def test_text_manager_format():
n_text = 3
text = 'confidence: {confidence:.2f}'
classes = np.array(['A', 'B', 'C'])
properties = {'class': classes, 'confidence': np.array([0.5, 0.3, 1])}
expected_text = np.array(
['confidence: 0.50', 'confidence: 0.30', 'confidence: 1.00']
)
text_manager = TextManager(text=text, n_text=n_text, properties=properties)
np.testing.assert_equal(text_manager.values, expected_text)
assert text_manager._mode == TextMode.FORMATTED
# add new text with properties
new_properties = {'class': np.array(['A']), 'confidence': np.array([0.5])}
text_manager.add(new_properties, 1)
expected_text_2 = np.concatenate([expected_text, ['confidence: 0.50']])
np.testing.assert_equal(text_manager.values, expected_text_2)
# test getting the text elements when there are none in view
text_view = text_manager.view_text([])
np.testing.assert_equal(text_view, [''])
# test getting the text elements when the first two elements are in view
text_view = text_manager.view_text([0, 1])
np.testing.assert_equal(text_view, expected_text_2[0:2])
text_manager.anchor = 'center'
coords = np.array([[0, 0], [10, 10], [20, 20]])
text_coords = text_manager.compute_text_coords(coords, ndisplay=3)
np.testing.assert_equal(text_coords, (coords, 'center', 'center'))
# remove the first text element
text_manager.remove({0})
np.testing.assert_equal(text_manager.values, expected_text_2[1::])
def test_add_with_text_constant_init_empty_then_ignored():
# TODO: we may choose not to ignore add as part of the properties refactor.
properties = {'class': np.array(['A', 'B', 'C'])}
text_manager = TextManager(text='point', n_text=0, properties=properties)
text_manager.add({'class': np.array(['C'])}, 2)
assert len(text_manager.values) == 0
def test_add_with_text_constant_init_empty():
properties = {}
text_manager = TextManager(text={'constant': 'point'},
n_text=0,
properties=properties)
text_manager.add({'class': np.array(['C'])}, 2)
np.testing.assert_array_equal(text_manager.values, ['point'] * 2)
class TextManagerSuite:
"""Benchmarks for creating and modifying a text manager."""
param_names = ['n', 'string']
params = [
[2**i for i in range(4, 18, 2)],
[
{
'constant': 'test'
},
'string_property',
'float_property',
'{string_property}: {float_property:.2f}',
],
]
def setup(self, n, string):
np.random.seed(0)
categories = ('cat', 'car')
self.features = pd.DataFrame({
'string_property':
pd.Series(
np.random.choice(categories, n),
dtype=pd.CategoricalDtype(categories),
),
'float_property':
np.random.rand(n),
})
self.current_properties = self.features.iloc[[-1]].to_dict('list')
self.manager = TextManager(string=string, features=self.features)
self.indices_to_remove = list(range(0, n, 2))
def time_create(self, n, string):
TextManager(string=string, features=self.features)
def time_refresh(self, n, string):
self.manager.refresh_text(self.features)
def time_add_iteratively(self, n, string):
for _ in range(512):
self.manager.add(self.current_properties, 1)
def time_remove_as_batch(self, n, string):
self.manager.remove(self.indices_to_remove)
# `time_remove_as_batch` can only run once per instance;
# otherwise it fails because the indices were already removed:
#
# IndexError: index 32768 is out of bounds for axis 0 with size 32768
#
# Why? ASV will run the same function after setup several times in two
# occasions: warmup and timing itself. We disable warmup and only
# allow one execution per state with these method-specific options:
time_remove_as_batch.number = 1
time_remove_as_batch.warmup_time = 0
def test_add_many_text_formatted():
properties = {'confidence': np.empty(0, dtype=float)}
text_manager = TextManager(
text='confidence: {confidence:.2f}',
n_text=0,
properties=properties,
)
text_manager.add({'confidence': np.array([0.5])}, 2)
np.testing.assert_equal(text_manager.values, ['confidence: 0.50'] * 2)
def test_add_with_text_constant():
n_text = 3
properties = {'class': np.array(['A', 'B', 'C'])}
text_manager = TextManager(text={'constant': 'point'},
n_text=n_text,
properties=properties)
np.testing.assert_array_equal(text_manager.values, ['point'] * 3)
text_manager.add({'class': np.array(['C'])}, 2)
np.testing.assert_array_equal(text_manager.values, ['point'] * 5)
def test_empty_text_manager_format():
"""Test creating an empty text manager in formatted mode.
This is for creating an empty layer with text initialized.
"""
properties = {'confidence': np.empty(0, dtype=float)}
text = 'confidence: {confidence:.2f}'
text_manager = TextManager(text=text, n_text=0, properties=properties)
assert text_manager.values.size == 0
# add a text element
new_properties = {'confidence': np.array([0.5])}
text_manager.add(new_properties, 1)
np.testing.assert_equal(text_manager.values, ['confidence: 0.50'])
def test_empty_text_manager_property():
"""Test creating an empty text manager in property mode.
This is for creating an empty layer with text initialized.
"""
properties = {'confidence': np.empty(0, dtype=float)}
text_manager = TextManager(
text='confidence', n_text=0, properties=properties
)
assert text_manager._mode == TextMode.PROPERTY
assert text_manager.values.size == 0
# add a text element
new_properties = {'confidence': np.array([0.5])}
text_manager.add(new_properties, 1)
np.testing.assert_equal(text_manager.values, ['0.5'])
def test_text_manager_property():
n_text = 3
text = 'class'
classes = np.array(['A', 'B', 'C'])
properties = {'class': classes, 'confidence': np.array([0.5, 0.3, 1])}
text_manager = TextManager(text=text, n_text=n_text, properties=properties)
np.testing.assert_equal(text_manager.values, classes)
# add new text with properties
new_properties = {'class': np.array(['A']), 'confidence': np.array([0.5])}
text_manager.add(new_properties, 1)
expected_text_2 = np.concatenate([classes, ['A']])
np.testing.assert_equal(text_manager.values, expected_text_2)
# remove the first text element
text_manager.remove({0})
np.testing.assert_equal(text_manager.values, expected_text_2[1::])
class TextManagerSuite:
"""Benchmarks for creating and modifying a text manager."""
param_names = ['n', 'text']
params = [
[2**i for i in range(4, 18, 2)],
[
None,
'constant',
'string_property',
'float_property',
'{string_property}: {float_property:.2f}',
],
]
def setup(self, n, text):
np.random.seed(0)
self.properties = {
'string_property': np.random.choice(('cat', 'car'), n),
'float_property': np.random.rand(n),
}
self.current_properties = {
k: np.array([v[-1]])
for k, v in self.properties.items()
}
self.manager = TextManager(n_text=n,
properties=self.properties,
text=text)
self.indices_to_remove = list(range(0, n, 2))
def time_create(self, n, text):
TextManager(n_text=n, properties=self.properties, text=text)
def time_refresh(self, n, text):
self.manager.refresh_text(self.properties)
def time_add_iteratively(self, n, text):
for _ in range(512):
self.manager.add(self.current_properties, 1)
def time_remove_as_batch(self, n, text):
self.manager.remove(self.indices_to_remove)
