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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from pylab import *
from PyMca import EdfFile
import glob
import string
from . import string_six
import sys
from numpy import fft
import os
import pickle
import numpy
from .congrid import congrid
try:
import pycuda
import pycuda.autoinit
import pycuda.elementwise
import pycuda.gpuarray as gpuarray
from pycuda.compiler import SourceModule
import scikits.cuda.fft as cu_fft
usecuda=1
modShifta = SourceModule("""
#include <cuComplex.h>
__global__ void shifta(cuDoubleComplex *a, double K0aV, int Ny, int Nx)
{
int gidx = threadIdx.x + blockIdx.x*blockDim.x;
int gidy = threadIdx.y + blockIdx.y*blockDim.y;
int gid = gidy*Nx+gidx;
cuDoubleComplex c;
if(gidx<Nx && gidy<Ny) {
c = a[gid] ;
a[gid] = cuCmul(c, make_cuDoubleComplex(cos(K0aV*gidx), sin(K0aV*gid)));
}
}
__global__ void propa(cuDoubleComplex *a, double K0, double Vox, double p, int Ny, int Nx) {
int gidx = threadIdx.x + blockIdx.x*blockDim.x;
int gidy = threadIdx.y + blockIdx.y*blockDim.y;
int gid = gidy*Nx+gidx;
cuDoubleComplex c;
double kperp, kpar, phase;
int Nx2 = Nx/2;
if(gidx<Nx && gidy<Ny) {
kperp = 2*M_PI/Vox/Nx * ( (gidx+Nx2 )%Nx -Nx2) ;
kpar = sqrt( K0*K0 - kperp*kperp ) ;
phase = kpar * p;
c = a[gid] ;
// c = cuCmul(c, make_cuDoubleComplex( exp( - kperp*kperp*Vox*Vox*1.4/8 ) , 0));
a[gid] = cuCmul(c, make_cuDoubleComplex(cos(phase), sin(phase)));
}
}
__global__ void intensity(cuDoubleComplex *a, double * inte, double w, int Ny, int Nx) {
int gidx = threadIdx.x + blockIdx.x*blockDim.x;
int gidy = threadIdx.y + blockIdx.y*blockDim.y;
int gid = gidy*Nx+gidx;
cuDoubleComplex c;
if(gidx<Nx && gidy<Ny) {
c = a[gid] ;
inte[gid] += w*( cuCreal(c)*cuCreal(c) + cuCimag(c)*cuCimag(c) ) ;
}
}
__global__ void blur(cuDoubleComplex *a, double sigma, int Ny, int Nx) {
int gidx = threadIdx.x + blockIdx.x*blockDim.x;
int gidy = threadIdx.y + blockIdx.y*blockDim.y;
int gid = gidy*Nx+gidx;
cuDoubleComplex c;
double kperp;
int Nx2 = Nx/2;
if(gidx<Nx && gidy<Ny) {
kperp = 2*M_PI/Nx * ( (gidx+Nx2 )%Nx -Nx2) ;
c = a[gid] ;
c = cuCmul(c, make_cuDoubleComplex(exp( - kperp*kperp*sigma*sigma/2.0 ) , 0));
a[gid] = c;
}
}
""")
except:
usecuda=0
try:
import pyfftw
usepyfftw = 1
except:
usepyfftw=0
import fftw3
##############################################################################################
