def test_005_mpi_sum_all_reduce(self):
# skip tests on windows until we work out proper
# mpi4py install for our windows ci
if sys.platform == "win32":
return
BEGIN_EXAMPLE("py_mpi_sum_all_reduce")
import conduit
import conduit.relay as relay
import conduit.relay.mpi
from mpi4py import MPI
# get a comm id from mpi4py world comm
comm_id = MPI.COMM_WORLD.py2f()
# get our rank and the comm's size
comm_rank = relay.mpi.rank(comm_id)
comm_size = relay.mpi.size(comm_id)
# gather data all ranks
# (ranks have nodes with compatible schemas)
n = conduit.Node(conduit.DataType.int64(4))
n_res = conduit.Node(conduit.DataType.int64(4))
# data to reduce
vals = n.value()
for i in range(4):
vals[i] = 1
relay.mpi.sum_all_reduce(n, n_res, comm=comm_id)
# answer should be an array with each value == comm_size
# show result on rank 0
if comm_rank == 0:
print("[rank: {}] sum reduce result: {}".format(
comm_rank, n_res.to_yaml()))
END_EXAMPLE("py_mpi_sum_all_reduce")
def test_006_mpi_all_gather(self):
# skip tests on windows until we work out proper
# mpi4py install for our windows ci
if sys.platform == "win32":
return
BEGIN_EXAMPLE("py_mpi_all_gather_using_schema")
import conduit
import conduit.relay as relay
import conduit.relay.mpi
from mpi4py import MPI
# get a comm id from mpi4py world comm
comm_id = MPI.COMM_WORLD.py2f()
# get our rank and the comm's size
comm_rank = relay.mpi.rank(comm_id)
comm_size = relay.mpi.size(comm_id)
n = conduit.Node(conduit.DataType.int64(4))
n_res = conduit.Node()
# data to gather
vals = n.value()
for i in range(4):
vals[i] = comm_rank
relay.mpi.all_gather_using_schema(n, n_res, comm=comm_id)
# show result on rank 0
if comm_rank == 0:
print("[rank: {}] all gather using schema result: {}".format(
comm_rank, n_res.to_yaml()))
END_EXAMPLE("py_mpi_all_gather_using_schema")
def process_args(args):
data = cb.load_node(args.input)
with open(args.schema, 'r') as f:
schema_json = f.read()
g = conduit.Generator(schema_json, 'json')
schema = conduit.Node()
g.walk_external(schema)
samples = data.child_names()
# Walk through all the samples and create a unified list (ie pack into a
# dictionary of lists)
all_dict = {}
for s in samples:
unfiltered_node = data[s]
filtered_node = conduit.Node()
g.walk_external(filtered_node)
filtered_node.update_compatible(unfiltered_node)
make_data_array_dict(all_dict, filtered_node)
# Save according to output extension, either numpy or conduit-compatible
protocol = cb.determine_protocol(args.output)
if protocol == 'npz':
np.savez(args.output, **all_dict)
else:
n = conduit.Node()
g.walk_external(n)
n.update_compatible(data)
for data_name in all_dict.keys():
n[data_name] = np.array(all_dict[data_name])
cb.dump_node(n, args.output)
def test_render_3d(self):
# if we don't have ascent, simply return
info = ascent.about()
if info["runtimes/ascent/vtkm/status"] != "enabled":
return
obase = "tout_python_ascent_render_3d"
ofile = obase + "100.png"
# clean up old results if they exist
if os.path.isfile(ofile):
os.remove(ofile)
# create example mesh
n_mesh = conduit.Node()
conduit.blueprint.mesh.examples.braid("hexs", 10, 10, 10, n_mesh)
# open ascent
a = ascent.Ascent()
a.open()
a.publish(n_mesh)
actions = conduit.Node()
scenes = conduit.Node()
scenes["s1/plots/p1/type"] = "pseudocolor"
scenes["s1/plots/p1/field"] = "braid"
scenes["s1/image_prefix"] = obase
add_act = actions.append()
add_act["action"] = "add_scenes"
add_act["scenes"] = scenes
a.execute(actions)
a.close()
self.assertTrue(os.path.isfile(ofile))
def test_bcast_using_schema(self):
# skip tests on windows until we work out proper
# mpi4py install for our windows ci
if sys.platform == "win32":
return
from mpi4py import MPI
comm_id = MPI.COMM_WORLD.py2f()
comm_rank = relay.mpi.rank(comm_id)
comm_size = relay.mpi.size(comm_id)
for root in range(comm_size):
n = conduit.Node()
if comm_rank == root:
n.set(conduit.DataType.int64(3))
vals = n.value()
vals[0] = 11
vals[1] = 22
vals[2] = 33
relay.mpi.broadcast_using_schema(n,root,comm_id)
print(n)
vals = n.value()
self.assertEqual(vals[0],11)
self.assertEqual(vals[1],22)
self.assertEqual(vals[2],33)
for root in range(comm_size):
n = conduit.Node()
if comm_rank == root:
n["a/b/c/d/e/f"].set(np.int64(10))
relay.mpi.broadcast_using_schema(n,root,comm_id)
val = n["a/b/c/d/e/f"]
