def rotations_faces(cube): U = cube.copy(); N = t3d.rotate(cube, axis=0, steps=1); W = t3d.rotate(cube, axis=1, steps=1); UNW = [U,N,W]; DSE = [t3d.reflect(X) for X in UNW]; return UNW + DSE;
def rotations_nodes(cube): UNW = cube.copy(); UNE = t3d.rotate(cube, axis=2,steps=1); USE = t3d.rotate(cube, axis=2,steps=2); USW = t3d.rotate(cube, axis=2,steps=3); R = [t3d.reflect(X) for X in [UNW,UNE,USE,USW]]; return [UNW,UNE, USE,USW] + R
def match_non_removable(index, verbose=True):
if verbose and index % 2**14 == 0:
print('PK12 LUT non-removables: %d / %d' % (index, 2**26))
cube = t3d.cube_from_index(index=index, center=False)
n = cube.sum()
if n < 2:
return True
if n > 3:
return False
x, y, z = np.where(cube)
if n == 2:
if np.any(np.abs([x[1] - x[0], y[1] - y[0], z[1] - z[0]]) == 2):
return True
else:
return False
else:
if np.any(np.abs(
[x[1] - x[0], y[1] - y[0], z[1] - z[0]]) == 2) and np.any(
np.abs([x[2] - x[0], y[2] - y[0], z[2] -
z[0]]) == 2) and np.any(
np.abs([x[1] - x[2], y[1] - y[2], z[1] -
z[2]]) == 2):
return True
else:
return False
def rotations_edges(cube): UN = cube.copy(); UE = t3d.rotate(cube, axis=2, steps=1); US = t3d.rotate(cube, axis=2, steps=2); UW = t3d.rotate(cube, axis=2, steps=3); NW = t3d.rotate(cube, axis=1, steps=3); NE = t3d.rotate(cube, axis=1, steps=1); R = [t3d.reflect(X) for X in [UN,UE,US,UW,NW,NE]]; return [UN,UE,US,UW,NW,NE] + R;
def rotations_node_faces(cube): U_UNW = cube.copy(); #U1 U_UNE = t3d.rotate(cube, axis=2, steps=1); #U3 U_USE = t3d.rotate(cube, axis=2, steps=2); #U5 U_USW = t3d.rotate(cube, axis=2, steps=3); #U7 Us = [U_UNW, U_UNE, U_USE, U_USW]; Ns = [t3d.rotate(X, axis=0, steps=1) for X in Us]; # N7. N1, N3, N5 Ws = [t3d.rotate(X, axis=1, steps=1) for X in Us]; # W3, W1, W7, W5 UNWs = Us + Ns + Ws; DSEs = [t3d.reflect(X) for X in UNWs]; return UNWs + DSEs;
def smooth_by_configuration_block(source, iterations = 1, verbose = False):
"""Smooth a binary source using the local configuration around each pixel.
Arguments
---------
source : array
The binary source to smooth.
iterations : int
Number of smoothing iterations.
verbose : bool
If True, print progress information.
Returns
-------
smoothed : array
Thre smoothed binary array.
"""
if isinstance(source, io.src.Source):
smoothed = source.array;
else:
smoothed = source;
smoothed = np.asarray(smoothed, dtype='uint32');
ndim = smoothed.ndim;
lut = np.asarray(initialize_lookup_table(verbose=verbose), dtype='uint32');
for i in range(iterations):
#index
for axis in range(ndim):
kernel = t3d.index_kernel(axis=axis);
smoothed = ndi.correlate1d(smoothed, kernel, axis=axis, output='uint32', mode='constant', cval=0);
smoothed = lut[smoothed];
if verbose:
print('Binary Smoothing: itertion %d / %d done!' % (i+1, iterations));
return np.asarray(smoothed, dtype=bool);
def index_to_smoothing(index, verbose = True):
"""Match index of configuration to smoothing action"""
if verbose and index % 2**14 == 0:
print('Smoothing LUT: %d / %d' % (index, 2**27));
cube = t3d.cube_from_index(index=index, center=None);
return cube_to_smoothing(cube);
def _test():
import numpy as np
import ClearMap.ParallelProcessing.DataProcessing.ArrayProcessing as ap
## Lookup table processing
#apply_lut
x = np.random.randint(0, 100, size=(20, 30))
lut = np.arange(100) + 1
y = ap.apply_lut(x, lut)
assert np.all(y == x + 1)
#apply_lut_to_index
import ClearMap.ImageProcessing.Topology.Topology3d as t3d
kernel = t3d.index_kernel(dtype=int)
import ClearMap.ImageProcessing.Binary.Smoothing as sm
lut = sm.initialize_lookup_table()
data = np.array(np.random.rand(150, 30, 40) > 0.75, order='F')
result = ap.apply_lut_to_index(data, kernel, lut, sink=None, verbose=True)
import ClearMap.Visualization.Plot3d as p3d
p3d.plot([[data, result]])
### Correlation
#correlate1d
kernel = np.array(range(11), dtype='uint32')
data = np.array(np.random.randint(0,
2**27, (300, 400, 1500),
dtype='uint32'),
order='F')
#data = np.array(np.random.rand(3,4,5), order='F');
data = np.empty((300, 400, 1500), order='F')
kernel = np.array([1, 2, 3, 4, 5], dtype='uint8')
sink = 'test.npy'
import ClearMap.Utils.Timer as tmr
import scipy.ndimage as ndi
timer = tmr.Timer()
for axis in range(3):
print(axis)
corr_ndi = ndi.correlate1d(data, axis=axis, mode='constant', cval=0)
timer.print_elapsed_time('ndi')
timer = tmr.Timer()
for axis in range(3):
print(axis)
corr = ap.correlate1d(data,
sink=sink,
kernel=kernel,
axis=axis,
verbose=False,
processes=None)
timer.print_elapsed_time('ap')
assert np.allclose(corr.array, corr_ndi)
# IO
import ClearMap.ParallelProcessing.DataProcessing.ArrayProcessing as ap
import numpy as np
reload(ap)
data = np.random.rand(10, 200, 10)
sink = ap.write('test.npy', data, verbose=True)
assert (np.all(sink.array == data))
read = ap.read('test.npy', verbose=True)
assert (np.all(read.array == data))
ap.io.delete_file('test.npy')
# where
reload(ap)
data = np.random.rand(30, 20, 40) > 0.5
where_np = np.array(np.where(data)).T
where = ap.where(data, cutoff=2**0)
check_np = np.zeros(data.shape, dtype=bool)
check = np.zeros(data.shape, dtype=bool)
check_np[tuple(where_np.T)] = True
check[tuple(where.array.T)] = True
assert (np.all(check_np == check))
def graph_from_skeleton(skeleton, points = None, radii = None, vertex_coordinates = True,
check_border = True, delete_border = False, verbose = False):
"""Converts a binary skeleton image to a graph-tool graph.
