def get_amp_data(data_path):
p = re.compile(r'amp(\d+).csv')
cond = pd.read_csv(filepath_or_buffer=os.path.join(data_path, 'cond.txt'),
sep=",",
header=None)
sample_rate = cond.at[0, 1]
mV_per_Pa = cond.at[2, 1]
dt = 1.0 / sample_rate
results_sound = np.zeros(256)
c = 0
for filepath in glob.glob(os.path.join(data_path, '*')):
m = p.match(filepath.split(os.path.sep)[-1])
if m is None:
continue
df = pd.read_csv(filepath_or_buffer=filepath, sep=",")
sound = df[' A Max [mV]']
duty = int(m.group(1))
results_sound[duty] = get_40kHz_amp(sound, dt) / mV_per_Pa / np.sqrt(2)
c += 1
print_progress(c, 256)
print()
print(f'max [Pa]: {results_sound.max()}')
return normalized(results_sound)
def get_input_data(data_path):
p = re.compile(r'input(\d+).csv')
cond = pd.read_csv(filepath_or_buffer=os.path.join(data_path, 'cond.txt'),
sep=",",
header=None)
sample_rate = cond.at[0, 1]
dt = 1.0 / sample_rate
results_input = np.zeros(256)
c = 0
for filepath in glob.glob(os.path.join(data_path, '*')):
m = p.match(filepath.split(os.path.sep)[-1])
if m is None:
continue
df = pd.read_csv(filepath_or_buffer=filepath, sep=",")
input_sig = df[' A Max [mV]']
duty = int(m.group(1))
results_input[duty] = get_40kHz_amp(input_sig, dt)
c += 1
print_progress(c, 256)
print()
return normalized(results_input)
def get_amp_data(data_path, total):
cond = pd.read_csv(filepath_or_buffer=os.path.join(data_path, 'cond.txt'),
sep=",",
header=None)
sample_rate = cond.at[0, 1]
mV_per_Pa = cond.at[2, 1]
dt = 1.0 / sample_rate
p = re.compile(
r'x([+-]?\d+\.?\d+?)y([+-]?\d+\.?\d+?)z([+-]?\d+\.?\d+?).csv')
results = pd.DataFrame(columns=['amp'])
c = 0
for dev_dir in glob.glob(os.path.join(data_path, '*')):
for tr_dir in glob.glob(os.path.join(dev_dir, '*')):
for filepath in glob.glob(os.path.join(tr_dir, '*')):
m = p.match(filepath.split(os.path.sep)[-1])
if m is None:
continue
z = int(float(m.group(3)))
if z != 200:
continue
df = pd.read_csv(filepath_or_buffer=filepath, sep=",")
sound = df[' A Max [mV]']
results.at[c, 'amp'] = get_40kHz_amp(
sound, dt) / mV_per_Pa / np.sqrt(2)
c += 1
print_progress(c, total)
print()
results.to_csv('individual_amp.csv')
def calc(data_path):
cond = pd.read_csv(filepath_or_buffer=os.path.join(data_path, 'cond.txt'), sep=",", header=None)
sample_rate = cond.at[0, 1]
mV_per_Pa = cond.at[2, 1]
dt = 1.0 / sample_rate
p = re.compile(r'x([+-]?\d+\.?\d+?)y([+-]?\d+\.?\d+?)z([+-]?\d+\.?\d+?).csv')
x_axis = []
y_axis = []
total = 0
for filepath in glob.glob(os.path.join(data_path, '*')):
m = p.match(filepath.split(os.path.sep)[-1])
if m is None:
continue
x = float(m.group(1))
y = float(m.group(2))
x_axis.append(x)
y_axis.append(y)
total += 1
x_axis = sorted(set(x_axis))
y_axis = sorted(set(y_axis))
rms = pd.DataFrame(index=y_axis, columns=x_axis)
c = 0
for filepath in glob.glob(os.path.join(data_path, '*')):
m = p.match(filepath.split(os.path.sep)[-1])
if m is None:
continue
x = float(m.group(1))
y = float(m.group(2))
df = pd.read_csv(filepath_or_buffer=filepath, sep=",")
sound = df[' A Max [mV]']
rms.at[y, x] = get_40kHz_amp(sound, dt) / mV_per_Pa / np.sqrt(2)
c += 1
print_progress(c, total)
print()
rms.to_csv('xy.csv')
def get_phase_data(data_path, total):
p = re.compile(r'dev(\d+)')
cond = pd.read_csv(filepath_or_buffer=os.path.join(data_path, 'cond.txt'),
sep=",",
header=None)
phases = np.array([])
print_progress(len(phases), total)
for dev_dir in glob.glob(os.path.join(data_path, '*')):
m = p.match(dev_dir.split(os.path.sep)[-1])
if m is None:
continue
phases = np.concatenate(
[phases, process_phase_data_dev(dev_dir, cond)])
print_progress(len(phases), total)
print()
results = pd.DataFrame(columns=['phase'])
results['phase'] = phases
results.to_csv('individual_phase.csv')
def get_amp_data(data_path):
p = re.compile(r'duty(\d+).csv')
cond = pd.read_csv(filepath_or_buffer=os.path.join(data_path, 'cond.txt'), sep=",", header=None)
sample_rate = cond.at[0, 1]
mV_per_Pa = cond.at[2, 1]
dt = 1.0 / sample_rate
duties = []
total = 0
