Tutorial

[aaristov]

Hi Guys,

As a Python developer in the lab, I'm very enthusiastic about using your tools for our mother machine data. However, the present repository looks very chaotic and makes hard to understand the analysis pipeline. Would you be able to provide a simple and to end tutorial on now you do processing? That implies providing some sample datasets in the releases section and listing all steps you do in a simple .md or wiki format. Even a jupyter notebook would be fantastic to start with. If you already have these somewhere, please refer to them in the README.md to be accessible from the start.

I'll be happy to contribute to better-structured project and portability to python 3 (python 2 will not be supported pretty soon) once I have a working pipeline. You can check Spot-On-Cli as an example of clear notebooks explaining the order of function calls. That allowed me to port the code to python 3 and improve the speed and functionality of the original code.

Thanks in advance.

Best,
Andrey

[jtsauls]

Hello Andrey,

Check out the /docs/Start_Here.md file for a walk through. mm3 already runs in Python 3.

I can link you to some test data if you do not have your own.

[itspuk]

Hello,

I would also be interested in some test data as I encounter problems with the .TIFF files I generated. It seems like the program expects the shape of each image (tif.asarray()) to be of the form [x,y,z]. I only have images with a single plane for now, so they do not have a first dimension x and I am not sure they can be handled.
The problem occurs in mm3_helpers.py, line 312:
img_shape = [image_data.shape[1],image_data.shape[2]]

But maybe it is a problem with my images, not with the code, so test data would be appreciated. Excuses if I'm mixing differetn issues here.

Thanks!

Best,
Niclas

[stevendbrown]

@itspuk When loading the images, you can do something like:

if len(img_data.shape) == 2:
    img_data = np.expand_dims(img_data, 2)