def heatmap_beam_pattern(self, save_fig=True):
'''
beampattern heatmap only available for frequency domain beamforming
case : draw beampatten for heatmap.
There will be as many subplots as beamforming numbers.
'''
if self._beamforming_cout == 0:
raise ValueError(
'No beamforming to analysis! Please append one firstly!')
beamforming_dict = self._beamforming_list[0]
if beamforming_dict.get('beam_domain') == 'time':
raise ValueError(
'Time domain beamforming donnot support heatmap plot!')
print('This may take a few seconds. Please wating...')
figsize = (10, 5) if self._beamforming_cout == 1 else (
10, 3 * self._beamforming_cout)
plot = Plot(figsize=figsize, subplot_rows=self._beamforming_cout)
plot.append_axes_combo()
xticks = np.arange(0, len(constants.get('angle_range')), 60)
yticks = np.arange(0, 900, 100)
xticklabels = np.arange(
0, len(constants.get('angle_range')), 60, dtype=int) / 2.0
yticklabels = np.linspace(8, 0, num=9, endpoint=True, dtype=int)
freq_range = np.linspace(8000, 1, num=800, endpoint=True)
for beam_index, beam in enumerate(self._beamforming_list):
response_matrix = np.zeros(
(len(freq_range), len(constants.get('angle_range'))))
for freq_index, freq in enumerate(freq_range):
response_matrix[freq_index:] = beam[
'beam_instance'].beam_pattern(freq)
plot.plot_heatmap(response_matrix,
label_pad=-65,
subplot_index=beam_index)
plot.set_pipeline(title=beam['beam_instance'].beam_label,
xticks=xticks,
yticks=yticks,
xticklabels=[int(tick) for tick in xticklabels],
yticklabels=yticklabels,
xlabel='Azimuth Angle[Deg]',
ylabel='Freq[kHz]',
wait_for_user=False,
subplot_index=beam_index)
if self._beamforming_cout > 1:
plot.suptitle(
'Beampattern Heatmap Compare for Different Beamforming',
hspace=0.5)
if save_fig:
fig_name = self.fig_name('Beampattern_Heatmap')
plot.save_figure(fig_name)
plot.wait_for_user()
def beam_pattern(self, polar, save_fig):
'''
plot either beamforming or beamforming group's beampattern
Case1 -- if the beamforming is a time domain beamforming, either ploar or catersian
all the beam instance are plotted in the same figure, because it's frequency invariant.
Case2-- if the beamforming is a frequency domain beamforming, then if polar, it will be plotted in 4 figures,
every figure represents a single frequency.
if catesian, the figure number is equal to the beamforming instance number. One figure represents
a beamforming, the different frequency plotted in this same figure.
Parameters
----------
polar: bool True or False
save_fig: bool True of False
'''
if self._beamforming_cout == 0:
raise ValueError(
'No beamforming to analysis! Please append one firstly!')
beamforming_dict = self._beamforming_list[0]
coordinate = 'Polar' if polar else 'Cartesian'
title = '{coordinate} Beampattern for {domain}Domain {dtype}Beamforming'.format(
coordinate=coordinate,
domain=(beamforming_dict.get('beam_domain')).capitalize(),
dtype=(beamforming_dict.get('beam_type')).capitalize())
if polar:
sweep_angle = np.concatenate(
(constants.get('angle_range'),
(constants.get('angle_range') + np.pi)),
axis=0)
xlim = (0, 2 * np.pi)
else:
sweep_angle = np.degrees(constants.get('angle_range'))
xlim = (0, 180)
if beamforming_dict.get('beam_domain') == 'time':
plot = Plot(figsize=(6, 6)) if polar else Plot(figsize=(8, 5))
plot.append_axes_combo(polar=polar)
axis_limit_max = 0
axis_limit_min = 0
for beam_index, beam in enumerate(self._beamforming_list):
response = beam['beam_instance'].beam_pattern()
axis_limit_max = np.maximum(axis_limit_max, np.max(response))
axis_limit_min = np.minimum(axis_limit_min, np.min(response))
if polar:
response = np.concatenate(
(response, np.fliplr([response])[0]), axis=0)
plot.plot_vector(response,
sweep_angle,
label=beam['beam_instance'].beam_label)
plot.set_pipeline(xlim=xlim,
ylim=(axis_limit_min - 1, axis_limit_max + 1),
title=title,
xlabel='Azimuth Angle[Deg]',
wait_for_user=False)
plot.set_legend(loc=(0.7, 0.01), fontsize=6)
elif beamforming_dict.get('beam_domain') == 'frequency':
if polar:
mini_freq_range = constants.get('freq_range_mini')
plot = Plot(figsize=(8, 8), subplot_cols=2, subplot_rows=2)
plot.append_axes_combo(polar=True)
# axis_limit_max = 0
# axis_limit_min = 0
for beam_index, beam in enumerate(self._beamforming_list):
for freq_index, freq in enumerate(mini_freq_range):
# get response
response = beam['beam_instance'].beam_pattern(freq)
# axis_limit_max = np.maximum(axis_limit_max, np.max(response))
# axis_limit_min = np.minimum(axis_limit_min, np.min(response))
response = np.concatenate(
(response, np.fliplr([response])[0]), axis=0)
# plot
plot.plot_vector(
response,
sweep_angle,
label=beam['beam_instance'].beam_label,
subplot_index=freq_index)
plot.set_pipeline(
xlim=(0, 2 * np.pi),
ylim=(-50, 1),
title='',
xlabel='Azimuth Angle[Deg]_{}Hz'.format(freq),
wait_for_user=False,
subplot_index=freq_index)
plot.set_legend(loc=(0.5, 0.01), fontsize=6, subplot_index=3)
plot.suptitle(title)
else:
plot = Plot(figsize=(8, 4 * self._beamforming_cout),
subplot_rows=self._beamforming_cout)
plot.append_axes_combo()
axis_limit_max = 0
axis_limit_min = 0
for beam_index, beam in enumerate(self._beamforming_list):
for freq in constants.get('freq_range_small'):
response = beam['beam_instance'].beam_pattern(freq)
axis_limit_max = np.maximum(axis_limit_max,
np.max(response))
axis_limit_min = np.minimum(axis_limit_min,
np.min(response))
plot.plot_vector(response,
np.degrees(
constants.get('angle_range')),
label='{} kHz'.format(freq / 1000),
subplot_index=beam_index)
plot.set_pipeline(
title=beam['beam_instance'].beam_label,
xlim=xlim,
ylim=(axis_limit_min - 1, axis_limit_max + 1),
xlabel='Azimuth Angle[Deg]',
ylabel='Beampatten[dB]',
wait_for_user=False,
subplot_index=beam_index)
plot.set_legend(subplot_index=self._beamforming_cout - 1)
if self._beamforming_cout > 1:
plot.suptitle(title, 0.5)
if save_fig:
fig_name = self.fig_name(
'Beampattern_{coordinate}'.format(coordinate=coordinate))
plot.save_figure(fig_name)
plot.wait_for_user()