docs/notebooks/Logging.ipynb

 %% Cell type:markdown id:ae2a75fe tags:
 
 # Logging system for qim3d
 
 Using proper logging instead of print statements is a recommended practice.
 
 While print statements can be helpful for quick debugging, logging provides a more powerful and versatile approach. Logging allows for better control over output, with options to configure log levels, filter messages, and redirect output to different destinations.
 
 %% Cell type:code id:a31b2245 tags:
 
 ``` python
 import qim3d
-from qim3d.io.logger import log
+from qim3d.utils._logger import log
 ```
 
 %% Cell type:code id:94022824 tags:
 
 ``` python
 # Here we test by sending one message for each level
-# Note that DEBUG and INFO do not appear
+# Note that DEBUG does not appear
 log.debug('debug level message')
 log.info('info level message')
 log.warning('warning level message')
 log.error('error level message')
 log.critical('critical level message')
 ```
 
 %% Output
 
+    info level message
     warning level message
     error level message
     critical level message
 
 %% Cell type:code id:b0856333 tags:
 
 ``` python
 # Change the level to debug
-qim3d.io.logger.level("debug")
+qim3d.utils._logger.level("debug")
 
 # Now all the levels get logged
 log.debug('debug level message')
 log.info('info level message')
 log.warning('warning level message')
 log.error('error level message')
 log.critical('critical level message')
 ```
 
 %% Output
 
     debug level message
     info level message
     warning level message
     error level message
     critical level message
 
 %% Cell type:code id:eb542404 tags:
 
 ``` python
 # Change the level to error
-qim3d.io.logger.level("error")
+qim3d.utils._logger.level("error")
 
 # And now only above ERROR is shown
 log.debug('debug level message')
 log.info('info level message')
 log.warning('warning level message')
 log.error('error level message')
 log.critical('critical level message')
 ```
 
 %% Output
 
     error level message
     critical level message
 
+%% Cell type:code id:75af4473 tags:
+
+``` python
+# Change the level back to info
+qim3d.utils._logger.level("info")
+
+# And now only above INFO is shown again
+log.debug('debug level message')
+log.info('info level message')
+log.warning('warning level message')
+log.error('error level message')
+log.critical('critical level message')
+```
+
+%% Output
+
+    info level message
+    warning level message
+    error level message
+    critical level message
+
 %% Cell type:code id:af3cc812 tags:
 
 ``` python
 # We can increase the level of detail
-qim3d.io.logger.set_detailed_output()
+qim3d.utils._logger.set_detailed_output()
 
-# Note that DEBUG and INFO are still not shown
+# Note that DEBUG is still not shown
 log.debug('debug level message')
 log.info('info level message')
 log.warning('warning level message')
 log.error('error level message')
 log.critical('critical level message')
 ```
 
 %% Output
 
-    ERROR     3224913348.py:8    error level message
-    CRITICAL  3224913348.py:9    critical level message
+    INFO      20327955.py:6    info level message
+    WARNING   20327955.py:7    warning level message
+    ERROR     20327955.py:8    error level message
+    CRITICAL  20327955.py:9    critical level message
 
 %% Cell type:code id:d7239b1b tags:
 
 ``` python
 # We can switch back to the simple output mode
-qim3d.io.logger.set_simple_output()
+qim3d.utils._logger.set_simple_output()
 
-# Now we see all the levels on simple mode
+# Now the levels are back to simple mode
 log.debug('debug level message')
 log.info('info level message')
 log.warning('warning level message')
 log.error('error level message')
 log.critical('critical level message')
 ```
 
 %% Output
 
+    info level message
+    warning level message
     error level message
     critical level message
 
 %% Cell type:code id:eaceb5b6 tags:
 
 ``` python
 # Change back to detailed and DEBUG level
-qim3d.io.logger.set_detailed_output()
-qim3d.io.logger.level("debug")
+qim3d.utils._logger.set_detailed_output()
+qim3d.utils._logger.level("debug")
 
 log.debug('debug level message')
 log.info('info level message')
 log.warning('warning level message')
 log.error('error level message')
 log.critical('critical level message')
 ```
 
 %% Output
 
-    DEBUG     1186198911.py:5    debug level message
-    INFO      1186198911.py:6    info level message
-    WARNING   1186198911.py:7    warning level message
-    ERROR     1186198911.py:8    error level message
-    CRITICAL  1186198911.py:9    critical level message
+    DEBUG     3380703693.py:5    debug level message
+    INFO      3380703693.py:6    info level message
+    WARNING   3380703693.py:7    warning level message
+    ERROR     3380703693.py:8    error level message
+    CRITICAL  3380703693.py:9    critical level message

docs/notebooks/References from DOI.ipynb

 %% Cell type:markdown id:855a28ff tags:
 
 # Get references from DOI
-This notebook shows how it is possible to use the `Qim3D` library to easily get well formatted references from a DOI
+This notebook shows how it is possible to use the `qim3d` library to easily get well formatted references from a DOI
 
 %% Cell type:code id:35b9fe6b tags:
 
 ``` python
 import qim3d
-qim3d.io.logger.level('info')
+qim3d.utils._logger.level("info")
 ```
 
 %% Cell type:code id:7962731c tags:
 
 ``` python
 doi = "https://doi.org/10.1007/s10851-021-01041-3"
 ```
 
 %% Cell type:code id:17720b94 tags:
 
 ``` python
-bibtext = qim3d.utils.doi.get_bibtex(doi)
+bibtext = qim3d.utils._doi.get_bibtex(doi)
 ```
 
 %% Output
 
-    @article{Stephensen_2021,
-    	doi = {10.1007/s10851-021-01041-3},
-    	url = {https://doi.org/10.1007%2Fs10851-021-01041-3},
-    	year = 2021,
-    	month = {jun},
-    	publisher = {Springer Science and Business Media {LLC}},
-    	volume = {63},
-    	number = {8},
-    	pages = {1069--1083},
-    	author = {Hans J. T. Stephensen and Anne Marie Svane and Carlos B. Villanueva and Steven A. Goldman and Jon Sporring},
-    	title = {Measuring Shape Relations Using r-Parallel Sets},
-    	journal = {Journal of Mathematical Imaging and Vision}
-    }
+     @article{Stephensen_2021, title={Measuring Shape Relations Using r-Parallel Sets}, volume={63}, ISSN={1573-7683}, url={http://dx.doi.org/10.1007/s10851-021-01041-3}, DOI={10.1007/s10851-021-01041-3}, number={8}, journal={Journal of Mathematical Imaging and Vision}, publisher={Springer Science and Business Media LLC}, author={Stephensen, Hans J. T. and Svane, Anne Marie and Villanueva, Carlos B. and Goldman, Steven A. and Sporring, Jon}, year={2021}, month=jun, pages={1069–1083} }
+    
 
