def __init__(self, img, **kwa):
flexbase.__init__(self, **kwa)
arr = kwa.get('arr', None)
if arr is None: arr = img #kwa['arr'] = arr = img
amin, amax = self._intensity_limits(arr, **kwa)
aspratio = float(img.shape[0]) / float(img.shape[1]) # heigh/width
kwa.setdefault('w_in', 9)
kwa.setdefault('h_in', 8)
_fig = gr_figure(**kwa)
kwfica = {}
fymin, fymax = 0.04, 0.93
self.fig, self.axim, self.axcb = gr.fig_img_cbar_axes(\
fig=_fig,\
win_axim = kwa.get('win_axim', (0.05, fymin, 0.86, fymax)),\
win_axcb = kwa.get('win_axcb', (0.915, fymin, 0.01, fymax)), **kwfica)
kwa.setdefault('amin', amin)
kwa.setdefault('amax', amax)
self.imsh, self.cbar = gr_imshow_cbar(self.fig, self.axim, self.axcb,
img, **kwa)
gr.draw_fig(self.fig)
def test01(tname):
PF = V3 if tname == '3' else V4
SHOW_PEAKS = tname != '0'
ds = data_hdf5_v0(FNAME)
#ds.print_images()
alg = peak_finder_algos(pbits=0)
if PF == V3:
alg.set_peak_selection_parameters(npix_min=0,
npix_max=1e6,
amax_thr=0,
atot_thr=0,
son_min=6)
if PF == V4:
alg.set_peak_selection_parameters(npix_min=0,
npix_max=1e6,
amax_thr=0,
atot_thr=0,
son_min=6)
img = ds.next_image()
shape = img.shape
mask = np.ones(shape, dtype=np.uint16)
INDS = np.indices((shape[0], shape[1]), dtype=np.int64)
imRow, imCol = INDS[0, :], INDS[1, :]
fig1, axim1, axcb1 = gr.fig_img_cbar_axes(gr.figure(figsize=(8, 7)))
for nev in range(min(EVTMAX, ds.nevmax)):
img = ds.next_image()
#ave, rms = img.mean(), img.std()
#amin, amax = ave-1*rms, ave+8*rms
#amin, amax = img.min(), img.max()
amin, amax = 0, img.max()
axim1.clear()
axcb1.clear()
#imsh1,cbar1=\
gr.imshow_cbar(fig1, axim1, axcb1, img, amin=amin, amax=amax, extent=None,\
interpolation='nearest', aspect='auto', origin='upper',\
orientation='vertical', cmap='inferno')
fig1.canvas.set_window_title('Event: %04d random data' % nev)
gr.move_fig(fig1, x0=400, y0=30)
if SHOW_PEAKS:
peaks = alg.peak_finder_v3r3_d2(img, mask, rank=5, r0=7, dr=2, nsigm=9) if PF == V3 else\
alg.peak_finder_v4r3_d2(img, mask, thr_low=100, thr_high=200, rank=5, r0=7, dr=2) if PF == V4 else\
None
plot_peaks_on_img(peaks, axim1, imRow, imCol, color='w', lw=1)
gr.show(mode='do not hold')
gr.show()
def plot_image(data):
fig, axim, axcb = gr.fig_img_cbar_axes(gr.figure(figsize=(10, 5), dpi=80))
ave, rms = np.mean(data), np.std(data)
_, _ = gr.imshow_cbar(fig,
axim,
axcb,
data,
amin=ave - 1 * rms,
amax=ave + 5 * rms,
extent=None,
cmap='inferno')
return axim
def test_xtcav_data_access():
tname = sys.argv[1] if len(sys.argv) > 1 else '0'
#fig, axim = fig_axis()
fig, axim, axcb = gr.fig_img_cbar_axes(fig=None,\
win_axim=(0.05, 0.05, 0.87, 0.93),\
win_axcb=(0.923, 0.05, 0.02, 0.93)) #, **kwargs)
ds = DataSource(files=data_file(tname))
orun = next(ds.runs())
det = orun.Detector('xtcav')
print('test_xtcav_data expt: %s runnum: %d\n' %
(orun.expt, orun.runnum))
for nev, evt in enumerate(orun.events()):
if nev > 10: break
print('Event %03d' % nev, end='')
nda = det.raw(evt)
print_ndarr(nda, ' det.raw(evt):')
mean, std = nda.mean(), nda.std()
