Merge 2741406 into ed20d29

tests/auto_variables.py

@@ -0,0 +1,17 @@
+import symfit as sf
+import pytest
+
+x, y, z = sf.variables('x, y, z')
+a, b, c = sf.parameters('a, b, c')
+
+
+@pytest.fixture(autouse=True)
+def initVariales():
+    global x, y, z
+    x, y, z = sf.variables('x, y, z')
+
+
+@pytest.fixture(autouse=True)
+def initParameters():
+    global a, b, c
+    a, b, c = sf.parameters('a, b, c')

tests/test_finite_difference.py

@@ -1,102 +1,57 @@
 import symfit as sf
 import numpy as np
 import pytest
+from tests.auto_variables import *
 
 
 def setup_method():
     np.random.seed(0)
 
 
-def test_1_1_model():
-    '''Tests the case with 1 component and 1 parameter'''
-    x, y = sf.variables('x, y')
-    a = sf.Parameter(name='a')
-    model = sf.Model({y: 3 * a * x**2})
-    x_data = np.arange(10)
-
-    exact = model.eval_jacobian(x=x_data, a=3.5)
-    approx = model.finite_difference(x=x_data, a=3.5)
-    _assert_equal(exact, approx)
-
-    exact = model.eval_jacobian(x=3, a=3.5)
-    approx = model.finite_difference(x=3, a=3.5)
-    _assert_equal(exact, approx)
-
-
-def test_1_multi_model():
-    '''Tests the case with 1 component and multiple parameters'''
-    x, y = sf.variables('x, y')
-    a, b = sf.parameters('a, b')
-    model = sf.Model({y: 3 * a * x**2 - sf.exp(b) * x})
-    x_data = np.arange(10)
-
-    exact = model.eval_jacobian(x=x_data, a=3.5, b=2)
-    approx = model.finite_difference(x=x_data, a=3.5, b=2)
-    _assert_equal(exact, approx)
-
-    exact = model.eval_jacobian(x=3, a=3.5, b=2)
-    approx = model.finite_difference(x=3, a=3.5, b=2)
-    _assert_equal(exact, approx)
-
-
-def test_multi_1_model():
-    '''Tests the case with multiple components and one parameter'''
-    x, y, z = sf.variables('x, y, z')
-    a, = sf.parameters('a')
-    model = sf.Model({y: 3 * a * x**2,
-                      z: sf.exp(a*x)})
-    x_data = np.arange(10)
-
-    exact = model.eval_jacobian(x=x_data, a=3.5)
-    approx = model.finite_difference(x=x_data, a=3.5)
-    _assert_equal(exact, approx)
-
-    exact = model.eval_jacobian(x=3, a=3.5)
-    approx = model.finite_difference(x=3, a=3.5)
-    _assert_equal(exact, approx)
-
-
-def test_multi_multi_model():
-    '''Tests the case with multiple components and multiple parameters'''
-    x, y, z = sf.variables('x, y, z')
-    a, b, c = sf.parameters('a, b, c')
-    model = sf.Model({y: 3 * a * x**2 + b * x - c,
-                      z: sf.exp(a*x - b) * c})
-    x_data = np.arange(10)
-
-    exact = model.eval_jacobian(x=x_data, a=3.5, b=2, c=5)
-    approx = model.finite_difference(x=x_data, a=3.5, b=2, c=5)
-    _assert_equal(exact, approx, rel=1e-3)
-
-    exact = model.eval_jacobian(x=3, a=3.5, b=2, c=5)
-    approx = model.finite_difference(x=3, a=3.5, b=2, c=5)
-    _assert_equal(exact, approx, rel=1e-3)
-
-
-def test_multi_indep():
+@pytest.mark.parametrize('x_data', [np.arange(10), 3])
+@pytest.mark.parametrize('init_model,init_par',
+    [
+        ({y: 3 * a * x**2}, {'a': 3.5}),
+        ({y: 3 * a * x**2 - sf.exp(b) * x},
+        {'a': 3.5, 'b': 2}),
+        ({y: 3 * a * x**2, z: sf.exp(a*x)}, {'a': 3.5}),
+        ({y: 3 * a * x**2 + b * x - c, z: sf.exp(a*x - b) * c},
+            {'a': 3.5, 'b': 2, 'c': 5})  
+    ]
+)
+def test_model(x_data, init_model, init_par):
+    '''
+    Tests cases with:   1 component and 1 parameter,
+                        1 component and multiple parameters,
+                        multiple components and one parameter,
+                        multiple components and multiple parameters
+    '''
+    model = sf.Model(init_model)
+    exact = model.eval_jacobian(x=x_data, **init_par)
+    approx = model.finite_difference(x=x_data, **init_par)
+    _assert_equal(exact, approx, rel=1e-5)
+
+
+@pytest.mark.parametrize('x_data, w_data',
+    [
+        (np.arange(10)/10, np.arange(10)),
+        (0.3, np.arange(10)),
+        (0.3, 5)
+    ]
+)
+def test_multi_indep(x_data, w_data):
     '''
     Tests the case with multiple components, multiple parameters and
     multiple independent variables
     '''
-    w, x, y, z = sf.variables('w, x, y, z')
-    a, b, c = sf.parameters('a, b, c')
+    w = sf.Variable('w')
     model = sf.Model({y: 3 * a * x**2 + b * x * w - c,
                       z: sf.exp(a*x - b) + c*w})
-    x_data = np.arange(10)/10
-    w_data = np.arange(10)
 
     exact = model.eval_jacobian(x=x_data, w=w_data, a=3.5, b=2, c=5)
     approx = model.finite_difference(x=x_data, w=w_data, a=3.5, b=2, c=5)
     _assert_equal(exact, approx)
 
-    exact = model.eval_jacobian(x=0.3, w=w_data, a=3.5, b=2, c=5)
-    approx = model.finite_difference(x=0.3, w=w_data, a=3.5, b=2, c=5)
-    _assert_equal(exact, approx)
-
-    exact = model.eval_jacobian(x=0.3, w=5, a=3.5, b=2, c=5)
-    approx = model.finite_difference(x=0.3, w=5, a=3.5, b=2, c=5)
-    _assert_equal(exact, approx)
-
 
 def test_ODE_stdev():
     """