def setup_method(self, method):
self.results = [
{
"rmssd": 30,
"sdnn": 52,
"sdsd": 30,
"nn50": 2,
"pnn50": 6,
"mrri": 1003,
"mhr": 59,
},
{
"rmssd": 31,
"sdnn": 53,
"sdsd": 31,
"nn50": 3,
"pnn50": 7,
"mrri": 1004,
"mhr": 60,
},
]
self.rri_segments = [
RRi([810, 800, 815], time=[1, 2, 3]),
RRi([810, 800, 815], time=[4, 5, 6]),
]
def test_rri_describe(self):
rri = RRi(FAKE_RRI)
rri_descr = rri.describe()
assert isinstance(rri_descr, MutableMapping)
expected = [
['', 'rri', 'hr'],
['min', 750.0, 73.61963190184049],
['max', 815.0, 80.0],
['amplitude', 65.0, 6.380368098159508],
['mean', 793.75, 75.67342649397864],
['median', 805.0, 74.53703703703704],
['var', 667.1875, 6.487185483887203],
['std', 25.829972899714782, 2.546995383562209],
]
expected__repr__ = ('----------------------------------------\n',
' rri hr\n',
'----------------------------------------\n',
'min 750.00 73.62\n',
'max 815.00 80.00\n',
'mean 793.75 75.67\n',
'var 667.19 6.49\n',
'std 25.83 2.55\n',
'median 805.00 74.54\n',
'amplitude 65.00 6.38\n')
for field in expected[1:]:
assert rri_descr[field[0]]['rri'] == field[1]
assert rri_descr[field[0]]['hr'] == field[2]
rri_descr_rep = rri_descr.__repr__()
for table_row in expected__repr__:
assert table_row in rri_descr_rep
def test_class_repr_long_array(self):
rri = RRi(range(1, 100000))
assert rri.__repr__() == (
"RRi array([1.0000e+00, 2.0000e+00, 3.0000e+00, ..., "
"9.9997e+04, 9.9998e+04,\n 9.9999e+04])"
)
def test_return_figure_objects(self):
rri = RRi(FAKE_RRI, time=[4, 5, 6, 7])
fig, ax = rri.plot()
assert isinstance(fig, matplotlib.figure.Figure)
assert isinstance(ax, matplotlib.figure.Axes)
def read_from_csv(pathname, rri_col_index=0, time_col_index=None,
row_offset=0, time_parser=int, sep=None):
with open(pathname, newline='') as csvfile:
if sep is None:
try:
sep = csv.Sniffer().sniff(csvfile.read(1024)).delimiter
except csv.Error:
sep = ','
csvfile.seek(0)
reader = csv.reader(csvfile, delimiter=sep)
for offset in range(row_offset):
next(reader)
if time_col_index is None:
return RRi([float(r[rri_col_index].strip()) for r in reader])
rri = []
time = []
for row in reader:
rri.append(float(row[rri_col_index].strip()))
time.append(time_parser(row[time_col_index].strip()))
return RRi(rri, time)
def test_split_rri_using_time_informarion(self):
rri = RRi([800, 810, 790, 795], time=[1, 5, 10, 20])
splitted_rri = rri.time_split(seg_size=10, overlap=0)
expected = [RRi([800, 810], time=[1, 5]), RRi([790, 795], time=[10, 20])]
self.assert_splitted_equal(splitted_rri, expected)
def test_rri_describe(self):
rri = RRi(FAKE_RRI)
rri_descr = rri.describe()
assert isinstance(rri_descr, MutableMapping)
expected = [
["", "rri", "hr"],
["min", 750.0, 73.61963190184049],
["max", 815.0, 80.0],
["amplitude", 65.0, 6.380368098159508],
["mean", 793.75, 75.67342649397864],
["median", 805.0, 74.53703703703704],
["var", 667.1875, 6.487185483887203],
["std", 25.829972899714782, 2.546995383562209],
]
expected__repr__ = (
"----------------------------------------\n",
" rri hr\n",
"----------------------------------------\n",
"min 750.00 73.62\n",
"max 815.00 80.00\n",
"mean 793.75 75.67\n",
"var 667.19 6.49\n",
"std 25.83 2.55\n",
"median 805.00 74.54\n",
"amplitude 65.00 6.38\n",
)
for field in expected[1:]:
assert rri_descr[field[0]]["rri"] == field[1]
assert rri_descr[field[0]]["hr"] == field[2]
rri_descr_rep = rri_descr.__repr__()
for table_row in expected__repr__:
assert table_row in rri_descr_rep
def test_return_fig_and_axes_hist_method(self):
rri = RRi(FAKE_RRI, time=[4, 5, 6, 7])
