def pre_run(self):
S = self.settings
# create arrays
self.x_array = np.linspace(S['x0'], S['x1'], S['n_steps'])
self.y_array = np.linspace(S['y0'], S['y1'], S['n_steps'])
if S['retrace']:
self.x_array = np.concatenate([self.x_array, self.x_array[::-1]])
self.y_array = np.concatenate([self.y_array, self.y_array[::-1]])
self.N = len(self.x_array)
self.stage = self.app.hardware['attocube_xy_stage']
# data file
self.h5file = h5_io.h5_base_file(self.app, measurement=self)
H = self.h5measure = h5_io.h5_create_measurement_group(
measurement=self, h5group=self.h5file, group_name=self.name)
# Create acquisition arrays
if S['collect_apd']:
self.apd_counter = self.app.hardware.apd_counter
self.apd_countrates = H.create_dataset(
'apd_countrates', self.N, dtype=float) # 'apd_countrate'
if S['collect_spectrum']:
self.spectra = H.create_dataset('spectra', (self.N, ccd_Nx),
dtype=float) # 'apd_countrate'
if S['collect_lifetime']:
self.picoharp = self.app.hardware.picoharp
self.time_traces = H.create_dataset(
'time_traces',
(self.N, self.picoharp.settings['histogram_channels']),
dtype=int)
def initH5CellCounter(self):
"""
Initialization operations for the h5 file.
"""
t0 = time.time()
f = self.app.settings['data_fname_format'].format(
app=self.app,
measurement=self,
timestamp=datetime.fromtimestamp(t0),
sample=self.app.settings["sample"] + "_cell_counter",
ext='h5')
fname = os.path.join(self.app.settings['save_dir'], f)
self.h5file_counter = h5_io.h5_base_file(app=self.app,
measurement=self,
fname=fname)
self.h5_group = h5_io.h5_create_measurement_group(
measurement=self, h5group=self.h5file_counter)
self.h5_mean = self.h5_group.create_dataset(name='t0/c0/mean',
shape=(self.N_frames, 1,
1),
dtype="int16",
chunks=(1, 1, 1))
self.h5_dt = self.h5_group.create_dataset(name='t0/c0/dt',
shape=(self.N_frames, 1, 1),
dtype="float",
chunks=(1, 1, 1))
self.h5_t1 = self.h5_group.create_dataset(name='t0/c0/t1',
shape=(self.N_frames, 1, 1),
dtype="float",
chunks=(1, 1, 1))
def run(self):
self.display_update_period = 1 #seconds
if self.save_data.val:
self.full_optimize_history = []
self.full_optimize_history_time = []
self.t0 = time.time()
while not self.interrupt_measurement_called:
self.optimize_ii += 1
self.optimize_ii %= self.OPTIMIZE_HISTORY_LEN
self.optimize_history_A[
self.optimize_ii] = self.ctrl.settings['T_A']
self.optimize_history_B[
self.optimize_ii] = self.ctrl.settings['T_B']
time.sleep(self.display_update_period)
if self.settings['save_data']:
try:
self.h5_file = h5_io.h5_base_file(self.app, measurement=self)
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = h5_io.h5_create_measurement_group(
self, self.h5_file)
#create h5 data arrays
H['power_optimze_history'] = self.full_optimize_history
H['optimze_history_time'] = self.full_optimize_history_time
finally:
self.h5_file.close()
def initH5_temp2(self):
"""
Initialization operations for the h5 file.
"""
t0 = time.time()
f = self.app.settings['data_fname_format'].format(
app=self.app,
measurement=self,
timestamp=datetime.fromtimestamp(t0),
sample=self.app.settings["sample"],
ext='h5')
fname = os.path.join(self.app.settings['save_dir'], f)
self.h5file = h5_io.h5_base_file(app=self.app, measurement=self, fname = fname)
self.h5_group = h5_io.h5_create_measurement_group(measurement=self, h5group=self.h5file)
img_size=self.image.shape
length=self.camera.hamamatsu.number_image_buffers
self.image_h5 = self.h5_group.create_dataset( name = 't0/c0/image',
shape = ( length, img_size[0], img_size[1]),
dtype = self.image.dtype, chunks = (1, self.eff_subarrayv, self.eff_subarrayh)
)
"""
THESE NAMES MUST BE CHANGED
"""
self.image_h5.dims[0].label = "z"
self.image_h5.dims[1].label = "y"
self.image_h5.dims[2].label = "x"
#self.image_h5.attrs['element_size_um'] = [self.settings['zsampling'], self.settings['ysampling'], self.settings['xsampling']]
self.image_h5.attrs['element_size_um'] = [1,1,1]
def run(self):
self.display_update_period = 0.02 #seconds
#self.apd_counter_hc = self.gui.apd_counter_hc
#self.apd_count_rate = self.apd_counter_hc.apd_count_rate
#self.pm_hc = self.gui.thorlabs_powermeter_hc
#self.pm_analog_readout_hc = self.gui.thorlabs_powermeter_analog_readout_hc
if self.save_data.val:
self.full_optimize_history = []
self.full_optimize_history_time = []
self.t0 = time.time()
while not self.interrupt_measurement_called:
self.optimize_ii += 1
self.optimize_ii %= self.OPTIMIZE_HISTORY_LEN
self.oo_spec.interrupt_measurement_called = self.interrupt_measurement_called
self.oo_spec.settings['continuous'] = False
self.oo_spec.settings['save_h5'] = False
# if ~self.oo_spec.activation.val:
# self.oo_spec.activation.update_value(True)
#self.oo_spec.run()
self.start_nested_measure_and_wait(self.oo_spec)
spec = self.oo_spec.hw.spectrum.copy()
if self.oo_spec.settings['baseline_subtract']:
new_value = (spec -
self.oo_spec.settings['baseline_val']).sum()
else:
new_value = self.pow_reading.update_value(spec.sum())
if isnan(new_value):
print('spec sum nan', self.optimize_ii)
self.pow_reading.update_value(
self.optimize_history[self.optimize_ii - 1])
else:
print('spec sum', self.optimize_ii, new_value)
self.pow_reading.update_value(new_value)
self.optimize_history[self.optimize_ii] = self.pow_reading.val
time.sleep(self.settings['update_period'])
#time.sleep(0.02)
if self.settings['save_data']:
try:
self.h5_file = h5_io.h5_base_file(self.app, measurement=self)
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = h5_io.h5_create_measurement_group(
self, self.h5_file)
#create h5 data arrays
H['power_optimze_history'] = self.full_optimize_history
H['optimze_history_time'] = self.full_optimize_history_time
finally:
self.h5_file.close()
def save_h5(self):
# h5 data file setup
self.t0 = time.time()
self.h5_file = h5_io.h5_base_file(self.app, measurement=self)
self.h5_filename = self.h5_file.filename
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = h5_io.h5_create_measurement_group(self, self.h5_file)
H['V'] = np.array(self.Varray)
H['I'] = np.array(self.Iarray)
self.h5_file.close()
def initH5(self):
"""
Initialization operations for the h5 file
"""
self.create_saving_directory()
# file name creation
timestamp = time.strftime("%y%m%d_%H%M%S", time.localtime())
