def setup_class(self):
"""Setup Parameters"""
if 'S3_ENDPOINT' in dir(settings):
self.endpoint_url = settings.S3_ENDPOINT
else:
self.endpoint_url = None
CuboidBucket.createBucket(endpoint_url=self.endpoint_url)
self.cuboid_bucket = CuboidBucket(nd_proj.project_name,
endpoint_url=self.endpoint_url)
def setup_class(self):
"""Setup class parameters"""
# create the tile index table. skip if it exists
try:
TileIndexDB.createTable(endpoint_url="http://localhost:8000")
CuboidIndexDB.createTable(endpoint_url="http://localhost:8000")
except Exception as e:
pass
self.tileindex_db = TileIndexDB(nd_proj.project_name,
endpoint_url="http://localhost:8000")
# create the tile bucket
TileBucket.createBucket(endpoint_url="http://localhost:4567")
self.tile_bucket = TileBucket(nd_proj.project_name,
endpoint_url="http://localhost:4567")
self.tiles = [self.x_tile, self.y_tile, self.z_tile] = [0, 0, 0]
message_id = "testing"
receipt_handle = "123456"
# insert SUPER_CUBOID_SIZE tiles in the bucket
for z_index in (self.z_tile, settings.SUPER_CUBOID_SIZE[2], 1):
tile_handle = cStringIO.StringIO()
self.tile_bucket.putObject(
tile_handle,
nd_proj.channel_name,
nd_proj.resolution,
self.x_tile,
self.y_tile,
z_index,
message_id,
receipt_handle,
)
# creating the cuboid bucket
CuboidBucket.createBucket(endpoint_url="http://localhost:4567")
self.cuboid_bucket = CuboidBucket(nd_proj.project_name,
endpoint_url="http://localhost:4567")
# create the ingest queue
IngestQueue.createQueue(nd_proj, endpoint_url="http://localhost:4568")
self.ingest_queue = IngestQueue(nd_proj,
endpoint_url="http://localhost:4568")
# send message to the ingest queue
morton_index = XYZMorton(self.tiles)
supercuboid_key = self.cuboid_bucket.generateSupercuboidKey(
nd_proj.channel_name, nd_proj.resolution, morton_index)
response = self.ingest_queue.sendMessage(supercuboid_key)
# create the cleanup queue
CleanupQueue.createQueue(nd_proj, endpoint_url="http://localhost:4568")
def createTestDB ( project_name, channel_list=['unit_anno'], channel_type=ANNOTATION, channel_datatype=UINT32, public=PUBLIC_TRUE, ximagesize=10000, yimagesize=10000, zimagesize=1000, xvoxelres=4.0, yvoxelres=4.0, zvoxelres=3.0, scalingoption=ZSLICES, scalinglevels=5, readonly=READONLY_FALSE, propagate=NOT_PROPAGATED, window=[0,0], time=[0,15], default=False, nd_version=ND_VERSION, token_name='unittest', user='******', dataset_name="unittest", base_resolution=0):
"""Create a unit test data base on the specified sit and name"""
# setting s3backend to true if Redis and creating s3 bucket and dynamo table
if KV_ENGINE == REDIS:
s3backend = S3_TRUE
CuboidIndexDB.createTable()
CuboidBucket.createBucket()
else:
s3backend = S3_FALSE
unituser = User.objects.get(username=user)
ds = NDDataset(Dataset ( dataset_name=dataset_name, user=unituser, ximagesize=ximagesize, yimagesize=yimagesize, zimagesize=zimagesize, xoffset=0, yoffset=0, zoffset=1, xvoxelres=xvoxelres, yvoxelres=yvoxelres, zvoxelres=zvoxelres, scalingoption=scalingoption, scalinglevels=scalinglevels, public=PUBLIC_TRUE, dataset_description="Unit test" ) )
ds.create()
# make the project entry
pr = NDProject(Project(project_name=project_name, project_description='Unit test', user=unituser, dataset=ds._ds, nd_version=nd_version, host='localhost', kvengine=KV_ENGINE, kvserver=KV_SERVER, s3backend=s3backend))
pr.create()
