def Perform_Measurement(self, event):
print("GUIController: Perform_Measurement")
# Using that instance we then run the lights,
# and measure the outputs
self.measurement_handler = MeasurementHandler()
measurement_handler.add_to_queue(
self.light_pulse.complete_waveform,
self.metadata
)
raw_data = self.measurement_handler.single_measurement()
self.Data.updateRawData(raw_data)
self.Data.Data = utils.bin_data(raw_data, self.metadata.binning)
# We then plot the datas, this has to be changed if the plots want
# to be updated on the fly.
pub.sendMessage('update.plot')
def single_measurement(self):
'''
This runs a single measurements:
1. Sets the femto gain
2. Generatres a waveform
3. Sends command to DAQ
4. Does next item
'''
# get measurement from list
measurement_settings = self._queue.popleft()
print 'The measurement settings are', type(measurement_settings)
print measurement_settings.ref_gain
print measurement_settings.pl_gain
# set preamp gains
self.preamps.configure(measurement_settings)
# gets the voltage pulse to control the waveform
waveform_array = LightPulse(measurement_settings).create_waveform()
# plt.figure()
# plt.plot(waveform_array)
# plt.show()
# initalises the DAQ ready for a measurement
thread = self.daq._int_thread(waveform_array, measurement_settings)
# lets get the averaging
averaging = measurement_settings.averaging
# set the variables before they are created?
thread_time = None
measurement_data = []
# perform a measurement
measurement_data, thread_time = self.daq.run_thread(thread)
# make sure the length makes sense
data_len = measurement_data.shape[0] / self.daq.NUM_CHANNELS
# print type(data_len), data_len
# print
assert (data_len.is_integer(),
"No of points per channel={0}".format(data_len))
data_len = int(data_len)
# if more averages were requested
if averaging > 1:
for i in range(averaging - 1):
thread_data, thread_time = self.daq.run_thread(thread)
# stack the data
measurement_data = np.vstack((thread_data, measurement_data))
# average the data, weighting it towards
# running total
measurement_data = np.average(measurement_data,
axis=0,
weights=(1, i + 1))
# convert into columns, putting the right data
# in the right place
measurement_data = measurement_data[:data_len *
int(self.daq.NUM_CHANNELS)]
measurement_data = measurement_data.reshape(
(data_len, int(self.daq.NUM_CHANNELS)), order='F')
assert measurement_data.shape[0] == thread_time.shape[
0], 'data Length differ {0} {1}'.format(measurement_data.shape,
thread_time.shape[0])
measurement_data = np.vstack((thread_time, measurement_data.T)).T
measurement_data = bin_data(measurement_data,
measurement_settings.binning)
return measurement_data
def single_measurement(self):
'''
This runs a single measurements:
1. Sets the femto gain
2. Generatres a waveform
3. Sends command to DAQ
4. Does next item
'''
# get measurement from list
measurement_settings = self._queue.popleft()
print 'The measurement settings are', type(measurement_settings)
print measurement_settings.ref_gain
print measurement_settings.pl_gain
# set preamp gains
self.preamps.configure(measurement_settings)
# gets the voltage pulse to control the waveform
waveform_array = LightPulse(measurement_settings).create_waveform()
# plt.figure()
# plt.plot(waveform_array)
# plt.show()
# initalises the DAQ ready for a measurement
thread = self.daq._int_thread(waveform_array, measurement_settings)
# lets get the averaging
averaging = measurement_settings.averaging
# set the variables before they are created?
thread_time = None
measurement_data = []
# perform a measurement
measurement_data, thread_time = self.daq.run_thread(thread)
# make sure the length makes sense
data_len = measurement_data.shape[0] / self.daq.NUM_CHANNELS
# print type(data_len), data_len
# print
assert (
data_len.is_integer(), "No of points per channel={0}".format(
data_len)
)
data_len = int(data_len)
# if more averages were requested
if averaging > 1:
for i in range(averaging - 1):
thread_data, thread_time = self.daq.run_thread(thread)
# stack the data
measurement_data = np.vstack((thread_data,
measurement_data))
# average the data, weighting it towards
# running total
measurement_data = np.average(measurement_data,
axis=0,
weights=(1, i + 1))
# convert into columns, putting the right data
# in the right place
measurement_data = measurement_data[:data_len*int(self.daq.NUM_CHANNELS)]
measurement_data = measurement_data.reshape(
(data_len, int(self.daq.NUM_CHANNELS)),
order = 'F')
assert measurement_data.shape[0] == thread_time.shape[0], 'data Length differ {0} {1}'.format( measurement_data.shape, thread_time.shape[0])
measurement_data = np.vstack((thread_time, measurement_data.T)).T
measurement_data = bin_data(measurement_data,measurement_settings.binning)
return measurement_data