Arguments
---------
skeleton : array
Source with 2d/3d binary skeleton.
points : array
List of skeleton points as 1d indices of flat skeleton array (optional to save processing time).
radii : array
List of radii associated with each vertex.
vertex_coordinates : bool
If True, store coordiantes of the vertices / edges.
check_border : bool
If True, check if the boder is empty. The algorithm reuqires this.
delete_border : bool
If True, delete the border.
verbose : bool
If True, print progress information.
Returns
-------
graph : Graph class
The graph corresponding to the skeleton.
Note
----
Edges are detected between neighbouring foreground pixels using 26-connectivty.
"""
skeleton = io.as_source(skeleton);
if delete_border:
skeleton = t3d.delete_border(skeleton);
check_border = False;
if check_border:
if not t3d.check_border(skeleton):
raise ValueError('The skeleton array needs to have no points on the border!');
if verbose:
timer = tmr.Timer();
timer_all = tmr.Timer();
print('Graph from skeleton calculation initialize.!');
if points is None:
points = ap.where(skeleton.reshape(-1, order = 'A')).array;
if verbose:
timer.print_elapsed_time('Point list generation');
timer.reset();
#create graph
n_vertices = points.shape[0];
g = ggt.Graph(n_vertices=n_vertices, directed=False);
g.shape = skeleton.shape;
if verbose:
timer.print_elapsed_time('Graph initialized with %d vertices' % n_vertices);
timer.reset();
#detect edges
edges_all = np.zeros((0,2), dtype = int);
for i,o in enumerate(t3d.orientations()):
# calculate off set
offset = np.sum((np.hstack(np.where(o))-[1,1,1]) * skeleton.strides)
edges = ap.neighbours(points, offset);
if len(edges) > 0:
edges_all = np.vstack([edges_all, edges]);
if verbose:
timer.print_elapsed_time('%d edges with orientation %d/13 found' % (edges.shape[0], i+1));
timer.reset();
if edges_all.shape[0] > 0:
g.add_edge(edges_all);
if verbose:
timer.print_elapsed_time('Added %d edges to graph' % (edges_all.shape[0]));
timer.reset();
if vertex_coordinates:
vertex_coordinates = np.array(np.unravel_index(points, skeleton.shape, order=skeleton.order)).T;
g.set_vertex_coordinates(vertex_coordinates);
if radii is not None:
g.set_vertex_radius(radii);
if verbose:
timer_all.print_elapsed_time('Skeleton to Graph');
return g;
def skeletonize_index(binary,
points=None,
steps=None,
removals=False,
radii=False,
return_points=False,
check_border=True,
delete_border=False,
verbose=True):
"""Skeletonize a binary 3d array using PK12 algorithm via index coordinates.
Arguments
---------
binary : array
Binary image to be skeletonized.
steps : int or None
Number of maximal iteration steps. If None, use maximal reduction.
removals :bool
If True, returns the steps in which the pixels in the input data
were removed.
radii :bool
If True, the estimate of the local radius is returned.
verbose :bool
If True, print progress info.
Returns
-------
skeleton : array
The skeleton of the binary input.
points : nxd array
The point coordinates of the skeleton.
"""
if verbose:
print('#############################################################')
print('Skeletonization PK12 [convolution, index]')
timer = tmr.Timer()
#TODO: make this work for any memmapable source
if not isinstance(binary, np.ndarray):
raise ValueError('Numpy array required for binary in skeletonization!')
if binary.ndim != 3:
raise ValueError('The binary array dimension is %d, 3 is required!' %
binary.ndim)
if delete_border:
binary = t3d.delete_border(binary)
check_border = False
if check_border:
if not t3d.check_border(binary):
raise ValueError(
'The binary array needs to have not points on the border!')
