Parse and load data-sets

´APR-pipelines´ makes it easy to automatically load a data-set. Several parser method are provided:

  1. A general parser where each tile is saved as ´row_col.tif´ (it can be 3D tiffs or 2D tiffs saved in a folder named ´row_col´)

  2. COLM (Clarity Optimised Lightsheet Microscope) data

  3. ClearScope data

Note that it is very easy to support new data architecture, you can get in touch for a given implementation. For the first case we use the ´tileParser´ class:

import paprica

tiles = paprica.tileParser(path='path to the folder containing the data',
                          frame_size=2048,
                          ftype='apr')

For the second case we use the ´colmParser´ class (less inputs are required since the sensor size and data type is fixed by the microscope):

tiles = paprica.parser.colmParser(path='path to the COLM folder acquisition')

And finally for the third case we use the ´clearscopeParser´ class:

tiles = paprica.parser.clearscopeParser(path='path to the ClearScope folder acquisition')

Note that each channel must be parsed independently (this implementation choice was made to give more flexibility to the user):

tiles_ch0 = paprica.parser.colmParser(path='path to the COLM folder acquisition', channel=0)
tiles_ch1 = paprica.parser.colmParser(path='path to the COLM folder acquisition', channel=1)
tiles_ch2 = paprica.parser.colmParser(path='path to the COLM folder acquisition', channel=2)