 %% Cell type:code id:a15baf83 tags:
 
 ``` python
-reference = qim3d.utils.doi.get_reference(doi)
+reference = qim3d.utils._doi.get_reference(doi)
 ```
 
-%% Output
-
-    Stephensen, H. J. T., Svane, A. M., Villanueva, C. B., Goldman, S. A., & Sporring, J. (2021). Measuring Shape Relations Using r-Parallel Sets. Journal of Mathematical Imaging and Vision, 63(8), 1069–1083. https://doi.org/10.1007/s10851-021-01041-3
-    
-
 %% Cell type:code id:6d4d215e tags:
 
 ``` python
-qim3d.utils.doi.get_metadata(doi)
+qim3d.utils._doi.get_metadata(doi)
 ```
 
 %% Output
 
-    {'indexed': {'date-parts': [[2023, 9, 21]],
-      'date-time': '2023-09-21T13:58:37Z',
-      'timestamp': 1695304717010},
+    {'indexed': {'date-parts': [[2023, 10, 20]],
+      'date-time': '2023-10-20T12:08:45Z',
+      'timestamp': 1697803725999},
      'reference-count': 26,
      'publisher': 'Springer Science and Business Media LLC',
      'issue': '8',
      'license': [{'start': {'date-parts': [[2021, 6, 26]],
         'date-time': '2021-06-26T00:00:00Z',
         'timestamp': 1624665600000},
        'content-version': 'tdm',
        'delay-in-days': 0,
        'URL': 'https://www.springer.com/tdm'},
       {'start': {'date-parts': [[2021, 6, 26]],
         'date-time': '2021-06-26T00:00:00Z',
         'timestamp': 1624665600000},
        'content-version': 'vor',
        'delay-in-days': 0,
        'URL': 'https://www.springer.com/tdm'}],
      'funder': [{'DOI': '10.13039/100008398',
        'name': 'Villum Fonden',
-       'doi-asserted-by': 'publisher'},
+       'doi-asserted-by': 'publisher',
+       'id': [{'id': '10.13039/100008398',
+         'id-type': 'DOI',
+         'asserted-by': 'publisher'}]},
       {'DOI': '10.13039/501100005275',
        'name': 'Region Hovedstaden',
-       'doi-asserted-by': 'publisher'}],
+       'doi-asserted-by': 'publisher',
+       'id': [{'id': '10.13039/501100005275',
+         'id-type': 'DOI',
+         'asserted-by': 'publisher'}]}],
      'content-domain': {'domain': ['link.springer.com'],
       'crossmark-restriction': False},
      'published-print': {'date-parts': [[2021, 10]]},
      'DOI': '10.1007/s10851-021-01041-3',
      'type': 'journal-article',
      'created': {'date-parts': [[2021, 6, 26]],
       'date-time': '2021-06-26T15:02:20Z',
       'timestamp': 1624719740000},
      'page': '1069-1083',
      'update-policy': 'http://dx.doi.org/10.1007/springer_crossmark_policy',
      'source': 'Crossref',
      'is-referenced-by-count': 3,
      'title': 'Measuring Shape Relations Using r-Parallel Sets',
      'prefix': '10.1007',
      'volume': '63',
      'author': [{'ORCID': 'http://orcid.org/0000-0001-8245-0571',
        'authenticated-orcid': False,
        'given': 'Hans J. T.',
        'family': 'Stephensen',
        'sequence': 'first',
        'affiliation': []},
       {'ORCID': 'http://orcid.org/0000-0001-6356-0484',
        'authenticated-orcid': False,
        'given': 'Anne Marie',
        'family': 'Svane',
        'sequence': 'additional',
        'affiliation': []},
       {'ORCID': 'http://orcid.org/0000-0001-9786-9439',
        'authenticated-orcid': False,
        'given': 'Carlos B.',
        'family': 'Villanueva',
        'sequence': 'additional',
        'affiliation': []},
       {'ORCID': 'http://orcid.org/0000-0002-5498-4303',
        'authenticated-orcid': False,
        'given': 'Steven A.',
        'family': 'Goldman',
        'sequence': 'additional',
        'affiliation': []},
       {'ORCID': 'http://orcid.org/0000-0003-1261-6702',
        'authenticated-orcid': False,
        'given': 'Jon',
        'family': 'Sporring',
        'sequence': 'additional',
        'affiliation': []}],
      'member': '297',
      'published-online': {'date-parts': [[2021, 6, 26]]},
      'reference': [{'key': '1041_CR1',
        'doi-asserted-by': 'publisher',
+       'DOI': '10.1201/b19708',
        'volume-title': 'Spatial Point Patterns: Methodology and Applications with R',
        'author': 'A Baddeley',
        'year': '2015',
-       'unstructured': 'Baddeley, A., Rubak, E., Turner, R.: Spatial Point Patterns: Methodology and Applications with R. CRC Press, Boca Raton (2015)',
-       'DOI': '10.1201/b19708'},
+       'unstructured': 'Baddeley, A., Rubak, E., Turner, R.: Spatial Point Patterns: Methodology and Applications with R. CRC Press, Boca Raton (2015)'},
       {'key': '1041_CR2',
        'series-title': 'Statistics Reference Online',
        'doi-asserted-by': 'publisher',
        'DOI': '10.1002/9781118445112.stat07751',
        'volume-title': 'Ripley’s $$k$$ Function',
        'author': 'PM Dixon',
        'year': '2014',
        'unstructured': 'Dixon, P.M.: Ripley’s $$k$$ Function. Statistics Reference Online, Wiley, New York (2014)'},
       {'issue': '1',
        'key': '1041_CR3',
        'doi-asserted-by': 'publisher',
        'first-page': '1',
        'DOI': '10.1016/0962-8924(94)90025-6',
        'volume': '4',
        'author': 'R Fesce',
        'year': '1994',
