initial commit

.github/dependabot.yml

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+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+ - package-ecosystem: "pip" # See documentation for possible values
+ directory: "/" # Location of package manifests
+ schedule:
+ interval: "weekly"
+ assignees:
+ - "sophiamaedler"
+ # Maintain dependencies for GitHub Actions
+ - package-ecosystem: "github-actions"
+ directory: "/"
+ schedule:
+ interval: "weekly"
+ assignees:
+ - "sophiamaedler"

.github/workflows/dependatbot-validate.yml

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+name: dependabot validate
+
+on:
+ pull_request:
+ paths:
+ - '.github/dependabot.yml'
+ - '.github/workflows/dependabot-validate.yml'
+jobs:
+ validate:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v3
+ - uses: marocchino/validate-dependabot@v2
+ id: validate
+ - uses: marocchino/sticky-pull-request-comment@v2
+ if: always()
+ with:
+ header: validate-dependabot
+ message: ${{ steps.validate.outputs.markdown }}

.gitignore

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+.ipynb_checkpoints
+.DS_store
+vipertools.egg-info
+src/vipertools/__pycache__
+src/vipertools.egg-info

LICENSE

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+MIT License
+
+Copyright (c) 2022 Sophia Mädler
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# SPARCStools
+
+documentation can be found at: https://mannlabs.github.io/SPARCStools/html/index.html
+
+## Installation
+
+Clone the github repository and navigate to the main directory:
+
+ git clone https://github.com/MannLabs/SPARCStools.git
+ cd SPARCStools
+
+Create a conda environment and activate it
+
+ conda create -n stitching python=3.10
+ conda activate stitching
+
+Install Java using conda
+
+ conda install -c conda-forge openjdk
+
+Install package via pip. This should install all dependencies as well.
+
+ pip install .

docs/html/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 58447cc7763988a8e2886fa512125b6f
+tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/html/_sources/index.rst.txt

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+.. sparcstools documentation master file
+
+Welcome to SPARCStools documentation!
+=======================================
+
+This python module contains wrapper functions to perform stitching with the `Ashlar API <https://labsyspharm.github.io/ashlar/>`_. In addition it contains
+data parsing functions to make imaging data aquired with the `Perkinelmer Opera Phenix Microscope <https://www.perkinelmer.com/uk/product/opera-phenix-plus-system-hh14001000>`_ accessible Ashlar 
+or also to other downstream applications. 
+
+The generated stitched images can then be used for downstream applications, for example the `SPARCSpy <link URL>`_ workflow or other analysis frameworks.
+
+Installation
+------------
+
+SPARCStools has been tested using python >= 3.8 on Linux and MacOS. Currently to run on Windows please utilize a Linux Virtual Machine.
+
+Clone the github repository and navigate to the main directory:
+
+.. code::
+
+ git clone https://github.com/MannLabs/SPARCStools.git
+ cd SPARCStools
+
+Create a conda environment and activate it
+
+.. code::
+
+ conda create -n stitching python=3.10
+ conda activate stitching
+
+Install Java using conda
+
+.. code::
+
+ conda install -c conda-forge openjdk
+
+Install package via pip. This should install all dependencies as well.
+
+.. code::
+
+ pip install .
+
+Citing our Work 
+----------------
+
+This code was developed by Sophia Maedler and Niklas Schmacke in the labs of Matthias Mann and Veit Hornung. 
+
+If you use our work please cite the following manuscript:
+
+INSERT CITATION HERE
+
+.. toctree::
+ :maxdepth: 3
+ :caption: Modules:
+
+ pages/modules.rst
+
+.. toctree::
+ :maxdepth: 3
+ :numbered:
+ :caption: Tutorials:
+
+ pages/tutorials
+
+.. toctree::
+ :maxdepth: 3
+ :numbered:
+ :caption: Example Notebooks:
+
+ pages/notebooks/example_stitching_notebook

docs/html/_sources/pages/modules.rst.txt

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+*******************
+Modules
+*******************
+
+.. automodule:: sparcstools.parse
+ :members:
+
+.. automodule:: sparcstools.stitch
+ :members:
+
+.. automodule:: sparcstools.image_processing
+ :members:
+
+.. toctree::
+ :maxdepth: 5
+ :caption: Contents:

