400 lines
15 KiB
Python
400 lines
15 KiB
Python
|
|
from CellData import CellData
|
|
from models.LIFACnoise import LifacNoiseModel
|
|
from stimuli.SinusoidalStepStimulus import SinusoidalStepStimulus
|
|
import helperFunctions as hF
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import pickle
|
|
from os.path import join, exists
|
|
|
|
|
|
class Baseline:
|
|
|
|
def __init__(self):
|
|
self.save_file_name = "baseline_values.pkl"
|
|
self.baseline_frequency = -1
|
|
self.serial_correlation = []
|
|
self.vector_strength = -1
|
|
self.coefficient_of_variation = -1
|
|
self.burstiness = -1
|
|
|
|
def get_baseline_frequency(self):
|
|
raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
|
|
|
|
def get_serial_correlation(self, max_lag):
|
|
raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
|
|
|
|
def get_vector_strength(self):
|
|
raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
|
|
|
|
def get_coefficient_of_variation(self):
|
|
raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
|
|
|
|
def get_burstiness(self):
|
|
raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
|
|
|
|
def __get_burstiness__(self, eod_freq):
|
|
isis = np.array(self.get_interspike_intervals())
|
|
if len(isis) == 0:
|
|
return 0
|
|
bursts = isis[isis < 1.5 * (1.0/eod_freq)]
|
|
|
|
return len(bursts) / float(len(isis))
|
|
|
|
def get_interspike_intervals(self):
|
|
raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
|
|
|
|
def get_spiketime_phases(self):
|
|
raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
|
|
|
|
def plot_baseline(self, save_path=None, time_length=0.2):
|
|
raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
|
|
|
|
@staticmethod
|
|
def _get_baseline_frequency_given_data(spiketimes):
|
|
base_freqs = []
|
|
for st in spiketimes:
|
|
base_freqs.append(hF.calculate_mean_isi_freq(st))
|
|
|
|
return np.median(base_freqs)
|
|
|
|
@staticmethod
|
|
def _get_serial_correlation_given_data(max_lag, spikestimes):
|
|
serial_cors = []
|
|
|
|
for st in spikestimes:
|
|
sc = hF.calculate_serial_correlation(st, max_lag)
|
|
serial_cors.append(sc)
|
|
serial_cors = np.array(serial_cors)
|
|
|
|
res = np.mean(serial_cors, axis=0)
|
|
return res
|
|
|
|
@staticmethod
|
|
def _get_vector_strength_given_data(times, eods, spiketimes, sampling_interval):
|
|
vs_per_trial = []
|
|
for i in range(len(spiketimes)):
|
|
vs = hF.calculate_vector_strength_from_spiketimes(times[i], eods[i], spiketimes[i], sampling_interval)
|
|
vs_per_trial.append(vs)
|
|
|
|
return np.mean(vs_per_trial)
|
|
|
|
@staticmethod
|
|
def _get_coefficient_of_variation_given_data(spiketimes):
|
|
# CV (stddev of ISI divided by mean ISI (np.diff(spiketimes))
|
|
cvs = []
|
|
for st in spiketimes:
|
|
st = np.array(st)
|
|
cvs.append(hF.calculate_coefficient_of_variation(st))
|
|
|
|
return np.mean(cvs)
|
|
|
|
@staticmethod
|
|
def _get_interspike_intervals_given_data(spiketimes):
|
|
isis = []
|
|
for st in spiketimes:
|
|
st = np.array(st)
|
|
isis.extend(np.diff(st))
|
|
|
|
return isis
|
|
|
|
@staticmethod
|
|
def _plot_baseline_given_data(time, eod, v1, spiketimes, sampling_interval, eod_freq="", save_path=None, position=0.5, time_length=0.2):
|
|
"""
|
|
plots the stimulus / eod, together with the v1, spiketimes and frequency
|
|
:return:
|
|
"""
|
|
length_data_points = int(time_length / sampling_interval)
|
|
|
|
start_idx = int(len(time) * position)
|
|
start_idx = start_idx if start_idx >= 0 else 0
|
|
end_idx = int(len(time) * position + length_data_points) + 1
|
|
end_idx = end_idx if end_idx <= len(time) else len(time)
|
|
|
|
spiketimes = np.array(spiketimes)
|
|
spiketimes_part = spiketimes[(spiketimes >= time[start_idx]) & (spiketimes < time[end_idx])]
|
|
|
|
