def __init__(self, n_particles, n_steps, dt, x_max, trans_matrix_v, trans_matrix_theta, mapping, init_count_v,
init_count_theta, inj_location = "start", verbose = True):
"""
:param n_particles: number of particles
:param n_steps: number of steps
:param dt: stencil time
:param x_max: maximum length of the domain
:param trans_matrix:
:param mapping:
:param init_class_count:
:param inj_location:
:param verbose:
"""
super(DispModelIndependent,self).__init__(n_particles, n_steps, inj_location, verbose)
self.y_array = np.zeros(self.x_array.shape)
self.trans_matrix_v = trans_matrix_v
self.trans_matrix_theta = trans_matrix_theta
self.fix_matrix_types()
self.mapping = mapping
self.dt = dt
self.x_max = x_max
# cdf for initial classes for whatever state definition being used
self.init_class_cdf_v = get_cdf(init_count_v)
self.init_class_cdf_theta = get_cdf(init_count_theta)
self.blocked_particles = []
self._switched_classes = {}
print 'Normalizing transition matrices...'
self.normalize_trans_mat_colums(self.trans_matrix_theta)
self.normalize_trans_mat_colums(self.trans_matrix_v)
def choose_next_class(self, current_class):
indptr = self.trans_matrix.indptr
start = indptr[current_class]
end = indptr[current_class + 1]
rows = self.trans_matrix.indices[start:end]
values = self.trans_matrix.data[start:end]
if len(values) == 0:
return -12
cdf = get_cdf(values)
return rows[bs.bisect_left(cdf, random.random())]
def __init__(self,
n_particles,
n_steps,
dt,
x_max,
trans_matrix,
class_velocity,
init_class_count,
inj_location="start",
verbose=True):
super(dispModelCorrelatedStencil,
self).__init__(n_particles, n_steps, inj_location, verbose)
self.trans_matrix = trans_matrix
self.init_class_count = init_class_count
self.class_velocity = class_velocity
self.dt = dt
self.x_max = x_max
self.init_class_cdf = get_cdf(init_class_count)
self.cdf_matrix = np.cumsum(trans_matrix, axis=0)
def __init__(self,
n_particles,
n_steps,
dt,
x_max,
trans_matrix,
mapping,
init_class_count,
inj_location="start",
verbose=True):
super(dispModelTime3d, self).__init__(n_particles, n_steps,
inj_location, verbose)
self.trans_matrix = trans_matrix
self.init_class_count = init_class_count
self.mapping = mapping
self.dt = dt
self.x_max = x_max
self.init_class_cdf = get_cdf(init_class_count)
self.n_class = np.sqrt(trans_matrix.shape[0])
self.blocked_particles = []
def __init__(self,
n_particles,
n_steps,
dx,
x_max,
trans_matrix,
class_log_edges,
init_class_count,
inj_location="start",
verbose=True):
super(dispModelCorrelatedSpaceKang,
self).__init__(n_particles, n_steps, inj_location, verbose)
self.trans_matrix = trans_matrix
self.init_class_count = init_class_count
self.class_log_edges = class_log_edges
self.class_velocity = self.get_class_velocity(class_log_edges)
self.dx = dx
self.x_max = x_max
self.init_class_cdf = get_cdf(init_class_count)
self.cdf_matrix = np.cumsum(trans_matrix, axis=0)
def __init__(self,
n_particles,
n_steps,
dx,
x_max,
trans_matrix,
class_log_edges,
init_class_count,
inj_location="start",
verbose=True):
super(dispModelOrderTwo, self).__init__(n_particles, n_steps,
inj_location, verbose)
self.trans_matrix = trans_matrix
self.init_class_count = init_class_count
self.class_log_edges = class_log_edges
self.class_velocity = self.get_class_velocity(class_log_edges)
self.dx = dx
self.x_max = x_max
self.init_class_cdf = get_cdf(init_class_count)
self.n_class = np.sqrt(trans_matrix.shape[0])
self.blocked_particles = []