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|
# -*- coding: utf-8 -*-
# Copyright © 2014, German Neuroinformatics Node (G-Node)
#
# All rights reserved.
#
# Redistribution and use in section and binary forms, with or without
# modification, are permitted under the terms of the BSD License. See
# LICENSE file in the root of the Project.
import os
import time
import unittest
from collections import OrderedDict
import numpy as np
import nixio as nix
from nixio.exceptions import UnsupportedLinkType
from .tmp import TempDir
class TestTags(unittest.TestCase):
def setUp(self):
self.tmpdir = TempDir("tagtest")
self.testfilename = os.path.join(self.tmpdir.path, "tagtest.nix")
self.file = nix.File.open(self.testfilename, nix.FileMode.Overwrite)
self.block = self.file.create_block("test block", "recordingsession")
self.my_array = self.block.create_data_array("my array", "test",
nix.DataType.Int16, (1,))
self.my_tag = self.block.create_tag(
"my tag", "tag", [0]
)
self.my_tag.references.append(self.my_array)
self.your_array = self.block.create_data_array(
"your array", "test", nix.DataType.Int16, (1,)
)
self.your_tag = self.block.create_tag(
"your tag", "tag", [0]
)
self.your_tag.references.append(self.your_array)
def tearDown(self):
del self.file.blocks[self.block.id]
self.file.close()
self.tmpdir.cleanup()
def test_flex(self):
tag1d = self.block.create_tag("1dim tag", "tag", [1])
tag2d = self.block.create_tag("2dim tag", "tag", [1, 1])
tag3d = self.block.create_tag("3dim tag", "tag", [1, 1, 1])
da1d = self.block.create_data_array("1d array", "da",
data=np.arange(5))
da1d.append_sampled_dimension(1., label="time", unit="s")
da2d = self.block.create_data_array("2d array", "da",
data=np.arange(25).reshape((5, 5)))
da2d.append_sampled_dimension(1., label="time", unit="s")
da2d.append_set_dimension()
da3d = self.block.create_data_array("3d array", "da",
data=np.arange(125)
.reshape((5, 5, 5)))
da3d.append_sampled_dimension(1., label="time", unit="s")
da3d.append_set_dimension()
da3d.append_set_dimension()
tag1d.extent = [1]
tag1d.references.extend([da1d, da2d, da3d])
assert list(tag1d.references) == [da1d, da2d, da3d]
ref1 = tag1d.tagged_data(1) # 1d tag to 2d data
for (ref_val, da_val) in zip(ref1[:].flatten(), da2d[1:3, :].flatten()):
assert ref_val == da_val
tag2d.extent = [1, 2]
tag2d.references.extend([da1d, da2d, da3d])
tag2d.tagged_data(0) # 2d tag to 1d data
tag3d.extent = [1, 2, 3]
tag3d.references.extend([da1d, da2d, da3d])
np.testing.assert_array_equal(tag3d.tagged_data(0), da1d[1:2])
np.testing.assert_array_equal(tag3d.tagged_data(1), da2d[1:2, 1:3])
np.testing.assert_array_equal(tag3d.tagged_data(2), da3d[1:2, 1:3, 1:4])
def test_tag_eq(self):
assert self.my_tag == self.my_tag
assert not self.my_tag == self.your_tag
assert self.my_tag is not None
def test_tag_id(self):
assert self.my_tag.id is not None
def test_tag_name(self):
assert self.my_tag.name is not None
def test_tag_type(self):
def set_none():
self.my_tag.type = None
assert self.my_tag.type is not None
self.assertRaises(Exception, set_none)
self.my_tag.type = "foo type"
assert self.my_tag.type == "foo type"
def test_tag_definition(self):
assert self.my_tag.definition is None
self.my_tag.definition = "definition"
assert self.my_tag.definition == "definition"
self.my_tag.definition = None
assert self.my_tag.definition is None
def test_tag_timestamps(self):
created_at = self.my_tag.created_at
assert created_at > 0
updated_at = self.my_tag.updated_at
assert updated_at > 0
self.my_tag.force_created_at(1403530068)
