File: test_number_validator.py

package info (click to toggle)
plotly 5.4.1%2Bdfsg-1
  • links: PTS, VCS
  • area: main
  • in suites: bookworm
  • size: 90,200 kB
  • sloc: python: 368,793; javascript: 213,159; sh: 49; makefile: 4
file content (152 lines) | stat: -rw-r--r-- 4,557 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
import pytest
from pytest import approx

from _plotly_utils.basevalidators import NumberValidator
import numpy as np
import pandas as pd

# Fixtures
# --------
@pytest.fixture
def validator(request):
    return NumberValidator("prop", "parent")


@pytest.fixture
def validator_min_max(request):
    return NumberValidator("prop", "parent", min=-1.0, max=2.0)


@pytest.fixture
def validator_min(request):
    return NumberValidator("prop", "parent", min=-1.0)


@pytest.fixture
def validator_max(request):
    return NumberValidator("prop", "parent", max=2.0)


@pytest.fixture
def validator_aok():
    return NumberValidator("prop", "parent", min=-1, max=1.5, array_ok=True)


# Array not ok
# ------------
# ### Acceptance ###
@pytest.mark.parametrize(
    "val", [1.0, 0.0, 1, -1234.5678, 54321, np.pi, np.nan, np.inf, -np.inf]
)
def test_acceptance(val, validator):
    assert validator.validate_coerce(val) == approx(val, nan_ok=True)


# ### Rejection by value ###
@pytest.mark.parametrize("val", ["hello", (), [], [1, 2, 3], set(), "34"])
def test_rejection_by_value(val, validator):
    with pytest.raises(ValueError) as validation_failure:
        validator.validate_coerce(val)

    assert "Invalid value" in str(validation_failure.value)


# ### With min/max ###
@pytest.mark.parametrize("val", [0, 0.0, -0.5, 1, 1.0, 2, 2.0, np.pi / 2.0])
def test_acceptance_min_max(val, validator_min_max):
    assert validator_min_max.validate_coerce(val) == approx(val)


@pytest.mark.parametrize("val", [-1.01, -10, 2.1, 234, -np.inf, np.nan, np.inf])
def test_rejection_min_max(val, validator_min_max):
    with pytest.raises(ValueError) as validation_failure:
        validator_min_max.validate_coerce(val)

    assert "in the interval [-1.0, 2.0]" in str(validation_failure.value)


# ### With min only ###
@pytest.mark.parametrize("val", [0, 0.0, -0.5, 99999, np.inf])
def test_acceptance_min(val, validator_min):
    assert validator_min.validate_coerce(val) == approx(val)


@pytest.mark.parametrize("val", [-1.01, -np.inf, np.nan])
def test_rejection_min(val, validator_min):
    with pytest.raises(ValueError) as validation_failure:
        validator_min.validate_coerce(val)

    assert "in the interval [-1.0, inf]" in str(validation_failure.value)


# ### With max only ###
@pytest.mark.parametrize("val", [0, 0.0, -np.inf, -123456, np.pi / 2])
def test_acceptance_max(val, validator_max):
    assert validator_max.validate_coerce(val) == approx(val)


@pytest.mark.parametrize("val", [2.01, np.inf, np.nan])
def test_rejection_max(val, validator_max):
    with pytest.raises(ValueError) as validation_failure:
        validator_max.validate_coerce(val)

    assert "in the interval [-inf, 2.0]" in str(validation_failure.value)


# Array ok
# --------
# ### Acceptance ###
@pytest.mark.parametrize("val", [1.0, 0.0, 1, 0.4])
def test_acceptance_aok_scalars(val, validator_aok):
    assert validator_aok.validate_coerce(val) == val


@pytest.mark.parametrize("val", [[1.0, 0.0], [1], [-0.1234, 0.41, -1.0]])
def test_acceptance_aok_list(val, validator_aok):
    assert np.array_equal(
        validator_aok.validate_coerce(val), np.array(val, dtype="float")
    )


# ### Coerce ###
#     Coerced to general consistent numeric type
@pytest.mark.parametrize(
    "val,expected",
    [
        ([1.0, 0], (1.0, 0)),
        (np.array([1, -1]), np.array([1, -1])),
        (pd.Series([1, -1]), np.array([1, -1])),
        (pd.Index([1, -1]), np.array([1, -1])),
        ((-0.1234, 0, -1), (-0.1234, 0.0, -1.0)),
    ],
)
def test_coercion_aok_list(val, expected, validator_aok):
    v = validator_aok.validate_coerce(val)
    if isinstance(val, (np.ndarray, pd.Series, pd.Index)):
        assert np.array_equal(v, expected)
    else:
        assert isinstance(v, list)
        assert validator_aok.present(v) == tuple(val)


# ### Rejection ###
#
@pytest.mark.parametrize("val", [["a", 4]])
def test_rejection_aok(val, validator_aok):
    with pytest.raises(ValueError) as validation_failure:
        validator_aok.validate_coerce(val)

    assert "Invalid element(s)" in str(validation_failure.value)


# ### Rejection by element ###
@pytest.mark.parametrize(
    "val",
    [[-1.6, 0.0], [1, 1.5, 2], [-0.1234, 0.41, np.nan], [0, np.inf], [0, -np.inf]],
)
def test_rejection_aok_min_max(val, validator_aok):
    with pytest.raises(ValueError) as validation_failure:
        validator_aok.validate_coerce(val)

    assert "Invalid element(s)" in str(validation_failure.value)
    assert "in the interval [-1, 1.5]" in str(validation_failure.value)