def set_parameters(self, params):
default_params = {
'sigma': 1.0,
'n_sources': 100,
'xmin': np.min(self.elec_pos[:, 0]),
'xmax': np.max(self.elec_pos[:, 0]),
'ymin': np.min(self.elec_pos[:, 1]),
'ymax': np.max(self.elec_pos[:, 1]),
'zmin': np.min(self.elec_pos[:, 2]),
'zmax': np.max(self.elec_pos[:, 2]),
'dist_table_density': 100,
'lambd': 0.0,
'R_init': 2 * parut.min_dist(self.elec_pos),
'ext_X': 0.0,
'ext_Y': 0.0,
'ext_Z': 0.0,
'h': 1.0,
'source_type': 'gauss'
}
for (prop, default) in default_params.iteritems():
setattr(self, prop, params.get(prop, default))
self.gdX = params.get('gdX', 0.05 * (self.xmax - self.xmin))
self.gdY = params.get('gdY', 0.05 * (self.ymax - self.ymin))
self.gdZ = params.get('gdZ', 0.05 * (self.zmax - self.zmin))
self.dist_table_density = 100
self.source_type = params.get('source_type', 'gauss')
basis_types = {
"step": bf.step_rescale_3D,
"gauss": bf.gauss_rescale_3D,
"gauss_lim": bf.gauss_rescale_lim_3D,
}
if self.source_type not in basis_types.keys():
raise Exception("Incorrect source type!")
else:
self.basis = basis_types.get(self.source_type)
nx = (self.xmax - self.xmin)/self.gdX + 1
ny = (self.ymax - self.ymin)/self.gdY + 1
nz = (self.zmax - self.zmin)/self.gdZ + 1
lin_x = np.linspace(self.xmin, self.xmax, nx)
lin_y = np.linspace(self.ymin, self.ymax, ny)
lin_z = np.linspace(self.zmin, self.ymax, nz)
self.space_X, self.space_Y, self.space_Z = np.meshgrid(lin_x,
lin_y,
lin_z)
(self.X_src, self.Y_src, self.Z_src, self.R) = sd.make_src_3D(
self.space_X, self.space_Y, self.space_Z,
self.n_sources,
self.ext_X, self.ext_Y, self.ext_Z,
self.R_init
)
Lx = np.max(self.X_src) - np.min(self.X_src) + self.R
Ly = np.max(self.Y_src) - np.min(self.Y_src) + self.R
Lz = np.max(self.Z_src) - np.min(self.Z_src) + self.R
self.dist_max = (Lx**2 + Ly**2 + Lz**2)**0.5
def set_parameters(self, params):
default_params = {
'sigma': 1.0,
'n_sources': 300,
'xmin': np.min(self.elec_pos[:, 0]),
'xmax': np.max(self.elec_pos[:, 0]),
'ymin': np.min(self.elec_pos[:, 1]),
'ymax': np.max(self.elec_pos[:, 1]),
'dist_table_density': 100,
'lambd': 0.0,
'R_init': 2 * parut.min_dist(self.elec_pos),
'ext_x': 0.0,
'ext_y': 0.0,
'h': 1.0,
'source_type': 'gauss'
}
for (prop, default) in default_params.iteritems():
setattr(self, prop, params.get(prop, default))
self.gdX = params.get('gdX', 0.01 * (self.xmax - self.xmin))
self.gdY = params.get('gdY', 0.01 * (self.ymax - self.ymin))
if self.source_type not in ["gauss", "step"]:
raise Exception("Incorrect source type!")
basis_types = {
"step": bf.step_rescale_2D,
"gauss": bf.gauss_rescale_2D,
"gauss_lim": bf.gauss_rescale_lim_2D,
}
if self.source_type not in basis_types.keys():
raise Exception("Incorrect source type!")
else:
self.basis = basis_types.get(self.source_type)
nx = (self.xmax - self.xmin)/self.gdX + 1
ny = (self.ymax - self.ymin)/self.gdY + 1
lin_x = np.linspace(self.xmin, self.xmax, nx)
lin_y = np.linspace(self.ymin, self.ymax, ny)
self.space_X, self.space_Y = np.meshgrid(lin_x, lin_y)
(self.X_src, self.Y_src, self.R) = sd.make_src_2D(
self.space_X,
self.space_Y,
self.n_sources,
self.ext_x,
self.ext_y,
self.R_init
)
Lx = np.max(self.X_src) - np.min(self.X_src) + self.R
Ly = np.max(self.Y_src) - np.min(self.Y_src) + self.R
self.dist_max = (Lx**2 + Ly**2)**0.5
def set_parameters(self, params):
default_params = {
"sigma": 1.0,
"n_sources": 100,
"xmin": np.min(self.elec_pos[:, 0]),
"xmax": np.max(self.elec_pos[:, 0]),
"ymin": np.min(self.elec_pos[:, 1]),
"ymax": np.max(self.elec_pos[:, 1]),
"zmin": np.min(self.elec_pos[:, 2]),
"zmax": np.max(self.elec_pos[:, 2]),
"dist_table_density": 100,
"lambd": 0.0,
"R_init": 2 * parut.min_dist(self.elec_pos),
"ext_X": 0.0,
"ext_Y": 0.0,
"ext_Z": 0.0,
"h": 1.0,
"source_type": "gauss",
}
for (prop, default) in default_params.iteritems():
setattr(self, prop, params.get(prop, default))
self.gdX = params.get("gdX", 0.05 * (self.xmax - self.xmin))
self.gdY = params.get("gdY", 0.05 * (self.ymax - self.ymin))
self.gdZ = params.get("gdZ", 0.05 * (self.zmax - self.zmin))
self.dist_table_density = 100
self.source_type = params.get("source_type", "gauss")
basis_types = {"step": bf.step_rescale_3D, "gauss": bf.gauss_rescale_3D, "gauss_lim": bf.gauss_rescale_lim_3D}
if self.source_type not in basis_types.keys():
raise Exception("Incorrect source type!")
