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removed wildcard imports

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cimatosa 2015-08-31 12:42:19 +02:00
parent d0ba98c0b8
commit 0c8b48a408
3 changed files with 77 additions and 71 deletions
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19
stocproc/__init__.py
View file

@ -1,10 +1,15 @@
# from . import stocproc_c as c
from .stocproc import *
from .class_stocproc_kle import *
# from .stocproc import *
#
# from .class_stocproc_kle import *
# from .class_stocproc import StocProc_FFT
# from .class_stocproc import StocProc_KLE
#
# import gquad
from .class_stocproc import StocProc_FFT
from .class_stocproc import StocProc_KLE
import gquad
from . import stocproc_c
from . import stocproc
from . import class_stocproc_kle
from . import class_stocproc
from . import gquad

3
stocproc/stocproc.py
View file

@ -57,6 +57,7 @@ solutions of the time discrete version.
.. todo:: implement convenient classes with fixed weights
"""
from .stocproc_c import auto_correlation as auto_correlation_c
import sys
import os
@ -64,7 +65,7 @@ from warnings import warn
sys.path.append(os.path.dirname(__file__))
import numpy as np
from scipy.linalg import eigh as scipy_eigh
from .stocproc_c import auto_correlation as auto_correlation_c
def solve_hom_fredholm(r, w, eig_val_min, verbose=1):
r"""Solves the discrete homogeneous Fredholm equation of the second kind

126
tests/test_stocproc.py
View file

@ -93,15 +93,15 @@ def stochastic_process_KLE_correlation_function(name, err_tol, plot=False):
sig_min = 1e-4
if name == 'mid_point':
method = sp.stochastic_process_mid_point_weight
method = sp.stocproc.stochastic_process_mid_point_weight
elif name == 'trapezoidal':
method = sp.stochastic_process_trapezoidal_weight
method = sp.stocproc.stochastic_process_trapezoidal_weight
elif name == 'simpson':
method = sp.stochastic_process_simpson_weight
method = sp.stocproc.stochastic_process_simpson_weight
print("use {} method".format(name))
x_t_array_KLE, t = method(r_tau, t_max, num_grid_points, num_samples, seed, sig_min)
autoCorr_KLE_conj, autoCorr_KLE_not_conj = sp.auto_correlation(x_t_array_KLE)
autoCorr_KLE_conj, autoCorr_KLE_not_conj = sp.stocproc.auto_correlation(x_t_array_KLE)
t_grid = np.linspace(0, t_max, num_grid_points)
ac_true = r_tau(t_grid.reshape(num_grid_points, 1) - t_grid.reshape(1, num_grid_points))
@ -166,8 +166,8 @@ def test_stochastic_process_FFT_correlation_function(plot = False):
seed = 0
x_t_array_FFT, t = sp.stochastic_process_fft(spectral_density_omega, t_max, num_grid_points, num_samples, seed)
autoCorr_KLE_conj, autoCorr_KLE_not_conj = sp.auto_correlation(x_t_array_FFT)
x_t_array_FFT, t = sp.stocproc.stochastic_process_fft(spectral_density_omega, t_max, num_grid_points, num_samples, seed)
autoCorr_KLE_conj, autoCorr_KLE_not_conj = sp.stocproc.auto_correlation(x_t_array_FFT)
t_grid = np.linspace(0, t_max, num_grid_points)
ac_true = r_tau(t_grid.reshape(num_grid_points, 1) - t_grid.reshape(1, num_grid_points))
@ -226,8 +226,8 @@ def test_func_vs_class_KLE_FFT():
seed = 0
sig_min = 0
x_t_array_func, t = sp.stochastic_process_trapezoidal_weight(r_tau, t_max, ng, num_samples, seed, sig_min)
stoc_proc = sp.StocProc.new_instance_by_name(name = 'trapezoidal',
