def readData(filename, x = all, y = all, z = all, resolution = 0, channel = 0, timepoint = 0, **args):
"""Read data from imaris file
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
resolution (int): resolution level
channel (int): color channel
timepoint (int): time point
Returns:
array: image data
"""
f = h5py.File(filename, "r");
dataset = readDataSet(f, resolution = resolution, channel = channel, timepoint = timepoint);
dsize = dataset.shape;
rz = io.toDataRange(dsize[0], r = z);
ry = io.toDataRange(dsize[1], r = y);
rx = io.toDataRange(dsize[2], r = x);
data = dataset[rz[0]:rz[1],ry[0]:ry[1],rx[0]:rx[1]];
data = data.transpose((2,1,0)); # imaris stores files in reverse x,y,z ordering
#data = dataset[x[0]:x[1],y[0]:y[1],z[0]:z[1]];
f.close();
return data;
def readDataFiles(filename, x=all, y=all, z=all, **args):
"""Read data from individual images assuming they are the z slices
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
array: image data
"""
fpath, fl = readFileList(filename)
nz = len(fl)
# read first image to get data size and type
rz = io.toDataRange(nz, r=z)
sz = io.toDataSize(nz, r=z)
fn = os.path.join(fpath, fl[rz[0]])
img = io.readData(fn, x=x, y=y)
nxy = img.shape
data = numpy.zeros(nxy + (sz,), dtype=img.dtype)
data[:, :, 0] = img
for i in range(rz[0] + 1, rz[1]):
fn = os.path.join(fpath, fl[i])
data[:, :, i - rz[0]] = io.readData(fn, x=x, y=y)
return data
def readData(filename, x=all, y=all, z=all, **args):
"""Read data from a single tif image or stack
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
array: image data
"""
dsize = dataSize(filename)
#print("dsize %s" % str(dsize);
if len(dsize) == 2:
data = tiff.imread(filename, key=0)
#print("data.shape %s" % str(data.shape);
return io.dataToRange(data.transpose([1, 0]), x=x, y=y)
#return io.dataToRange(data, x = x, y = y);
else:
if z is all:
data = tiff.imread(filename)
if data.ndim == 2:
# data = data
data = data.transpose([1, 0])
elif data.ndim == 3:
#data = data.transpose([1,2,0]);
data = data.transpose([2, 1, 0])
elif data.ndim == 4: # multi channel image
#data = data.transpose([1,2,0,3]);
data = data.transpose([2, 1, 0, 3])
else:
raise RuntimeError('readData: dimension %d not supproted!' %
data.ndim)
return io.dataToRange(data, x=x, y=y, z=all)
else: #optimize for z ranges
ds = io.dataSizeFromDataRange(dsize, x=x, y=y, z=z)
t = tiff.TiffFile(filename)
p = t.pages[0]
data = numpy.zeros(ds, dtype=p.dtype)
rz = io.toDataRange(dsize[2], r=z)
#print("test"
#print(rz;
#print(dsize
for i in range(rz[0], rz[1]):
xydata = t.pages[i].asarray()
#data[:,:,i-rz[0]] = io.dataToRange(xydata, x = x, y = y);
data[:, :, i - rz[0]] = io.dataToRange(xydata.transpose([1,
0]),
x=x,
y=y)
return data
def readData(filename, x = all, y = all, z = all, **args):
"""Read data from a single tif image or stack
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
Returns:
array: image data
"""
dsize = dataSize(filename);
#print "dsize %s" % str(dsize);
if len(dsize) == 2:
data = tiff.imread(filename, key = 0);
#print "data.shape %s" % str(data.shape);
return io.dataToRange(data.transpose([1,0]), x = x, y = y);
#return io.dataToRange(data, x = x, y = y);
else:
if z is all:
data = tiff.imread(filename);
if data.ndim == 2:
# data = data
data = data.transpose([1,0]);
elif data.ndim == 3:
#data = data.transpose([1,2,0]);
data = data.transpose([2,1,0]);
elif data.ndim == 4: # multi channel image
#data = data.transpose([1,2,0,3]);
data = data.transpose([2,1,0,3]);
else:
raise RuntimeError('readData: dimension %d not supproted!' % data.ndim)
return io.dataToRange(data, x = x, y = y, z = all);
else: #optimize for z ranges
ds = io.dataSizeFromDataRange(dsize, x = x, y = y, z = z);