binary_flat = binary.reshape(-1, order='A')
# detect points
if points is None:
points = ap.where(binary_flat).array
npoints = points.shape[0]
if verbose:
timer.print_elapsed_time('Foreground points: %d' % (points.shape[0], ))
if removals is True or radii is True:
#birth = np.zeros(binary.shape, dtype = 'uint16');
order = 'C'
if binary.flags.f_contiguous:
order = 'F'
death = np.zeros(binary.shape, dtype='uint16', order=order)
deathflat = death.reshape(-1, order='A')
with_info = True
else:
with_info = False
# iterate
if steps is None:
steps = -1
step = 1
nnonrem = 0
while True:
if verbose:
print(
'#############################################################'
)
print('Iteration %d' % step)
timer_iter = tmr.Timer()
print(type(points), points.dtype, binary.dtype)
border = cpl.convolve_3d_indices_if_smaller_than(
binary, t3d.n6, points, 6)
borderpoints = points[border]
#borderids = np.nonzero(border)[0];
borderids = ap.where(border).array
keep = np.ones(len(border), dtype=bool)
if verbose:
timer_iter.print_elapsed_time('Border points: %d' %
(len(borderpoints), ))
#if info is not None:
# b = birth[borderpoints[:,0], borderpoints[:,1], borderpoints[:,2]];
# bids = b == 0;
# birth[borderpoints[bids,0], borderpoints[bids,1], borderpoints[bids,2]] = step;
# sub iterations
remiter = 0
for i in range(12):
if verbose:
print(
'-------------------------------------------------------------'
)
print('Sub-Iteration %d' % i)
timer_sub_iter = tmr.Timer()
remborder = delete[cpl.convolve_3d_indices(binary, rotations[i],
borderpoints)]
rempoints = borderpoints[remborder]
if verbose:
timer_sub_iter.print_elapsed_time('Matched points : %d' %
(len(rempoints), ))
binary_flat[rempoints] = 0
keep[borderids[remborder]] = False
rem = len(rempoints)
remiter += rem
#death times
if with_info is True:
#remo = np.logical_not(keep);
deathflat[rempoints] = 12 * step + i
if verbose:
timer_sub_iter.print_elapsed_time('Sub-Iteration %d' % (i, ))
if verbose:
print(
'-------------------------------------------------------------'
)
#update foregroud
points = points[keep]
if step % 3 == 0:
npts = len(points)
points = points[consider[cpl.convolve_3d_indices(
binary, base, points)]]
nnonrem += npts - len(points)
if verbose:
print('Non-removable points: %d' % (npts - len(points)))
if verbose:
print('Foreground points : %d' % points.shape[0])
if verbose:
print(
'-------------------------------------------------------------'
)
timer_iter.print_elapsed_time('Iteration %d' % (step, ))
step += 1
if steps >= 0 and step >= steps:
break
if remiter == 0:
break
if verbose:
print('#############################################################')
timer.print_elapsed_time('Skeletonization done')
print('Total removed: %d' % (npoints - (len(points) + nnonrem)))
print('Total remaining: %d' % (len(points) + nnonrem))
if radii is True or return_points is True:
points = ap.where(binary_flat).array
if radii is True:
#calculate average diameter as death average death of neighbourhood
radii = cpl.convolve_3d_indices(death,
t3d.n18,
points,
out_dtype='uint16')
else:
radii = None
result = [binary]
if return_points:
result.append(points)
if removals is True:
result.append(death)
if radii is not None:
result.append(radii)
if len(result) > 1:
return tuple(result)
else:
return result[0]
def skeletonize(binary,
points=None,
steps=None,
removals=False,
radii=False,
check_border=True,
delete_border=False,
return_points=False,
verbose=True):
"""Skeletonize a binary 3d array using PK12 algorithm.
Arguments
---------
binary : array
Binary image to skeletonize.
points : array or None.
Optional list of points in the binary to speed up processing.
steps : int or None
Number of maximal iteration steps (if None maximal reduction).
removals : bool
If True, returns also the steps at which the pixels in the input data
where removed.
radii : bool
If True, the estimate of the local radius is returned.
check_border : bool
If True, check if the boder is empty. The algorithm reuqires this.
delete_border : bool
If True, delete the border.
verbose : bool
If True print progress info.
Returns
-------
skeleton : array
The skeleton of the binary.
points : array
The point coordinates of the skeleton nx3
Note
----
The skeletonization is done in place on the binary. Copy the binary if
needed for further processing.
"""
if verbose:
print('#############################################################')
print('Skeletonization PK12 [convolution]')
timer = tmr.Timer()
#TODO: make this work for any memmapable source !
if not isinstance(binary, np.ndarray):
raise ValueError('Numpy array required for binary in skeletonization!')
if binary.ndim != 3:
raise ValueError('The binary array dimension is %d, 3 is required!' %
binary.ndim)
if delete_border:
binary = t3d.delete_border(binary)
check_border = False
if check_border:
if not t3d.check_border(binary):
raise ValueError(
'The binary array needs to have no points on the border!')
