bliqtools 1.1.1

A simple module for testing used at Bliq Photonics

BliqTools

Cross-platform Python tools to manipulate any type of images and data from Bliq microscopes (and general). Used by Bliq programmers internally and by the community. For now, fairly limited with only tools for unittesting and limited tools for nirvana files and folders, but the goal is to add more and more this project public.

Project objectives

1. To collect often-used tools in a single package, easy to install.
2. To make manipulation of images task-oriented by removing the boiler-plate, generic code.
3. To let the community look at the code, and tailor it to their needs.

Installing

Python 3.12 is the version used for development, and setuptools have evolved away from setup.py. The best method to install bliqtools is pip:

pip install bliqtools

or install from source:

cd bliqtools/ # In root directory of project (with setup.cfg file)
python3 -m pip install .

This will install with the current version of Python. You can then use it in scripts with:

from bliqtools.testing import *

Tools for unittesting

In bliqtools.testing, you will find these functions. You can also run the tests that are incorporated directly into the file by running it.

1. Progress is a simple class used with contextlib that can provide reasonable feedback. The user provides total number of iterations and calls next() every iterations.

        with Progress(self.total, description="Volume creation") as progress:
            for i,j,k in self.index_list:
                tile = self.tile(i,j,k)
                volume[i-1,j-1,k-1,:,:] = tile
                progress.next()

2. TimeIt is a simple class to time a small section of code:

        with TimeIt(f"Get layers (single)"):
            stitcher.get_layers()

3. Debuggable can be used as a parent class to get useful debugging functions, like _dump_internals() or to see what variables have changed between calls.

    def test_base_stitcher_still_working(self):
        stitcher = Stitcher(self.valid_root_dir, overlap=0.2, channel="Grayscale")
        self.assertIsNotNone(stitcher)
        # stitcher._save_state()
        stitcher.array_from_tiff_dirs()
        stitcher.make_z_stack()
        stitcher._dump_internals()

will print:

-- begin test_base_stitcher_still_working (/Users/dccote/GitHub/Stitching/stitcher.py @line 276)

   no_c_directory [<class 'str'>                           ] : /Users/dccote/Downloads/Test_maps
   app_directory  [<class 'NoneType'>                      ] : None
   overlap        [<class 'float'>                         ] : 0.2
   dtype          [<class 'numpy.dtypes.UInt8DType'>       ] : uint8
   channel        [<class 'str'>                           ] : Grayscale
   channels       [<class 'NoneType'>                      ] : None
   start_memory   [<class 'int'>                           ] : 8276803584
   save_name      [<class 'NoneType'>                      ] : None
   array_paths    [<class 'list'>                          ] : []
   sorted_files   [<class 'list'>                          ] : len=945
   use_zarr_arrays[<class 'bool'>                          ] : False
   og_arrays      [<class 'NoneType'>                      ] : None
   z_stacks       [<class 'numpy.ndarray'>                 ] : shape=(5, 740, 2780)

-- end test_base_stitcher_still_working (/Users/dccote/GitHub/Stitching/stitcher.py @line 276)

4. MemoryMonitor can monitor memory in a separate thread during a long calculation to provide insight on performance. It will print out the time and memory available when done:

        with MemoryMonitor(self.id()) as monitor:
            stitcher = ArrayStitcher(volume_kji=volume)
            with TimeIt(f"Save Zarr {volume_shape}"):
                stitcher.save("/tmp/test-zarr.tif")

and can be used to provide a graph of memory available vs time: