def calculate_p_global(grid: Grid, univ_ele: UniversalElement,
boundaries_info: dict):
nodes_count = grid.get_spec().mN
global_p = np.zeros(shape=(nodes_count, 1))
for i in range(grid.get_spec().mE):
element = grid.getElement(i + 1)
local_p = calculate_p_matrix(element, univ_ele, boundaries_info)
indexes = [element[i].id - 1
for i in range(4)] # -1 to align with matrix indexing
for local_row_index, global_row_index in zip(range(4), indexes):
global_p[global_row_index, 0] += local_p[local_row_index, 0]
return global_p
def calculate_c_global(grid: Grid, univ_elem: UniversalElement,
layer_info: dict):
global_c = np.zeros(shape=(grid.get_spec().mN, grid.get_spec().mN))
for i in range(grid.get_spec().mE):
element = grid.getElement(i + 1)
local_c = calculate_c_matrix(element, univ_elem, layer_info)
indexes = [element[i].id - 1
for i in range(4)] # -1 to align with matrix indexing
for local_row_index, global_row_index in zip(range(4), indexes):
for local_column_index, global_column_index in zip(
range(4), indexes):
global_c[global_row_index,
global_column_index] += local_c[local_row_index,
local_column_index]
return global_c
def calculate_h_global(grid: Grid, univ_elem: UniversalElement,
layer_info: dict, boundaries_info: dict):
# creating global matrix with mNxmN size(num_nodes_in_grid x num_nodes_in_grid)
global_h = np.zeros(shape=(grid.get_spec().mN, grid.get_spec().mN))
# iterating over every element in grid
for i in range(grid.get_spec().mE):
element = grid.getElement(i + 1)
local_h = calculate_h_matrix(element, univ_elem, layer_info,
boundaries_info)
# list to store id of element nodes corresponding to shape function order
indexes = [element[i].id - 1
for i in range(4)] # -1 to align with matrix indexing
# aggregating matrix
for local_row_index, global_row_index in zip(range(4), indexes):
for local_column_index, global_column_index in zip(
range(4), indexes):
global_h[global_row_index,
global_column_index] += local_h[local_row_index,
local_column_index]
return global_h