        'unstructured': 'Fesce, R., Grohovaz, F., Valtorta, F., Meldolesi, J.: Neurotransmitter release: fusion or kiss-and-run? Trends Cell Biol. 4(1), 1–4 (1994)',
        'journal-title': 'Trends Cell Biol.'},
       {'issue': '8',
        'key': '1041_CR4',
        'doi-asserted-by': 'publisher',
        'first-page': '1953',
        'DOI': '10.1007/s00138-014-0625-2',
        'volume': '25',
        'author': 'Y Gavet',
        'year': '2014',
        'unstructured': 'Gavet, Y., Fernandes, M., Debayle, J., Pinoli, J.C.: Dissimilarity criteria and their comparison for quantitative evaluation of image segmentation: application to human retina vessels. Mach. Vis. Appl. 25(8), 1953–1966 (2014)',
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       {'key': '1041_CR5',
        'doi-asserted-by': 'publisher',
        'first-page': '248',
        'DOI': '10.3389/fncel.2018.00248',
        'volume': '12',
        'author': 'N Gavrilov',
        'year': '2018',
        'unstructured': 'Gavrilov, N., Golyagina, I., Brazhe, A., Scimemi, A., Turlapov, V., Semyanov, A.: Astrocytic coverage of dendritic spines, dendritic shafts, and axonal boutons in hippocampal neuropil. Front. Cell. Neurosci. 12, 248 (2018)',
        'journal-title': 'Front. Cell. Neurosci.'},
       {'issue': '498',
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        'doi-asserted-by': 'publisher',
        'first-page': '754',
        'DOI': '10.1080/01621459.2012.688463',
        'volume': '107',
        'author': 'U Hahn',
        'year': '2012',
        'unstructured': 'Hahn, U.: A studentized permutation test for the comparison of spatial point patterns. J. Am. Stat. Assoc. 107(498), 754–764 (2012). https://doi.org/10.1080/01621459.2012.688463',
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       {'issue': '4',
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        'first-page': '60:1',
        'DOI': '10.1145/3197517.3201353',
        'volume': '37',
        'author': 'Y Hu',
        'year': '2018',
        'unstructured': 'Hu, Y., Zhou, Q., Gao, X., Jacobson, A., Zorin, D., Panozzo, D.: Tetrahedral meshing in the wild. ACM Trans. Gr. 37(4), 60:1-60:14 (2018). https://doi.org/10.1145/3197517.3201353',
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       {'key': '1041_CR8',
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        'unstructured': 'Khanmohammadi, M., Waagepetersen, R.P., Sporring, J.: Analysis of shape and spatial interaction of synaptic vesicles using data from focused ion beam scanning electron microscopy (FIB-SEM). Front. Neuroanatomy (2015). https://doi.org/10.3389/fnana.2015.00116',
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        'first-page': '797',
        'DOI': '10.1083/jcb.135.3.797',
        'volume': '135',
        'author': 'J Koenig',
        'year': '1996',
        'unstructured': 'Koenig, J., Ikeda, K.: Synaptic vesicles have two distinct recycling pathways. J. Cell Biol. 135(3), 797–808 (1996)',
        'journal-title': 'J. Cell Biol.'},
       {'key': '1041_CR10',
        'unstructured': 'Lucchi, A., Li, Y., Becker, C., Fua, P.: Electron microscopy dataset. https://cvlab.epfl.ch/data/data-em/. Accessed 14 Mar 2020'},
       {'issue': '2',
        'key': '1041_CR11',
        'doi-asserted-by': 'publisher',
        'first-page': '568',
        'DOI': '10.1002/mrm.24477',
        'volume': '70',
        'author': 'J Marques',
        'year': '2013',
        'unstructured': 'Marques, J., Genant, H.K., Lillholm, M., Dam, E.B.: Diagnosis of osteoarthritis and prognosis of tibial cartilage loss by quantification of tibia trabecular bone from MRI. Magn. Reson. Med. 70(2), 568–575 (2013). https://doi.org/10.1002/mrm.24477',
        'journal-title': 'Magn. Reson. Med.'},
       {'issue': '1654',
        'key': '1041_CR12',
        'doi-asserted-by': 'publisher',
        'first-page': '20140047',
        'DOI': '10.1098/rstb.2014.0047',
        'volume': '369',
        'author': 'N Medvedev',
        'year': '2014',
        'unstructured': 'Medvedev, N., Popov, V., Henneberger, C., Kraev, I., Rusakov, D.A., Stewart, M.G.: Glia selectively approach synapses on thin dendritic spines. Philos. Trans. R Soc. B Biol. Sci. 369(1654), 20140047 (2014)',
        'journal-title': 'Philos. Trans. R Soc. B Biol. Sci.'},
       {'issue': '3',
        'key': '1041_CR13',
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        'first-page': '551',
        'DOI': '10.1016/S0896-6273(00)00065-9',
        'volume': '27',
        'author': 'D Richards',
        'year': '2000',
        'unstructured': 'Richards, D., Guatimosim, C., Betz, W.: Two endocytic recycling routes selectively fill two vesicle pools in frog motor nerve terminals. Neuron 27(3), 551–559 (2000)',
        'journal-title': 'Neuron'},
       {'issue': '3',
        'key': '1041_CR14',
        'doi-asserted-by': 'publisher',
        'first-page': '368',
        'DOI': '10.1111/j.2517-6161.1979.tb01091.x',
        'volume': '41',
        'author': 'BD Ripley',
