def test_read_txrm_gets_metadata(self):
image_data, metadata = reader.read_txrm(
os.path.join(
TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"))
np.testing.utils.assert_(metadata["thetas"] is not False)
np.testing.utils.assert_(metadata["x_positions"] is not False)
np.testing.utils.assert_(metadata["y_positions"] is not False)
def test_read_txrm_gets_theta_set(self):
array_data, metadata = reader.read_txrm(
os.path.join(TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"))
self.assertEqual(metadata["thetas"], [
-45.018001556396484, -30.01412582397461, -15.01412582397461,
-0.014125000685453415, 14.985876083374023, 29.985876083374023,
44.985877990722656
])
def test_read_txrm_returns_properly_sliced_np_array_x_slice(self):
array_data, metadata = reader.read_txrm(
os.path.join(TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"),
((0, 5), ),
)
self.assertEqual(array_data.shape, (
5,
256,
256,
))
def test_read_txrm_gets_y_position_set(self):
array_data, metadata = reader.read_txrm(
os.path.join(TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"))
self.assertEqual(metadata["y_positions"], [
-3965.869384765625,
-3965.889404296875,
-3965.889404296875,
-3965.889404296875,
-3965.869384765625,
-3965.869384765625,
-3965.869384765625,
])
def test_read_txrm_returns_properly_sliced_np_array_z_y_x_slice(self):
array_data, metadata = reader.read_txrm(
os.path.join(
TEST_DIR,
"test_data/txrm_test_chip_tomo.txrm"
),
((0, 5), (0, 128), (59, 177), ),
)
self.assertEqual(
array_data.shape,
(5, 128, 118, )
)
def test_read_txrm_gets_x_position_set(self):
array_data, metadata = reader.read_txrm(
os.path.join(TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"))
self.assertEqual(metadata["x_positions"], [
1019.4400024414062,
1019.4400024414062,
1019.4599609375,
1019.4400024414062,
1019.4599609375,
1019.4599609375,
1019.4599609375,
])
def test_read_txrm_gets_y_position_set(self):
array_data, metadata = reader.read_txrm(
os.path.join(
TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"))
self.assertEqual(
metadata["y_positions"],
[
-3965.869384765625,
-3965.889404296875,
-3965.889404296875,
-3965.889404296875,
-3965.869384765625,
-3965.869384765625,
-3965.869384765625,
]
)
def test_read_txrm_gets_theta_set(self):
array_data, metadata = reader.read_txrm(
os.path.join(
TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"))
self.assertEqual(
metadata["thetas"],
[
-45.018001556396484,
-30.01412582397461,
-15.01412582397461,
-0.014125000685453415,
14.985876083374023,
29.985876083374023,
44.985877990722656
]
)
def test_read_txrm_gets_x_position_set(self):
array_data, metadata = reader.read_txrm(
os.path.join(
TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"))
self.assertEqual(
metadata["x_positions"],
[
1019.4400024414062,
1019.4400024414062,
1019.4599609375,
1019.4400024414062,
1019.4599609375,
1019.4599609375,
1019.4599609375,
]
)
def __init__(self, ct_file):
self.data, self.metadata = dxreader.read_txrm('/camp/home/kollom/working/mkollo/CHIME/BR_200710/ct/stitch-A/BR_200710_stitch-A_Stitch.txm')
self.pixel_size=metadata['pixel_size']
self.wire_pixels = int(50/self.pixel_size)
class Ct():
# CODE DUMP 1 - latest
import dxchange
import dxchange.reader as dxreader
import numpy as np
from tqdm import trange
from scipy.spatial.distance import cdist
from scipy.ndimage.measurements import center_of_mass, label
from skimage.feature import peak_local_max
# Load data
print("Loadig data...")
data, metadata = dxreader.read_txrm('/camp/home/kollom/working/mkollo/CHIME/BR_200710/ct/stitch-A/BR_200710_stitch-A_Stitch.txm')
pixel_size=metadata['pixel_size']
wire_pixels = int(25/pixel_size)
# Find wire locations at middle plane
print("Detecting mid-plane wire locations...")
