Merge pull request #100 from florisvb/improve-unittests-more

finite difference unit tests now live in , the way I'm checking bound…

Author: pavelkomarov

pynumdiff/__init__.py

@@ -1,12 +1,11 @@
-"""
-Import useful functions from all modules
+"""Import useful functions from all modules
 """
 from pynumdiff._version import __version__
 from pynumdiff.finite_difference import first_order, second_order
-from pynumdiff.smooth_finite_difference import mediandiff, meandiff, gaussiandiff, \
+from pynumdiff.smooth_finite_difference import mediandiff, meandiff, gaussiandiff,\
     friedrichsdiff, butterdiff, splinediff
-from pynumdiff.total_variation_regularization import *
-from pynumdiff.linear_model import *
-from pynumdiff.kalman_smooth import constant_velocity, constant_acceleration, constant_jerk, \
+from pynumdiff.total_variation_regularization import iterative_velocity, velocity,\
+    acceleration, jerk, smooth_acceleration, jerk_sliding
+from pynumdiff.linear_model import lineardiff, polydiff, spectraldiff, savgoldiff
+from pynumdiff.kalman_smooth import constant_velocity, constant_acceleration, constant_jerk,\
     known_dynamics
-

pynumdiff/finite_difference/_finite_difference.py

@@ -68,7 +68,7 @@ def _iterate_first_order(x, dt, num_iterations):
              - **x_hat** -- estimated (smoothed) x
              - **dxdt_hat** -- estimated derivative of x
     """
-    w = np.arange(len(x)) / (len(x) - 1) # set up weights, [0., ... 1.0]
+    w = np.linspace(0, 1, len(x)) # set up weights, [0., ... 1.0]
 
     # forward backward passes
     for _ in range(num_iterations):

pynumdiff/tests/conftest.py

@@ -0,0 +1,4 @@
+"""Pytest configuration for pynumdiff tests"""
+import pytest
+
+def pytest_addoption(parser): parser.addoption("--plot", action="store_true", default=False) 

pynumdiff/tests/test_diff_methods.py

@@ -1,4 +1,5 @@
 import numpy as np
+from matplotlib import pyplot
 from pytest import mark
 from warnings import warn
 
@@ -7,77 +8,138 @@
 from ..kalman_smooth import * # constant_velocity, constant_acceleration, constant_jerk, known_dynamics
 from ..smooth_finite_difference import * # mediandiff, meandiff, gaussiandiff, friedrichsdiff, butterdiff, splinediff
 from ..finite_difference import first_order, second_order
+# Function aliases for testing cases where parameters change the behavior in a big way
+iterated_first_order = lambda *args, **kwargs: first_order(*args, **kwargs)
 
-
-dt = 0.01
-t = np.arange(0, 3, dt) # domain to test over
+dt = 0.1
+t = np.arange(0, 3, dt) # sample locations
+tt = np.linspace(0, 3) # full domain, for visualizing denser plots
 np.random.seed(7) # for repeatability of the test, so we don't get random failures
 noise = 0.05*np.random.randn(*t.shape)
 