        'year': '1979',
        'unstructured': 'Ripley, B.D.: Tests of randomness for spatial point patterns. J. Roy. Stat. Soc. Ser. B (Methodol.) 41(3), 368–374 (1979). https://doi.org/10.1111/j.2517-6161.1979.tb01091.x',
        'journal-title': 'J. Roy. Stat. Soc. Ser. B (Methodol.)'},
       {'key': '1041_CR15',
        'doi-asserted-by': 'publisher',
+       'DOI': '10.1017/CBO9780511624131',
        'volume-title': 'Statistical Inference for Spatial Processes',
        'author': 'BD Ripley',
        'year': '1988',
-       'unstructured': 'Ripley, B.D.: Statistical Inference for Spatial Processes. Cambridge University Press, Cambridge (1988)',
-       'DOI': '10.1017/CBO9780511624131'},
+       'unstructured': 'Ripley, B.D.: Statistical Inference for Spatial Processes. Cambridge University Press, Cambridge (1988)'},
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        'doi-asserted-by': 'crossref',
        'unstructured': 'Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer (2015)',
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+       'DOI': '10.1007/978-3-540-78859-1',
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        'year': '2008',
-       'unstructured': 'Schneider, R., Weil, W.: Stochastic and Integral Geometry. Springer, Heidelberg (2008)',
-       'DOI': '10.1007/978-3-540-78859-1'},
+       'unstructured': 'Schneider, R., Weil, W.: Stochastic and Integral Geometry. Springer, Heidelberg (2008)'},
       {'key': '1041_CR18',
        'unstructured': 'Scientific, T.F.: Amira-avizo software (2020)'},
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        'doi-asserted-by': 'crossref',
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        'volume': '41',
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        'year': '2015',
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        'year': '2020',
        'unstructured': 'Stephensen, H.J.T., Darkner, S., Sporring, J.: Restoring drifted electron microscope volumes using synaptic vesicles at sub-pixel accuracy. Commun. Biol. 3(1), 1–7 (2020)',
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       {'key': '1041_CR23',
        'unstructured': 'Stephensen, H.J.T., Sporring, J.: Rodent neuronal volume annotations and segmentations (2020). https://www.doi.org/10.17894/ucph.33bd30d2-5796-48f4-a0a8-96fcc0ce6af5'},
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        'volume': '37',
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        'year': '2004',
        'unstructured': 'Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recogn. 37(1), 1–19 (2004). https://doi.org/10.1016/j.patcog.2003.07.008',
        'journal-title': 'Pattern Recogn.'}],
      'container-title': 'Journal of Mathematical Imaging and Vision',
      'original-title': [],
      'language': 'en',
      'link': [{'URL': 'https://link.springer.com/content/pdf/10.1007/s10851-021-01041-3.pdf',
        'content-type': 'application/pdf',
        'content-version': 'vor',
        'intended-application': 'text-mining'},
       {'URL': 'https://link.springer.com/article/10.1007/s10851-021-01041-3/fulltext.html',
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       'date-time': '2023-01-01T16:48:24Z',
       'timestamp': 1672591704000},
      'score': 1,
      'resource': {'primary': {'URL': 'https://link.springer.com/10.1007/s10851-021-01041-3'}},
      'subtitle': [],
      'short-title': [],
      'issued': {'date-parts': [[2021, 6, 26]]},
      'references-count': 26,
      'journal-issue': {'issue': '8',
       'published-print': {'date-parts': [[2021, 10]]}},
      'alternative-id': ['1041'],
      'URL': 'http://dx.doi.org/10.1007/s10851-021-01041-3',
      'relation': {},
      'ISSN': ['0924-9907', '1573-7683'],
-     'subject': ['Applied Mathematics',
-      'Geometry and Topology',
-      'Computer Vision and Pattern Recognition',
-      'Condensed Matter Physics',
-      'Modeling and Simulation',
-      'Statistics and Probability'],
+     'subject': [],
      'container-title-short': 'J Math Imaging Vis',
      'published': {'date-parts': [[2021, 6, 26]]},
      'assertion': [{'value': '7 November 2020',
        'order': 1,
        'name': 'received',
        'label': 'Received',
        'group': {'name': 'ArticleHistory', 'label': 'Article History'}},
       {'value': '2 June 2021',
        'order': 2,
        'name': 'accepted',
        'label': 'Accepted',
        'group': {'name': 'ArticleHistory', 'label': 'Article History'}},
       {'value': '26 June 2021',
        'order': 3,
        'name': 'first_online',
        'label': 'First Online',
        'group': {'name': 'ArticleHistory', 'label': 'Article History'}},
       {'order': 1,
        'name': 'Ethics',
        'group': {'name': 'EthicsHeading', 'label': 'Declarations'}},
       {'value': 'The authors declare that they have no conflict of interest.',
        'order': 2,
        'name': 'Ethics',
        'group': {'name': 'EthicsHeading', 'label': 'Conflict of interest'}}]}

