def run_mp(json_path, pyd_path, verbose=False, n_core=4):
start_time = time.time()
config = readjson(json_path=json_path)
transducer_config = config['transducer']
coor_file_path = "data/transducer_position.txt"
textarray = np.loadtxt(coor_file_path)
coordinates = textarray[::, 1:]
T = Transducer(element_coordinates=coordinates, **transducer_config)
mc = MediaComplex(config_json=config)
init_medium_config = config['init_medium']
init_medium = InitMedium.new_markoil(init_medium_config['boundary'])
T.initialize(
init_medium,
**transducer_config["element_init_paras"],
verbose=verbose,
n_core=n_core)
end_time = time.time()
if verbose:
print("--- initialization:", end_time - start_time, "seconds ---")
# start sampling
start_time = end_time
box_config = config['box']
B = Box(*box_config['min'], *box_config['max'], box_config['step'])
# parallelization, give transducer casting and measuring job to process pool
pool = Pool(n_core) # process worker pool, 10 CPU cores
async_results = []
for te in T:
ar = pool.apply_async(measure_kernel, args=(te, B, mc, verbose))
async_results.append(ar)
pool.close()
pool.join()
if verbose: print("--- all process finished ---")
complex_pressure = np.zeros(B.nxyz, dtype=np.complex128)
for r in async_results:
complex_pressure += r.get()
end_time = time.time()
if verbose:
print("--- casting time:", end_time - start_time, "seconds ---")
# save ndarray to file
if pyd_path is None:
pyd_path = 'pressure' + '_l' + str(B.lx) + "_n" + str(len(
T[0])) + "_t" + str(int(time.time()))
real_pressure = np.abs(complex_pressure)
np.save('npydata/' + pyd_path, real_pressure)
np.save('npydata/' + 'c' + pyd_path, complex_pressure)
if verbose:
# TODO plot surface
plot_sliced_tensor(real_pressure, slicing_axis=2)
def run_hifu(config_path, pyd_path, verbose=False, n_core=4):
start_time = time.time()
config = readjson(json_path=config_path)
transducer_config = config['transducer']
coor_file_path = "data/transducer_position.txt"
textarray = np.loadtxt(coor_file_path)
coordinates = textarray[::, 1:]
# coordinates = coordinates[9]
# coordinates = np.reshape(coordinates, (1, 3))
T = Transducer(element_coordinates=coordinates, **transducer_config)
mc = MediaComplex(medium_list=config["medium_list"])
box_config = config['box']
B = Box(*box_config['min'], *box_config['max'], box_config['step'])
esp = transducer_config["element_init_paras"]
myhifu = HIFU(T)
cp = myhifu.run(mc,
B,
esp['n_rays'],
esp['trident_angle'],
esp['theta_max'],
n_core=n_core)
end_time = time.time()
print(f'use {end_time - start_time} seconds')
np.save(f'npydata/complex_pressure_{start_time}', cp)
plot_sliced_tensor(np.abs(cp), slicing_axis=2)
def run(json_path, pyd_path, verbose=False):
start_time = time.time()
config = readjson(json_path=json_path)
transducer_config = config['transducer']
coor_file_path = "data/transducer_position.txt"
textarray = np.loadtxt(coor_file_path)
coordinates = textarray[::, 1:]
T = Transducer(element_coordinates=coordinates, **transducer_config)
mc = MediaComplex(config_json=config)
init_medium_config = config['init_medium']
init_medium = InitMedium.new_markoil(init_medium_config['boundary'])
T.initialize(
init_medium,
**transducer_config["element_init_paras"],
verbose=verbose)
end_time = time.time()
if verbose:
print("--- initialization:", end_time - start_time, "seconds ---")
start_time = end_time
bundle_dict = T.cast(mc)
end_time = time.time()
if verbose:
print("--- casting time:", end_time - start_time, "seconds ---")
# start sampling
start_time = end_time
box_config = config['box']
B = Box(*box_config['min'], *box_config['max'], box_config['step'])
# print(len(B.lattrix))
complex_pressure = measure(B, bundle_dict, verbose=verbose)
end_time = time.time()
if verbose:
print("--- sampling time:", end_time - start_time, "seconds ---")
if pyd_path is None:
pyd_path = 'pressure' + '_l' + str(B.lx) + "_n" + str(len(
T[0])) + "_t" + str(int(time.time()))
real_pressure = np.abs(complex_pressure)
np.save('npydata/' + pyd_path, real_pressure)
np.save('npydata/' + 'c' + pyd_path, complex_pressure)
if verbose:
# TODO plot surface
plot_sliced_tensor(real_pressure, slicing_axis=2)
def run_hifu(config_path, pyd_path, verbose=False, n_core=4):
start_time = time.time()
config = readjson(json_path=config_path)
logging.basicConfig(filename="run_hifu"+str(start_time)+".log",
filemode="w",