# HOW MANY GPUS?
##
try:
args=["oarprint", "host", "-P", "host,gpu_num", "-F", "'% %'" ]
p = sub.Popen(args=args ,stdout=sub.PIPE,stderr=sub.PIPE)
resources, errors = p.communicate()
except:
resources=""
print( " resources gpu ", resources)
if(len(resources)):
## example
# gpu0102 1,0
# gpu0101 1
resources_L = resources.split( "\n" )
gpus_string=" "
for l in resources_L:
if len(l)==0:
continue
if l[0]==l[-1] and l[0] in ["'", '"']:
l=l[1:-1]
l=l.strip()
node, gpus = l.split( " ")
gpus_s=gpus.split( ",")
if(len(gpus_s)>1):
print( " WARNING : you have been allocated ", len(gpus_s)," gpus ")
print( " but you will use ony one cpu! ")
gpus_string = gpus
gpu=string.atoi(gpus_string)
import pycuda.autoinit
import pycuda.tools
if gpu!=-1:
dev=pycuda.driver.Device(gpu)
dev.make_context()
else:
pycuda.tools.make_default_context()
# else:
# pass
# # la linea gpu-string sara presa dalla linea di comando
# gpus_string=""
# if len(sys.argv)==3:
# gpus_string=" "+sys.argv[2]
# del sys.argv[2]
print( "Ma carte est ", pycuda.driver.Context.get_device().name())
parinput = open(sys.argv[1],"r")
for line in parinput:
if "NPROJ" in line:
exec(line)
if "PROJ_OUTPUT_FILE" in line:
items = line.split()
path = items[2]
numberpos = path.find("%")
postfixpos = path.rfind(".")
path = path[:numberpos]+"*"+path[postfixpos:]
if "PROPA_OUTPUT_FILE" in line:
items = line.split()
PROPAPROJ_OUTPUT_FILE = items[2]
if "dbratio" in line or "plength" in line or "OVERSAMPLE" in line or "SIGMAPIXEL" in line or "IMAGE_PIXEL_SIZE_1" in line or "SSOURCE_RAD" in line or "SENE" in line or "NENE" in line :
exec(line)
if "ekev" in line :
exec(line)
print( "\n* THE INPUT FILE SAYS YOU HAVE GENERATED ", NPROJ, " PROJECTION ")
print( " AND PROJECTIONS ARE SEARCHED AS PATTERN ", path)
fl=glob.glob(path)
fl.sort()
fl=fl[ :NPROJ]
print( "\n\n* FIRST PROJECTION ", fl[0])
print( " LAST ", fl[-1])
print( "\n")
data = EdfFile.EdfFile(fl[0]).GetData(0)
sino = zeros([NPROJ, data.shape[1]],"d")
voxelsize = IMAGE_PIXEL_SIZE_1*1.0
ekev = ekev*1.0
dbratio=dbratio*1.0
print( "\n* THE INPUT FILE SAYS YOU VOXEL IS ", voxelsize, " MICRON ")
print( " DELTA/BETA ", dbratio)
print( " PROPAGATION ", plength , " METERS ")
print( " ENERGY ", ekev , " KeV ")
print( "\n")
print( "* CONCERNING COHERENCE:")
print( " SSOURCE_RAD ", SSOURCE_RAD , " Radians ")
print( " SENE ", SENE , " Kev ")
print( " NENE ", NENE , " Points ")
print( " SIGMAPIXEL ", SIGMAPIXEL)
print( " OVERSAMPLE ", OVERSAMPLE)
## OVERSAMPLE = 10
voxelsize_A = voxelsize *10000.0
plength_A = plength *1.0e10
print( " leggo .... " )
for i,nome in enumerate(fl):
sino[i] = EdfFile.EdfFile(nome).GetData(0)
print( " congrid " )
voxelsize_A = voxelsize_A/OVERSAMPLE
sinonew = zeros([sino.shape[0], data.shape[1]*OVERSAMPLE],"d")
for i in range(0,sino.shape[0],2):
print( i)
sinonew[i:i+2,:] = congrid(sino[i:i+2,:],[1,sino.shape[1]*OVERSAMPLE ] , minusone=False, method="linear" )
