Skip to content

Commit cfebdb6

Browse files
phobsonphobson
committed
PEP8 cleanup in the tests
1 parent 219ba0e commit cfebdb6

File tree

3 files changed

+239
-140
lines changed

3 files changed

+239
-140
lines changed

‎probscale/tests/test_algo.py

Lines changed: 45 additions & 42 deletions
Original file line numberDiff line numberDiff line change
@@ -16,23 +16,24 @@ def test__make_boot_index():
1616
@pytest.fixture
1717
def plot_data():
1818
data = numpy.array([
19-
3.113, 3.606, 4.046, 4.046, 4.710, 6.140, 6.978,
20-
2.000, 4.200, 4.620, 5.570, 5.660, 5.860, 6.650,
21-
6.780, 6.790, 7.500, 7.500, 7.500, 8.630, 8.710,
22-
8.990, 9.850, 10.820, 11.250, 11.250, 12.200, 14.920,
23-
16.770, 17.810, 19.160, 19.190, 19.640, 20.180, 22.970,
19+
3.113, 3.606, 4.046, 4.046, 4.710, 6.140, 6.978,
20+
2.000, 4.200, 4.620, 5.570, 5.660, 5.860, 6.650,
21+
6.780, 6.790, 7.500, 7.500, 7.500, 8.630, 8.710,
22+
8.990, 9.850, 10.820, 11.250, 11.250, 12.200, 14.920,
23+
16.770, 17.810, 19.160, 19.190, 19.640, 20.180, 22.970,
2424
])
2525
return data
2626

2727

2828
@pytest.mark.parametrize(('fitlogs', 'known_yhat'), [
2929
(None, numpy.array([0.7887, 3.8946, 7.0005, 10.1065, 13.2124, 16.3183])),
3030
('x', numpy.array([0.2711, 1.2784, 1.5988, 1.7953, 1.9373, 2.0487])),
31-
('y', numpy.array([2.2006e+00, 4.9139e+01, 1.0972e+03, 2.4501e+04, 5.4711e+05, 1.2217e+07])),
31+
('y', numpy.array([2.2006e+00, 4.9139e+01, 1.0972e+03,
32+
2.4501e+04, 5.4711e+05, 1.2217e+07])),
3233
('both', numpy.array([1.3114, 3.5908, 4.9472, 6.0211, 6.9402, 7.7577])),
3334
])
3435
def test__fit_simple(plot_data, fitlogs, known_yhat):
35-
x = numpy.arange(1, len(plot_data)+1)
36+
x = numpy.arange(1, len(plot_data) + 1)
3637
known_results = {'slope': 0.5177, 'intercept': 0.2711}
3738
xhat = x[::6]
3839
yhat, results = algo._fit_simple(x, plot_data, xhat, fitlogs=fitlogs)
@@ -43,19 +44,22 @@ def test__fit_simple(plot_data, fitlogs, known_yhat):
4344

4445
@pytest.mark.parametrize(('fitlogs', 'known_lo', 'known_hi'), [
4546
(None, numpy.array([-0.7944, 2.7051, 6.1974, 9.2612, 11.9382, 14.4290]),
46-
numpy.array([ 2.1447, 4.8360, 7.7140, 10.8646, 14.1014, 17.4432])),
47+
numpy.array([2.1447, 4.8360, 7.7140, 10.8646, 14.1014, 17.4432])),
4748
('x', numpy.array([-1.4098, -0.2210, 0.1387, 0.3585, 0.5147, 0.6417]),
48-
numpy.array([ 1.7067, 2.5661, 2.8468, 3.0169, 3.1400, 3.2341])),
49-
('y', numpy.array([4.5187e-01, 1.4956e+01, 4.9145e+02, 1.0522e+04, 1.5299e+05, 1.8468e+06]),
50-
numpy.array([8.5396e+00, 1.2596e+02, 2.2396e+03, 5.2290e+04, 1.3310e+06, 3.7627e+07])),
51-
('both', numpy.array([0.2442, 0.8017, 1.1488, 1.4312, 1.6731, 1.8997]),
52-
numpy.array([5.5107, 13.0148 , 17.232, 20.4285, 23.1035, 25.3843])),
49+
numpy.array([1.7067, 2.5661, 2.8468, 3.0169, 3.1400, 3.2341])),
50+
('y', numpy.array([4.5187e-01, 1.4956e+01, 4.9145e+02,
51+
1.0522e+04, 1.5299e+05, 1.8468e+06]),
52+
numpy.array([8.5396e+00, 1.2596e+02, 2.2396e+03,
53+
5.2290e+04, 1.3310e+06, 3.7627e+07])),
54+
('both', numpy.array([0.2442, 0.8017, 1.1488, 1.4312, 1.6731, 1.8997]),
55+
numpy.array([5.5107, 13.0148, 17.232, 20.4285, 23.1035, 25.3843])),
5356
])
5457
def test__bs_fit(plot_data, fitlogs, known_lo, known_hi):
5558
numpy.random.seed(0)
56-
x = numpy.arange(1, len(plot_data)+1)
59+
x = numpy.arange(1, len(plot_data) + 1)
5760
xhat = x[::6]
58-
yhat_lo, yhat_hi = algo._bs_fit(x, plot_data, xhat, fitlogs=fitlogs, niter=1000)
61+
yhat_lo, yhat_hi = algo._bs_fit(x, plot_data, xhat,
62+
fitlogs=fitlogs, niter=1000)
5963