t = tiff.TiffFile(filename);
p = t.pages[0];
data = numpy.zeros(ds, dtype = p.dtype);
rz = io.toDataRange(dsize[2], r = z);
#print "test"
#print rz;
#print dsize
for i in range(rz[0], rz[1]):
xydata = t.pages[i].asarray();
#data[:,:,i-rz[0]] = io.dataToRange(xydata, x = x, y = y);
data[:,:,i-rz[0]] = io.dataToRange(xydata.transpose([1,0]), x = x, y = y);
return data
def calculateSubStacks(source, z = all, x = all, y = all, **args):
"""Calculates the chunksize and other info for parallel processing and returns a list of sub-stack objects
The sub-stack information is described in :ref:`SubStack`
Arguments:
source (str): image source
x,y,z (tuple or all): range specifications
processes (int): number of parallel processes
chunkSizeMax (int): maximal size of a sub-stack
chunkSizeMin (int): minial size of a sub-stack
chunkOverlap (int): minimal sub-stack overlap
chunkOptimization (bool): optimize chunck sizes to best fit number of processes
chunkOptimizationSize (bool or all): if True only decrease the chunk size when optimizing
verbose (bool): print information on sub-stack generation
Returns:
list: list of sub-stack objects
"""
#determine z ranges
fs = io.dataSize(source);
zs = fs[2];
zr = io.toDataRange(zs, r = z);
nz = zr[1] - zr[0];
#calculate optimal chunk sizes
nchunks, zranges, zcenters = calculateChunkSize(nz, **args);
#adjust for the zrange
zcenters = [c + zr[0] for c in zcenters];
zranges = [(zc[0] + zr[0], zc[1] + zr[0]) for zc in zranges];
#create substacks
subStacks = [];
indexlo = zr[0];
for i in range(nchunks):
indexhi = int(round(zcenters[i+1]));
if indexhi > zr[1] or i == nchunks - 1:
indexhi = zr[1];
zs = zranges[i][1] - zranges[i][0];
subStacks.append({"stackId" : i, "nStacks" : nchunks,
"source" : source, "x" : x, "y" : y, "z" : zranges[i],
"zCenters" : (zcenters[i], zcenters[i+1]),
"zCenterIndices" : (indexlo, indexhi),
"zSubStackCenterIndices" : (indexlo - zranges[i][0], zs - (zranges[i][1] - indexhi))});
indexlo = indexhi; # + 1;
return subStacks;
def calculateSubStacks(source, z = all, x = all, y = all, **args):
"""Calculates the chunksize and other info for parallel processing and returns a list of sub-stack objects
The sub-stack information is described in :ref:`SubStack`
Arguments:
source (str): image source
x,y,z (tuple or all): range specifications
processes (int): number of parallel processes
chunkSizeMax (int): maximal size of a sub-stack
chunkSizeMin (int): minial size of a sub-stack
chunkOverlap (int): minimal sub-stack overlap
chunkOptimization (bool): optimize chunck sizes to best fit number of processes
chunkOptimizationSize (bool or all): if True only decrease the chunk size when optimizing
verbose (bool): print information on sub-stack generation
Returns:
list: list of sub-stack objects
"""
#determine z ranges
fs = io.dataSize(source);
zs = fs[2];
zr = io.toDataRange(zs, r = z);
nz = zr[1] - zr[0];
#calculate optimal chunk sizes
nchunks, zranges, zcenters = calculateChunkSize(nz, **args);
#adjust for the zrange
zcenters = [c + zr[0] for c in zcenters];
zranges = [(zc[0] + zr[0], zc[1] + zr[0]) for zc in zranges];
#create substacks
subStacks = [];
indexlo = zr[0];
for i in range(nchunks):
indexhi = int(round(zcenters[i+1]));
if indexhi > zr[1] or i == nchunks - 1:
indexhi = zr[1];
zs = zranges[i][1] - zranges[i][0];
subStacks.append({"stackId" : i, "nStacks" : nchunks,
"source" : source, "x" : x, "y" : y, "z" : zranges[i],
"zCenters" : (zcenters[i], zcenters[i+1]),
"zCenterIndices" : (indexlo, indexhi),
"zSubStackCenterIndices" : (indexlo - zranges[i][0], zs - (zranges[i][1] - indexhi))});
indexlo = indexhi; # + 1;
return subStacks;
def readData(filename,
x=all,
y=all,
z=all,
resolution=0,
channel=0,