assert self.my_tag.created_at == 1403530068
def test_tag_units(self):
assert self.my_tag.units == ()
self.my_tag.units = ["mV", "ms"]
assert self.my_tag.units == ("mV", "ms")
self.my_tag.units = []
assert self.my_tag.units == ()
def test_tag_position(self):
assert self.my_tag.position == (0,)
self.my_tag.position = (1.0, 2.0, 3.0)
assert self.my_tag.position == (1.0, 2.0, 3.0)
def test_tag_extent(self):
assert self.my_tag.extent == ()
self.my_tag.extent = (1.0, 2.0, 3.0)
assert self.my_tag.extent == (1.0, 2.0, 3.0)
self.my_tag.extent = []
assert self.my_tag.extent == ()
def test_tag_references(self):
assert len(self.my_tag.references) == 1
self.assertRaises(TypeError, self.my_tag.references.append, 100)
reference1 = self.block.create_data_array("reference1", "stimuli",
nix.DataType.Int16, (1,))
reference2 = self.block.create_data_array("reference2", "stimuli",
nix.DataType.Int16, (1,))
self.my_tag.references.append(reference1)
self.my_tag.references.append(reference2)
assert reference1.name in self.my_tag.references
assert len(self.my_tag.references) == 3
assert reference1 in self.my_tag.references
assert reference2 in self.my_tag.references
# id and name access
assert reference1 == self.my_tag.references[reference1.name]
assert reference1 == self.my_tag.references[reference1.id]
assert reference2 == self.my_tag.references[reference2.name]
assert reference2 == self.my_tag.references[reference2.id]
assert reference1.name in self.my_tag.references
assert reference2.name in self.my_tag.references
assert reference1.id in self.my_tag.references
assert reference2.id in self.my_tag.references
del self.my_tag.references[reference2]
assert self.my_array in self.my_tag.references
assert reference1 in self.my_tag.references
del self.my_tag.references[reference1]
assert len(self.my_tag.references) == 1
def test_tag_features(self):
assert len(self.my_tag.features) == 0
data_array = self.block.create_data_array("feature", "stimuli",
nix.DataType.Int16, (1,))
da_feature = self.my_tag.create_feature(data_array, nix.LinkType.Untagged)
assert len(self.my_tag.features) == 1
assert da_feature in self.my_tag.features
assert da_feature.id in self.my_tag.features
assert "notexist" not in self.my_tag.features
assert da_feature.id == self.my_tag.features[0].id
assert da_feature.id == self.my_tag.features[-1].id
# id and name access
assert da_feature.id == self.my_tag.features[da_feature.id].id
assert da_feature.id == self.my_tag.features[data_array.id].id
assert da_feature.id == self.my_tag.features[data_array.name].id
assert data_array == self.my_tag.features[data_array.id].data
assert data_array == self.my_tag.features[data_array.name].data
assert data_array.id in self.my_tag.features
assert data_array.name in self.my_tag.features
data_frame = self.block.create_data_frame(
"dataframe feature", "test",
col_dict=OrderedDict([("number", nix.DataType.Float)]),
data=[(10.,)]
)
df_feature = self.my_tag.create_feature(data_frame, nix.LinkType.Untagged)
assert len(self.my_tag.features) == 2
assert df_feature in self.my_tag.features
assert df_feature.id in self.my_tag.features
assert df_feature.id == self.my_tag.features[1].id
assert df_feature.id == self.my_tag.features[-1].id
# id and name access
assert df_feature.id == self.my_tag.features[df_feature.id].id
assert df_feature.id == self.my_tag.features[data_frame.id].id
assert df_feature.id == self.my_tag.features[data_frame.name].id
assert data_frame == self.my_tag.features[data_frame.id].data