# detect points
#points = np.array(np.nonzero(binary)).T;
if points is None:
points = ap.where(binary).array
if verbose:
timer.print_elapsed_time(head='Foreground points: %d' %
(points.shape[0], ))
if removals is True or radii is True:
#birth = np.zeros(binary.shape, dtype = 'uint16');
death = np.zeros(binary.shape, dtype='uint16')
with_info = True
else:
with_info = False
# iterate
if steps is None:
steps = -1
step = 1
removed = 0
while True:
if verbose:
print(
'#############################################################'
)
print('Iteration %d' % step)
timer_iter = tmr.Timer()
border = cpl.convolve_3d_points(binary, t3d.n6, points) < 6
borderpoints = points[border]
borderids = np.nonzero(border)[0]
keep = np.ones(len(border), dtype=bool)
if verbose:
timer_iter.print_elapsed_time('Border points: %d' %
(len(borderpoints), ))
#if info is not None:
# b = birth[borderpoints[:,0], borderpoints[:,1], borderpoints[:,2]];
# bids = b == 0;
# birth[borderpoints[bids,0], borderpoints[bids,1], borderpoints[bids,2]] = step;
# sub iterations
remiter = 0
for i in range(12):
if verbose:
print(
'-------------------------------------------------------------'
)
print('Sub-Iteration %d' % i)
timer_sub_iter = tmr.Timer()
remborder = delete[cpl.convolve_3d_points(binary, rotations[i],
borderpoints)]
rempoints = borderpoints[remborder]
if verbose:
timer_sub_iter.print_elapsed_time('Matched points: %d' %
(len(rempoints), ))
binary[rempoints[:, 0], rempoints[:, 1], rempoints[:, 2]] = 0
keep[borderids[remborder]] = False
rem = len(rempoints)
remiter += rem
removed += rem
if verbose:
print('Deleted points: %d' % (rem))
timer_sub_iter.print_elapsed_time('Sub-Iteration %d' % (i))
#death times
if with_info is True:
#remo = np.logical_not(keep);
death[rempoints[:, 0], rempoints[:, 1],
rempoints[:, 2]] = 12 * step + i
#update foreground
points = points[keep]
if verbose:
print('Foreground points: %d' % points.shape[0])
if verbose:
print(
'-------------------------------------------------------------'
)
timer_iter.print_elapsed_time('Iteration %d' % (step, ))
step += 1
if steps >= 0 and step >= steps:
break
if remiter == 0:
break
if verbose:
print('#############################################################')
print('Total removed: %d' % (removed))
print('Total remaining: %d' % (len(points)))
timer.print_elapsed_time('Skeletonization')
result = [binary]
if return_points:
result.append(points)
if removals is True:
result.append(death)
if radii is True:
#calculate average diameter as average death of neighbourhood
radii = cpl.convolve_3d(death, np.array(t3d.n18, dtype='uint16'),
points)
result.append(radii)
if len(result) > 1:
return tuple(result)
else:
return result[0]
def match_index(index, verbose=True):
if verbose and index % 2**14 == 0:
print('PK12 LUT: %d / %d' % (index, 2**26))
cube = t3d.cube_from_index(index=index, center=True)
return match(cube)
filename = os.path.join(os.path.dirname(os.path.abspath(__file__)),
filename)
#check if only compressed file exists
fu.uncompress(filename)
if os.path.exists(filename):
return np.load(filename)
else:
lut = generate_lookup_table(function=function)
np.save(filename, lut)
return lut
base = t3d.cube_base_2(center=False)
"""Base kernel to multiply with cube to obtain index of cube"""
delete = initialize_lookup_table()
"""Lookup table mapping cube index to its deleteability"""
keep = np.logical_not(delete)
"""Lookup table mapping cube index to its non-deleteability"""
filename_non_removable = "PK12nr.npy"
"""Filename for the lookup table mapping a cube configuration to the non-removeability of the center pixel"""
non_removable = initialize_lookup_table(filename=filename_non_removable,
function=match_non_removable)
"""Lookup table mapping cube index to its non-removeability"""
def _test():
import numpy as np
import ClearMap.ParallelProcessing.DataProcessing.ArrayProcessing as ap
from importlib import reload
reload(ap)
## Lookup table processing
#apply_lut
x = np.random.randint(0, 100, size=(20,30));
lut = np.arange(100) + 1;
y = ap.apply_lut(x, lut)
assert np.all(y == x+1)
#apply_lut_to_index
import ClearMap.ImageProcessing.Topology.Topology3d as t3d
kernel = t3d.index_kernel(dtype=int);
import ClearMap.ImageProcessing.Binary.Smoothing as sm
lut = sm.initialize_lookup_table()
data = np.random.randint(0, 2, (1500,300,400), dtype = bool)
#reload(ap)
result = ap.apply_lut_to_index(data, kernel, lut, sink=None, verbose=True)
import ClearMap.Visualization.Plot3d as p3d
p3d.plot([data, result])
## Correlation
#correlate1d
#reload(ap)
axis = 1;
kernel = np.array(range(11), dtype='uint32');
data = np.random.randint(0, 2**27, (1000, 1500,100), dtype='uint32');
corr = ap.correlate1d(data, kernel, axis=axis, verbose=True, processes=10);
import scipy.ndimage as ndi
import ClearMap.Utils.Timer as tmr
timer = tmr.Timer();
corr_ndi = ndi.correlate1d(data, kernel, axis=axis, mode='constant',cval=0);
timer.print_elapsed_time('ndi')
assert np.allclose(corr, corr_ndi)
#
#
#
#
#
#default_blocks_per_process = 10;
#"""Default number of blocks per process to split the data.
#
#Note
#----
#10 blocks per process is a good choice.
#"""
#
#default_cutoff = 20000000;
#"""Default size of array below which ordinary numpy is used.
#
#Note
#----
#Ideally test this on your machine for different array sizes.