with mock.patch('hrv.rri.plt.show'):
fig, ax = rri.hist()
assert isinstance(fig, matplotlib.figure.Figure)
assert isinstance(ax, matplotlib.figure.Axes)
def test_rri_info(self, _stdout):
rri = RRi(FAKE_RRI)
rri.info()
msg = 'N Points: 4\nDuration: 2.38s\nInterpolated: False\n'\
'Detrended: False\nMemory Usage: 0.03Kb'
_stdout.write.assert_called_once_with(msg)
def test_rri_class_encapsulation(self):
rri = RRi(FAKE_RRI)
with pytest.raises(AttributeError):
rri.rri = [1, 2, 3, 4]
with pytest.raises(AttributeError):
rri.time = [1, 2, 3, 4]
def test_get_rri_time_interval(self):
rri = RRi(FAKE_RRI + [817, 785, 910], time=[2, 4, 6, 8, 10, 12, 14])
rri_interval = rri.time_range(start=10, end=14)
expected = RRi([817, 785, 910], time=[10, 12, 14])
assert isinstance(rri_interval, RRi)
np.testing.assert_array_equal(rri_interval.values, expected.values)
np.testing.assert_array_equal(rri_interval.time, expected.time)
def test__getitem__method(self):
rri = RRi(FAKE_RRI)
rri_slice = rri[:2]
expected = RRi([800, 810])
assert isinstance(rri_slice, RRi)
np.testing.assert_equal(rri_slice.values, expected.values)
np.testing.assert_equal(rri_slice.time, expected.time)
def test_reset_time_offset_inplace(self):
rri = RRi(FAKE_RRI, time=[4, 5, 6, 7])
rri.reset_time(inplace=True)
expected = RRi(FAKE_RRI, time=[0, 1, 2, 3])
assert isinstance(rri, RRi)
np.testing.assert_array_equal(rri.values, expected.values)
np.testing.assert_array_equal(rri.time, expected.time)
def test_quotient_filter_receiving_and_return_rri_class(self):
fake_rri = RRi([810, 580, 805, 790])
rri_filt = quotient(fake_rri)
expected = RRi(rri=[805, 790], time=[1.385, 2.175])
assert isinstance(rri_filt, RRi)
np.testing.assert_almost_equal(rri_filt.values, expected.values, decimal=2)
np.testing.assert_almost_equal(rri_filt.time, expected.time, decimal=2)
def test_rri_info_time_range(self, _stdout):
rri = RRi(FAKE_RRI, time=[2, 3, 4, 5])
rri.info()
msg = (
"N Points: 4\nDuration: 3.00s\nInterpolated: False\n"
"Detrended: False\nMemory Usage: 0.03Kb"
)
_stdout.write.assert_called_once_with(msg)
def __getitem__(self, index):
assert (index < self._dataset_size)
#if self._is_for_train: # random
# index = random.randint(0, self._dataset_size-1)
# get sample data
data = self._data[index] / 1000
data = list(data)
data_reverse = copy.deepcopy(data)
data_reverse.reverse()
filt_rri1 = list(moving_average(RRi(data), order=1))
filt_rri2 = list(moving_average(RRi(data), order=2))
filt_rri3 = list(moving_average(RRi(data), order=3))
filt_rri1_reverse = copy.deepcopy(filt_rri1)
filt_rri2_reverse = copy.deepcopy(filt_rri2)
filt_rri3_reverse = copy.deepcopy(filt_rri3)
filt_rri1_reverse.reverse()
filt_rri2_reverse.reverse()
filt_rri3_reverse.reverse()
order_data = [filt_rri1, filt_rri2, filt_rri3]
order_data_reverse = [
filt_rri1_reverse, filt_rri2_reverse, filt_rri3_reverse
]
label = int(self._label[index])
subject = self._subject[index]
mean = self._mean_train[index]
sdnn = self._sdnn_train[index]
pnn50 = self._pnn50_train[index]
rmssd = self._rmssd_train[index]
lnrmssd = self._lnrmssd_train[index]
vlf = self._vlf_train[index]
lf = self._lf_train[index]
hf = self._hf_train[index]
rlh = self._rlh_train[index]
features = list(np.stack((mean, sdnn, pnn50, rmssd, lnrmssd, \
vlf, lf, hf, rlh )))
makeup_length = 512 - len(data)
if len(data) > 512:
data = data[:512]
else:
data.extend(0 for _ in range(makeup_length))
return data, data_reverse, order_data, order_data_reverse, label, subject, features
def test_operations_with_other_rri_instance(self):
