def __init__(self, options):
self.options = options
self.grid_size = int(options['mesh_size'][0] / options['downsample_by'])
self.max_domain = 2 * np.pi # Not variable for now. Domain is fixed to [0, 2*pi]x[0, 2*pi]
self.filter_size = options['filter_size']
self.batch_size = options['batch_size']
self.dt = options['dt']
self.boundary_cond = options['boundary_cond']
self.max_order = options['max_order']
self.iterations = options['iterations']
# Number of filters:
self.N = int((self.max_order + 2) * (self.max_order + 1) / 2)
# Positioning the 1 in the moment-matrices
self.ind = mm.index(self.filter_size)
self.coefs = [] # Storing the coefficients here
self.M = [] # Storing the moment-matrices here
# Generating the data
self.batch, self.inits = gd.generate(options)
def __init__(self, options):
self.options = options
self.grid_size = 50 # Only fixed it to 50 for this test. Shouldn't do it in general!
self.max_domain = 2 * np.pi # Not variable for now. Domain is fixed to [0, 2*pi]x[0, 2*pi]
self.filter_size = options['filter_size']
self.batch_size = options['batch_size']
self.dt = options['dt']
self.boundary_cond = options['boundary_cond']
self.max_order = options['max_order']
self.iterations = options['iterations']
# Number of filters:
self.N = int((self.max_order + 2) * (self.max_order + 1) / 2)
# Positioning the 1 in the moment-matrices
self.ind = mm.index(self.filter_size)
self.coefs = [] # Storing the coefficients here
self.M = [] # Storing the moment-matrices here
# Generating the data
self.batch, self.inits = gd.generate(
options, method='low_freq') # For inference