print( " OK " )
sinonew[:,-1] = sino[:,-1]
sinoold=sino
NSTEP=sino.shape[0]/OVERSAMPLE
for NSTART in range(0,sinonew.shape[0], NSTEP):
sino=sinonew[NSTART:min(NSTART+NSTEP,sinonew.shape[0])]
sigma_pixel = SSOURCE_RAD*plength/voxelsize_A*1.0e6
sigma_pixel = numpy.sqrt( sigma_pixel*sigma_pixel+SIGMAPIXEL*SIGMAPIXEL )
if usecuda:
aux_a = numpy.zeros([ sino.shape[0], sino.shape[1]*2 ], dtype= 'D')
aux_b = numpy.zeros([ sino.shape[0], sino.shape[1]*2 ], dtype= 'D')
arrayin = gpuarray.empty([ sino.shape[0], sino.shape[1]*2 ], numpy.complex128)
print( sino.shape[0]*sino.shape[1]*2)
print( sino.shape[0],sino.shape[1]*2)
arrayout = gpuarray.empty([ sino.shape[0], sino.shape[1]*2 ], numpy.complex128)
array_signal = gpuarray.empty([ sino.shape[0], sino.shape[1]*2 ], numpy.complex128)
array_convoluted = gpuarray.zeros([ sino.shape[0], sino.shape[1]*2 ], numpy.float64)
data=numpy.zeros([sino.shape[0], sino.shape[1]*2 ],numpy.complex128)
fplan =cu_fft.Plan([sino.shape[1]*2 ],numpy.complex128, numpy.complex128 , batch=sino.shape[0])
# fplan_float =cu_fft.Plan([sino.shape[1]*2 ],numpy.float64, numpy.float64 , batch=sino.shape[0])
fact4norm = 1.0/aux_a.shape[1]
# if usecuda:
aux_a[:]=0
aux_a[:,:sino.shape[1]].real= sino
aux_a[:,:].imag= 0
arrayin.set(aux_a)
cu_fft.fft( arrayin , arrayout, fplan)
Ny,Nx = aux_a.shape[0], aux_a.shape[1]
modShifta.get_function("blur")( arrayout , numpy.float64(OVERSAMPLE/3.0) ,
numpy.int32(Ny),numpy.int32(Nx) , block = (32, 8, 1), grid=( Nx/32+1,Ny/8+1) )
cu_fft.ifft(arrayout, arrayin , fplan)
sino = arrayin.get().real[:,:sino.shape[1]]* fact4norm
# else:
# fft_object_a_float()
# convoluted_fft *= blurring
# fft_object_b_float()
# ressino=convoluted[:,:sino.shape[1]] * fact4norm
elif not usepyfftw:
try:
if os.path.exists("fftw3_wisdom"):
fftw3.import_wisdom_from_file("fftw3_wisdom")
except:
pass
aux_a = fftw3.create_aligned_array([ sino.shape[0], sino.shape[1]*2 ], dtype= 'D')
aux_b = fftw3.create_aligned_array([ sino.shape[0], sino.shape[1]*2 ], dtype= 'D')
convoluted = fftw3.create_aligned_array(aux_a.shape,"d")
convoluted_fft = fftw3.create_aligned_array(aux_a.shape,"d")
convoluted[:]=0.0
fft_object_a = fftw3.Plan(aux_a,aux_b, direction="forward", flags=('measure', ) )
fft_object_b = fftw3.Plan(aux_b,aux_a, direction="backward", flags=('measure', ) )
fft_object_a_float = fftw3.Plan(convoluted , convoluted_fft, direction="forward", flags=('measure', ) )
fft_object_b_float = fftw3.Plan(convoluted_fft , convoluted , direction="backward", flags=('measure', ) )
fftw3.export_wisdom_to_file("fftw3_wisdom")
fact4norm = 1.0/ aux_a.shape[1]/aux_a.shape[0]
else:
try:
if os.path.exists("pyfftw_wisdom"):
wisdom = pickle.load(open("pyfftw_wisdom","r") )
pyfftw.import_wisdom(wisdom)
except:
pass
aux_a = pyfftw.n_byte_align_empty([ sino.shape[0], sino.shape[1]*2 ],16,'complex128')
aux_b = pyfftw.n_byte_align_empty([ sino.shape[0], sino.shape[1]*2 ],16,'complex128')
