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| − | =Python Notebooks=
| + | #REDIRECT [[SNT:_Scripting#Python_Notebooks]] |
| − | Direct access to the SNT API from the [https://www.python.org/ Python] programming language is made possible through the [https://pypi.org/project/pyimagej/ pyimagej] module. This enables full integration between SNT and any library in the Python ecosystem.
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| − | ==Installing pyimagej==
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| − | Follow the instructions given [https://github.com/imagej/pyimagej#installation here]
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| − | ==Getting Started==
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| − | To initialize Fiji from Python:
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| − | <source lang="python">
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| − | import imagej
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| − | ij = imagej.init('sc.fiji:fiji')
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| − | </source>
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| − | Then, use [https://github.com/kivy/pyjnius pyjnius] (bundled with pyimagej) to import the SNT (Java) classes you need. For example, to download a neuron reconstruction from the MouseLight database and calculate summary statistics on it, you would import the [http://javadoc.scijava.org/Fiji/tracing/io/MouseLightLoader MouseLightLoader] and [http://javadoc.scijava.org/Fiji/tracing/analysis/TreeStatistics.html TreeStatistics] classes:
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| − | <source lang="python">
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| − | from jnius import autoclass
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| − | MouseLightLoader = autoclass('tracing.io.MouseLightLoader')
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| − | TreeStatistics = autoclass('tracing.analysis.TreeStatistics')
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| − | </source>
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| − | Now you can access all the attributes and methods these classes offer. Let's get a summary of the inter-node distances for a specific mouse cortical motor neuron (UUID = "AA0100" in the MouseLight database).
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| − | <source lang="python">
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| − | loader = MouseLightLoader("AA0100") # one of the largest cells in the database
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| − | if not loader.isDatabaseAvailable():
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| − | ui.showDialog("Could not connect to ML database", "Error")
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| − | return
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| − | if not loader.idExists():
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| − | ui.showDialog("Somehow the specified id was not found", "Error")
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| − | return
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| − | a_tree = loader.getTree('axon', None) # compartment, color
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| − | s_stats = TreeStatistics(a_tree)
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| − | metric = TreeStatistics.INTER_NODE_DISTANCE
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| − | summary_stats = s_stats.getSummaryStats(metric)
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| − | s_stats.getHistogram(metric).show()
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| − | print("The average inter-node distance is %d" % summary_stats.getMean())
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| − | </source>
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