aind-ephys-utils

Helpful methods for exploring in vivo electrophysiology data.

Installation

pip install aind-ephys-utils

Key concepts

Analysis happens on Xarray DataArray objects with labeled dimensions and coordinates, via the ephys accessor: - da.ephys.align(...) - da.ephys.reduce(...) - da.ephys.psth(...) - da.ephys.plot.raster(...)

This allows functions to be run in sequence and combined with built-in Xarray functions, e.g.:

da.ephys.align(...).sel(unit=1).mean('trial').ephys.smooth(...)

Example usage (with NWB)

from aind_ephys_utils import from_dataframe
from pynwb import NWBHDF5IO

# read the file
nwb = NWBHDF5IO('/path/to/file.nwb', 'r').read()

# load units and trials dataframes
units = nwb.units.to_dataframe()
trials = nwb.trials.to_dataframe()

# align all units to all trials in a specific time window
spikes = from_dataframe(units, trials, window=(-0.5, 1.0))

# plot a spike raster for one unit, grouped by the value in the "choice" column:
ax = spikes.sel(unit=1).ephys.plot.raster(group_by="choice")

# bin the spikes in 0.01 s intervals and smooth
binned = spikes.ephys.bin(0.01).ephys.smooth(window=0.05)

# plot a PSTH for all units and conditions:
ax = binned.ephys.plot.psth()

Next steps

• Quickstart

• Core concepts

• Operations

Documentation

• Quickstart
  • Install
  • Load and convert
  • Data filtering and selection
  • Bin, smooth, plot
  • Next steps
• Core concepts
  • Data model
  • Xarray primer
  • Conventions
• Design principles
  • Package layers
  • Data model assumptions
  • Coords and attributes are first‑class
  • Error handling
  • Extensibility
  • Guidelines for contributions
• Adapters
  • from_dataframe
• Operations
  • NumPy compatibility
• Plotting
  • raster
  • psth
  • trajectory
• API
  • aind_ephys_utils package

• Index

• Module Index