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Diffstat (limited to '')
| -rw-r--r-- | main.py | 159 |
1 files changed, 0 insertions, 159 deletions
diff --git a/main.py b/main.py deleted file mode 100644 index 4079ac5..0000000 --- a/main.py +++ /dev/null @@ -1,159 +0,0 @@ -import sympy -import scipy.special -import scipy.sparse -import numpy as np -from polymatrix.polystruct import init_equation, init_poly_matrix, init_poly_vector -from polymatrix.sympyutils import poly_to_data_coord, poly_to_matrix -from polymatrix.utils import variable_to_index - -binom = scipy.special.binom - -def skew_symmetric(degree, p_row, p_col): - if p_col < p_row: - return p_col, p_row, -1 - -def zero_diagonal_for_uneven_degree(degree, p_row, p_col): - if degree % 2 == 1 and p_row == p_col: - return 0 - -def gradient(degree, v_row, monom): - if degree == 1: - factor = sum(v_row==e for e in monom) + 1 - - if monom[-1] < v_row: - n_v_row = monom[-1] - n_monom = sorted(monom + (v_row,), reverse=True) - - if v_row <= monom[-1]: - n_v_row = v_row - n_monom = monom - - return n_v_row, n_monom, factor - -x = sympy.symbols('v_dc, i_d, i_q') -vdc, id, iq = x - -g_dc = 3 -wl = 106 - -jr_poly_list = [ - g_dc, -1, g_dc*wl, - 1, 0, wl, - -g_dc*wl, -wl, 0 -] -jr_terms = poly_to_data_coord(list(sympy.poly(p, x) for p in jr_poly_list)) -# print(jr_terms) - -h = vdc**2/2 + id**2/2 + iq**2/2 -dh_poly_list = [ - sympy.diff(h, vdc), sympy.diff(h, id), sympy.diff(h, iq) -] -dh_terms = poly_to_data_coord(list(sympy.poly(p, x) for p in dh_poly_list)) -# print(dh_terms) - -mg_poly_list = [ - g_dc-id, -iq, - vdc+1, 0, - 0, vdc+1 -] -mg_terms = poly_to_data_coord(list(sympy.poly(p, x) for p in mg_poly_list)) -# print(mg_terms) - -nabla_h = init_poly_vector(subs=dh_terms) -JR = init_poly_matrix(subs=jr_terms) -mg = init_poly_matrix(subs=mg_terms) - -nabla_ha = init_poly_vector( - degrees=(1,), - re_index_func_2=gradient, -) -JRa = init_poly_matrix( - degrees=(0,1,2), - re_index_func=skew_symmetric, - subs_func=zero_diagonal_for_uneven_degree, -) -u = init_poly_vector(degrees=(1,2)) - -eq = init_equation( - terms = [(JR, nabla_ha), (JRa, nabla_ha), (JRa, nabla_h), (mg, u)], - n_var = 2, - # terms = [(JR, nabla_ha)], - # n_var = 2, -) - -# n_var = 2 -# n_param_nabla_ha = sum(n_var*binom(n_var+p-1, p) for p in nabla_ha.degrees) -# n_param_JRa = sum(n_var**2*binom(n_var+p-1, p) for p in JRa.non_zero_degrees) -# n_param_u = sum(n_var*binom(n_var+p-1, p) for p in u.non_zero_degrees) -# total = n_param_nabla_ha+n_param_JRa+n_param_u - -# print(f'{n_param_nabla_ha=}') -# print(f'{n_param_JRa=}') -# print(f'{n_param_u=}') -# print(f'{total=}') - -# print(binom(total+2-1, 2)) - -# mat = init_poly_matrix( -# degrees=(1,), -# re_index_func=skew_symmetric, -# subs_func=zero_diagonal_for_uneven_degree, -# ) -# vec = init_poly_vector( -# subs={0: {(0, 0): 1, (1, 0): 1}}, -# ) - -# # mat = init_poly_matrix( -# # subs={0: {(0, 0): 1, (1, 0): 1, (0, 1): 1, (1, 1): 1}}, -# # ) - -# # vec = init_poly_vector( -# # degrees=(1,), -# # re_index_func_2=gradient, -# # ) - -# eq = init_equation( -# terms = [(mat, vec)], -# n_var = 2, -# ) - -eq_tuples, offset_dict, n_param = eq.create() - -print(f'{list(offset_dict.values())=}') -print(f'{n_param=}') - -# mapping from row index to entry in eq_tuples -rows_to_eq = list(set(key for eq_tuple_degree in eq_tuples.values() for key in eq_tuple_degree.keys())) -eq_to_rows = {eq: idx for idx, eq in enumerate(rows_to_eq)} - -print(f'n_eq = {len(rows_to_eq)}') - -# # mapping from col index to entry in eq_tuples -# cols_to_var = list(set(key for eq in eq_tuples[degree].values() for key in eq.keys())) -# var_to_cols = {var: idx for idx, var in enumerate(cols_to_var)} - -def gen_matrix_per_degree(): - for degree, eq_tuple_degree in eq_tuples.items(): - - if 0 < degree: - def gen_coords(eq_tuple_degree=eq_tuple_degree): - for eq, var_dict in eq_tuple_degree.items(): - zero_degree_val = eq_tuples[0][eq][0] - row = eq_to_rows[eq] - for var, value in var_dict.items(): - # col = variable_to_index(n_param, var) - # col = var_to_cols[var] - col = var - yield row, col, value / zero_degree_val - - row, col, data = list(zip(*gen_coords())) - - n_col = int(binom(n_param+degree-1, degree)) - n_row = int(max(row) + 1) - sparse_matrix = scipy.sparse.coo_matrix((data, (row, col)), dtype=np.float64, shape=(n_row, n_col)).toarray() - - yield degree, sparse_matrix - -result = dict(gen_matrix_per_degree()) - -print(result[1].shape) |