<commit_before>

![alt tag](https://raw.githubusercontent.com/mpicbg-scicomp/tissue_miner/master/docs/readme_screenshots/stripes_0.jpg)
![alt tag](https://raw.githubusercontent.com/mpicbg-scicomp/tissue_miner/master/docs/readme_screenshots/veins_0.jpg)


About
=================

**TissueMiner** is a framework to perform a multiscale analysis of an entire developing epithelium acquired at cellular resolution over several hours or days. It works in [three steps](misc/TM_description.md). 


How to get started?
================
Install the [docker engine](https://docs.docker.com/) on your system. And start it (a shell window pops up). Docker can run the TissueMiner automated workflow on your system (MacOSX, Linux, Windows) without any OS setup. To do so, you once need to download the TissuerMiner [docker image](https://registry.hub.docker.com/u/brandl/tissue_miner/) that we preconfigured. Just do

     ## Step 1: download some example data for the demo
     curl https://cloud.mpi-cbg.de/index.php/s/EspCWSQn3k6NKLA/download  | tar -zxvf -
     
     ## Step 2: download the TissueMiner application (bundled in a docker image called "brandl/tissue_miner")
     docker pull brandl/tissue_miner
     
     ## Step 3: Run the TissueMiner automated workflow on the provided example
     docker run --rm -ti -v $(pwd)/example_movies:/movies -w /movies/demo_ForTissueMiner brandl/tissue_miner sm all

For troubleshooting in case of out-of-date operating systems [click here](misc/docker_troubleshooting.md).

To run TissueMiner over your own data, you'll need to go to the source directory containing your movie directories (use the *cd* command + drag and drop the folder containing your movie directories onto the shell + press enter) and to replace the example movie name (demo_ForTissueMiner) with your movie/directory of interest.

    ## Example for "my_favorite_movie"
    cd path_to_your_movie_repository
    docker run --rm -ti -v $(pwd):/movies -w /movies/my_favorite_movie brandl/tissue_miner sm all

Once your tracked-cell data have been processed by the TissueMiner automated workflow, you can perform a custom multiscale analysis of epithelial morphogenesis using the detailed [R-tutorial](https://mpicbg-scicomp.github.io/tissue_miner/tm_tutorial/R-tutorial.html) or the [Python-tutorial](https://github.com/mpicbg-scicomp/tissue_miner/blob/master/docs/TM_tutorial_in_Python/TissueMiner_pythonTutorial-3WT_Demo.md).


Data structure
================

We advice the user to store all movies in a movie repository folder \<movie_repository\> to facilitate automated movie comparison.

Here is the **required structure** of a movie:


\<movie_repository\>/\<movie_directory\>/**Segmentation**/\<movie_directory_name\>_%03d.png

%03d represents a number padded with 3 digits. The number of digits can be modified [FAQ](https://github.com/mpicbg-scicomp/tissue_miner/blob/master/faq.md).<commit_msg>cosmetics<commit_after>

![alt tag](https://raw.githubusercontent.com/mpicbg-scicomp/tissue_miner/master/docs/readme_screenshots/stripes_0.jpg)
![alt tag](https://raw.githubusercontent.com/mpicbg-scicomp/tissue_miner/master/docs/readme_screenshots/veins_0.jpg)


About
=================

**TissueMiner** is a framework to perform a multiscale analysis of an entire developing epithelium acquired at cellular resolution over several hours or days. It works in [three steps](misc/TM_description.md). 


How to get started?
================
Install the [docker engine](https://docs.docker.com/) on your system. And start it (a shell window pops up). Docker can run the TissueMiner automated workflow on your system (MacOSX, Linux, Windows) without any OS setup. To do so, you once need to download the TissuerMiner [docker image](https://registry.hub.docker.com/u/brandl/tissue_miner/) that we preconfigured. Just use the opened shell window (Unix-shell interpreter) and do

     ## DEMO:
     ## Step 1: download some example data for the demo
     curl https://cloud.mpi-cbg.de/index.php/s/EspCWSQn3k6NKLA/download  | tar -zxvf -
     
     ## Step 2: download the TissueMiner application (bundled in a docker image called "brandl/tissue_miner")
     docker pull brandl/tissue_miner
     
     ## Step 3: Run the TissueMiner automated workflow on the provided example
     docker run --rm -ti -v $(pwd)/example_movies:/movies -w /movies/demo_ForTissueMiner brandl/tissue_miner sm all

For troubleshooting in case of out-of-date operating systems [click here](misc/docker_troubleshooting.md).

To run TissueMiner over **your own data**, you'll need to go to the source directory containing your movie directories (use the *cd* command + drag and drop the folder containing your movie directories onto the shell + press enter) and to replace the example movie name (demo_ForTissueMiner) with your movie/directory of interest.

    ## Example for "my_favorite_movie"
    cd path_to_your_movie_repository
    docker run --rm -ti -v $(pwd):/movies -w /movies/my_favorite_movie brandl/tissue_miner sm all

Once your tracked-cell data have been processed by the TissueMiner automated workflow, you can perform a custom multiscale analysis of epithelial morphogenesis using the detailed [R-tutorial](https://mpicbg-scicomp.github.io/tissue_miner/tm_tutorial/R-tutorial.html) or the [Python-tutorial](https://github.com/mpicbg-scicomp/tissue_miner/blob/master/docs/TM_tutorial_in_Python/TissueMiner_pythonTutorial-3WT_Demo.md).


Data structure
================

We advice the user to store all movies in a movie repository folder \<movie_repository\> to facilitate automated movie comparison.

Here is the **required structure** of a movie:


\<movie_repository\>/\<movie_directory\>/**Segmentation**/\<movie_directory_name\>_%03d.png

%03d represents a number padded with 3 digits. The number of digits can be modified [FAQ](https://github.com/mpicbg-scicomp/tissue_miner/blob/master/faq.md).
{'fraction_non_alphanumeric': 0.10479905036459217, 'fraction_numerical': 0.0044090215363744275, 'mean_word_length': 4.676612127045236, 'pattern_counts': {'":': 0, '<': 11, '<?xml version=': 0, '>': 11, 'https://': 16, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 8, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '432922', 'n_tokens_mistral': 1900, 'n_tokens_neox': 1741, 'n_words': 586}