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RabiyaFRabiyaF
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fixed linting issues
1 parent 0e9e405 commit b70ebab

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2 files changed

+81
-60
lines changed

2 files changed

+81
-60
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‎fitbenchmarking/parsing/fitting_problem.py

Lines changed: 5 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -296,9 +296,11 @@ def ini_y(self, parameter_set=0):
296296
"""
297297
if parameter_set not in self._ini_y:
298298
params = self.starting_values[parameter_set].values()
299-
self._ini_y[parameter_set] = self.eval_model(params=params,
300-
x=self.data_x[0]) \
301-
if self.multifit else self.eval_model(params=params)
299+
self._ini_y[parameter_set] = (
300+
self.eval_model(params=params, x=self.data_x[0])
301+
if self.multifit
302+
else self.eval_model(params=params)
303+
)
302304
return self._ini_y[parameter_set]
303305

304306

‎fitbenchmarking/parsing/tests/test_fitting_problem.py

Lines changed: 76 additions & 57 deletions
Original file line numberDiff line numberDiff line change
@@ -162,12 +162,15 @@ def test_correct_data_single_fit(self):
162162
Tests that correct data gives the expected result
163163
"""
164164
fitting_problem = FittingProblem(self.options)
165-
fitting_problem.data_x = np.array([-0.5, 0.0, 1.0, 0.5, 1.5,
166-
2.0, 2.5, 3.0, 4.0])
167-
fitting_problem.data_y = np.array([0.0, 1.0, 2.0, 3.0, 4.0,
168-
5.0, 6.0, 7.0, 8.0])
169-
fitting_problem.data_e = np.array([1.0, 20.0, 30.0, 40.0, 50.0,
170-
60.0, 70.0, 80.0, 9.0])
165+
fitting_problem.data_x = np.array(
166+
[-0.5, 0.0, 1.0, 0.5, 1.5, 2.0, 2.5, 3.0, 4.0]
167+
)
168+
fitting_problem.data_y = np.array(
169+
[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]
170+
)
171+
fitting_problem.data_e = np.array(
172+
[1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 9.0]
173+
)
171174
fitting_problem.start_x = 0.5
172175
fitting_problem.end_x = 2.5
173176

@@ -178,19 +181,24 @@ def test_correct_data_single_fit(self):
178181
fitting_problem.correct_data()
179182

180183
sort = fitting_problem.sorted_index
181-
self.assertTrue((fitting_problem.data_x[sort]
182-
== expected_x_data).all())
183-
self.assertTrue((fitting_problem.data_y[sort]
184-
== expected_y_data).all())
185-
self.assertTrue((fitting_problem.data_e[sort]
186-
== expected_e_data).all())
184+
self.assertTrue(
185+
(fitting_problem.data_x[sort] == expected_x_data).all()
186+
)
187+
self.assertTrue(
188+
(fitting_problem.data_y[sort] == expected_y_data).all()
189+
)
190+
self.assertTrue(
191+
(fitting_problem.data_e[sort] == expected_e_data).all()
192+
)
187193

188194
self.options.cost_func_type = ["nlls"]
189195
fitting_problem.correct_data()
190-
self.assertTrue((fitting_problem.data_x[sort]
191-
== expected_x_data).all())
192-
self.assertTrue((fitting_problem.data_y[sort]
193-
== expected_y_data).all())
196+
self.assertTrue(
197+
(fitting_problem.data_x[sort] == expected_x_data).all()
198+
)
199+
self.assertTrue(
200+
(fitting_problem.data_y[sort] == expected_y_data).all()
201+
)
194202
self.assertIs(fitting_problem.data_e, None)
195203

196204
def test_correct_data_multi_fit(self):
@@ -199,59 +207,70 @@ def test_correct_data_multi_fit(self):
199207
"""
200208
fitting_problem = FittingProblem(self.options)
201209
fitting_problem.multifit = True
202-
fitting_problem.data_x = [np.array([-0.5, 0.0, 1.0, 0.5, 1.5,
203-
2.0, 2.5, 3.0, 4.0]),
204-
np.array([-0.5, 0.0, 1.0, 0.5, 1.4,
205-
2.0, 2.5, 3.0, 4.0]),
206-
np.array([-0.5, 0.0, 1.0, 0.5, 1.7,
207-
2.0, 2.5, 3.0, 4.0])]
208-
fitting_problem.data_y = [np.array([0.0, 1.0, 2.0, 3.0, 4.0,
209-
5.0, 6.0, 7.0, 8.0]),
210-
np.array([0.0, 1.0, 2.0, 3.0, 24.0,
211-
5.0, 6.0, 7.0, 8.0]),
212-
np.array([0.0, 1.0, 2.8, 3.0, 4.0,
213-
5.0, 6.0, 7.0, 8.0])]
214-
fitting_problem.data_e = [np.array([1.0, 20.0, 30.0, 40.0, 50.0,
215-
60.0, 1.0, 6.0, 9.0]),
216-
np.array([1.0, 20.0, 30.0, 40.0, 50.0,
217-
60.0, 1.0, 6.0, 9.0]),
218-
np.array([1.0, 20.0, 30.0, 40.0, 50.0,
219-
60.0, 1.0, 6.0, 9.0])]
210+
fitting_problem.data_x = [
211+
np.array([-0.5, 0.0, 1.0, 0.5, 1.5, 2.0, 2.5, 3.0, 4.0]),
212+
np.array([-0.5, 0.0, 1.0, 0.5, 1.4, 2.0, 2.5, 3.0, 4.0]),
213+
np.array([-0.5, 0.0, 1.0, 0.5, 1.7, 2.0, 2.5, 3.0, 4.0]),
214+
]
215+
fitting_problem.data_y = [
216+
np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]),
217+
np.array([0.0, 1.0, 2.0, 3.0, 24.0, 5.0, 6.0, 7.0, 8.0]),
218+
np.array([0.0, 1.0, 2.8, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]),
219+
]
220+
fitting_problem.data_e = [
221+
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0]),
222+
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0]),
223+
np.array([1.0, 20.0, 30.0, 40.0, 50.0, 60.0, 1.0, 6.0, 9.0]),
224+
]
220225
fitting_problem.start_x = [0.5, 1.1, 0.0]
221226
fitting_problem.end_x = [2.5, 2.6, 1.0]
222227