aran = (mean - 3 * std, mean + 5 * std)
axim.clear()
axcb.clear()
imsh = gr.imshow(axim, nda, amp_range=aran, extent=None, interpolation='nearest',\
aspect='auto', origin='upper', orientation='horizontal', cmap='inferno')
cbar = gr.colorbar(fig,
imsh,
axcb,
orientation='vertical',
amp_range=aran)
gr.set_win_title(fig, 'Event: %d' % nev)
gr.draw_fig(fig)
gr.show(mode='non-hold')
gr.save_fig(
fig,
prefix='./img-%s-r%04d-e%06d-' % (orun.expt, orun.runnum, nev),
suffix='.png',
)
gr.show()
def __init__(self, img, **kwa):
"""
"""
flexbase.__init__(self, **kwa)
arr = kwa.setdefault('arr', img)
amin, amax = self._intensity_limits(arr, kwa)
w_in = kwa.pop('w_in', 9)
h_in = kwa.pop('h_in', 8)
aspratio = float(img.shape[0]) / float(img.shape[1]) # heigh/width
kwfig = {}
_fig=gr.plt.figure(\
num = kwa.get('num',None),\
figsize = kwa.get('figsize',(w_in, h_in)),\
dpi = kwa.get('dpi',80),\
facecolor = kwa.get('facecolor','w'),\
edgecolor = kwa.get('edgecolor','w'),\
frameon = kwa.get('frameon',True),\
clear = kwa.get('clear',False),\
**kwfig)
kwfica = {}
self.fig, self.axim, self.axcb = gr.fig_img_cbar_axes(\
fig=_fig,\
win_axim = kwa.get('win_axim', (0.05,0.03,0.87,0.94)),\
win_axcb = kwa.get('win_axcb', (0.915,0.03,0.01,0.94)), **kwfica)
kwic = {
'amin': amin,
'amax': amax,
'extent': kwa.get('extent', None),
'interpolation': kwa.get('interpolation', 'nearest'),
'aspect': kwa.get('aspect', 'equal'),
'origin': kwa.get('origin', 'upper'),
'orientation': kwa.get('orientation', 'vertical'),
'cmap': kwa.get('cmap', 'inferno'),
}
self.imsh, self.cbar = gr.imshow_cbar(self.fig, self.axim, self.axcb,
img, **kwic)
gr.draw_fig(self.fig)
def __init__(self, args):
"""
"""
#self.args = args
fname = getattr(
args, 'fname',
'/reg/g/psdm/detector/data2_test/xtc/data-amox23616-r0131-e000200-xtcav-v2.xtc2'
)
experiment = getattr(args, 'experiment', 'amox23616')
run_number = getattr(args, 'run_number', 131)
max_shots = getattr(args, 'max_shots',
401) #Maximum number of shots to process
validity_range = getattr(args, 'validity_range', None)
save_to_file = getattr(args, 'save_to_file', True)
calibration_path = getattr(args, 'calibration_path', '')
start_image = getattr(args, 'start_image', 0)
dark_reference_path = getattr(args, 'dark_reference_path',
None) #Dark reference information
num_bunches = getattr(args, 'num_bunches', 1) #Number of bunches
num_groups = getattr(args, 'num_groups',
None) #Number of profiles to average together
snr_filter = getattr(args, 'snr_filter',
10) #Number of sigmas for the noise threshold
roi_expand = getattr(args, 'roi_expand',
1) #Parameter for the roi location
roi_fraction = getattr(args, 'roi_fraction', cons.ROI_PIXEL_FRACTION)
island_split_method = getattr(
args, 'island_split_method',
cons.DEFAULT_SPLIT_METHOD) #Method for island splitting
island_split_par1 = getattr(
args, 'island_split_par1', 3.0
) #Ratio between number of pixels between largest and second largest groups when calling scipy.label
island_split_par2 = getattr(
args, 'island_split_par2', 5.
