def save_last_histogram(self, name):
# y,x = self._hist, self._hist_bins[1:]*self._hist_binsize_ns#np.histogram(self._dts_ns, bins = np.linspace(self.get_roi_min(), self.get_roi_max(),pts))#np.min(dts[dts>0]),np.max(dts),pts))
# f = h5py.File(self._last_filepath,'a')
dat = h5.HDF5Data(name='qutau_counter_' + name)
print dat.filepath()
dat.create_dataset('x', data=self.x)
dat.create_dataset('y', data=self.y)
dat.close()
def save(self):
name = self.get_name()
if self._setup_controller != None:
name += '_' + self._setup_controller.get_keyword()
# NOTE a test of hdf5 data
dat = h5.HDF5Data(name=name)
dat.create_dataset('x', data=self._x)
dat.create_dataset('y', data=self._y)
dat.create_dataset('countrate', data=self._data['countrates'])
m2.save_instrument_settings_file(dat)
self._last_filepath = dat.filepath()
dat.close()
def __init__(self, name, save=True):
self.name = name
self.params = MeasurementParameters()
if save:
self.dataset_idx = 0
self.h5data = h5.HDF5Data(name=self.mprefix + '_' + self.name)
self.h5datapath = self.h5data.filepath()
self.h5base = '/' + self.name + '/'
self.h5basegroup = self.h5data.create_group(self.name)
self.datafolder = self.h5data.folder()
self.keystroke_monitors = {}
self.params['measurement_type'] = self.mprefix
def save_current_settings(self, parent=None, name=''):
if parent == None:
parent = h5.HDF5Data(name='OKOTech_DM' + '_' + name)
flush = True
instrument_grp = parent.create_group('instrument_settings')
#inslist = dict_to_ordered_tuples(qt.instruments.get_instruments())
#for (iname, ins) in inslist:
insgroup = instrument_grp.create_group(self._name)
parlist = dict_to_ordered_tuples(self.get_parameters())
for (param, popts) in parlist:
try:
insgroup.attrs[param] = self.get(param, query=True) \
if 'remote' in self.get_options()['tags'] \
else self.get(param, query=False)
except (ValueError, TypeError):
insgroup.attrs[param] = str(self.get(param, query=False))
if flush: parent.flush()
def start_scan_debug():
blobs_list = []
xs_blobs = np.array([])
ys_blobs = np.array([])
filter_str = None
folder = r'D:\measuring\data\20180208\194717_scan2d'
search_range = 2
x_o = 0
y_o = 0
store = []
z = -3
x_scan = [-12, 12]
y_scan = [-17, 17]
#dat = h5.HDF5Data(name='blobs_coordinates')
ip = testcv.Image_processor()
ip.config(search_range)
S = NV_search()
# I=nir.CNN()
# I.train(r'D:\measuring\data\20180210\data\train',saved_file='trained_explorer_68',folder_validate=r'D:\measuring\data\20180210\data\validate')
# folder_name=r'D:\measuring\data\20180211\130859_scan2d\images_to analyze\to_test'
# I.load_trained_model(load_file='trained_explorer_68')
# I.analyze_images_from_folder(folder_name)
# # e=esr_NV_search.esr_on_blob()
# success=e.run(name='test_esr')
# print success
# d= ds.DisplayScanFlim(folder)
# d.get_data()
# d.split_image(folder,x_scan,y_scan,save=True,colormap='gist_earth',search_range=search_range)
#filter_str=['x_c=-1 y_c=-3 ','x_c=-1 y_c=-10', 'x_c=-2 y_c=-1 ']
filter_str = ['x_c']
qt.instruments['GreenAOM'].turn_on()
for filename in os.listdir(folder):
if any(s in filename for s in filter_str):
if not 'search_range' in filename:
cropped_image = ip.crop_image(folder, filename)
x_blobs, y_blobs = ip.find_blobs(folder, filename,
cropped_image, search_range,
z)
xs_blobs = np.append(xs_blobs, x_blobs)
ys_blobs = np.append(ys_blobs, y_blobs)
#xy_blobs=np.append([x_blobs],[y_blobs],axis=0)
#xy_blobs=np.transpose(xy_blobs)
blobs_list = np.append([xs_blobs], [ys_blobs], axis=0)
print 'number of blobs', len(xs_blobs)
del filter_str
name = 'outcome_cordinates'
dat = h5.HDF5Data(name=name)
dat.create_dataset('z=' + str(z) + ' blobs_coordinates',
data=np.transpose(blobs_list))
m2.save_instrument_settings_file(dat)
dat.close()
def DataDumperThread ( StopQueue, ProcessedDataQueue, MAX_DATA_LEN, data_filepath ):
rawdata_idx = 0
t_ofl = 0
t_lastsync = 0
last_sync_number = 0
length = 0
current_dset_length = 0
head, tail = os.path.split(data_filepath)
h5data = h5.HDF5Data( name = head + '/PQData_'+ tail)
h5datapath = h5data.filepath()
print 'Filepath '+str( h5datapath )
#h5data = h5py.File(data_filepath, 'r+')
print 'Dumper Process started'
while True : #The process is stopped when an item is put to the stop queue. Then it will process the remaining queue items and exit.
