence is a kaparoo.data.sequences.DataSequence, so it indexes, slices, and iterates lazily; same-shape sources also expose frame_shape by mixing in iivs.common.data.FrameShapedMixin (so a uniform source is its <Modality>FloatSequence / <Modality>ImageSequence plus that mixin). For phase and intensity the quantitative float32 sources are <Modality>FloatSequence and the uint8 Image/*.tif previews are <Modality>ImageSequence, both under the <Modality>Sequence base. Numbered-folder sequences share the KoalaFrameFolder discovery/validation base in iivs.dhm.data.koala, and validate their arrays via iivs.common.data's validate_float32_array / validate_uint8_array.

iivs.dhm.analysis

Physical quantities derived from phase, each via an engine object that precomputes its conversion factor (with one-shot function conveniences):

• opd — optical path difference (OPD = phase * wavelength / (2*pi), in nm). OPDConverter (convert_to_opd / convert_to_phase, scale opd_scale); phase_to_opd / opd_to_phase.
• drymass — dry mass (pg) via the Barer relation. DryMassCalculator (calc_from_opd / calc_from_phase over a background-corrected map, optionally masked by a boolean or integer-label region; scale drymass_scale) delegates its masked sum to the iivs.common.data Sum reduction; calc_drymass / calc_drymass_from_phase are the one-shots.

The wavelength and alpha defaults come from iivs.dhm.constants, the lab's microscope settings — shared by analysis and data, and belonging to neither. Override per experiment when the setup differs.

Using with PyTorch (autograd)

The convert_* / calc_* methods operate on NumPy arrays. Install the iivs-lib[torch] extra for iivs.dhm.analysis.pytorch — tensor-in / tensor-out twins that keep the input tensor's device, dtype, and autograd graph (the calibration scalars are shared with the NumPy engines). The Torch OpticalPathDifference / DryMass are pure pointwise nn.Modules (so they fit nn.Sequential / torch.compile); masking and reduction are the separate iivs.common.data.pytorch reductions, which the calc_* one-shots compose:

from iivs.dhm.analysis.pytorch.opd import phase_to_opd
from iivs.dhm.analysis.pytorch.drymass import calc_drymass_from_phase

opd = phase_to_opd(phase, wavelength=666e-9)   # Tensor (CPU/GPU), grad kept
mass = calc_drymass_from_phase(phase, pixel_size=px, mask=cell)  # Tensor, grad kept

Or, without the dependency, multiply by the cached scale factors (plain floats) with native ops yourself:

opd = phase * conv.opd_scale                  # phase: Tensor -> OPD (nm), grad kept
mass = opd[mask].sum() * calc.drymass_scale   # OPD -> dry mass (pg), grad kept

iivs.common.data

Technique-agnostic data primitives — nothing here knows what a hologram is, so a future technique can build on them without importing dhm.

• ArrayFileList[U] — the dtype-generic base every file-backed sequence extends, with FrameShapedMixin marking the same-shape sources and ValueRangeMixin adding a cached value_range() over all frames.
• load_float32_npy / save_float32_npy and the uint8 pair, plus read_npy_shape / write_npy — header-less .npy I/O, keyed by dtype (which is all that varies) rather than by modality. Pickle is disabled, so an object array cannot be written and one written elsewhere is refused rather than unpickled.
• validate_float32_array / validate_uint8_array and their wider float / uint forms — dtype + shape + non-finite checks, with on_nonfinite ("ignore" / "warn" / "raise") the policy every loader and saver in the library threads through.
• Sum / Mean / Norm / Variance / Std — masked region reductions: reduce a (..., H, W) map over the regions of a mask (a boolean image or stack, or an integer label image; regions may overlap), with region_stack / apply_mask the shared primitives. A Torch twin in iivs.common.data.pytorch mirrors them as autograd-friendly nn.Modules.
• Timestamp / TimestampSequence / TimestampsFixedFPS — per-frame timing, which any time-lapse acquisition has; the Koala timestamps.txt reader implements this interface from dhm.

iivs.common.visualization

Technique-agnostic display helpers, shared across every modality.

• auto_rescale — ImageJ-style auto-contrast (Enhance Contrast + Normalize): clips saturated% of pixels via NaN-safe percentile bounds, stretches onto out_range (or the dtype's full span), and casts back to the input dtype. Works on any numeric image or stack, so phase / intensity / hologram share it.

📋 TODO

See TODO.md for tracked open items.

📜 Changelog

See CHANGELOG.md for the version history.

🙏 Acknowledgements

The file formats read and written by iivs.dhm.data are Lyncée Tec's proprietary Koala formats. The .bin format — Koala's float32 binary container, shared by the phase and intensity reconstructions — was cross-checked against their reference implementation, pyKoalaUtils (MIT). iivs-lib is an independent reimplementation and contains no code from it.

⚖️ License

This project is distributed under the terms of the MIT license.