plot looks better now
This commit is contained in:
weygoldt
parent
2dec8fd508
commit
9a7ac66f4a
@ -43,10 +43,12 @@ class PlotBuffer:
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time: np.ndarray
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baseline: np.ndarray
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baseline_envelope_unfiltered: np.ndarray
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baseline_envelope: np.ndarray
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baseline_peaks: np.ndarray
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search_frequency: float
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search: np.ndarray
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search_envelope_unfiltered: np.ndarray
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search_envelope: np.ndarray
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search_peaks: np.ndarray
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@ -92,92 +94,144 @@ class PlotBuffer:
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self.time = self.time - self.t0
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self.frequency_time = self.frequency_time - self.t0
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chirps = np.ararray(chirps) - self.t0
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chirps = np.asarray(chirps) - self.t0
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self.t0_old = self.t0
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self.t0 = 0
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fig = plt.figure(
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figsize=(16 / 2.54, 24 / 2.54), constrained_layout=True
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figsize=(16 / 2.54, 20 / 2.54)
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)
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grid = gr.GridSpec(
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9, 1, figure=fig, height_ratios=[4, 0.5, 1, 1, 1, 0.5, 1, 1, 1]
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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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)
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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(grid[0, 0])
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ax1 = fig.add_subplot(grid[2, 0], sharex=ax0)
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ax2 = fig.add_subplot(grid[3, 0], sharex=ax0)
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ax3 = fig.add_subplot(grid[4, 0], sharex=ax0)
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ax4 = fig.add_subplot(grid[6, 0], sharex=ax0)
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ax5 = fig.add_subplot(grid[7, 0], sharex=ax0)
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ax6 = fig.add_subplot(grid[8, 0], sharex=ax0)
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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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# ax_leg = fig.add_subplot(gs0[1, 0])
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waveform_scaler = 1000
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# plot spectrogram
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plot_spectrogram(ax0, data_oi, self.data.raw_rate, self.t0)
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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=0,
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alpha=0.2,
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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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[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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search_lower,
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search_upper,
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color=ps.black,
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lw=0,
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alpha=0.2,
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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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# 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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# spec_times[0], spec_times[-1],
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# color=ps.gblue2, lw=2, ls="dashed")
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for chirp in chirps:
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ax0.scatter(
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chirp, np.median(self.frequency), c=ps.black, marker="x"
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chirp, np.median(self.frequency) + 150, c=ps.black, marker="v"
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)
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# plot waveform of filtered signal
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ax1.plot(self.time, norm(self.baseline))
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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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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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# plot waveform of filtered search signal
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ax2.plot(self.time, norm(self.search))
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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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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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# plot baseline instantaneous frequency
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ax3.plot(self.frequency_time, self.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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# plot filtered and rectified envelope
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ax4.plot(self.time, self.baseline_envelope)
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ax4.plot(self.time, self.baseline_envelope, c=ps.gblue1, lw=2)
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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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zorder=10,
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)
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# plot envelope of search signal
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ax5.plot(self.time, self.search_envelope)
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ax5.plot(self.time, self.search_envelope, c=ps.gblue2, lw=2)
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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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zorder=10,
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)
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# plot filtered instantaneous frequency
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ax6.plot(self.frequency_time, self.frequency_filtered)
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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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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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zorder=10,
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)
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ax0.set_ylim(
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np.max(self.frequency) - 200, top=np.max(self.frequency) + 400
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)
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ax0.set_title("Spectrogram of raw data")
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ax1.set_title("Extracted features")
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ax4.set_title("Filtered and rectified features")
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ax6.set_xlabel("Time [s]")
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ax0.set_xlim(0, 5)
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ax0.set_ylabel("frequency [Hz]")
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ax1.set_ylabel("a.u.")
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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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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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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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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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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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@ -187,13 +241,17 @@ class PlotBuffer:
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self.config.outputdir + self.data.datapath.split("/")[-2] + "/"
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)
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plt.savefig(f"{out}{self.track_id}_{self.t0}.pdf")
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plt.savefig(f"{out}{self.track_id}_{self.t0_old}.pdf")
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plt.close()
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def plot_spectrogram(
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axis, signal: np.ndarray, samplerate: float, window_start_seconds: float
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) -> None:
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axis,
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signal: np.ndarray,
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samplerate: float,
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window_start_seconds: float,
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ylims: list[float]
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) -> np.ndarray:
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"""
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Plot a spectrogram of a signal.