docs/notebooks/Unet.ipynb

 %% Cell type:code id:be66055b-8ee9-46be-ad9d-f15edf2654a4 tags:
 ``` python
 %load_ext autoreload
 %autoreload 2
 ```
 %% Cell type:code id:0c61dd11-5a2b-44ff-b0e5-989360bbb677 tags:
 ``` python
 from os.path import join
 import qim3d
 import os
 %matplotlib inline
 ```
 %% Cell type:code id:cd6bb832-1297-462f-8d35-1738a9c37ffd tags:
 ``` python
 # Define function for getting dataset path from string
 def get_dataset_path(name: str, datasets):
     assert name in datasets, 'Dataset name must be ' + ' or '.join(datasets)
     dataset_idx = datasets.index(name)
     if os.name == 'nt':
         datasets_path = [
             '//home.cc.dtu.dk/3dimage/projects/2023_STUDIOS_SD/analysis/data/Belialev2020/side',
             '//home.cc.dtu.dk/3dimage/projects/2023_STUDIOS_SD/analysis/data/Gaudez2022/3d',
             '//home.cc.dtu.dk/3dimage/projects/2023_STUDIOS_SD/analysis/data/Guo2023/2d/',
             '//home.cc.dtu.dk/3dimage/projects/2023_STUDIOS_SD/analysis/data/Stan2020/2d',
             '//home.cc.dtu.dk/3dimage/projects/2023_STUDIOS_SD/analysis/data/Reichardt2021/2d',
             '//home.cc.dtu.dk/3dimage/projects/2023_STUDIOS_SD/analysis/data/TestCircles/2d_binary'
         ]
     else:
         datasets_path = [
             '/dtu/3d-imaging-center/projects/2023_STUDIOS_SD/analysis/data/Belialev2020/side',
             '/dtu/3d-imaging-center/projects/2023_STUDIOS_SD/analysis/data/Gaudez2022/3d',
             '/dtu/3d-imaging-center/projects/2023_STUDIOS_SD/analysis/data/Guo2023/2d/',
             '/dtu/3d-imaging-center/projects/2023_STUDIOS_SD/analysis/data/Stan2020/2d',
             '/dtu/3d-imaging-center/projects/2023_STUDIOS_SD/analysis/data/Reichardt2021/2d',
             '/dtu/3d-imaging-center/projects/2023_STUDIOS_SD/analysis/data/TestCircles/2d_binary'
         ]
     return datasets_path[dataset_idx]
 ```
 %% Cell type:markdown id:7d07077a-cce3-4448-89f5-02413345becc tags:
 ### Datasets
 %% Cell type:code id:9a3b9c3c-4bbb-4a19-9685-f68c437e8bee tags:
 ``` python
 datasets = ['belialev2020_side', 'gaudez2022_3d', 'guo2023_2d', 'stan2020_2d', 'reichardt2021_2d', 'testcircles_2dbinary']
 dataset = datasets[3]
 root = get_dataset_path(dataset,datasets)
 # should not use gaudez2022: 3d image
 # reichardt2021: multiclass segmentation
 ```
 %% Cell type:markdown id:254dc8cb-6f24-4b57-91c0-98fb6f62602c tags:
 ### Model and Augmentation
 %% Cell type:code id:30098003-ec06-48e0-809f-82f44166fb2b tags:
 ``` python
 # defining model
-my_model = qim3d.models.UNet(size = 'medium', dropout = 0.25)
+my_model = qim3d.ml.models.UNet(size = 'medium', dropout = 0.25)
 # defining augmentation
-my_aug = qim3d.utils.Augmentation(resize = 'crop', transform_train = 'light')
+my_aug = qim3d.ml.Augmentation(resize = 'crop', transform_train = 'light')
 ```
 %% Cell type:markdown id:7b56c654-720d-4c5f-8545-749daa5dbaf2 tags:
 ### Loading the data
 %% Cell type:code id:84141298-054d-4322-8bda-5ec514528985 tags:
 ``` python
 # level of logging
-qim3d.io.logger.level('info')
+qim3d.utils._logger.level('info')
 # datasets and dataloaders
-train_set, val_set, test_set = qim3d.utils.prepare_datasets(path = root, val_fraction = 0.3,
+train_set, val_set, test_set = qim3d.ml.prepare_datasets(path = root, val_fraction = 0.3,
                                                             model = my_model , augmentation = my_aug)
-train_loader, val_loader, test_loader = qim3d.utils.prepare_dataloaders(train_set, val_set,
+train_loader, val_loader, test_loader = qim3d.ml.prepare_dataloaders(train_set, val_set,
                                                                         test_set, batch_size = 6)
 ```
 %% Output
     The image size doesn't match the Unet model's depth. The image is changed with 'crop', from (852, 852) to (832, 832).
 %% Cell type:code id:f320a4ae-f063-430c-b5a0-0d9fb64c2725 tags:
 ``` python
 qim3d.viz.grid_overview(train_set,alpha = 1)
 ```
 %% Output
     <Figure size 1400x600 with 14 Axes>
 %% Cell type:code id:7fa3aa57-ba61-4c9a-934c-dce26bbc9e97 tags:
 ``` python
 # Summary of model
-model_s = qim3d.utils.model_summary(train_loader,my_model)
+model_s = qim3d.ml.model_summary(train_loader,my_model)
 print(model_s)
 ```
 %% Output
     =======================================================================================================================================
     Layer (type:depth-idx)                                                                Output Shape              Param #
     =======================================================================================================================================
     UNet                                                                                  [6, 1, 832, 832]          --
     ├─UNet: 1-1                                                                           [6, 1, 832, 832]          --
     │    └─Sequential: 2-1                                                                [6, 1, 832, 832]          --
     │    │    └─Convolution: 3-1                                                          [6, 64, 416, 416]         --
     │    │    │    └─Conv2d: 4-1                                                          [6, 64, 416, 416]         640
     │    │    │    └─ADN: 4-2                                                             [6, 64, 416, 416]         --
     │    │    │    │    └─InstanceNorm2d: 5-1                                             [6, 64, 416, 416]         --
     │    │    │    │    └─Dropout: 5-2                                                    [6, 64, 416, 416]         --
     │    │    │    │    └─PReLU: 5-3                                                      [6, 64, 416, 416]         1
     │    │    └─SkipConnection: 3-2                                                       [6, 128, 416, 416]        --
     │    │    │    └─Sequential: 4-3                                                      [6, 64, 416, 416]         --
     │    │    │    │    └─Convolution: 5-4                                                [6, 128, 208, 208]        --
     │    │    │    │    │    └─Conv2d: 6-1                                                [6, 128, 208, 208]        73,856
     │    │    │    │    │    └─ADN: 6-2                                                   [6, 128, 208, 208]        --
     │    │    │    │    │    │    └─InstanceNorm2d: 7-1                                   [6, 128, 208, 208]        --
     │    │    │    │    │    │    └─Dropout: 7-2                                          [6, 128, 208, 208]        --
     │    │    │    │    │    │    └─PReLU: 7-3                                            [6, 128, 208, 208]        1
     │    │    │    │    └─SkipConnection: 5-5                                             [6, 256, 208, 208]        --
