from __future__ import division from scitbx.array_family import flex from dials.scratch.luiso_s import model_2d, write_2d, test_compare_2d from dials.algorithms.integration import add_2d nrow = 10 ncol = 10 sumation = flex.double(flex.grid(21, 21)) descr = flex.double(flex.grid(1, 3)) descr[0, 0] = 0.5 descr[0, 1] = 5 descr[0, 2] = 5 ref2d = model_2d(5, 5, 2, 1, 0.2, 55, 0.5) sumation = add_2d(descr, ref2d, sumation) # sumation = test_compare_2d(descr, ref2d, sumation) write_2d(sumation)
t_bbox = flex.int6(num_ref)
t_xyzobs = flex.vec3_double(num_ref)
t_xyzcal = flex.vec3_double(num_ref)
t_row = 0
for ypos in range(n_y):
for xpos in range(n_x):
row_str = ypos * nrow
col_str = xpos * ncol
dx = col_str - xmax / 2
dy = row_str - ymax / 2
ref_ang = float(math.atan2(dx, dy) / pi)
i_loc = random.randint(0, 999)
thold = i_loc / 8
ref2d = model_2d(nrow, ncol, 5, 1, ref_ang, i_loc, 0.5)
data2d_tmp = ref2d.as_numpy_array()
data2d[col_str:col_str + ncol,
row_str:row_str + nrow] += numpy.float64(data2d_tmp)
t_bbox[t_row] = [
col_str, col_str + ncol, row_str, row_str + nrow, 0, 1
]
t_xyzobs[t_row] = [col_str + centr_col, row_str + centr_row, 0.5]
t_xyzcal[t_row] = [col_str + centr_col, row_str + centr_row, 0.5]
np_shoebox = numpy.copy(data2d[col_str:col_str + ncol,
row_str:row_str + nrow])
# tmp_2d_fl_shoebox = flex.double(np_shoebox)
# tmp_2d_fl_shoebox.reshape(flex.grid(1, ncol, nrow))
nrow = 60
ncol = 60
for xpos in range(45):
for ypos in range(25):
row_str = ypos * 20
col_str = xpos * 20
ref_ang = float(ypos / 10)
local_i = (68 - xpos - ypos) * 5
'''
print "xpos =", xpos
print "ypos =", ypos
print "local_i =", local_i
print "____________________________"
'''
ref2d = model_2d(nrow, ncol, 3, 2, ref_ang, local_i, 0.5)
data2d_tmp = ref2d.as_numpy_array()
np_data2d[row_str:row_str + 60, col_str:col_str + 60] += numpy.float64(data2d_tmp)
big_ref2d = model_2d(550, 950, 280, 140, 1.0, 955, 0.5)
data2d_tmp = big_ref2d.as_numpy_array()
np_data2d[:, :] += numpy.float64(data2d_tmp)
#tmp='''
print "adding noise ...."
import random
from __future__ import division
from __future__ import print_function
from scitbx.array_family import flex
from dials.scratch.luiso_s import model_2d
nrow = 35
ncol = 35
ref_ang = 5
local_i = 170
data2d = model_2d(nrow, ncol, 6, 3, ref_ang, local_i, 0.5)
ext_siz = 2
mask2d = flex.int(flex.grid(nrow, ncol))
for x_loc in range(ncol):
for y_loc in range(nrow):
if data2d[y_loc, x_loc] > 10:
mask2d[y_loc, x_loc] = 5
else:
mask2d[y_loc, x_loc] = 3
ext_siz = 1
tmp_mask = mask2d[:, :]
mask2d = mask2d.as_numpy_array()
for x_loc in range(ncol):
for y_loc in range(nrow):
if tmp_mask[y_loc, x_loc] == 5:
mask2d[y_loc - ext_siz:y_loc + ext_siz,
x_loc - ext_siz:x_loc + ext_siz] = 5
mask2d = flex.int(mask2d)
sumation = flex.double(flex.grid(21, 21))
descr = flex.double(flex.grid(1, 3))
descr[0, 0] = .5
descr[0, 1] = 5