#"""
#
#
#
#def blockRanges(data, blocks = None, processes = defaultProcesses):
# """Ranges of evenly spaced blocks in array
#
# Arguments:
# data : array
# array to divide in blocks
# blocks : int or None
# number of blocks to split array into
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# list of the range boundaries
# """
# if processes is None:
# processes = defaultProcesses;
# if blocks is None:
# blocks = processes * defaultBlocksPerProcess;
#
# d = data.reshape(-1, order = 'A');
# blocks = min(blocks, d.shape[0]);
# return np.array(np.linspace(0, d.shape[0], blocks + 1), dtype = int);
#
#
#def blockSums(data, blocks = None, processes = defaultProcesses):
# """Sums of evenly spaced blocks in array
#
# Arguments:
# data : array
# array to perform the block sums on
# blocks : int or None
# number of blocks to split array into
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# sums of the values in the different blocks
# """
# if processes is None:
# processes = defaultProcesses;
# if blocks is None:
# blocks = processes * defaultBlocksPerProcess;
#
# d = data.reshape(-1, order = 'A');
# if data.dtype == bool:
# d = d.view('uint8')
#
# return code.blockSums1d(d, blocks = blocks, processes = processes);
#
#
#def where(data, out = None, blocks = None, cutoff = defaultCutoff, processes = defaultProcesses):
# """Returns the indices of the non-zero entries of the array
#
# Arguments:
# data : array
# array to search for nonzero indices
# out : array or None
# if not None results is written into this array
# blocks : int or None
# number of blocks to split array into for parallel processing
# cutoff : int
# number of elements below whih to switch to numpy.where
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# positions of the nonzero entries of the input array
#
# Note:
# Uses numpy.where if there is no match of dimension implemented!
# """
# if data.ndim != 1 and data.ndim != 3:
# raise Warning('Using numpy where for dimension %d and type %s!' % (data.ndim, data.dtype))
# return np.vstack(np.where(data)).T;
#
# if cutoff is None:
# cutoff = 1;
# cutoff = min(1, cutoff);
# if data.size <= cutoff:
# return np.vstack(np.where(data)).T;
#
# if processes is None:
# processes = defaultProcesses;
# if blocks is None:
# blocks = processes * defaultBlocksPerProcess;
#
# if data.dtype == bool:
# d = data.view('uint8')
# else:
# d = data;
#
# if out is None:
# if d.ndim == 1:
# sums = code.blockSums1d(d, blocks = blocks, processes = processes);
# else:
# sums = code.blockSums3d(d, blocks = blocks, processes = processes);
# out = np.squeeze(np.zeros((np.sum(sums), data.ndim), dtype = np.int));
# else:
# sums = None;
#
# if d.ndim == 1:
# code.where1d(d, out = out, sums = sums, blocks = blocks, processes = processes);
# else: # d.ndim == 3:
# code.where3d(d, out = out, sums = sums, blocks = blocks, processes = processes);
#
# return out;
#
#
#
#
#def setValue(data, indices, value, cutoff = defaultCutoff, processes = defaultProcesses):
# """Set value at specified indices of an array
#
# Arguments:
# data : array
# array to search for nonzero indices
# indices : array or None
# list of indices to set
# value : numeric or bool
# value to set elements in data to
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# array with specified entries set to new value
#
# Note:
# Uses numpy if there is no match of dimension implemented!
# """
# if data.ndim != 1:
# raise Warning('Using numpy where for dimension %d and type %s!' % (data.ndim, data.dtype))
# data[indices] = value;
# return data;
#
# if cutoff is None:
# cutoff = 1;
# cutoff = min(1, cutoff);
# if data.size <= cutoff:
# data[indices] = value;
# return data;
#
# if processes is None:
# processes = defaultProcesses;
#
# if data.dtype == bool:
# d = data.view('uint8')
# else:
# d = data;
#
# code.set1d(d, indices, value, processes = processes);
#
# return data;
#
#
#def setArray(data, indices, values, cutoff = defaultCutoff, processes = defaultProcesses):
# """Set value at specified indices of an array
#
# Arguments:
# data : array
# array to search for nonzero indices
# indices : array or None
# list of indices to set
# values : array
# values to set elements in data to
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# array with specified entries set to new value
#
# Note:
# Uses numpy if there is no match of dimension implemented!
# """
# if data.ndim != 1:
# raise Warning('Using numpy where for dimension %d and type %s!' % (data.ndim, data.dtype))
# data[indices] = values;
# return data;
#
# if cutoff is None:
# cutoff = 1;
# cutoff = min(1, cutoff);
# if data.size <= cutoff:
# data[indices] = values;
# return data;
#
# if processes is None:
# processes = defaultProcesses;
#
# if data.dtype == bool:
# d = data.view('uint8')
# else:
# d = data;
#
# code.set1darray(d, indices, values, processes = processes);
#
# return data;
#
#
#
#def take(data, indices, out = None, cutoff = defaultCutoff, processes = defaultProcesses):