223-
expected_x_data = [np.array([0.5, 1.0, 1.5, 2.0, 2.5]),
224-
np.array([1.4, 2.0, 2.5]),
225-
np.array([0.0, 0.5, 1.0])]
226-
expected_y_data = [np.array([3.0, 2.0, 4.0, 5.0, 6.0]),
227-
np.array([24.0, 5.0, 6.0]),
228-
np.array([1.0, 3.0, 2.8])]
229-
expected_e_data = [np.array([40.0, 30.0, 50.0, 60.0, 1.0]),
230-
np.array([50.0, 60.0, 1.0]),
231-
np.array([20.0, 40.0, 30.0])]
228+
expected_x_data = [
229+
np.array([0.5, 1.0, 1.5, 2.0, 2.5]),
230+
np.array([1.4, 2.0, 2.5]),
231+
np.array([0.0, 0.5, 1.0]),
232+
]
233+
expected_y_data = [
234+
np.array([3.0, 2.0, 4.0, 5.0, 6.0]),
235+
np.array([24.0, 5.0, 6.0]),
236+
np.array([1.0, 3.0, 2.8]),
237+
]
238+
expected_e_data = [
239+
np.array([40.0, 30.0, 50.0, 60.0, 1.0]),
240+
np.array([50.0, 60.0, 1.0]),
241+
np.array([20.0, 40.0, 30.0]),
242+
]
232243

233244
fitting_problem.correct_data()
234245

235246
for ix, sort in enumerate(fitting_problem.sorted_index):
236-
self.assertTrue((fitting_problem.data_x[ix][sort]
237-
== expected_x_data[ix]).all())
238-
self.assertTrue((fitting_problem.data_y[ix][sort]
239-
== expected_y_data[ix]).all())
240-
self.assertTrue((fitting_problem.data_e[ix][sort]
241-
== expected_e_data[ix]).all())
247+
self.assertTrue(
248+
(fitting_problem.data_x[ix][sort] == expected_x_data[ix]).all()
249+
)
250+
self.assertTrue(
251+
(fitting_problem.data_y[ix][sort] == expected_y_data[ix]).all()
252+
)
253+
self.assertTrue(
254+
(fitting_problem.data_e[ix][sort] == expected_e_data[ix]).all()
255+
)
242256

243257
self.options.cost_func_type = ["nlls"]
244258
fitting_problem.correct_data()
245259
for ix, sort in enumerate(fitting_problem.sorted_index):
246-
self.assertTrue((fitting_problem.data_x[ix][sort]
247-
== expected_x_data[ix]).all())
248-
self.assertTrue((fitting_problem.data_y[ix][sort]
249-
== expected_y_data[ix]).all())
260+
self.assertTrue(
261+
(fitting_problem.data_x[ix][sort] == expected_x_data[ix]).all()
262+
)
263+
self.assertTrue(
264+
(fitting_problem.data_y[ix][sort] == expected_y_data[ix]).all()
265+
)
250266
self.assertIs(fitting_problem.data_e[ix], None)
251267

252-
@parameterized.expand([(True, [np.array([1, 2]), np.array([3, 4])],
253-
['params', 'x']),
254-
(False, np.array([1, 2]), ['params'])])
268+
@parameterized.expand(
269+
[
270+
(True, [np.array([1, 2]), np.array([3, 4])], ["params", "x"]),
271+
(False, np.array([1, 2]), ["params"]),
272+
]
273+
)
255274
@patch("fitbenchmarking.parsing.fitting_problem.FittingProblem.eval_model")
256275
def test_ini_y_args(self, multifit, data_x, args, mock):
257276
"""
@@ -260,7 +279,7 @@ def test_ini_y_args(self, multifit, data_x, args, mock):
260279
fitting_problem = FittingProblem(self.options)
261280
fitting_problem.multifit = multifit
262281
fitting_problem.data_x = data_x
263-
fitting_problem.starting_values = [{0: '0'}]
282+
fitting_problem.starting_values = [{0: "0"}]
264283

265284
fitting_problem.ini_y()
266285
self.assertEqual(mock.call_count, 1)

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