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
iwf = 3
std = wfs[iwf, basemin:basemax].std()
mean = wfs[iwf, basemin:basemax].mean()
wf = wfs[iwf, binmin:binmax] - mean
nbins = wf.size
THR = -5 * std
print('XXXX wf base level mean:%.3f std:%.3f THR:%.3f' % (mean, std, THR))
ones_wf = np.ones_like(wf)
zeros_wf = np.zeros_like(wf)
kwargs = {}
fig = gr.figure(figsize=(12, 10),
dpi=80,
facecolor='w',
edgecolor='w',
frameon=True,
**kwargs)
fig.canvas.set_window_title('Waveform filters', **kwargs)
fig.clear()
def axes_h(fig, naxes=4, x0=0.07, y0=0.03, width=0.87, ygap=0.04):
dy = 1. / naxes
return [
gr.add_axes(fig, axwin=(x0, y0 + i * dy, width, dy - ygap))
for i in range(naxes)
]
ax0, ax1, ax2, ax3 = axes = axes_h(fig)
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()
'X1',
'X2',
'Y1',
'Y2',
'MCP',
'XX',
'YY',
)
dy = 1. / naxes
lw = 1
w = 0.87
h = dy - 0.04
x0, y0 = 0.07, 0.03
fig = gr.figure(figsize=(15, 15), title='Image')
fig.clear()
ax = [gr.add_axes(fig, axwin=(x0, y0 + i * dy, w, h)) for i in range(naxes)]
def array_of_selected_channels(a, ch=(2, 3, 4, 5, 6)):
"""converts shape:(8, 44000) -> shape:(5, 44000)"""
return a[ch, :]
def draw_waveforms(wfs, wts, nev):
"""Draws all waveforms on figure axes, one waveform per axis.
Parameters:
- wfs [np.array] shape=(NUM_CHANNELS, NUM_SAMPLES) - waveform intensities
- wts [np.array] shape=(NUM_CHANNELS, NUM_SAMPLES) - waveform times
"""
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()
import psana.pyalgos.generic.Graphics as gr
from psana.pyalgos.generic.NDArrUtils import print_ndarr
#----------
tname = sys.argv[1] if len(sys.argv) > 1 else '1'
print('%s\nTEST %s' % (50 * '_', tname))
ifname = '/reg/g/psdm/detector/data2_test/npy/wavelets-amox27716-r0091.npy' if tname=='1' else\
'/reg/g/psdm/detector/data2_test/npy/waveforms-amox27716-r0100-e000010.npy'
wf = np.load(ifname)
print('Waveforms loaded from file %s' % ifname)
print_ndarr(wf, 'Array of waveforms')
fig = gr.figure(figsize=(15, 15), title='Waveforms')
fig.clear()
naxes = 5
dy = 1. / naxes
w, h = 0.87, (dy - 0.04)
x0, y0 = 0.07, 0.03
gfmt = (
'b-',
'r-',
'g-',
'k-',
'm-',
'y-',
'c-',
)
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()