descr[0, 2] = 5
print "____________________________________________________________________"
for xpos in range(3):
for ypos in range(3):
row_str = ypos * 12
col_str = xpos * 20
ref_ang = float((ypos + 1) / 10)
# flex_int model_2d(int nrow, int ncol, float a, float b,
# float delta_ang, float imax, float asp)
ref2d = model_2d(nrow, ncol, 2, 1, ref_ang, 55, 0.5)
data2d_tmp = ref2d.as_numpy_array()
data2d[row_str:row_str + nrow, col_str:col_str + ncol] += numpy.float64(data2d_tmp)
sumation = add_2d(descr, flex.double (numpy.float64 (data2d_tmp)), sumation)
write_2d(sumation)
print "Plotting data2d"
plt.imshow(data2d, interpolation = "nearest")
plt.show()
print "Plotting reslt"
img_suma = sumation.as_numpy_array()
plt.imshow(img_suma, interpolation = "nearest")
plt.show()
from __future__ import division from scitbx.array_family import flex from dials.scratch.luiso_s import model_2d, write_2d, test_compare_2d from dials.algorithms.integration import add_2d nrow = 10 ncol = 10 sumation = flex.double(flex.grid(21, 21)) descr = flex.double(flex.grid(1, 3)) descr[0, 0] = .5 descr[0, 1] = 5 descr[0, 2] = 5 ref2d = model_2d(5, 5, 2, 1, 0.2, 55, 0.5) sumation = add_2d(descr, ref2d, sumation) #sumation = test_compare_2d(descr, ref2d, sumation) write_2d(sumation)
col_str = xpos * ncol
dx = col_str - 1200
dy = row_str - 1300
if dx < 0 and dy < 0:
ref_ang = 0.25
elif dx > 0 and dy < 0:
ref_ang = 0.75
elif dx < 0 and dy > 0:
ref_ang = 0.75
else:
ref_ang = 0.20
ref_ang = float(math.atan2(dx, dy) / pi)
i_loc = random.randint(0, 999)
thold = i_loc / 20
ref2d = model_2d(nrow, ncol, 5, 1, ref_ang, i_loc, 0.5)
data2d_tmp = ref2d.as_numpy_array()
data2d[col_str : col_str + ncol, row_str : row_str + nrow] += numpy.float64(data2d_tmp)
# new_r = Reflection()
# new_r.bounding_box = [col_str, col_str + ncol, row_str, row_str + nrow, 0, 1]
t_bbox[t_row] = [col_str, col_str + ncol, row_str, row_str + nrow, 0, 1]
# new_r.centroid_position = [col_str + 14.5, row_str + 14.5, 0.5]
t_xyzobs[t_row] = [col_str + 14.5, row_str + 14.5, 0.5]
t_xyzcal[t_row] = [col_str + 14.5, row_str + 14.5, 0.5]
# new_r.image_coord_px = [col_str + 14.5, row_str + 14.5]
from __future__ import division
from scitbx.array_family import flex
from dials.scratch.luiso_s import model_2d
nrow = 35
ncol = 35
ref_ang = 5
local_i = 170
data2d = model_2d(nrow, ncol, 6, 3, ref_ang, local_i, 0.5)
ext_siz = 2
mask2d = flex.int(flex.grid(nrow, ncol))
for x_loc in range(ncol):
for y_loc in range(nrow):
if( data2d[y_loc, x_loc] > 10):
mask2d[y_loc, x_loc] = 5
else:
mask2d[y_loc, x_loc] = 3
ext_siz = 1
tmp_mask = mask2d[:,:]
mask2d = mask2d.as_numpy_array()
for x_loc in range(ncol):
for y_loc in range(nrow):
if( tmp_mask[y_loc, x_loc] == 5):
mask2d[y_loc - ext_siz:y_loc + ext_siz \
, x_loc - ext_siz : x_loc + ext_siz] = 5
mask2d = flex.int(mask2d)
print "adding noise ...."