fig, axes = plt.subplots(3, 1, sharex="col", figsize=(12, 8))
|
|
fig.suptitle("Baseline middle part ({:.2f} seconds)".format(time_length))
|
|
axes[0].plot(time[start_idx:end_idx], eod[start_idx:end_idx])
|
|
axes[0].set_ylabel("Stimulus [mV] - Freq:" + eod_freq)
|
|
|
|
max_v1 = max(v1[start_idx:end_idx])
|
|
axes[1].plot(time[start_idx:end_idx], v1[start_idx:end_idx])
|
|
axes[1].plot(spiketimes_part, [max_v1 for _ in range(len(spiketimes_part))],
|
|
'o', color='orange')
|
|
axes[1].set_ylabel("V1-Trace [mV]")
|
|
|
|
t, f = hF.calculate_time_and_frequency_trace(spiketimes_part, sampling_interval)
|
|
axes[2].plot(t, f)
|
|
axes[2].set_ylabel("ISI-Frequency [Hz]")
|
|
axes[2].set_xlabel("Time [s]")
|
|
|
|
if save_path is not None:
|
|
plt.savefig(save_path + "baseline.png")
|
|
else:
|
|
plt.show()
|
|
|
|
plt.close()
|
|
|
|
@staticmethod
|
|
def plot_isi_histogram_comparision(cell_isis, model_isis, save_path=None):
|
|
cell_isis = np.array(cell_isis) * 1000
|
|
model_isis = np.array(model_isis) * 1000
|
|
maximum = max(max(cell_isis), max(model_isis))
|
|
bins = np.arange(0, maximum * 1.01, 0.1)
|
|
|
|
plt.title('Baseline ISIs')
|
|
plt.xlabel('ISI in ms')
|
|
plt.ylabel('Count')
|
|
plt.hist(cell_isis, bins=bins, label="cell", alpha=0.5, density=True)
|
|
plt.hist(model_isis, bins=bins, label="model", alpha=0.5, density=True)
|
|
plt.legend()
|
|
if save_path is not None:
|
|
plt.savefig(save_path + "isi-histogram_comparision.png")
|
|
else:
|
|
plt.show()
|
|
|
|
plt.close()
|
|
|
|
def plot_polar_vector_strength(self, save_path=None):
|
|
phases = self.get_spiketime_phases()
|
|
fig = plt.figure()
|
|
ax = fig.add_subplot(111, polar=True)
|
|
# r = np.arange(0, 1, 0.001)
|
|
# theta = 2 * 2 * np.pi * r
|
|
# line, = ax.plot(theta, r, color='#ee8d18', lw=3)
|
|
bins = np.arange(0, np.pi * 2, 0.1)
|
|
ax.hist(phases, bins=bins)
|
|
|
|
if save_path is not None:
|
|
plt.savefig(save_path + "vector_strength_polar_plot.png")
|
|
else:
|
|
plt.show()
|
|
|
|
plt.close()
|
|
|
|
def plot_interspike_interval_histogram(self, save_path=None):
|
|
|
|
isi = np.array(self.get_interspike_intervals()) * 1000 # change unit to milliseconds
|
|
if len(isi) == 0:
|
|
print("NON SPIKES IN BASELINE OF CELL/MODEL")
|
|
plt.title('Baseline ISIs - NO SPIKES!')
|
|
plt.xlabel('ISI in ms')
|
|
plt.ylabel('Count')
|
|
plt.hist(isi, bins=np.arange(0, 1, 0.1))
|
|
|
|
if save_path is not None:
|
|
plt.savefig(save_path + "isi-histogram.png")
|
|
else:
|
|
plt.show()
|
|
|
|
plt.close()
|
|
return
|
|
maximum = max(isi)
|
|
bins = np.arange(0, maximum * 1.01, 0.1)
|
|
|
|
plt.title('Baseline ISIs')
|
|
plt.xlabel('ISI in ms')
|
|
plt.ylabel('Count')
|
|
plt.hist(isi, bins=bins)
|
|
|
|
if save_path is not None:
|
|
plt.savefig(save_path + "isi-histogram.png")
|
|
else:
|
|
plt.show()
|
|
|
|
plt.close()
|
|
|
|
def plot_serial_correlation(self, max_lag, save_path=None):
|
|
plt.title("Baseline Serial correlation")
|
|
plt.xlabel("Lag")
|
|
plt.ylabel("Correlation")
|
|
plt.ylim((-1, 1))
|
|
plt.plot(np.arange(1, max_lag+1, 1), self.get_serial_correlation(max_lag))
|
|
|
|
if save_path is not None:
|
|
plt.savefig(save_path + "serial_correlation.png")
|
|
else:
|
|
plt.show()
|
|
|
|
plt.close()
|
|
|
|
def save_values(self, save_directory):
|
|
values = {}
|
|
values["baseline_frequency"] = self.get_baseline_frequency()
|
|
values["serial correlation"] = self.get_serial_correlation(max_lag=10)
|
|
values["vector strength"] = self.get_vector_strength()
|
|
values["coefficient of variation"] = self.get_coefficient_of_variation()
|
|
values["burstiness"] = self.get_burstiness()
|
|
|
|
with open(join(save_directory, self.save_file_name), "wb") as file:
|
|
pickle.dump(values, file)
|
|
print("Baseline: Values saved!")
