even better bufferplot
This commit is contained in:
weygoldt
parent
943de7c023
commit
cc96501f5e
@ -71,10 +71,11 @@ class PlotBuffer:
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# get tracked frequencies and their times
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freq_temp = self.data.freq[window_idx]
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time_temp = self.data.time[
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(self.data.time >= self.t0)
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& (self.data.time <= (self.t0 + self.dt))
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]
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# time_temp = self.data.time[
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# self.data.idx[self.data.ident == self.track_id]][
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# (self.data.time >= self.t0)
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# & (self.data.time <= (self.t0 + self.dt))
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# ]
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# remake the band we filtered in
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q25, q50, q75 = np.percentile(freq_temp, [25, 50, 75])
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@ -84,8 +85,8 @@ class PlotBuffer:
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)
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# get indices on raw data
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start_idx = self.t0 * self.data.raw_rate
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window_duration = self.dt * self.data.raw_rate
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start_idx = (self.t0 - 5) * self.data.raw_rate
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window_duration = (self.dt + 10) * self.data.raw_rate
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stop_idx = start_idx + window_duration
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# get raw data
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@ -98,58 +99,81 @@ class PlotBuffer:
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self.t0 = 0
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fig = plt.figure(
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figsize=(16 / 2.54, 20 / 2.54)
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figsize=(14 / 2.54, 20 / 2.54)
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)
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gs0 = gr.GridSpec(
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6, 1, figure=fig, height_ratios=[1, 0.05, 1, 0.05, 1, 0.05]
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3, 1, figure=fig, height_ratios=[1, 1, 1]
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)
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gs1 = gs0[0].subgridspec(1, 1)
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gs2 = gs0[2].subgridspec(3, 1)
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gs3 = gs0[4].subgridspec(3, 1)
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gs4 = gs0[5].subgridspec(1, 1)
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ax0 = fig.add_subplot(gs1[0, 0])
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ax1 = fig.add_subplot(gs2[0, 0], sharex=ax0)
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ax2 = fig.add_subplot(gs2[1, 0], sharex=ax0)
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ax3 = fig.add_subplot(gs2[2, 0], sharex=ax0)
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ax4 = fig.add_subplot(gs3[0, 0], sharex=ax0)
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ax5 = fig.add_subplot(gs3[1, 0], sharex=ax0)
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ax6 = fig.add_subplot(gs3[2, 0], sharex=ax0)
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ax7 = fig.add_subplot(gs4[0, 0], sharex=ax0)
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gs2 = gs0[1].subgridspec(3, 1, hspace=0.4)
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gs3 = gs0[2].subgridspec(3, 1, hspace=0.4)
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# gs4 = gs0[5].subgridspec(1, 1)
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ax6 = fig.add_subplot(gs3[2, 0])
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ax0 = fig.add_subplot(gs1[0, 0], sharex=ax6)
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ax1 = fig.add_subplot(gs2[0, 0], sharex=ax6)
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ax2 = fig.add_subplot(gs2[1, 0], sharex=ax6)
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ax3 = fig.add_subplot(gs2[2, 0], sharex=ax6)
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ax4 = fig.add_subplot(gs3[0, 0], sharex=ax6)
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ax5 = fig.add_subplot(gs3[1, 0], sharex=ax6)
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# ax7 = fig.add_subplot(gs4[0, 0], sharex=ax0)
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# ax_leg = fig.add_subplot(gs0[1, 0])
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waveform_scaler = 1000
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lw = 1.5
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# plot spectrogram
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_ = plot_spectrogram(
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ax0,
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data_oi,
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self.data.raw_rate,
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self.t0,
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self.t0 - 5,
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[np.max(self.frequency) - 200, np.max(self.frequency) + 200]
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)
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# ax0.fill_between(
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# np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate),
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# q50 - self.config.minimal_bandwidth / 2,
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# q50 + self.config.minimal_bandwidth / 2,
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# color=ps.black,
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# lw=1,
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# ls="dashed",
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# alpha=0.5,
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# )
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# ax0.fill_between(
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# np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate),
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# search_lower,
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# search_upper,
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# color=ps.black,
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# lw=1,
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# ls="dashed",
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# alpha=0.5,
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# )
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for track_id in self.data.ids:
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t0_track = self.t0_old - 5
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dt_track = self.dt + 10
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window_idx = np.arange(len(self.data.idx))[
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(self.data.ident == track_id)
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& (self.data.time[self.data.idx] >= t0_track)
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& (self.data.time[self.data.idx] <= (t0_track + dt_track))