else:
self.basis = basis_types.get(self.source_type)
nx = (self.xmax - self.xmin) / self.gdX + 1
ny = (self.ymax - self.ymin) / self.gdY + 1
nz = (self.zmax - self.zmin) / self.gdZ + 1
lin_x = np.linspace(self.xmin, self.xmax, nx)
lin_y = np.linspace(self.ymin, self.ymax, ny)
lin_z = np.linspace(self.zmin, self.ymax, nz)
self.space_X, self.space_Y, self.space_Z = np.meshgrid(lin_x, lin_y, lin_z)
(self.X_src, self.Y_src, self.Z_src, self.R) = sd.make_src_3D(
self.space_X, self.space_Y, self.space_Z, self.n_sources, self.ext_X, self.ext_Y, self.ext_Z, self.R_init
)
Lx = np.max(self.X_src) - np.min(self.X_src) + self.R
Ly = np.max(self.Y_src) - np.min(self.Y_src) + self.R
Lz = np.max(self.Z_src) - np.min(self.Z_src) + self.R
self.dist_max = (Lx ** 2 + Ly ** 2 + Lz ** 2) ** 0.5
def set_parameters(self, params):
default_params = {
'sigma': 1.0,
'n_sources': 300,
'xmin': np.min(self.elec_pos),
'xmax': np.max(self.elec_pos),
'dist_density': 200,
'lambd': 0.0,
'R_init': 2 * parut.min_dist(self.elec_pos),
'ext': 0.0,
'h': 1.0,
'source_type': 'gauss_lim'
}
for (prop, default) in default_params.iteritems():
setattr(self, prop, params.get(prop, default))
self.gdX = params.get('gdX', 0.01 * (self.xmax - self.xmin))
basis_types = {
"step": bf.step_rescale_1D,
"gauss": bf.gauss_rescale_1D,
"gauss_lim": bf.gauss_rescale_lim_1D,
}
if self.source_type not in basis_types.keys():
raise Exception("Incorrect source type!")
else:
self.basis = basis_types.get(self.source_type)
self.nx = int(np.ceil((self.xmax - self.xmin)/self.gdX))
# space_X is the estimation area
self.space_X = np.linspace(self.xmin - self.ext,
self.xmax + self.ext,
self.nx)
(self.X_src, self.R) = sd.make_src_1D(self.space_X, self.ext,
self.n_sources, self.R_init)
Lx = np.max(self.X_src) - np.min(self.X_src) + self.R
self.dist_max = Lx
def set_parameters(self, params):
default_params = {
'sigma': 1.0,
'n_sources': 300,
'xmin': np.min(self.elec_pos),
'xmax': np.max(self.elec_pos),
'dist_density': 200,
'lambd': 0.0,
'R_init': 2 * parut.min_dist(self.elec_pos),
'ext': 0.0,
'h': 1.0,
'source_type': 'gauss_lim'
}
for (prop, default) in default_params.iteritems():
setattr(self, prop, params.get(prop, default))
self.gdX = params.get('gdX', 0.01 * (self.xmax - self.xmin))
basis_types = {
"step": bf.step_rescale_1D,
"gauss": bf.gauss_rescale_1D,
"gauss_lim": bf.gauss_rescale_lim_1D,
}
if self.source_type not in basis_types.keys():
raise Exception("Incorrect source type!")
else:
self.basis = basis_types.get(self.source_type)
self.nx = int(np.ceil((self.xmax - self.xmin) / self.gdX))
# space_X is the estimation area
self.space_X = np.linspace(self.xmin - self.ext, self.xmax + self.ext,
self.nx)
(self.X_src, self.R) = sd.make_src_1D(self.space_X, self.ext,
self.n_sources, self.R_init)
Lx = np.max(self.X_src) - np.min(self.X_src) + self.R
self.dist_max = Lx