sample = self.app.settings['sample']
#sample_name = f'{timestamp}_{self.name}_{sample}.h5'
if sample == '':
sample_name = '_'.join([timestamp, self.name])
else:
sample_name = '_'.join([timestamp, self.name, sample])
fname = os.path.join(self.app.settings['save_dir'],
sample_name + '.h5')
# file creation
self.h5file = h5_io.h5_base_file(app=self.app,
measurement=self,
fname=fname)
self.h5_group = h5_io.h5_create_measurement_group(measurement=self,
h5group=self.h5file)
img_size = self.im.image[
0].shape # both image[0] and image[1] are valid, since they have the same shape
number_of_channels = len(self.channels)
# take as third dimension of the file the total number of images collected in the buffer
if self.camera.hamamatsu.last_frame_number < self.camera.hamamatsu.number_image_buffers:
length = int((self.camera.hamamatsu.last_frame_number + 1) /
number_of_channels)
else:
length = self.camera.hamamatsu.number_image_buffers / number_of_channels
# dataset creation
for ch_index in self.channels:
name = f't0/c{ch_index}/image'
self.image_h5[ch_index] = self.h5_group.create_dataset(
name=name,
shape=(length, img_size[0], img_size[1]),
dtype=self.im.image[0].dtype,
chunks=(1, img_size[0], img_size[1]))
self.image_h5[ch_index].attrs['element_size_um'] = [
self.settings['zsampling'], self.settings['ysampling'],
self.settings['xsampling']
]
self.image_h5[ch_index].attrs['acq_time'] = timestamp
self.image_h5[ch_index].attrs['flowrate'] = self.settings[
'flowrate']
def run(self):
cam_hw = self.app.hardware.ascom_camera
print(self.name, 'interrupted', self.interrupt_measurement_called)
while not self.interrupt_measurement_called:
# Blocking version
#self.img = cam_hw.acq_single_exposure()
# non blocking version
cam_hw.ac.StartExposure(cam_hw.settings['exp_time'])
t0 = time.time()
while not cam_hw.ac.is_image_ready():
if self.interrupt_measurement_called:
break
time.sleep(0.05)
self.settings['progress'] = 100.0 * (
time.time() - t0) / cam_hw.settings['exp_time']
self.img = cam_hw.ac.read_image_data()
if not self.settings['continuous']:
# save image
try:
t0 = time.time()
fname = os.path.join(self.app.settings['save_dir'],
"%i_%s" % (t0, self.name))
print(self.name, 'asdf', self.img.dtype, fname)
if self.settings['save_ini']:
self.app.settings_save_ini(fname + ".ini")
if self.settings['save_png']:
scipy.misc.imsave(fname + ".png", self.img)
if self.settings['save_tif']:
#im = PIL.Image.fromarray(self.img.T)
#im.save("%i_%s.tif" % (t0, self.name), compression=6)
tif_imsave(fname + ".tif",
self.img.T.astype(np.uint16),
compress=0)
if self.settings['save_h5']:
with h5_io.h5_base_file(self.app,
fname=fname + ".h5") as H:
M = h5_io.h5_create_measurement_group(
measurement=self, h5group=H)
M.create_dataset('img',
data=self.img,
compression='gzip')
except Exception as e:
print('Error saving files!', e)
raise e
finally:
break # end the while loop for non-continuous scans
else:
pass
def run(self):
# Set up Hardware
self.andor_ccd.settings['acq_mode'] = 'single'
self.andor_ccd.settings['trigger_mode'] = 'internal'
self.andor_ccd.set_readout()
ccd_hw = self.app.hardware['andor_ccd']
ccd_dev = ccd_hw.ccd_dev
width_px = ccd_dev.Nx_ro
height_px = ccd_dev.Ny_ro
try:
# create data file and array
self.h5_file = h5_io.h5_base_file(app=self.app, measurement=self)
self.h5m = h5_io.h5_create_measurement_group(measurement=self,
h5group=self.h5_file)
self.sweep_wls = self.settings.ranges['sweep_wls'].array
self.h5m['sweep_wls'] = self.sweep_wls
self.spectra = np.zeros((len(self.sweep_wls), width_px),
dtype=float)
self.spectra_h5 = self.h5m.create_dataset(
'spectra', shape=(len(self.sweep_wls), width_px), dtype=float)
for ii, center_wl in enumerate(self.sweep_wls):
if self.interrupt_measurement_called:
break
# move spectrometer to center wavelength
self.spec.settings['center_wl'] = center_wl
ccd_dev.start_acquisition()
stat = ccd_hw.settings.ccd_status.read_from_hardware()
while stat == 'ACQUIRING':
if self.interrupt_measurement_called:
break
time.sleep(0.01)
stat = ccd_hw.settings.ccd_status.read_from_hardware()
if stat == 'IDLE':
self.ccd_img = ccd_dev.get_acquired_data()
self.spectrum = np.average(self.ccd_img, axis=0)
self.spectra[ii, :] = self.spectrum
self.spectra_h5[ii, :] = self.spectrum
finally:
print(self.name, 'done')
self.h5_file.close()
def run(self):
cam = self.spec_hw.cam
print("rois|-->", cam.read_rois())
cam.commit_parameters()
while not self.interrupt_measurement_called:
self.t0 = time.time()
dat = cam.acquire(readout_count=1, readout_timeout=-1)
self.roi_data = cam.reshape_frame_data(dat)
#print "roi_data shapes", [d.shape for d in self.roi_data]
self.spec = spec = np.average(self.roi_data[0], axis=0)
px_index = np.arange(self.spec.shape[-1])
self.hbin = self.spec_hw.settings['roi_x_bin']
if 'acton_spectrometer' in self.app.hardware:
self.wls = self.app.hardware['acton_spectrometer'].get_wl_calibration(px_index, self.hbin)
else:
self.wls = self.hbin*px_index + 0.5*(self.hbin-1)
self.pixels = self.hbin*px_index + 0.5*(self.hbin-1)
self.raw_pixels = px_index
self.wave_numbers = 1.0e7/self.wls
self.raman_shifts = 1.0e7/self.laser_wl.val - 1.0e7/self.wls
self.wls_mean = self.wls.mean()
if not self.continuous.val:
break
if self.settings['save_h5']:
self.h5_file = h5_io.h5_base_file(self.app, measurement=self )
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = h5_io.h5_create_measurement_group(self, self.h5_file)
H['spectrum'] = spec
H['wavelength'] = self.wls
H['wave_numbers'] = self.wave_numbers
H['raman_shifts'] = self.raman_shifts
print('saved file')
self.h5_file.close()
def save_data(self):
t = time.localtime(time.time())
t_string = "{:02d}{:02d}{:02d}_{:02d}{:02d}{:02d}".format(
int(str(t[0])[:-2]), t[1], t[2], t[3], t[4], t[5])
fname = os.path.join(self.app.settings['save_dir'],
"%s_%s" % (t_string, self.name))
with h5_io.h5_base_file(self.app, fname=fname + ".h5") as H:
print("Saving " + fname + ".h5")
M = h5_io.h5_create_measurement_group(measurement=self, h5group=H)
M.create_dataset('spectrum',
data=self.spectrum,
compression='gzip')
M.create_dataset('wavelengths',
data=self.wavelengths,
compression='gzip')
def run(self):
self.ph_hw = self.app.hardware['picoharp']