self.assertEqual(val,10)
def test_render_flow_inspect(self):
# if we don't have ascent, simply return
info = ascent.about()
if info["runtimes/ascent/status"] != "enabled":
return
flow.Workspace.register_builtin_filter_types()
flow.Workspace.register_filter_type(flow.wrap_function(inspect));
# create example mesh
n_mesh = conduit.Node()
conduit.blueprint.mesh.examples.braid("quads",
10,
10,
0,
n_mesh)
# open ascent
a = ascent.Ascent()
open_opts = conduit.Node()
open_opts["runtime/type"].set("flow")
a.open(open_opts)
a.publish(n_mesh)
actions = conduit.Node()
add_py = actions.append()
add_py["action"] = "add_filter";
add_py["type_name"] = "ensure_python";
add_py["name"] = "py";
add_ins = actions.append()
add_ins["action"] = "add_filter";
add_ins["type_name"] = "inspect";
add_ins["name"] = "my_inspect";
conn_py = actions.append()
conn_py["action"] = "connect";
conn_py["src"] = "source";
conn_py["dest"] = "py";
conn_ins = actions.append()
conn_ins["action"] = "connect";
conn_ins["src"] = "py";
conn_ins["dest"] = "my_inspect";
print(actions)
actions.append()["action"] = "execute"
# this will fail for static libs case
# b/c the py libs are still dynamic, and they each
# link their own static copy of flow
try:
a.execute(actions)
except RuntimeError as e:
if not e.message.count("ensure_python") > 0:
raise(e)
a.close()
def PPL3toCDT3(args):
nHeader = conduit.Node()
nHeader['date'] = "Created by PPL3toCDT3hdf5.py at " + datetime.datetime.now().strftime("%m-%d-%Y %H:%M:%S")
print("Created by PPL3toCDT3hdf5.py at " + datetime.datetime.now().strftime("%m-%d-%Y %H:%M:%S"))
if not os.path.exists(args.outfile):
os.makedirs(args.outfile)
hdf5pathDir = os.path.abspath(args.outfile)
hdf5path=os.path.join(hdf5pathDir, "dock_proc1.hdf5")
print(hdf5path)
conduit.relay.io.save(nHeader, hdf5path)
comDirPath = os.path.abspath(args.scrDir + "/com")
print(comDirPath)
os.chdir(comDirPath)
dirs = os.listdir(".")
for recid in dirs:
recPath = os.path.join(comDirPath, recid+"/dock")
if os.path.isdir(recPath):
os.chdir(recPath)
print(os.getcwd())
n = conduit.Node()
ligs=os.listdir(".")
for ligid in ligs:
ligPath = os.path.join(recPath, ligid)
if os.path.isdir(ligPath):
os.chdir(ligPath)
scorefile=os.path.join(ligPath, 'scores.log')
if os.path.isfile(scorefile):
entryKey = "/dock/" + recid + "/" + ligid
scores=getScore(scorefile)
numPose=len(scores)
n[entryKey + "/meta/numPose"] = np.int32(numPose)
if numPose>0:
n[entryKey + "/status"] = np.int32(1)
n[entryKey + "/meta/Mesg"] ="Finished!"
else:
n[entryKey + "/status"] = np.int32(0)
n[entryKey + "/meta/Mesg"] = "No Scores!"
for key, val in scores.iteritems():
n[entryKey+"/meta/scores/"+key]=val
fileList = ['poses.pdbqt', 'scores.log']
filesToHDF(n, entryKey, fileList)
try:
conduit.relay.io.save_merged(n, hdf5path)
except:
print(recid+ " cannot be saved into HDF5")
def test_render_mpi_2d(self):
# if we don't have ascent, or mpi simply return
info = ascent.mpi.about()
if info["runtimes/ascent/status"] != "enabled":
print("ascent runtime not enabled, skipping mpi render test")
return
obase = "tout_python_ascent_mpi_render_2d"
ofile = obase + ".png"
# clean up old results if they exist
if MPI.COMM_WORLD.rank == 0 and os.path.isfile(ofile):
os.remove(ofile)
# create example mesh using conduit blueprint
n_mesh = conduit.Node()
conduit.blueprint.mesh.examples.braid("uniform",
10,
10,
0,
n_mesh)
# shift data for rank > 1
x_origin = MPI.COMM_WORLD.rank * 20 - 10;
n_mesh["state/domain_id"] = MPI.COMM_WORLD.rank
n_mesh["coordsets/coords/origin/x"] = x_origin
n_mesh["fields/braid/values"][:] = MPI.COMM_WORLD.rank
# open ascent
a = ascent.mpi.Ascent()
ascent_opts = conduit.Node()
ascent_opts["mpi_comm"].set(MPI.COMM_WORLD.py2f())
a.open(ascent_opts)
a.publish(n_mesh)
actions = conduit.Node()
scenes = conduit.Node()
scenes["s1/plots/p1/type"] = "pseudocolor"
scenes["s1/plots/p1/params/field"] = "braid"
scenes["s1/image_prefix"] = obase
add_act =actions.append()
add_act["action"] = "add_scenes"
add_act["scenes"] = scenes
actions.append()["action"] = "execute"
a.execute(actions)
a.close()
# use barrier to avoid problems
# some ranks may finish before file is written
MPI.COMM_WORLD.Barrier()
self.assertTrue(os.path.isfile(ofile))
def test_io_handle(self):
tbase = "tout_python_relay_io_handle."
