def get_threshold_current(file):
"""Get the threshold current to initialize AP for a neuron.
Input:
file: An abf file with voltage under ramp current recorded.
Return:
A generator containing (cell_ID, trace/sweep, ramp slope(pA/s), threshold current(pA), threshold voltage(mV), spiking_time(s))."""
abf = pyabf.ABF(file)
cellid = getCellID(file)
dt = abf.dataSecPerPoint
start, end = abf.sweepEpochs.p1s[2], abf.sweepEpochs.p2s[2]
for sweep in abf.sweepList:
abf.setSweep(sweep)
dy = np.diff(abf.sweepY[start:end])
dc = np.diff(abf.sweepC[start:end])
dyt = dy / dt
dct = dc / dt
try:
if dyt.max() / dyt.std() < 10:
continue
# if dv/dt > 5*std(dy/dt), set as threshold point
spike_p = np.where(dyt > 5 * dyt.std())[0]
th_i = abf.sweepC[spike_p[0] + start]
th_v = abf.sweepY[spike_p[0] + start]
th_t = abf.sweepX[spike_p[0] + start]
yield (cellid, sweep + 1, dct[0], th_i, th_v, th_t)
except ValueError:
print("Cannot compute rheobase for `{}` trace {}".format(
getFileName(file), sweep + 1))
def plot_demo_distribution(self, term, file_id, to_save=""):
"""Plot a bar plot of AP number vs. sweep number.
term: String. Include 'freq', 'amp'.
file_id: Interger. the n-th 'freq' file.
to_save: file path. If left empty, not to save."""
demoData = self.read_demo_stat(term, file_id)
demoData.plot("sweep", "n", kind="bar", legend=False)
identifier = getCellID(self.files[term][file_id])
plt.title(identifier)
plt.ylabel("AP number")
if bool(to_save):
to_save_figure(term + " distribution of " + to_save)
def read_demo_stat(self, term, file_ID):
"""Read statistic data from txt file generated by mini.
term: String. Include 'freq', 'amp'.
file_ID: demo file order."""
if term in ["freq", "amp"]:
filepath = self.files[term][file_ID]
dt = pd.read_csv(filepath, sep="\t", skiprows=1)
dt.dropna(how="all", inplace=True)
dt.rename({"Unnamed: 0": "sweep"}, axis=1, inplace=True)
dt.sweep = dt.sweep.str.extract("Group # (\d+)").astype("int64")
dt.n = dt.n.astype("int64")
dt["CellID"] = getCellID(filepath)
dt = dt[np.roll(dt.columns, 1)]
return dt
def get_matched_file(self, item1, item2, file_id=0, cell_id=""):
"""Match pairwise file for 'abf', 'iti', 'event', 'fit', 'freq', 'amp',
return actual path instead of a map. Input either file_id in item1 or cell_id.
file_id: interger. The n-th file in item1 group.
cell_id: string. A cell id what item1_files contains.
See `ap_parser.match_file()`"""
if not (cell_id):
identifier = getCellID(self.files[item1][file_id])
else:
identifier = cell_id
matched = pd.Series(self.files[item2])
result = matched[matched.str.contains(identifier)].values
if len(result) == 0:
return ""
elif len(result) >= 2:
return result.tolist()
else:
return result[0]
def extract_demo_ap_features(file, features, extract_average=False):
"""Extract electrophysiological features from an abf file about AP recording.
features: list. All features to be extracted.
extrace_average: Average the values of the same sweep."""
result = []
visited = []
for f in file:
traces = abf2trace(f)
cellid = getCellID(f)
if cellid in visited:
continue
cur_result = efel.getFeatureValues(traces,
features,
raise_warnings=False)
cur_result = {cellid: cur_result}
visited.append(cellid)
if not extract_average:
result.append(
pd.DataFrame(
((cellid, ind + 1, itemk, itemv[0]) if itemv else
(cellid, ind + 1, itemk, None)
for cellid, v in cur_result.items()
for ind, vv in enumerate(v)
for itemk, itemv in vv.items()),
columns=["CellID", "trace", "featureName", "featureValue"],
))
else:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
result.append(
pd.DataFrame(
((cellid, ind + 1, "mean_" + itemk,
itemv.mean()) if isinstance(itemv, np.ndarray) else
(cellid, ind + 1, itemk, None)
for cellid, v in cur_result.items()
for ind, vv in enumerate(v)
for itemk, itemv in vv.items()),
columns=[
"CellID", "trace", "featureName", "featureValue"
],
))
return pd.concat(result, ignore_index=True)
def plot_demo_IF(self, file_id, to_save=""):
"""Plot instant frequency of each current level for a demo neuron.
file_id: the n-th 'event' file.
to_save: String. The figure to be saved.
Default: empty (not to save)."""
IFdemoData = self.get_demo_IF_data(file_id)
if IFdemoData is not None:
sns_line = sns.lineplot(
x="relative_time",
y="instantaneous_freq (Hz)",
hue="Group",
data=IFdemoData[IFdemoData["instantaneous_freq (Hz)"].between(
0, 500, False)
& (IFdemoData["Group"] != 0)],
estimator=None,
)
plt.xlabel("AP onset time (ms)")
plt.ylabel("Instantaneous frequency (Hz)")
title = getCellID(self.files["event"][file_id])
plt.title(title)
plt.show()
figIFdemo = sns_line.get_figure()
if bool(to_save):
figIFdemo.savefig("instantaneous freq of" + title, dpi=600)