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pybdv

★ 23 Open GitHub ↗

python tools for BigDataViewer

// repository documentation

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# pyBDV

Python tools for [BigDataViewer](https://imagej.net/BigDataViewer).

## Installation

`pybdv` is availabe on `PyPI` and on `conda-forge`. Hence, you can install it via `pip`:
```bash
pip install pybdv
```
or via `conda`:
```bash
conda install -c conda-forge pybdv
```

Alternatively, you can install it from source by downloading it from git and then installing `pybdv` locally:
```bash
git clone https://github.com/constantinpape/pybdv
cd pybdv
pip install -e .
```

## Usage

### Python

Write out numpy array `volume` to bdv format:
```python
from pybdv import make_bdv

out_path = '/path/to/out'

# the scale factors determine the levels of the multi-scale pyramid
# that will be created by pybdv.
# the downscaling factors are interpreted relative to the previous factor
# (rather than absolute) and the zeroth scale level (corresponding to [1, 1, 1])
# is implicit, i.e. DON'T specify it
scale_factors = [[2, 2, 2], [2, 2, 2], [4, 4, 4]]

# the downscale mode determines the method for downscaling:
# - interpolate: cubic interpolation
# - max: downscale by maximum
# - mean: downscale by averaging
# - min: downscale by minimum
# - nearest: nearest neighbor downscaling
mode = 'mean'

# specify a resolution of 0.5 micron per pixel (for zeroth scale level)
make_bdv(volume, out_path,
         downscale_factors=scale_factors, downscale_mode=mode,
         resolution=[0.5, 0.5, 0.5], unit='micrometer')
```

Convert hdf5 dataset to bdv format:
```python
from pybdv import convert_to_bdv

in_path = '/path/to/in.h5'
in_key = 'data'
out_path = '/path/to/out'

# keyword arguments are same as for 'make_bdv'
convert_to_bdv(in_path, in_key, out_path,
               resolution=[0.5, 0.5, 0.5], unit='micrometer')
```

### Command line

You can also call `convert_to_bdv` via the command line:
```bash
convert_to_bdv /path/to/in.h5 data /path/to/out --downscale_factors "[[2, 2, 2], [2, 2, 2], [4, 4, 4]]" --downscale_mode nearest --resolution 0.5 0.5 0.5 --unit micrometer
```

The downscale factors need to be encoded as json list.

### Conversion to n5-bdv format

Bigdatviewer core also supports an [n5 based data format](https://github.com/bigdataviewer/bigdataviewer-core/blob/master/BDV%20N5%20format.md). The data can be converted to this format by passing a path with n5 ending as output path: `/path/to/out.n5`. In order to support this, you need to install [z5py](https://github.com/constantinpape/z5).

### Advanced IO options

If [elf](https://github.com/constantinpape/elf) is available, additional file input formats are supported.
For example, it is possible to convert inputs from tif slices

```python
import os
import imageio
import numpy as np
from pybdv import convert_to_bdv

input_path = './slices'
os.makedirs(input_path, exist_ok=True)
n_slices = 25
shape = (256, 256)

for slice_id in range(n_slices):
    imageio.imsave('./slices/im%03i.tif', np.random.randint(0, 255, size=shape, dtype='uint8'))

input_key = '*.tif'
output_path = 'from_slices.h5'
convert_to_bdv(input_path, input_key, output_path)
```

or tif stacks:

```python
import imageio
import numpy as np
from pybdv import convert_to_bdv

input_path = './stack.tif'
shape = (25, 256, 256)

imageio.volsave(input_path, np.random.randint(0, 255, size=shape, dtype='uint8'))

input_key = ''
output_path = 'from_stack.h5'
convert_to_bdv(input_path, input_key, output_path)
```

### On-the-fly processing

Data can also be added on the fly, using `pybdv.initialize_dataset` to create the bdv file and then `BdvDataset` to 
add (and downscale) new sub-regions of the data on the fly. See [examples/on-the-fly.py](https://github.com/constantinpape/pybdv/blob/master/examples/on_the_fly.py) for details.

### Dask array support

You can use the `pybdv.make_bdv_from_dask_array` function and pass a [dask array](https://docs.dask.org/en/stable/array.html). Currently only `zarr` and `n5` are supported and a limited options for downsampling (using [`dask.array.coarsen`](https://docs.dask.org/en/stable/generated/dask.array.coarsen.html#dask.array.coarsen)). See [examples/dask_array.py](https://github.com/constantinpape/pybdv/blob/master/examples/dask_array.py) for details.

pybdv

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