river20140407/live-coding

This is a repository containing notebooks demonstrating along corresponding with YouTube videos, coding strategies to various problems.

live-coding

This is a repository containing Jupyter lab notebooks demonstrating how to code in Python and are associated with corresponding live-coding YouTube videos. The goal of this work is to show you how to approach image analysis in Python in a very practical sense. You will learn about image structure and formats and about the various libraries which can support image analysis.

How to Solve a Maze:

In this notebook and associated video I do some live-coding in Python to describe and demonstrate how to solve a maze rendered in graphical form. In this video I implement a simple breadth-first graph searching algorithm to find the shortest route through a maze, as well as show simple thresholding and image plotting. The notebook can be found in the directory 'how-to-solve-a-maze', or through this link:
How to Solve a Maze - notebook.
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Object colocalization:

In this notebook and assoicated video I demonstrate how to go about performing an object-based colocalization analysis procedure, using fluorescence microscopy derived images. I showcase histogram-based thresholding and also Find-maxima methods of segmentation. Finally I show how to compare the distances between foci in two different channels and how to organise the data using pandas. The notebook can be found in the directory 'how-to-solve-a-maze', or through this link:
Object Colocalization - notebook.
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Find Maxima 2-D:

In this notebook and associated video I demonstrate how to go about applying a 2-D maxima algorithm to wide-field and fluorescence microscopy derived images. axima finding (or peak finding) is a technique for finding the locations of intensity maxima within a data signal. To understand more about theory, please visit the associated video tutorial which goes through the theory and functioning of the algorithmic approach also: https://youtu.be/8YEPRf2C8Dw The code created from this practical is here: Find Maxima - notebook.
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Microscopy Figures - Creating linear LUT and merging microscopy images in Python

Python and the Matplotlib library are great for creating figures. In microscopy however we frequently use linear LUT with a single colour tone, which Matplotlib doesn't support very well. In this notebook I show you how to produce nice linear LUT and also how to merge these images to create multi-channel comparisons. To see a video explaining this notebook, please see:https://youtu.be/dBXUqeXW18c The code created from this practical is here:
Microscopy Figures - notebook
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