# create a token
tk = NDToken(Token (token_name = token_name, user = unituser, token_description = 'Unit test token', project_id = pr.project_name, public = public))
tk.create()
# get the correct object for the kvengine
# pd = NDProjectsDB.getProjDB(NDProjectpr)
# create the database
# pd.newNDProject()
try:
for channel_name in channel_list:
ch = NDChannel(Channel (channel_name=channel_name, channel_type=channel_type, channel_datatype=channel_datatype, channel_description='Unit test channel', project_id=pr.project_name, readonly=readonly, propagate=propagate, resolution=base_resolution, exceptions=1, starttime=time[0], endtime=time[1] ,startwindow=window[0], endwindow=window[1], default=default))
# create a channel
ch.create()
# create the channel table
# pd.newNDChannel(ch.channel_name)
except Exception, e:
print(e)
raise e
# Copyright 2014 NeuroData (http://neurodata.io)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
sys.path += [os.path.abspath('../django')]
import ND.settings
os.environ['DJANGO_SETTINGS_MODULE'] = 'ND.settings'
from ndingest.nddynamo.cuboidindexdb import CuboidIndexDB
from ndingest.ndbucket.cuboidbucket import CuboidBucket
# setup cuboid indext table and bucket locally
CuboidIndexDB.createTable()
CuboidBucket.createBucket()
def createTestDB(project_name,
channel_list=['unit_anno'],
channel_type=ANNOTATION,
channel_datatype=UINT32,
public=PUBLIC_TRUE,
ximagesize=10000,
yimagesize=10000,
zimagesize=1000,
xvoxelres=4.0,
yvoxelres=4.0,
zvoxelres=3.0,
scalingoption=ZSLICES,
scalinglevels=5,
readonly=READONLY_FALSE,
propagate=NOT_PROPAGATED,
window=[0, 0],
time=[0, 15],
default=False,
nd_version=ND_VERSION,
token_name='unittest',
user='******',
dataset_name="unittest",
base_resolution=0):
"""Create a unit test data base on the specified sit and name"""
# setting s3backend to true if Redis and creating s3 bucket and dynamo table
if KV_ENGINE == REDIS:
s3backend = S3_TRUE
CuboidIndexDB.createTable()
CuboidBucket.createBucket()
else:
s3backend = S3_FALSE
unituser = User.objects.get(username=user)
ds = NDDataset(
Dataset(dataset_name=dataset_name,
user=unituser,
ximagesize=ximagesize,
yimagesize=yimagesize,
zimagesize=zimagesize,
xoffset=0,
yoffset=0,
zoffset=1,
xvoxelres=xvoxelres,
yvoxelres=yvoxelres,
zvoxelres=zvoxelres,
scalingoption=scalingoption,
scalinglevels=scalinglevels,
public=PUBLIC_TRUE,
dataset_description="Unit test"))
ds.create()
# make the project entry
pr = NDProject(
Project(project_name=project_name,
project_description='Unit test',
user=unituser,
dataset=ds._ds,
nd_version=nd_version,
host='localhost',
kvengine=KV_ENGINE,
kvserver=KV_SERVER,
s3backend=s3backend))
pr.create()
# create a token
tk = NDToken(
Token(token_name=token_name,
user=unituser,
token_description='Unit test token',
project_id=pr.project_name,
public=public))
tk.create()
# get the correct object for the kvengine
# pd = NDProjectsDB.getProjDB(NDProjectpr)
# create the database
# pd.newNDProject()
try:
for channel_name in channel_list:
ch = NDChannel(
Channel(channel_name=channel_name,
channel_type=channel_type,
channel_datatype=channel_datatype,
channel_description='Unit test channel',
project_id=pr.project_name,
readonly=readonly,
propagate=propagate,
resolution=base_resolution,
exceptions=1,
starttime=time[0],
endtime=time[1],
startwindow=window[0],
endwindow=window[1],
default=default))
# create a channel
ch.create()
# create the channel table
# pd.newNDChannel(ch.channel_name)
except Exception, e:
print(e)
raise e