generic_spikedetekt.prm

"""Contain default parameters."""

VOLTAGE_GAIN = 10.
WAVEFORMS_NSAMPLES = 32

assert sample_rate > 0

# Filtering
# ---------
filter_low = 600. # Low pass frequency (Hz)
filter_high = 0.95 * .5 * sample_rate
filter_butter_order = 3  # Order of Butterworth filter.

# Chunks
# ------
chunk_size = int(1. * sample_rate)  # 1 second
chunk_overlap = int(.015 * sample_rate)  # 15 ms

# Saving raw/filtered data
# ------------------------
save_raw = False
save_high = False
save_low = False

# Spike detection
# ---------------
# Uniformly scattered chunks, for computing the threshold from the std of the
# signal across the whole recording.

nexcerpts = 60
excerpt_size = int(1. * sample_rate)
use_single_threshold = False
threshold_strong_std_factor = 5.
threshold_weak_std_factor = 2.5
detect_spikes = 'negative'

# Connected component
# -------------------
connected_component_join_size = int(.00005 * sample_rate)

# Spike extraction
# ----------------
extract_s_before = 16
extract_s_after = 16

# Features
# --------
nfeatures_per_channel = 5  # Number of features per channel.
pca_nwaveforms_max = 20000
features_contiguous = True  # Whether to make the features array contiguous

# Put a NumPy array here if you don't want the PCs to be computed
# automatically from the filtered waveforms. This is normally obtained with
# compute_pcs(waveforms, npcs=npcs). This array size should be
# (npcs, nsamples, nchannels).
canonical_pcs = None

#Waveform alignment
# -----------------
weight_power = 2

# DEBUG: diagnostics
# ------------------
# Full path to a debug script.
diagnostics_path = None