if StopQueue.empty() == False:
if ProcessedDataQueue.empty() == True:
break
print 'Stopping the Dumper... waiting until the queue is empty. Current length ' + str( ProcessedDataQueue.qsize() )
if rawdata_idx == 0 or current_dset_length > MAX_DATA_LEN :
rawdata_idx += 1
current_dset_length = 0
print 'I create a new dataset.'
dset_hhtime = h5data.create_dataset('PQ_time-{}'.format(rawdata_idx),
(0,), 'u8', maxshape=(None,))
dset_hhchannel = h5data.create_dataset('PQ_channel-{}'.format(rawdata_idx),
(0,), 'u1', maxshape=(None,))
dset_hhspecial = h5data.create_dataset('PQ_special-{}'.format(rawdata_idx),
(0,), 'u1', maxshape=(None,))
dset_hhsynctime = h5data.create_dataset('PQ_sync_time-{}'.format(rawdata_idx),
(0,), 'u8', maxshape=(None,))
dset_hhsyncnumber = h5data.create_dataset('PQ_sync_number-{}'.format(rawdata_idx),
(0,), 'u4', maxshape=(None,))
h5data.flush()
if ProcessedDataQueue.qsize() > 0:
try:
hhtime, hhchannel, hhspecial, sync_time, sync_number, \
newlength, t_ofl, t_lastsync, last_sync_number = ProcessedDataQueue.get(True, 1)
except Exception as E:
print 'exception: timeout during get() in the dumper queue'
print E.args
continue # do not process data when getting from the queue has not worked out
if newlength > 0:
dset_hhtime.resize((current_dset_length+newlength,))
dset_hhchannel.resize((current_dset_length+newlength,))
dset_hhspecial.resize((current_dset_length+newlength,))
dset_hhsynctime.resize((current_dset_length+newlength,))
dset_hhsyncnumber.resize((current_dset_length+newlength,))
dset_hhtime[current_dset_length:] = hhtime
dset_hhchannel[current_dset_length:] = hhchannel
dset_hhspecial[current_dset_length:] = hhspecial
dset_hhsynctime[current_dset_length:] = sync_time
dset_hhsyncnumber[current_dset_length:] = sync_number
current_dset_length += newlength
h5data.flush()
print 'Dumper thread stopped'
print 'PQ total datasets, events last datase, last sync number:', rawdata_idx, current_dset_length, last_sync_number
Example to illustrate the usage of the hdf5_data module;
To check out the resulting file, you can use, besides python,
the HDFView Program from the HDF group (hdfgroup.com).
About HDF5 implementation in python see h5py.alfven.org.
Latest version: 2012/12/26, Wolfgang Pfaff <wolfgangpfff at gmail dot com>
"""
import hdf5_data as h5
import numpy as np
### Direct access to hdf5 container
# create data; follows the data storage scheme of qtlab
dat = h5.HDF5Data(name='data_number_one')
# this function is a simple wrapper for the h5py method of the same name
print 'create our first dataset'
dset1 = dat.create_dataset('first set', (5, 5), 'i')
dset1[...] = 42
print dset1 # this is the dataset object
print dset1.value # this is a numpy array
# simpler access (equivalent)
print ''
print 'again...'
print dat['/first set']
