def load_reference_intensites(ref_file):
t_intens = (read_results.extract_value_from_h5(ref_file, "/data/data")).astype("float")
intens_len = len(t_intens)
qmax = intens_len/2
(q_low, q_high) = (15, int(0.9*qmax))
qRange1 = numpy.arange(-q_high, q_high + 1)
qRange2 = numpy.arange(-qmax, qmax + 1)
qPos0 = numpy.array([[i,j,0] for i in qRange1 for j in qRange1 if numpy.sqrt(i*i+j*j) > q_low]).astype("float")
qPos1 = numpy.array([[i,0,j] for i in qRange1 for j in qRange1 if numpy.sqrt(i*i+j*j) > q_low]).astype("float")
qPos2 = numpy.array([[0,i,j] for i in qRange1 for j in qRange1 if numpy.sqrt(i*i+j*j) > q_low]).astype("float")
qPos = numpy.concatenate((qPos0, qPos1, qPos2))
qPos_full = numpy.array([[i,j,k] for i in qRange2 for j in qRange2 for k in qRange2]).astype("float")
return (qmax, t_intens, intens_len, qPos, qPos_full)
def load_reference_intensites(ref_file):
t_intens = (read_results.extract_value_from_h5(
ref_file, "/data/data")).astype("float")
intens_len = len(t_intens)
qmax = intens_len / 2
(q_low, q_high) = (15, int(0.9 * qmax))
qRange1 = numpy.arange(-q_high, q_high + 1)
qRange2 = numpy.arange(-qmax, qmax + 1)
qPos0 = numpy.array([[i, j, 0] for i in qRange1 for j in qRange1
if numpy.sqrt(i * i + j * j) > q_low]).astype("float")
qPos1 = numpy.array([[i, 0, j] for i in qRange1 for j in qRange1
if numpy.sqrt(i * i + j * j) > q_low]).astype("float")
qPos2 = numpy.array([[0, i, j] for i in qRange1 for j in qRange1
if numpy.sqrt(i * i + j * j) > q_low]).astype("float")
qPos = numpy.concatenate((qPos0, qPos1, qPos2))
qPos_full = numpy.array([[i, j, k] for i in qRange2 for j in qRange2
for k in qRange2]).astype("float")
return (qmax, t_intens, intens_len, qPos, qPos_full)
def do_analysis(args):
# Scan directories for reconstructions that were done.
# Reference directory is the zeroth (or first) one.
dirs = glob.glob(args.input_directory + "/")
cwd = os.getcwd()
curr_dir = dirs[0]
curr_file = glob.glob(os.path.join(curr_dir, args.tmp_fn))[0]
print "Will read default parameters from reconstruction in " + curr_file
(qmax, t_intens, intens_len, qPos,
qPos_full) = load_reference_intensites(curr_file)
quats = load_quaternions(os.path.join(curr_dir, "quaternion.dat"))
num_dirs = len(dirs)
intens_stack = numpy.zeros((num_dirs, intens_len, intens_len, intens_len))
intens_stack[0] = t_intens.copy()
dir_ct = 1
i_off = 1.E-7
tt_intens = (t_intens > 0.) * t_intens
tt_intens /= tt_intens.max()
tt_intens = numpy.abs(numpy.log(tt_intens + i_off))
if num_dirs > 1:
for dir_ct in range(num_dirs):
dir = dirs[dir_ct]
t0 = time.time()
curr_file = glob.glob(os.path.join(dir, args.tmp_fn))[0]
c_intens = (read_results.extract_value_from_h5(
curr_file, "/data/data")).astype("float")
cc_intens = (c_intens > 0.) * c_intens
cc_intens /= cc_intens.max()
cc_intens = numpy.abs(numpy.log(cc_intens + i_off))
scores = rotateIntens.orient_two_intensities(
tt_intens.ravel(), cc_intens.ravel(), qPos.ravel(),
quats.ravel(), intens_len)
ml_quat = quats[(scores.argsort())[0]]
out_intens = numpy.zeros_like(cc_intens)
rotateIntens.interp_intensities(c_intens.ravel(),
out_intens.ravel(),
qPos_full.ravel(), ml_quat,
intens_len)
t1 = time.time()
intens_stack[dir_ct] = out_intens.copy()
print "Done orienting intensity %d of %d. Took %lf s." % (
dir_ct, num_dirs, t1 - t0)
# Save stack.
h5 = h5py.File("3d_stack.h5", "w")
h5.create_group("data")
h5.create_dataset("data/oriented_diffraction_volumes", data=intens_stack)
h5.close()
# Make diagnostic images from individual reconstructions
# only if make_diag_imgs option is true
if args.make_diag_imgs:
for dir in dirs:
os.chdir(dir)
tmp_file = glob.glob(args.tmp_fn)[0]
print tmp_file
print "=" * 80
print "Making images for %s " % dir + "." * 20
try:
viewRecon.make_panel_of_intensity_slices(tmp_file, c_n=16)
except:
print "Making of intensity slices failed!"
