spikeinterface

name

spikeinterface

description

Unified extracellular electrophysiology workflows for spike sorting, preprocessing, postprocessing, metrics, curation, and visualization. Use when working with Recording/Sorting objects, building SortingAnalyzer pipelines, running sorters, comparing sortings to ground truth, and applying automated/manual curation in SpikeInterface. Keywords: generate_ground_truth_recording, NumpyRecording, load_extractor, create_sorting_analyzer, quality_metrics, run_sorter, compare_sorter_to_ground_truth, apply_curation, threshold_metrics_label_units, plot_traces.

SpikeInterface

SpikeInterface provides a modular API to move from raw extracellular recordings to sorted, analyzed, curated, and visualized units.

Version

Built against: spikeinterface==0.103.3 Python: 3.13.5

Runtime import/execution status in this build: [UNVERIFIED: install denied in selected environment] (see assets/version.txt).

Environment Gate

Before any install command, confirm environment + permission and record in assets/version.txt:

environment: <env name/path>
python_executable: <path or `python`>
install_permission: yes|no
install_scope: none|current-env|named-env|new-env
package_manager: pip|conda|mamba|uv|other
coverage_profile: workflow|dictionary|hybrid

Current build metadata is already captured in assets/version.txt.

Installation

# Requires install_permission: yes
pip install spikeinterface

# Optional extras are task-dependent and not enforced in this skill build:
# [REQUIRES: sorter-specific external binaries/containers for some spikeinterface.sorters workflows]
# [REQUIRES: plotting backend dependencies for some spikeinterface.widgets backends]

---

Core I/O and Synthetic Data

import numpy as np
import spikeinterface as si

# tested against spikeinterface==0.103.3
# Build an in-memory recording and query traces
traces = np.random.normal(0, 5, size=(3000, 4)).astype("float32")
rec = si.NumpyRecording([traces], sampling_frequency=30000.0)
print(rec.get_num_channels(), rec.get_num_segments())

# Generate toy ground truth pair
recording, sorting = si.generate_ground_truth_recording(num_channels=4, num_units=6, durations=[2.0], seed=0)
print(recording.get_num_channels(), sorting.get_num_units())

See references/core-io-and-synthetic.md for signatures and pitfalls around NumpyRecording, binary I/O, load_extractor, and synthetic generators.

---

Preprocessing and Motion

import spikeinterface.preprocessing as spre
import spikeinterface as si

# tested against spikeinterface==0.103.3
recording, _ = si.generate_ground_truth_recording(num_channels=4, durations=[2.0], seed=0)
rec_bp = spre.bandpass_filter(recording, freq_min=300.0, freq_max=6000.0)
print(rec_bp.get_num_channels())

# [VERSION: changed in 0.103.x — unsigned data is not auto-cast; use unsigned_to_signed() explicitly]

See references/preprocessing-and-motion.md for preprocessing pipelines, motion correction entry points, and version-sensitive behavior.

---

SortingAnalyzer and Metrics

import spikeinterface as si

# tested against spikeinterface==0.103.3
recording, sorting = si.generate_ground_truth_recording(num_channels=4, num_units=8, durations=[3.0], seed=0)
analyzer = si.create_sorting_analyzer(sorting=sorting, recording=recording, format="memory", return_in_uV=True)
analyzer.compute("random_spikes", max_spikes_per_unit=200)
analyzer.compute("waveforms", ms_before=1.0, ms_after=2.0)
qm = analyzer.compute("quality_metrics", metric_names=["snr", "presence_ratio"]).get_data()
print(qm.shape)

# [VERSION: changed in 0.103.x — prefer return_in_uV over return_scaled]

See references/sorting-analyzer-and-metrics.md for extension dependencies, signature details, and metrics workflow patterns.