for filepath in glob.glob(os.path.join(data_path, '*')):
m = p.match(filepath.split(os.path.sep)[-1])
if m is None:
continue
duty = int(m.group(1))
duties.append(duty)
total += 1
duties = np.array(sorted(duties))
results_sound = pd.DataFrame(index=duties, columns=['rms'])
c = 0
for filepath in glob.glob(os.path.join(data_path, '*')):
m = p.match(filepath.split(os.path.sep)[-1])
if m is None:
continue
df = pd.read_csv(filepath_or_buffer=filepath, sep=",")
sound = df[' A Max [mV]']
duty = int(m.group(1))
results_sound.at[duty, 'rms'] = get_40kHz_amp(sound, dt) / mV_per_Pa / np.sqrt(2)
c += 1
print_progress(c, total)
print()
return results_sound
def phase(data_path):
p = re.compile(r'phase(\d+).csv')
cond = pd.read_csv(filepath_or_buffer=os.path.join(data_path, 'cond.txt'),
sep=",",
header=None)
sample_rate = cond.at[0, 1]
dt = 1.0 / sample_rate
period = 25e-6
df = pd.read_csv(filepath_or_buffer=os.path.join(data_path, 'phase0.csv'),
sep=",")
sig_base = df[' A Max [mV]']
sig_base = sig_base - sig_base.mean()
results = np.zeros(256)
c = 0
for filepath in glob.glob(os.path.join(data_path, '*')):
m = p.match(filepath.split(os.path.sep)[-1])
if m is None:
continue
df = pd.read_csv(filepath_or_buffer=filepath, sep=",")
sig = df[' A Max [mV]']
sig = sig - sig.mean()
corr = np.correlate(sig, sig_base, "full")
delay = corr.argmax() - (len(sig) - 1)
phases_delay = delay * dt / period * 2 * math.pi
if phases_delay < 0:
phases_delay += 2 * math.pi
phases_delay %= 2 * math.pi
phase_value = int(m.group(1))
results[phase_value] = phases_delay
c += 1
print_progress(c, 256)
print()
x = np.linspace(0, 255, 256)
fig = plt.figure(figsize=(6, 6), dpi=DPI)
ax = fig.add_subplot(111)
ax.plot(x,
results,
marker='.',
zorder=2,
markersize=4,
linestyle='None',
label='measured')
ax.plot(x,
x / 256.0 * 2 * math.pi,
zorder=1,
linewidth=1,
label='theoretical')
plt.ylabel('Phase [rad]', fontname='Arial', fontsize=18)
plt.xlabel(r'$S$ [-]', fontname='Arial', fontsize=18)
plt.yticks([0, math.pi, 2 * math.pi], [r'$0$', r'$\pi$', r'$2\pi$'],
fontname='Arial',
fontsize=16)
plt.xticks([0, 127, 255], ['0', '127', '255'],
fontname='Arial',
fontsize=16)
plt.legend(bbox_to_anchor=(1, 0),
loc='lower right',
borderaxespad=1,
fontsize=16,
frameon=False,
markerscale=2)
# delete right up frame
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
ax.set_xlim(0, 255)
ax.set_ylim(0, 2 * math.pi)
plt.tight_layout()
plt.savefig(os.path.join('plot', 'measured_phase' + ext),
bbox_inches='tight',
pad_inches=0)
def calc():
NUM_TRANS_X = 18 * 3
NUM_TRANS_Y = 14 * 3
TRANS_SIZE = 10.16
FREQUENCY = 40e3
TEMPERATURE = 287.6843037730883
Z_DIR = np.array([0., 0., 1.]) # sound source direction
R = 8.5
Z = 500.0
array_center = np.array([
TRANS_SIZE * (NUM_TRANS_X - 1) / 2, TRANS_SIZE * (NUM_TRANS_Y - 1) / 2,
Z
])
# Observe properties, units are mm
X_RANGE = (array_center[0], array_center[0] + 2 * R)
# Initialize calculator
calculator = CpuCalculator()
# initialize position, direction, amplitude and phase of each sound source
system = UniformSystem(TEMPERATURE)
for y in range(NUM_TRANS_Y):
for x in range(NUM_TRANS_X):
pos = np.array([TRANS_SIZE * x, TRANS_SIZE * y, 0.])
source = T4010A1(pos, Z_DIR, 1.0, 0.0, FREQUENCY)
system.add_wave_source(source)
# Generate observe area, units are mm
observe_area = GridAreaBuilder()\
.x_range(X_RANGE)\
.y_at(array_center[1])\
.z_at(Z)\
.resolution(RESOLUTION)\
.generate()
field = PressureFieldBuffer()
foci_x = np.array([x * RESOLUTION for x in range(85 + 1)])
phase_div = range(2, 256)
df = pd.DataFrame(columns=['x'].extend(phase_div))
df['x'] = foci_x
c = 0
total = len(foci_x) * len(phase_div)
for i in phase_div:
N = 85 + 1
results = np.zeros(N)
d = 0
for focus_x in foci_x:
focal_pos = array_center + np.array([focus_x, 0, 0])
Optimizer.focus(system, focal_pos)
to_digital(system, i)
result = calculator.calculate(system, observe_area, field)
max_index = np.argmax(result)
results[d] = max_index
d += 1
c += 1
print_progress(c, total)
df[i] = results
df.to_csv('pos_vs_argmax.csv')