Relaxing scipy version

aydin/__init__.py

@@ -2,7 +2,7 @@
 from aydin.restoration.denoise.noise2selfcnn import noise2self_cnn  # noqa: F401
 from aydin.restoration.denoise.classic import Classic  # noqa: F401
 from aydin.restoration.denoise.classic import classic_denoise  # noqa: F401
-from aydin.restoration.deconvolve.lr import lucyrichardson  # noqa: F401
+
 
 try:
     from ._version import version as __version__

aydin/analysis/demo/demo_blind_spot_analysis.py

@@ -2,7 +2,7 @@
 from aydin.analysis.blind_spot_analysis import auto_detect_blindspots
 from aydin.features.standard_features import StandardFeatureGenerator
 from aydin.io.datasets import examples_single
-from aydin.it.deconvolution.lr_deconv_scipy import ImageTranslatorLRDeconvScipy
+
 from aydin.it.fgr import ImageTranslatorFGR
 from aydin.it.transforms.range import RangeTransform
 from aydin.regression.cb import CBRegressor
@@ -19,11 +19,6 @@ def demo_blind_spot_analysis():
     # Here are the blind spots that should be used with N2S:
     print(blind_spots)
 
-    lr = ImageTranslatorLRDeconvScipy(psf_kernel=noise_auto, max_num_iterations=100)
-    lr.add_transform(RangeTransform())
-    lr.train(image)
-    deconvolved_image = lr.translate(image)
-
     generator = StandardFeatureGenerator(
         include_corner_features=True,
         include_scale_one=True,
@@ -40,17 +35,12 @@ def demo_blind_spot_analysis():
     it.train(image)
     denoised_image = it.translate(image)
 
-    it.train(deconvolved_image)
-    denoised_deconvolved_image = it.translate(deconvolved_image)
-
     import napari
 
     with napari.gui_qt():
         viewer = napari.Viewer()
         viewer.add_image(image, name='image')
         viewer.add_image(denoised_image, name='denoised_image')
-        viewer.add_image(deconvolved_image, name='deconvolved_image')
-        viewer.add_image(denoised_deconvolved_image, name='denoised_deconvolved_image')
         viewer.add_image(noise_auto, name='noise_auto')
 
 

aydin/cli/cli.py

@@ -16,7 +16,6 @@
 from aydin.gui.gui import run
 from aydin.io.datasets import normalise
 from aydin.it.base import ImageTranslatorBase
-from aydin.restoration.deconvolve.lr import LucyRichardson
 from aydin.io.io import imwrite, imread
 from aydin.io.utils import (
     get_output_image_path,
@@ -184,44 +183,6 @@ def denoise(files, **kwargs):
         lprint("DONE")
 
 
-@cli.command()
-@click.argument('files', nargs=-1, required=True)
-@click.argument('psf_path', nargs=1, required=True)
-@click.option('-s', '--slicing', default='', type=str)
-@click.option('-b', '--backend', default=None)
-@click.option('--output-folder', default='')
-def lucyrichardson(files, psf_path, **kwargs):
-    """lucyrichardson command
-
-    Parameters
-    ----------
-    files
-    psf_kernel
-    kwargs : dict
-
-    """
-
-    psf_kernel = imread(psf_path)[0]
-    psf_kernel = psf_kernel.astype(numpy.float32, copy=False)
-    psf_kernel /= psf_kernel.sum()
-
-    filepaths, image_arrays, metadatas = handle_files(files, kwargs['slicing'])
-    for filepath, input_image in zip(filepaths, image_arrays):
-        lr = LucyRichardson(
-            psf_kernel=psf_kernel, max_num_iterations=20, backend=kwargs['backend']
-        )
-
-        lr.train(input_image, input_image)
-        deconvolved = lr.deconvolve(input_image)
-
-        path, index_counter = get_output_image_path(
-            filepath,
-            operation_type="deconvolved",
-            output_folder=kwargs["output_folder"],
-        )
-        imwrite(deconvolved, path)
-
-
 @cli.command()
 @click.argument('files', nargs=-1)
 @click.option('-s', '--slicing', default='', type=str)

aydin/io/utils.py

@@ -101,7 +101,7 @@ def get_output_image_path(
     ----------
     path : str
     operation_type : str
-        Currently supported values: 'denoised', 'deconvolved', 'hyperstacked'.
+        Currently supported values: 'denoised', 'hyperstacked'.
     output_folder : str
 
     Returns
@@ -110,7 +110,7 @@ def get_output_image_path(
         (Correct output path, counter).
 
     """
-    if operation_type not in ["denoised", "deconvolved", "hyperstacked"]:
+    if operation_type not in ["denoised", "hyperstacked"]:
         raise ValueError(
             f"invalud value for operation_type parameter: {operation_type}"
         )