|
|
|
|
def load_values(self, save_directory):
|
|
file_path = join(save_directory, self.save_file_name)
|
|
if not exists(file_path):
|
|
print("Baseline: No file to load")
|
|
return False
|
|
|
|
file = open(file_path, "rb")
|
|
values = pickle.load(file)
|
|
self.baseline_frequency = values["baseline_frequency"]
|
|
self.serial_correlation = values["serial correlation"]
|
|
self.vector_strength = values["vector strength"]
|
|
self.coefficient_of_variation = values["coefficient of variation"]
|
|
self.burstiness = values["burstiness"]
|
|
print("Baseline: Values loaded!")
|
|
return True
|
|
|
|
|
|
class BaselineCellData(Baseline):
|
|
|
|
def __init__(self, cell_data: CellData):
|
|
super().__init__()
|
|
self.data = cell_data
|
|
|
|
def get_baseline_frequency(self):
|
|
if self.baseline_frequency == -1:
|
|
spiketimes = self.data.get_base_spikes()
|
|
self.baseline_frequency = self._get_baseline_frequency_given_data(spiketimes)
|
|
|
|
return self.baseline_frequency
|
|
|
|
def get_vector_strength(self):
|
|
if self.vector_strength == -1:
|
|
times = self.data.get_base_traces(self.data.TIME)
|
|
eods = self.data.get_base_traces(self.data.EOD)
|
|
spiketimes = self.data.get_base_spikes()
|
|
sampling_interval = self.data.get_sampling_interval()
|
|
self.vector_strength = self._get_vector_strength_given_data(times, eods, spiketimes, sampling_interval)
|
|
return self.vector_strength
|
|
|
|
def get_serial_correlation(self, max_lag):
|
|
if len(self.serial_correlation) < max_lag:
|
|
self.serial_correlation = self._get_serial_correlation_given_data(max_lag, self.data.get_base_spikes())
|
|
return self.serial_correlation[:max_lag]
|
|
|
|
def get_coefficient_of_variation(self):
|
|
if self.coefficient_of_variation == -1:
|
|
self.coefficient_of_variation = self._get_coefficient_of_variation_given_data(self.data.get_base_spikes())
|
|
return self.coefficient_of_variation
|
|
|
|
def get_interspike_intervals(self):
|
|
return self._get_interspike_intervals_given_data(self.data.get_base_spikes())
|
|
|
|
def get_spiketime_phases(self):
|
|
times = self.data.get_base_traces(self.data.TIME)
|
|
spiketimes = self.data.get_base_spikes()
|
|
eods = self.data.get_base_traces(self.data.EOD)
|
|
sampling_interval = self.data.get_sampling_interval()
|
|
|
|
phase_list = []
|
|
for i in range(len(times)):
|
|
spiketime_indices = np.array(np.around((np.array(spiketimes[i]) + times[i][0]) / sampling_interval), dtype=int)
|
|
rel_spikes, eod_durs = hF.eods_around_spikes(times[i], eods[i], spiketime_indices)
|
|
|
|
phase_times = (rel_spikes / eod_durs) * 2 * np.pi
|
|
phase_list.extend(phase_times)
|
|
|
|
return phase_list
|
|
|
|
def get_burstiness(self):
|
|
if self.burstiness == -1:
|
|
self.burstiness = self.__get_burstiness__(self.data.get_eod_frequency())
|
|
return self.burstiness
|
|
|
|
def plot_baseline(self, save_path=None, position=0.5, time_length=0.2):
|
|
# eod, v1, spiketimes, frequency
|
|
|
|
time = self.data.get_base_traces(self.data.TIME)[0]
|
|
eod = self.data.get_base_traces(self.data.EOD)[0]
|
|
v1_trace = self.data.get_base_traces(self.data.V1)[0]
|
|
spiketimes = self.data.get_base_spikes()[0]
|
|
|
|
self._plot_baseline_given_data(time, eod, v1_trace, spiketimes,
|
|
self.data.get_sampling_interval(), "{:.0f}".format(self.data.get_eod_frequency()), save_path, position, time_length)
