{"library":"neo","title":"Neo","description":"Neo is a Python package for representing electrophysiology data, with support for reading a wide range of neurophysiology file formats. Current version 0.14.4, requires Python >=3.10. Release cadence is irregular.","language":"python","status":"active","last_verified":"Mon Apr 27","install":{"commands":["pip install neo"],"cli":null},"imports":["from neo.core import Block","from neo.core import Segment","from neo.core import AnalogSignal","from neo.core import SpikeTrain"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import neo\nfrom neo.core import Block, Segment, AnalogSignal, SpikeTrain\nimport quantities as pq\nimport numpy as np\n\n# Create a Block with a Segment\nblock = Block(name='example')\nseg = Segment(name='segment1')\nblock.segments.append(seg)\n\n# Create an AnalogSignal (1 second of data, 100 Hz)\nsignal = AnalogSignal(np.random.rand(100), sampling_rate=100*pq.Hz, units='mV', name='test')\nseg.analogsignals.append(signal)\n\n# Create a SpikeTrain\nst = SpikeTrain([0.1, 0.2, 0.3, 0.5]*pq.s, t_stop=1.0*pq.s, units='s', name='test_spikes')\nseg.spiketrains.append(st)\n\nprint('Block:', block)\nprint('Signals:', seg.analogsignals)\nprint('Spike trains:', seg.spiketrains)","lang":"python","description":"Quickstart: creating a basic hierarchical data structure with Block, Segment, AnalogSignal, and SpikeTrain.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}