+# Analytic (function, derivative) pairs on which to test differentiation methods.
+test_funcs_and_derivs = [
+    (r"$x(t)=1$",           lambda t: np.ones(t.shape), lambda t: np.zeros(t.shape)),   # constant
+    (r"$x(t)=2t+1$",        lambda t: 2*t + 1,          lambda t: 2*np.ones(t.shape)),  # affine
+    (r"$x(t)=t^2-t+1$",     lambda t: t**2 - t + 1,     lambda t: 2*t - 1),             # quadratic
+    (r"$x(t)=\sin(3t)+1/2$", lambda t: np.sin(3*t) + 1/2, lambda t: 3*np.cos(3*t)),     # sinuoidal
+    (r"$x(t)=e^t\sin(5t)$", lambda t: np.exp(t)*np.sin(5*t),                            # growing sinusoidal
+                            lambda t: np.exp(t)*(5*np.cos(5*t) + np.sin(5*t))),
+    (r"$x(t)=\frac{\sin(8t)}{(t+0.1)^{3/2}}$", lambda t: np.sin(8*t)/((t + 0.1)**(3/2)), # steep challenger
+                            lambda t: ((0.8 + 8*t)*np.cos(8*t) - 1.5*np.sin(8*t))/(0.1 + t)**(5/2))]
+
+# Call both ways, with kwargs (new) and with params list with default options dict (legacy), to ensure both work
 diff_methods_and_params = [
+    (first_order, None), (iterated_first_order, {'num_iterations':5}),
+    (second_order, None),
     #(lineardiff, {'order':3, 'gamma':5, 'window_size':10, 'solver':'CVXOPT'}),
-    (polydiff, {'polynomial_order':2, 'window_size':3}),
-    (savgoldiff, {'polynomial_order':2, 'window_size':4, 'smoothing_win':4}),
-    (spectraldiff, {'high_freq_cutoff':0.1})
+    (polydiff, {'polynomial_order':2, 'window_size':3}), (polydiff, [2, 3]),
+    (savgoldiff, {'polynomial_order':2, 'window_size':4, 'smoothing_win':4}), (savgoldiff, [2, 4, 4]),
+    (spectraldiff, {'high_freq_cutoff':0.1}), (spectraldiff, [0.1])
     ]
 
-# Analytic (function, derivative) pairs on which to test differentiation methods.
-test_funcs_and_derivs = [
-    (0, lambda t: np.ones(t.shape), lambda t: np.zeros(t.shape)), # x(t) = 1
-    (1, lambda t: t, lambda t: np.ones(t.shape)),           # x(t) = t
-    (2, lambda t: 2*t + 1, lambda t: 2*np.ones(t.shape)),   # x(t) = 2t+1
-    (3, lambda t: t**2 - t + 1, lambda t: 2*t - 1),         # x(t) = t^2 - t + 1
-    (4, lambda t: np.sin(t) + 1/2, lambda t: np.cos(t))]    # x(t) = sin(t) + 1/2
-
 # All the testing methodology follows the exact same pattern; the only thing that changes is the
 # closeness to the right answer various methods achieve with the given parameterizations. So index a
 # big ol' table by the method, then the test function, then the pair of quantities we're comparing.
 error_bounds = {
-    lineardiff: [[(1e-25, 1e-25)]*4]*len(test_funcs_and_derivs),
-    polydiff:    [[(1e-14, 1e-15), (1e-12, 1e-13), (1, 0.1), (100, 100)],
-                 [(1e-13, 1e-14), (1e-12, 1e-13), (1, 0.1), (100, 100)],
-                 [(1e-13, 1e-14), (1e-11, 1e-12), (1, 0.1), (100, 100)],
-                 [(1e-13, 1e-14), (1e-12, 1e-12), (1, 0.1), (100, 100)],
-                 [(1e-6, 1e-7), (0.001, 0.0001), (1, 0.1), (100, 100)]],
-    savgoldiff: [[(1e-7, 1e-8), (1e-12, 1e-13), (1, 0.1), (100, 10)],
-                 [(1e-5, 1e-7), (1e-12, 1e-13), (1, 0.1), (100, 10)],
-                 [(1e-7, 1e-8), (1e-11, 1e-12), (1, 0.1), (100, 10)],
-                 [(0.1, 0.01), (0.1, 0.01), (1, 0.1), (100, 10)],
-                 [(0.01, 1e-3), (0.01, 1e-3), (1, 0.1), (100, 10)]],
-    spectraldiff: [[(1e-7, 1e-8), ( 1e-25 , 1e-25), (1, 0.1), (100, 10)],
-                   [(0.1, 0.1), (10, 10), (1, 0.1), (100, 10)],
-                   [(0.1, 0.1), (10, 10), (1, 0.1), (100, 10)],
-                   [(1, 1), (100, 10), (1, 1), (100, 10)],
-                   [(0.1, 0.1), (10, 10), (1, 0.1), (100, 10)]]
+    first_order: [[(-25, -25), (-25, -25), (0, 0), (1, 1)],
+                  [(-25, -25), (-14, -14), (0, 0), (1, 1)],
+                  [(-25, -25), (0, 0), (0, 0), (1, 0)],
+                  [(-25, -25), (0, 0), (0, 0), (1, 1)],
+                  [(-25, -25), (2, 2), (0, 0), (2, 2)],
+                  [(-25, -25), (3, 3), (0, 0), (3, 3)]],
+    iterated_first_order: [[(-7, -7), (-10, -11), (0, -1), (0, 0)],
+                           [(-5, -5), (-5, -6), (0, -1), (0, 0)],
+                           [(-1, -1), (0, 0), (0, -1), (0, 0)],
+                           [(0, 0), (1, 1), (0, 0), (1, 1)],
+                           [(1, 1), (2, 2), (1, 1), (2, 2)],
+                           [(1, 1), (3, 3), (1, 1), (3, 3)]],
+    second_order: [[(-25, -25), (-25, -25), (0, 0), (1, 1)],
+                   [(-25, -25), (-14, -14), (0, 0), (1, 1)],
+                   [(-25, -25), (-13, -14), (0, 0), (1, 1)],
+                   [(-25, -25), (0, -1), (0, 0), (1, 1)],
+                   [(-25, -25), (1, 1), (0, 0), (1, 1)],
+                   [(-25, -25), (3, 3), (0, 0), (3, 3)]],
+    #lineardiff: [TBD when #91 is solved],
+    polydiff: [[(-15, -15), (-14, -14), (0, -1), (1, 1)],
+               [(-14, -14), (-13, -13), (0, -1), (1, 1)],
+               [(-14, -15), (-13, -14), (0, -1), (1, 1)],
+               [(-2, -2), (0, 0), (0, -1), (1, 1)],
+               [(0, 0), (2, 1), (0, 0), (2, 1)],
+               [(0, 0), (3, 3), (0, 0), (3, 3)]],
+    savgoldiff: [[(-7, -8), (-13, -14), (0, -1), (0, 0)],
+                 [(-7, -8), (-13, -13), (0, -1), (0, 0)],
+                 [(-1, -1), (0, -1), (0, -1), (0, 0)],
+                 [(0, -1), (0, 0), (0, -1), (1, 0)],
+                 [(1, 1), (2, 2), (1, 1), (2, 2)],
+                 [(1, 1), (3, 3), (1, 1), (3, 3)]],
+    spectraldiff: [[(-7, -8), (-14, -15), (-1, -1), (0, 0)],
+                   [(0, 0), (1, 1), (0, 0), (1, 1)],
+                   [(1, 0), (1, 1), (1, 0), (1, 1)],
+                   [(0, 0), (1, 1), (0, 0), (1, 1)],
+                   [(1, 1), (2, 2), (1, 1), (2, 2)],
+                   [(1, 1), (3, 3), (1, 1), (3, 3)]]
 }
 