# """Extracts the values at specified indices
#
# Arguments:
# data : array
# array to search for nonzero indices
# out : array or None
# if not None results is written into this array
# cutoff : int
# number of elements below whih to switch to numpy.where
# processes : None or int
# number of processes, if None use number of cpus
#
# Returns:
# array
# positions of the nonzero entries of the input array
#
# Note:
# Uses numpy data[indices] if there is no match of dimension implemented!
# """
# if data.ndim != 1:
# raise Warning('Using numpy where for dimension %d and type %s!' % (data.ndim, data.dtype))
# return data[indices];
#
# if cutoff is None:
# cutoff = 1;
# cutoff = min(1, cutoff);
# if data.size < cutoff:
# return data[indices];
#
# if processes is None:
# processes = defaultProcesses;
#
# if data.dtype == bool:
# d = data.view('uint8')
# else:
# d = data;
#
# if out is None:
# out = np.empty(len(indices), dtype = data.dtype);
# if out.dtype == bool:
# o = out.view('uint8');
# else:
# o = out;
#
# code.take1d(d, indices, o, processes = processes);
#
# return out;
#
#
#def match(match, indices, out = None):
# """Matches a sorted list of 1d indices to another larger one
#
# Arguments:
# match : array
# array of indices to match to indices
# indices : array or None
# array of indices
#
# Returns:
# array
# array with specified entries set to new value
#
# Note:
# Uses numpy if there is no match of dimension implemented!
# """
# if match.ndim != 1:
# raise ValueError('Match array dimension required to be 1d, found %d!' % (match.ndim))
# if indices.ndim != 1:
# raise ValueError('Indices array dimension required to be 1d, found %d!' % (indices.ndim))
#
# if out is None:
# out = np.empty(len(match), dtype = match.dtype);
#
# code.match1d(match, indices, out);
#
# return out;
#
#
# Find neighbours in an index list
#
#
#def neighbours(indices, offset, processes = defaultProcesses):
# """Returns all pairs of indices that are apart a specified offset"""
# return code.neighbours(indices, offset = offset, processes = processes);
#
#
#def findNeighbours(indices, center, shape, strides, mask):
# """Finds all indices within a specified kernel region centered at a point"""
#
# if len(strides) != 3 or len(shape) != 3 or (strides[0] != 1 and strides[2] != 1):
# raise RuntimeError('only 3d C or F contiguous arrays suported');
#
# if isinstance(mask, int):
# mask = (mask,);
# if isinstance(mask, tuple):
# mask = mask * 3;
# return code.neighbourlistRadius(indices, center, shape[0], shape[1], shape[2],
# strides[0], strides[1], strides[2],
# mask[0], mask[1], mask[2]);
# else:
# if mask.dtype == bool:
# mask = mask.view(dtype = 'uint8');
#
# return code.neighbourlistMask(indices, center, shape[0], shape[1], shape[2], strides[0], strides[1], strides[2], mask);
#
# Loading and saving
#
#def readNumpyHeader(filename):
# """Read numpy array information including offset to data
#
# Arguments:
# filename : str
# file name of the numpy file
#
# Returns:
# shape : tuple
# shape of the array
# dtype : dtype
# data type of array
# order : str
# 'C' for c and 'F' for fortran order
# offset : int
# offset in bytes to data buffer in file
# """
# with open(filename, 'rb') as fhandle:
# major, minor = np.lib.format.read_magic(fhandle);
# shape, fortran, dtype = np.lib.format.read_array_header_1_0(fhandle);
# offset = fhandle.tell()
#
# order = 'C';
# if fortran:
# order = 'F';
#
# return (shape, dtype, order, offset)
#
#
#def _offsetFromSlice(sourceSlice, order = 'F'):
# """Checks if slice is compatible with the large data loader and returns z coordiante"""
#
# if order == 'C':
# os = 1; oe = 3; oi = 0;
# else:
# os = 0; oe = 2; oi = 2;
#
# for s in sourceSlice[os:oe]:
# if s.start is not None or s.stop is not None or s.step is not None:
# raise RuntimeError('sub-regions other than in slowest dimension %d not supported! slice = %r' % (oi, sourceSlice))
#
# s = sourceSlice[oi];
# if s.step is not None:
# raise RuntimeError('sub-regions with non unity steps not supported')
#
# if s.start is None:
# s = 0;
# else:
# s = s.start;
#
# return s;
#
#
#def load(filename, region = None, shared = False, blocks = None, processes = cpu_count(), verbose = False):