self.ph = self.ph_hw.picoharp
if self.settings['use_calc_hist_chans']:
self.ph_hw.settings[
'histogram_channels'] = self.ph_hw.calc_num_hist_chans()
self.sleep_time = min((max(0.1 * self.ph.Tacq * 1e-3, 0.010), 0.100))
self.t0 = time.time()
while not self.interrupt_measurement_called:
self.ph.start_histogram()
if self.ph.Tacq < 101:
time.sleep(self.ph.Tacq * 1e-3 + 5e-3)
else:
while not self.ph.check_done_scanning():
self.set_progress(100 * (time.time() - self.t0) /
self.ph_hw.settings['Tacq'])
if self.interrupt_measurement_called:
break
self.ph.read_histogram_data()
self.ph_hw.settings.count_rate0.read_from_hardware()
self.ph_hw.settings.count_rate1.read_from_hardware()
time.sleep(self.sleep_time)
self.ph_hw.settings.count_rate0.read_from_hardware()
self.ph_hw.settings.count_rate1.read_from_hardware()
self.ph.stop_histogram()
self.ph.read_histogram_data()
if not self.settings['continuous']:
break
self.data_slice = slice(0, self.ph_hw.settings['histogram_channels'])
histogram_data = self.ph.histogram_data[self.data_slice]
time_array = self.ph.time_array[self.data_slice]
elapsed_meas_time = self.ph.read_elapsed_meas_time()
if self.settings['save_h5']:
self.h5_file = h5_io.h5_base_file(self.app, measurement=self)
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = h5_io.h5_create_measurement_group(
self, self.h5_file)
H['time_histogram'] = histogram_data
H['time_array'] = time_array
H['elapsed_meas_time'] = elapsed_meas_time
self.h5_file.close()
def init_roi_h5(self):
"""
Initialization operations for the h5 file
"""
self.create_saving_directory()
# file name creation
timestamp = time.strftime("%y%m%d_%H%M%S", time.localtime())
sample = self.app.settings['sample']
#sample_name = f'{timestamp}_{self.name}_{sample}_ROI.h5'
sample_name = '_'.join([timestamp, self.name, sample, 'ROI.h5'])
fname = os.path.join(self.app.settings['save_dir'], sample_name)
# file creation
self.h5_roi_file = h5_io.h5_base_file(app=self.app, measurement=self, fname = fname)
self.h5_roi_group = h5_io.h5_create_measurement_group(measurement=self, h5group=self.h5_roi_file)
def snap_h5(self):
#
from ScopeFoundry import h5_io
#self.app.hardware['asi_stage'].correct_backlash(0.02)
self.h5_file = h5_io.h5_base_file(app=self.app, measurement=self)
H = self.h5_meas_group = h5_io.h5_create_measurement_group(
self, self.h5_file)
im = self.get_rgb_image()
im = np.flip(im.swapaxes(0, 1), 0)
H['image'] = im
self.h5_file.close()
print('saved file successfully', im.sum())
return im
def _run(self):
self.initial_scan_setup_plotting = True
#Hardware
self.keithley_hc = self.gui.keithley_sourcemeter_hc
K1 = self.keithley = self.keithley_hc.keithley
# h5 data file setup
self.t0 = time.time()
self.h5_file = h5_io.h5_base_file(self.gui,
"%i_%s.h5" % (self.t0, self.name))
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = self.h5_file.create_group(self.name)
h5_io.h5_save_measurement_settings(self, H)
h5_io.h5_save_hardware_lq(self.gui, H)
K1.resetA()
K1.setAutoranges_A()
K1.switchV_A_on()
I, V = K1.measureIV_A(self.source_voltage_steps.val,
Vmin=self.source_voltage_min.val,
Vmax=self.source_voltage_max.val,
KeithleyADCIntTime=1,
delay=0)
K1.switchV_A_off()
print I
print V
self.Iarray = I
self.Varray = V
#save some data
save_dict = {'I': self.Iarray, 'V': self.Varray}
self.fname = "%i_photocurrent_iv.npz" % time.time()
np.savez_compressed(self.fname, **save_dict)
print "photocurrent_iv Saved", self.fname
#create h5 data arrays
H['I'] = self.Iarray
H['V'] = self.Varray
self.h5_file.close()
def run(self):
self.display_ready = False
self.hw = hw = self.app.hardware['hydraharp']
Tacq = hw.settings['Tacq']
if self.settings['auto_HistogramBins']:
self.hw.update_HistogramBins()
self.sleep_time = min((max(0.1*Tacq, 0.010), 0.100))
self.t0 = time.time()
while not self.interrupt_measurement_called:
hw.start_histogram()
if Tacq < 0.1:
time.sleep(Tacq+5e-3)
else:
while not hw.check_done_scanning():
self.set_progress( 100*(time.time() - self.t0)/Tacq )
if self.interrupt_measurement_called:
break
self.hist_data = hw.read_histogram_data(clear_after=False)
time.sleep(self.sleep_time)
hw.stop_histogram()
self.hist_data = hw.read_histogram_data(clear_after=True)
if not self.settings['continuous']:
break
elapsed_meas_time = hw.settings.ElapsedMeasTime.read_from_hardware()
if self.settings['save_h5']:
self.h5_file = h5_io.h5_base_file(self.app, measurement=self )
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = h5_io.h5_create_measurement_group(self, self.h5_file)
H['time_histogram'] = self.hist_data[self.hw.hist_slice]
H['time_array'] = self.hw.time_array[self.hw.hist_slice[-1]]
H['elapsed_meas_time'] = elapsed_meas_time
self.h5_file.close()
def save_image(self):
print('thor_cam_capture save_image')
S = self.settings
t0 = time.time()
fname = os.path.join(self.app.settings['save_dir'],
"%i_%s" % (t0, self.name))
if S['save_ini']:
self.app.settings_save_ini(fname + ".ini")
if S['save_png']:
self.imview.export(fname + ".png")
if S['save_tif']:
self.imview.export(fname + ".tif")
if S['save_h5']:
with h5_io.h5_base_file(app=self.app, measurement=self) as H:
M = h5_io.h5_create_measurement_group(measurement=self,
h5group=H)
M.create_dataset('img', data=self.img, compression='gzip')
def run(self):
self.t0 = time.time()
#Hardware
self.keithley_hw = self.app.hardware['keithley_sourcemeter']
KS = self.keithley_hw.settings
self.keithley = self.keithley_hw.keithley
KS['output_a_on'] = True
time.sleep(0.5)
#measure IV
self.V_sources = self.voltage_range.sweep_array
self.I_array = np.zeros_like(self.V_sources, float)
self.V_array = np.zeros_like(self.V_sources, float)
for i, Vs in enumerate(self.V_sources):
self.ii = i
self.set_progress(i / len(self.V_sources) * 100)
KS['source_V_a'] = Vs
time.sleep(KS['delay_time_a'])
self.keithley_hw.settings.I_a.read_from_hardware()
self.keithley_hw.settings.V_a.read_from_hardware()
self.I_array[i] = KS["I_a"]
self.V_array[i] = KS["V_a"]
self.collect_Vs(i, Vs, KS["I_a"], KS["V_a"])
KS['output_a_on'] = False
if self.settings['save_h5']:
self.h5_file = h5_io.h5_base_file(self.app, measurement=self)
self.h5_filename = self.h5_file.filename
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = h5_io.h5_create_measurement_group(
