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Heimdall

Python based viewer for large multi-dimensional datasets.

Based on napari and inspired by BigDataViewer. Can display datasets in memory or stored in hdf5, bdv-format, zarr, n5 or knossos file format. This is work in progress.

Installation

From Source

After cloning this repository, you can install heimdall via

python setup.py install

It requires the following dependencies:

  • napari
  • elf (will be available via pip and conda soon)

Optionally dependencies for viewing big data:

  • h5py
  • z5py

Via pip

Coming soon ;).

Via conda

Coming soon ;).

Usage

Heimdall is a wrapper around napari that makes common visualisation tasks for large volumetric data more convenient. In particular, it supports visualizing data from numpy arrays and hdf5 as well as zarr/n5 datasets and knossos files. It also supports some pyramid specifications for these file formats.

It is the easiest to use it through the convenience functions view_arrays, which displays a list of numpy arrays and view_container, which displays the content of a hdf5 or zarr/n5 file:

import numpy as np
from heimdall import view_arrays

shape = (128,) * 3
x = np.random.rand(*shape)
y = np.random.randint(0, 1000, size=shape, dtype='uint32')
# Display x as raw data and y as labels (automatically inferred from the dtypes).
view_arrays([x, y])
from heimdall import view_container
path = '/path/to/file.h5'  # or .n5/.zarr
# Display all 3d datasets in the container.
# To exclude selected datasets, pass their names as list `exclude_names`.
# To only show selected datasets, pass their names as list `include_names`.
view_container(path, ndim=3)

view_container is also installed as command-line script.

In order to use heimdall in a more flexible manner, use the function view. It can be called with numpy arrays as well as z5py/h5py datasets or groups (for pyramids). It also supports heimdall.sources, which allow to customize the viewer further.

import numpy as np
import h5py
from heimdall import view, to_source

shape = (128,) * 3
x = np.random.rand(*shape)

path = '/path/to/file.h5'
dset_name = 'some/name'
with h5py.File(path, 'r') as f:
    ds = f[dset_name]

    # We wrap the h5 dataset in a source to specifiy additional options.
    # Here, we specify that the dataset has a channel dimension 
    # and set the min_val and max_val that will be used for normalization by napari.
    y = to_source(ds, min_val=0, max_val=100, multichannel=True)

    # All sources need to have the same shape, otherwise `view` will fail.
    assert x.shape == y.shape
    view(x, y)

Pyramid sources

For now, heimdall supports three different multi-scale pyramid formats:

You can load a pyramid, by passing the z5py.Group / h5py.Group or the corresponding knossos file to view, or wrapping it into a PyramidSource with to_source in order to specify further options.

import h5py
import z5py
from heimdall import view, to_source

f1 = z5py.File('/path/to/file.n5')
# this needs to be a group containing an n5 pyramid
pyramid1 = f1['n5-pyramid-group']

# this needs to be a bdv hdf5 file
with h5py.File('/path/to/file.h5', 'r') as f2:
    # this is the pyramid for timepoint 0, channel 0 in the bdv format
    pyramid2 = f2['t00000/s00']
    # we wrap it into a source to specify further options
    # (here: the maximum scale level to be loaded)
    pyramid2 = to_source(pyramid2, n_scales=3)

    # both pyramid data-sets need to have the same shape (at scale 0)
    # note that we can call shape on pyramid2 directly, because this is exposed by 
    # the `PyramidSource` 
    asserrt pyramid1['s0'].shape == pyramid2.shape
    view(pyramid1, pyramid2)

Source wrappers

Heimdall provides several source wrappers - classes that wrap a source and perform some tranformation on the fly. For example, the RoiWrapper only exposes a sub-region of the data to the viewer:

import z5py
from heimdall import view, to_source
from heimdall.source_wrappers import RoiWrapper

# load the dataset source we want to view
ds = z5py.File('/path/to/file.n5')['some/name']
# wrap it into a source (this is required in order to pass it to the wrapper)
source = to_source(ds)

# specify the roi (assuming this is 3d data with a matching shape!)
roi_start = (0, 100, 150)
roi_stop = (200, 250, 400)
# wrap the source
source = RoiWrapper(source, roi_start, roi_stop)
print(source.shape)
# (200, 150, 250)

view(source)

Source wrappers can be chained.

They can also be applied to pyramids, however a PyramidSource cannot be wrapped into a SourceWrapper directly. Instead, the PyramidSource can be passed a factory function factory(source, level, scale), that needs to adjust the transformation to the individual scale level. For many source wrappers, this function is alrady implemented:

from functools import partial
import z5py
from heimdall import view, to_source
from heimdall.source_wrappers import roi_wrapper_pyramid_factory

# load the pyramid source we want to view
g = z5py.File('/path/to/file.n5')['some/n5-pyramid']

# construct the wrapper factory function,
# the values for `roi_start` and `roi_stop` are specified with partial
roi_start = (0, 100, 150)
roi_stop = (200, 250, 400)
factory = partial(roi_wrapper_pyramid_factory, roi_start=roi_start, roi_stop=roi_stop)

# construct the source with wrapper factory
source = to_source(g, wrapper_factory=factory)

view(source)

Interacting with napari

Heimdall can be combined with napari in order to make use of additional functionality. For this use view with return_viewer=True and wrap the function call into napari.gui_qt().

import numpy as np
import napari
from heimdall import view

shape = (128,) * 3
x = np.random.rand(*shape)
y = np.random.randint(0, 1000, size=shape, dtype='uint32')

with napari.gui_qt():
    viewer = view(x, y, return_viewer=True)

    # We add an additional napary points layer.
    points = np.array([[64, 64, 64], [32, 64, 96]])
    sizes = np.array([10, 25])
    viewer.add_points(points, size=sizes)

See examples/ for additional usage examples.

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Python based viewer for large mulit-dimensional datasets.

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