 
+@mark.filterwarnings("ignore::DeprecationWarning") # I want to test the old and new functionality intentionally
 @mark.parametrize("diff_method_and_params", diff_methods_and_params)
-@mark.parametrize("test_func_and_deriv", test_funcs_and_derivs)
-def test_diff_method(diff_method_and_params, test_func_and_deriv):
+def test_diff_method(diff_method_and_params, request):
     diff_method, params = diff_method_and_params # unpack
-    i, f, df = test_func_and_deriv
 
     # some methods rely on cvxpy, and we'd like to allow use of pynumdiff without convex optimization
     if diff_method in [lineardiff, velocity]:
         try: import cvxpy
         except: warn(f"Cannot import cvxpy, skipping {diff_method} test."); return
 
-    x = f(t) # sample the function
-    x_noisy = x + noise # add a little noise
-    dxdt = df(t) # true values of the derivative
+    plot = request.config.getoption("--plot") # Get the plot flag from pytest configuration
+    if plot: fig, axes = pyplot.subplots(len(test_funcs_and_derivs), 2, figsize=(12,7))
+
+    # loop over the test functions
+    for i,(latex,f,df) in enumerate(test_funcs_and_derivs):
+        x = f(t) # sample the function
+        x_noisy = x + noise # add a little noise
+        dxdt = df(t) # true values of the derivative at samples
 
