from blockbased_synapseaware.makeflow_example.makeflow_helperfunctions import *
from blockbased_synapseaware.benchmark.kimimaro.connectskeletons import ConnectSkeletons
# pass arguments
# read passed arguments
if len(sys.argv) != 2:
raise ValueError(" Scripts needs exactley 1 input arguments (Prefix) ")
else:
meta_fp = sys.argv[1]
# read in the data for this block
data = ReadMetaData(meta_fp)
# Redirect stdout and stderr
RedirectOutStreams(data.BlockSize(), "KM", 2, "all", "all", "all")
# check that beforehand step has executed successfully
for iz in range(data.StartZ(), data.EndZ()):
for iy in range(data.StartY(), data.EndY()):
for ix in range(data.StartX(), data.EndX()):
CheckSuccessFile(data.BlockSize(), "KM", 1, iz, iy, ix)
# users must provide an output directory
assert (not data.SkeletonOutputDirectory() == None)
os.makedirs(data.SkeletonOutputDirectory(), exist_ok=True)
# compute the second step to find adjacencies between borders
ConnectSkeletons(data)
# Create and Write Success File
if __name__ == "__main__":
# read meta filepath
meta_fp, mf_param_fp, prefix = readArgv(sys.argv)
# import parameters from file
import_name = "blockbased_synapseaware.makeflow_example." + mf_param_fp[:-3].replace(
"/", ".")
mf_param = importlib.import_module(import_name)
# read in the data for this block
data = ReadMetaData(meta_fp)
# create working directory and directory for temporary makeflow files
working_dir = data.MFCodeDirectory(
) + "/working_directories/{}_working_dir_{:04d}x{:04d}x{:04d}/".format(
prefix,
data.BlockSize()[OR_X],
data.BlockSize()[OR_Y],
data.BlockSize()[OR_Z])
temp_file_dir = working_dir + "temp_files/"
if not os.path.exists(working_dir):
os.mkdir(working_dir)
if not os.path.exists(temp_file_dir):
os.mkdir(temp_file_dir)
# write pipeline and batch deploy file
writePipelineFile(working_dir, data, meta_fp, prefix)
writeBatchFile(working_dir, data)