     │    │    │    │    │    └─Sequential: 6-3                                            [6, 128, 208, 208]        --
     │    │    │    │    │    │    └─Convolution: 7-4                                      [6, 256, 104, 104]        --
     │    │    │    │    │    │    │    └─Conv2d: 8-1                                      [6, 256, 104, 104]        295,168
     │    │    │    │    │    │    │    └─ADN: 8-2                                         [6, 256, 104, 104]        --
     │    │    │    │    │    │    │    │    └─InstanceNorm2d: 9-1                         [6, 256, 104, 104]        --
     │    │    │    │    │    │    │    │    └─Dropout: 9-2                                [6, 256, 104, 104]        --
     │    │    │    │    │    │    │    │    └─PReLU: 9-3                                  [6, 256, 104, 104]        1
     │    │    │    │    │    │    └─SkipConnection: 7-5                                   [6, 512, 104, 104]        --
     │    │    │    │    │    │    │    └─Sequential: 8-3                                  [6, 256, 104, 104]        --
     │    │    │    │    │    │    │    │    └─Convolution: 9-4                            [6, 512, 52, 52]          --
     │    │    │    │    │    │    │    │    │    └─Conv2d: 10-1                           [6, 512, 52, 52]          1,180,160
     │    │    │    │    │    │    │    │    │    └─ADN: 10-2                              [6, 512, 52, 52]          --
     │    │    │    │    │    │    │    │    │    │    └─InstanceNorm2d: 11-1              [6, 512, 52, 52]          --
     │    │    │    │    │    │    │    │    │    │    └─Dropout: 11-2                     [6, 512, 52, 52]          --
     │    │    │    │    │    │    │    │    │    │    └─PReLU: 11-3                       [6, 512, 52, 52]          1
     │    │    │    │    │    │    │    │    └─SkipConnection: 9-5                         [6, 1536, 52, 52]         --
     │    │    │    │    │    │    │    │    │    └─Convolution: 10-3                      [6, 1024, 52, 52]         --
     │    │    │    │    │    │    │    │    │    │    └─Conv2d: 11-4                      [6, 1024, 52, 52]         4,719,616
     │    │    │    │    │    │    │    │    │    │    └─ADN: 11-5                         [6, 1024, 52, 52]         --
     │    │    │    │    │    │    │    │    │    │    │    └─InstanceNorm2d: 12-1         [6, 1024, 52, 52]         --
     │    │    │    │    │    │    │    │    │    │    │    └─Dropout: 12-2                [6, 1024, 52, 52]         --
     │    │    │    │    │    │    │    │    │    │    │    └─PReLU: 12-3                  [6, 1024, 52, 52]         1
     │    │    │    │    │    │    │    │    └─Convolution: 9-6                            [6, 256, 104, 104]        --
     │    │    │    │    │    │    │    │    │    └─ConvTranspose2d: 10-4                  [6, 256, 104, 104]        3,539,200
     │    │    │    │    │    │    │    │    │    └─ADN: 10-5                              [6, 256, 104, 104]        --
     │    │    │    │    │    │    │    │    │    │    └─InstanceNorm2d: 11-6              [6, 256, 104, 104]        --
     │    │    │    │    │    │    │    │    │    │    └─Dropout: 11-7                     [6, 256, 104, 104]        --
     │    │    │    │    │    │    │    │    │    │    └─PReLU: 11-8                       [6, 256, 104, 104]        1
     │    │    │    │    │    │    └─Convolution: 7-6                                      [6, 128, 208, 208]        --
     │    │    │    │    │    │    │    └─ConvTranspose2d: 8-4                             [6, 128, 208, 208]        589,952
     │    │    │    │    │    │    │    └─ADN: 8-5                                         [6, 128, 208, 208]        --
     │    │    │    │    │    │    │    │    └─InstanceNorm2d: 9-7                         [6, 128, 208, 208]        --
     │    │    │    │    │    │    │    │    └─Dropout: 9-8                                [6, 128, 208, 208]        --
     │    │    │    │    │    │    │    │    └─PReLU: 9-9                                  [6, 128, 208, 208]        1
     │    │    │    │    └─Convolution: 5-6                                                [6, 64, 416, 416]         --
     │    │    │    │    │    └─ConvTranspose2d: 6-4                                       [6, 64, 416, 416]         147,520
     │    │    │    │    │    └─ADN: 6-5                                                   [6, 64, 416, 416]         --
     │    │    │    │    │    │    └─InstanceNorm2d: 7-7                                   [6, 64, 416, 416]         --
     │    │    │    │    │    │    └─Dropout: 7-8                                          [6, 64, 416, 416]         --
     │    │    │    │    │    │    └─PReLU: 7-9                                            [6, 64, 416, 416]         1
     │    │    └─Convolution: 3-3                                                          [6, 1, 832, 832]          --
     │    │    │    └─ConvTranspose2d: 4-4                                                 [6, 1, 832, 832]          1,153
     =======================================================================================================================================
     Total params: 10,547,273
     Trainable params: 10,547,273
     Non-trainable params: 0
     Total mult-adds (G): 675.50
     =======================================================================================================================================
     Input size (MB): 16.61
     Forward/backward pass size (MB): 4153.34
     Params size (MB): 42.19
     Estimated Total Size (MB): 4212.15
     =======================================================================================================================================
 %% Cell type:markdown id:a665ae28-d9a6-419f-9131-54283b47582c tags:
 ### Hyperparameters and training
 %% Cell type:code id:ce64ae65-01fb-45a9-bdcb-a3806de8469e tags:
 ``` python
 # model hyperparameters
-my_hyperparameters = qim3d.models.Hyperparameters(my_model, n_epochs=5,
+my_hyperparameters = qim3d.ml.Hyperparameters(my_model, n_epochs=5,
                                                   learning_rate = 5e-3, loss_function='DiceCE',weight_decay=1e-3)
 # training model
-qim3d.utils.train_model(my_model, my_hyperparameters, train_loader, val_loader, plot=True)
+qim3d.ml.train_model(my_model, my_hyperparameters, train_loader, val_loader, plot=True)
 ```
 %% Output
     Epoch   0, train loss: 0.7937, val loss: 0.5800
 %% Cell type:markdown id:7e14fac8-4fd3-4725-bd0d-9e2a95552278 tags:
 ### Plotting
 %% Cell type:code id:f8684cb0-5673-4409-8d22-f00b7d099ca4 tags:
 ``` python
-in_targ_preds_test = qim3d.utils.inference(test_set,my_model)
+in_targ_preds_test = qim3d.ml.inference(test_set,my_model)
 qim3d.viz.grid_pred(in_targ_preds_test,alpha=1)
 ```
 %% Output
     <Figure size 1400x1000 with 28 Axes>

