def __init__(self, token, channel, resolution, file_type="tif"):
# set the channel, res
self.channel = channel
self.resolution = resolution
# load the project here
self.proj = ndproj.NDProjectsDB().loadToken(token)
self.file_type = file_type
def deleteChannel(webargs, post_data):
"""Delete a list of channels using a JSON file"""
# Get the token and load the project
try:
m = re.match("(\w+)/deleteChannel/$", webargs)
token_name = m.group(1)
except Exception as e:
print("Error in URL format")
raise
nd_dict = json.loads(post_data)
try:
channels = nd_dict["channels"]
except Exception as e:
print("Missing requred fields")
raise
tk = Token.objects.get(token_name=token_name)
ur = User.objects.get(id=tk.user_id)
pr = Project.objects.get(project_name=tk.project_id)
try:
# Iterating over channel list to store channels
for channel_name in channels:
# Checking if the channel already exists or not
if Channel.objects.get(channel_name=channel_name,
project=pr.project_name):
ch = Channel.objects.get(channel_name=channel_name,
project=pr.project_name)
# Checking if channel is readonly or not
if ch.readonly == READONLY_FALSE:
# delete channel table using the ndproj interface
pd = ndproj.NDProjectsDB().getProjDB(pr.project_name)
pd.deleteNDChannel(ch.channel_name)
ch.delete()
return_json = "SUCCESS"
except Exception as e:
print("Error saving models")
return_json = "FAILED"
return json.dumps(return_json)
def __init__(self, token, channels, centroid):
# arguments
self.token = token
self.channels = channels
self.centroid = centroid
# parameter defaults. set be accessors.
self.sog_width = 200
self.sog_frame = 20
self.width = 11
self.normalize = True
self.normalize2 = False
self.resolution = 0
self.refchannels = []
self.emchannels = []
self.enhance = None
# pattern for using contexts to close databases
# get the project
with closing(ndproj.NDProjectsDB()) as projdb:
self.proj = projdb.loadToken(token)
def uploadExistingProject(self):
"""Upload an existing project to S3"""
# Uploading to a bucket
with closing(ndproj.NDProjectsDB()) as projdb:
proj = projdb.loadToken(self.token)
with closing(spatialdb.SpatialDB(proj)) as db:
ch = proj.getChannelObj(self.channel_name)
if self.res == 0:
start_res = 0
stop_res = proj.datasetcfg.scalinglevels
else:
start_res = self.res
stop_res = self.res + 1
for cur_res in range(start_res, stop_res):
start = time.time()
# Get the source database sizes
[[ximagesz, yimagesz, zimagesz],
timerange] = proj.datasetcfg.imageSize(cur_res)
[xcubedim, ycubedim,
zcubedim] = cubedim = proj.datasetcfg.getCubeDims()[cur_res]
[xoffset, yoffset,
zoffset] = proj.datasetcfg.getOffset()[cur_res]
[xs, ys, zs] = supercubedim = map(mul, cubedim, SUPERCUBESIZE)
# Set the limits for iteration on the number of cubes in each dimension
xlimit = (ximagesz - 1) / (xs) + 1
ylimit = (yimagesz - 1) / (ys) + 1
zlimit = (zimagesz - 1) / (zs) + 1
for z in range(zlimit):
for y in range(ylimit):
for x in range(xlimit):
# cutout the data at the current resolution
data = db.cutout(ch, [x * xs, y * ys, z * zs],
[xs, ys, zs], cur_res).data
zidx = ndlib.XYZMorton([x, y, z])
# m = hashlib.md5()
# m.update('{}_{}'.format(zidx,cur_res))
# s3_key = m.hexdigest()
# generateS3Key(ch.getChannelName(), cur_res, zidx)
print "Inserting Cube {} at res {}".format(
zidx, cur_res), [x, y, z]
# data = blosc.pack_array(data)
s3io.putCube(ch, cur_res, zidx,
blosc.pack_array(data))
# Uploading the object to S3
# bucket.put_object(Key=s3_key, Body=data)
print "Time for Resolution {} : {} secs".format(
cur_res,
time.time() - start)
def uploadCatmaidProject(self):
"""Ingest a CATMAID tile stack"""
tilesz = 1024
# Load a database
proj = ndproj.NDProjectsDB().loadToken(self.token)
db = spatialdb.SpatialDB(proj)
s3db = s3io.S3IO(db)
ch = proj.getChannelObj(self.channel_name)
# creating bucket
# self.createS3Bucket(proj.getProjectName())
# get the dataset configuration
[[ximagesz, yimagesz, zimagesz],
(starttime, endtime)] = proj.datasetcfg.imageSize(self.resolution)
[xcubedim, ycubedim,
zcubedim] = cubedim = proj.datasetcfg.getCubeDims()[self.resolution]
[xoffset, yoffset,
zoffset] = proj.datasetcfg.getOffset()[self.resolution]
[xsupercubedim, ysupercubedim,
zsupercubedim] = supercubedim = map(mul, cubedim, SUPERCUBESIZE)
if ch.getChannelType() in TIMESERIES_CHANNELS:
logger.error(
"Timeseries data not supported for CATMAID data. Error in {}".
format(self.token))
raise NDWSError(
"Timeseries data not supported for CATMAID data. Error in {}".
