#%% argument generation
def beamforming_arg_gen_funct(
dim, num_args): #generate num_cells_x,num_cells_y as dim
return [dim, np.cdouble]
def beamforming_arg_gen_funct_single(
dim, num_args): #generate num_cells_x,num_cells_y as dim
return [dim, np.csingle]
#%% now run
[solve_stats_double,
_] = fancy_timeit_matrix_sweep([beamform_speed], ['beamforming'], [2],
dim_list,
num_reps,
arg_gen_funct=beamforming_arg_gen_funct)
[solve_stats_single,
_] = fancy_timeit_matrix_sweep([beamform_speed], ['beamforming'], [2],
dim_list,
num_reps,
arg_gen_funct=beamforming_arg_gen_funct_single)
stats_np_double.update(solve_stats_double)
stats_np_single.update(solve_stats_single)
#%% now save out (single is not used)
import scipy.io as sio
sio.savemat(out_file, {'single': stats_np_single, 'double': stats_np_double})
out_file = os.path.join(output_directory, 'stats_py_np_extended.mat')
#%%
'''
NUMPY TESTS
'''
#%% Test the fancy_timeit_matrix_sweep capability
functs = [np.sum]
funct_names = ['sum']
num_args = [1]
#% now run
[stats_np_double, _] = fancy_timeit_matrix_sweep(functs,
funct_names,
num_args,
mat_dim_list,
num_reps,
dtype=np.cdouble)
[stats_np_single, _] = fancy_timeit_matrix_sweep(functs,
funct_names,
num_args,
mat_dim_list,
num_reps,
dtype=np.csingle)
#%% now measure our fft
fft_reps = num_reps
#%% generate our arguments
def fft_arg_gen_funct(dim, num_args): #vector generation function
def ssolve_arg_gen_funct_single(dim,
num_args): #generate A and b for dense solving
numel = np.floor(dim * 5).astype(np.uint32)
return [
generate_random_sparse((dim, dim), numel, np.csingle),
(np.random.rand(dim) + 1j * np.random.rand(dim)).astype(np.csingle)
]
#now get the results
[ssolve_stats_double,
_] = fancy_timeit_matrix_sweep([scipy.sparse.linalg.spsolve], ['ssolve'], [2],
ssolve_dim_list,
ssolve_reps,
arg_gen_funct=ssolve_arg_gen_funct,
no_fail=True)
#now get the results
[ssolve_stats_single,
_] = fancy_timeit_matrix_sweep([scipy.sparse.linalg.spsolve], ['ssolve'], [2],
ssolve_dim_list,
ssolve_reps,
arg_gen_funct=ssolve_arg_gen_funct_single,
no_fail=True)
stats_np_double = ssolve_stats_double
stats_np_single = ssolve_stats_single
#%% now save out
import scipy.io as sio
mat_dim_list = np.floor(np.linspace(1, 10000, 51)).astype(np.uint32)
solve_dim_list = np.floor(np.linspace(1, 5000, 51)).astype(np.uint32)
num_reps = 25
out_file = os.path.join(output_directory, 'stats_py_np_matrix.mat')
#%% Test the fancy_timeit_matrix_sweep capability
#lu_factor is used as opposed to lu to match the output of MATLAB's lu() function
functs = [scipy.linalg.lu_factor, np.matmul]
funct_names = ['lu', 'matmul']
num_args = [1, 2]
#% now run
[stats_np_double, _] = fancy_timeit_matrix_sweep(functs,
funct_names,
num_args,
mat_dim_list,
num_reps,
dtype=np.cdouble)
[stats_np_single, _] = fancy_timeit_matrix_sweep(functs,
funct_names,
num_args,
mat_dim_list,
num_reps,
dtype=np.csingle)
#%% and now matrix solving
import scipy.linalg
solve_reps = num_reps
#argument generation functions
#argument generation
def fdfd_arg_gen_funct(dim,
num_args): #generate num_cells_x,num_cells_y as dim
return [dim, dim, np.cdouble]
def fdfd_arg_gen_funct_single(
dim, num_args): #generate num_cells_x,num_cells_y as dim
return [dim, dim, np.csingle]
#now run
[solve_stats_double,
_] = fancy_timeit_matrix_sweep([FDFD_2D], ['fdfd'], [3],
dim_list,
num_reps,
arg_gen_funct=fdfd_arg_gen_funct)
[solve_stats_single,
_] = fancy_timeit_matrix_sweep([FDFD_2D], ['fdfd'], [3],
dim_list,
num_reps,
arg_gen_funct=fdfd_arg_gen_funct_single)
#stats_np_double.update(solve_stats_double)
stats_np_single.update(solve_stats_single)
#%% now save out (single is not used)
import scipy.io as sio
sio.savemat(out_file, {'single': stats_np_single, 'double': stats_np_double})
out_file_nb = os.path.join(output_directory, 'stats_py_nb_basic.mat')
#%%
'''
NUMPY TESTS
'''
#%% Test the fancy_timeit_matrix_sweep capability
combo_fun = lambda x, y: x + y * np.exp(x)
functs = [np.add, np.subtract, np.multiply, np.divide, np.exp, combo_fun]
funct_names = ['add', 'sub', 'mult', 'div', 'exp', 'combo']
num_args = [2, 2, 2, 2, 1, 2]
#% now run
[stats_np_double, _] = fancy_timeit_matrix_sweep(functs,
funct_names,
num_args,
mat_dim_list,
num_reps,
dtype=np.cdouble)
[stats_np_single, _] = fancy_timeit_matrix_sweep(functs,
funct_names,
num_args,
mat_dim_list,
num_reps,
dtype=np.csingle)
#%%
'''
NUMBA TESTS
'''
#%% Function declarations
import cmath