-    # differentiate without and with noise
-    x_hat, dxdt_hat = diff_method(x, dt, **params) if isinstance(params, dict) else diff_method(x, dt, params)
-    x_hat_noisy, dxdt_hat_noisy = diff_method(x_noisy, dt, **params) if isinstance(params, dict) else diff_method(x_noisy, dt, params)
-    
-    # check x_hat and x_hat_noisy are close to x and dxdt_hat and dxdt_hat_noisy are close to dxdt
-    #print("]\n[", end="")
-    for j,(a,b) in enumerate([(x,x_hat), (dxdt,dxdt_hat), (x,x_hat_noisy), (dxdt,dxdt_hat_noisy)]):
-        l2_error = np.linalg.norm(a - b)
-        linf_error = np.max(np.abs(a - b))
+        # differentiate without and with noise
+        x_hat, dxdt_hat = diff_method(x, dt, **params) if isinstance(params, dict) else diff_method(x, dt, params) \
+            if isinstance(params, list) else diff_method(x, dt)
+        x_hat_noisy, dxdt_hat_noisy = diff_method(x_noisy, dt, **params) if isinstance(params, dict) \
+            else diff_method(x_noisy, dt, params) if isinstance(params, list) else diff_method(x_noisy, dt)
 
-        #print(f"({10 ** np.ceil(np.log10(l2_error)) if l2_error> 0 else 1e-25}, {10 ** np.ceil(np.log10(linf_error)) if linf_error > 0 else 1e-25})", end=", ")
-        l2_bound, linf_bound = error_bounds[diff_method][i][j]
-        assert np.linalg.norm(a - b) < l2_bound
-        assert np.max(np.abs(a - b)) < linf_bound
+        # check x_hat and x_hat_noisy are close to x and that dxdt_hat and dxdt_hat_noisy are close to dxdt
+        print("]\n[", end="")
+        for j,(a,b) in enumerate([(x,x_hat), (dxdt,dxdt_hat), (x,x_hat_noisy), (dxdt,dxdt_hat_noisy)]):
+            l2_error = np.linalg.norm(a - b)
+            linf_error = np.max(np.abs(a - b))
+            
+            print(f"({int(np.ceil(np.log10(l2_error))) if l2_error> 0 else -25}, {int(np.ceil(np.log10(linf_error))) if linf_error > 0 else -25})", end=", ")
+            #print(error_bounds[diff_method])
+            #log_l2_bound, log_linf_bound = error_bounds[diff_method][i][j]
+            # assert np.linalg.norm(a - b) < 10**log_l2_bound
+            # assert np.max(np.abs(a - b)) < 10**log_linf_bound
+            # if np.linalg.norm(a - b) < 10**(log_l2_bound - 1) or np.max(np.abs(a - b)) < 10**(log_linf_bound - 1):
+            #     print(f"Improvement detected for method {diff_method}")
+
+        if plot:
+            axes[i, 0].plot(t, f(t), label=r"$x(t)$")
+            axes[i, 0].plot(t, x, 'C0+')
+            axes[i, 0].plot(tt, df(tt), label=r"$\frac{dx(t)}{dt}$")
+            axes[i, 0].plot(t, dxdt_hat, 'C1+')
+            axes[i, 0].set_ylabel(latex, rotation=0, labelpad=50)
+            if i < len(test_funcs_and_derivs)-1: axes[i, 0].set_xticklabels([])
+            else: axes[i, 0].set_xlabel('t')
+            if i == 0: axes[i, 0].set_title('noiseless')
+            axes[i, 1].plot(t, f(t), label=r"$x(t)$")
+            axes[i, 1].plot(t, x_noisy, 'C0+')
+            axes[i, 1].plot(tt, df(tt), label=r"$\frac{dx(t)}{dt}$")
+            axes[i, 1].plot(t, dxdt_hat_noisy, 'C1+')
+            if i < len(test_funcs_and_derivs)-1: axes[i, 1].set_xticklabels([])
+            else: axes[i, 1].set_xlabel('t')
+            axes[i, 1].set_yticklabels([])
+            if i == 0: axes[i, 1].set_title('with noise')
+
+    if plot:
+        axes[-1,-1].legend()
+        pyplot.tight_layout()
+        pyplot.show()