def measure(node: MetrolabTHM1176Node,
N=10,
max_num_retrials=5,
average=False):
"""
Measures the magnetic field with the Metrolab sensor. Returns either raw measured values of field components (x, y, z)
or mean and standard deviation in each direction.
Args:
node (MetrolabTHM1176Node): represents the Metrolab THM 1176 sensor
N (int, optional): number of data points collected for each average. Defaults to 10.
max_num_retrials (int, optional): How many times to reattempt measurement before raising an exception. Defaults to 5.
average (bool, optional): Average over the N measurements. Defaults to False.
Raises:
MeasurementError: a problem occured during measurement.
Returns:
tuple of 2 np.array(3): This is returned if average is true. mean and std of the three field componenents over N measurements.
tuple (np.ndarray((N,3)), 0): This is returned if average is false. N measured values of each component are contained. Second return is 0.
"""
# if dataDir is not None:
# ensure_dir_exists(dataDir, verbose=False)
# perform measurement and collect the raw data
for _ in range(max_num_retrials):
try:
# an N by 3 array
meas_data = np.array(node.measureFieldArraymT(N)).swapaxes(0, 1)
except BaseException:
pass
else:
break
try:
# due to the setup, transform sensor coordinates to magnet coordinates
meas_data = sensor_to_magnet_coordinates(meas_data)
# if it was not possible to obtain valid measurement results after
# max_num_retrials, raise MeasurementError, too
except BaseException:
raise MeasurementException()
if average:
# compute the mean and std from raw data for each sensor
mean_data = np.mean(meas_data, axis=0)
std_data = np.std(meas_data, axis=0)
ret1, ret2 = mean_data, std_data
else:
# This option is more for getting time-field measurements.
ret1, ret2 = meas_data, 0
return ret1, ret2
def get_mean_dataset_MetrolabSensor(node: MetrolabTHM1176Node,
sampling_size,
verbose=False,
max_num_retrials=5):
"""
Estimate field vectors with Metrolab sensor sampling_size-times and return the mean and std
as 1d-ndarrays of length 3.
Args:
- node (MetrolabTHM1176Node): represents the Metrolab THM 1176 sensor
- sampling_size (int): sampling size to estimate mean magnetic field vector and std,
i.e. number of times the sensor is read out in series before averaging
- verbose (bool): switching on/off print-statements for displaying progress
Return:
- mean_data, std_data (ndarrays of of shape (number sensors, 3)): Mean magnetic field and
its standard deviation as a vector.
Raises MeasurementError if no valid measurement data could be aquired after max_num_retrials tries.
"""
# perform measurement and collect the raw data
for _ in range(max_num_retrials):
try:
meas_data = np.array(
node.measureFieldArraymT(sampling_size)).swapaxes(0, 1)
except:
pass
else:
break
try:
# due to the setup, transform sensor coordinates to magnet coordinates
meas_data = sensor_to_magnet_coordinates(meas_data)
# if it was not possible to obtain valid measurement results after max_num_retrials, raise MeasurementError, too
except UnboundLocalError:
raise MeasurementError
# estimate the mean and std from raw data for each sensor
mean_data = np.mean(meas_data, axis=0)
std_data = np.std(meas_data, axis=0)
# # save raw data if desired
# if save_mean_data:
# # if no directory is provided, just take current working directory
# if directory is None:
# directory = os.getcwd()
# save_in_dir(meas_data, directory, 'data', now=True)
# return mean and std fields either for only this sensor or for all sensors
return mean_data, std_data
def readoutMetrolabSensor(node: MetrolabTHM1176Node,
measure_runs=1,
fname_postfix='data_sets',
directory='./data_sets',
verbose=False,
save_data=True):
"""
Read measurement outcomes of Metrolab THM1176 sensor and return the estimated B-field [mT] in magnet coordinates,
also save data if desired.
Magnet coordinates: coil 2 is mounted along -y axis.
Args:
- node (MetrolabTHM1176Node): represents the Metrolab THM 1176 sensor
- measure_runs (int): Number of samples per fuction call per sensor
- save_data (bool): if True, the results are stored in a csv-file
- directory (string): valid path of the folder where data should be stored. The default name of the data file is
'yy_mm_dd_hh-mm-ss_{fname_postfix}.csv'
- fname_postfix (string): postfix of data file (csv).
Return:
- meas_time (ndarray of length=measure_runs): Contains the time of each measurement relative
to start of measurements
- meas_data (ndarray of shape=(measure_runs, 3)): Contains the measured field components
Exceptions:
Raise a MeasurementError if something went wrong during measurements.
Possible reasons are that a sensor was skipped or that an incomplete message was received from the sensor.
"""
if measure_runs == 1:
# initialize ndarray to store the measurement data and time
meas_data = np.zeros(3)
meas_time = 0
# get current time before starting
t_start = time()
# read the current output of sensor and save measured magnetic field
meas_data = np.array(node.measureFieldmT())
# save the current time
meas_time = time() - t_start
elif measure_runs > 1:
# get current time before starting
# t_start = time()
# read the current output of sensor and save measured magnetic field
meas_data = np.array(node.measureFieldArraymT(measure_runs)).swapaxes(
0, 1)
# assume that each measurement took the same time, such that the times of measurements
# are equally spaced between initial and final time and offset such that t_start = 0
meas_time = np.linspace(0, time() - t_start, num=len(meas_data))
else:
raise ValueError("measure_runs must be >= 1!")
# due to the setup, transform sensor coordinates to magnet coordinates
meas_data = sensor_to_magnet_coordinates(meas_data)
# save data if desired
if save_data:
# Measurement Time (s), Sensor Number, X-Axis (mT), Y-Axis (mT), Z-Axis (mT)
df = pd.DataFrame({
'Measurement Time (s)': meas_time,
'Bx [mT]': meas_data[:, 0],
'By [mT]': meas_data[:, 1],
'Bz [mT]': meas_data[:, 2]
})
if directory is None:
directory = os.getcwd()
ensure_dir_exists(directory)
now = datetime.now().strftime("%y_%m_%d_%H-%M-%S")
output_file_name = "{}_{}.csv".format(now, fname_postfix)
data_filepath = os.path.join(directory, output_file_name)
try:
df.to_csv(data_filepath, index=False, header=True)
except FileNotFoundError:
data_filepath = os.path.join(os.getcwd(), output_file_name)
df.to_csv(data_filepath, index=False, header=True)
return meas_time, meas_data