def test_load_cosine(self, mock_pd):
mock_pd.read_csv.return_value = mock_return = MagicMock()
mock_return.values = expected_return = np.array([1, 2, 3, 4, 5])
actual_returned_value = TimeBasedFeatureService.load_cosine("subjectA")
self.assertListEqual(expected_return.tolist(),
actual_returned_value.tolist())
mock_pd.read_csv.assert_called_once_with(
str(TimeBasedFeatureService.get_path_for_cosine("subjectA")))
def build(subject_id):
feature_count = ActivityCountFeatureService.load(subject_id)
feature_hr = HeartRateFeatureService.load(subject_id)
feature_time = TimeBasedFeatureService.load_time(subject_id)
#feature_circadian = TimeBasedFeatureService.load_circadian_model(subject_id)
feature_cosine = TimeBasedFeatureService.load_cosine(subject_id)
labeled_sleep = PSGLabelService.load(subject_id)
feature_dictionary = {
FeatureType.count:
feature_count,
FeatureType.heart_rate:
feature_hr,
FeatureType.time:
feature_time,
#FeatureType.circadian_model: feature_circadian,
FeatureType.cosine:
feature_cosine
}
subject = Subject(subject_id=subject_id,
labeled_sleep=labeled_sleep,
feature_dictionary=feature_dictionary)
# Uncomment to save plots of every subject's data:
ax = plt.subplot(5, 1, 1)
ax.plot(range(len(feature_hr)), feature_hr, label="Heart-Rate")
ax.legend()
ax = plt.subplot(5, 1, 2)
ax.plot(range(len(feature_count)), feature_count, label="Motion")
ax.legend()
ax = plt.subplot(5, 1, 3)
ax.plot(range(len(feature_cosine)),
feature_cosine,
label="Clock proxy")
ax.legend()
# ax = plt.subplot(5, 1, 4)
# ax.plot(range(len(feature_circadian)), feature_circadian)
ax = plt.subplot(5, 1, 4)
ax.plot(range(len(labeled_sleep)), labeled_sleep, label="Sleep Stages")
ax.legend()
#
plt.savefig(
str(Constants.FIGURE_FILE_PATH.joinpath(subject_id + '_info.png')))
plt.close()
return subject
def build(subject_id):
feature_count = ActivityCountFeatureService.load(subject_id)
feature_hr = HeartRateFeatureService.load(subject_id)
feature_time = TimeBasedFeatureService.load_time(subject_id)
if Constants.INCLUDE_CIRCADIAN:
feature_circadian = TimeBasedFeatureService.load_circadian_model(
subject_id)
else:
feature_circadian = None
feature_cosine = TimeBasedFeatureService.load_cosine(subject_id)
labeled_sleep = PSGLabelService.load(subject_id)
feature_dictionary = {
FeatureType.count: feature_count,
FeatureType.heart_rate: feature_hr,
FeatureType.time: feature_time,
FeatureType.circadian_model: feature_circadian,
FeatureType.cosine: feature_cosine
}
subject = Subject(subject_id=subject_id,
labeled_sleep=labeled_sleep,
feature_dictionary=feature_dictionary)
# Uncomment to save plots of every subject's data:
# ax = plt.subplot(5, 1, 1)
# ax.plot(range(len(feature_hr)), feature_hr)
# ax = plt.subplot(5, 1, 2)
# ax.plot(range(len(feature_count)), feature_count)
# ax = plt.subplot(5, 1, 3)
# ax.plot(range(len(feature_cosine)), feature_cosine)
# ax = plt.subplot(5, 1, 4)
# ax.plot(range(len(feature_circadian)), feature_circadian)
# ax = plt.subplot(5, 1, 5)
# ax.plot(range(len(labeled_sleep)), labeled_sleep)
#
# plt.savefig(str(Constants.FIGURE_FILE_PATH.joinpath(subject_id + '_applewatch.png')))
# plt.close()
return subject