def test_read_csv():
filename = './test_data/test_data1.csv'
time, voltage = file_io.read_csv(filename)
assert float(time[0]) == 0
assert float(voltage[0]) == -0.145
assert float(time[1]) == 0.003
assert float(voltage[1]) == -0.145
filename = './test_data/test_data2.csv'
time, voltage = file_io.read_csv(filename)
assert float(time[0]) == 0
assert float(voltage[0]) == -0.345
assert float(time[1]) == 0.003
assert float(voltage[1]) == -0.345
def process(test_file, index, reverse, metrics):
test_data = file_io.read_csv('data', test_file)
header = file_io.get_csv_header('data', test_file)
print "Test result of feature %s Reverse %s" % (header[index] , str(reverse))
for metric in metrics.split(','):
methodToCall = getattr(sys.modules[__name__], metric.rstrip())
methodToCall(test_data, index, reverse, test_file)
def main():
dir = './test_data/*.csv'
filenames_list = file_io.collect_csv_filenames(dir)
filename_ref = filenames_list[0]
time, voltage_ref = file_io.read_csv(filename_ref)
time_unit = input("Time is in min or sec (default = sec)? ")
if time_unit != "sec" and time_unit != "min":
print("Warning: Input was neither 'sec' or 'min'. Data saved as "
"seconds.")
for filename in filenames_list:
time, voltage = file_io.read_csv(filename)
hrm = process_ecg_data.HeartRateMonitor(time, voltage, voltage_ref,
time_unit, filename)
file_io.write_json(filename, hrm.output())
print("Finished.")
def plot_multi_pressure(dir, filename, format):
""" Gathers multiple filenames, extracts data, facilitates range selection
and plots data sets onto a single axes
:param dir: Directory containing the data files
:param filename: List containing all filenames of data files
:param format: File type for exported graphs
:return:
"""
temp = []
new_pressure = []
new_time = []
for count, file in enumerate(filename):
raw_time, pressure, units, start, end, min, max = file_io.read_csv(
dir + file)
start = normalize_single_time(start)
end = normalize_single_time(end) - start
time = normalize_time_list(raw_time, start)
temp.append(plot_single_pressure(pressure, time, units))
temp[count].show()
try:
new_pressure, new_time = \
handle_inputs.handle_interval_selection(
pressure,time,end,new_pressure,new_time)
except TypeError:
return
save_dir = str("./Exported/" + datetime.now().strftime("%Y-%m-%d "
"%H-%M-%S"))
os.mkdir(save_dir)
if format != "":
for count, i in enumerate(new_pressure):
single = (plot_single_pressure(i, new_time[count], units))
file_io.render_figure(single, [filename[count]],
save_dir + "/" + filename[count], format,
False)
single.clf()
for count, i in enumerate(new_pressure):
graph = plot_single_pressure(i, new_time[count], units, int(count))
prepare_output_data(filename[count], save_dir, i, new_time[count])
export_filename = save_dir + "/" + "combined_plots"
file_io.render_figure(graph, filename, export_filename, format)
return
def handle_single(dir):
""" Handles case of when only one file is to be analyzed, executing
functions for data range selection, plotting and export
:param dir: Directory of where the data file is located
:return:
"""
filename = input("Filename? ")
try:
raw_time, pressure, units, start, end, min, max = file_io.read_csv(
dir + filename)
except FileNotFoundError:
print("Error: File not found in specified directory.")
