Name		Name	Last commit message	Last commit date
Latest commit History 32 Commits
.ipynb_checkpoints		.ipynb_checkpoints
__pycache__		__pycache__
tools		tools
.DS_Store		.DS_Store
Master_ImageAnalysis.ipynb		Master_ImageAnalysis.ipynb
README.md		README.md
_DS_Store		_DS_Store
__init__.py		__init__.py
__init__.pyc		__init__.pyc
analyze.py		analyze.py
analyze.pyc		analyze.pyc
cluster.py		cluster.py
cluster.pyc		cluster.pyc
config.yml		config.yml
droplets.py		droplets.py
droplets.pyc		droplets.pyc
io.py		io.py
io.pyc		io.pyc
matchmask.py		matchmask.py
matchmask.pyc		matchmask.pyc
phase.py		phase.py
playing.py		playing.py
register.py		register.py
register.pyc		register.pyc
well_mask.tif		well_mask.tif

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kChip Python Package

Find droplets from 5-channel images.
Split the droplets by UV bin
Save each the droplets in each bin separately to a csv file
Import each csv file individually into the Interactive Clustering notebook and assign clusters
Save csv’s from Interactive Clustering for each UV bin
Bring those back into the Analysis notebook, concatenate them, and name the final DataFrame “droplets”
Proceed with post-merge image analysis
Do distance and area filtering
Save the final DataFrame as a csv with the format: “YYYYMMDD_experimentName_trimmed_distance_and_area_filtered.csv”