mirror of https://github.com/morpheus65535/bazarr
151 lines
4.8 KiB
Python
151 lines
4.8 KiB
Python
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
AUDITOK_PLOT_THEME = {
|
|
"figure": {"facecolor": "#482a36", "alpha": 0.2},
|
|
"plot": {"facecolor": "#282a36"},
|
|
"energy_threshold": {
|
|
"color": "#e31f8f",
|
|
"linestyle": "--",
|
|
"linewidth": 1,
|
|
},
|
|
"signal": {"color": "#40d970", "linestyle": "-", "linewidth": 1},
|
|
"detections": {
|
|
"facecolor": "#777777",
|
|
"edgecolor": "#ff8c1a",
|
|
"linewidth": 1,
|
|
"alpha": 0.75,
|
|
},
|
|
}
|
|
|
|
|
|
def _make_time_axis(nb_samples, sampling_rate):
|
|
sample_duration = 1 / sampling_rate
|
|
x = np.linspace(0, sample_duration * (nb_samples - 1), nb_samples)
|
|
return x
|
|
|
|
|
|
def _plot_line(x, y, theme, xlabel=None, ylabel=None, **kwargs):
|
|
color = theme.get("color", theme.get("c"))
|
|
ls = theme.get("linestyle", theme.get("ls"))
|
|
lw = theme.get("linewidth", theme.get("lw"))
|
|
plt.plot(x, y, c=color, ls=ls, lw=lw, **kwargs)
|
|
plt.xlabel(xlabel, fontsize=8)
|
|
plt.ylabel(ylabel, fontsize=8)
|
|
|
|
|
|
def _plot_detections(subplot, detections, theme):
|
|
fc = theme.get("facecolor", theme.get("fc"))
|
|
ec = theme.get("edgecolor", theme.get("ec"))
|
|
ls = theme.get("linestyle", theme.get("ls"))
|
|
lw = theme.get("linewidth", theme.get("lw"))
|
|
alpha = theme.get("alpha")
|
|
for (start, end) in detections:
|
|
subplot.axvspan(start, end, fc=fc, ec=ec, ls=ls, lw=lw, alpha=alpha)
|
|
|
|
|
|
def plot(
|
|
audio_region,
|
|
scale_signal=True,
|
|
detections=None,
|
|
energy_threshold=None,
|
|
show=True,
|
|
figsize=None,
|
|
save_as=None,
|
|
dpi=120,
|
|
theme="auditok",
|
|
):
|
|
y = np.asarray(audio_region)
|
|
if len(y.shape) == 1:
|
|
y = y.reshape(1, -1)
|
|
nb_subplots, nb_samples = y.shape
|
|
sampling_rate = audio_region.sampling_rate
|
|
time_axis = _make_time_axis(nb_samples, sampling_rate)
|
|
if energy_threshold is not None:
|
|
eth_log10 = energy_threshold * np.log(10) / 10
|
|
amplitude_threshold = np.sqrt(np.exp(eth_log10))
|
|
else:
|
|
amplitude_threshold = None
|
|
if detections is None:
|
|
detections = []
|
|
else:
|
|
# End of detection corresponds to the end of the last sample but
|
|
# to stay compatible with the time axis of signal plotting we want end
|
|
# of detection to correspond to the *start* of the that last sample.
|
|
detections = [
|
|
(start, end - (1 / sampling_rate)) for (start, end) in detections
|
|
]
|
|
if theme == "auditok":
|
|
theme = AUDITOK_PLOT_THEME
|
|
|
|
fig = plt.figure(figsize=figsize, dpi=dpi)
|
|
fig_theme = theme.get("figure", theme.get("fig", {}))
|
|
fig_fc = fig_theme.get("facecolor", fig_theme.get("ffc"))
|
|
fig_alpha = fig_theme.get("alpha", 1)
|
|
fig.patch.set_facecolor(fig_fc)
|
|
fig.patch.set_alpha(fig_alpha)
|
|
|
|
plot_theme = theme.get("plot", {})
|
|
plot_fc = plot_theme.get("facecolor", plot_theme.get("pfc"))
|
|
|
|
if nb_subplots > 2 and nb_subplots % 2 == 0:
|
|
nb_rows = nb_subplots // 2
|
|
nb_columns = 2
|
|
else:
|
|
nb_rows = nb_subplots
|
|
nb_columns = 1
|
|
|
|
for sid, samples in enumerate(y, 1):
|
|
ax = fig.add_subplot(nb_rows, nb_columns, sid)
|
|
ax.set_facecolor(plot_fc)
|
|
if scale_signal:
|
|
std = samples.std()
|
|
if std > 0:
|
|
mean = samples.mean()
|
|
std = samples.std()
|
|
samples = (samples - mean) / std
|
|
max_ = samples.max()
|
|
plt.ylim(-1.5 * max_, 1.5 * max_)
|
|
if amplitude_threshold is not None:
|
|
if scale_signal and std > 0:
|
|
amp_th = (amplitude_threshold - mean) / std
|
|
else:
|
|
amp_th = amplitude_threshold
|
|
eth_theme = theme.get("energy_threshold", theme.get("eth", {}))
|
|
_plot_line(
|
|
[time_axis[0], time_axis[-1]],
|
|
[amp_th] * 2,
|
|
eth_theme,
|
|
label="Detection threshold",
|
|
)
|
|
if sid == 1:
|
|
legend = plt.legend(
|
|
["Detection threshold"],
|
|
facecolor=fig_fc,
|
|
framealpha=0.1,
|
|
bbox_to_anchor=(0.0, 1.15, 1.0, 0.102),
|
|
loc=2,
|
|
)
|
|
legend = plt.gca().add_artist(legend)
|
|
|
|
signal_theme = theme.get("signal", {})
|
|
_plot_line(
|
|
time_axis,
|
|
samples,
|
|
signal_theme,
|
|
xlabel="Time (seconds)",
|
|
ylabel="Signal{}".format(" (scaled)" if scale_signal else ""),
|
|
)
|
|
detections_theme = theme.get("detections", {})
|
|
_plot_detections(ax, detections, detections_theme)
|
|
plt.title("Channel {}".format(sid), fontsize=10)
|
|
|
|
plt.xticks(fontsize=8)
|
|
plt.yticks(fontsize=8)
|
|
plt.tight_layout()
|
|
|
|
if save_as is not None:
|
|
plt.savefig(save_as, dpi=dpi)
|
|
if show:
|
|
plt.show()
|