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@ -219,18 +277,24 @@ def plot_spectrogram(
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overlap_frac=0.5,
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)
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fmask = np.zeros(spec_freqs.shape, dtype=bool)
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fmask[(spec_freqs > ylims[0]) & (spec_freqs < ylims[1])] = True
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axis.imshow(
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decibel(spec_power),
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decibel(spec_power[fmask, :]),
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extent=[
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spec_times[0] + window_start_seconds,
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spec_times[-1] + window_start_seconds,
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spec_freqs[0],
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spec_freqs[-1],
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spec_freqs[fmask][0],
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spec_freqs[fmask][-1],
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],
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aspect="auto",
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origin="lower",
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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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return spec_times
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def extract_frequency_bands(
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@ -477,16 +541,16 @@ 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_seconds = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate
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# window_duration_seconds = 60 * data.raw_rate
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window_start_index = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate
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window_duration_index = 60 * data.raw_rate
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# t0 = 0
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# dt = data.raw.shape[0]
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# window_start_seconds = (23495 + ((28336-23495)/3)) * data.raw_rate
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# window_duration_seconds = (28336 - 23495) * data.raw_rate
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window_start_index = 0
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window_duration_index = data.raw.shape[0]
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# window_start_index = 0
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# window_duration_index = data.raw.shape[0]
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# generate starting points of rolling window
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window_start_indices = np.arange(
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@ -648,11 +712,12 @@ def main(datapath: str, plot: str) -> None:
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# band envelope correspond to troughs in the baseline envelope
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# during chirps
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search_envelope = envelope(
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search_envelope_unfiltered = envelope(
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signal=searchband,
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samplerate=data.raw_rate,
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cutoff_frequency=config.search_envelope_cutoff,
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)
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search_envelope = search_envelope_unfiltered
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# compute instantaneous frequency of the baseline band to find
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# anomalies during a chirp, i.e. a frequency jump upwards or
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@ -706,8 +771,10 @@ def main(datapath: str, plot: str) -> None:
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)
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current_raw_time = current_raw_time[no_edges]
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baselineband = baselineband[no_edges]
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baseline_envelope_unfiltered = baseline_envelope_unfiltered[no_edges]
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searchband = searchband[no_edges]
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baseline_envelope = baseline_envelope[no_edges]
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search_envelope_unfiltered = search_envelope_unfiltered[no_edges]
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search_envelope = search_envelope[no_edges]
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# get instantaneous frequency withoup edges
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@ -822,11 +889,13 @@ def main(datapath: str, plot: str) -> None:
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track_id=track_id,
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data=data,
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time=current_raw_time,
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baseline_envelope_unfiltered=baseline_envelope_unfiltered,
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baseline=baselineband,
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baseline_envelope=baseline_envelope,
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baseline_peaks=baseline_peak_indices,
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search_frequency=search_frequency,
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search=searchband,
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search_envelope_unfiltered=search_envelope_unfiltered,
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search_envelope=search_envelope,
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search_peaks=search_peak_indices,
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frequency_time=baseline_frequency_time,
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@ -864,9 +933,8 @@ def main(datapath: str, plot: str) -> None:
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multiwindow_ids.append(track_id)
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logger.info(
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"Found %d chirps for fish %d"
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% len(multielectrode_chirps_validated),
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track_id,
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f"Found {len(multielectrode_chirps_validated)}"
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f" chirps for fish {track_id} in this window!"
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)
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# if chirps are detected and the plot flag is set, plot the
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# chirps, otheswise try to delete the buffer if it exists
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@ -930,7 +998,7 @@ def main(datapath: str, plot: str) -> None:
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if __name__ == "__main__":
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# datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-05-13-10_00/"
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# datapath = "../data/2022-06-02-10_00/"
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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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# datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/"
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main(datapath, plot="save")
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@ -30,10 +30,14 @@ def PlotStyle() -> None:
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purple = "#cba6f7"
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pink = "#f5c2e7"
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lavender = "#b4befe"
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gblue1 = "#8cb8ff"
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gblue2 = "#7cdcdc"
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gblue3 = "#82e896"
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@classmethod
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def lims(cls, track1, track2):
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"""Helper function to get frequency y axis limits from two fundamental frequency tracks.
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"""Helper function to get frequency y axis limits from two
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fundamental frequency tracks.
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Args:
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track1 (array): First track
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@ -91,6 +95,16 @@ def PlotStyle() -> None:
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ax.tick_params(left=False, labelleft=False)
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ax.patch.set_visible(False)
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@classmethod
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def hide_xax(cls, ax):
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ax.xaxis.set_visible(False)
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ax.spines["bottom"].set_visible(False)
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@classmethod
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def hide_yax(cls, ax):
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ax.yaxis.set_visible(False)
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ax.spines["left"].set_visible(False)
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@classmethod
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def set_boxplot_color(cls, bp, color):
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plt.setp(bp["boxes"], color=color)
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@ -216,8 +230,8 @@ def PlotStyle() -> None:
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plt.rc("figure", titlesize=BIGGER_SIZE) # fontsize of the figure title
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plt.rcParams["image.cmap"] = 'cmo.haline'
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# plt.rcParams["axes.xmargin"] = 0.1
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# plt.rcParams["axes.ymargin"] = 0.15
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plt.rcParams["axes.xmargin"] = 0.05
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plt.rcParams["axes.ymargin"] = 0.1
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plt.rcParams["axes.titlelocation"] = "left"
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plt.rcParams["axes.titlesize"] = BIGGER_SIZE
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# plt.rcParams["axes.titlepad"] = -10
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@ -230,9 +244,9 @@ def PlotStyle() -> None:
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plt.rcParams["legend.borderaxespad"] = 0.5
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plt.rcParams["legend.fancybox"] = False
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# specify the custom font to use
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plt.rcParams["font.family"] = "sans-serif"
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plt.rcParams["font.sans-serif"] = "Helvetica Now Text"
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# # specify the custom font to use
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# plt.rcParams["font.family"] = "sans-serif"
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# plt.rcParams["font.sans-serif"] = "Helvetica Now Text"
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# dark mode modifications
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plt.rcParams["boxplot.flierprops.color"] = white
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@ -271,7 +285,7 @@ def PlotStyle() -> None:
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plt.rcParams["ytick.color"] = gray # color of the ticks
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plt.rcParams["grid.color"] = dark_gray # grid color
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plt.rcParams["figure.facecolor"] = black # figure face color
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plt.rcParams["figure.edgecolor"] = "#555169" # figure edge color
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plt.rcParams["figure.edgecolor"] = black # figure edge color
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plt.rcParams["savefig.facecolor"] = black # figure face color when saving
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return style
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