Clean up

Remove unused imports

Remove unused imports

Removed unneeded line endings

Remove header in test_finite_difference

Remove unneeded empty lines

tests/test_argument.py

@@ -3,19 +3,8 @@
 import pytest
 import sys
 import sympy
-import warnings
 
-import numpy as np
-import scipy.stats
-from scipy.optimize import curve_fit, minimize
-
-from symfit import (
-    Variable, Parameter, Fit, FitResults, log, variables,
-    parameters, Model, Eq, Ge
-)
-from symfit.core.minimizers import BFGS, MINPACK, SLSQP, LBFGSB
-from symfit.core.objectives import LogLikelihood
-from symfit.distributions import Gaussian, Exp
+from symfit import Variable, Parameter
 
 if sys.version_info >= (3, 0):
     import inspect as inspect_sig
@@ -62,13 +51,11 @@ def test_pickle():
     """
     A = Parameter('A', min=0., max=1e3, fixed=True)
     new_A = pickle.loads(pickle.dumps(A))
-    assert (A.min, A.value, A.max, A.fixed, A.name) == (
-        new_A.min, new_A.value, new_A.max, new_A.fixed, new_A.name)
+    assert (A.min, A.value, A.max, A.fixed, A.name) == (new_A.min, new_A.value, new_A.max, new_A.fixed, new_A.name)
 
     A = Parameter(min=0., max=1e3, fixed=True)
     new_A = pickle.loads(pickle.dumps(A))
-    assert (A.min, A.value, A.max, A.fixed, A.name) == (
-        new_A.min, new_A.value, new_A.max, new_A.fixed, new_A.name)
+    assert (A.min, A.value, A.max, A.fixed, A.name) == (new_A.min, new_A.value, new_A.max, new_A.fixed, new_A.name)
 
 
 def test_slots():

tests/test_auto_fit.py

@@ -217,7 +217,7 @@ def test_global_fitting():
         y_1: a_1 * x_1**2 + b_1 * x_1,
     })
     fit = Fit(model, x_1=xdata[0], y_1=ydata[0])
-    assert model.shared_parameters == False
+    assert model.shared_parameters is False
     assert isinstance(fit.minimizer, BFGS)
 
 
@@ -229,6 +229,9 @@ def test_gaussian_2d_fitting():
     mean = (0.6, 0.4)  # x, y mean 0.6, 0.4
     cov = [[0.2**2, 0], [0, 0.1**2]]
 
+    # TODO: Since we bin this data later on in a 100 bins, just evaluate 100
+    #       points on a Gaussian, and add an appropriate amount of noise. This
+    #       burns CPU cycles without good reason.
     data = np.random.multivariate_normal(mean, cov, 1000000)
 
     # Insert them as y,x here as np fucks up cartesian conventions.
@@ -256,10 +259,8 @@ def test_gaussian_2d_fitting():
 
     assert fit_result.value(x0) == pytest.approx(np.mean(data[:, 0]), 1e-3)
     assert fit_result.value(y0) == pytest.approx(np.mean(data[:, 1]), 1e-3)
-    assert np.abs(fit_result.value(sig_x)) == pytest.approx(
-        np.std(data[:, 0]), 1e-2)
-    assert np.abs(fit_result.value(sig_y)) == pytest.approx(
-        np.std(data[:, 1]), 1e-2)
+    assert np.abs(fit_result.value(sig_x)) == pytest.approx(np.std(data[:, 0]), 1e-2)
+    assert np.abs(fit_result.value(sig_y)) == pytest.approx(np.std(data[:, 1]), 1e-2)
     assert fit_result.r_squared >= 0.96
 
 
@@ -272,6 +273,9 @@ def test_gaussian_2d_fitting_background():
     cov = [[0.2**2, 0], [0, 0.1**2]]
     background = 3.0
 
+    # TODO: Since we bin this data later on in a 100 bins, just evaluate 100
+    #       points on a Gaussian, and add an appropriate amount of noise. This
+    #       burns CPU cycles without good reason.
     data = np.random.multivariate_normal(mean, cov, 500000)
     # print(data.shape)
     # Insert them as y,x here as np fucks up cartesian conventions.
@@ -294,20 +298,15 @@ def test_gaussian_2d_fitting_background():
     y = Variable('y')
     g = Variable('g')
 
-    model = GradientModel(
-        {g: A * Gaussian(x, x0, sig_x) * Gaussian(y, y0, sig_y) + b})
+    model = GradientModel({g: A * Gaussian(x, x0, sig_x) * Gaussian(y, y0, sig_y) + b})
 
     # ydata, = model(x=xx, y=yy, x0=mean[0], y0=mean[1], sig_x=np.sqrt(cov[0][0]), sig_y=np.sqrt(cov[1][1]), A=1, b=3.0)
     fit = Fit(model, x=xx, y=yy, g=ydata)
     fit_result = fit.execute()
 
-    assert fit_result.value(
-        x0) / np.mean(data[:, 0]) == pytest.approx(1.0, 1e-2)
-    assert fit_result.value(
-        y0) / np.mean(data[:, 1]) == pytest.approx(1.0, 1e-2)
-    assert np.abs(fit_result.value(sig_x)) / \
-        np.std(data[:, 0]) == pytest.approx(1.0, 1e-2)
-    assert np.abs(fit_result.value(sig_y)) / \
-        np.std(data[:, 1]) == pytest.approx(1.0, 1e-2)
+    assert fit_result.value(x0) / np.mean(data[:, 0]) == pytest.approx(1.0, 1e-2)
+    assert fit_result.value(y0) / np.mean(data[:, 1]) == pytest.approx(1.0, 1e-2)
+    assert np.abs(fit_result.value(sig_x)) / np.std(data[:, 0]) == pytest.approx(1.0, 1e-2)
+    assert np.abs(fit_result.value(sig_y)) / np.std(data[:, 1]) == pytest.approx(1.0, 1e-2)
     assert background / fit_result.value(b) == pytest.approx(1.0, 1e-1)
     assert fit_result.r_squared >= 0.96