Difference between revisions of "SNT: Python Notebooks"

(Getting Started)
(Getting Started)
Line 27: Line 27:
  
 
a_tree = loader.getTree('axon', None) # compartment, color
 
a_tree = loader.getTree('axon', None) # compartment, color
d_stats = TreeStatistics(a_tree)
+
s_stats = TreeStatistics(a_tree)
 
metric = TreeStatistics.INTER_NODE_DISTANCE
 
metric = TreeStatistics.INTER_NODE_DISTANCE
summary_stats = d_stats.getSummaryStats(metric)
+
summary_stats = s_stats.getSummaryStats(metric)
d_stats.getHistogram(metric).show()
+
s_stats.getHistogram(metric).show()
 
print("The average inter-node distance is %d" % summary_stats.getMean())
 
print("The average inter-node distance is %d" % summary_stats.getMean())
 
</source>
 
</source>

Revision as of 18:40, 11 April 2019

Python Notebooks

Direct access to the SNT API from the Python programming language is made possible with the pyimagej module. This enables full integration between SNT and any library in the Python ecosystem.

Installing pyimagej

Follow the instructions given here

Getting Started

To initialize Fiji from Python:

import imagej
ij = imagej.init('sc.fiji:fiji')

Then, use 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 MouseLightLoader and TreeStatistics classes:

from jnius import autoclass
MouseLightLoader = autoclass('tracing.io.MouseLightLoader')
TreeStatistics   = autoclass('tracing.analysis.TreeStatistics')

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).

loader = MouseLightLoader("AA0100")  # one of the largest cells in the database
if not loader.isDatabaseAvailable():
    ui.showDialog("Could not connect to ML database", "Error")
    return
if not loader.idExists():
    ui.showDialog("Somehow the specified id was not found", "Error")
    return

a_tree = loader.getTree('axon', None) # compartment, color
s_stats = TreeStatistics(a_tree)
metric = TreeStatistics.INTER_NODE_DISTANCE
summary_stats = s_stats.getSummaryStats(metric)
s_stats.getHistogram(metric).show()
print("The average inter-node distance is %d" % summary_stats.getMean())