add plot_polar for vector strength
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a.ott
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676a0e4945
commit
869923a2be
66
Baseline.py
66
Baseline.py
@ -30,6 +30,9 @@ class Baseline:
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def get_interspike_intervals(self):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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def get_spiketime_phases(self):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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def plot_baseline(self, save_path=None, time_length=0.2):
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raise NotImplementedError("NOT YET OVERRIDDEN FROM ABSTRACT CLASS")
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@ -80,16 +83,16 @@ class Baseline:
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return isis
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@staticmethod
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def _plot_baseline_given_data(time, eod, v1, spiketimes, sampling_interval, save_path=None, time_length=0.2):
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def _plot_baseline_given_data(time, eod, v1, spiketimes, sampling_interval, eod_freq="", save_path=None, position=0.5, time_length=0.2):
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"""
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plots the stimulus / eod, together with the v1, spiketimes and frequency
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:return:
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"""
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length_data_points = int(time_length / sampling_interval)
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start_idx = int(len(time) * 0.5 - length_data_points * 0.5)
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start_idx = int(len(time) * position)
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start_idx = start_idx if start_idx >= 0 else 0
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end_idx = int(len(time) * 0.5 + length_data_points * 0.5) + 1
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end_idx = int(len(time) * position + length_data_points) + 1
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end_idx = end_idx if end_idx <= len(time) else len(time)
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spiketimes = np.array(spiketimes)
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@ -98,7 +101,7 @@ class Baseline:
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fig, axes = plt.subplots(3, 1, sharex="col", figsize=(12, 8))
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fig.suptitle("Baseline middle part ({:.2f} seconds)".format(time_length))
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axes[0].plot(time[start_idx:end_idx], eod[start_idx:end_idx])
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axes[0].set_ylabel("Stimulus [mV]")
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axes[0].set_ylabel("Stimulus [mV] - Freq:" + eod_freq)
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max_v1 = max(v1[start_idx:end_idx])
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axes[1].plot(time[start_idx:end_idx], v1[start_idx:end_idx])
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@ -118,6 +121,23 @@ class Baseline:
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plt.close()
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def plot_polar_vector_strength(self, save_path=None):
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phases = self.get_spiketime_phases()
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fig = plt.figure()
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ax = fig.add_subplot(111, polar=True)
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# r = np.arange(0, 1, 0.001)
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# theta = 2 * 2 * np.pi * r
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# line, = ax.plot(theta, r, color='#ee8d18', lw=3)
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bins = np.arange(0, np.pi * 2, 0.1)
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ax.hist(phases, bins=bins)
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if save_path is not None:
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plt.savefig(save_path + "isi-histogram.png")
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else:
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plt.show()
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plt.close()
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def plot_interspike_interval_histogram(self, save_path=None):
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isi = np.array(self.get_interspike_intervals()) * 1000 # change unit to milliseconds
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maximum = max(isi)
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@ -185,7 +205,23 @@ class BaselineCellData(Baseline):
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def get_interspike_intervals(self):
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return self._get_interspike_intervals_given_data(self.data.get_base_spikes())
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def plot_baseline(self, save_path=None, time_length=0.2):
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def get_spiketime_phases(self):
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times = self.data.get_base_traces(self.data.TIME)
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spiketimes = self.data.get_base_spikes()
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eods = self.data.get_base_traces(self.data.EOD)
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sampling_interval = self.data.get_sampling_interval()
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phase_list = []
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for i in range(len(times)):
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spiketime_indices = np.array(np.around((np.array(spiketimes[i]) + times[i][0]) / sampling_interval), dtype=int)
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rel_spikes, eod_durs = hF.eods_around_spikes(times[i], eods[i], spiketime_indices)
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phase_times = (rel_spikes / eod_durs) * 2 * np.pi
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phase_list.extend(phase_times)
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return phase_list
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def plot_baseline(self, save_path=None, position=0.5, time_length=0.2):
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# eod, v1, spiketimes, frequency
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time = self.data.get_base_traces(self.data.TIME)[0]
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@ -194,7 +230,7 @@ class BaselineCellData(Baseline):
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spiketimes = self.data.get_base_spikes()[0]
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self._plot_baseline_given_data(time, eod, v1_trace, spiketimes,
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self.data.get_sampling_interval(), save_path, time_length)
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self.data.get_sampling_interval(), "{:.0f}".format(self.data.get_eod_frequency()), save_path, position, time_length)
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class BaselineModel(Baseline):
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@ -243,9 +279,23 @@ class BaselineModel(Baseline):
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def get_interspike_intervals(self):
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return self._get_interspike_intervals_given_data(self.spiketimes)
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def plot_baseline(self, save_path=None, time_length=0.2):
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def get_spiketime_phases(self):
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sampling_interval = self.model.get_sampling_interval()
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phase_list = []
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for i in range(len(self.spiketimes)):
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spiketime_indices = np.array(np.around((np.array(self.spiketimes[i]) + self.time[0]) / sampling_interval), dtype=int)
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rel_spikes, eod_durs = hF.eods_around_spikes(self.time, self.eod, spiketime_indices)
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phase_times = (rel_spikes / eod_durs) * 2 * np.pi
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phase_list.extend(phase_times)
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return phase_list
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def plot_baseline(self, save_path=None, position=0.5, time_length=0.2):
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self._plot_baseline_given_data(self.time, self.eod, self.v1_traces[0], self.spiketimes[0],
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self.model.get_sampling_interval(), save_path, time_length)
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self.model.get_sampling_interval(), "{:.0f}".format(self.eod_frequency),
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save_path, position, time_length)
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def get_baseline_class(data, eod_freq=None) -> Baseline:
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