mid_z_plane=data.shape[0]//2+4
image=data[mid_z_plane,:,:]
midwire_locs = peak_local_max(image, min_distance=wire_pixels, threshold_abs=8500)
midwire_locs=midwire_locs[midwire_locs[:,0]<720]
midwire_locs=midwire_locs[midwire_locs[:,1]<800]
wires=np.full((data.shape[0],midwire_locs.shape[0],3), np.inf, dtype=np.float)
wires[mid_z_plane,:,:2]=midwire_locs
wires[mid_z_plane,:,2]=0
# Search down
for z_plane in trange(mid_z_plane-1,0,-1, desc="Searching down",position=0, leave=True):
image=data[z_plane,:,:]
if z_plane==mid_z_plane-1:
pred_loc=wires[mid_z_plane,:,:2]
else:
pred_loc=wires[z_plane+1,:,:2]*2-wires[z_plane+2,:,:2]
wire_locs = peak_local_max(image, min_distance=wire_pixels, threshold_abs=8500)
distances = cdist(wires[z_plane+1,:,:2], wire_locs)
while not np.isinf(distances).all():
best_pair=np.unravel_index(np.argmin(distances),distances.shape)
wires[z_plane,best_pair[0],:2]=wire_locs[best_pair[1],:]
wires[z_plane,best_pair[0],2]=distances[best_pair]
distances[best_pair[0],:]=np.inf
distances[:,best_pair[1]]=np.inf
# Search up
for z_plane in trange(mid_z_plane,wires.shape[0],1, desc="Searching up",position=0, leave=True):
image=data[z_plane,:,:]
pred_loc=wires[z_plane-1,:,:2]*2-wires[z_plane-2,:,:2]
wire_locs = peak_local_max(image, min_distance=wire_pixels, threshold_abs=7900)
distances = cdist(wires[z_plane-1,:,:2], wire_locs)
while not np.isinf(distances).all():
best_pair=np.unravel_index(np.argmin(distances),distances.shape)
wires[z_plane,best_pair[0],:2]=wire_locs[best_pair[1],:]
wires[z_plane,best_pair[0],2]=distances[best_pair]
distances[best_pair[0],:]=np.inf
distances[:,best_pair[1]]=np.inf
# if distances[best_pair]<5:
# wires[z_plane,best_pair[0],:]=np.nan
# Plot plane and markings
import matplotlib.pyplot as plt
# clean_wires=wires.copy()
z_plane=40
wire_selection=1
fig=plt.figure(1, figsize=(10,10))
plt.imshow(data[z_plane,:,:])
# wire_locs = peak_local_max(data[z_plane,:,:], min_distance=wire_pixels, threshold_abs=8200)
# plt.scatter(wires[:,1],wires[:,0],s=0.25,c='r'
plt.scatter(clean_wires[z_plane,:,1],clean_wires[z_plane,:,0],s=0.25,c='r')
# plt.xlim(300,800)
# plt.ylim(1000,500)
plt.show()
# Fitting plane
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
N_POINTS = 10
TARGET_X_SLOPE = 2
TARGET_y_SLOPE = 3
TARGET_OFFSET = 5
EXTENTS = 5
NOISE = 5
# create random data
xs = tops[:,0]
ys = tops[:,1]
zs = tops[:,2]
# plot raw data
plt.figure()
ax = plt.subplot(111, projection='3d')
ax.scatter(xs, ys, zs, color='b')
# do fit
tmp_A = []
tmp_b = []
for i in range(len(xs)):
tmp_A.append([xs[i], ys[i], 1])
tmp_b.append(zs[i])
b = np.matrix(tmp_b).T
A = np.matrix(tmp_A)
fit = (A.T * A).I * A.T * b
errors = b - A * fit
residual = np.linalg.norm(errors)
print("solution:")
print("%f x + %f y + %f = z" % (fit[0], fit[1], fit[2]))
# print("errors:")
# print(errors)
# print("residual:")
# print(residual)
# plot plane
xlim = ax.get_xlim()
ylim = ax.get_ylim()
X,Y = np.meshgrid(np.arange(xlim[0], xlim[1],30),
np.arange(ylim[0], ylim[1],30))
Z = np.zeros(X.shape)
for r in range(X.shape[0]):
for c in range(X.shape[1]):
Z[r,c] = fit[0] * X[r,c] + fit[1] * Y[r,c] + fit[2]
ax.plot_wireframe(X,Y,Z, color='k')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
plt.show()
# Project point on plane
origo=np.array([0,0,3115.671])
normal=np.array([0.009, 0.012, 1])
print(tops[0,:])
print(origo-np.dot(tops[0,:]-origo, normal)*normal)
# CODE DUMP 2
import dxchange
import dxchange.reader as dxreader
import numpy as np
from tqdm import trange
from scipy.spatial.distance import cdist
# Load data
print("Loadig data...")
data, metadata = dxreader.read_txrm('/camp/home/kollom/working/mkollo/CHIME/BR_200710/ct/stitch-A/BR_200710_stitch-A_Stitch.txm')
pixel_size=metadata['pixel_size']
wire_pixels = int(25/pixel_size)
# Find wire locations at middle plane
print("Detecting mid-plane wire locations...")
