def read_data_from_binary_file(ifname = 'data.bin',\
npixels = 32*185*388,\
dtype = np.int16,\
verbos = True) :
"""Test read/unpack binary file.
Binary file does not have shape, so image size in pixels and data type should be provided.
"""
print 'Read file %s' % ifname
BUF_SIZE_BYTE = npixels * 2
f = open(ifname, 'rb')
buf = f.read()
f.close()
nmax = len(buf) / BUF_SIZE_BYTE
print 'len(buf)', len(buf), 'nmax', nmax
for nevt in range(nmax):
nda = np.frombuffer(buf,
dtype=dtype,
count=npixels,
offset=nevt * BUF_SIZE_BYTE)
print_ndarr(nda, name='%4d nda' % (nevt), first=0, last=10)
#------------------------------
#------------------------------
def print_raw(args, opts, defs):
print 'Command:', ' '.join(sys.argv)
print ' opts: ', opts
print ' defs: ', defs
print ' args: ', args
def test_pfv3r3(tname):
import numpy as np
from pyimgalgos.GlobalUtils import print_ndarr
print 'test_pfv3r3: %s' % {
'1': '2-d np.array',
'2': '3-d np.array',
'3': 'list of 2-d np.array'
}[tname]
sh = None
data = None
mask = None
sh = (1000, 1000) if tname == '1' else (32, 185, 388)
mu, sigma = 200, 25
data = np.array(mu + sigma * np.random.standard_normal(sh),
dtype=np.double)
mask = np.ones(sh, dtype=np.uint16)
if tname == '3':
data = [data[i, :, :] for i in range(sh[0])]
mask = [mask[i, :, :] for i in range(sh[0])]
#print 'data object', str(data)
#print 'mask object', str(mask)
#print 'data[0].shape', data[0].shape
print_ndarr(data, 'input data')
print_ndarr(mask, 'input mask')
t0_sec = time()
peaks = peaks_adaptive(data, mask, rank=5, r0=7.0, dr=2.0, nsigm=3,\
npix_min=1, npix_max=None, amax_thr=0, atot_thr=0, son_min=8)
print 'peaks_adaptive: img.shape=%s consumed time = %.6f(sec)' % (
str(sh), time() - t0_sec)
for p in peaks:
#print dir(p)
print ' seg:%4d, row:%4d, col:%4d, npix:%4d, son:%4.1f' % (
p.seg, p.row, p.col, p.npix, p.son)
def test03():
import numpy as np
from pyimgalgos.GlobalUtils import print_ndarr
print 'test numpy.array'
af8 = np.ones((5, 5), dtype=np.float64)
print_ndarr(af8, 'input array ')
#psalgos.test_nda_f8(af8)
psalgos.test_nda_v1(af8)
print_ndarr(af8, 'output array')
ai2 = np.ones((6, 3), dtype=np.int16)
print_ndarr(ai2, 'input array ')
#psalgos.test_nda_i2(ai2)
psalgos.test_nda_v1(ai2)
print_ndarr(ai2, 'output array')
def make_data_binary_file(dsname = 'exp=xpptut15:run=54',\
dname = 'CxiDs2.0:Cspad.0',\
nevs = 1000000,\
nskip = 0,\
do_calib = False,\
ofname = 'data.bin',\
verbos = True) :
ds = psana.DataSource(dsname)
det = psana.Detector(dname)
#run = ds.runs().next()
nev_min = nskip
nev_max = nskip + nevs
f = open(ofname, 'wb')
myrun = next(ds.runs())
for nevt, evt in enumerate(myrun.events()):
if nevt % 100 == 0: print 'Event %d' % nevt
if nevt < nev_min: continue
if nevt >= nev_max: break
data = det.calib(evt) if do_calib else\
det.raw(evt)
if data is None: continue
if do_calib: data = data.astype(np.int16)
if verbos:
ent = entropy(data)
msg = '%4d: data entropy=%.3f' % (nevt, ent)
print_ndarr(data, name=msg, first=0, last=10)
#data.tofile(f)
f.write(data.tobytes())
f.close()
print '%d datasets saved file %s' % (nevt, ofname)
xtcav_OTRS_DMP1_695_TCAL_X[nevt] = Detector('OTRS:DMP1:695:TCAL_X')(evt)
xtcav_XTCAV_Amp_Des_calib_MV[nevt] = Detector('XTCAV_Amp_Des_calib_MV')(
evt)