format(self.token))
num_xtiles = ximagesz / tilesz
num_ytiles = yimagesz / tilesz
# over all the tiles in the slice
for ytile in range(0, num_ytiles, 1):
for xtile in range(0, num_xtiles, 1):
# Get a list of the files in the directories
for slice_number in range(zoffset, zimagesz, zsupercubedim):
# empty slab
slab = np.zeros([zsupercubedim, tilesz, tilesz],
dtype=ND_dtypetonp.get(ch.getDataType()))
# prefetch data
self.fetchCatmaidData(
range(slice_number, slice_number + zsupercubedim)
if slice_number + zsupercubedim <= zimagesz else range(
slice_number, zimagesz), xtile, ytile)
for b in range(zsupercubedim):
if (slice_number + b < zimagesz):
try:
# reading the raw data
file_name = "{}{}".format(
self.data_location,
self.generateCatmaidFileName(
slice_number + b, xtile, ytile))
logger.info(
"Open filename {}".format(file_name))
# print "Open filename {}".format(file_name)
slab[b, :, :] = np.asarray(
Image.open(file_name, 'r'))[:, :, 0]
except IOError, e:
logger.warning("IOError {}.".format(e))
slab[b, :, :] = np.zeros(
(tilesz, tilesz),
dtype=ND_dtypetonp.get(ch.getDataType()))
for y in range(ytile * tilesz, (ytile + 1) * tilesz,
ysupercubedim):
for x in range(xtile * tilesz, (xtile + 1) * tilesz,
xsupercubedim):
# Getting a Cube id and ingesting the data one cube at a time
zidx = ndlib.XYZMorton([
(x - xoffset) / xsupercubedim,
(y - yoffset) / ysupercubedim,
(slice_number - zoffset) / zsupercubedim
])
cube = Cube.getCube(supercubedim,
ch.getChannelType(),
ch.getDataType())
cube.zeros()
xmin = x % tilesz
ymin = y % tilesz
xmax = (min(ximagesz - xoffset - 1,
x + xsupercubedim - 1) % tilesz) + 1
ymax = (min(yimagesz - yoffset - 1,
y + ysupercubedim - 1) % tilesz) + 1
zmin = 0
zmax = min(slice_number - zoffset + zsupercubedim,
zimagesz + 1)
cube.data[0:zmax - zmin, 0:ymax - ymin,
0:xmax - xmin] = slab[zmin:zmax,
ymin:ymax,
xmin:xmax]
if cube.isNotZeros():
s3db.putCube(ch, self.resolution, zidx,
cube.toBlosc())
# clean up the slices fetched
self.cleanCatmaidData(
range(slice_number, slice_number + zsupercubedim)
if slice_number + zsupercubedim <= zimagesz else range(
slice_number, zimagesz), xtile, ytile)
def main():
parser = argparse.ArgumentParser(description='Ingest the TIFF data')
parser.add_argument('token',
action="store",
type=str,
help='Token for the project')
parser.add_argument('channel',
action="store",
type=str,
help='Channel for the project')
parser.add_argument('path',
action="store",
type=str,
help='Directory with the image files')
parser.add_argument('resolution',
action="store",
type=int,
help='Resolution of data')
result = parser.parse_args()
# Load a database
with closing(ndproj.NDProjectsDB()) as projdb:
proj = projdb.loadToken(result.token)
with closing(SpatialDB(proj)) as db:
ch = proj.getChannelObj(result.channel)
# get the dataset configuration
[[ximagesz, yimagesz, zimagesz],
(starttime, endtime)] = proj.datasetcfg.imageSize(result.resolution)
[xcubedim, ycubedim,
zcubedim] = cubedim = proj.datasetcfg.getCubeDims()[result.resolution]
[xoffset, yoffset,
zoffset] = proj.datasetcfg.getOffset()[result.resolution]
# Get a list of the files in the directories
for slice_number in range(zoffset, zimagesz + 1, zcubedim):
slab = np.zeros([zcubedim, yimagesz, ximagesz], dtype=np.uint32)
for b in range(zcubedim):
if (slice_number + b <= zimagesz):
try:
# reading the raw data
file_name = "{}/{:0>4}.tif".format(
result.path, slice_number + b)
print "Open filename {}".format(file_name)
img = Image.open(file_name, 'r').convert("RGBA")
imgdata = np.asarray(img)
slab[b, :, :] = np.left_shift(
imgdata[:, :, 3], 24,
dtype=np.uint32) | np.left_shift(
imgdata[:, :, 2], 16,
dtype=np.uint32) | np.left_shift(
imgdata[:, :, 1], 8,
dtype=np.uint32) | np.uint32(imgdata[:, :,
0])
except IOError, e:
print e
imgdata = np.zeros((yimagesz, ximagesz),
dtype=np.uint32)
slab[b, :, :] = imgdata
for y in range(0, yimagesz + 1, ycubedim):
for x in range(0, ximagesz + 1, xcubedim):
# Getting a Cube id and ingesting the data one cube at a time
zidx = ndlib.XYZMorton([
x / xcubedim, y / ycubedim,
(slice_number - zoffset) / zcubedim
])
cube = Cube.getCube(cubedim, ch.getChannelType(),
ch.getDataType())
cube.zeros()
xmin = x
ymin = y
xmax = min(ximagesz, x + xcubedim)
ymax = min(yimagesz, y + ycubedim)
zmin = 0
zmax = min(slice_number + zcubedim, zimagesz + 1)
cube.data[0:zmax - zmin, 0:ymax - ymin,
0:xmax - xmin] = slab[zmin:zmax, ymin:ymax,
xmin:xmax]
db.putCube(ch, zidx, result.resolution, cube, update=True)
