removed unnecessary imports from tests

tests/test_fits.py

@@ -1,16 +1,13 @@
 import autograd.numpy as np
-import os
-import random
-import string
-import copy
 import math
 import scipy.optimize
-from scipy.odr import ODR, Model, Data, RealData
+from scipy.odr import ODR, Model, RealData
 import pyerrors as pe
 import pytest
 
 np.random.seed(0)
 
+
 def test_standard_fit():
     dim = 10 + int(30 * np.random.rand())
     x = np.arange(dim)
@@ -69,7 +66,7 @@ def test_odr_fit():
 
     data = RealData([o.value for o in ox], [o.value for o in oy], sx=[o.dvalue for o in ox], sy=[o.dvalue for o in oy])
     model = Model(func)
-    odr = ODR(data, model, [0,0], partol=np.finfo(np.float64).eps)
+    odr = ODR(data, model, [0, 0], partol=np.finfo(np.float64).eps)
     odr.set_job(fit_type=0, deriv=1)
     output = odr.run()
 
@@ -79,8 +76,8 @@ def test_odr_fit():
     for i in range(2):
         beta[i].gamma_method(e_tag=5, S=1.0)
         assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)
-        assert math.isclose(output.cov_beta[i,i], beta[i].dvalue**2, rel_tol=2.5e-1), str(output.cov_beta[i,i]) + ' ' + str(beta[i].dvalue**2)
-    assert math.isclose(pe.covariance(beta[0], beta[1]), output.cov_beta[0,1], rel_tol=2.5e-1)
+        assert math.isclose(output.cov_beta[i, i], beta[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(beta[i].dvalue ** 2)
+    assert math.isclose(pe.covariance(beta[0], beta[1]), output.cov_beta[0, 1], rel_tol=2.5e-1)
     pe.Obs.e_tag_global = 0
 
 
@@ -94,7 +91,7 @@ def test_odr_derivatives():
         loc_xvalue = n + np.random.normal(0.0, x_err)
         x.append(pe.pseudo_Obs(loc_xvalue, x_err, str(n)))
         y.append(pe.pseudo_Obs((lambda x: x ** 2 - 1)(loc_xvalue) +
-        np.random.normal(0.0, y_err), y_err, str(n)))
+                               np.random.normal(0.0, y_err), y_err, str(n)))
 
         def func(a, x):
             return a[0] + a[1] * x ** 2
@@ -103,4 +100,4 @@ def test_odr_derivatives():
 
     tfit = pe.fits.fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
     assert np.abs(np.max(np.array(list(fit1[1].deltas.values()))
-                       - np.array(list(tfit[1].deltas.values())))) < 10e-8
+                  - np.array(list(tfit[1].deltas.values())))) < 10e-8

tests/test_linalg.py

@@ -1,18 +1,11 @@
-import sys
-sys.path.append('..')
 import autograd.numpy as np
-import os
-import random
 import math
-import string
-import copy
-import scipy.optimize
-from scipy.odr import ODR, Model, Data, RealData
 import pyerrors as pe
 import pytest
 
 np.random.seed(0)
 
+
 def test_matrix_functions():
     dim = 3 + int(4 * np.random.rand())
     print(dim)
@@ -55,4 +48,3 @@ def test_matrix_functions():
             tmp[j].gamma_method()
             assert math.isclose(tmp[j].value, 0.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j)
             assert math.isclose(tmp[j].dvalue, 0.0, abs_tol=1e-9), 'dvalue ' + str(i) + ',' + str(j)
-

tests/test_pyerrors.py

@@ -8,6 +8,7 @@ import pytest
 
 np.random.seed(0)
 
+
 def test_dump():
     value = np.random.normal(5, 10)
     dvalue = np.abs(np.random.normal(0, 1))
@@ -38,8 +39,8 @@ def test_function_overloading():
           lambda x: np.sinh(x[0]), lambda x: np.cosh(x[0]), lambda x: np.tanh(x[0])]
 
     for i, f in enumerate(fs):
-        t1 = f([a,b])
-        t2 = pe.derived_observable(f, [a,b])
+        t1 = f([a, b])
+        t2 = pe.derived_observable(f, [a, b])
         c = t2 - t1
         assert c.value == 0.0, str(i)
         assert np.all(np.abs(c.deltas['e1']) < 1e-14), str(i)

tests/test_roots.py

@@ -4,6 +4,7 @@ import pytest
 
 np.random.seed(0)
 
+
 def test_root_linear():
 
     def root_function(x, d):
@@ -16,4 +17,3 @@ def test_root_linear():
     assert np.isclose(my_root.value, value)
     difference = my_obs - my_root
     assert all(np.isclose(0.0, difference.deltas['t']))
-