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Changelog.txt
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Changelog.txt
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2024/03/05:
Data are now stored as JSON files (Plate and Project). No more use of data_well.txt files.
Using pandas dataframe for Project Overview.
Viewing of selected drop classification over time in Project tab.
Possibility to export to a single csv file all drops with a given classification for further analysis.
Added tool to create JSON files for a Plate from data_well.txt files (./tools/Create_json.py)
Possibility to visualize Human Scores as histogram.
Updated GUI.
Updated setup scripts to setup keyboard layout.
Updated manual.
2022/12/19
Changed GUI:
Added tabs with new Project tab to look for all plates of a given project.
Possibility to filter results for a given target name.
Updated Manual
Updated README and INSTALL (removing Raspbian)
2022/08/03
Updated Setup.py script to hopefully deal with later get-pip.py min_version increase.
Added possibility to score manually the drops (score 1 to 10)
If the user do not want to use the proposed nomenclature for scoring, just edit the file "preferences.py" and change
USESCORECLASS = True # True or False
to
USESCORECLASS = False # True or False
The assigned score will be displayed in the HeatMap.
However, you can modify the proposed nomenclature by editing in "preferences.py" the python list scoreclass.
#Define score classes. Edit to your need but Order is IMPORTANT !!!
scoreclass=['heavy prec',
'clear',
'phase sep',
'granular prec',
'crystalline prec',
'spherulites',
'micro-crystals',
'multiple crystals',
'small crystals',
'3D crystal']
2022/05/20
Clicking on a well in the Plate overview windows allows loading of the corresponding well in the mainframe (picture + notes + Timeline)
modified a conflicting shortcut for autocrop (change from shift+C to shift+K)
2021/09/28
Added Docker build file and instructions
2021/09/09
Changed version to 1.2.4.1
Added possibility to use autoMARCO on a single image within the GUI
More efficient load of MARCO model.
2021/06/24
Changed version to 1.2.4
Added possibility to have an overview of the plate and export image of the plate (per subwell)
Added kit Hampton additives HT to database.
2021/06/18
Added functionality to import RockMaker XML files. Therefore, one can import XML files downloaded from RockMaker website
or import custom screens performed with the SPTLabtech Dragonfly and exported to XML.
Added Filters to only show subwells
2021/05/20
Clicking a well in the Timeline spawns a new viewing window.
Added the option to class as "Unknown" in the drop score section.
2021/03/11:
Default folder tree has one more folder (see case 2 in INSTALL.txt).
If the local tree does not have this folder, use the following command at install "python3 Setup.py --no-ProjectID".
Also, if the local folder containing the unmerged Z-stacks is not called "rawimages", edit the parameter "_rawimages" in utils.py before installation.
2021/03/05:
By default AMi_Image_Analysis expects the individual Z focus images to be located in a folder "rawimages". As of 2021/03/05, a parameter named "_rawimages" can be modified in utils.py before installation.
2020/12/04:
Changed parallel processing. Defined MAX\_CPU in Merge\_Zstack.py, Merge\_AllNewPlates.py and preferences.py. Value can be set to limit number of processes if for example, RAM is completely used leading to the use of swap memory.
2020/09/22:
Two new command line tools: Merge_AllNewPlates.py and SaveDiskSpace.py to automate the tedious processing of several datasets or save disk space.
Release 1.2.3.7
2020/07/03:
Easier installation procedure using Setup.py. Creation of an uninstallation script.
Release 1.2.3.1
2020/05/20:
Improved navigation with shortcuts: Now only visible wells through "Filter" option are selected.
Added Screens Tables to quickly check crystallization conditions. Crystallization Tables are read from csv files stored in the folder "Screen_Database".
2020/03/03:
Merging Z-stacks can be done within AMI_Image_Analysis or command line using the script Merge_Zstack.py. It is done in python using openCV and is much faster than using Hugin.
2020/02/03:
Images in Timeline can be clicked to load corresponding image in main viewing frame for closer inspection.
autoMARCO windows can all be displayed at the same time (previously only a single window could be loaded)
2020/01/23:
Fixed autoMARCO on Raspbian Buster.
Fixed bug leading to program crash when stopping autoMARCO and filtering results.
Release 1.1.8.
2020/01/16:
Changed startup routine to include python virtual environment initialisation.
Release 1.1.6.
2020/01/10:
Parameters controlling circle detection are set in preferences.py
The color of the field indicating the actual classification changes dynamically.
New release.
2019/12/17:
Added a tool to automatically crop images using opencv (autocrop.py). This enhances autoMARCO results.
Added a tool to check circle detection on a given image (Check_Circle_detection.py).
2019/12/13:
Added visualisation of autoMARCO results
2019/12/12:
Automated annotation using TensorFlow (https://www.tensorflow.org/) and MARCO (https://marco.ccr.buffalo.edu/) has been added.
For now, this requires TensorFlow version previous to v2.
**Important Note:** a threshold is applied to accept or not autoMARCO most probable prediction. If the prediction probability is below
this threshold (currently 0.6 and can be modified in preferences.py), the drop classification is set to "Unknown".