self, self.h5_file)
H['V_sourced'] = self.V_sources
H['V'] = np.array(self.V_array)
H['I'] = np.array(self.I_array)
self.save_collected()
self.h5_file.close()
def run(self):
self.display_update_period = 0.02 #seconds
#self.apd_counter_hc = self.gui.apd_counter_hc
#self.apd_count_rate = self.apd_counter_hc.apd_count_rate
#self.pm_hc = self.gui.thorlabs_powermeter_hc
#self.pm_analog_readout_hc = self.gui.thorlabs_powermeter_analog_readout_hc
if self.save_data.val:
self.full_optimize_history = []
self.full_optimize_history_time = []
self.t0 = time.time()
while not self.interrupt_measurement_called:
self.optimize_ii += 1
self.optimize_ii %= self.OPTIMIZE_HISTORY_LEN
pow_reading = self.powermeter.settings.power.read_from_hardware()
#pow_reading = self.powermeter.power_meter.measure_power()
self.optimize_history[self.optimize_ii] = pow_reading
#self.pm_analog_readout_hc.voltage.read_from_hardware()
if self.save_data.val:
self.full_optimize_history.append(pow_reading)
self.full_optimize_history_time.append(time.time() - self.t0)
time.sleep(self.settings['update_period'])
#time.sleep(0.02)
if self.settings['save_data']:
try:
self.h5_file = h5_io.h5_base_file(self.app, measurement=self)
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = h5_io.h5_create_measurement_group(
self, self.h5_file)
#create h5 data arrays
H['power_optimze_history'] = self.full_optimize_history
H['optimze_history_time'] = self.full_optimize_history_time
finally:
self.h5_file.close()
def initH5(self):
"""
Initialization operations for the h5 file.
"""
self.h5file = h5_io.h5_base_file(app=self.app, measurement=self)
self.h5_group = h5_io.h5_create_measurement_group(measurement=self,
h5group=self.h5file)
img_size = self.image.shape
length = self.camera.pixelink.number_frames
self.image_h5 = self.h5_group.create_dataset(
name='t0/c0/image',
shape=(self.camera.pixelink.number_frames, self.eff_img_height,
self.eff_img_width),
dtype=self.image.dtype)
self.image_h5.dims[0].label = "z"
self.image_h5.dims[1].label = "y"
self.image_h5.dims[2].label = "x"
self.image_h5.attrs['element_size_um'] = [1, 1, 1]
def initH5(self):
"""
Initialization operations for the h5 file.
"""
self.create_saving_directory()
# file name creation
timestamp = time.strftime("%y%m%d_%H%M%S", time.localtime())
sample = self.app.settings['sample']
#sample_name = f'{timestamp}_{self.name}_{sample}.h5'
if sample == '':
sample_name = '_'.join([timestamp, self.name])
else:
sample_name = '_'.join([timestamp, self.name, sample])
fname = os.path.join(self.app.settings['save_dir'],
sample_name + '.h5')
# file creation
self.h5file = h5_io.h5_base_file(app=self.app,
measurement=self,
fname=fname)
self.h5_group = h5_io.h5_create_measurement_group(measurement=self,
h5group=self.h5file)
img_size = self.image.shape
length = self.camera.hamamatsu.number_image_buffers
self.image_h5 = self.h5_group.create_dataset(
name='t0/c0/image',
shape=(length, img_size[0], img_size[1]),
dtype=self.image.dtype,
chunks=(1, self.eff_subarrayv, self.eff_subarrayh))
self.image_h5.dims[0].label = "z"
self.image_h5.dims[1].label = "y"
self.image_h5.dims[2].label = "x"
#self.image_h5.attrs['element_size_um'] = [self.settings['zsampling'], self.settings['ysampling'], self.settings['xsampling']]
self.image_h5.attrs['element_size_um'] = [
1, 1, 1
] # required for compatibility with imageJ
def initH5(self):
"""
Initialization operations for the h5 file.
"""
self.h5file = h5_io.h5_base_file(app=self.app, measurement=self)
self.h5_group = h5_io.h5_create_measurement_group(measurement=self, h5group=self.h5file)
img_size=self.image.shape
length=self.camera.hamamatsu.number_image_buffers
self.image_h5 = self.h5_group.create_dataset( name = 't0/c0/image',
shape = ( length, img_size[0], img_size[1]),
dtype = self.image.dtype, chunks = (1, self.eff_subarrayv, self.eff_subarrayh)
)
"""
THESE NAMES MUST BE CHANGED
"""
self.image_h5.dims[0].label = "z"
self.image_h5.dims[1].label = "y"
self.image_h5.dims[2].label = "x"
#self.image_h5.attrs['element_size_um'] = [self.settings['zsampling'], self.settings['ysampling'], self.settings['xsampling']]
self.image_h5.attrs['element_size_um'] = [1,1,1]
def create_h5_file(self):
self.create_saving_directory()
# file name creation
timestamp = time.strftime("%y%m%d_%H%M%S", time.localtime())
sample = self.app.settings['sample']
#sample_name = f'{timestamp}_{self.name}_{sample}.h5'
if sample == '':
sample_name = '_'.join([timestamp, self.name])
else:
sample_name = '_'.join([timestamp, self.name, sample])
fname = os.path.join(self.app.settings['save_dir'], sample_name + '.h5')
self.h5file = h5_io.h5_base_file(app=self.app, measurement=self, fname = fname)
self.h5_group = h5_io.h5_create_measurement_group(measurement=self, h5group=self.h5file)
img_size = self.img.shape
dtype=self.img.dtype
length = self.image_gen.frame_num.val
self.image_h5 = self.h5_group.create_dataset(name = 't0/c0/image',
shape = [length, img_size[0], img_size[1]],
dtype = dtype)
self.image_h5.attrs['element_size_um'] = [self.settings['zsampling'],self.settings['ysampling'],self.settings['xsampling']]
def run(self):
self.t0 = time.time()
self.spec_readout.settings['continuous'] = False
self.spectra = []
self.center_wls = [] #center wls read from spectrometer
for center_wl in self.center_wl_range.array:
self.spec_center_wl.update_value(center_wl)
time.sleep(1)
self.start_nested_measure_and_wait(self.spec_readout,
polling_time=0.1,
start_time=0.1)
self.spectra.append(self.spec_readout.spec)
self.center_wls.append(self.spec_center_wl.val)
self.h5_file = h5_io.h5_base_file(self.app, measurement=self)
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = h5_io.h5_create_measurement_group(
self, self.h5_file)
H['spectra'] = np.array(self.spectra)
H['center_wls'] = np.array(self.center_wls)
self.h5_file.close()
def run(self):
if len(self.used_hws) == 0:
self.status = {'title':'Select a Collection Option and press Start', 'color':'r'}
return
else:
ETA = datetime.timedelta(seconds=int(self.total_acquisition_time) )
self.status = {'title':'Power scan total acquisition time in h:mm:ss {}'.format(str(ETA)), 'color':'g'}
self.move_to_min_pos()