) #Ratio between number of pixels between second/third largest groups when calling scipy.label
PLOT_IMAGE = getattr(args, 'plot_image', False)
if PLOT_IMAGE:
self.fig, self.axim, self.axcb = gr.fig_img_cbar_axes(fig=None,\
win_axim=(0.05, 0.05, 0.87, 0.93),\
win_axcb=(0.923, 0.05, 0.02, 0.93)) #, **kwargs)
#if type(run_number) == int:
# run_number = str(run_number)
self.parameters = LasingOffParameters(
experiment=experiment,
max_shots=max_shots,
run_number=run_number,
start_image=start_image,
validity_range=validity_range,
dark_reference_path=dark_reference_path,
num_bunches=num_bunches,
num_groups=num_groups,
snr_filter=snr_filter,
roi_expand=roi_expand,
roi_fraction=roi_fraction,
island_split_method=island_split_method,
island_split_par2=island_split_par2,
island_split_par1=island_split_par1,
calibration_path=calibration_path,
fname=fname,
version=1)
if rank == 0:
print('Lasing off reference')
print('\t File name: %s' % self.parameters.fname)
print('\t Experiment: %s' % self.parameters.experiment)
print('\t Runs: %s' % self.parameters.run_number)
print('\t Number of bunches: %d' % self.parameters.num_bunches)
print('\t Valid shots to process: %d' % self.parameters.max_shots)
print('\t Dark reference run: %s' %
self.parameters.dark_reference_path)
#Loading the data, this way of working should be compatible with both xtc and hdf5 files
#ds = psana.DataSource("exp=%s:run=%s:idx" % (self.parameters.experiment, self.parameters.run_number))
ds = DataSource(files=fname)
run = next(ds.runs()) # run = ds.runs().next()
#env = SimulatorEnvironment() # ds.env()
#Camera for the xtcav images, Ebeam type, eventid, gas detectors
camera = run.Detector(cons.DETNAME) # psana.Detector(cons.DETNAME)
ebeam = run.Detector(
cons.EBEAM) #SimulatorEBeam() # psana.Detector(cons.EBEAM)
eventid = run.Detector(
cons.EVENTID) #SimulatorEventId() # evt.get(psana.EventId)
gasdetector = run.Detector(
cons.GAS_DETECTOR
) #SimulatorGasDetector() # psana.Detector(cons.GAS_DETECTOR)
xtcavpars = run.Detector(cons.XTCAVPARS)
# Empty list for the statistics obtained from each image, the shot to shot properties,
# and the ROI of each image (although this ROI is initially the same for each shot,
# it becomes different when the image is cropped around the trace)
list_image_profiles = []
#dark_background = self._getDarkBackground(env)
dark_data, dark_meta = xtup.get_calibconst(camera, 'xtcav_pedestals',
cons.DETNAME, experiment,
run_number)
logger.debug('==== dark_meta:\n%s' % str(dark_meta))
dark_background = xtu.xtcav_calib_object_from_dict(dark_data)
logger.debug('==== dir(dark_background):\n%s' %
str(dir(dark_background)))
logger.debug('==== dark_background.ROI:\n%s' %
str(dark_background.ROI))
logger.debug(
info_ndarr(dark_background.image, '==== dark_background.image:'))
print('\n', 100 * '_', '\n')
camraw = xtup.get_attribute(camera, 'raw')
valsxtp = xtup.get_attribute(xtcavpars, 'valsxtp')
valsebm = xtup.get_attribute(ebeam, 'valsebm')
valseid = xtup.get_attribute(eventid, 'valseid')
valsgd = xtup.get_attribute(gasdetector, 'valsgd')
if None in (camraw, valsxtp, valsebm, eventid, valsgd):
sys.error(
'FATAL ERROR IN THE DETECTOR INTERFACE: MISSING ATTRIBUTE MUST BE IMPLEMENTED'
)
#times = run.times()
#image_numbers = xtup.divideImageTasks(first_event, len(times), rank, size)
roi_xtcav, global_calibration, saturation_value = None, None, None
num_processed = 0 #Counter for the total number of xtcav images processed within the run
for nev, evt in enumerate(run.events()):
#logger.info('Event %03d'%nev)
img = camraw(evt)
if img is None: continue
if roi_xtcav is None:
