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181 | class Kilosort:
_kilosort_core_files = [
"params.py",
"amplitudes.npy",
"channel_map.npy",
"channel_positions.npy",
"pc_features.npy",
"pc_feature_ind.npy",
"similar_templates.npy",
"spike_templates.npy",
"spike_times.npy",
"template_features.npy",
"template_feature_ind.npy",
"templates.npy",
"templates_ind.npy",
"whitening_mat.npy",
"whitening_mat_inv.npy",
"spike_clusters.npy",
]
_kilosort_additional_files = [
"spike_times_sec.npy",
"spike_times_sec_adj.npy",
"cluster_groups.csv",
"cluster_KSLabel.tsv",
]
kilosort_files = _kilosort_core_files + _kilosort_additional_files
def __init__(self, kilosort_dir):
self._kilosort_dir = pathlib.Path(kilosort_dir)
self._files = {}
self._data = None
self._clusters = None
self.validate()
params_filepath = kilosort_dir / "params.py"
self._info = {
"time_created": datetime.fromtimestamp(params_filepath.stat().st_ctime),
"time_modified": datetime.fromtimestamp(params_filepath.stat().st_mtime),
}
@property
def data(self):
if self._data is None:
self._load()
return self._data
@property
def info(self):
return self._info
def validate(self):
"""
Check if this is a valid set of kilosort outputs - i.e. all crucial files exist
"""
missing_files = []
for f in Kilosort._kilosort_core_files:
full_path = self._kilosort_dir / f
if not full_path.exists():
missing_files.append(f)
if missing_files:
raise FileNotFoundError(
f"Kilosort files missing in ({self._kilosort_dir}):" f" {missing_files}"
)
def _load(self):
self._data = {}
for kilosort_filename in Kilosort.kilosort_files:
kilosort_filepath = self._kilosort_dir / kilosort_filename
if not kilosort_filepath.exists():
log.debug("skipping {} - does not exist".format(kilosort_filepath))
continue
base, ext = path.splitext(kilosort_filename)
self._files[base] = kilosort_filepath
if kilosort_filename == "params.py":
log.debug("loading params.py {}".format(kilosort_filepath))
# params.py is a 'key = val' file
params = {}
for line in open(kilosort_filepath, "r").readlines():
k, v = line.strip("\n").split("=")
params[k.strip()] = convert_to_number(v.strip())
log.debug("params: {}".format(params))
self._data[base] = params
if ext == ".npy":
log.debug("loading npy {}".format(kilosort_filepath))
d = np.load(
kilosort_filepath,
mmap_mode="r",
allow_pickle=False,
fix_imports=False,
)
self._data[base] = (
np.reshape(d, d.shape[0]) if d.ndim == 2 and d.shape[1] == 1 else d
)
self._data["channel_map"] = self._data["channel_map"].flatten()
# Read the Cluster Groups
for cluster_pattern, cluster_col_name in zip(
["cluster_group.*", "cluster_KSLabel.*"], ["group", "KSLabel"]
):
try:
cluster_file = next(self._kilosort_dir.glob(cluster_pattern))
except StopIteration:
pass
else:
cluster_file_suffix = cluster_file.suffix
assert cluster_file_suffix in (".tsv", ".xlsx")
break
else:
raise FileNotFoundError(
'Neither "cluster_groups" nor "cluster_KSLabel" file found!'
)
if cluster_file_suffix == ".tsv":
df = pd.read_csv(cluster_file, sep="\t", header=0)
elif cluster_file_suffix == ".xlsx":
df = pd.read_excel(cluster_file, engine="openpyxl")
else:
df = pd.read_csv(cluster_file, delimiter="\t")
self._data["cluster_groups"] = np.array(df[cluster_col_name].values)
self._data["cluster_ids"] = np.array(df["cluster_id"].values)
def get_best_channel(self, unit):
template_idx = self.data["spike_templates"][
np.where(self.data["spike_clusters"] == unit)[0][0]
]
channel_templates = self.data["templates"][template_idx, :, :]
max_channel_idx = np.abs(channel_templates).max(axis=0).argmax()
max_channel = self.data["channel_map"][max_channel_idx]
return max_channel, max_channel_idx
def extract_spike_depths(self):
"""Reimplemented from https://github.com/cortex-lab/spikes/blob/master/analysis/ksDriftmap.m"""
if "pc_features" in self.data:
ycoords = self.data["channel_positions"][:, 1]
pc_features = self.data["pc_features"][:, 0, :] # 1st PC only
pc_features = np.where(pc_features < 0, 0, pc_features)
# ---- compute center of mass of these features (spike depths) ----
# which channels for each spike?
spk_feature_ind = self.data["pc_feature_ind"][
self.data["spike_templates"], :
]
# ycoords of those channels?
spk_feature_ycoord = ycoords[spk_feature_ind]
# center of mass is sum(coords.*features)/sum(features)
self._data["spike_depths"] = np.sum(
spk_feature_ycoord * pc_features**2, axis=1
) / np.sum(pc_features**2, axis=1)
else:
self._data["spike_depths"] = None
# ---- extract spike sites ----
max_site_ind = np.argmax(np.abs(self.data["templates"]).max(axis=1), axis=1)
spike_site_ind = max_site_ind[self.data["spike_templates"]]
self._data["spike_sites"] = self.data["channel_map"][spike_site_ind]
|