assert data_frame == self.my_tag.features[data_frame.name].data
assert data_frame.id in self.my_tag.features
assert data_frame.name in self.my_tag.features
assert isinstance(self.my_tag.features[0].data, nix.DataArray)
assert isinstance(self.my_tag.features[1].data, nix.DataFrame)
del self.my_tag.features[0]
assert len(self.my_tag.features) == 1
del self.my_tag.features[0]
assert len(self.my_tag.features) == 0
def test_tag_tagged_data(self):
sample_iv = 1.0
ticks = [1.2, 2.3, 3.4, 4.5, 6.7]
unit = "ms"
pos = [0.0, 2.0, 3.4]
ext = [0.1, 6.0, 2.3]
units = ["none", "ms", "ms"]
data = np.random.random_sample((2, 10, 5))
da = self.block.create_data_array("dimtest", "test",
data=data)
setdim = da.append_set_dimension()
setdim.labels = ["Label A", "Label B"]
samdim = da.append_sampled_dimension(sample_iv)
samdim.unit = unit
randim = da.append_range_dimension(ticks)
randim.unit = unit
postag = self.block.create_tag("postag", "event", pos)
postag.references.append(da)
postag.units = units
segtag = self.block.create_tag("region", "segment", pos)
segtag.references.append(da)
segtag.extent = ext
segtag.units = units
posdata = postag.tagged_data(0)
assert len(posdata.shape) == 3
assert posdata.shape == (1, 1, 1)
segdata = segtag.tagged_data(0)
assert len(segdata.shape) == 3
assert segdata.shape == (1, 6, 2)
# retrieve data by id and name
posdata = postag.tagged_data(da.name)
assert len(posdata.shape) == 3
assert posdata.shape == (1, 1, 1)
segdata = segtag.tagged_data(da.name)
assert len(segdata.shape) == 3
assert segdata.shape == (1, 6, 2)
posdata = postag.tagged_data(da.id)
assert len(posdata.shape) == 3
assert posdata.shape == (1, 1, 1)
segdata = segtag.tagged_data(da.id)
assert len(segdata.shape) == 3
assert segdata.shape == (1, 6, 2)
def test_tag_tagged_data_slice_mode(self):
data = np.random.random_sample((3, 100, 10))
da = self.block.create_data_array("signals", "test.signals", data=data)
da.unit = "mV"
da.append_set_dimension(labels=["A", "B", "C"])
sample_iv = 0.001
timedim = da.append_sampled_dimension(sampling_interval=sample_iv)
timedim.unit = "s"
posdim = da.append_range_dimension([1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9])
posdim.unit = "mm"
# exact_tag has a pos+ext that is exactly equal to a dimension tick
exact_tag = self.block.create_tag("tickpoint", "test.tag", position=[0, 0.03, 0.0011])
exact_tag.extent = [0.2, 0.02, 0.0005]
exact_tag.units = ["none", "s", "m"]
exact_tag.references.append(da)
# dim2: [0.001, 0.002, ..., 0.03, 0.031, ..., 0.049, 0.05, 0.051, ...]
# ^ pos [30] ^ pos+ext [50]
# Inclusive mode includes index 50, exclusive does not
#
# dim3: [1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9]
# ^ pos [1] ^ pos+ext [6]
# Inclusive mode includes index 6, exclusive does not
slice_default = exact_tag.tagged_data(0)
assert slice_default.shape == (1, 20, 5)
np.testing.assert_array_equal(slice_default, da[0:1, 30:50, 1:6]) # default exclusive
slice_inclusive = exact_tag.tagged_data(0, stop_rule=nix.SliceMode.Inclusive)
assert slice_inclusive.shape == (1, 21, 6)
np.testing.assert_array_equal(slice_inclusive, da[0:1, 30:51, 1:7])
slice_exclusive = exact_tag.tagged_data(0, stop_rule=nix.SliceMode.Exclusive)
assert slice_exclusive.shape == (1, 20, 5)
np.testing.assert_array_equal(slice_exclusive, da[0:1, 30:50, 1:6])
# midpoint_tag has a pos+ext that falls between dimension ticks
midpoint_tag = self.block.create_tag("midpoint", "test.tag", position=[0, 0.03, 0.0011])
midpoint_tag.extent = [0.1, 0.0301, 0.00051] # .1 offset
midpoint_tag.units = ["none", "s", "m"]