# get calibration values needed to process images.
resp = self._getCalibrationValues(nev, evt, camraw, valsxtp)
if resp is None: continue
roi_xtcav, global_calibration, saturation_value = resp
#Obtain the shot to shot parameters necessary for the retrieval of the x and y axis in time and energy units
shot_to_shot = xtup.getShotToShotParameters(
evt, valsebm, valsgd, valseid)
#logger.debug('shot_to_shot: %s' % str(shot_to_shot))
if not shot_to_shot.valid: continue
image_profile, _ = xtu.processImage(img, self.parameters,
dark_background,
global_calibration,
saturation_value, roi_xtcav,
shot_to_shot)
#logger.debug(info_ndarr(image_profile, 'LasingOffReference image_profile'))
if not image_profile:
continue
#Append only image profile, omit processed image
list_image_profiles.append(image_profile)
num_processed += 1
self._printProgressStatements(num_processed)
if num_processed >= np.ceil(
self.parameters.max_shots / float(size)):
break
if PLOT_IMAGE:
nda = img
mean, std = nda.mean(), nda.std()
aran = (mean - 3 * std, mean + 5 * std)
self.axim.clear()
self.axcb.clear()
imsh = gr.imshow(self.axim, nda, amp_range=aran, extent=None, interpolation='nearest',\
aspect='auto', origin='upper', orientation='horizontal', cmap='inferno')
cbar = gr.colorbar(self.fig,
imsh,
self.axcb,
orientation='vertical',
amp_range=aran)
gr.set_win_title(self.fig, 'Event: %d' % nev)
gr.draw_fig(self.fig)
gr.show(mode='non-hold')
# here gather all shots in one core, add all lists
#image_profiles = comm.gather(list_image_profiles, root=0)
image_profiles = list_image_profiles
if rank != 0: return
sys.stdout.write('\n')
# Flatten gathered arrays
#image_profiles = [item for sublist in image_profiles for item in sublist]
#for i,ipf in enumerate(image_profiles) :
# print('XXX image_profiles %d:\n %s'%(i,str(ipf)))
#Since there are 12 cores it is possible that there are more references than needed. In that case we discard some
if len(image_profiles) > self.parameters.max_shots:
image_profiles = image_profiles[0:self.parameters.max_shots]
#At the end, all the reference profiles are converted to Physical units, grouped and averaged together
averaged_profiles = xtu.averageXTCAVProfilesGroups(
image_profiles, self.parameters.num_groups)
self.averaged_profiles, num_groups = averaged_profiles
self.n = num_processed
self.parameters = self.parameters._replace(num_groups=num_groups)
logger.debug('self.parameters.validity_range: %s type: %s' %
(self.parameters.validity_range,
type(self.parameters.validity_range)))
logger.debug(
'self.parameters.run_number: %d type: %s' %
(self.parameters.run_number, type(self.parameters.run_number)))
# Set validity range, replace 'end' -> 9999 othervise save does not work...
if not self.parameters.validity_range or not type(
self.parameters.validity_range) == tuple:
self.parameters = self.parameters._replace(
validity_range=(self.parameters.run_number,
9999)) # IT WAS 'end'))
elif len(self.parameters.validity_range) == 1:
self.parameters = self.parameters._replace(
validity_range=(self.parameters.validity_range[0],
9999)) # 'end'))
#=====================
#sys.exit('TEST EXIT')
#=====================
if save_to_file:
#cp = CalibrationPaths(env, self.parameters.calibration_path)