# loop through power wheel positions and measure active components.
for ii in range(self.Np):
self.ii = ii
self.settings['progress'] = 100.*ii/self.Np
if self.interrupt_measurement_called:
break
for hw, Tacq_lq, acq_times_array in self.Tacq_arrays:
print("power scan {} of {}, {} acq_times {}".format(ii + 1, self.Np, hw, acq_times_array[ii]))
Tacq_lq.update_value(acq_times_array[ii])
print("moving power wheel to " + str(self.power_wheel_hw.settings['position']) )
self.power_wheel_hw.settings['position'] = self.power_wheel_position[ii]
time.sleep(0.25)
# collect power meter value
self.pm_powers[ii]=self.collect_pm_power_data()
# read detectors
if self.settings['collect_apd_counter']:
time.sleep(self.apd_counter_hw.settings['int_time'])
self.apd_count_rates[ii] = \
self.apd_counter_hw.settings.count_rate.read_from_hardware()
if self.settings['collect_picoharp']:
ph = self.ph_hw.picoharp
ph.start_histogram()
while not ph.check_done_scanning():
if self.interrupt_measurement_called:
break
ph.read_histogram_data()
time.sleep(0.1)
ph.stop_histogram()
ph.read_histogram_data()
Nt = self.ph_hw.settings['histogram_channels']
self.picoharp_elapsed_time[ii] = ph.read_elapsed_meas_time()
self.picoharp_histograms[ii,:] = ph.histogram_data[0:Nt]
self.picoharp_time_array = ph.time_array[0:Nt]
if self.settings['collect_hydraharp']:
self.hydraharp_histograms[ii,:] = self.aquire_histogram(self.hh_hw)
self.hydraharp_time_array = self.hh_hw.sliced_time_array
self.hydraharp_elapsed_time[ii] = self.hh_hw.settings['ElapsedMeasTime']
if self.settings['collect_winspec_remote_client']:
#self.spec_readout.run()
self.spec_readout.settings['continuous'] = False
self.spec_readout.settings['save_h5'] = False
self.spec_readout.interrupt_measurement_called = False
self.spec_readout.run()
time.sleep(0.5)
Tacq_lq = self.installed_hw['winspec_remote_client']
#time.sleep(Tacq_lq.val)
spec = np.array(self.spec_readout.data)
self.spectra.append(spec)
self.integrated_spectra.append(spec.sum())
if self.settings['collect_andor_ccd']:
self.andor_readout.settings['read_single'] = True
self.start_nested_measure_and_wait(self.andor_readout)
spec = self.andor_readout.get_spectrum()
self.spectra.append(spec)
self.integrated_spectra.append(spec.sum())
if self.settings['collect_ascom_img']:
self.ascom_camera_capture.interrupt_measurement_called = False
self.ascom_camera_capture.run()
img = self.ascom_camera_capture.img.copy()/self.ascom_camera_capture.settings['exp_time']
self.ascom_img_stack.append(img)
self.ascom_img_integrated.append(img.astype(float).sum())
# collect power meter value after measurement
self.pm_powers_after[ii]=self.collect_pm_power_data()
self.status = {'title':'Power scan finished', 'color':'y'}
# write data to h5 file on disk
self.t0 = time.time()
self.h5_file = h5_io.h5_base_file(app=self.app,measurement=self)
try:
self.h5_file.attrs['time_id'] = self.t0
H = self.h5_meas_group = h5_io.h5_create_measurement_group(self, self.h5_file)
if self.settings['collect_apd_counter']:
H['apd_count_rates'] = self.apd_count_rates
if self.settings['collect_picoharp']:
H['picoharp_elapsed_time'] = self.picoharp_elapsed_time
H['picoharp_histograms'] = self.picoharp_histograms
H['picoharp_time_array'] = self.picoharp_time_array
if self.settings['collect_hydraharp']:
H['hydraharp_elapsed_time'] = self.hydraharp_elapsed_time
H['hydraharp_histograms'] = self.hydraharp_histograms
H['hydraharp_time_array'] = self.hydraharp_time_array
if self.settings['collect_winspec_remote_client']:
H['wls'] = self.spec_readout.wls
H['spectra'] = np.squeeze(np.array(self.spectra))
H['integrated_spectra'] = np.array(self.integrated_spectra)
if self.settings['collect_andor_ccd']:
H['wls'] = self.andor_readout.wls
H['spectra'] = np.squeeze(np.array(self.spectra))
H['integrated_spectra'] = np.array(self.integrated_spectra)
if self.settings['collect_ascom_img']:
H['ascom_img_stack'] = np.array(self.ascom_img_stack)
H['ascom_img_integrated'] = np.array(self.ascom_img_integrated)
H['pm_powers'] = self.pm_powers
H['pm_powers_after'] = self.pm_powers_after
H['power_wheel_position'] = self.power_wheel_position
for hw, Tacq_lq, acq_times_array in self.Tacq_arrays:
H[hw + '_acq_times_array'] = acq_times_array
print('saving ' + hw + '_acq_times_array')
finally:
self.log.info("data saved " + self.h5_file.filename)
self.h5_file.close()
def run(self):
self.first_display = True
self.do_callback_times = []
self.img= np.zeros((self.settings['image_size_x'],self.settings['image_size_y']), dtype=np.float)
self.counts= np.zeros((self.settings['image_size_x'],self.settings['image_size_y']), dtype=np.float)
try:
self.setup_tasks()
# Create h5 arrays
self.h5_file = h5_io.h5_base_file(self.app, measurement=self)
self.h5_filename = self.h5_file.filename
self.h5_m = h5_io.h5_create_measurement_group(measurement=self, h5group=self.h5_file)
self.ao_data_h5 = self.h5_m.create_dataset('ao_data',
shape=(self.N_ao, self.ao_chan_count),
maxshape=(None, self.ao_chan_count), dtype=float,
chunks=(self.N_ao, self.ao_chan_count))
self.ai_data_h5 = self.h5_m.create_dataset('ai_data',
shape=(self.N_ai, self.ai_chan_count),
maxshape=(None, self.ai_chan_count), dtype=float,
chunks=(self.N_ai, self.ai_chan_count))
self.i_processed = 0
#KDE interp (it overwrites the one created in setup())