xtcav_SIOC_SYS0_ML01_AO214[nevt] = Detector('SIOC:SYS0:ML01:AO214')(evt)
xtcav_XTCAV_Phas_Des_calib_deg[nevt] = Detector(
'XTCAV_Phas_Des_calib_deg')(evt)
xtcav_SIOC_SYS0_ML01_AO215[nevt] = Detector('XTCAV_Beam_energy_dump_GeV')(
evt)
xtcav_XTCAV_Beam_energy_dump_GeV[nevt] = Detector('REFS:DMP1:400:EDES')(
evt)
xtcav_REFS_DMP1_400_EDES[nevt] = Detector('REFS:DMP1:400:EDES')(evt)
xtcav_XTCAV_calib_disp_posToEnergy[nevt] = Detector(
'XTCAV_calib_disp_posToEnergy')(evt)
xtcav_SIOC_SYS0_ML01_AO216[nevt] = Detector('SIOC:SYS0:ML01:AO216')(evt)
raw_ds[nevt] = raw
###smldata.event(raw=raw) # waveforms=wfs,times=times)
if not (nevt < EVENTS): break
print 'End of event loop, ev: %3d' % nevt
print_ndarr(raw, 'raw')
###smldata.save()
h5out.close()
print usage()
print 'saved file %s' % OFNAME
#----------
def test01(tname='1', NUMBER_OF_EVENTS=5, DO_PRINT=False):
print 'local extrema : %s' % ('minimums' if tname=='1'\
else 'maximums' if tname=='2'\
else 'maximums runk=1 cross' if tname=='3'\
else 'two-threshold maximums')
from time import time
from pyimgalgos.GlobalUtils import print_ndarr
import pyimgalgos.GlobalGraphics as gg
#sh, fs = (200,200), (11,10)
sh, fs = (1000, 1000), (11, 10)
#sh, fs = (185,388), (11,5)
fig1, axim1, axcb1, imsh1 = gg.fig_axim_axcb_imsh(figsize=fs)
fig2, axim2, axcb2, imsh2 = gg.fig_axim_axcb_imsh(figsize=fs)
print 'Image shape: %s' % str(sh)
mu, sigma = 200, 25
for evnum in range(NUMBER_OF_EVENTS):
data = 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 == '1':
nmax = algos.local_minima_2d(data, mask, rank, extrema)
elif tname == '2':
nmax = algos.local_maxima_2d(data, mask, rank, extrema)
elif tname == '3':
nmax = algos.local_maxima_rank1_cross_2d(data, mask, extrema)
else:
nmax = algos.threshold_maxima_2d(data, mask, rank, thr_low,
thr_high, extrema)
#----------
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()
if imsh1 is not None: del imsh1
imsh1 = None
axim2.clear()
if imsh2 is not None: del imsh2
imsh2 = None
ave, rms = img1.mean(), img1.std()
amin, amax = ave - 1 * rms, ave + 5 * rms
gg.plot_imgcb(fig1,
axim1,
axcb1,
imsh1,
img1,
amin=amin,
amax=amax,
title='Event: %d, Data' % evnum,
cmap='inferno')
gg.move_fig(fig1, x0=400, y0=30)
gg.plot_imgcb(fig2,
axim2,
axcb2,
imsh2,
img2,
amin=0,
amax=5,
title='Event: %d, Local extrema' % evnum,
cmap='inferno')
gg.move_fig(fig2, x0=0, y0=30)
gg.show(mode='DO_NOT_HOLD')
gg.show()
#myrun = next(ds.runs())
#evt = next(myrun.events())
#runnum = evt.run()
#run = ds.runs().next()
#runnum = run.run()
#for key in evt.keys() : print key
##-----------------------------
det = AreaDetector(src, env, pbits=0)
print 85 * '_', '\nInstrument: %s run number: %d' % (det.instrument(), runnum)
nda_peds = det.pedestals(runnum)
print_ndarr(nda_peds, 'nda_peds')
#nda_bkgd = det.bkgd(runnum)
#smask = det.mask(runnum, calib=False, status=True, edges=True, central=True, unbond=True, unbondnbrs=True)
mask = det.mask(runnum, calib=False, status=True, edges=True).astype(np.uint16)
mask = np.ones(nda_peds.shape, dtype=np.uint16)
##-----------------------------
#mask_img = np.loadtxt('../rel-mengning/work/roi_mask_nda_equ_arc.txt')
#mask_arc.shape = mask_equ.shape = mask_img.shape = nda_peds.shape
#------------------------------
#xoffset, yoffset = 300, 300
#xsize, ysize = 1150, 1150