def plot_results(sr, audio, onsets, savefile):
if audio.ndim > 1:
audio = np.mean(audio, axis=1)
t = np.linspace(0, len(audio) / sr, len(audio))
plt.plot(t, audio, color='dodgerblue')
if len(onsets) > 0:
for o in onsets:
plt.axvline(o / sr, color='red', linestyle='--')
plt.xlabel('Time (s)')
plt.grid()
sp = SavePlot(True, savefile=savefile, auto_overwrite=True)
sp.plot(plt)
ax2.set_yticks(ytx)
ax1.spines['bottom'].set_visible(False)
ax2.spines['top'].set_visible(False)
ax1.xaxis.tick_top()
ax1.tick_params(labeltop=False) # don't put tick labels at the top
ax2.xaxis.tick_bottom()
d = .015 # how big to make the diagonal lines in axes coordinates
# arguments to pass plot, just so we don't keep repeating them
grad = ratio[1] / ratio[0]
kwargs = dict(transform=ax1.transAxes, color='k', clip_on=False)
ax1.plot((-d, +d), (-grad * d, +grad * d), **kwargs) # top-left diagonal
ax1.plot((1 - d, 1 + d), (-grad * d, +grad * d),
**kwargs) # top-right diagonal
kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonal
ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs) # bottom-right diagonal
handles, labels = ax1.get_legend_handles_labels()
labels, handles = zip(*sorted(zip(map(float, labels), handles)))
labels = map(formatLabel, labels)
ax2.legend(handles, labels, title='Damping factor', bbox_to_anchor=(0.96, 1.2))
ax2.set_xlabel('Time (s)')
ax2.set_ylabel('|z|', rotation='horizontal')
ax2.yaxis.set_label_coords(-0.11, (ax1_size + ax2_size) / 2)
sp.plot(plt)
def plot_segments(file, threshold=None, save=False, audio_file=None):
sns.set_style('whitegrid')
plot_onsets = True # False #
plot_ms = False #True #
savepath = '/home/keziah/onsets/hsj/Visualisation/Iowa/'
# if plot_onsets:
# figdir = '{}_octaves_with_onsets'.format('xylophone') #'piano'
# else:
# figdir = '{}_octaves_without_onsets'.format('xylophone') #'piano'
figdir = ''
figdir = os.path.join(savepath, figdir)
if not os.path.exists(figdir):
os.makedirs(figdir)
arr = np.loadtxt(file)
# arr = arr[:-1]
# plot first 500 segments
i0 = 0
i1 = len(arr) # 500 # 20 #
seg_size = 20e-3
title = file.split(os.path.sep)[-2]
sr, manual_onsets = get_manual_onsets(file)
sr, found_onsets = get_found_onsets(file)
found_onsets /= sr
if audio_file is not None:
sp = SavePlot(True,
os.path.join(savepath, title + '_waveform.pdf'),
auto_overwrite=True)
plot_audio(audio_file, sp) #, manual_onsets, found_onsets)
t = np.arange(0, len(arr), dtype=float)
t *= seg_size
if plot_ms:
t *= 1000
plt.plot(t[i0:i1], arr[i0:i1])
correct, diff = CheckOnsets.compare(manual_onsets, found_onsets, 50)
if plot_onsets:
if correct.size > 0:
for n, idx in enumerate(correct):
# correctly found onset
onset = found_onsets[idx]
# remove corresponding onset from manual_onsets
man = manual_onsets + (diff[n] / 1000)
w = np.where(np.isclose(man, onset))[0]
manual_onsets = np.delete(manual_onsets, w)
# plot onset
if onset <= i1 * seg_size:
if plot_ms:
onset *= 1000
plt.axvline(onset, color='lime', label='True positive')
# remove correct onsets from found
found_onsets = np.delete(found_onsets, correct)
# else:
# for onset in manual_onsets:
# plt.axvline(onset, color='red', linestyle='--')
# plot false negatives
for onset in manual_onsets:
if onset <= i1 * seg_size:
if plot_ms:
onset *= 1000
plt.axvline(onset,
color='indigo',
linestyle='--',
label='False negative')
# plot false positives
for onset in found_onsets:
if onset <= i1 * seg_size: # t[i1-1]:
if plot_ms:
onset *= 1000
plt.axvline(onset,
color='mediumorchid',
linestyle='--',
label='False positive')
if threshold is not None:
threshold = np.log(threshold)
plt.axhline(threshold, color='green', linestyle='--')
# plt.title(title)
plt.grid(True)
if plot_ms:
xlabel = 'Time (ms)'
else:
xlabel = 'Time (s)'
plt.xlabel(xlabel)
plt.ylabel('Mean log')
# plt.legend()
sp = SavePlot(
save,
os.path.join(figdir, title + '.pdf'), #+'_w_legend.pdf'),
auto_overwrite=True)
sp.plot(plt)