viewRecon.make_error_time_plot(tmp_file)
try:
viewRecon.make_mutual_info_plot(tmp_file)
except:
print "Making of mutual information plot failed!"
print "=" * 80
os.chdir(cwd)
tarBallName = (os.getcwd().split('/')[-1]) + ".tgz"
os.system("tar -czf %s " % tarBallName +
''.join([s + "*.pdf " for s in dirs]))
print "Images saved in %s" % tarBallName
# Make images from merging individual reconstructions
# only if merge_imgs option is true
if args.merge_imgs:
(rows, cols) = (3, max([2, num_dirs / 3 + 1]))
matplotlib.rcParams.update({'font.size': 13})
fig, ax = plt.subplots(rows,
cols,
sharex=True,
sharey=True,
figsize=(2.5 * cols, 2.5 * rows))
fig.subplots_adjust(wspace=0.01)
stack_ct = 0
for r in range(rows):
for c in range(cols):
if stack_ct >= num_dirs:
break
out_intens = intens_stack[stack_ct]
im = ax[r, c].imshow(
numpy.log(numpy.abs(out_intens[qmax]) + 1.E-7),
cmap=plt.cm.coolwarm,
aspect='auto')
plt.draw()
stack_ct += 1
cbar_ax = fig.add_axes([0.9, 0.1, 0.025, 0.8])
fig.colorbar(im, cax=cbar_ax, label="log10(intensities)")
(shx, shy) = t_intens[qmax].shape
(h_shx, h_shy) = (shx / 2, shy / 2)
xt = numpy.linspace(0.5 * h_shx, shx - .5 * h_shx - 1, 3).astype('int')
xt_l = numpy.linspace(-0.5 * h_shx, 0.5 * h_shx, 3).astype('int')
yt = numpy.linspace(0, shy - 1, 3).astype('int')
yt_l = numpy.linspace(-1 * h_shy, h_shy, 3).astype('int')
plt.setp(ax, xticks=xt, xticklabels=xt_l, yticks=yt, yticklabels=yt_l)
img_name = "slices.pdf"
plt.savefig(img_name, bbox_inches='tight')
plt.close(fig)
fig2, ax2 = plt.subplots(1, 1)
im = ax2.imshow(
numpy.log(
numpy.abs((numpy.median(intens_stack, axis=0))[qmax]) + 1.E-7))
fig2.subplots_adjust(wspace=0.01)
cbar_ax2 = fig2.add_axes([0.9, 0.1, 0.025, 0.8])
fig2.colorbar(im, cax=cbar_ax2, label="log10(intensities)")
plt.setp(ax2, xticks=xt, xticklabels=xt_l, yticks=yt, yticklabels=yt_l)
img_name = "merged.pdf"
plt.savefig(img_name, bbox_inches='tight')
plt.close(fig2)
def do_analysis(args):
