Getting Started with Nelpy¶

Welcome to nelpy! This notebook will guide you through the basics of using nelpy for neural data analysis.

We'll cover installation, basic data structures, and a minimal workflow.

Installation¶

If you haven't already, install nelpy (and dependencies) using pip or conda.

pip install nelpy

Or, for the latest development version:

pip install git+https://github.com/nelpy/nelpy.git

Importing nelpy¶

Let's import the main nelpy modules.

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import matplotlib.pyplot as plt
import numpy as np

import nelpy as nel
import nelpy.plotting as npl

# assume default aesthetics
npl.setup()
import matplotlib.pyplot as plt import numpy as np import nelpy as nel import nelpy.plotting as npl # assume default aesthetics npl.setup()

Simulate or Load Data¶

For this demo, we'll simulate some spike times and position data.

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# Simulate spike times for 3 units
np.random.seed(42)
n_units = 3
duration = 10.0  # seconds
spike_times = [np.sort(np.random.uniform(0, duration, size=50)) for _ in range(n_units)]

# Simulate 1D position sampled at 100 Hz
fs = 100
t = np.arange(0, duration, 1 / fs)
position = np.sin(2 * np.pi * t / duration)  # simple oscillatory trajectory
# Simulate spike times for 3 units np.random.seed(42) n_units = 3 duration = 10.0 # seconds spike_times = [np.sort(np.random.uniform(0, duration, size=50)) for _ in range(n_units)] # Simulate 1D position sampled at 100 Hz fs = 100 t = np.arange(0, duration, 1 / fs) position = np.sin(2 * np.pi * t / duration) # simple oscillatory trajectory

Create Nelpy Objects¶

Let's create an SpikeTrainArray for spikes and an AnalogSignalArray for position.

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# Create EventArray for spikes
spk = nel.SpikeTrainArray(timestamps=spike_times, fs=fs)

# Create AnalogSignalArray for position
asa = nel.AnalogSignalArray(data=position, fs=fs)

epoch_1 = nel.EpochArray([3, 4])
print(spk)
print(asa)
print(epoch_1)
# Create EventArray for spikes spk = nel.SpikeTrainArray(timestamps=spike_times, fs=fs) # Create AnalogSignalArray for position asa = nel.AnalogSignalArray(data=position, fs=fs) epoch_1 = nel.EpochArray([3, 4]) print(spk) print(asa) print(epoch_1)

WARNING:root:creating support from abscissa_vals and sampling rate, fs!
WARNING:root:'fs' has been deprecated; use 'step' instead
WARNING:root:some steps in the data are smaller than the requested step size.

<SpikeTrainArray at 0x1d392f43590: 3 units> at 100 Hz
<AnalogSignalArray at 0x1d392dbd590: 1 signals> for a total of 10 seconds
<EpochArray at 0x1d3911abd90: 1 epoch> of length 1 seconds

Basic Plotting¶

Let's plot the spike raster and position trace.

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fig, ax = plt.subplots(2, 1, figsize=(14, 5), sharex=True)

# use nelpy to plot the spike raster
npl.rasterplot(spk, ax=ax[0])
ax[0].set_ylabel("unit")
ax[0].set_title("Spike Raster")

npl.plot(asa, ax=ax[1])

ax[1].set_xlabel("time [seconds]")
ax[1].set_ylabel("Position")
ax[1].set_title("Position Trace")
plt.show()
fig, ax = plt.subplots(2, 1, figsize=(14, 5), sharex=True) # use nelpy to plot the spike raster npl.rasterplot(spk, ax=ax[0]) ax[0].set_ylabel("unit") ax[0].set_title("Spike Raster") npl.plot(asa, ax=ax[1]) ax[1].set_xlabel("time [seconds]") ax[1].set_ylabel("Position") ax[1].set_title("Position Trace") plt.show()

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slice the data with EpochArray¶

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fig, ax = plt.subplots(2, 1, figsize=(14, 5), sharex=True)

# use nelpy to plot the spike raster
npl.rasterplot(spk[epoch_1], ax=ax[0])
ax[0].set_ylabel("unit")
ax[0].set_title("Spike Raster")

npl.plot(asa[epoch_1], ax=ax[1])

ax[1].set_xlabel("time [seconds]")
ax[1].set_ylabel("Position")
ax[1].set_title("Position Trace")
plt.show()
fig, ax = plt.subplots(2, 1, figsize=(14, 5), sharex=True) # use nelpy to plot the spike raster npl.rasterplot(spk[epoch_1], ax=ax[0]) ax[0].set_ylabel("unit") ax[0].set_title("Spike Raster") npl.plot(asa[epoch_1], ax=ax[1]) ax[1].set_xlabel("time [seconds]") ax[1].set_ylabel("Position") ax[1].set_title("Position Trace") plt.show()

WARNING:root:ignoring events outside of eventarray support
WARNING:root:ignoring signal outside of support

Minimal End-to-End Workflow¶

Let's bin the spikes, compute a simple firing rate, and plot it alongside position.

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# Bin spikes
bin_size = 0.1  # seconds
binned = spk.bin(ds=bin_size)

# Compute firing rate (spikes per bin / bin size)
firing_rate = binned.data / bin_size
bin_centers = binned.bin_centers

# Plot firing rate for first unit
fig, ax = plt.subplots(2, 1, figsize=(14, 5), sharex=True)
ax[0].plot(bin_centers, firing_rate[0], label="Unit 0")
ax[0].set_ylabel("Firing Rate (Hz)")
ax[0].set_title("Binned Firing Rate (Unit 0)")

npl.plot(asa, ax=ax[1])

ax[1].set_xlabel("time [seconds]")
ax[1].set_ylabel("Position")
ax[1].set_title("Position Trace")

plt.show()
# Bin spikes bin_size = 0.1 # seconds binned = spk.bin(ds=bin_size) # Compute firing rate (spikes per bin / bin size) firing_rate = binned.data / bin_size bin_centers = binned.bin_centers # Plot firing rate for first unit fig, ax = plt.subplots(2, 1, figsize=(14, 5), sharex=True) ax[0].plot(bin_centers, firing_rate[0], label="Unit 0") ax[0].set_ylabel("Firing Rate (Hz)") ax[0].set_title("Binned Firing Rate (Unit 0)") npl.plot(asa, ax=ax[1]) ax[1].set_xlabel("time [seconds]") ax[1].set_ylabel("Position") ax[1].set_title("Position Trace") plt.show()

Next Steps¶

Explore the API Reference for more details on nelpy objects.
Try running the other tutorials in the tutorials/ folder.
Check out the GitHub repo for more info and updates.

Happy analyzing!