docs/notebooks/Untitled.ipynb

-%% Cell type:code id:0b73f2d8 tags:
-
-``` python
-import qim3d
-```
-
-%% Cell type:code id:73db6886 tags:
-
-``` python
-vol = qim3d.examples.bone_128x128x128
-```
-
-%% Cell type:code id:22d86d4d tags:
-
-``` python
-qim3d.viz.orthogonal(vol)
-```
-
-%% Output
-
-    HBox(children=(interactive(children=(IntSlider(value=64, description='Z', max=127), Output()), layout=Layout(a…

docs/notebooks/annotation_tool.ipynb

 %% Cell type:code id: tags:
 
 ``` python
 import qim3d
 import matplotlib.pyplot as plt
 
 # Load example image
 vol = qim3d.examples.bone_128x128x128
 
 # Start annotation tool
 annotation_tool = qim3d.gui.annotation_tool.Interface()
 
 # We can directly pass the image we loaded to the interface
-app = annotation_tool.launch(vol[0], server_name="10.197.104.229")
+app = annotation_tool.launch(vol[0]) # , server_name="10.197.104.229")
 ```
 
+%% Output
+
+
+    c:\Users\s193396\AppData\Local\miniconda3\envs\qim3d\lib\site-packages\gradio\analytics.py:106: UserWarning: IMPORTANT: You are using gradio version 4.44.0, however version 4.44.1 is available, please upgrade.
+    --------
+      warnings.warn(
+
 %% Cell type:code id: tags:
 
 ``` python
 annotation_tool.get_result()
 ```
 
+%% Output
+
+    Loaded shape: (128, 128)
+    INFO:qim3d:Loaded shape: (128, 128)
+    Volume using 16.0 KB of memory
+    
+    INFO:qim3d:Volume using 16.0 KB of memory
+    
+    System memory:
+     • Total.: 31.6 GB
+     • Used..: 18.0 GB (56.8%)
+     • Free..: 13.7 GB (43.2%)
+    INFO:qim3d:System memory:
+     • Total.: 31.6 GB
+     • Used..: 18.0 GB (56.8%)
+     • Free..: 13.7 GB (43.2%)
+
+    {'mask_red': array([[False, False, False, ..., False, False, False],
+            [False, False, False, ..., False, False, False],
+            [False, False, False, ..., False, False, False],
+            ...,
+            [False, False, False, ..., False, False, False],
+            [False, False, False, ..., False, False, False],
+            [False, False, False, ..., False, False, False]])}
+
 %% Cell type:code id: tags:
 
 ``` python
 %matplotlib inline
 import matplotlib.pyplot as plt
 from IPython.display import clear_output
 import numpy as np
 import time
 
-while True:
-    clear_output(wait=True)
-    masks_dict = annotation_tool.get_result()
-    if len(masks_dict) == 0:
-        masks_dict["No mask"] = np.zeros((32,32))
 
-    fig, axs = plt.subplots(1, len(masks_dict), figsize=(8,3))
+clear_output(wait=True)
+masks_dict = annotation_tool.get_result()
+if len(masks_dict) == 0:
+    masks_dict["No mask"] = np.zeros((32,32))
 
-    if len(masks_dict) == 1:
-        axs = [axs]
-    for idx, (name, mask) in enumerate(masks_dict.items()):
+fig, axs = plt.subplots(1, len(masks_dict), figsize=(8,3))
 
-        axs[idx].imshow(mask, cmap='gray', interpolation='none')
-        axs[idx].set_title(name)
-        axs[idx].axis('off')
+if len(masks_dict) == 1:
+    axs = [axs]
+for idx, (name, mask) in enumerate(masks_dict.items()):
 
-    plt.tight_layout()
+    axs[idx].imshow(mask, cmap='gray', interpolation='none')
+    axs[idx].set_title(name)
+    axs[idx].axis('off')
 
+plt.tight_layout()
 
-    plt.show()
-    time.sleep(2)
+
+plt.show()
+time.sleep(2)
 ```
+
+%% Output
+
+    Loaded shape: (128, 128)
+
+    INFO:qim3d:Loaded shape: (128, 128)
+    Volume using 16.0 KB of memory
+    
+    INFO:qim3d:Volume using 16.0 KB of memory
+    
+    System memory:
+     • Total.: 31.6 GB
+     • Used..: 17.9 GB (56.7%)
+     • Free..: 13.7 GB (43.3%)
+    INFO:qim3d:System memory:
+     • Total.: 31.6 GB
+     • Used..: 17.9 GB (56.7%)
+     • Free..: 13.7 GB (43.3%)
+
+

docs/notebooks/filters.ipynb

 %% Cell type:code id: tags:
 
 ``` python
 import qim3d
-import qim3d.processing.filters as filters
+import qim3d.filters as filters
 import numpy as np
 from scipy import ndimage
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 vol = qim3d.examples.fly_150x256x256
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Using the filter functions directly
 
 %% Cell type:code id: tags:
 
 ``` python
 ### Gaussian filter
-out1_gauss = filters.gaussian(vol,3)
-# or
-out2_gauss = filters.gaussian(vol,sigma=3) # sigma is positional, but can be passed as a kwarg
+out1_gauss = filters.gaussian(vol, sigma=3)
 
 ### Median filter
-out_median = filters.median(vol,size=5)
+out_median = filters.median(vol, size=5)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Using filter classes
 
 %% Cell type:code id: tags:
 
 ``` python
 gaussian_fn = filters.Gaussian(sigma=3)
 out3_gauss = gaussian_fn(vol)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## Using filter classes to construct a pipeline of filters
 
 %% Cell type:code id: tags:
 
 ``` python
 pipeline = filters.Pipeline(
     filters.Gaussian(sigma=3),
     filters.Median(size=10))
 out_seq = pipeline(vol)
 ```
 
 %% Cell type:markdown id: tags:
 
 Filter functions can also be appended to the sequence after defining the class instance:
 
 %% Cell type:code id: tags:
 
 ``` python
 pipeline.append(filters.Maximum(size=5))
 out_seq2 = pipeline(vol)
 ```
 
 %% Cell type:markdown id: tags:
 
 The filter objects are stored in the `filters` dictionary:
 
 %% Cell type:code id: tags:
 
 ``` python
 print(pipeline.filters)
 ```
 
 %% Output
 
-    {'0': <qim3d.processing.filters.Gaussian object at 0x7b3fbdad7bb0>, '1': <qim3d.processing.filters.Median object at 0x7b3fbdad52a0>, '2': <qim3d.processing.filters.Maximum object at 0x7b40f7d3f6d0>}
+    {'0': <qim3d.filters._common_filter_methods.Gaussian object at 0x000001AB34BC5F90>, '1': <qim3d.filters._common_filter_methods.Median object at 0x000001AB34BC5FC0>, '2': <qim3d.filters._common_filter_methods.Maximum object at 0x000001AB34BC61A0>}

docs/notebooks/structure_tensor.ipynb

 %% Cell type:code id: tags:
 