mid_z_plane=data.shape[0]//2+4
image=data[mid_z_plane,:,:]
midwire_locs = peak_local_max(image, min_distance=wire_pixels, threshold_abs=8200)
midwire_locs=midwire_locs[midwire_locs[:,0]<720]
midwire_locs=midwire_locs[midwire_locs[:,1]<800]
wires=np.full((data.shape[0],midwire_locs.shape[0],3), 5000, dtype=np.float)
wires[mid_z_plane,:,:2]=midwire_locs
wires[mid_z_plane,:,2]=0
# Search down
for z_plane in trange(mid_z_plane-1,0,-1, desc="Searching down",position=0, leave=True):
image=data[z_plane,:,:]
if z_plane==mid_z_plane-1:
pred_loc=wires[mid_z_plane,:,:2]
else:
pred_loc=wires[z_plane+1,:,:2]*2-wires[z_plane+2,:,:2]
wire_locs = peak_local_max(image, min_distance=wire_pixels, threshold_abs=8200)
distances = cdist(pred_loc, wire_locs)
while not np.isinf(distances).all():
best_pair=np.unravel_index(np.argmin(distances),distances.shape)
wires[z_plane,best_pair[0],:2]=wire_locs[best_pair[1],:]
wires[z_plane,best_pair[0],2]=distances[best_pair]
distances[best_pair[0],:]=np.inf
distances[:,best_pair[1]]=np.inf
# Search up
for z_plane in trange(mid_z_plane,wires.shape[0],1, desc="Searching up",position=0, leave=True):
image=data[z_plane,:,:]
pred_loc=wires[z_plane-1,:,:2]*2-wires[z_plane-2,:,:2]
wire_locs = peak_local_max(image, min_distance=wire_pixels, threshold_abs=8000)
distances = cdist(pred_loc, wire_locs)
while not np.isinf(distances).all():
best_pair=np.unravel_index(np.argmin(distances),distances.shape)
wires[z_plane,best_pair[0],:2]=wire_locs[best_pair[1],:]
wires[z_plane,best_pair[0],2]=distances[best_pair]
distances[best_pair[0],:]=np.inf
distances[:,best_pair[1]]=np.inf
# Resect breaks
x=np.where(wires[:,:,2]>3000)[0]
y=np.where(wires[:,:,2]>3000)[1]
wires[x,y,:]=np.nan
clean_wires=wires.copy()
for i in range(wires.shape[1]):
speed=np.abs(np.diff(wires[:1500,i,0]))+np.abs(np.diff(wires[:1500,i,1]))
speed=np.convolve(speed, np.ones((3,))/3, mode='valid')
clean_wires[:np.max(np.where(speed>10)[0])+1,i,:]=np.nan
speed=np.abs(np.diff(wires[1500:,i,0]))+np.abs(np.diff(wires[1500:,i,1]))
if len(np.where(speed>10)[0])>0:
clean_wires[np.max(np.where(speed>10)[0])-1:,i,:]=np.nan
def read_txm(file, slices=None):
data, meta = dxreader.read_txrm(file, slice_range=slices)
return data, meta
def test_read_txrm_works_on_a_list_of_slice_data(self):
image_data, metadata = reader.read_txrm(
os.path.join(TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"),
[1, 2])
self.assertEqual(image_data.shape, (1, 2, 256))
def test_read_txrm_uses_slice_data(self):
image_data, metadata = reader.read_txrm(
os.path.join(TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"),
(5, 128, 54))
self.assertEqual(image_data.shape, (5, 128, 54))
def test_read_txrm_gets_metadata(self):
image_data, metadata = reader.read_txrm(
os.path.join(TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"))
np.testing.utils.assert_(metadata["thetas"] is not False)
np.testing.utils.assert_(metadata["x_positions"] is not False)
np.testing.utils.assert_(metadata["y_positions"] is not False)
def test_read_txrm_reads_a_txrm_file(self):
image_data, metadata = reader.read_txrm(
os.path.join(TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"))
np.testing.utils.assert_(image_data is not False)
np.testing.utils.assert_(metadata is not False)
def test_read_txrm_reads_a_txrm_file(self):
image_data, metadata = reader.read_txrm(
os.path.join(
TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"))
np.testing.utils.assert_(image_data is not False)
np.testing.utils.assert_(metadata is not False)
def test_read_txrm_uses_slice_data(self):
image_data, metadata = reader.read_txrm(
os.path.join(
TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"), (5, 128, 54))
self.assertEqual(image_data.shape, (5, 128, 54))
def test_read_txrm_works_on_a_list_of_slice_data(self):
image_data, metadata = reader.read_txrm(
os.path.join(
TEST_DIR, "test_data/txrm_test_chip_tomo.txrm"), [1, 2])
self.assertEqual(image_data.shape, (1, 2, 256))