fixed imports and arguments reorder

Author: pavelkomarov

pynumdiff/optimize/__init__.py

@@ -2,13 +2,13 @@
 """
 try:
     import cvxpy
-    from .optimize import total_variation_regularization
+    from . import total_variation_regularization
 except ImportError:
     from warnings import warn
     warn("Limited support for total variation regularization and linear model detected! " + 
         "Some functions in the `total_variation_regularization` and `linear_model` modules require " +
         "CVXPY to be installed. You can still pynumdiff.optimize for other functions.")
 
-from .optimize import finite_difference, smooth_finite_difference, linear_model, kalman_smooth
+from . import finite_difference, smooth_finite_difference, linear_model, kalman_smooth
 
 __all__ = ['finite_difference', 'smooth_finite_difference', 'linear_model', 'kalman_smooth', 'total_variation_regularization'] # So these get treated as direct members of the module by sphinx

pynumdiff/tests/test_optimize.py

@@ -12,18 +12,15 @@
 from pynumdiff.optimize.linear_model import *
 from pynumdiff.optimize.kalman_smooth import constant_velocity, constant_acceleration, \
     constant_jerk
-from pynumdiff.utils import simulate
+from pynumdiff.utils.simulate import pi_control
 
 
 # simulation
 noise_type = 'normal'
 noise_parameters = [0, 0.01]
 dt = 0.01
-timeseries_length = 2
-problem = 'pi_control'
-x, x_truth, dxdt_truth, extras = simulate.__dict__[problem](timeseries_length,
-                                                            noise_parameters=noise_parameters,
-                                                            dt=dt)
+duration = 2
+x, x_truth, dxdt_truth, extras = pi_control(duration, noise_parameters=noise_parameters, dt=dt)
 cutoff_frequency = 0.1
 log_gamma = -1.6 * np.log(cutoff_frequency) - 0.71 * np.log(dt) - 5.1
 tvgamma = np.exp(log_gamma)

pynumdiff/utils/simulate.py

@@ -11,7 +11,7 @@
 
 
 # pylint: disable-msg=too-many-locals, too-many-arguments, no-member
-def _add_noise(x, random_seed, noise_type, *noise_parameters):
+def _add_noise(x, random_seed, noise_type, noise_parameters):
     """Add synthetic noise to data
 
     :param np.array[float] x: data
@@ -57,7 +57,7 @@ def sine(duration=4, noise_type='normal', noise_parameters=(0, 0.5), random_seed
         x += magnitude/len(frequencies)*np.sin(t*2*np.pi*f)
         dxdt += magnitude/len(frequencies)*np.cos(t*2*np.pi*f)*2*np.pi*f
     actual_vals = np.array(np.vstack((x, dxdt)))
-    noisy_x = _add_noise(x, noise_type, noise_parameters, random_seed)
+    noisy_x = _add_noise(x, random_seed, noise_type, noise_parameters)
     #
     noisy_measurements = np.array(noisy_x)
     #
@@ -115,7 +115,7 @@ def triangle(duration=4, noise_type='normal', noise_parameters=(0, 0.5), random_
     x = np.interp(t, reversal_ts, reversal_vals)
     _, dxdt = _finite_difference(x, dt=simdt)
 
-    noisy_x = _add_noise(x, noise_type, noise_parameters, random_seed)
+    noisy_x = _add_noise(x, random_seed, noise_type, noise_parameters)
 
     actual_vals = np.array(np.vstack((x, dxdt)))
     noisy_measurements = np.array(noisy_x)
@@ -161,7 +161,7 @@ def pop_dyn(duration=4, noise_type='normal', noise_parameters=(0, 0.5), random_s
     x = np.array(x)
     dxdt = np.array(dxdt)
 
-    noisy_x = _add_noise(x, noise_type, noise_parameters, random_seed)
+    noisy_x = _add_noise(x, random_seed, noise_type, noise_parameters)
 
     actual_vals = np.array(np.vstack((x, dxdt)))
     noisy_measurements = np.array(noisy_x)
@@ -206,7 +206,7 @@ def linear_autonomous(duration=4, noise_type='normal', noise_parameters=(0, 0.5)
     x *= 2
 
     smooth_x, dxdt = _finite_difference( np.ravel(x), simdt)
-    noisy_x = _add_noise(x, noise_type, noise_parameters, random_seed)
+    noisy_x = _add_noise(x, random_seed, noise_type, noise_parameters)
 
     idx = np.arange(0, len(t), int(dt/simdt))
     return np.ravel(noisy_x)[1:][idx], smooth_x[1:][idx], dxdt[1:][idx], None
@@ -239,7 +239,7 @@ def pi_control(duration=4, noise_type='normal', noise_parameters=(0, 0.5),
     x = np.ravel(actual_vals[0, :])
     dxdt = np.ravel(actual_vals[1, :])
 
-    noisy_x = _add_noise(x, noise_type, noise_parameters, random_seed)
+    noisy_x = _add_noise(x, random_seed, noise_type, noise_parameters)
 
     actual_vals = np.array(np.vstack((x, dxdt)))
     noisy_measurements = np.array(noisy_x)
@@ -343,9 +343,9 @@ def lorenz_xyz(duration=4, noise_type='normal', noise_parameters=(0, 0.5), rando
     z = xyz[2, 0:-1] / f
     dzdt = xyz_dot[2, :] / f
 
-    noisy_x = _add_noise(x, noise_type, noise_parameters, random_seed)
-    noisy_y = _add_noise(y, noise_type, noise_parameters, random_seed+1)
-    noisy_z = _add_noise(z, noise_type, noise_parameters, random_seed+2)
+    noisy_x = _add_noise(x, random_seed, noise_type, noise_parameters)
+    noisy_y = _add_noise(y, random_seed+1, noise_type, noise_parameters)
+    noisy_z = _add_noise(z, random_seed+2, noise_type, noise_parameters)
 
     actual_vals = np.array(np.vstack((x, y, z, dxdt, dydt, dzdt)))
     noisy_measurements = np.array(np.vstack((noisy_x, noisy_y, noisy_z)))
@@ -404,9 +404,9 @@ def dxyz_dt(xyz):
         y = vals[1, :]
         z = vals[2, :]
 
-    noisy_x = _add_noise(x, noise_type, noise_parameters, random_seed)
-    noisy_y = _add_noise(y, noise_type, noise_parameters, random_seed+1)
-    noisy_z = _add_noise(z, noise_type, noise_parameters, random_seed+2)
+    noisy_x = _add_noise(x, random_seed, noise_type, noise_parameters)
+    noisy_y = _add_noise(y, random_seed+1, noise_type, noise_parameters)
+    noisy_z = _add_noise(z, random_seed+2, noise_type, noise_parameters)
 
     _, dxdt = _finite_difference(x, dt)
     _, dydt = _finite_difference(y, dt)