Plotting and updated .gitignore
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j-hartling
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9701ef1a4d
commit
d78dcf4f4a
2
.gitignore
vendored
2
.gitignore
vendored
@ -1,2 +1,2 @@
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data/*
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./_pycache_/
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./__pycache__/
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@ -6,10 +6,10 @@ from thunderhopper.modeltools import load_data
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from IPython import embed
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# GENERAL SETTINGS:
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data_paths = glob.glob('../data/*.npz')
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data_paths = glob.glob('../data/processed/*.npz')
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stages = ['filt', 'env', 'log', 'inv',
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'conv', 'bi', 'feat']
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channel = 0
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stages_pre = ['raw', 'filt', 'env', 'log', 'inv']
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stages_feat = ['conv', 'bi', 'feat', 'norm']
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# PLOT SETTINGS:
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fig_kwargs = dict(
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@ -19,42 +19,115 @@ fig_kwargs = dict(
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sharey = 'row'
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)
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zoom_rel = np.array([0.4, 0.6])
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colors_pre = ['k' for _ in range(len(stages_pre))]
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colors_feat = ['k' for _ in range(len(stages_feat))]
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colors = dict(
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filt='k',
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env='k',
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log='k',
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inv='k',
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conv='k',
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bi='k',
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feat='k'
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)
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linewidths = dict(
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filt=0.25,
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env=0.5,
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log=0.5,
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inv=0.5,
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conv=0.5,
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bi=1,
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feat=1
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)
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# EXECUTION:
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for data_path in data_paths:
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if 'Gomphocerippus' in data_path:
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continue
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print(f'Processing {data_path}')
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# Load overall data:
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data, config = load_data(data_path)
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t_full = np.arange(data['raw'].shape[0]) / config['rate']
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data, config = load_data(data_path, stages)
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t_full = np.arange(data['filt'].shape[0]) / config['rate']
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# Establish zoom frame:
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zoom_abs = zoom_rel * t_full[-1]
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zoom_mask = (t_full >= zoom_abs[0]) & (t_full <= zoom_abs[1])
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t_zoom = t_full[zoom_mask]
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# PART I: PREPROCESSING STAGE
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fig, axes = plt.subplots(len(stages_pre), 2, **fig_kwargs)
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fig, axes = plt.subplots(4, 2, **fig_kwargs)
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fig.supylabel('amplitude', fontsize=plt.rcParams['axes.labelsize'])
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fig.supxlabel('time [s]', fontsize=plt.rcParams['axes.labelsize'])
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for i, stage in enumerate(stages_pre):
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signal = data[stage][:, channel]
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ax_full, ax_zoom = axes[i, :]
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ax_full.plot(t_full, signal, c=colors_pre[i])
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ax_full.set_ylim(signal.min(), signal.max())
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# Bandpass-filtered signal:
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signal = data['filt'][:, channel]
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ax_full, ax_zoom = axes[0, :]
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c, lw = colors['filt'], linewidths['filt']
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ax_full.plot(t_full, signal, c=c, lw=lw)
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ax_zoom.plot(t_zoom, signal[zoom_mask], c=c, lw=lw)
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ax_full.set_ylim(signal.min(), signal.max())
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ax_zoom.plot(t_zoom, signal[zoom_mask], c=colors_pre[i])
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# Signal envelope:
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signal = data['env'][:, channel]
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ax_full, ax_zoom = axes[1, :]
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c, lw = colors['env'], linewidths['env']
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ax_full.plot(t_full, signal, c=c, lw=lw)
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ax_zoom.plot(t_zoom, signal[zoom_mask], c=c, lw=lw)
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ax_full.set_ylim(0, signal.max())
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# Logarithmic envelope:
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signal = data['log'][:, channel]
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ax_full, ax_zoom = axes[2, :]
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c, lw = colors['log'], linewidths['log']
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ax_full.plot(t_full, signal, c=c, lw=lw)
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ax_zoom.plot(t_zoom, signal[zoom_mask], c=c, lw=lw)
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ax_full.set_ylim(signal.min(), 0)
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# Adapted envelope:
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signal = data['inv'][:, channel]
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ax_full, ax_zoom = axes[3, :]
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c, lw = colors['inv'], linewidths['inv']
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ax_full.plot(t_full, signal, c=c, lw=lw)
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ax_zoom.plot(t_zoom, signal[zoom_mask], c=c, lw=lw)
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ax_full.set_ylim(signal.min(), signal.max())
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# Posthoc adjustments:
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ax_full.set_xlim(t_full[0], t_full[-1])
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ax_zoom.set_xlim(t_zoom[0], t_zoom[-1])
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# PART II: FEATURE EXTRACTION STAGE:
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fig, axes = plt.subplots(len(stages_feat), 2, **fig_kwargs)
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fig, axes = plt.subplots(3, 2, **fig_kwargs)
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fig.supylabel('amplitude', fontsize=plt.rcParams['axes.labelsize'])
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fig.supxlabel('time [s]', fontsize=plt.rcParams['axes.labelsize'])
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for i, stage in enumerate(stages_feat):
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signal = data[stage][:, ..., channel]
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ax_full, ax_zoom = axes[i, :]
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ax_full.plot(t_full, signal, c=colors_feat[i])
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ax_full.set_ylim(signal.min(), signal.max())
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# Convolutional filter responses:
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signal = data['conv'][:, :, channel]
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ax_full, ax_zoom = axes[0, :]
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c, lw = colors['conv'], linewidths['conv']
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ax_full.plot(t_full, signal, c=c, lw=lw)
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ax_zoom.plot(t_zoom, signal[zoom_mask, :], c=c, lw=lw)
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ax_full.set_ylim(signal.min(), signal.max())
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ax_zoom.plot(t_zoom, signal[zoom_mask, ...], c=colors_feat[i])
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# Binary responses:
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signal = data['bi'][:, :, channel]
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ax_full, ax_zoom = axes[1, :]
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c, lw = colors['bi'], linewidths['bi']
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ax_full.plot(t_full, signal, c=c, lw=lw)
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ax_zoom.plot(t_zoom, signal[zoom_mask, :], c=c, lw=lw)
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ax_full.set_ylim(signal.min(), signal.max())
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# Finalized features:
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signal = data['feat'][:, :, channel]
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ax_full, ax_zoom = axes[2, :]
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c, lw = colors['feat'], linewidths['feat']
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ax_full.plot(t_full, signal, c=c, lw=lw)
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ax_zoom.plot(t_zoom, signal[zoom_mask, :], c=c, lw=lw)
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ax_full.set_ylim(0, 1)
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# Posthoc adjustments:
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ax_full.set_xlim(t_full[0], t_full[-1])
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ax_zoom.set_xlim(t_zoom[0], t_zoom[-1])
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plt.show()
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