def standard_recover(file_path):
dm = data_manupulation.standard_dm(1000, default=True)
stge = STGE()
stge.register_data_manager(dm)
store_dict = load_obj(file_path)
stge.Pi_list = [np.array(Pi) for Pi in store_dict['Pi_list']]
stge.mDelta_list = [
np.array(mDelta) for mDelta in store_dict['mDelta_list']
]
stge.set_params(store_dict['l_corr'], store_dict['t_corr'],
store_dict['sigma_f'], store_dict['sigma_s'],
store_dict['sigma_t'])
stge.gene_id_list = store_dict['gene_id_list']
stge.gene_name_list = store_dict['gene_name_list']
stge.A = stge.dm.get_ts_assignment_matrix()
stge.Ys = stge.dm.get_sc_exp_mat(stge.gene_id_list)
stge.Yt = stge.dm.get_ts_exp_mat(stge.gene_id_list)
return (stge)
def initiate_sim_stge(sim_dm,
l_corr=200,
t_corr=5,
sigma_f=1000,
sigma_s=1000,
sigma_t=5000,
diag_val=0):
stge = STGE()
stge.register_data_manager(sim_dm)
stge.diag_val = diag_val
stge.set_params(l_corr, t_corr, sigma_f, sigma_s, sigma_t)
stge.A = stge.dm.get_ts_assignment_matrix()
stge.Yt = stge.dm.Yt
if len(stge.dm.sc_t_vec) > 0:
stge.Ys = np.concatenate(
[np.transpose(stge.dm.sc_dict[t]) for t in stge.dm.sc_t_vec],
axis=0)
gene_num = stge.Yt.shape[1]
stge.gene_id_list = np.arange(gene_num)
stge.gene_name_list = np.arange(gene_num)
return (stge)
def initiate_YsYt_dict_stge(l_corr=200,
t_corr=5,
sigma_f=1000,
sigma_s=200,
sigma_t=5000,
diag_val=0,
ref_cell_num=100):
dm = data_manupulation.ts_dm(ref_cell_num)
store_dict = load_obj("selected_cluster_gene_Ys_Yt.obj")
dm.t_vec = store_dict['t_vec']
dm.sc_t_vec = store_dict['sc_t_vec']
dm.sc_t_nums = store_dict['sc_t_nums']
dm.sc_t_breaks = store_dict['sc_t_breaks']
dm.refresh_ref_t()
stge = STGE()
stge.register_data_manager(dm)
stge.Yt = store_dict['Yt']
stge.Ys = store_dict['Ys']
stge.diag_val = diag_val
stge.A = stge.dm.get_ts_assignment_matrix()
stge.set_params(l_corr, t_corr, sigma_f, sigma_s, sigma_t)
return (stge)