convoluted = pyfftw.n_byte_align_empty(aux_a.shape,"d")
convoluted_fft = pyfftw.n_byte_align_empty(aux_a.shape,"d")
convoluted[:]=0.0
fft_object_a = pyfftw.FFTW(aux_a,aux_b, direction="FFTW_FORWARD", flags=('FFTW_MEASURE', ),axes=(-1,),threads=4 ) #
fft_object_b = pyfftw.FFTW(aux_b,aux_a, direction="FFTW_BACKWARD", flags=('FFTW_MEASURE', ),axes=(-1,),threads=4 ) #
fft_object_a_float = pyfftw.FFTW(convoluted , convoluted_fft, direction="FFTW_FORWARD", flags=('FFTW_MEASURE', ),axes=(-1,),threads=4 ) #
fft_object_b_float = pyfftw.FFTW(convoluted_fft , convoluted , direction="FFTW_BACKWARD", flags=('FFTW_MEASURE', ),axes=(-1,),threads=4 ) #
wisdom = pyfftw.export_wisdom()
pickle.dump(wisdom, open("pyfftw_wisdom","w") )
fact4norm = 1.0
absorption = sino/2.0
phase = dbratio * absorption
complex_signal = exp( -absorption - phase*(0+1.0j) )
if NENE>1:
enes = numpy.linspace(-1*SENE, 1*SENE, num=NENE, endpoint=True, retstep=False)
enew = numpy.exp( -enes*enes/SENE/SENE/2.0 )
enew = enew/ enew.sum()
else:
enes=[0.0]
enew=[1.0]
print( aux_a.shape)
freqs = fft.fftfreq(aux_a.shape[1], voxelsize_A)*2*pi
freqs_pixel = fft.fftfreq(aux_a.shape[1], voxelsize_A)*2*pi
blurring = exp( -freqs_pixel*freqs_pixel*sigma_pixel*sigma_pixel/2.0 )
if usecuda:
aux_a[:,:sino.shape[1]]=complex_signal
aux_a[:,sino.shape[1]:]= 1.0+0.0j
array_signal.set(aux_a)
kperp = fft.fftfreq(aux_a.shape[1], voxelsize_A)*2*pi
for ed, ew in zip( enes, enew):
K0 = 2*pi*(ekev+ed)/12.39842
if 1:
print( " ed, ew " , ed, ew)
if usecuda:
Ny,Nx = aux_a.shape[0], aux_a.shape[1]
pycuda.driver.memcpy_dtod( arrayin.gpudata, array_signal.gpudata, array_signal.nbytes)
cu_fft.fft(arrayin, arrayout, fplan)
modShifta.get_function("propa")( arrayout, numpy.float64(K0) , numpy.float64(voxelsize_A)
, numpy.float64(plength_A),
numpy.int32(Ny),numpy.int32(Nx) , block = (32, 8, 1), grid=( Nx/32+1,Ny/8+1) )
else:
aux_a[:,:sino.shape[1]]=complex_signal
aux_a[:,sino.shape[1]:]= 1.0+0.0j
fft_object_a()
pp = sqrt(K0*K0 - kperp*kperp)
pp_forw = exp( 1.0j * pp * plength_A)
pp_back = exp( -1.0j * pp * plength_A)
aux_b[:] = aux_b * pp_forw
if usecuda:
cu_fft.ifft(arrayout, arrayin, fplan)
modShifta.get_function("intensity")( arrayin, array_convoluted, numpy.float64(ew* fact4norm*fact4norm),
numpy.int32(Ny),numpy.int32(Nx) , block = (32, 8, 1), grid=( Nx/32+1,Ny/8+1) )
else:
fft_object_b()
result = array( aux_a[:,:] * fact4norm )
ressino = abs(result)
ressino=ressino*ressino
convoluted+=ew* ressino
if usecuda:
aux_a[:,:]= array_convoluted.get()
arrayin.set(aux_a)
cu_fft.fft( arrayin , arrayout, fplan)
modShifta.get_function("blur")( arrayout , numpy.float64(sigma_pixel) ,
numpy.int32(Ny),numpy.int32(Nx) , block = (32, 8, 1), grid=( Nx/32+1,Ny/8+1) )
cu_fft.ifft(arrayout, arrayin , fplan)
ressino = arrayin.get().real[:,:sino.shape[1]]* fact4norm
ressino.shape = [ressino.shape[0], ressino.shape[1]/OVERSAMPLE, OVERSAMPLE]
