def test_peak_detection(file_name, expected_num_peaks):
test_data = importdata(file_name)
data_valid = is_data_number(test_data)
source = filter_data(data_valid)
peaks = detect_peak(source)
num_peaks = len(peaks)
assert num_peaks <= expected_num_peaks
def __init__(self, filename_arg):
"""this is the initial function
:param filename_arg: input the filename for the test_data
"""
self.filename = filename_arg
self.data = importdata(self.filename + '.csv')
self.peaks = None
self.mean_hr_bpm = None
self.voltage_extreme = None
self.time_duration = None
self.num_beats = None
self.beats = None
def test_is_filter_data_valid():
filename = "test_data28.csv"
test_data = importdata(filename)
data_valid = is_data_number(test_data)
source = filter_data(data_valid)
sample = len(source)
noise = 0.000000000000008 * np.asarray(
random.sample(range(0, sample), sample))
source.voltage = source.voltage + noise
filtered = filter_data(source)
corr = filtered.voltage.corr(source.voltage)
assert corr >= 0.95
def test_is_importdata(file_name, expected):
output = importdata(file_name)
output_array = pd.concat([output.head(1), output.tail(1)])
assert (output_array == expected).all
def test_is_data_number(source, expected):
test_data = importdata(source)
output = is_data_number(test_data)
len_output = output.shape[0]
assert len_output == expected