#file = cp.newCalFileName(cons.LOR_FILE_NAME, self.parameters.validity_range[0], self.parameters.validity_range[1])
fname = 'cons-%s-%04d-xtcav-lasingoff.data' % (run.expt, run.runnum
) # , cons.DETNAME)
self.save(fname)
def test01(tname='1', NUMBER_OF_EVENTS=5, DO_PRINT=True):
print('local extrema : %s' % ('minimums' if tname in ('1','2')\
else 'maximums' if tname in ('3','4')\
else 'maximums runk=1 cross' if tname in ('5','6')\
else 'two-threshold maximums' if tname == '7'\
else 'unknown test'))
from time import time #, sleep
from psana.pyalgos.generic.NDArrUtils import print_ndarr
import psana.pyalgos.generic.Graphics as gr
sh, fs = (50, 50), (7, 6)
fig1, axim1, axcb1 = gr.fig_img_cbar_axes(gr.figure(figsize=fs))
fig2, axim2, axcb2 = gr.fig_img_cbar_axes(gr.figure(figsize=fs))
imsh1 = None
imsh2 = None
print('Image shape: %s' % str(sh))
mu, sigma = 200, 25
for evnum in range(NUMBER_OF_EVENTS):
data = 10.*np.ones(sh, dtype=np.float64) if tname in ('2','4','6') else\
np.array(mu + sigma*np.random.standard_normal(sh), dtype=np.float64)
mask = np.ones(sh, dtype=np.uint16)
extrema = np.zeros(sh, dtype=np.uint16)
rank = 5
thr_low = mu + 3 * sigma
thr_high = mu + 4 * sigma
nmax = 0
if DO_PRINT: print_ndarr(data, ' input data')
t0_sec = time()
#----------
if tname in ('1', '2'):
nmax = algos.local_minimums(data, mask, rank, extrema)
elif tname in ('3', '4'):
nmax = algos.local_maximums(data, mask, rank, extrema)
elif tname in ('5', '6'):
nmax = algos.local_maximums_rank1_cross(data, mask, extrema)
elif tname == '7':
nmax = algos.threshold_maximums(data, mask, rank, thr_low,
thr_high, extrema)
elif tname == '8':
nmax = algos.local_maximums_rank1_cross(data, mask, extrema)
else:
contunue
#----------
print('Event: %2d, consumed time = %10.6f(sec), nmax = %d' %
(evnum, time() - t0_sec, nmax))
if DO_PRINT: print_ndarr(extrema, ' output extrema')
img1 = data
img2 = extrema
axim1.clear()
axcb1.clear()
if imsh1 is not None: del imsh1
imsh1 = None
axim2.clear()
axcb2.clear()
if imsh2 is not None: del imsh2
imsh2 = None
#ave, rms = img1.mean(), img1.std()
#amin, amax = ave-1*rms, ave+5*rms
amin, amax = img1.min(), img1.max()
#imsh1,cbar1=\
gr.imshow_cbar(fig1, axim1, axcb1, img1, amin=amin, amax=amax, extent=None,\
interpolation='nearest', aspect='auto', origin='upper',\
orientation='vertical', cmap='inferno')
fig1.canvas.set_window_title('Event: %d Random data' % evnum)
gr.move_fig(fig1, x0=560, y0=30)
#imsh2,cbar2=\
gr.imshow_cbar(fig2, axim2, axcb2, img2, amin=0, amax=img2.max(), extent=None,\
interpolation='nearest', aspect='auto', origin='upper',\
orientation='vertical', cmap='inferno')
fig2.canvas.set_window_title('Event: %d Local extrema' % evnum)
gr.move_fig(fig2, x0=0, y0=30)
gr.show(mode='DO_NOT_HOLD')
gr.show()
def test01(tname='1', NUMBER_OF_EVENTS=3, DO_PRINT=False):
print('local extrema : %s' % ('minimums' if tname in ('1','2')\
else 'maximums' if tname in ('3','4')\
else 'maximums runk=1 cross' if tname in ('5','6')\
else 'two-threshold maximums' if tname == '7'\
else 'unknown test'))
from time import time
from psana.pyalgos.generic.NDArrUtils import print_ndarr
import psana.pyalgos.generic.Graphics as gg
#sh, fs = (185,388), (11,5)
sh, fs = (50, 50), (7, 7)
fig1, axim1, axcb1 = gg.fig_img_cbar_axes(gg.figure(figsize=fs),
(0.05, 0.05, 0.87, 0.90),