ressino=ressino.sum(axis=-1)/OVERSAMPLE
else:
fft_object_a_float()
convoluted_fft *= blurring
fft_object_b_float()
ressino=convoluted[:,:sino.shape[1]] * fact4norm
if( NSTART==0) :
fig = plt.figure()
ax = fig.add_subplot(111)
ax.imshow(sino, cmap=cm.jet, interpolation='nearest')
numrows, numcols = sino.shape
def format_coord(x, y):
col = int(x+0.5)
row = int(y+0.5)
if col>=0 and col<numcols and row>=0 and row<numrows:
z = sino[row,col]
return 'x=%1.4f, y=%1.4f, z=%1.4f'%(x, y, z)
else:
return 'x=%1.4f, y=%1.4f'%(x, y)
ax.format_coord = format_coord
fig2 = plt.figure()
ax = fig2.add_subplot(111)
ax.imshow(ressino, cmap=cm.jet, interpolation='nearest')
numrows, numcols = ressino.shape
def format_coord(x, y):
col = int(x+0.5)
row = int(y+0.5)
if col>=0 and col<numcols and row>=0 and row<numrows:
z = ressino[row,col]
return 'x=%1.4f, y=%1.4f, z=%1.4f'%(x, y, z)
else:
return 'x=%1.4f, y=%1.4f'%(x, y)
ax.format_coord = format_coord
plt.show()
print( "PROJECTIONS FROM " , NSTART, " TO " , NSTART+ressino.shape[0])
for i, line in enumerate(ressino):
line=array(line,"f")
edf=EdfFile.EdfFile(PROPAPROJ_OUTPUT_FILE%(i+NSTART),"w+")
edf.WriteImage({},line)
Datas = array(ressino)
# ## INIZIO
# absorption = -log(ressino)/2.0
# phase = dbratio * absorption
# complex_signal = exp( -absorption - phase*(0+1.0j) )
# # complex_signal = result
# # for i in range(12):
# #
# # WAVE SYNTHESIS AT THE DETECTOR
# aux_a[:,:sino.shape[1]]=complex_signal
# bordervalue = (complex_signal[:,0]+complex_signal[:,-1])/2.0
# aux_a[:,sino.shape[1]:]= bordervalue[:, newaxis]
# ## BACKWARD
# fft_object_a()
# aux_b[:] = aux_b * pp_back
# fft_object_b()
# result = array( aux_a[:,:sino.shape[1]] * fact4norm )
# ## SIMULATED SIGNAL AT THE SAMPLE
# ressino = abs(result)
# ressino=ressino*ressino
# absorption = -log(ressino)/2.0
# # A PRIORI PHASE
# phase = dbratio * absorption
# complex_signal = exp( -absorption - phase*(0+1.0j) )
# print( complex_signal[0,0])
# print( complex_signal[0,-1])
# # WAVE SYNTHESIS AT THE SAMPLE
# aux_a[:,:sino.shape[1]]=complex_signal
# bordervalue = (complex_signal[:,0]+complex_signal[:,-1])/2.0
# aux_a[:,sino.shape[1]:]= bordervalue[:, newaxis]
# ## FORWARD
# fft_object_a()
# aux_b[:] = aux_b * pp_forw
# fft_object_b()
# result = array( aux_a[:,:sino.shape[1]] * fact4norm )
# ## SIMULATED SIGNAL AT THE DETECTOR
# simphase = log( result ).imag # simulated phase
# complex_signal = exp( + simphase*(0+1.0j) ) * sqrt(Datas)
# fig3 = plt.figure()
# ax = fig3.add_subplot(111)
# ax.imshow(ressino, cmap=cm.jet, interpolation='nearest')
# numrows, numcols = ressino.shape
# def format_coord(x, y):
# col = int(x+0.5)
# row = int(y+0.5)
# if col>=0 and col<numcols and row>=0 and row<numrows:
# z = ressino[row,col]
# return 'x=%1.4f, y=%1.4f, z=%1.4f'%(x, y, z)
# else:
# return 'x=%1.4f, y=%1.4f'%(x, y)
# ax.format_coord = format_coord
# plt.show()
|