 ``` python
 import qim3d
 ```
 
 %% Cell type:markdown id: tags:
 
 ### Structure tensor notebook
 
 %% Cell type:markdown id: tags:
 
 This notebook shows how to compute eigenvalues and eigenvectors of the **structure tensor** of a 3D volume using the `qim3d` library. The structure tensor (matrix) represents information about the local gradient directions in the volume, such that the eigenvectors represent the orientation of the structure in the volume, and the corresponding eigenvaleus indicate the magnitude.
 
 The function `qim3d.processing.structure_tensor` returns two arrays `val` and `vec` for the eigenvalues and eigenvectors, respectively.\
 By having the argument `visulize = True`, the function displays a figure of three subplots:
 
 * Slice of volume with vector field of the eigenvectors
 * Orientation histogram of the eigenvectors
 * Slice of volume with overlaying colors of the orientation
 
 For all three subplots, the colors used to visualize the orientation within the volume are from the HSV colorspace. In these visualizations, the saturation of the color corresponds to the vector component of the slicing direction (i.e. $z$-component when choosing visualization along `axis = 0`). Hence, if an orientation in the volume is orthogonal to the slicing direction, the corresponding color of the visualization will be gray.
 
 %% Cell type:markdown id: tags:
 
 ### **Example:** Structure tensor of brain tissue volume
 
 %% Cell type:code id: tags:
 
 ``` python
 # Import 3D volume of brain tissue
 NT = qim3d.examples.NT_128x128x128
 
 # Visuaize the 3D volume
-qim3d.viz.vol(NT)
+qim3d.viz.volumetric(NT)
 ```
 
 %% Output
 
 
 %% Cell type:markdown id: tags:
 
 From the visualization of the full volume, it can be seen that the circular structures of the brain tissue are aligned orthogonal to the $z$-axis (`axis = 0`). By choosing to slice the volume in this direction, the structure tensor visualizations will largely be gray, since the $z$-component of the eigenvectors are close to $0$, meaning the saturation of the coloring will be close to $0$ (i.e. gray). This can be seen below.
 
 %% Cell type:code id: tags:
 
 ``` python
 # Compute eigenvalues and eigenvectors of the structure tensor
 val, vec = qim3d.processing.structure_tensor(NT, visualize = True, axis = 0) # Slicing in z-direction
 ```
 
 %% Output
 
 
 %% Cell type:markdown id: tags:
 
 By slicing the volume in the $x$-direction (`axis = 2`) instead, the orientation along the length of the structures in the brain tissue can be seen instead. Then the structure tensor visualizations will be largely blue, corresponding to eigenvectors along the $x$-direction with angles of $\approx \frac{\pi}{2}$ radians ($90$ degrees).
 
 %% Cell type:code id: tags:
 
 ``` python
 # Compute eigenvalues and eigenvectors of the structure tensor
 val, vec = qim3d.processing.structure_tensor(NT, visualize = True, axis = 2) # Slicing in x-direction
 ```
 
 %% Output
 

qim3d/ml/__init__.py

 from ._augmentations import Augmentation
 from ._data import Dataset, prepare_dataloaders, prepare_datasets
-from ._ml_utils import inference, model_summary, train_model
+from ._ml_utils import inference, volume_inference, model_summary, train_model
 from .models import *
\ No newline at end of file

qim3d/tests/notebooks/test_notebooks.py

+from testbook import testbook
+
+def test_blob_detection_notebook():
+    with testbook('./docs/notebooks/blob_detection.ipynb', execute=True) as tb:
+        pass
+
+def test_filters_notebook():
+    with testbook('./docs/notebooks/filters.ipynb', execute=True) as tb:
+        pass
+
+def test_local_thickness_notebook():
+    with testbook('./docs/notebooks/local_thickness.ipynb', execute=True) as tb:
+        pass
+
+def test_logging_notebook():
+    with testbook('./docs/notebooks/Logging.ipynb', execute=True) as tb:
+        pass
+
+def test_references_from_doi_notebook():
+    with testbook('./docs/notebooks/References from DOI.ipynb', execute=True) as tb:
+        pass
+
+def test_structure_tensor_notebook():
+    with testbook('./docs/notebooks/structure_tensor.ipynb', execute=True) as tb:
+        pass
\ No newline at end of file

qim3d/utils/_logger.py

@@ -2,6 +2,7 @@
 
 import logging
 
+logger = logging.getLogger("qim3d")
 
 def set_detailed_output():
     """Configures the logging output to display detailed information.
@@ -18,7 +19,6 @@ def set_detailed_output():
     )
     handler = logging.StreamHandler()
     handler.setFormatter(formatter)
-    logger = logging.getLogger("qim3d")
     logger.handlers = []
     logger.addHandler(handler)
 
@@ -36,7 +36,6 @@ def set_simple_output():
     formatter = logging.Formatter("%(message)s")
     handler = logging.StreamHandler()
     handler.setFormatter(formatter)
-    logger = logging.getLogger()
     logger.handlers = []
     logger.addHandler(handler)
 
@@ -48,7 +47,7 @@ def set_level_debug():
         >>> set_level_debug()
     """
 
-    logging.getLogger("qim3d").setLevel(logging.DEBUG)
+    logger.setLevel(logging.DEBUG)
 
 
 def set_level_info():
@@ -58,7 +57,7 @@ def set_level_info():
         >>> set_level_info()
     """
 
-    logging.getLogger("qim3d").setLevel(logging.INFO)
+    logger.setLevel(logging.INFO)
 
 
 def set_level_warning():
@@ -68,7 +67,7 @@ def set_level_warning():
         >>> set_level_warning()
     """
 
-    logging.getLogger("qim3d").setLevel(logging.WARNING)
+    logger.setLevel(logging.WARNING)
 
 
 def set_level_error():
@@ -77,7 +76,7 @@ def set_level_error():
     Example:
         >>> set_level_error()
     """
-    logging.getLogger("qim3d").setLevel(logging.ERROR)
+    logger.setLevel(logging.ERROR)
 
 
 def set_level_critical():
@@ -86,7 +85,7 @@ def set_level_critical():
     Example:
         >>> set_level_critical()
     """
-    logging.getLogger("qim3d").setLevel(logging.CRITICAL)
+    logger.setLevel(logging.CRITICAL)
 
 
 def level(log_level):
@@ -121,7 +120,7 @@ def level(log_level):
         set_level_critical()
 
     elif isinstance(log_level, int):
-        logging.getLogger("qim3d").setLevel(log_level)
+        logger.setLevel(log_level)
 
     else:
         raise ValueError(
@@ -130,7 +129,7 @@ def level(log_level):
         )
 
 
-# create the logger
+# Create the logger
 log = logging.getLogger("qim3d")
 set_level_info()
 set_simple_output()

requirements.txt

@@ -31,3 +31,4 @@ ome_zarr>=0.9.0
 dask-image>=2024.5.3
 trimesh>=4.4.9
 slgbuilder>=0.2.1
+testbook>=0.4.2