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]
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# get tracked frequencies and their times
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f = self.data.freq[window_idx]
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t = self.data.time[
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self.data.idx[self.data.ident == self.track_id]]
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tmask = (t >= t0_track) & (t <= (t0_track + dt_track))
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if track_id == self.track_id:
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ax0.plot(t[tmask]-self.t0_old, f, lw=lw,
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zorder=10, color=ps.gblue1)
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else:
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ax0.plot(t[tmask]-self.t0_old, f, lw=lw,
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zorder=10, color=ps.gray, alpha=0.5)
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ax0.fill_between(
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np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate),
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q50 - self.config.minimal_bandwidth / 2,
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q50 + self.config.minimal_bandwidth / 2,
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color=ps.gblue1,
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lw=1,
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ls="dashed",
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alpha=0.5,
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)
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ax0.fill_between(
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np.arange(self.t0, self.t0 + self.dt, 1 / self.data.raw_rate),
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search_lower,
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search_upper,
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color=ps.gblue2,
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lw=1,
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ls="dashed",
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alpha=0.5,
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)
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# ax0.axhline(q50, spec_times[0], spec_times[-1],
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# color=ps.gblue1, lw=2, ls="dashed")
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# ax0.axhline(q50 + self.search_frequency,
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@ -163,46 +187,52 @@ class PlotBuffer:
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# plot waveform of filtered signal
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ax1.plot(self.time, self.baseline * waveform_scaler,
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c=ps.gray, lw=2, alpha=0.5)
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c=ps.gray, lw=lw, alpha=0.5)
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ax1.plot(self.time, self.baseline_envelope_unfiltered *
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waveform_scaler, c=ps.gblue1, lw=2, label="baseline envelope")
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waveform_scaler, c=ps.gblue1, lw=lw, label="baseline envelope")
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# plot waveform of filtered search signal
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ax2.plot(self.time, self.search * waveform_scaler,
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c=ps.gray, lw=2, alpha=0.5)
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c=ps.gray, lw=lw, alpha=0.5)
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ax2.plot(self.time, self.search_envelope_unfiltered *
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waveform_scaler, c=ps.gblue2, lw=2, label="search envelope")
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waveform_scaler, c=ps.gblue2, lw=lw, label="search envelope")
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# plot baseline instantaneous frequency
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ax3.plot(self.frequency_time, self.frequency,
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c=ps.gblue3, lw=2, label="baseline inst. freq.")
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c=ps.gblue3, lw=lw, label="baseline inst. freq.")
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# plot filtered and rectified envelope
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ax4.plot(self.time, self.baseline_envelope, c=ps.gblue1, lw=2)
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ax4.plot(self.time, self.baseline_envelope, c=ps.gblue1, lw=lw)
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ax4.scatter(
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(self.time)[self.baseline_peaks],
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self.baseline_envelope[self.baseline_peaks],
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c=ps.red,
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edgecolors=ps.red,
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zorder=10,
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marker="o",
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facecolors="none",
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)
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# plot envelope of search signal
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ax5.plot(self.time, self.search_envelope, c=ps.gblue2, lw=2)
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ax5.plot(self.time, self.search_envelope, c=ps.gblue2, lw=lw)
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ax5.scatter(
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(self.time)[self.search_peaks],
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self.search_envelope[self.search_peaks],
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c=ps.red,
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edgecolors=ps.red,
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zorder=10,
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marker="o",
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facecolors="none",
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)
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# plot filtered instantaneous frequency
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ax6.plot(self.frequency_time,
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self.frequency_filtered, c=ps.gblue3, lw=2)
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self.frequency_filtered, c=ps.gblue3, lw=lw)
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ax6.scatter(
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self.frequency_time[self.frequency_peaks],
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self.frequency_filtered[self.frequency_peaks],
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c=ps.red,
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edgecolors=ps.red,
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zorder=10,
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marker="o",
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facecolors="none",
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)
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ax0.set_ylabel("frequency [Hz]")
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@ -210,27 +240,24 @@ class PlotBuffer:
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ax2.set_ylabel("a.u.")
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ax3.set_ylabel("Hz")
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ax5.set_ylabel("a.u.")
