def get_qr_intensity( qr, data,vert_rect,mask=None, show_roi=True ):
V_K_label_array = roi.rectangles(vert_rect, data.shape) #(y,x, hight, wdith)
if mask is not None:V_K_label_array =V_K_label_array * mask
if show_roi:
data_ = data*V_K_label_array
fig, ax = plt.subplots()
im = plt.imshow(data_,origin='lower',norm= LogNorm( vmin=.1, vmax=1e0 ) )
fig.colorbar(im)
plt.show()
fig, ax = plt.subplots()
for i, vr in enumerate( vert_rect):
print (i, vr)
V_K_label_array_i = roi.rectangles((vr,), data.shape) #(y,x, hight, wdith)
if mask is not None:V_K_label_array_i =V_K_label_array_i * mask
roi_pixel_num = np.sum( V_K_label_array_i, axis=0)
qr_ = qr *V_K_label_array_i
data_ = data*V_K_label_array_i
qr_ave = np.sum( qr_, axis=0)/roi_pixel_num
data_ave = np.sum( data_, axis=0)/roi_pixel_num
ax.plot( qr_ave, data_ave, '--o', label= 'interest_roi_%i'%i)
ax.set_xlabel( r'$q_r$', fontsize=15)
ax.set_yscale('log')
ax.set_xscale('log')
ax.legend()
def test_rectangles():
shape = (15, 26)
roi_data = np.array(([2, 2, 6, 3], [6, 7, 8, 5], [8, 18, 5, 10]),
dtype=np.int64)
all_roi_inds = roi.rectangles(roi_data, shape)
roi_inds, pixel_list = corr.extract_label_indices(all_roi_inds)
ty = np.zeros(shape).ravel()
ty[pixel_list] = roi_inds
num_pixels_m = (np.bincount(ty.astype(int)))[1:]
re_mesh = ty.reshape(*shape)
for i, (col_coor, row_coor, col_val, row_val) in enumerate(roi_data, 0):
ind_co = np.column_stack(np.where(re_mesh == i + 1))
left, right = np.max([col_coor, 0]), np.min([col_coor + col_val,
shape[0]])
top, bottom = np.max([row_coor, 0]), np.min([row_coor + row_val,
shape[1]])
assert_almost_equal(left, ind_co[0][0])
assert_almost_equal(right-1, ind_co[-1][0])
assert_almost_equal(top, ind_co[0][1])
assert_almost_equal(bottom-1, ind_co[-1][-1])
def test_correlation():
num_levels = 4
num_bufs = 8 # must be even
num_qs = 2 # number of interested roi's (rings)
img_dim = (50, 50) # detector size
roi_data = np.array(([10, 20, 12, 14], [40, 10, 9, 10]), dtype=np.int64)
indices = roi.rectangles(roi_data, img_dim)
img_stack = np.random.randint(1, 5, size=(500,) + img_dim)
g2, lag_steps = corr.multi_tau_auto_corr(num_levels, num_bufs, indices, img_stack)
assert_array_almost_equal(
lag_steps, np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 24, 28, 32, 40, 48, 56])
)
assert_array_almost_equal(g2[1:, 0], 1.00, decimal=2)
assert_array_almost_equal(g2[1:, 1], 1.00, decimal=2)
def get_qr_intensity_series( qr, data,vert_rect, mask=None,show_roi=True ):
V_K_label_array = roi.rectangles(vert_rect, data.shape) #(y,x, hight, wdith)
if mask is not None:V_K_label_array =V_K_label_array * mask
qr_ = qr *V_K_label_array
data_ = data*V_K_label_array
if False:
fig, ax = plt.subplots()
im = plt.imshow(data_,origin='lower',norm= LogNorm( vmin=.1, vmax=1e0 ) )
fig.colorbar(im)
plt.show()
data_ave = np.average( data_, axis=0)
qr_ave = np.average( qr_, axis=0)
if show_roi:
fig, ax = plt.subplots()
im = plt.imshow(data_,origin='lower',norm= LogNorm( vmin=.1, vmax=1e0 ) )
fig.colorbar(im)
plt.show()
def test_static_test_sets():
img_stack1 = np.random.randint(0, 60, size=(50, ) + (50, 50))
label_array = np.zeros((25, 25))
# different shapes for the images and labels
assert_raises(ValueError,
lambda: roi.mean_intensity(img_stack1, label_array))
images1 = []
for i in range(10):
int_array = np.tril(i*np.ones(50))
int_array[int_array == 0] = i*100
images1.append(int_array)
images2 = []
for i in range(20):
int_array = np.triu(i*np.ones(50))
int_array[int_array == 0] = i*100
images2.append(int_array)
samples = np.array((np.asarray(images1), np.asarray(images2)))
roi_data = np.array(([2, 30, 12, 15], [40, 20, 15, 10]), dtype=np.int64)
label_array = roi.rectangles(roi_data, shape=(50, 50))
# test mean_intensity function
average_intensity, index = roi.mean_intensity(np.asarray(images1),
label_array)
# test mean_intensity_sets function
average_int_sets, index_list = roi.mean_intensity_sets(samples,
label_array)
assert_array_equal((list(average_int_sets)[0][:, 0]),
[float(x) for x in range(0, 1000, 100)])
assert_array_equal((list(average_int_sets)[1][:, 0]),
[float(x) for x in range(0, 20, 1)])
assert_array_equal((list(average_int_sets)[0][:, 1]),
[float(x) for x in range(0, 10, 1)])
assert_array_equal((list(average_int_sets)[1][:, 1]),
[float(x) for x in range(0, 2000, 100)])
# test combine_mean_intensity function
combine_mean_int = roi.combine_mean_intensity(average_int_sets,
index_list)
roi_data2 = np.array(([2, 30, 12, 15], [40, 20, 15, 10],
[20, 2, 4, 5]), dtype=np.int64)
label_array2 = roi.rectangles(roi_data2, shape=(50, 50))
average_int2, index2 = roi.mean_intensity(np.asarray(images1),
label_array2)
index_list2 = [index_list, index2]
average_int_sets.append(average_int2)
# raise ValueError when there is different labels in different image sets
# when trying to combine the values
assert_raises(ValueError,
lambda: roi.combine_mean_intensity(average_int_sets,
index_list2))