6064
nptest.assert_allclose(yhat_lo, known_lo, rtol=0.001)
6165
nptest.assert_allclose(yhat_hi, known_hi, rtol=0.001)
@@ -68,58 +72,57 @@ def setup(self):
6872
self.intercept = 3.5
6973

7074
self.known_ylinlin = numpy.array([
71-
5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5,
72-
14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5,
73-
23.5, 24.5
75+
5.5, 6.5, 7.5, 8.5, 9.5, 10.5, 11.5, 12.5, 13.5,
76+
14.5, 15.5, 16.5, 17.5, 18.5, 19.5, 20.5, 21.5, 22.5,
77+
23.5, 24.5
7478
])
7579

76-
7780
self.known_yloglin = numpy.array([
78-
3.5 , 4.31093022, 4.88629436, 5.33258146, 5.69722458,
79-
6.00552594, 6.27258872, 6.50815479, 6.71887582, 6.90949618,
80-
7.08351894, 7.24360435, 7.3918203 , 7.52980604, 7.65888308,
81-
7.78013233, 7.89444915, 8.0025836 , 8.10517019, 8.20275051
81+
3.50000000, 4.31093022, 4.88629436, 5.33258146, 5.69722458,
82+
6.00552594, 6.27258872, 6.50815479, 6.71887582, 6.90949618,
83+
7.08351894, 7.24360435, 7.39182030, 7.52980604, 7.65888308,
84+
7.78013233, 7.89444915, 8.00258360, 8.10517019, 8.20275051
8285
])
8386

8487
self.known_yloglog = numpy.array([
85-
33.11545196, 74.50976691, 132.46180783, 206.97157474,
86-
298.03906763, 405.66428649, 529.84723134, 670.58790216,
87-
827.88629897, 1001.74242175, 1192.15627051, 1399.12784525,
88-
1622.65714598, 1862.74417268, 2119.38892536, 2392.59140402,
89-
2682.35160865, 2988.66953927, 3311.54519587, 3650.97857845
88+
33.11545196, 74.50976691, 132.46180783, 206.97157474,
89+
298.03906763, 405.66428649, 529.84723134, 670.58790216,
90+
827.88629897, 1001.74242175, 1192.15627051, 1399.12784525,
91+
1622.65714598, 1862.74417268, 2119.38892536, 2392.59140402,
92+
2682.35160865, 2988.66953927, 3311.54519587, 3650.97857845
9093
])
9194

9295
self.known_ylinlog = numpy.array([
93-
2.44691932e+02, 6.65141633e+02, 1.80804241e+03,
94-
4.91476884e+03, 1.33597268e+04, 3.63155027e+04,
95-
9.87157710e+04, 2.68337287e+05, 7.29416370e+05,
96-
1.98275926e+06, 5.38969848e+06, 1.46507194e+07,
97-
3.98247844e+07, 1.08254988e+08, 2.94267566e+08,
98-
7.99902177e+08, 2.17435955e+09, 5.91052206e+09,
99-
1.60664647e+10, 4.36731791e+10
100-
])
96+
2.44691932e+02, 6.65141633e+02, 1.80804241e+03,
97+
4.91476884e+03, 1.33597268e+04, 3.63155027e+04,
98+
9.87157710e+04, 2.68337287e+05, 7.29416370e+05,
99+
1.98275926e+06, 5.38969848e+06, 1.46507194e+07,
100+
3.98247844e+07, 1.08254988e+08, 2.94267566e+08,
101+
7.99902177e+08, 2.17435955e+09, 5.91052206e+09,
102+
1.60664647e+10, 4.36731791e+10
103+
])
101104

102105
def test_linlin(self):
103106
ylinlin = algo._estimate_from_fit(self.x, self.slope, self.intercept,
104-
xlog=False, ylog=False)
107+
xlog=False, ylog=False)
105108
nptest.assert_array_almost_equal(ylinlin, self.known_ylinlin)
106109

107110
def test_loglin(self):
108111
yloglin = algo._estimate_from_fit(self.x, self.slope, self.intercept,
109-
xlog=True, ylog=False)
112+
xlog=True, ylog=False)
110113
nptest.assert_array_almost_equal(yloglin, self.known_yloglin)
111114

112115
def test_loglog(self):
113116
yloglog = algo._estimate_from_fit(self.x, self.slope, self.intercept,
114-
xlog=True, ylog=True)
117+
xlog=True, ylog=True)
115118
nptest.assert_array_almost_equal(yloglog, self.known_yloglog)
116119

117120
def test_linlog(self):
118121
ylinlog = algo._estimate_from_fit(self.x, self.slope, self.intercept,
119-
xlog=False, ylog=True)
120-
percent_diff = numpy.abs(ylinlog - self.known_ylinlog) / self.known_ylinlog
122+
xlog=False, ylog=True)
123+
diff = numpy.abs(ylinlog - self.known_ylinlog) / self.known_ylinlog
121124
nptest.assert_array_almost_equal(
122-
percent_diff,
125+
diff,
123126
numpy.zeros(self.x.shape[0]),
124127
decimal=5
125128
)

‎probscale/tests/test_validate.py

Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -6,7 +6,6 @@
66
from probscale import algo
77

88

9-
109
def test_axes_object_invalid():
1110
with pytest.raises(ValueError):
1211
validate.axes_object('junk')

0 commit comments

Comments
 (0)