# dim2: [0.001, 0.002, ..., 0.03, 0.031, ..., 0.059, 0.06,| 0.061, ...]
# ^ pos [30] ^ pos+ext [60] + 0.1
# Both inclusive and exclusive include index 60
#
# dim3: [1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6,| 1.7, 1.8, 1.9]
# ^ pos [1] ^ pos+ext [6] + 0.1
# Both inclusive and exclusive include index 6
midpoint_tag.references.append(da)
# all slicing is inclusive since the pos+ext points are between ticks
slice_default = midpoint_tag.tagged_data(0)
assert slice_default.shape == (1, 31, 6)
np.testing.assert_array_equal(slice_default, da[0:1, 30:61, 1:7])
slice_inclusive = midpoint_tag.tagged_data(0, stop_rule=nix.SliceMode.Inclusive)
assert slice_inclusive.shape == (1, 31, 6)
np.testing.assert_array_equal(slice_inclusive, da[0:1, 30:61, 1:7])
slice_exclusive = midpoint_tag.tagged_data(0, stop_rule=nix.SliceMode.Exclusive)
assert slice_exclusive.shape == (1, 31, 6)
np.testing.assert_array_equal(slice_exclusive, da[0:1, 30:61, 1:7])
def test_tag_feature_data(self):
number_data = np.random.random(20)
number_feat = self.block.create_data_array("number feature", "test",
data=number_data)
dim = number_feat.append_sampled_dimension(1.0)
dim.unit = "ms"
dim.offset = 1.0
ramp_data = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
ramp_feat = self.block.create_data_array("ramp feature", "test",
data=np.asarray(ramp_data))
ramp_feat.label = "voltage"
ramp_feat.unit = "mV"
dim = ramp_feat.append_sampled_dimension(1.0)
dim.unit = "ms"
dim.offset = 2.0
pos_tag = self.block.create_tag("feature test", "test", [5.0])
pos_tag.units = ["ms"]
pos_tag.create_feature(number_feat, nix.LinkType.Tagged)
pos_tag.create_feature(number_feat, nix.LinkType.Untagged)
pos_tag.create_feature(number_feat, nix.LinkType.Indexed)
pos_tag.create_feature(ramp_feat, nix.LinkType.Tagged)
pos_tag.create_feature(ramp_feat, nix.LinkType.Untagged)
pos_tag.create_feature(ramp_feat, nix.LinkType.Indexed)
assert len(pos_tag.features) == 6
data1 = pos_tag.feature_data(0)
data2 = pos_tag.feature_data(1)
data3 = pos_tag.feature_data(2)
data4 = pos_tag.feature_data(3)
data5 = pos_tag.feature_data(4)
data6 = pos_tag.feature_data(5)
assert data1.size == 1
assert data2.size == len(number_data)
assert data3.size == len(number_data)
assert data4.size == 1
assert data5.size == len(ramp_data)
assert data6.size == len(ramp_data)
# check expected data
# For Tag, both Indexed and Untagged just return the full data
assert np.all(data1[:] == number_data[4:5])
assert np.all(data2[:] == number_data[:])
assert np.all(data3[:] == number_data[:])
assert np.all(data4[:] == ramp_data[3:4])
assert np.all(data5[:] == ramp_data[:])
assert np.all(data6[:] == ramp_data[:])
# make the tag pointing to a slice
pos_tag.extent = [2.0]
data1 = pos_tag.feature_data(0)
data2 = pos_tag.feature_data(1)
data3 = pos_tag.feature_data(2)
data4 = pos_tag.feature_data(3)
data5 = pos_tag.feature_data(4)
data6 = pos_tag.feature_data(5)
assert np.all(data1[:] == number_data[4:6])
assert np.all(data2[:] == number_data[:])
assert np.all(data3[:] == number_data[:])
assert np.all(data4[:] == ramp_data[3:5])
assert np.all(data5[:] == ramp_data[:])
assert np.all(data6[:] == ramp_data[:])
def test_tag_feature_dataframe(self):
numberdata = np.random.random(20)
number_feat = self.block.create_data_frame(
"number feature", "test",
col_dict=OrderedDict([("number", nix.DataType.Float)]),
data=[(n,) for n in numberdata]
)
column_descriptions = OrderedDict([("name", nix.DataType.String), ("duration", nix.DataType.Double)])
values = [("One", 0.1), ("Two", 0.2), ("Three", 0.3), ("Four", 0.4), ("Five", 0.5),
("Six", 0.6), ("Seven", 0.7), ("Eight", 0.8), ("Nine", 0.9), ("Ten", 1.0)]
ramp_feat = self.block.create_data_frame("ramp feature", "test", col_dict=column_descriptions, data=values)
ramp_feat.label = "voltage"
ramp_feat.units = (None, "s")
pos_tag = self.block.create_tag("feature test", "test", [5.0])
with self.assertRaises(UnsupportedLinkType):
pos_tag.create_feature(number_feat, nix.LinkType.Tagged)