# Scan directories for reconstructions that were done.
# Reference directory is the zeroth (or first) one.
dirs = glob.glob(args.input_directory+"/")
cwd = os.getcwd()
curr_dir = dirs[0]
curr_file = glob.glob(os.path.join(curr_dir, args.tmp_fn))[0]
print "Will read default parameters from reconstruction in " + curr_file
(qmax, t_intens, intens_len, qPos, qPos_full) = load_reference_intensites(curr_file)
quats = load_quaternions(os.path.join(curr_dir, "quaternion.dat"))
num_dirs = len(dirs)
intens_stack = numpy.zeros((num_dirs, intens_len, intens_len, intens_len))
intens_stack[0] = t_intens.copy()
dir_ct = 1
i_off = 1.E-7
tt_intens = (t_intens>0.)*t_intens
tt_intens /= tt_intens.max()
tt_intens = numpy.abs(numpy.log(tt_intens+i_off))
if num_dirs > 1:
for dir_ct in range(num_dirs):
dir = dirs[dir_ct]
t0 = time.time()
curr_file = glob.glob(os.path.join(dir, args.tmp_fn))[0]
c_intens = (read_results.extract_value_from_h5(curr_file, "/data/data")).astype("float")
cc_intens = (c_intens>0.)*c_intens
cc_intens /= cc_intens.max()
cc_intens = numpy.abs(numpy.log(cc_intens+i_off))
scores = rotateIntens.orient_two_intensities(tt_intens.ravel(), cc_intens.ravel(), qPos.ravel(), quats.ravel(), intens_len)
ml_quat = quats[(scores.argsort())[0]]
out_intens = numpy.zeros_like(cc_intens)
rotateIntens.interp_intensities(c_intens.ravel(), out_intens.ravel(), qPos_full.ravel(), ml_quat, intens_len)
t1 = time.time()
intens_stack[dir_ct] = out_intens.copy()
print "Done orienting intensity %d of %d. Took %lf s."%(dir_ct, num_dirs, t1-t0)
# Save stack.
h5 = h5py.File("3d_stack.h5", "w")
h5.create_group("data")
h5.create_dataset("data/oriented_diffraction_volumes", data=intens_stack )
h5.close()
# Make diagnostic images from individual reconstructions
# only if make_diag_imgs option is true
if args.make_diag_imgs:
for dir in dirs:
os.chdir(dir)
tmp_file = glob.glob(args.tmp_fn)[0]
print tmp_file
print "="*80
print "Making images for %s "%dir + "."*20
try:
viewRecon.make_panel_of_intensity_slices(tmp_file, c_n=16)
except:
print "Making of intensity slices failed!"
viewRecon.make_error_time_plot(tmp_file)
try:
viewRecon.make_mutual_info_plot(tmp_file)
except:
print "Making of mutual information plot failed!"
print "="*80
os.chdir(cwd)
tarBallName = (os.getcwd().split('/')[-1])+".tgz"
os.system("tar -czf %s "%tarBallName + ''.join([s+"*.pdf " for s in dirs]))
print "Images saved in %s" % tarBallName
# Make images from merging individual reconstructions
# only if merge_imgs option is true
if args.merge_imgs:
(rows, cols)= (3, max([2,num_dirs/3+1]))
matplotlib.rcParams.update({'font.size': 13})
fig, ax = plt.subplots(rows, cols, sharex=True, sharey=True, figsize=(2.5*cols, 2.5*rows))
fig.subplots_adjust(wspace=0.01)
stack_ct = 0
for r in range(rows):
for c in range(cols):
if stack_ct >= num_dirs:
break
out_intens = intens_stack[stack_ct]
im = ax[r, c].imshow(numpy.log(numpy.abs(out_intens[qmax])+1.E-7), cmap=plt.cm.coolwarm, aspect='auto')
plt.draw()
stack_ct += 1
cbar_ax = fig.add_axes([0.9, 0.1, 0.025, 0.8])
fig.colorbar(im, cax=cbar_ax, label="log10(intensities)")
(shx, shy) = t_intens[qmax].shape
(h_shx, h_shy) = (shx/2, shy/2)
xt = numpy.linspace(0.5*h_shx, shx-.5*h_shx-1, 3).astype('int')
xt_l = numpy.linspace(-0.5*h_shx, 0.5*h_shx, 3).astype('int')
yt = numpy.linspace(0, shy-1, 3).astype('int')
yt_l = numpy.linspace(-1*h_shy, h_shy, 3).astype('int')
plt.setp(ax, xticks=xt, xticklabels=xt_l, yticks=yt, yticklabels=yt_l)
img_name = "slices.pdf"
plt.savefig(img_name, bbox_inches='tight')
plt.close(fig)
fig2, ax2 = plt.subplots(1, 1)
im = ax2.imshow(numpy.log(numpy.abs((numpy.median(intens_stack, axis=0))[qmax])+1.E-7))
fig2.subplots_adjust(wspace=0.01)
cbar_ax2 = fig2.add_axes([0.9, 0.1, 0.025, 0.8])
fig2.colorbar(im, cax=cbar_ax2, label="log10(intensities)")
plt.setp(ax2, xticks=xt, xticklabels=xt_l, yticks=yt, yticklabels=yt_l)
img_name = "merged.pdf"
plt.savefig(img_name, bbox_inches='tight')
plt.close(fig2)