Nx=self.settings['image_size_x']
Ny=self.settings['image_size_y']
Ax=self.settings['amplitude_x']
Ay=self.settings['amplitude_y']
self.kde_img = KDE_Image((Ny,Nx), self.settings['kde_sigma'])
### Start Output Tasks
self.ao_task.StartTask()
self.do_task.StartTask()
block_i=0
t00=time.time()
while not self.interrupt_measurement_called:
#time.sleep(0.1)
if len(self.ao_block_queue) > 0:
# pop things off queue
i_ao, ao_block = self.ao_block_queue.pop(0)
assert ao_block.shape[0] == self.N_ao
# store in h5 dataset
if i_ao >= self.ao_data_h5.shape[0]:
self.ao_data_h5.resize((i_ao+self.N_ao, self.ao_chan_count))
self.ao_data_h5[i_ao:i_ao+self.N_ao,:] = ao_block
# process block
if len(self.ai_block_queue) > 0:
# pop things off queue
i_ai, ai_block = self.ai_block_queue.pop(0)
assert ai_block.shape[0] == self.N_ai
# store in h5 dataset
if i_ai >= self.ai_data_h5.shape[0]:
self.ai_data_h5.resize((i_ai+self.N_ai, self.ai_chan_count))
self.ai_data_h5[i_ai:i_ai+self.N_ai,:] = ai_block
# process block
if self.ai_data_h5.shape[0] > self.i_processed:
x = self.ao_data_h5[self.i_processed: self.i_processed + self.N_ao, 0]
y = self.ao_data_h5[self.i_processed: self.i_processed + self.N_ao, 1]
#we are choosing here analog input channel 0 as signal
z = self.ai_data_h5[self.i_processed: self.i_processed + self.N_ai, 0]
# print ("x",x)
# print ("y",y)
# print ("z",z)
#
# Quick interpolation
x_img=np.uint32((Nx-1)/2*x/Ax+(Nx)/2)
y_img=np.uint32((Ny-1)/2*y/Ay+(Ny)/2)
# for ii in range(len(x)):
# #self.img[x_img[ii],y_img[ii]]+=z[ii]
# self.counts[x_img[ii],y_img[ii]]+=1
# cc=self.counts[x_img[ii],y_img[ii]]
# old_z=self.img[x_img[ii],y_img[ii]]
# new_z=(old_z*(cc-1)+z[ii])/cc
# self.img[x_img[ii],y_img[ii]]=new_z
#
### KDE
x_img=(Nx-1)/2*x/Ax+(Nx)/2
y_img=(Ny-1)/2*y/Ay+(Ny)/2
x_img = np.clip(x_img, 4, Nx-4)
y_img = np.clip(y_img, 4, Ny-4)
t0=time.time()
self.kde_img.add_datapoints(x_img, y_img,z)
print('Elapsed time for adding chunk to image:', round(time.time()-t0,3))
self.i_processed += self.N_ao
block_i+=1
#print('Acquired samples:' , block_i*self.N_ai)
if (block_i*self.N_ai) > 2e6:
print('While loop terminated')
break
print('Elapsed time for all in while loop:', round(time.time()-t00,3))
# Optional: store old blocks in a finite sized buffer
finally:
self.stop_tasks()
def saveMeasurements(self):
if list_equal(self.imageRaw_store, self.imageRAW):
self.show_text("Raw images are not saved: the same measurement.")
else:
timestamp = time.strftime("%y%m%d_%H%M%S", time.localtime())
sample = self.app.settings['sample']
if sample == '':
sample_name = '_'.join([timestamp, self.name])
else:
sample_name = '_'.join([timestamp, self.name, sample])
# create file path for both h5 and other types of files
pathname = os.path.join(self.app.settings['save_dir'], sample_name)
Path(pathname).mkdir(parents=True, exist_ok=True)
self.pathname = pathname
self.sample_name = sample_name
# create h5 base file if the h5 file is not exist
if self.ui.saveH5.isChecked():
# create h5 file for raw
fname_raw = os.path.join(pathname, sample_name + '_Raw.h5')
self.h5file_raw = h5_io.h5_base_file(app=self.app,
measurement=self,
fname=fname_raw)
# save measure component settings
h5_io.h5_create_measurement_group(measurement=self,
h5group=self.h5file_raw)
for ch_idx in range(2):
gname = f'data/c{ch_idx}/raw'
if np.sum(self.imageRAW[ch_idx]
) == 0: # remove the empty channel
self.h5file_raw.create_dataset(gname,
data=h5py.Empty("f"))
self.show_text("[H5] Raw images are empty.")
else:
self.h5file_raw.create_dataset(
gname, data=self.imageRAW[ch_idx])
self.show_text("[H5] Raw images are saved.")
self.h5file_raw.close()
if self.ui.saveTif.isChecked():
for ch_idx in range(2):
fname_raw = os.path.join(
pathname, sample_name + f'_Raw_Ch{ch_idx}.tif')
if np.sum(self.imageRAW[ch_idx]
) != 0: # remove the empty channel
tif.imwrite(fname_raw,
np.single(self.imageRAW[ch_idx]))
self.show_text("[Tif] Raw images are saved.")
else:
self.show_text("[Tif] Raw images are empty.")
self.imageRaw_store = self.imageRAW # store the imageRAW for comparision
if self.isCalibrated:
if self.ui.saveH5.isChecked():
fname_pro = os.path.join(
self.pathname, self.sample_name +
f'_C{self.current_channel_display()}_Processed.h5')
self.h5file_pro = h5_io.h5_base_file(app=self.app,
measurement=self,
fname=fname_pro)
h5_io.h5_create_measurement_group(measurement=self,
h5group=self.h5file_pro)
name = f'data/sim'
if np.sum(self.imageSIM) == 0:
dset = self.h5file_pro.create_dataset(name,
data=h5py.Empty("f"))
self.show_text("[H5] SIM images are empty.")
else:
dset = self.h5file_pro.create_dataset(name,
data=self.imageSIM)
self.show_text("[H5] SIM images are saved.")
dset.attrs['kx'] = self.kx_full
dset.attrs['ky'] = self.ky_full
if self.numSets != 0:
for idx in range(self.numSets):
roi_group_name = f'data/roi/{idx:03}'
raw_set = self.h5file_pro.create_dataset(
roi_group_name + '/raw',
data=self.imageRaw_ROI[idx])
raw_set.attrs['cx'] = self.oSegment.selected_cx[idx]
raw_set.attrs['cy'] = self.oSegment.selected_cy[idx]
sim_set = self.h5file_pro.create_dataset(
roi_group_name + '/sim',
data=self.imageSIM_ROI[idx])
sim_set.attrs['kx'] = self.kx_roi[idx]
sim_set.attrs['ky'] = self.ky_roi[idx]
self.show_text("[H5] ROI images are saved.")