x_t_array_func, t = sp.stocproc.stochastic_process_trapezoidal_weight(r_tau, t_max, ng, num_samples, seed, sig_min)
stoc_proc = sp.class_stocproc_kle.StocProc.new_instance_by_name(name = 'trapezoidal',
r_tau = r_tau,
t_max = t_max,
ng = ng,
@ -238,13 +238,13 @@ def test_func_vs_class_KLE_FFT():
print("max diff:", np.max(np.abs(x_t_array_func - x_t_array_class)))
assert np.all(x_t_array_func == x_t_array_class), "stochastic_process_kle vs. StocProc Class not identical"
x_t_array_func, t = sp.stochastic_process_fft(spectral_density = J,
x_t_array_func, t = sp.stocproc.stochastic_process_fft(spectral_density = J,
t_max = t_max,
num_grid_points = ng,
num_samples = num_samples,
seed = seed)
stoc_proc = sp.StocProc_FFT(spectral_density = J,
stoc_proc = sp.class_stocproc.StocProc_FFT(spectral_density = J,
t_max = t_max,
num_grid_points = ng,
seed = seed)
@ -287,7 +287,7 @@ def test_stocproc_KLE_memsave():
seed = 0
sig_min = 1e-4
stoc_proc = sp.StocProc.new_instance_by_name(name = 'simpson',
stoc_proc = sp.class_stocproc_kle.StocProc.new_instance_by_name(name = 'simpson',
r_tau = r_tau,
t_max = t_max,
ng = ng,
@ -367,7 +367,7 @@ def test_stochastic_process_KLE_interpolation(plot=False):
seed = 0
sig_min = 1e-5
stoc_proc = sp.StocProc.new_instance_by_name(name = 'trapezoidal',
stoc_proc = sp.class_stocproc_kle.StocProc.new_instance_by_name(name = 'trapezoidal',
r_tau = r_tau,
t_max = t_max,
ng = ng,
@ -388,8 +388,8 @@ def test_stochastic_process_KLE_interpolation(plot=False):
x_t_samples[n,:] = stoc_proc(finer_t)
x_t_samples_ms[n,:] = stoc_proc.x_t_mem_save(delta_t_fac=3, kahanSum=True)
print("done!")
ac_kle_int_conj, ac_kle_int_not_conj = sp.auto_correlation(x_t_samples)
ac_kle_int_conj_ms, ac_kle_int_not_conj_ms = sp.auto_correlation(x_t_samples_ms)
ac_kle_int_conj, ac_kle_int_not_conj = sp.stocproc.auto_correlation(x_t_samples)
ac_kle_int_conj_ms, ac_kle_int_not_conj_ms = sp.stocproc.auto_correlation(x_t_samples_ms)
t_grid = np.linspace(0, t_max, ng_fine)
t_memsave = stoc_proc.t_mem_save(delta_t_fac=3)
@ -473,7 +473,7 @@ def test_stocproc_KLE_splineinterpolation(plot=False):
seed = 0
sig_min = 1e-4
stoc_proc = sp.StocProc_KLE(r_tau = r_tau,
stoc_proc = sp.class_stocproc.StocProc_KLE(r_tau = r_tau,
t_max = t_max,
ng_fredholm = ng_fredholm,
ng_fac = ng_fac,
@ -493,7 +493,7 @@ def test_stocproc_KLE_splineinterpolation(plot=False):
x_t_samples[n] = stoc_proc(finer_t)
print("done!")
ac_conj, ac_not_conj = sp.auto_correlation(x_t_samples)
ac_conj, ac_not_conj = sp.stocproc.auto_correlation(x_t_samples)
t_grid = np.linspace(0, t_max, ng_fine)
ac_true = r_tau(t_grid.reshape(ng_fine, 1) - t_grid.reshape(1, ng_fine))
@ -579,7 +579,7 @@ def test_stochastic_process_FFT_interpolation(plot=False):
seed = 0
stoc_proc = sp.StocProc_FFT(spectral_density = J,
stoc_proc = sp.class_stocproc.StocProc_FFT(spectral_density = J,
t_max = t_max,
num_grid_points = ng,
seed = seed,
@ -681,8 +681,8 @@ def test_stocProc_eigenfunction_extraction():
seed = 0
sig_min = 1e-4
t, w = sp.get_trapezoidal_weights_times(t_max, ng)
stoc_proc = sp.StocProc(r_tau, t, w, seed, sig_min)
t, w = sp.stocproc.get_trapezoidal_weights_times(t_max, ng)
stoc_proc = sp.class_stocproc_kle.StocProc(r_tau, t, w, seed, sig_min)
t_large = np.linspace(t[0], t[-1], int(8.7*ng))