(0.923, 0.05, 0.02, 0.90))
fig2, axim2, axcb2 = gg.fig_img_cbar_axes(gg.figure(figsize=fs),
(0.05, 0.05, 0.87, 0.90),
(0.923, 0.05, 0.02, 0.90))
print('Image shape: %s' % str(sh))
mu, sigma = 200, 25
for evnum in range(NUMBER_OF_EVENTS):
data = 10.*np.ones(sh, dtype=np.float64) if tname in ('2','4','6') else\
np.array(mu + sigma*np.random.standard_normal(sh), dtype=np.float64)
mask = np.ones(sh, dtype=np.uint16)
extrema = np.zeros(sh, dtype=np.uint16)
rank = 5
thr_low = mu + 3 * sigma
thr_high = mu + 4 * sigma
nmax = 0
if DO_PRINT: print_ndarr(data, 'input data')
t0_sec = time()
#----------
if tname in ('1', '2'):
nmax = algos.local_minimums(data, mask, rank, extrema)
elif tname in ('3', '4'):
nmax = algos.local_maximums(data, mask, rank, extrema)
elif tname in ('5', '6'):
nmax = algos.local_maximums_rank1_cross(data, mask, extrema)
elif tname == '7':
nmax = algos.threshold_maximums(data, mask, rank, thr_low,
thr_high, extrema)
else:
contunue
#----------
print('Event: %4d, consumed time = %10.6f(sec), nmax = %d' %
(evnum, time() - t0_sec, nmax))
if DO_PRINT: print_ndarr(extrema, 'output extrema')
img1 = data
img2 = extrema
axim1.clear()
axim2.clear()
ave, rms = img1.mean(), img1.std()
amin, amax = ave - 1 * rms, ave + 5 * rms
gg.imshow_cbar(fig1,
axim1,
axcb1,
img1,
amin=amin,
amax=amax,
cmap='inferno')
axim1.set_title('Event: %d, Data' % evnum, color='k', fontsize=20)
gg.move_fig(fig1, x0=550, y0=30)
gg.imshow_cbar(fig2,
axim2,
axcb2,
img2,
amin=0,
amax=5,
cmap='inferno')
axim2.set_title('Event: %d, Local extrema' % evnum,
color='k',
fontsize=20)
gg.move_fig(fig2, x0=0, y0=30)
gg.show(mode='DO_NOT_HOLD')
gg.show()
def test_pf(tname):
##-----------------------------
PF = V4 # default
if tname == '1': PF = V1
if tname == '2': PF = V2
if tname == '3': PF = V3
if tname == '4': PF = V4
SKIP = 0
EVTMAX = 10 + SKIP
DO_PLOT_IMAGE = True
DO_PLOT_PIXEL_STATUS = False #True if PF in (V2,V4) else False
DO_PLOT_CONNECED_PIXELS = False #True if PF in (V2,V3,V4) else False
DO_PLOT_LOCAL_MAXIMUMS = False #True if PF == V3 else False
DO_PLOT_LOCAL_MINIMUMS = False #True if PF == V3 else False
shape = (1000, 1000)
mask = np.ones(shape, dtype=np.uint16)
# Pixel image indexes
#arr3d = np.array((1,shape[0],shape[1]))
INDS = np.indices((1, shape[0], shape[1]), dtype=np.int64)
imRow, imCol = INDS[1, :], INDS[2, :]
#iX = np.array(det.indexes_x(evt), dtype=np.int64) #- xoffset
#iY = np.array(det.indexes_y(evt), dtype=np.int64) #- yoffset
##-----------------------------
fs = (8, 7) # (11,10)
fig1, axim1, axcb1 = gr.fig_img_cbar_axes(gr.figure(
figsize=fs)) if DO_PLOT_IMAGE else (None, None, None)
fig2, axim2, axcb2 = gr.fig_img_cbar_axes(gr.figure(
figsize=fs)) if DO_PLOT_PIXEL_STATUS else (None, None, None)
fig3, axim3, axcb3 = gr.fig_img_cbar_axes(gr.figure(
figsize=fs)) if DO_PLOT_CONNECED_PIXELS else (None, None, None)
fig4, axim4, axcb4 = gr.fig_img_cbar_axes(gr.figure(
figsize=fs)) if DO_PLOT_LOCAL_MAXIMUMS else (None, None, None)
fig5, axim5, axcb5 = gr.fig_img_cbar_axes(gr.figure(
figsize=fs)) if DO_PLOT_LOCAL_MINIMUMS else (None, None, None)
imsh1 = None
imsh2 = None
imsh3 = None
imsh4 = None
imsh5 = None
##-----------------------------
alg = peak_finder_algos(pbits=0)
if PF == V1:
alg.set_peak_selection_parameters(npix_min=0,