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ax7.set_xlabel("time [s]")
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ax6.set_xlabel("time [s]")
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ps.hide_xax(ax0)
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ps.hide_xax(ax1)
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ps.hide_xax(ax2)
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ps.hide_xax(ax3)
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ps.hide_xax(ax4)
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ps.hide_xax(ax5)
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ps.hide_xax(ax6)
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ps.hide_yax(ax7)
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plt.setp(ax0.get_xticklabels(), visible=False)
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plt.setp(ax1.get_xticklabels(), visible=False)
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plt.setp(ax2.get_xticklabels(), visible=False)
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plt.setp(ax3.get_xticklabels(), visible=False)
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plt.setp(ax4.get_xticklabels(), visible=False)
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plt.setp(ax5.get_xticklabels(), visible=False)
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ps.letter_subplots([ax0, ax1, ax4], xoffset=-0.21)
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# ps.letter_subplots([ax0, ax1, ax4], xoffset=-0.21)
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ax7.set_xticks(np.arange(0, 5.5, 1))
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ax7.spines.bottom.set_bounds((0, 5))
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# ax7.set_xticks(np.arange(0, 5.5, 1))
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# ax7.spines.bottom.set_bounds((0, 5))
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ax0.set_ymargin(0)
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plt.subplots_adjust(left=0.19, right=0.99,
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top=0.98, bottom=0.08, hspace=0.15)
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ax0.set_xlim(0, self.config.window)
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plt.subplots_adjust(left=0.165, right=0.975,
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top=0.98, bottom=0.074, hspace=0.2)
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fig.align_labels()
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ax0.autoscale(enable=True)
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if plot == "show":
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plt.show()
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@ -241,6 +268,7 @@ class PlotBuffer:
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)
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plt.savefig(f"{out}{self.track_id}_{self.t0_old}.pdf")
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plt.savefig(f"{out}{self.track_id}_{self.t0_old}.svg")
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plt.close()
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@ -292,7 +320,7 @@ def plot_spectrogram(
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interpolation="gaussian",
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alpha=1,
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)
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axis.use_sticky_edges = False
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# axis.use_sticky_edges = False
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return spec_times
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@ -561,7 +589,7 @@ def main(datapath: str, plot: str) -> None:
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raw_time = np.arange(data.raw.shape[0]) / data.raw_rate
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# good chirp times for data: 2022-06-02-10_00
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window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate
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window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5 + 5) * data.raw_rate
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window_duration_index = 60 * data.raw_rate
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# t0 = 0
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@ -1021,4 +1049,4 @@ if __name__ == "__main__":
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datapath = "../data/2022-06-02-10_00/"
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# datapath = "/home/weygoldt/Data/uni/efishdata/2016-colombia/fishgrid/2016-04-09-22_25/"
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# datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/"
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main(datapath, plot="show")
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main(datapath, plot="save")
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@ -3,7 +3,7 @@ dataroot: "../data/"
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outputdir: "../output/"
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# Duration and overlap of the analysis window in seconds
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window: 5
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window: 10
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overlap: 1
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edge: 0.25
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@ -12,7 +12,7 @@ number_electrodes: 3
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minimum_electrodes: 2
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# Search window bandwidth and minimal baseline bandwidth
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minimal_bandwidth: 10
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minimal_bandwidth: 20
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# Instantaneous frequency smoothing usint a gaussian kernel of this width
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baseline_frequency_smoothing: 5
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Binary file not shown.
BIN
poster/figs/algorithm.pdf
Normal file
BIN
poster/figs/algorithm.pdf
Normal file
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BIN
poster/main.pdf
BIN
poster/main.pdf
Binary file not shown.
@ -35,29 +35,51 @@ University of Tübingen}
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}
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\myblock[TranspBlock]{A chirp detection algorithm}{
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\begin{minipage}[t]{0.55\linewidth}
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\begin{minipage}[t]{0.45\linewidth}
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\vspace{-1.5cm}
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\begin{tikzfigure}[]
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\label{modulations}
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\includegraphics[width=\linewidth]{figs/10.0_11245.5.pdf}
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\includegraphics[width=\linewidth]{figs/algorithm}
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\end{tikzfigure}
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\end{minipage} \hfill
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\begin{minipage}[t]{0.40\linewidth}
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\begin{minipage}[t]{0.50\linewidth}
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\lipsum[3][1-5]
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\end{minipage}
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}
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\column{0.5}
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\myblock[TranspBlock]{Chirps and diadic competitions}{
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\begin{minipage}[t]{0.66\linewidth}
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\begin{minipage}[t]{0.7\linewidth}
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\begin{tikzfigure}[]
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\label{modulations}
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\includegraphics[width=\linewidth]{figs/placeholder1}
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\end{tikzfigure}
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\end{minipage} \hfill
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\begin{minipage}[t]{0.25\linewidth}
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\lipsum[3][1-3]
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\end{minipage}
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\begin{minipage}[t]{0.7\linewidth}
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\begin{tikzfigure}[]
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\label{modulations}
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\includegraphics[width=\linewidth]{figs/placeholder1}
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\end{tikzfigure}
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\end{minipage} \hfill
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\begin{minipage}[t]{0.30\linewidth}
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\lipsum[3][1-5]
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\begin{minipage}[t]{0.25\linewidth}
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\lipsum[3][1-3]
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\end{minipage}
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\begin{minipage}[t]{0.7\linewidth}
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\begin{tikzfigure}[]
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\label{modulations}
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\includegraphics[width=\linewidth]{figs/placeholder1}
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\end{tikzfigure}
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\end{minipage} \hfill
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\begin{minipage}[t]{0.25\linewidth}
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\lipsum[3][1-3]
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\end{minipage}
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}
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\myblock[TranspBlock]{Conclusion}{
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