ui_all = stoc_proc.u_i_all(t_large)
@ -705,8 +705,8 @@ def test_orthonomality():
seed = 0
sig_min = 1e-4
t, w = sp.get_trapezoidal_weights_times(t_max, ng)
stoc_proc = sp.StocProc(r_tau, t, w, seed, sig_min)
t, w = sp.stocproc.get_trapezoidal_weights_times(t_max, ng)
stoc_proc = sp.class_stocproc_kle.StocProc(r_tau, t, w, seed, sig_min)
# check integral norm of eigenfunctions (non interpolated eigenfunctions)
ev = stoc_proc.eigen_vector_i_all()
@ -746,7 +746,7 @@ def test_auto_grid_points():
t_max = 15
tol = 1e-8
ng = sp.auto_grid_points(r_tau = r_tau,
ng = sp.class_stocproc_kle.auto_grid_points(r_tau = r_tau,
t_max = t_max,
tol = tol,
sig_min = 0)
@ -762,7 +762,7 @@ def test_chache():
seed = 0
sig_min = 1e-8
stocproc = sp.StocProc.new_instance_with_trapezoidal_weights(r_tau, t_max, ng, seed, sig_min)
stocproc = sp.class_stocproc_kle.StocProc.new_instance_with_trapezoidal_weights(r_tau, t_max, ng, seed, sig_min)
t = {}
t[1] = 3
@ -790,7 +790,7 @@ def test_dump_load():
seed = 0
sig_min = 1e-8
stocproc = sp.StocProc.new_instance_with_trapezoidal_weights(r_tau, t_max, ng, seed, sig_min)
stocproc = sp.class_stocproc_kle.StocProc.new_instance_with_trapezoidal_weights(r_tau, t_max, ng, seed, sig_min)
t = np.linspace(0,4,30)
@ -800,7 +800,7 @@ def test_dump_load():
stocproc.save_to_file(fname)
stocproc_2 = sp.StocProc(seed = seed, fname = fname)
stocproc_2 = sp.class_stocproc_kle.StocProc(seed = seed, fname = fname)
x_t_2 = stocproc_2.x_t_array(t)
assert np.all(x_t == x_t_2)
@ -824,16 +824,16 @@ def show_auto_grid_points_result():
# name = 'trapezoidal'
# name = 'gauss_legendre'
ng = sp.auto_grid_points(r_tau, t_max, tol, name=name, sig_min=sig_min)
ng = sp.class_stocproc_kle.auto_grid_points(r_tau, t_max, tol, name=name, sig_min=sig_min)
t, w = sp.get_trapezoidal_weights_times(t_max, ng)
stoc_proc = sp.StocProc(r_tau, t, w, seed, sig_min)
t, w = sp.stocproc.get_trapezoidal_weights_times(t_max, ng)
stoc_proc = sp.class_stocproc_kle.StocProc(r_tau, t, w, seed, sig_min)
r_t_s = stoc_proc.recons_corr(t_large)
r_t_s_exact = r_tau(t_large.reshape(ng_interpolation,1) - t_large.reshape(1, ng_interpolation))
diff = sp.mean_error(r_t_s, r_t_s_exact)
diff_max = sp.max_error(r_t_s, r_t_s_exact)
diff = sp.class_stocproc_kle.mean_error(r_t_s, r_t_s_exact)
diff_max = sp.class_stocproc_kle.max_error(r_t_s, r_t_s_exact)
# plt.plot(t_large, diff)
# plt.plot(t_large, diff_max)
@ -855,7 +855,7 @@ def test_ui_mem_save():
assert abs( (t_max/(N1-1)) - a*(t_fine[1]-t_fine[0]) ) < 1e-14, "{}".format(abs( (t_max/(N1-1)) - (t_fine[1]-t_fine[0]) ))
stoc_proc = sp.StocProc.new_instance_with_trapezoidal_weights(r_tau, t_max, ng=N1, sig_min = 1e-4)
stoc_proc = sp.class_stocproc_kle.StocProc.new_instance_with_trapezoidal_weights(r_tau, t_max, ng=N1, sig_min = 1e-4)
ui_all_ms = stoc_proc.u_i_all_mem_save(delta_t_fac=a)
@ -895,7 +895,7 @@ def test_z_t_mem_save():
assert abs( (t_max/(N1-1)) - a*(t_fine[1]-t_fine[0]) ) < 1e-14, "{}".format(abs( (t_max/(N1-1)) - (t_fine[1]-t_fine[0]) ))
stoc_proc = sp.StocProc.new_instance_with_trapezoidal_weights(r_tau, t_max, ng=N1, sig_min=sig_min)
stoc_proc = sp.class_stocproc_kle.StocProc.new_instance_with_trapezoidal_weights(r_tau, t_max, ng=N1, sig_min=sig_min)
z_t_mem_save = stoc_proc.x_t_mem_save(delta_t_fac = a)