|
|
|
|
|
|
class BaselineModel(Baseline):
|
|
|
|
simulation_time = 30
|
|
|
|
def __init__(self, model: LifacNoiseModel, eod_frequency, trials=1):
|
|
super().__init__()
|
|
self.model = model
|
|
self.eod_frequency = eod_frequency
|
|
self.stimulus = SinusoidalStepStimulus(eod_frequency, 0)
|
|
self.eod = self.stimulus.as_array(0, self.simulation_time, model.get_sampling_interval())
|
|
self.time = np.arange(0, self.simulation_time, model.get_sampling_interval())
|
|
|
|
self.v1_traces = []
|
|
self.spiketimes = []
|
|
for i in range(trials):
|
|
v, st = model.simulate_fast(self.stimulus, self.simulation_time)
|
|
self.v1_traces.append(v)
|
|
self.spiketimes.append(st)
|
|
|
|
def get_baseline_frequency(self):
|
|
if self.baseline_frequency == -1:
|
|
self.baseline_frequency = self._get_baseline_frequency_given_data(self.spiketimes)
|
|
return self.baseline_frequency
|
|
|
|
def get_vector_strength(self):
|
|
if self.vector_strength == -1:
|
|
times = [self.time] * len(self.spiketimes)
|
|
eods = [self.eod] * len(self.spiketimes)
|
|
sampling_interval = self.model.get_sampling_interval()
|
|
self.vector_strength = self._get_vector_strength_given_data(times, eods, self.spiketimes, sampling_interval)
|
|
|
|
return self.vector_strength
|
|
|
|
def get_serial_correlation(self, max_lag):
|
|
if len(self.serial_correlation) != max_lag:
|
|
self.serial_correlation = self._get_serial_correlation_given_data(max_lag, self.spiketimes)
|
|
return self.serial_correlation
|
|
|
|
def get_coefficient_of_variation(self):
|
|
if self.coefficient_of_variation == -1:
|
|
self.coefficient_of_variation = self._get_coefficient_of_variation_given_data(self.spiketimes)
|
|
return self.coefficient_of_variation
|
|
|
|
def get_interspike_intervals(self):
|
|
return self._get_interspike_intervals_given_data(self.spiketimes)
|
|
|
|
def get_burstiness(self):
|
|
if self.burstiness == -1:
|
|
self.burstiness = self.__get_burstiness__(self.eod_frequency)
|
|
return self.burstiness
|
|
|
|
def get_spiketime_phases(self):
|
|
sampling_interval = self.model.get_sampling_interval()
|
|
|
|
phase_list = []
|
|
for i in range(len(self.spiketimes)):
|
|
spiketime_indices = np.array(np.around((np.array(self.spiketimes[i]) + self.time[0]) / sampling_interval), dtype=int)
|
|
rel_spikes, eod_durs = hF.eods_around_spikes(self.time, self.eod, spiketime_indices)
|
|
|
|
phase_times = (rel_spikes / eod_durs) * 2 * np.pi
|
|
phase_list.extend(phase_times)
|
|
|
|
return phase_list
|
|
|
|
def plot_baseline(self, save_path=None, position=0.5, time_length=0.2):
|
|
self._plot_baseline_given_data(self.time, self.eod, self.v1_traces[0], self.spiketimes[0],
|
|
self.model.get_sampling_interval(), "{:.0f}".format(self.eod_frequency),
|
|
save_path, position, time_length)
|
|
|
|
|
|
def get_baseline_class(data, eod_freq=None, trials=1) -> Baseline:
|
|
if isinstance(data, CellData):
|
|
return BaselineCellData(data)
|
|
if isinstance(data, LifacNoiseModel):
|
|
if eod_freq is None:
|
|
raise ValueError("The EOD frequency is needed for the BaselineModel Class.")
|
|
return BaselineModel(data, eod_freq, trials=trials)
|
|
|
|
raise ValueError("Unknown type: Cannot find corresponding Baseline class. data was type:" + str(type(data)))
|