rri = RRi(FAKE_RRI)
another_rri = RRi([750, 765, 755, 742])
mul_result = rri * another_rri
div_result = rri / another_rri
add_result = rri + another_rri
sub_result = rri - another_rri
pow_result = rri**another_rri
results = (mul_result, div_result, add_result, sub_result, pow_result)
for result in results:
assert isinstance(result, RRi)
def test_rri_statistical_values(self):
rri = RRi(FAKE_RRI)
np.testing.assert_array_equal(rri.mean(), np.mean(FAKE_RRI))
np.testing.assert_array_equal(rri.var(), np.var(FAKE_RRI))
np.testing.assert_array_equal(rri.std(), np.std(FAKE_RRI))
np.testing.assert_array_equal(rri.median(), np.median(FAKE_RRI))
np.testing.assert_array_equal(rri.max(), np.max(FAKE_RRI))
np.testing.assert_array_equal(rri.min(), np.min(FAKE_RRI))
np.testing.assert_array_equal(
rri.amplitude(), np.max(FAKE_RRI) - np.min(FAKE_RRI),
)
np.testing.assert_array_equal(
rri.rms(), np.sqrt(np.mean(np.square(FAKE_RRI))),
)
def test_smoothness_priors_detrend(self):
fake_rri = RRi([810, 830, 860, 790, 804])
detrended_rri = smoothness_priors(fake_rri, l=500, fs=4.0)
expected_rri = [
799.0018,
794.1204,
801.8663,
817.2193,
835.156,
850.6497,
858.6705,
854.2143,
836.2049,
811.4113,
787.5311,
772.2589,
0.0,
0.0,
]
np.testing.assert_almost_equal(detrended_rri, expected_rri, 4)
assert isinstance(detrended_rri, RRiDetrended)
assert detrended_rri.interpolated
assert detrended_rri.detrended
def test_calculate_min_with_numpy_function(self):
rri = RRi(FAKE_RRI, time=[4, 5, 6, 7])
avg = np.min(rri)
expected = 750.0
np.testing.assert_almost_equal(avg, expected)
def test_calculate_variance_with_numpy_function(self):
rri = RRi(FAKE_RRI, time=[4, 5, 6, 7])
avg = np.var(rri)
expected = 667.1875
np.testing.assert_almost_equal(avg, expected)
def test_calculate_standard_deviation_with_numpy_function(self):
rri = RRi(FAKE_RRI, time=[4, 5, 6, 7])
avg = np.std(rri)
expected = 25.82
np.testing.assert_almost_equal(avg, expected, decimal=2)
def test__pow__method(self):
rri = RRi(FAKE_RRI)
result = rri**2
expected = [640000., 656100., 664225., 562500.]
np.testing.assert_equal(result.values, expected)
def test__mul__method(self):
rri = RRi(FAKE_RRI)
result = rri * 10
expected = [8000, 8100, 8150, 7500]
assert isinstance(result, RRi)
np.testing.assert_equal(result.values, expected)
def test_raises_exception_if_rri_and_time_havent_same_length(self):
with pytest.raises(ValueError) as e:
_validate_time(FAKE_RRI, [1, 2, 3])
with pytest.raises(ValueError):
RRi(FAKE_RRI, [1, 2, 3])
assert e.value.args[0] == ("rri and time series must have the same length")
def test__add__method(self):
rri = RRi(FAKE_RRI)
result = rri + 10
expected = [810, 820, 825, 760]
assert isinstance(result, RRi)
np.testing.assert_equal(result.values, expected)
def test__sub__method(self):
rri = RRi(FAKE_RRI)
result = rri - 10
expected = [790, 800, 805, 740]
assert isinstance(result, RRi)
np.testing.assert_equal(result.values, expected)
def test__truediv__method(self):
rri = RRi(FAKE_RRI)
result = rri / 10
expected = np.array(FAKE_RRI) / 10
assert isinstance(result, RRi)
np.testing.assert_equal(result.values, expected)
def test__abs__method(self):
rri = RRi(FAKE_RRI)
result = abs(rri)
expected = np.array(FAKE_RRI)
assert isinstance(result, RRi)
np.testing.assert_equal(result.values, expected)
def test__getitem__method_with_numpy_array(self):
rri = RRi(FAKE_RRI)
indexes = np.array([False, True, True, False])
rri_slice = rri[indexes]
expected = [810, 815]
assert isinstance(rri_slice, RRi)
np.testing.assert_equal(rri_slice.values, expected)