level=logging.INFO)
transducer_config = config['transducer']
coor_file_path = "data/transducer_position.txt"
textarray = np.loadtxt(coor_file_path)
coordinates = textarray[::, 1:]
# coordinates = coordinates[36]
# coordinates = np.reshape(coordinates, (1, 3))
T = Transducer(element_coordinates=coordinates, **transducer_config)
mc = MediaComplex(medium_list=config["medium_list"])
box_config = config['box']
B = Box(*box_config['min'], *box_config['max'], box_config['step'])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
plot_transducer(T, ax)
plot_box(B, ax)
plt.show()
return
esp = transducer_config["element_init_paras"]
myhifu = HIFU(T)
cp = myhifu.run(
mc,
B,
esp['n_rays'],
esp['trident_angle'],
esp['theta_max'],
n_core=n_core)
end_time = time.time()
print(f'use {end_time - start_time} seconds')
np.save(f'npydata/complex_pressure_{start_time}', cp)
plot_sliced_tensor(np.abs(cp), slicing_axis=1)
def __init__(self, medium_list=None, config_file_path=None):
super().__init__()
if medium_list is None:
medium_list = readjson(json_path=config_file_path)["medium_list"]
for item in medium_list:
if 'is_init' in item and item['is_init']:
for m in self:
m.id += 1
self.insert(0, Medium(**item, med_id=0))
else:
n = len(self)
self.append(Medium(**item, med_id=n))
# adj_mtx[i,j] = [1,2] : 1,2 are the indices of the faces of self[i]
# adjacent to self[j]
self.adj_mtx = np.ones((len(self), len(self))) * -1
# traverse all media (double compare loop)
for i, item1 in enumerate(self):
for j, item2 in enumerate(self):
if i != j:
self.adj_mtx[i, j] = item1.shape.adj_at(item2.shape)
import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from pyHIFU.transducer import Transducer, TElement from pyHIFU.io.config import readjson from pyHIFU.physics.medium import InitMedium config = readjson(json_path='HIFU.py/data/test_case1.json') transducer_config = config['transducer'] coor_file_path = "HIFU.py/data/transducer_position.txt" textarray = np.loadtxt(coor_file_path) coordinates = textarray[::, 1:] init_medium_config = config['init_medium'] init_medium = InitMedium.new_markoil(init_medium_config['boundary']) te = TElement(0, [0, 0, 0], 3.5e-3, 40, 1.2e6, np.array([0.14, 0, 0])) te.initialize(init_medium, 0, n=3000, trident_angle=5e-2, theta_max=np.pi / 20) from pyHIFU.visualization.mkplots import plot_TElements from pyHIFU.visualization.figure import create_ax fig = plt.figure() ax = create_ax(fig) plot_TElements(te, ax) plt.show()
import matplotlib.pyplot as plt
import numpy as np
from pyHIFU.io.config import readjson
from matplotlib.ticker import FormatStrFormatter
n_rays_data = readjson(json_path="data/d_v_n_rays.json")
theta_max_data = readjson(json_path="data/d_v_theta_max.json")
trident_angle_data = readjson(json_path="data/d_v_trident_angle.json")
n_rays_l = list(n_rays_data.keys())
n_rays_l = np.array(n_rays_l, dtype=np.float64)
n_rays_v = n_rays_data.values()
theta_max_l = list(theta_max_data.keys())
theta_max_l = np.array(theta_max_l, dtype=np.float64)
theta_max_v = theta_max_data.values()
trident_angle_l = list(trident_angle_data.keys())
trident_angle_l = np.array(trident_angle_l, dtype=np.float64)
trident_angle_v = trident_angle_data.values()
fig, ax = plt.subplots()
# ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
# ax.plot(n_rays_l, n_rays_v)
# ax.title.set_text("number of rays")
# ax.set_xlabel("n_rays")
# ax.set_ylabel("pnorm distance")
# ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
# ax.plot(theta_max_l, theta_max_v)
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from pyHIFU.transducer import Transducer, TElement
from pyHIFU.io.config import readjson
from pyHIFU.physics.medium import Medium
config = readjson(json_path='../data/case1.json')
transducer_config = config['transducer']
coor_file_path = "../data/transducer_position.txt"
textarray = np.loadtxt(coor_file_path)
coordinates = textarray[::, 1:]
init_medium_config = config['medium_list'][0]
init_medium = Medium(**init_medium_config)
te = TElement(0, [0,0,0], 3.5e-3, 40, 1.2e6, np.array([0.14, 0,0]))
te.initialize(init_medium, 0, n=1, trident_angle=5e-2, theta_max=np.pi/20)
delta = 0.001
x = np.arange(0.02, 0.15, delta)
y = np.arange(-0.03, 0.03, delta)
X, Y = np.meshgrid(x, y)
z = np.zeros(X.shape, dtype=np.complex128)
for i in range(len(x)):
for j in range(len(y)):
coor = [x[i], y[j], 0]
pc = te.ffa(coor)