sumation = flex.double(flex.grid(21, 21))
descr = flex.double(flex.grid(1, 3))
descr[0, 0] = 0.5
descr[0, 1] = 5
descr[0, 2] = 5
print("____________________________________________________________________")
for xpos in range(3):
for ypos in range(3):
row_str = ypos * 12
col_str = xpos * 20
ref_ang = float((ypos + 1) / 10)
# flex_int model_2d(int nrow, int ncol, float a, float b,
# float delta_ang, float imax, float asp)
ref2d = model_2d(nrow, ncol, 2, 1, ref_ang, 55, 0.5)
data2d_tmp = ref2d.as_numpy_array()
data2d[row_str:row_str + nrow,
col_str:col_str + ncol] += numpy.float64(data2d_tmp)
sumation = add_2d(descr, flex.double(numpy.float64(data2d_tmp)),
sumation)
write_2d(sumation)
print("Plotting data2d")
plt.imshow(data2d, interpolation="nearest")
plt.show()
print("Plotting reslt")
img_suma = sumation.as_numpy_array()
plt.imshow(img_suma, interpolation="nearest")
from __future__ import print_function from dials.algorithms.integration import add_2d from scitbx.array_family import flex from dials.scratch.luiso_s import model_2d nrow = ncol = 25 ref2d_01 = model_2d(nrow, ncol, 3, 6, 0.2, 85, 0.5) ref2d_02 = model_2d(nrow + 1, ncol + 1, 3, 6, 0.8, 85, 0.5) descr = flex.double(flex.grid(1, 3)) descr[0, 0] = 12.5 descr[0, 1] = 12.5 descr[0, 2] = 2.5 # tmp_ref01 = ref2d_01[:,:] # tmp_ref02 = ref2d_02[:,:] print(id(ref2d_01)) print(id(ref2d_02)) print() sumation = add_2d(descr, ref2d_01, ref2d_02) # sumation = add_2d(descr, tmp_ref01, tmp_ref02) print() print(id(ref2d_01)) print(id(ref2d_02)) print(id(sumation)) from matplotlib import pyplot as plt plt.imshow(ref2d_01.as_numpy_array(), interpolation="nearest") plt.show() plt.imshow(ref2d_02.as_numpy_array(), interpolation="nearest") plt.show()
import numpy
data2d = numpy.zeros((550, 950), dtype = numpy.float64)
nrow = 60
ncol = 60
for xpos in range(10):
for ypos in range(10):
row_str = ypos * 50
col_str = xpos * 90
ref_ang = float(ypos / 10)
'''flex_int model_2d(int nrow, int ncol, float a, float b,
float delta_ang, float imax, float asp)'''
ref2d = model_2d(nrow, ncol, 3, 2, ref_ang, 955, 0.5)
data2d_tmp = ref2d.as_numpy_array()
data2d[row_str:row_str + 60, col_str:col_str + 60] += numpy.float64(data2d_tmp)
ref2d = model_2d(550, 950, 280, 140, 1.0, 955, 0.5)
data2d_tmp = ref2d.as_numpy_array()
data2d[:, :] += numpy.float64(data2d_tmp)
#n = write_2d(flex.double(data2d))
from matplotlib import pyplot as plt
print "Plotting data2d"
plt.imshow(data2d, interpolation = "nearest")
from dials.algorithms.integration import add_2d from scitbx.array_family import flex from dials.scratch.luiso_s import model_2d nrow = ncol = 25 ref2d_01 = model_2d(nrow, ncol, 3, 6, 0.2, 85, 0.5) ref2d_02 = model_2d(nrow+1, ncol+1, 3, 6, 0.8, 85, 0.5) descr = flex.double(flex.grid(1, 3)) descr[0, 0] = 12.5 descr[0, 1] = 12.5 descr[0, 2] = 2.5 #tmp_ref01 = ref2d_01[:,:] #tmp_ref02 = ref2d_02[:,:] print id(ref2d_01) print id(ref2d_02) print sumation = add_2d(descr, ref2d_01, ref2d_02) #sumation = add_2d(descr, tmp_ref01, tmp_ref02) print print id(ref2d_01) print id(ref2d_02) print id(sumation) from matplotlib import pyplot as plt plt.imshow(ref2d_01.as_numpy_array(), interpolation = "nearest") plt.show() plt.imshow(ref2d_02.as_numpy_array(), interpolation = "nearest") plt.show()
nrow = 60
ncol = 60
for xpos in range(45):
for ypos in range(25):
row_str = ypos * 20
col_str = xpos * 20
ref_ang = float(ypos / 10)
local_i = (68 - xpos - ypos) * 5
"""
print "xpos =", xpos
print "ypos =", ypos
print "local_i =", local_i
print "____________________________"
"""
ref2d = model_2d(nrow, ncol, 3, 2, ref_ang, local_i, 0.5)
data2d_tmp = ref2d.as_numpy_array()
np_data2d[row_str:row_str + 60,
col_str:col_str + 60] += numpy.float64(data2d_tmp)
big_ref2d = model_2d(550, 950, 280, 140, 1.0, 955, 0.5)
data2d_tmp = big_ref2d.as_numpy_array()
np_data2d[:, :] += numpy.float64(data2d_tmp)
# tmp='''
print("adding noise ....")
import random
for x_loc in range(950):
for y_loc in range(550):
roll_the_dice = random.randint(1, 50)