timepoint=0,
**args):
"""Read data from imaris file
Arguments:
filename (str): file name as regular expression
x,y,z (tuple): data range specifications
resolution (int): resolution level
channel (int): color channel
timepoint (int): time point
Returns:
array: image data
"""
f = h5py.File(filename, "r")
dataset = readDataSet(f,
resolution=resolution,
channel=channel,
timepoint=timepoint)
dsize = dataset.shape
rz = io.toDataRange(dsize[0], r=z)
ry = io.toDataRange(dsize[1], r=y)
rx = io.toDataRange(dsize[2], r=x)
data = dataset[rz[0]:rz[1], ry[0]:ry[1], rx[0]:rx[1]]
data = data.transpose((2, 1, 0))
# imaris stores files in reverse x,y,z ordering
#data = dataset[x[0]:x[1],y[0]:y[1],z[0]:z[1]];
f.close()
return data
def cropData(source, sink=None, x=all, y=all, z=all, adjustOverlap=False, verbose=True, processes=all):
"""Crop source from start to stop point
Arguments:
source (str or array): filename or data array of source
sink (str or None): filename or sink
x,y,z (tuple or all): the range to crop the data to
adjustOverlap (bool): correct overlap meta data if exists
Return:
str or array: array or filename with cropped data
"""
if sink is None:
return readDataFiles(source, x=x, y=y, z=z)
else: # sink assumed to be file expression
if not io.isFileExpression(sink):
raise RuntimeError("cropping data to different format not supported!")
fileheader, fileext, digitfrmt = splitFileExpression(sink)
# read first image to get data size and type
fp, fl = readFileList(source)
nz = len(fl)
rz = io.toDataRange(nz, r=z)
if adjustOverlap: # change overlap in first file
try:
fn = os.path.join(fp, fl[0])
info = io.readMetaData(fn, info=["description", "overlap", "resolution"])
description = str(info["description"])
overlap = numpy.array(info["overlap"], dtype=float)
resolution = numpy.array(info["resolution"], dtype=float)
except:
raise RuntimeWarning("could not modify overlap!")
fullsize = io.dataSize(fn)
data = io.readData(fn, x=x, y=y)
# overlap in pixels
poverlap = overlap[:2] / resolution[:2]
print poverlap
# cropped pixel
xr = io.toDataRange(fullsize[0], r=x)
yr = io.toDataRange(fullsize[1], r=y)
print xr
print yr
print fullsize
poverlap[0] = poverlap[0] - xr[0] - (fullsize[0] - xr[1])
poverlap[1] = poverlap[1] - yr[0] - (fullsize[1] - yr[1])
print poverlap
# new overlap in microns
overlap = poverlap * resolution[:2]
# check for consistency
if numpy.abs(fullsize[0] - xr[1] - xr[0]) > 1 or numpy.abs(fullsize[1] - yr[1] - yr[0]) > 1:
raise RuntimeWarning("cropping is inconsistent with overlap )modification!")
# change image description
import ClearMap.IO.TIF as CMTIF
description = CMTIF.changeOMEMetaDataString(description, {"overlap": overlap})
print len(description)
# write first file
fnout = fileheader + (digitfrmt % 0) + fileext
io.writeData(fnout, data, info=description)
zr = range(rz[0] + 1, rz[1])
else:
zr = range(rz[0], rz[1])
print zr
nZ = len(zr)
if processes is None:
processes = 1
if processes is all:
processes = multiprocessing.cpu_count()
if processes > 1: # parallel processing
pool = multiprocessing.Pool(processes=processes)
argdata = []
for i, z in enumerate(zr):
if verbose:
argdata.append(
(os.path.join(fp, fl[z]), fileheader + (digitfrmt % (i + 1)) + fileext, x, y, (i + 1), (nZ + 1))
)
else:
argdata.append(
(os.path.join(fp, fl[z]), fileheader + (digitfrmt % (i + 1)) + fileext, x, y, None, None)
)
pool.map(_cropParallel, argdata)
else: # sequential processing
for i, z in enumerate(zr):
if verbose:
print "cropData: corpping image %d / %d" % (i + 1, nZ + 1)
fileSource = os.path.join(fp, fl[z])
data = io.readData(fileSource, x=x, y=y)
fileSink = fileheader + (digitfrmt % (i + 1)) + fileext
io.writeData(fileSink, data)
return sink
