Convert czi files to Codex-Processor format.
from multicycle, multi-region czi files czi2codex
will generate
the following folder structure:
outdir |_ cyc001_reg001 | | | |_ 1_00001_Z001_CH1.tif | |_ 1_00001_Z001_CH2.tif | |_ ... |_ cyc002_reg001 |_ cyc003_reg001 |_ ... |_ experiment.json |_ channelnames.txt |_ exposure_times.txt |_ options.yaml
Clone the git repository, which will be located in your current directory.
$ git clone https://github.com/erikadudki/czi2codex.git
Create a conda-environment where all needed packages with the needed correct versions will be installed. Type in your terminal:
$ conda create --name codex-env python=3.8
Your conda-environment will be then called condex-env
.
Now, everytime you want to work within this environment, which will contain all necessary,
installed packages call:
$ conda activate codex-env
Enter the czi2codex directory, in which the setup.py
file is
located, and run:
$ pip install .
With that all necessary side-packages will be automatically installed and the conversion tool is ready to go.
Enter the directory of the source code (where the python files are located,
exemplarily the file run_czi2codex.py
) :
$ cd czi2codex
A prerequisite of using the czi2codex conversion-tool is having an
options.yaml
file, where mandatory user options can be saved/changed. In order
to generate the backbone of this file, which then needs to be filled by the
user, you can run:
$ python3 run_generate_std_options_file.py /dir/to/optionsfile/
with /dir/to/optionsfile/
, being the directory path, where this
options-file should be saved.
You can find an example in the folder examples/options.yaml
for getting an idea how the options.yaml
file could look like.
Then you can call the czi2codex conversion tool:
$ python3 run_czi2codex.py /dir/to/optionsfile/options.yaml
with /dir/to/optionsfile/options.yaml
, being the directory path, where
options.yaml
is located.
This will generate
- the
.tif
files for each cycle, channel and tile exposure_times.txt
experiment.json