docs/html/_sources/pages/tutorials.rst.txt

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+*******************
+Tutorials
+*******************
+
+Parsing and Stitching Data from Opera Phenix
+============================================
+
+First you need to export your data from Harmony and rename the path to eliminate any spaces in the name.
+Then you can run the following script to parses and stitch your data.
+
+.. code-block:: python
+ :caption: example script for parsing and stitching phenix data
+
+ #import relevant libraries
+ import os
+ from sparcstools.parse import parse_phenix
+ from sparcstools.stitch import generate_stitched
+
+ #parse image data
+ path = "path to exported harmony project without any spaces"
+ parse_phenix(path, flatfield_exported = True, export_as_symlink = True) #export as symlink true enabled for better speed and to not duplicate data, set to False if you want to work with hardcopies or plan on accessing the data from multiple OS
+
+ #define important information for your slide that you want to stitch
+
+ # the code below needs to be run for each slide contained in the imaging experiment! 
+ # Can be put into a loop for example to automate this or also can be subset to seperate 
+ # jobs when running on a HPC
+
+ input_dir = os.path.join(path, "parsed_images")
+ slidename = "Slide1"
+ outdir = os.path.join(path, "stitched", slidename)
+ overlap = 0.1 #adjust in case your data was aquired with another overlap
+
+ #define parameters to find correct slide in experiment folder
+ row = 1
+ well = 1
+ zstack_value = 1
+ timepoint = str(1)
+
+ #define on which channel should be stitched
+ stitching_channel = "Alexa647"
+ output_filetype = [".tif", "ome.zarr"] #one of .tif, .ome.tif, .ome.zarr (can pass several if you want to generate all filetypes)
+
+ #adjust cropping parameter
+ crop = {'top':0, 'bottom':0, 'left':0, 'right':0} #this does no cropping
+ #crop = {'top':72, 'bottom':52, 'left':48, 'right':58} #this is good default values for an entire PPS slide with cell culture samples imaged with the SPARCSpy protocol
+
+ #create output directory if it does not exist
+ if not os.path.exists(outdir):
+ os.makedirs(outdir)
+
+ #define pattern to recognize which slide should be stitched
+ #remember to adjust the zstack value if you aquired zstacks and want to stitch a speciifc one in the parameters above 
+
+ pattern = "Timepoint"+str(timepoint.zfill(3) +"_Row"+ str(row).zfill(2) + "_" + "Well" + str(well).zfill(2) + "_{channel}_"+"zstack"+str(zstack_value).zfill(3)+"_r{row:03}_c{col:03}.tif")
+ generate_stitched(input_dir, 
+ slidename,
+ pattern,
+ outdir,
+ overlap,
+ crop = crop ,
+ stitching_channel = stitching_channel, 
+ filetype = output_filetype)
+
+Generated output
+------------------
+
+The stitching script will generate the following files:
+
+.. code::
+
+ .
+ ├── QC_edge_quality.pdf
+ ├── QC_edge_scatter.pdf
+ ├── stitching_test.XML
+ ├── stitching_test_Alexa488.tif
+ ├── stitching_test_DAPI.tif
+ ├── stitching_test_mCherry.tif
+ └── stitching_test_tile_positions.tsv
+
+
+.. list-table:: Generated Files
+ :widths: 25 70
+ :header-rows: 1
+
+ * - FileName
+ - Contents
+ * - `QC_edge_quality.pdf`
+ - This plot shows which tiles are connected with one another.
+ * - `QC_edge_scatter.pdf`
+ - Plots the alignment quality against each other
+ * - `stitching_test.XML`
+ - contains the required input for reading the stitched files into BIAS
+ * - `<slidename>_<channel_name>.tif`
+ - stitched image for the given channel
+ * - `stitching_test_tile_positions.tsv`
+ - coordinate position of where each tile is located
+
+
+Example Results
+---------------
+
+.. image:: ../_static/stitched_channels.png
+ :width: 100 %
+
+