z_t = stoc_proc.x_t_array(t_fine)
@ -932,7 +932,7 @@ def show_ef():
seed = 0
sig_min = 1e-5
stoc_proc = sp.StocProc.new_instance_by_name(name = 'trapezoidal',
stoc_proc = sp.class_stocproc_kle.StocProc.new_instance_by_name(name = 'trapezoidal',
r_tau = r_tau,
t_max = t_max,
ng = ng,
@ -979,7 +979,7 @@ def test_integral_equation():
# two parameter correlation function -> correlation matrix
r_tau = lambda tau : corr(tau, s_param, gamma_s_plus_1)
stocproc_simp = sp.StocProc.new_instance_with_simpson_weights(r_tau = r_tau,
stocproc_simp = sp.class_stocproc_kle.StocProc.new_instance_with_simpson_weights(r_tau = r_tau,
t_max = tmax,
ng = 1001,
sig_min = 0,
@ -1029,7 +1029,7 @@ def test_solve_fredholm_ordered_eigen_values():
eig_val_min = 1e-6
verbose=2
eval, evec = sp.solve_hom_fredholm(r, w, eig_val_min, verbose)
eval, evec = sp.stocproc.solve_hom_fredholm(r, w, eig_val_min, verbose)
eval_old = np.Inf
@ -1054,15 +1054,15 @@ def test_ac_vs_ac_from_c():
seed = 0
sig_min = 0
x_t_array_KLE, t = sp.stochastic_process_trapezoidal_weight(r_tau, t_max, num_grid_points, num_samples, seed, sig_min)
x_t_array_KLE, t = sp.stocproc.stochastic_process_trapezoidal_weight(r_tau, t_max, num_grid_points, num_samples, seed, sig_min)
t1 = time.clock()
ac, ac_prime = sp.auto_correlation_numpy(x_t_array_KLE)
ac, ac_prime = sp.stocproc.auto_correlation_numpy(x_t_array_KLE)
t2 = time.clock()
print("ac (numpy): {:.3g}s".format(t2-t1))
# import stocproc_c as spc
t1 = time.clock()
ac_c, ac_prime_c = sp.auto_correlation(x_t_array_KLE)
ac_c, ac_prime_c = sp.stocproc.auto_correlation(x_t_array_KLE)
t2 = time.clock()
print("ac (cython): {:.3g}s".format(t2-t1))
@ -1073,31 +1073,31 @@ def test_ac_vs_ac_from_c():
if __name__ == "__main__":
# test_solve_fredholm_ordered_eigen_values()
# test_ac_vs_ac_from_c()
# test_stochastic_process_KLE_correlation_function_midpoint()
# test_stochastic_process_KLE_correlation_function_trapezoidal()
# test_stochastic_process_KLE_correlation_function_simpson()
# test_stochastic_process_FFT_correlation_function(plot=False)
#
# test_func_vs_class_KLE_FFT()
# test_stocproc_KLE_memsave()
# test_stochastic_process_KLE_interpolation(plot=False)
# test_stocproc_KLE_splineinterpolation(plot=False)
# test_stochastic_process_FFT_interpolation(plot=False)
# test_stocProc_eigenfunction_extraction()
# test_orthonomality()
# test_auto_grid_points()
#
# test_chache()
# test_dump_load()
# test_ui_mem_save()
# test_z_t_mem_save()
#
# test_matrix_build()
# test_integral_equation()
#
test_solve_fredholm_ordered_eigen_values()
test_ac_vs_ac_from_c()
test_stochastic_process_KLE_correlation_function_midpoint()
test_stochastic_process_KLE_correlation_function_trapezoidal()
test_stochastic_process_KLE_correlation_function_simpson()
test_stochastic_process_FFT_correlation_function(plot=False)
test_func_vs_class_KLE_FFT()
test_stocproc_KLE_memsave()
test_stochastic_process_KLE_interpolation(plot=False)
test_stocproc_KLE_splineinterpolation(plot=False)
test_stochastic_process_FFT_interpolation(plot=False)
test_stocProc_eigenfunction_extraction()
test_orthonomality()
test_auto_grid_points()
test_chache()
test_dump_load()
test_ui_mem_save()
test_z_t_mem_save()
test_matrix_build()
test_integral_equation()
# show_auto_grid_points_result()
show_ef()
# show_ef()
pass