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 _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 _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]
#
#
#