# """Load a large npy array into memory in parallel
#
# Arguments:
# filename : str
# filename of array to load
# region : Region or None
# if not None this specifies the sub-region to read
# shared : bool
# if True read into shared memory
# blocks : int or None
# number of blocks to split array into for parallel processing
# processes : None or int
# number of processes, if None use number of cpus
# verbose : bool
# print info about the file to be loaded
#
# Returns:
# array
# the data as numpy array
# """
# if processes is None:
# processes = cpu_count();
# if blocks is None:
# blocks = processes * defaultBlocksPerProcess;
#
# #get specs from header specs
# shape, dtype, order, offset = readNumpyHeader(filename);
# if verbose:
# timer = tmr.Timer();
# print('Loading array of shape = %r, dtype = %r, order = %r, offset = %r' %(shape, dtype, order, offset));
#
# if region is not None:
# shape = region.shape();
# sourceSlice = region.sourceSlice();
# off = _offsetFromSlice(sourceSlice, order = order);
#
# if shared:
# data = shm.create(shape, dtype = dtype, order = order);
# else:
# data = np.empty(shape, dtype = dtype, order = order);
#
# d = data.reshape(-1, order = 'A');
# if dtype == bool:
# d = d.view('uint8');
#
# if region is not None:
# if order == 'F':
# offset += data.strides[-1] * off;
# else:
# offset += data.strides[1] * off;
#
# code.load(data = d, filename = filename, offset = offset, blocks = blocks, processes = processes);
#
# if verbose:
# timer.printElapsedTime(head = 'Loading array from %s' % filename);
#
# return data;
#
#
#
#
#def save(filename, data, region = None, blocks = None, processes = cpu_count(), verbose = False):
# """Save a large npy array to disk in parallel
#
# Arguments:
# filename : str
# filename of array to load
# data : array
# array to save to disk
# blocks : int or None
# number of blocks to split array into for parallel processing
# processes : None or int
# number of processes, if None use number of cpus
# verbose : bool
# print info about the file to be loaded
#
# Returns:
# str
# the filename of the numpy array on disk
# """
# if processes is None:
# processes = cpu_count();
# if blocks is None:
# blocks = processes * defaultBlocksPerProcess;
#
# if region is None:
# #create file on disk via memmap
# memmap = np.lib.format.open_memmap(filename, mode = 'w+', shape = data.shape, dtype = data.dtype, fortran_order = np.isfortran(data));
# memmap.flush();
# del(memmap);
#
# #get specs from header specs
# shape, dtype, order, offset = readNumpyHeader(filename);
# if verbose:
# timer = tmr.Timer();
# print('Saving array of shape = %r, dtype = %r, order = %r, offset = %r' %(shape, dtype, order, offset));
#
# if (np.isfortran(data) and order != 'F') or (not np.isfortran(data) and order != 'C'):
# raise RuntimeError('Order of arrays do not match isfortran=%r and order=%s' % (np.isfortran(data), order));
#
# d = data.reshape(-1, order = 'A');
# if dtype == bool:
# d = d.view('uint8');
#
# if region is not None:
# sourceSlice = region.sourceSlice();
# off = _offsetFromSlice(sourceSlice, order = order);
# if order == 'F':
# offset += data.strides[-1] * off;
# else:
# offset += data.strides[1] * off;
#
# #print d.dtype, filename, offset, blocks, processes
#
# code.save(data = d, filename = filename, offset = offset, blocks = blocks, processes = processes);
#
# if verbose:
# timer.printElapsedTime(head = 'Saving array to %s' % filename);
#
# return filename;
#
#
#
#
#
#
#if __name__ == "__main__":
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
#
# #dat = np.random.rand(2000,2000,1000) > 0.5;
# #dat = np.random.rand(1000,1000,500) > 0.5;
# dat = np.random.rand(200,300,400) > 0.5;
# #datan = io.MMP.writeData('test.npy', dat);
#
# dat = np.load('data.npy')
# xyz1 = np.load('points.npy')
#
# s = ld.sum(dat)
# print(s == np.sum(s))
#
#
# timer = Timer();
# xyz = ld.where(dat)
# timer.printElapsedTime('parallel')
# #parallel: elapsed time: 0:00:25.807
#
# timer = Timer();
# xyz1 = np.vstack(np.where(dat)).T
# timer.printElapsedTime('numpy')
# #numpy: elapsed time: 0:05:45.590
#
#
# d0 = np.zeros(dat.shape, dtype = bool);
# d1 = np.zeros(dat.shape, dtype = bool);
#
# d0[xyz[:,0], xyz[:,1], xyz[:,2]] = True;
# d1[xyz1[:,0], xyz1[:,1], xyz1[:,2]] = True;
# np.all(d0 == d1)
#
# dat2 = np.array(np.random.rand(1000, 1000, 1000) > 0, dtype = 'bool');
# filename = 'test.npy';
# np.save(filename, dat2)
#
# filename = '/disque/raid/vasculature/4X-test2/170824_IgG_2/170824_IgG_16-23-46/rank_threshold.npy'
#
# timer = Timer();
# ldat = ld.load(filename, verbose = True);
# timer.printElapsedTime('load')
# #load: elapsed time: 0:00:04.867
#
# timer = Timer();
# ldat2 = np.load(filename);
# timer.printElapsedTime('numpy')
# #numpy: elapsed time: 0:00:27.982
#
# np.all(ldat == ldat2)
#
# timer = Timer();
# xyz = ld.where(ldat)
# timer.printElapsedTime('parallel')
# #parallel: elapsed time: 0:07:25.698
#
# lldat = ldat.reshape(-1, order = 'A')
# timer = Timer();
# xyz = ld.where(lldat)
# timer.printElapsedTime('parallel 1d')
# #parallel 1d: elapsed time: 0:00:49.034
#
# timer = Timer();
# xyz = np.where(ldat)
# timer.printElapsedTime('numpy')
#
#
# import os
# #os.remove(filename)
#
# filename = './ClearMap/Test/Skeletonization/test_bin.npy';
# timer = Timer();
# ldat = ld.load(filename, shared = True, verbose = True);
# timer.printElapsedTime('load')
#
# ld.shm.isShared(ldat);
#
#
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