npix_max=1e6,
amax_thr=0,
atot_thr=0,
son_min=6)
elif PF == V2:
alg.set_peak_selection_parameters(npix_min=0,
npix_max=1e6,
amax_thr=0,
atot_thr=0,
son_min=6)
elif PF == V3:
alg.set_peak_selection_parameters(npix_min=0,
npix_max=1e6,
amax_thr=0,
atot_thr=0,
son_min=8)
elif PF == V4:
alg.set_peak_selection_parameters(npix_min=0,
npix_max=1e6,
amax_thr=0,
atot_thr=0,
son_min=6)
#alg.print_attributes()
for ev in range(EVTMAX):
ev1 = ev + 1
if ev < SKIP: continue
#if ev>=EVTMAX : break
print(50 * '_', '\nEvent %04d' % ev1)
img, peaks_sim = image_with_random_peaks(shape)
# --- for debugging
#np.save('xxx-image', img)
#np.save('xxx-peaks', np.array(peaks_sim))
#img = np.load('xxx-image-crash.npy')
#peaks_sim = np.load('xxx-peaks-crash.npy')
# ---
peaks_gen = [(0, r, c, a, a * s, 9 * s * s)
for r, c, a, s in peaks_sim]
t0_sec = time()
peaks = alg.peak_finder_v3r3_d2(img, mask, rank=5, r0=7, dr=2, nsigm=3) if PF == V3 else\
alg.peak_finder_v4r3_d2(img, mask, thr_low=20, thr_high=40, rank=6, r0=7, dr=2) if PF == V4 else\
None
#alg.peak_finder_v3r3_d2(img, rank=5, r0=7, dr=2, nsigm=3) if PF == V3 else\
#alg.peak_finder_v4r3_d2(img, thr_low=20, thr_high=40, rank=6, r0=7, dr=2) if PF == V3 else\
print(' Time consumed by the peak_finder = %10.6f(sec)' %
(time() - t0_sec))
map2 = reshape_to_2d(alg.maps_of_pixel_status(
)) if DO_PLOT_PIXEL_STATUS else None # np.zeros((10,10))
map3 = reshape_to_2d(alg.maps_of_connected_pixels(
)) if DO_PLOT_CONNECED_PIXELS else None # np.zeros((10,10))
map4 = reshape_to_2d(alg.maps_of_local_maximums(
)) if DO_PLOT_LOCAL_MAXIMUMS else None # np.zeros((10,10))
map5 = reshape_to_2d(alg.maps_of_local_minimums(
)) if DO_PLOT_LOCAL_MINIMUMS else None # np.zeros((10,10))
print('arrays are extracted')
#print_arr(map2, 'map_of_pixel_status')
#print_arr(map3, 'map_of_connected_pixels')
#maps.shape = shape
print('Simulated peaks:')
for i, (r0, c0, a0, sigma) in enumerate(peaks_sim):
print(' %04d row=%6.1f col=%6.1f amp=%6.1f sigma=%6.3f' %
(i, r0, c0, a0, sigma))
#plot_image(img)
print('Found peaks:')
print(hdr)
reg = 'IMG'
#for pk in peaks :
# seg,row,col,npix,amax,atot,rcent,ccent,rsigma,csigma,\
# rmin,rmax,cmin,cmax,bkgd,rms,son = pk[0:17]
# rec = fmt % (ev, reg, seg, row, col, npix, amax, atot, rcent, ccent, rsigma, csigma,\
# rmin, rmax, cmin, cmax, bkgd, rms, son) #,\
# #imrow, imcol, xum, yum, rum, phi)
# print(rec)
for p in peaks:
#print(' algos:', p.parameters())
print(' row:%4d, col:%4d, npix:%4d, son:%4.1f amp_tot:%4.1f' %
(p.row, p.col, p.npix, p.son, p.amp_tot))
if DO_PLOT_PIXEL_STATUS:
gr.plot_imgcb(fig2,
axim2,
axcb2,
imsh2,
map2,
amin=0,
amax=30,
title='Pixel status, ev: %04d' % ev1)
gr.move_fig(fig2, x0=0, y0=30)
if DO_PLOT_CONNECED_PIXELS:
cmin, cmax = (map3.min(),
map3.max()) if map3 is not None else (None, None)
print('Connected pixel groups min/max:', cmin, cmax)
gr.plot_imgcb(fig3,
axim3,
axcb3,
imsh3,
map3,
amin=cmin,
amax=cmax,
title='Connected pixel groups, ev: %04d' % ev1)
gr.move_fig(fig3, x0=100, y0=30)
if DO_PLOT_LOCAL_MAXIMUMS:
gr.plot_imgcb(fig4,
axim4,
axcb4,
imsh4,
map4,
amin=0,
amax=10,
title='Local maximums, ev: %04d' % ev1)
gr.move_fig(fig4, x0=200, y0=30)
if DO_PLOT_LOCAL_MINIMUMS:
gr.plot_imgcb(fig5,
axim5,
axcb5,
imsh5,
map5,
amin=0,
amax=10,