---

Running Sorters

import spikeinterface as si
import spikeinterface.sorters as ss

# tested against spikeinterface==0.103.3
recording, _ = si.generate_ground_truth_recording(num_channels=4, durations=[1.0], seed=0)
print("available", ss.available_sorters())
print("installed", ss.installed_sorters())

# [REQUIRES: sorter backends and optional external installations]
# sorting = ss.run_sorter("kilosort2_5", recording, folder="sorter_out", with_output=True)

See references/sorters-and-execution.md for local/container runner signatures and safe execution patterns.

---

Comparison, Curation, and Widgets

import spikeinterface as si
import spikeinterface.comparison as scmp
import spikeinterface.curation as scur

# tested against spikeinterface==0.103.3
recording, gt_sorting = si.generate_ground_truth_recording(num_channels=4, num_units=6, durations=[2.0], seed=0)
comp = scmp.compare_sorter_to_ground_truth(gt_sorting, gt_sorting, exhaustive_gt=True)
print(comp.count_well_detected_units(well_detected_score=0.8))

analyzer = si.create_sorting_analyzer(gt_sorting, recording, format="memory")
analyzer.compute("random_spikes", max_spikes_per_unit=200)
analyzer.compute("waveforms", ms_before=1.0, ms_after=2.0)
analyzer.compute("templates")
analyzer.compute("noise_levels")
metrics = analyzer.compute("quality_metrics", metric_names=["snr", "presence_ratio"]).get_data()
labels = scur.threshold_metrics_label_units(metrics, thresholds={"snr": (">=", 5)})
print(labels.columns)

See references/comparison-curation-widgets.md for comparison classes, curation model APIs, and widget function aliases.

---

Verification (Medium+)

python opensci-skill/scripts/verify-snippets.py --root spikeinterface --fail-fast

[UNVERIFIED: verify against installed runtime in an environment where spikeinterface and runtime deps are available].

API Dictionary (Dictionary/Hybrid)

• assets/symbol-index.md - module-level dictionary navigation
• assets/symbol-index.jsonl - machine-readable symbol lookup
• assets/symbol-cards/ - per-module symbol cards with signatures and source anchors

Quick Reference

Function / Class	Purpose
NumpyRecording(...)	Create an in-memory Recording from NumPy arrays.
generate_ground_truth_recording(...)	Produce synthetic recording/sorting pairs for testing and tutorials.
create_sorting_analyzer(...)	Pair recording+sorting and orchestrate extension computations.
run_sorter(...)	Execute a sorter backend (local or container depending on config).
compare_sorter_to_ground_truth(...)	Score sorter output against reference sorting.
threshold_metrics_label_units(...)	Apply metric thresholds to produce curation labels.

Module Map

Submodule	Contents	Notes
spikeinterface.core	Base recording/sorting classes, I/O, synthetic generation, sorting analyzer	large API surface
spikeinterface.preprocessing	Filtering, scaling, bad-channel handling, motion correction, pipeline API	mixed eager export + deprecation __getattr__
spikeinterface.sorters	Sorter registry and execution wrappers	external tool dependencies for many sorters
spikeinterface.metrics	Quality/template/spiketrain metrics via analyzer extensions	top-level star exports
spikeinterface.comparison	Pairwise and multi-sorter comparisons	agreement/confusion/performance tooling
spikeinterface.curation	Merge/split/threshold/model-based curation helpers	model workflows use pydantic/skops integrations
spikeinterface.widgets	Plotting widgets + plot_* aliases	backend-dependent behavior

Import style: mixed (eager star imports in several __init__.py, plus lazy/deprecation __getattr__ in extractors and preprocessing).

See assets/module-map.md for full submodule inventory.

References

• references/core-io-and-synthetic.md - in-memory/binary I/O and synthetic data generation.
• references/preprocessing-and-motion.md - preprocessing wrappers, pipelines, and motion correction.
• references/sorting-analyzer-and-metrics.md - analyzer lifecycle, extension dependencies, metrics retrieval.
• references/sorters-and-execution.md - sorter registry, local/container execution, and result loading.
• references/comparison-curation-widgets.md - comparison classes, curation APIs, and plotting aliases.