Anonymous OP 16w

Now, if you’re going to reuse model and view functions, those need to go at the base level, in a “shared utilities” section. That gets around the scope limitations of nesting. So you’re getting the MOST neat code posible, very human readable, but you don’t need to rewrite logic

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Anonymous OP 16w

Also, the concept of DRY is important too. Building abstract functions applicable to many tasks. For example, the handleFilterOperation function is called by 8 different user interface interactions, just using different input parameters. No code duplication

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Anonymous #2 16w

ur so original im sure you have a 6 figure job lined up

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Anonymous #3 16w

if u took his comment seriously idk what to tell u

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Anonymous #1 16w

replying to someone genuinely excited about getting better at coding with some shit like that is why mediocrity is celebrated these days

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Anonymous #3 16w

#2 is probably watching Northern Lion get demolished by rotating shapes right now

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Anonymous #3 16w

i fear this is the society we live in

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Anonymous #1 16w

Yeah

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Anonymous OP 16w

can I see what ur doing I like matlab

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Anonymous #1 16w

Petri dish analysis has always been a mess of fragmented tools. Researchers end up juggling ImageJ for measurements, Excel for data organization, JMP for statistics - managing hundreds of files across multiple platforms while developing custom pipelines for each project. QuantaColony fixes this by integrating the entire workflow into one platform, from image analysis to statistical results.

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Anonymous OP 16w

The platform organizes experiments around two key variables: ordinal (like time points, drug concentrations) and nominal (like strain types, treatment groups). You assign different sections of your petri dishes to these variable combinations, and each unique pairing creates what we call a "colony set" - basically all the colonies under those specific experimental conditions.

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Anonymous OP 16w

Colony Detection uses "User Supervised Automation" to measure these colony sets. You get 5 tunable parameters that update in real-time as you adjust them: brightness threshold, max area, sensitivity, and min/max radius. You can flip between circular detection (MATLAB's imfindcircles) and irregular detection (regionprops) depending on your organism. No two photos use the same parameters since lighting and imaging artifacts vary.

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Anonymous OP 16w

The algorithm gets you about 90% there quickly, then you use surgical tools like "Add Many," "Delete Individual" to polish the rest. Software handles bulk work, humans handle edge cases.

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Anonymous OP 16w

Those colony sets and their measurements become the foundation for statistical analysis. Population stability scoring, decline rate analysis, density distribution shifts, subpopulation tracking - all building on the quantitative data extracted during detection. The whole workflow maintains the connection between your experimental variables and the biological insights, something that's impossible when your analysis is scattered across multiple tools.

1