ATTENTION: This is a modified version used by UC2 and mirrored by PyPi to make it installable for ImSwitch.
Ashlar performs fast, high-quality stitching of microscopy images. It also co-registers multiple rounds of cyclic imaging for methods such as CyCIF and CODEX. Ashlar can read image data directly from BioFormats-supported microscope vendor file formats as well as a directory of plain TIFF files. Output is saved as pyramidal, tiled OME-TIFF.
Note that Ashlar requires unstitched individual "tile" images as input, so it is not suitable for microscopes or slide scanners that only provide pre-stitched images.
Visit labsyspharm.github.io/ashlar/ for the most up-to-date information on ASHLAR.
ashlar [-h] [-o PATH] [-c CHANNEL] [--flip-x] [--flip-y]
[--flip-mosaic-x] [--flip-mosaic-y]
[--output-channels CHANNEL [CHANNEL ...]] [-m SHIFT]
[--stitch-alpha ALPHA] [--filter-sigma SIGMA]
[--tile-size PIXELS] [--ffp FILE [FILE ...]]
[--dfp FILE [FILE ...]] [--plates] [-q] [--version]
FILE [FILE ...]
Stitch and align multi-tile cyclic microscope images
positional arguments:
FILE Image file(s) to be processed, one per cycle
optional arguments:
-h, --help Show this help message and exit
-o PATH, --output PATH
Output file. If PATH ends in .ome.tif a pyramidal OME-
TIFF will be written. If PATH ends in just .tif and
includes {cycle} and {channel} placeholders, a series
of single-channel plain TIFF files will be written. If
PATH starts with a relative or absolute path to
another directory, that directory must already exist.
(default: ashlar_output.ome.tif)
-c CHANNEL, --align-channel CHANNEL
Reference channel number for image alignment.
Numbering starts at 0. (default: 0)
--flip-x Flip tile positions left-to-right
--flip-y Flip tile positions top-to-bottom
--flip-mosaic-x Flip output image left-to-right
--flip-mosaic-y Flip output image top-to-bottom
--output-channels CHANNEL [CHANNEL ...]
Output only specified channels for each cycle.
Numbering starts at 0. (default: all channels)
-m SHIFT, --maximum-shift SHIFT
Maximum allowed per-tile corrective shift in microns
(default: 15)
--stitch-alpha ALP
76F2
HA Significance level for permutation testing during
alignment error quantification. Larger values include
more tile pairs in the spanning tree at the cost of
increased false positives. (default: 0.01)
--filter-sigma SIGMA Filter images before alignment using a Gaussian kernel
with s.d. of SIGMA pixels (default: no filtering)
--tile-size PIXELS Pyramid tile size for OME-TIFF output (default: 1024)
--ffp FILE [FILE ...]
Perform flat field illumination correction using the
given profile image. Specify one common file for all
cycles or one file for every cycle. Channel counts
must match input files. (default: no flat field
correction)
--dfp FILE [FILE ...]
Perform dark field illumination correction using the
given profile image. Specify one common file for all
cycles or one file for every cycle. Channel counts
must match input files. (default: no dark field
correction)
--plates Enable plate mode for HTS data
-q, --quiet Suppress progress display
--version Show program's version number and exit
Ashlar can be installed in most Python environments using pip:
pip install ashlarIf you don't already have miniconda or Anaconda, download the python 3.x version and install. Then, run the following commands from a terminal (Linux/Mac) or command prompt (Windows):
Create a named conda environment with python 3.10:
conda create -y -n ashlar python=3.10Activate the conda environment:
conda activate ashlarIn the activated environment, install dependencies and ashlar itself:
conda install -y -c conda-forge numpy scipy matplotlib networkx scikit-image=0.19 scikit-learn "tifffile>=2023.3.15" zarr pyjnius blessed
pip install ashlarThe docker image of ashlar is on DockerHub at labsyspharm/ashlar which should be
suitable for many use cases.