In [1]:
from __future__ import absolute_import, division, print_function
import numpy as np
import numpy.ma as ma
import os
import matplotlib.pyplot as plt
from skbeam.core import recip
from skbeam.core.utils import grid3d
import zipfile
import six
import time as ttime
%matplotlib inline
In [2]:
def recip_ex(detector_size, pixel_size, calibrated_center, dist_sample,
ub_mat, wavelength, motors, i_stack, H_range, K_range, L_range):
# convert to Q space
q_values = recip.process_to_q(motors, detector_size, pixel_size,
calibrated_center, dist_sample,
wavelength, ub_mat)
# minimum and maximum values of the voxel
q_min = np.array([H_range[0], K_range[0], L_range[0]])
q_max = np.array([H_range[1], K_range[1], L_range[1]])
# no. of bins
dqn = np.array([40, 40, 1])
# process the grid values
(grid_data, grid_occu, std_err,
bounds) = grid3d(q_values, i_stack, dqn[0], dqn[1],
dqn[2])
grid = np.mgrid[0:dqn[0], 0:dqn[1], 0:dqn[2]]
r = (q_max - q_min) / dqn
X = grid[0] * r[0] + q_min[0]
Y = grid[1] * r[1] + q_min[1]
Z = grid[2] * r[2] + q_min[2]
# creating a mask
_mask = grid_occu <= 10
grid_mask_data = ma.masked_array(grid_data, _mask)
grid_mask_std_err = ma.masked_array(std_err, _mask)
grid_mask_occu = ma.masked_array(grid_occu, _mask)
return X, Y, Z, grid_mask_data
In [3]:
def plot_slice(ax, x, y, i_slice, lx, H_range, K_range):
# plot the HK plane
i_slice_range = [0.006, 0.0115]
cnt = ax.contourf(x, y, i_slice, np.linspace(i_slice_range[0],
i_slice_range[1],
50, endpoint=True),
cmap='hot',
extend='both')
subp.axis('scaled')
subp.set_xlim(H_range)
subp.set_ylim(K_range)
subp.set_xlabel("H", size=10)
subp.set_ylabel("K", size=10)
subp.tick_params(labelsize=9)
cbar = plt.colorbar(cnt, ticks=np.linspace(i_slice_range[0],
i_slice_range[1],
3, endpoint=True),
format='%.4f')
cbar.ax.tick_params(labelsize=8)
In [4]:
def get_data(X, Y, grid_mask_data, plane):
HKL = 'HKL'
for i in plane:
HKL = HKL.replace(i, '')
HH = X[:, :, :].squeeze()
H = X[:, 0, 0]
KK = Y[:, :, :].squeeze()
K = Y[0, :, 0]
i_slice = grid_mask_data[:, :, :].squeeze() # intensity slice
lx = eval(plane[0])
return i_slice, lx
In [5]:
H_range = [-0.270, -0.200]
K_range = [+0.010, -0.010]
L_range = [+1.370, +1.410]
detector_size = (256, 256)
pixel_size = (0.0135*8, 0.0135*8) # (mm)
calibrated_center = (256/2.0, 256/2.0) # (mm)
dist_sample = 355.0 # (mm)
# ub matrix data
ub_mat = np. array([[-1.39772305e-01, -1.65559565e+00, -1.40501716e-02],
[-1.65632438e+00, 1.39853170e-01, -1.84650965e-04],
[4.79923390e-03, 4.91318724e-02, -4.72922724e-01]])
# wavelength data
wavelength = np.array([13.28559417])
# six angles data
motors = np.array([[102.3546, 77.874608, -90., 0., 0., 1.0205],
[102.6738, 78.285008, -90., 0., 0., 1.0575],
[102.9969, 78.696608, -90., 0., 0., 1.0945],
[103.3236, 79.108808, -90., 0., 0., 1.1315],
[103.6545, 79.521908, -90., 0., 0., 1.1685],
[103.9893, 79.935908, -90., 0., 0., 1.2055],
[104.3283, 80.350808, -90., 0., 0., 1.243],
[104.6712, 80.766608, -90., 0., 0., 1.28],
[105.018, 81.183308, -90., 0., 0., 1.317],
[105.369, 81.600908, -90., 0., 0., 1.354],
[105.7242, 82.019408, -90., 0., 0., 1.391],
[106.0836, 82.438808, -90., 0., 0., 1.428],
[106.4472, 82.859108, -90., 0., 0., 1.465],
[106.815, 83.280608, -90., 0., 0., 1.502],
[107.187, 83.703308, -90., 0., 0., 1.539],
[107.5632, 84.126608, -90., 0., 0., 1.576],
[107.9442, 84.551108, -90., 0., 0., 1.6135],
[108.3291, 84.976808, -90., 0., 0., 1.6505],
[108.7188, 85.403709, -90., 0., 0., 1.6875],
[109.113, 85.831509, -90., 0., 0., 1.7245],
[109.5117, 86.260509, -90., 0., 0., 1.7615],
[109.9149, 86.690709, -90., 0., 0., 1.7985],
[110.3229, 87.122109, -90., 0., 0., 1.8355],
[110.7357, 87.554709, -90., 0., 0., 1.8725],
[111.153, 87.988809, -90., 0., 0., 1.91],
[111.5754, 88.424109, -90., 0., 0., 1.947],
[112.0026, 88.860609, -90., 0., 0., 1.984],
[112.4349, 89.298609, -90., 0., 0., 2.021],
[112.8723, 89.737809, -90., 0., 0., 2.058],
[113.3145, 90.178809, -90., 0., 0., 2.095],
[113.7621, 90.621009, -90., 0., 0., 2.132],
[114.2151, 91.065009, -90., 0., 0., 2.169],
[114.6735, 91.510209, -90., 0., 0., 2.2065]])
# Data folder path
# path = "LSCO_Nov12_broker"
In [6]:
# intensity of the image stack data
try:
i_stack = np.load(os.path.join("LSCO_Nov12_broker", "i_stack.npy"))
except IOError:
zipfile.ZipFile(os.path.join("LSCO_Nov12_broker.zip")).extractall()
i_stack = np.load(os.path.join("LSCO_Nov12_broker", "i_stack.npy"))
In [7]:
X, Y, Z, grid_mask_data = recip_ex(detector_size, pixel_size,
calibrated_center, dist_sample,
ub_mat, wavelength, motors, i_stack,
H_range, K_range, L_range)
i_slice, lx = get_data(X, Y, grid_mask_data, plane='HK')
x = X.reshape(40, 40)
y = Y.reshape(40, 40)
In [8]:
f = plt.figure("HK plane", figsize=(15, 5))
plt.subplots_adjust(left=0.10, bottom=0.155555,
right=1.05, top=0.95,
wspace=0.2, hspace=0.45)
subp = f.add_subplot(111)
plot_slice(subp, x, y, i_slice, lx, H_range, K_range)
plt.show()
In [9]:
import skbeam
In [10]:
skbeam.__version__
Out[10]:
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