Dev

README.md

@@ -30,9 +30,9 @@ ___________
 
 - The ```example_analysis.ipynb``` notebook demonstrates a systematic and comprehensive analysis workflow using real plaque assay data. It illustrates how to load fluorescence microscopy images of viral plaques using the ```FluorescenceMicroscopy``` class, generate plate level readouts (readouts for all wells of a plate) and filter them into viral and control groups based on well identifier metadata additionally stored in the class. We then use internal visualisation tools to validate our analysis by showcasing comparison barplots for readouts that indicate infectivity that should be different between viral and control wells. This notebook can be tried in Colab here. <a href="https://colab.research.google.com/github/plaque2/PyPlaque/blob/master/notebooks/visualization/example_analysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 
-- The ```cvp_detailed.ipynb``` notebook demonstrates functionality provided by PyPlaque for the analysis of the crystal violet stained plaques using the ```CrystalViolet``` class to load and store crystal violet stained, mobile photography images of viral plaques in 6-well plates available as individual wells. Using inbuilt classes and functions, steps are demonstrated to generate rudimentary binary detection masks for plaques if they are unavailable, stitching individual wells to create partial composite overviews of the plate, detection of plaques based on size criteria from available or generated detection masks, getting measures that describe the shape characteristics and spread of plaques in a well, visualise the spread of plaque measures in a well and finally generate individual well images and well and plaque detection masks from full plate images and full plate well and plaque detection masks in case they are unavailable. This notebook can be tried in Colab here. <a href="https://colab.research.google.com/https://github.com/plaque2/PyPlaque/blob/master/notebooks/cvp_detailed.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
+- The ```cvp_detailed.ipynb``` notebook demonstrates functionality provided by PyPlaque for the analysis of the crystal violet stained plaques using the ```CrystalViolet``` class to load and store crystal violet stained, mobile photography images of viral plaques in 6-well plates available as individual wells. Using inbuilt classes and functions, steps are demonstrated to generate rudimentary binary detection masks for plaques if they are unavailable, stitching individual wells to create partial composite overviews of the plate, detection of plaques based on size criteria from available or generated detection masks, getting measures that describe the shape characteristics and spread of plaques in a well, visualise the spread of plaque measures in a well and finally generate individual well images and well and plaque detection masks from full plate images and full plate well and plaque detection masks in case they are unavailable. This notebook can be tried in Colab here. <a href="https://colab.research.google.com/github/plaque2/PyPlaque/blob/master/notebooks/cvp_detailed.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 
-- The ```fp_detailed.ipynb``` notebook demonstrates functionality provided by PyPlaque for the analysis of the fluorescence microscopy plaques using the ```FluorescenceMicroscopy``` class to load and store fluorescence microscopy images of viral plaques in 384-well plates available as individual wells. Using inbuilt classes and functions, steps are demonstrated to load virus and nuclei channel images for the wells, generate binary detection masks for both channels if unavailable, stitching individual wells to create partial composite overviews of the plate, and generation of plate level, well level and plaque level readouts at these 3 granularity levels. This notebook can be tried in Colab here. <a href="https://colab.research.google.com/https://github.com/plaque2/PyPlaque/blob/master/notebooks/fp_detailed.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
+- The ```fp_detailed.ipynb``` notebook demonstrates functionality provided by PyPlaque for the analysis of the fluorescence microscopy plaques using the ```FluorescenceMicroscopy``` class to load and store fluorescence microscopy images of viral plaques in 384-well plates available as individual wells. Using inbuilt classes and functions, steps are demonstrated to load virus and nuclei channel images for the wells, generate binary detection masks for both channels if unavailable, stitching individual wells to create partial composite overviews of the plate, and generation of plate level, well level and plaque level readouts at these 3 granularity levels. This notebook can be tried in Colab here. <a href="https://colab.research.google.com/github/plaque2/PyPlaque/blob/master/notebooks/fp_detailed.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 
 
 Note : In notebooks, custom packages and additional data need to be loaded. Load your own data into the Colab environment by connecting via Google Drive or direct upload or using our sample analysis data using the code in the `load_data.ipynb` notebook above and then run the analysis.

setup.py

@@ -22,7 +22,7 @@
   license='GPLv3',
   description = 'Python package for virus plaque analysis based on Plaque2.0',
   author = 'Plaque2.0 Team, AILS Institute, CASUS HZDR',
-  author_email = 'ayakimovich@ails.institute',
+  author_email = 'ayakimovich@ails.institute, trinade96@gmail.com',
   url = 'https://github.com/plaque2/PyPlaque',
   download_url = 'https://github.com/plaque2/PyPlaque/archive/{}.tar.gz' \
             .format(version),
@@ -47,7 +47,7 @@
   classifiers=[
      'Development Status :: 3 - Alpha',
      'Intended Audience :: Science/Research',
-     'Topic :: Scientific/Engineering :: Bio-Informatics ',
+     'Topic :: Scientific/Engineering :: Bio-Informatics',
      'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
      'Programming Language :: Python :: 3.9',
      'Programming Language :: Python :: 3.10',