# create something in a group, by simple access (there's also a create_group
# method that's a simple wrapper for the h5py method).
def go_to_membrane(where, **kw):
save_fit = kw.pop('save_fit', False)
opt_ins = qt.instruments['opt1d_counts']
mos_ins = qt.instruments['master_of_space']
x, y = opt_ins.run(dimension='z',
scan_length=8,
nr_of_points=61,
pixel_time=80,
return_data=True,
gaussian_fit=False)
Dx = 1.5
#g_a1, g_A1, g_x01, g_sigma1, g_A2, g_Dx, g_sigma2
fitargs = (0, np.max(y), x[np.argmax(y)], 0.5, np.max(y) * 0.7, Dx, 0.5)
#print fitargs, len(p)
gaussian_fit = fit.fit1d(x,
y,
common.fit_offset_double_gauss,
*fitargs,
fixed=[5],
do_print=False,
ret=True)
if type(gaussian_fit) != dict:
print 'double gaussian fit failed'
return
print gaussian_fit['success']
# plot(x,y,name='test',clear=True)
# xp=linspace(min(x),max(x),100)
# plot(xp,gaussian_fit['fitfunc'](xp), name='test')
fits = gaussian_fit['params_dict']
if where == 'middle':
D = Dx / 2. - 0.1
elif where == 'surface':
D = 0
else:
D = Dx / 2. - 0.1
print 'Middle of membrane'
if gaussian_fit['success']:
print 'fit succeeded, going to Z=', fits['x01'] + D
mos_ins.set_z(fits['x01'] + D)
else:
mos_ins.set_z(mos_ins.get_z() + D)
# fdir = os.path.join(self.datafolder, self.FILES_DIR)
# if not os.path.isdir(fdir):
# os.makedirs(fdir)
if save_fit:
dat = h5.HDF5Data(name='optimize_z_double_gauss_fit')
print dat.filepath()
fit.write_to_hdf(gaussian_fit, dat.filepath())
dat.close()
qt.msleep(1)
return gaussian_fit
def do_long_scan(bleaching_scan=False, save_fits=False, name=''):
scan2d_ins = qt.instruments['scan2d']
save = qt.instruments['setup_controller']
mos_ins = qt.instruments['master_of_space']
GreenAOM = qt.instruments['GreenAOM']
opt1d_ins = qt.instruments['opt1d_counts']
ystarts = [20, 40, 60, 80] #np.linspace(-10,70,5)
delta_y = 20
xstarts = [-60] #np.linspace(-90,-10,5)
delta_x = 20
ystep = delta_y * 10 + 1
xstep = delta_x * 10 + 1
bleaching_time = 20
depths = np.array([0, -2, -3])
aborted = False
#testing
# ystarts=np.linspace(-20,0,2)
# xstarts=np.linspace(-100,-80,2)
# ystep=21
# xstep=21
# bleaching_time = 10
for ystart in ystarts:
print 'y start=', ystart
ystop = ystart + delta_y
for xstart in xstarts:
print 'x start=', xstart
xstop = xstart + delta_x
scan2d_ins.set_ystart(ystart)
scan2d_ins.set_xstart(xstart)
scan2d_ins.set_ystop(ystop)
scan2d_ins.set_xstop(xstop)
scan2d_ins.set_ysteps(ystep)
scan2d_ins.set_xsteps(xstep)
mos_ins.set_x(xstart)
qt.msleep(1)
mos_ins.set_y(ystart)
qt.msleep(1)
GreenAOM.set_power(400.e-6)
#if xstart == xstarts[0]:
# qt.msleep(4)
# fitres = opt1d_ins.run(dimension='z', scan_length=15, nr_of_points=101, pixel_time=100, gaussian_fit=False, return_fitresult = False)
# qt.msleep(4)
# if save_fits and type(fitres)==dict:
# dat = h5.HDF5Data(name='optimize_z_gauss_fit'+'_y_'+str(ystart))
# print dat.filepath()
# fit.write_to_hdf(fitres,dat.filepath())
# dat.close()
qt.msleep(3)
opt1d_ins.run(dimension='z',
scan_length=15,
nr_of_points=151,
pixel_time=100,
gaussian_fit=False)
qt.msleep(5)
# fitres = opt1d_ins.run(dimension='z', scan_length=5, nr_of_points=51, pixel_time=100, gaussian_fit=True, return_fitresult = True)
# qt.msleep(2)
z_surface = mos_ins.get_z()
if save_fits and type(fitres) == dict:
dat = h5.HDF5Data(name='optimize_z_gauss_fit' + '_y_' +
str(ystart) + '_x_' + str(xstart))
print dat.filepath()
fit.write_to_hdf(fitres, dat.filepath())
dat.close()
# fit_ress = go_to_membrane('surface',save_fit=True)
for depth in depths:
print 'depth under surface', depth
mos_ins.set_z(z_surface + depth)
if bleaching_scan:
save.set_keyword(name +
'_bleaching_x=%d,y=%d,z=%.1f+%.1f' %
(xstart, ystart, z_surface, depth))
scan2d_ins.set_pixel_time(bleaching_time)
GreenAOM.turn_on()
scan2d_ins.set_is_running(True)
while scan2d_ins.get_is_running():
if (msvcrt.kbhit() and (msvcrt.getch() == 'q')):
aborted = True
break
qt.msleep(1)
qt.msleep(3)
if aborted == True:
break
mos_ins.set_x(xstart)
qt.msleep(0.5)
mos_ins.set_y(ystart)
qt.msleep(0.5)
save.set_keyword(name + 'x=%d,y=%d,z=%.1f+%.1f' %
(xstart, ystart, z_surface, depth))
# fit_res=go_to_membrane('middle', save_fit = True)
# qt.msleep(2)
scan2d_ins.set_pixel_time(10)
GreenAOM.set_power(400e-6)
scan2d_ins.set_is_running(True)
while scan2d_ins.get_is_running():
if (msvcrt.kbhit() and (msvcrt.getch() == 'q')):
aborted = True
break
qt.msleep(1)
qt.msleep(3)
if aborted == True:
break
mos_ins.set_x(xstart)
qt.msleep(0.5)
mos_ins.set_y(ystart)
qt.msleep(0.5)
#set z back so that for the next scan it will be easier to find the surface
mos_ins.set_z(z_surface)
if aborted == True:
break
mos_ins.set_x(xstart)
qt.msleep(0.5)
mos_ins.set_y(ystart)
qt.msleep(0.5)
if aborted == True:
break