self.h5file_pro.close()
if self.ui.saveTif.isChecked():
fname_sim = os.path.join(
self.pathname, self.sample_name +
f'_C{self.current_channel_display()}_SIM' + '.tif')
fname_ini = os.path.join(
self.pathname, self.sample_name +
f'_C{self.current_channel_display()}_Settings' + '.ini')
if np.sum(self.imageSIM) != 0:
tif.imwrite(fname_sim, np.single(self.imageSIM))
self.app.settings_save_ini(fname_ini, save_ro=False)
self.show_text("[Tif] SIM images are saved.")
else:
self.show_text("[Tif] SIM images are empty.")
if self.numSets != 0:
for idx in range(self.numSets):
fname_roi = os.path.join(
self.pathname, self.sample_name +
f'_Roi_C{self.current_channel_display()}_{idx:003}_SIM'
+ '.tif')
tif.imwrite(fname_roi,
np.single(self.imageSIM_ROI[idx]))
self.show_text("[Tif] ROI images are saved.")
def _run(self):
#hardware
self.stage = self.gui.mcl_xyz_stage_hc
self.nanodrive = self.stage.nanodrive
# Data arrays
self.h_array = np.arange(self.h0.val,
self.h1.val,
self.dh.val,
dtype=float)
self.v_array = np.arange(self.v0.val,
self.v1.val,
self.dv.val,
dtype=float)
self.Nh = len(self.h_array)
self.Nv = len(self.v_array)
self.range_extent = [
self.h0.val, self.h1.val, self.v0.val, self.v1.val
]
self.corners = [
self.h_array[0], self.h_array[-1], self.v_array[0],
self.v_array[-1]
]
self.imshow_extent = [
self.h_array[0] - 0.5 * self.dh.val,
self.h_array[-1] + 0.5 * self.dh.val,
self.v_array[0] - 0.5 * self.dv.val,
self.v_array[-1] + 0.5 * self.dv.val
]
# h5 data file setup
self.t0 = time.time()
self.h5_file = h5_io.h5_base_file(self.gui,
"%i_%s.h5" % (self.t0, self.name))
self.h5_file.attrs['time_id'] = self.t0
self.h5_meas_group = self.h5_file.create_group(self.name)
#h5_io.h5_save_measurement(self, self.h5_meas_group)
h5_io.h5_save_measurement_settings(self, self.h5_file['/'])
self.h5_meas_group.create_dataset(name='h_array',
data=self.h_array,
compression='gzip',
shuffle=True)
self.h5_meas_group.create_dataset(name='v_array',
data=self.v_array,
compression='gzip',
shuffle=True)
self.h5_meas_group.attrs['Nv'] = self.Nv
self.h5_meas_group.attrs['Nh'] = self.Nh
self.h5_meas_group.attrs['range_extent'] = self.range_extent
self.h5_meas_group.attrs['corners'] = self.corners
self.h5_meas_group.attrs['imshow_extent'] = self.imshow_extent
#scan specific setup
self.pre_scan_setup()
# TODO Stop other timers?!
print "scanning"
self.current_pixel_num = 0
self.t_scan_start = time.time()
try:
v_axis_id = self.stage.v_axis_id
h_axis_id = self.stage.h_axis_id
# move slowly to start position
start_pos = [None, None, None]
start_pos[v_axis_id - 1] = self.v_array[0]
start_pos[h_axis_id - 1] = self.h_array[0]
#print "scan.."
print start_pos
self.nanodrive.set_pos_slow(*start_pos)
# Scan!
line_time0 = time.time()
for i_v in range(self.Nv):
if self.interrupt_measurement_called:
break
self.v_pos = self.v_array[i_v]
self.nanodrive.set_pos_ax(self.v_pos, v_axis_id)
#self.read_stage_position()
if i_v % 2: #odd lines
h_line_indicies = range(self.Nh)[::-1]
else: #even lines -- traverse in opposite direction
h_line_indicies = range(self.Nh)
for i_h in h_line_indicies:
if self.interrupt_measurement_called:
break
self.h_pos = self.h_array[i_h]
self.nanodrive.set_pos_ax(self.h_pos, h_axis_id)
# collect data
self.collect_pixel(i_h, i_v)
self.current_pixel_num += 1
T_pixel = float(time.time() - line_time0) / self.Nh
print "line time:", time.time() - line_time0
line_time0 = time.time()
self.progress.update_value(self.current_pixel_num * 100. /
(self.Nv * self.Nh))
total_px = self.Nv * self.Nh
print "time per pixel:", T_pixel, '| estimated total time (h)', total_px * T_pixel / 3600, '| Nh, Nv:', self.Nh, self.Nv,
Time_finish = time.localtime(total_px * T_pixel +
self.t_scan_start)
print '| scan finishes at: {:02d}:{:02d}'.format(
Time_finish.tm_hour, Time_finish.tm_min)
# read stage position every line
self.stage.read_pos()
#scanning done
self.post_scan_cleanup()
#except Exception as err:
# self.interrupt()
# raise err
finally:
#save data file
save_dict = {
'h_array': self.h_array,
'v_array': self.v_array,
'Nv': self.Nv,
'Nh': self.Nh,
'range_extent': self.range_extent,
'corners': self.corners,
'imshow_extent': self.imshow_extent,
}
save_dict.update(self.scan_specific_savedict())
for lqname, lq in self.gui.logged_quantities.items():
save_dict[lqname] = lq.val
for hc in self.gui.hardware_components.values():
for lqname, lq in hc.logged_quantities.items():
save_dict[hc.name + "_" + lqname] = lq.val
for lqname, lq in self.logged_quantities.items():
save_dict[self.name + "_" + lqname] = lq.val
self.fname = "%i_%s.npz" % (self.t0, self.name)
np.savez_compressed(self.fname, **save_dict)
print self.name, "saved:", self.fname
#h5 file
self.h5_file.close()
if not self.interrupt_measurement_called:
self.measurement_sucessfully_completed.emit()
else:
pass
def saveMeasurements(self):
timestamp = time.strftime("%y%m%d_%H%M%S", time.localtime())
ch = self.current_channel_process() # selected channel
if self.isCalibrated:
if self.ui.saveH5.isChecked():
if list_equal(self.imageSIM_store, self.imageSIM):
self.show_text("[NOT SAVED]\tSIM images are identical.")
else:
# create file name for the processed file
fname_pro = os.path.join(
self.filepath, self.filetitle +
f'_{timestamp}_C{self.current_channel_process()}_Processed.h5'
)
# create H5 file
self.h5file_pro = h5_io.h5_base_file(app=self.app,
measurement=self,
fname=fname_pro)
# create measurement group and save measurement settings
h5_io.h5_create_measurement_group(measurement=self,
h5group=self.h5file_pro)
# name of the sim image group
sim_name = f'data/sim'
avg_name = f'data/avg'
std_name = f'data/std'
if np.sum(self.imageSIM) == 0:
dset = self.h5file_pro.create_dataset(
sim_name, data=h5py.Empty("f"))
dset_1 = self.h5file_pro.create_dataset(
avg_name, data=h5py.Empty("f"))
dset_2 = self.h5file_pro.create_dataset(
std_name, data=h5py.Empty("f"))
# self.show_text("[UNSAVED] SIM images are empty.")
else:
dset = self.h5file_pro.create_dataset(
sim_name, data=self.imageSIM)
dset_1 = self.h5file_pro.create_dataset(
avg_name, data=self.imageAVG)
dset_2 = self.h5file_pro.create_dataset(
std_name, data=self.imageSTD)
self.show_text("[SAVED]\tSIM images to <H5>.")