# filename = 'test_save.npy';
#
# dat = np.random.rand(100,200,100);
#
# ld.save(filename, dat)
#
#
# dat2 = ld.load(filename)
#
# np.all(dat == dat2)
#
# os.remove(filename)
#
#
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
# dat = np.zeros(100, dtype = bool);
# dat2 = dat.copy();
#
# indices = np.array([5,6,7,8,13,42])
#
# ld.setValue(dat, indices, True, cutoff = 0);
#
# dat2[indices] = True;
# np.all(dat2 == dat)
#
# d = ld.take(dat, indices, cutoff = 0)
# np.all(d)
#
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
#
# pts = np.array([0,1,5,6,10,11], dtype = int);
#
# ld.neighbours(pts, -10)
#
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# import ClearMap.ImageProcessing.Filter.StructureElement as sel;
# reload(ld)
#
# dat = np.random.rand(30,40,50) > 0.5;
# mask = sel.structureElement('Disk', (5,5,5));
# indices = np.where(dat.reshape(-1))[0];
# c_id = len(indices)/2;
# c = indices[c_id];
# xyz = np.unravel_index(c, dat.shape)
# l = np.array(mask.shape)/2
# r = np.array(mask.shape) - l;
# dlo = [max(0,xx-ll) for xx,ll in zip(xyz,l)];
# dhi = [min(xx+rr,ss) for xx,rr,ss in zip(xyz,r, dat.shape)]
# mlo = [-min(0,xx-ll) for xx,ll in zip(xyz,l)];
# mhi = [mm + min(0, ss-xx-rr) for xx,rr,ss,mm in zip(xyz,r, dat.shape, mask.shape)]
#
# nbh = dat[dlo[0]:dhi[0], dlo[1]:dhi[1], dlo[2]:dhi[2]];
# nbhm = np.logical_and(nbh, mask[mlo[0]:mhi[0], mlo[1]:mhi[1], mlo[2]:mhi[2]] > 0);
# nxyz = np.where(nbhm);
# nxyz = [nn + dl for nn,dl in zip(nxyz, dlo)];
# nbi = np.ravel_multi_index(nxyz, dat.shape);
#
# nbs = ld.findNeighbours(indices, c_id , dat.shape, dat.strides, mask)
#
# nbs.sort();
# print np.all(nbs == nbi)
#
#
# dat = np.random.rand(30,40,50) > 0.5;
# indices = np.where(dat.reshape(-1))[0];
# c_id = len(indices)/2;
# c = indices[c_id];
# xyz = np.unravel_index(c, dat.shape)
# l = np.array([2,2,2]);
# r = l + 1;
# dlo = [max(0,xx-ll) for xx,ll in zip(xyz,l)];
# dhi = [min(xx+rr,ss) for xx,rr,ss in zip(xyz, r, dat.shape)]
# nbh = dat[dlo[0]:dhi[0], dlo[1]:dhi[1], dlo[2]:dhi[2]];
# nxyz = np.where(nbh);
# nxyz = [nn + dl for nn,dl in zip(nxyz, dlo)];
# nbi = np.ravel_multi_index(nxyz, dat.shape);
#
# nbs = ld.findNeighbours(indices, c_id , dat.shape, dat.strides, tuple(l))
#
# nbs.sort();
# print np.all(nbs == nbi)
#
# print nbs
# print nbi
#
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
# data = np.random.rand(100);
# values =np.random.rand(50);
# indices = np.arange(50);
# ld.setArray(data, indices, values, cutoff = 1)
# print np.all(data[:50] == values)
#
# import numpy as np
# from ClearMap.Utils.Timer import Timer;
# import ClearMap.DataProcessing.LargeData as ld
# reload(ld)
#
# m = np.array([1,3,6,7,10]);
# i = np.array([1,2,3,4,6,7,8,9]);
#
# o = ld.match(m,i)
#
# o2 = [np.where(i==l)[0][0] for l in m]
#
#
#
def _test():
import numpy as np
import ClearMap.ImageProcessing.Topology.Topology3d as top
from importlib import reload
reload(top)
label = top.cube_labeled()
top.print_cube(label)
# Test rotations
c = np.zeros((3, 3, 3), dtype=bool)
c[1, 0, 0] = True
top.print_cube(c)
cs = [top.rotate(c, axis=2, steps=r) for r in range(4)]
[top.print_cube(cc) for cc in cs]
reload(top)
l = top.cube_labeled()
rts = top.rotations6(l)
[top.print_cube(r) for r in rts]
reload(top)
b = top.cube_from_index(6)
i = top.cube_to_index(b)
print(i, 6)
us = np.zeros((3, 3, 3), dtype=int)
us[1, 1, 2] = 1
us[1, 0, 1] = 1
us[1, 2, 0] = 2
r12 = top.rotations12(us)
[top.print_cube(cc) for cc in r12]
#check configuration utlity
reload(top)
index = 11607
source = top.cube_from_index(index)
c = top.index_from_binary(source)
c[1, 1, 1] == index
x = np.random.rand(1500, 500, 500) > 0.6
c = top.index_from_binary(x)
import numpy as np
import ClearMap.ImageProcessing.Topology.Topology3d as top
#check fortran vs c order
x = np.random.rand(5, 5, 5) > 0.35
y = np.asanyarray(x, order='F')
ix = top.index_from_binary(x)
iy = top.index_from_binary(y)
ax = ix.array
ay = iy.array
#%% profile
import io
io.DEFAULT_BUFFER_SIZE = 2**32
import pstats, cProfile
import numpy as np
import ClearMap.ImageProcessing.Topology.Topology3d as top
x = np.ones((3000, 500, 1000), dtype=bool, order='F')
import ClearMap.IO.IO as io
import ClearMap.ParallelProcessing.DataProcessing.ArrayProcessing as ap
ap.write('test.npy', x)
y = io.as_source('test.npy')
z = io.create('resuly.npy', shape=y.shape, order='C', dtype='uint32')
cProfile.runctx(
"c =top.index_from_binary(y, method='!shared', sink=z, verbose=True, processes=None)",
globals(), locals(), "Profile.prof")
s = pstats.Stats("Profile.prof")
s.strip_dirs().sort_stats("time").print_stats()
import mmap
mmap.ACCESS_COPY