title='Local minimums, ev: %04d' % ev1)
gr.move_fig(fig5, x0=300, y0=30)
if DO_PLOT_IMAGE:
#nda = maps_of_conpix_arc
#nda = maps_of_conpix_equ
#img = det.image(evt, nda)[xoffset:xoffset+xsize,yoffset:yoffset+ysize]
#img = det.image(evt, mask_img*nda)[xoffset:xoffset+xsize,yoffset:yoffset+ysize]
#img = det.image(evt, maps_of_conpix_equ)[xoffset:xoffset+xsize,yoffset:yoffset+ysize]
ave, rms = img.mean(), img.std()
amin, amax = ave - 1 * rms, ave + 8 * rms
axim1.clear()
if imsh1 is not None: del imsh1
imsh1 = None
gr.imshow_cbar(fig1, axim1, axcb1, img, amin=amin, amax=amax, extent=None,\
interpolation='nearest', aspect='auto', origin='upper',\
orientation='vertical', cmap='inferno')
#gr.plot_imgcb(fig1, axim1, axcb1, imsh1, img, amin=amin, amax=amax, title='Image, ev: %04d' % ev1)
fig1.canvas.set_window_title('Event: %04d random data' % ev1)
gr.move_fig(fig1, x0=400, y0=30)
#plot_peaks_on_img(peaks_gen, axim1, imRow, imCol, color='g', lw=5)
plot_peaks_on_img(peaks, axim1, imRow, imCol, color='w', lw=1)
fig1.canvas.draw() # re-draw figure content
#gr.plotHistogram(nda, amp_range=(-100,100), bins=200, title='Event %d' % i)
gr.show(mode='do not hold')
gr.show()
def test01(tname='1', NUMBER_OF_EVENTS=10, DO_PRINT=False):
print('local extrema : %s' % ('minimums' if tname in ('1','2')\
else 'maximums'))
#sh, fs = (200,200), (11,10)
sh, fs = (50, 50), (7, 6)
#sh, fs = (185,388), (11,5)
fig1, axim1, axcb1 = gr.fig_img_cbar_axes(gr.figure(figsize=fs))
fig2, axim2, axcb2 = gr.fig_img_cbar_axes(gr.figure(figsize=fs))
imsh1 = None
imsh2 = None
print('Image shape: %s' % str(sh))
mu, sigma = 200, 25
for evnum in range(NUMBER_OF_EVENTS):
data = 10*np.ones(sh, dtype=np.float64) if tname in ('2','4') else\
np.array(mu + sigma*np.random.standard_normal(sh), dtype=np.float64)
mask = np.ones(sh, dtype=np.uint16).flatten()
#mask = np.random.binomial(2, 0.80, data.size).astype(dtype=np.uint16)
extrema = np.zeros(sh, dtype=np.uint16).flatten()
rank = 5
nmax = 0
if DO_PRINT: print_ndarr(data, 'input data')
t0_sec = time()
#----------
if tname in ('1', '2'):
nmax = algos.local_minima_1d(data.flatten(), mask, rank, extrema)
elif tname in ('3', '4'):
nmax = algos.local_maxima_1d(data.flatten(), mask, rank, extrema)
#----------
print('Event: %4d, consumed time = %10.6f(sec), nmax = %d' %
(evnum, time() - t0_sec, nmax))
extrema.shape = sh
if DO_PRINT: print_ndarr(extrema, 'output extrema')
img1 = data
img2 = extrema
axim1.clear()
axcb1.clear()
if imsh1 is not None: del imsh1
imsh1 = None
axim2.clear()
axcb2.clear()
if imsh2 is not None: del imsh2
imsh2 = None
ave, rms = img1.mean(), img1.std()
amin, amax = ave - 1 * rms, ave + 5 * rms
#imsh1,cbar1=\
gr.imshow_cbar(fig1, axim1, axcb1, img1, amin=amin, amax=amax, extent=None,\
interpolation='nearest', aspect='auto', origin='upper',\
orientation='vertical', cmap='inferno')
fig1.canvas.set_window_title('Event: %d Random data' % evnum)
gr.move_fig(fig1, x0=560, y0=30)
#imsh2,cbar2=\
gr.imshow_cbar(fig2, axim2, axcb2, img2, amin=0, amax=5, extent=None,\
interpolation='nearest', aspect='auto', origin='upper',\
orientation='vertical', cmap='inferno')
fig2.canvas.set_window_title(
'Event: %d Local extrema (1d-folded in image)' % evnum)
gr.move_fig(fig2, x0=0, y0=30)
gr.show(mode='DO_NOT_HOLD')
gr.show()