pos_tag.create_feature(number_feat, nix.LinkType.Untagged)
pos_tag.create_feature(number_feat, nix.LinkType.Indexed)
with self.assertRaises(UnsupportedLinkType):
pos_tag.create_feature(ramp_feat, nix.LinkType.Tagged)
pos_tag.create_feature(ramp_feat, nix.LinkType.Untagged)
pos_tag.create_feature(ramp_feat, nix.LinkType.Indexed)
assert len(pos_tag.features) == 4
data1 = pos_tag.feature_data(0)
data2 = pos_tag.feature_data(1)
data3 = pos_tag.feature_data(2)
data4 = pos_tag.feature_data(3)
# check expected data
# For Tag, both Indexed and Untagged just return the full data
assert np.all(data1[:] == number_feat[:])
assert np.all(data2[:] == number_feat[:])
assert np.all(data3[:] == ramp_feat[:])
assert np.all(data4[:] == ramp_feat[:])
# Extent should have no effect
pos_tag.extent = [2.0]
data1 = pos_tag.feature_data(0)
data2 = pos_tag.feature_data(1)
data3 = pos_tag.feature_data(2)
data4 = pos_tag.feature_data(3)
assert np.all(data1[:] == number_feat[:])
assert np.all(data2[:] == number_feat[:])
assert np.all(data3[:] == ramp_feat[:])
assert np.all(data4[:] == ramp_feat[:])
def test_timestamp_autoupdate(self):
tag = self.block.create_tag("tag.time", "test.time", [-1])
tagtime = tag.updated_at
time.sleep(1) # wait for time to change
tag.position = [-100]
self.assertNotEqual(tag.updated_at, tagtime)
tagtime = tag.updated_at
time.sleep(1) # wait for time to change
tag.extent = [30]
self.assertNotEqual(tag.updated_at, tagtime)
tagtime = tag.updated_at
time.sleep(1) # wait for time to change
tag.units = "Mm"
self.assertNotEqual(tag.updated_at, tagtime)
def test_timestamp_noautoupdate(self):
self.file.auto_update_timestamps = False
tag = self.block.create_tag("tag.time", "test.time", [-1])
tagtime = tag.updated_at
time.sleep(1) # wait for time to change
tag.position = [-100]
self.assertEqual(tag.updated_at, tagtime)
tagtime = tag.updated_at
time.sleep(1) # wait for time to change
tag.extent = [30]
self.assertEqual(tag.updated_at, tagtime)
tagtime = tag.updated_at
time.sleep(1) # wait for time to change
tag.units = "Mm"
self.assertEqual(tag.updated_at, tagtime)
def test_tagged_set_dim(self):
"""
Simple test where the slice can be calculated directly from the position and extent and compared to the original
data.
Set dimension slicing.
"""
nsignals = 10
data = np.random.random_sample((nsignals, 100))
da = self.block.create_data_array("data", "data", data=data)
da.append_set_dimension()
da.append_sampled_dimension(sampling_interval=1).unit = "s"
tag = self.block.create_tag("tag", "simple", position=[])
tag.references.append(da)
for pos in range(nsignals):
for ext in range(2, nsignals-pos):
tag.position = [pos]
tag.extent = [ext]
np.testing.assert_array_almost_equal(tag.tagged_data(0), da[pos:pos+ext])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Exclusive), da[pos:pos+ext])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Inclusive), da[pos:pos+ext+1])
# +0.1 should round up (ceil) the start position
# +0.1 * 2 should round down (floor) the stop position and works the same for both inclusive and
# exclusive
tag.position = [pos+0.1]
tag.extent = [ext+0.1]
start = pos+1
stop = pos+ext+1
np.testing.assert_array_almost_equal(tag.tagged_data(0), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Exclusive), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Inclusive), da[start:stop])
if pos+ext+2 < len(da):
# +0.9 should round up (ceil) the start position
# +0.9 *2 should round down (floor) the stop position and works the same for both inclusive and
# exclusive
tag.position = [pos+0.9]
tag.extent = [ext+0.9]
start = pos+1
stop = pos+ext+2
np.testing.assert_array_almost_equal(tag.tagged_data(0), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Exclusive), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Inclusive), da[start:stop])
def test_tagged_range_dim(self):
"""
Simple test where the slice can be calculated directly from the position and extent and compared to the original
data.
Range dimension slicing.