dset.attrs['kx'] = self.kx_full
dset.attrs['ky'] = self.ky_full
if self.numSets != 0:
for idx in range(self.numSets):
roi_group_name = f'data/roi/{idx:03}'
raw_set = self.h5file_pro.create_dataset(
roi_group_name + '/raw',
data=self.imageRAW_ROI[idx])
raw_set.attrs['cx'] = self.oSegment.selected_cx[
idx]
raw_set.attrs['cy'] = self.oSegment.selected_cy[
idx]
sim_set = self.h5file_pro.create_dataset(
roi_group_name + '/sim',
data=self.imageSIM_ROI[idx])
sim_set.attrs['kx'] = self.kx_roi[idx]
sim_set.attrs['ky'] = self.ky_roi[idx]
avg_set = self.h5file_pro.create_dataset(
roi_group_name + '/avg',
data=self.imageAVG_ROI[idx])
std_set = self.h5file_pro.create_dataset(
roi_group_name + '/std',
data=self.imageSTD_ROI[idx])
self.show_text("[SAVED]\tROI images to <H5>.")
self.h5file_pro.close()
if self.ui.saveTif.isChecked():
fname_sim = os.path.join(
self.filepath, self.filetitle +
f'_{timestamp}_C{self.current_channel_process()}_SIM' + '.tif')
fname_ini = os.path.join(
self.filepath, self.filetitle +
f'_{timestamp}_C{self.current_channel_process()}_Settings' +
'.ini')
fname_avg = os.path.join(
self.filepath, self.filetitle +
f'_{timestamp}_C{self.current_channel_process()}_AVG' + '.tif')
fname_std = os.path.join(
self.filepath, self.filetitle +
f'_{timestamp}_C{self.current_channel_process()}_STD' + '.tif')
self.app.settings_save_ini(fname_ini, save_ro=False)
if np.sum(self.imageSIM) != 0:
tif.imwrite(fname_sim, np.single(self.imageSIM))
tif.imwrite(fname_avg, np.single(self.imageAVG))
tif.imwrite(fname_std, np.single(self.imageSTD))
self.show_text("[SAVED]\tSIM images to <TIFF>.")
# else:
# self.show_text("[UNSAVED] SIM images are empty.")
if self.numSets != 0:
for idx in range(self.numSets):
fname_roi_sim = os.path.join(
self.filepath, self.filetitle +
f'_{timestamp}_Roi_C{self.current_channel_process()}_{idx:003}_SIM'
+ '.tif')
fname_roi_avg = os.path.join(
self.filepath, self.filetitle +
f'_{timestamp}_Roi_C{self.current_channel_process()}_{idx:003}_AVG'
+ '.tif')
fname_roi_std = os.path.join(
self.filepath, self.filetitle +
f'_{timestamp}_Roi_C{self.current_channel_process()}_{idx:003}_STD'
+ '.tif')
tif.imwrite(fname_roi_sim,
np.single(self.imageSIM_ROI[idx]))
tif.imwrite(fname_roi_avg,
np.single(self.imageAVG_ROI[idx]))
tif.imwrite(fname_roi_std,
np.single(self.imageSTD_ROI[idx]))
self.show_text("[SAVED]\tROI images to <TIFF>.")
def run(self):
"""
Runs when measurement is started. Runs in a separate thread from GUI.
It should not update the graphical interface directly, and should only
focus on data acquisition.
"""
self.buffer = np.zeros((60000,2))
self.track_cam._dev.set_buffer_count(500)
# if enabled will create an HDF5 file with the plotted data
# first we create an H5 file (by default autosaved to app.settings['save_dir']
# This stores all the hardware and app meta-data in the H5 file
if self.settings.save_video.value():
save_dir = self.app.settings.save_dir.value()
data_path = os.path.join(save_dir,self.app.settings.sample.value())
try:
os.makedirs(data_path)
except OSError:
print('directory already exist, writing to existing directory')
frame_rate = self.track_cam.settings.frame_rate.value()
self.recorder.settings.path.update_value(data_path)
self.recorder.create_file('track_mov',frame_rate)
#save h5
file_name_index=0
file_name=os.path.join(self.recorder.settings.path.value(),'trail_'+str(file_name_index)+'.h5')
while os.path.exists(file_name):
file_name_index+=1
file_name=os.path.join(self.recorder.settings.path.value(),'trail_'+str(file_name_index)+'.h5')
self.h5file = h5_io.h5_base_file(app=self.app, measurement=self,fname = file_name)
# create a measurement H5 group (folder) within self.h5file
# This stores all the measurement meta-data in this group
self.h5_group = h5_io.h5_create_measurement_group(measurement=self, h5group=self.h5file)
# create an h5 dataset to store the data
self.buffer_h5 = self.h5_group.create_dataset(name = 'buffer',
shape = self.buffer.shape,
dtype = self.buffer.dtype)
# self.recorder.create_file('wide_mov',frame_rate)
self.track_disp_queue = queue.Queue(1000)
# self.wide_disp_queue = queue.Queue(1000)
self.motor_queue = queue.Queue(1000)
self.comp_thread = SubMeasurementQThread(self.camera_action)
self.motor_thread = SubMeasurementQThread(self.motor_action)
self.interrupt_subthread.connect(self.comp_thread.interrupt)
self.interrupt_subthread.connect(self.motor_thread.interrupt)
self.pid = PIDController(p = self.settings.proportional.value(),
i = self.settings.integral.value(),
d = self.settings.derivative.value())
self.midpoint = (self.track_cam.settings.height.value()//self.settings.binning.value())//2
self.pix_size = self.settings.pixel_size.value() * self.settings.binning.value()
try:
self.track_i = 0
self.i = 0
# self.wide_i = 0
self.track_flag = False
self.track_cam.start()
# self.wide_cam.start()
self.comp_thread.start()
self.motor_thread.start()
# Will run forever until interrupt is called.
while not self.interrupt_measurement_called:
#wait for 0.1ms
time.sleep(0.5)
if self.interrupt_measurement_called:
# Listen for interrupt_measurement_called flag.
# This is critical to do, if you don't the measurement will
# never stop.
# The interrupt button is a polite request to the
# Measurement thread. We must periodically check for
# an interrupt request
self.interrupt_subthread.emit()
break
finally:
self.track_cam.stop()
# self.wide_cam.stop()
if self.settings.save_video.value():
self.recorder.close()
del self.motor_thread
del self.comp_thread
del self.motor_queue
del self.track_disp_queue
if self.settings.save_video.value():
self.recorder.close()
self.h5file.close()