return
start = pressure_processor.normalize_single_time(start)
end = pressure_processor.normalize_single_time(end) - start
time = pressure_processor.normalize_time_list(raw_time, start)
temp = pressure_processor.plot_single_pressure(pressure, time, units)
temp.show()
try:
pressure, time = handle_interval_selection(pressure, time, end)
except TypeError:
return
format = input("Export filetype ('pdf' or 'png'; other input for view "
"only) ")
save_dir = str("./Exported/" + datetime.now().strftime("%Y-%m-%d "
"%H-%M-%S"))
os.mkdir(save_dir)
export_filename = save_dir + '/' + filename + ' ' + '.' + format
graph = pressure_processor.plot_single_pressure(pressure[0], time[0],
units)
file_io.render_figure(graph, [filename], export_filename, format)
pressure_processor.prepare_output_data(filename, save_dir, pressure[0],
time[0])
return
def _get_row_description(file_name: str):
""" Generator providing description for each row
:param file_name: complete path of file containing the description of the
contents of a flat file
:returns: a RowDescription object containing row description
"""
start_index = 0
for field_name, char_length, data_type in read_csv(file_name):
if data_type not in DataTypes.__members__:
raise DataDescriptorParseError(f"Unexpected datatype {data_type}")
char_length = int(char_length)
yield RowDescription(field_name, start_index,
start_index + char_length, DataTypes[data_type])
start_index += char_length
params['n_log_n_fit'] = bool(args.n_log_n_fit)
if args.linear_fit == 'True':
params['linear_fit'] = bool(args.linear_fit)
if args.logarithmic_fit == 'True':
params['logarithmic_fit'] = bool(args.logarithmic_fit)
if args.upper_bound == 'True':
params['upper_bound'] = bool(args.upper_bound)
if args.lower_bound == 'True':
params['lower_bound'] = bool(args.lower_bound)
# Read the x and y components from the .csv file
x_data, y_data = read_csv(params['csv_directory'] + params['input_file_name'] + '.csv')
# Plot the data with the updated configuration parameters
plot_data(output_file_path = params['png_directory'] + params['input_file_name'] + '.png',
plot_title = params['plot_title'],
input_data_label = params['input_data_label'],
x_label = params['x_axis_label'],
y_label = params['y_axis_label'],
x_data = x_data,
y_data = y_data,
dependent_variable = params['dependent_variable'],
independent_variable = params['independent_variable'],
plot_original_data = params['original_data'],
desired_fits = {
"exponential": params['exponential_fit'],
"cubic": params['cubic_fit'],
def main():
test_1 = process_ecg_data.HeartRateMonitor()
# Testing is_float
output_1 = test_1.is_float("abc")
assert output_1 is False
output_2 = test_1.is_float(4)
assert output_2 is True
# Testing process_input
with pytest.raises(ValueError):
test_1.voltage = [
0.1, -0.2, math.sqrt(2),
math.sqrt(-5), -(math.sqrt(8))
]
test_1.process_input()
with pytest.raises(ValueError):
test_1.time = [6, 7, math.sqrt(-2)]
test_1.process_input()
test_1.voltage = []
test_1.time = []
test_1.time_input = ["6", 7, 8, 9]
test_1.voltage_input = [5, 7, 3, 4]
output_3 = test_1.process_input()
assert output_3 is True
with pytest.raises(TypeError):
test_1.time_input = tuple("abc", 5, 7, 1)
test_1.process_input()
with pytest.raises(TypeError):
test_1.voltage_input = tuple("g", 5.2, 8.7, 1.0)
test_1.process_input()
test_1.time_input = ["6", 7, "bad data", 9]
output_4 = test_1.process_input()
assert output_4 is True
test_1.voltage = []
test_1.time = []
test_1.voltage_input = [2.3, 5.2, 8.7, 1.0]
test_1.time_input = [6.7, 2.6, 2.3]
assert test_1.process_input() is False
# Testing mean_hr_bpm
test_1.voltage = []
test_1.time = []
test_1.time_input = [0, 10, 20, 30, 40, 50, 60, 70, 80]
test_1.voltage_input = [2, 3, 4, 5, 6, 7, 8, 9, 10]
test_1.process_input()
output_5 = test_1.mean_hr_bpm(130)
assert output_5 == 97.5
# Testing voltage_extremes
test_1.voltage = []
test_1.time = []
test_1.voltage_input = [-5.0, -2.0, 0.0, 1.0, 7.0]
test_1.time_input = [1, 2, 3, 4, 5]
test_1.process_input()
output_6 = test_1.voltage_extremes()
assert output_6[0] == -5.0
assert output_6[1] == 7.0
# Testing duration
test_1.time = []
test_1.time_input = [0, 10, 20, 30, 40, 50, 60, 70, 80]
test_1.process_input()
output_7 = test_1.duration()
assert output_7 == 80
# Testing num_beats
dir = "./test_data/test_data1.csv"
time, voltage_ref = file_io.read_csv("./test_data/test_data1.csv")
time, voltage = file_io.read_csv("./test_data/test_data1.csv")
test_2 = process_ecg_data.HeartRateMonitor(time, voltage, voltage_ref,
"sec",
"./test_data/test_data1.csv")
output_8 = test_2.num_beats()
assert output_8 == 37
# Testing beats
output_9 = test_2.beats()
assert round(output_9[0], 5) == [1.91919]
assert round(output_9[1], 5) == [7.27613]
def test_read_csv_content(self):
""" test for reading contents of a csv """
for line in read_csv(self.temp_csv_path):
self.assertEqual(line, self.expected_csv_content)