"""
nticks = 10
data = np.random.random_sample((nticks, 100))
da = self.block.create_data_array("data", "data", data=data)
da.append_range_dimension(ticks=range(nticks))
da.append_sampled_dimension(sampling_interval=1).unit = "s"
tag = self.block.create_tag("tag", "simple", position=[])
tag.references.append(da)
for pos in range(nticks):
for ext in range(2, nticks-pos):
tag.position = [pos]
tag.extent = [ext]
np.testing.assert_array_almost_equal(tag.tagged_data(0), da[pos:pos+ext])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Exclusive), da[pos:pos+ext])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Inclusive), da[pos:pos+ext+1])
# +0.1 should round up (ceil) the start position
# +0.1 * 2 should round down (floor) the stop position and works the same for both inclusive and
# exclusive
tag.position = [pos + 0.1]
tag.extent = [ext + 0.1]
start = pos + 1
stop = pos + ext + 1
np.testing.assert_array_almost_equal(tag.tagged_data(0), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Exclusive), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Inclusive), da[start:stop])
if pos + ext + 2 < len(da):
# +0.9 should round up (ceil) the start position
# +0.9 * 2 should round down (floor) the stop position and works the same for both inclusive and
# exclusive
tag.position = [pos + 0.9]
tag.extent = [ext + 0.9]
start = pos + 1
stop = pos + ext + 2
np.testing.assert_array_almost_equal(tag.tagged_data(0), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Exclusive), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Inclusive), da[start:stop])
time_vector = np.arange(0.0, 10., 0.001)
indices = np.random.rand(len(time_vector))
event_data = time_vector[(indices < 0.1)]
event_data = event_data[(event_data < 4) | (event_data > 7)]
event_da = self.block.create_data_array("event_data", "nix.events", data=event_data, unit="s")
event_da.append_range_dimension_using_self()
tt = self.block.create_tag("no_event_segment", "nix.segment", 4.5)
tt.extent = 1.0
tt.references.append(event_da)
sl = tt.tagged_data(0)
self.assertFalse(sl.valid)
tt2 = self.block.create_tag("beyond data", "nix.segment", 12.0)
tt2.extent = 3.0
tt2.references.append(event_da)
sl = tt2.tagged_data(0)
self.assertFalse(sl.valid)
tt3 = self.block.create_tag("reachingbeyonddata", "nix.segment", 8.5)
tt3.extent = [3.0]
tt3.references.append(event_da)
sl = tt3.tagged_data(0)
self.assertTrue(sl.valid)
def test_tagged_sampled_dim(self):
"""
Simple test where the slice can be calculated directly from the position and extent and compared to the original
data.
Sampled dimension slicing.
"""
nticks = 10
data = np.random.random_sample((nticks, 100))
da = self.block.create_data_array("data", "data", data=data)
da.append_sampled_dimension(sampling_interval=1).unit = "V"
da.append_sampled_dimension(sampling_interval=1).unit = "s"
tag = self.block.create_tag("tag", "simple", position=[])
tag.units = ["V", "s"]
tag.references.append(da)
for pos in range(nticks):
for ext in range(2, nticks-pos):
tag.position = [pos]
tag.extent = [ext]
np.testing.assert_array_almost_equal(tag.tagged_data(0), da[pos:pos+ext])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Exclusive), da[pos:pos+ext])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Inclusive), da[pos:pos+ext+1])
# +0.1 should round up (ceil) the start position
# +0.1 * 2 should round down (floor) the stop position and works the same for both inclusive and
# exclusive
tag.position = [pos+0.1]
tag.extent = [ext+0.1]
start = pos+1
stop = pos+ext+1
np.testing.assert_array_almost_equal(tag.tagged_data(0), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Exclusive), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Inclusive), da[start:stop])
if pos+ext+2 < len(da):
# +0.9 should round up (ceil) the start position
# +0.9 * 2 should round down (floor) the stop position and works the same for both inclusive and
# exclusive
tag.position = [pos+0.9]
tag.extent = [ext+0.9]
start = pos+1
stop = pos+ext+2
np.testing.assert_array_almost_equal(tag.tagged_data(0), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Exclusive), da[start:stop])
np.testing.assert_array_almost_equal(tag.tagged_data(0, nix.SliceMode.Inclusive), da[start:stop])
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