protos = ["conduit_bin",
"json",
"conduit_json",
"conduit_base64_json",
"yaml"]
# only test hdf5 if relay was built with hdf5 support
if relay.io.about()["protocols/hdf5"] == "enabled":
protos.append("hdf5")
for proto in protos:
test_file = tbase + proto
if os.path.isfile(test_file):
os.remove(test_file)
n = conduit.Node();
n["a"] = int64(20);
n["b"] = int64(8);
n["c"] = int64(12);
n["d/here"] = int64(10);
h = conduit.relay.io.IOHandle();
h.open(test_file)
h.write(n)
self.assertTrue(h.has_path("d/here"))
cnames = h.list_child_names()
self.assertTrue(cnames[0] == "a")
self.assertTrue(cnames[1] == "b")
self.assertTrue(cnames[2] == "c")
self.assertTrue(cnames[3] == "d")
cnames = h.list_child_names("d")
self.assertTrue(cnames[0] == "here")
h.remove("d");
self.assertFalse(h.has_path("d"))
self.assertFalse(h.has_path("d/here"))
h.close();
n2 = conduit.Node()
h2 = conduit.relay.io.IOHandle();
h2.open(test_file);
cnames = h2.list_child_names();
self.assertTrue(cnames[0] == "a")
self.assertTrue(cnames[1] == "b")
self.assertTrue(cnames[2] == "c")
n_val = conduit.Node()
n_val.set(int64(10))
h2.write(n_val,"d/here")
h2.read(n2);
info = conduit.Node()
self.assertFalse(n.diff(n2,info,0.0))
n_val.reset()
h2.read(n_val,"c");
self.assertTrue(n_val.value() == 12)
h2.close()
def test_reduce_helpers(self):
# skip tests on windows until we work out proper
# mpi4py install for our windows ci
if sys.platform == "win32":
return
from mpi4py import MPI
comm_id = MPI.COMM_WORLD.py2f()
comm_rank = relay.mpi.rank(comm_id)
comm_size = relay.mpi.size(comm_id)
snd = conduit.Node(conduit.DataType.int64(5))
rcv = conduit.Node(conduit.DataType.int64(5))
snd_vals = snd.value()
rcv_vals = rcv.value()
# sum
print("sum")
for i in range(5):
snd_vals[i] = 10
relay.mpi.sum_reduce(snd, rcv, 0, comm_id);
if comm_rank == 0:
print(rcv_vals)
for i in range(5):
self.assertEqual(rcv_vals[i], 10 * comm_size)
# prod
print("prod")
for i in range(5):
snd_vals[i] = 2
relay.mpi.prod_reduce(snd, rcv, 0, comm_id)
if comm_rank == 0:
print(rcv_vals)
for i in range(5):
self.assertEqual(rcv_vals[i], math.pow(comm_size,2) )
# max
print("max")
for i in range(5):
snd_vals[i] = comm_rank * 10 +1
relay.mpi.max_reduce(snd, rcv, 0, comm_id)
if comm_rank == 0:
print(rcv_vals)
for i in range(5):
self.assertEqual(rcv_vals[i], 10 * (comm_size-1) + 1 )
# min
print("min")
for i in range(5):
snd_vals[i] = comm_rank * 10 +1
relay.mpi.min_reduce(snd, rcv, 0, comm_id)
if comm_rank == 0:
print(rcv_vals)
for i in range(5):
self.assertEqual(rcv_vals[i], 1)
def test_001_io_handle(self):
import conduit.relay
if conduit.relay.io.about()["protocols/hdf5"] != "enabled":
return
BEGIN_EXAMPLE("py_relay_io_handle")
import conduit
import conduit.relay.io
n = conduit.Node()
n["a/data"] = 1.0
n["a/more_data"] = 2.0
n["a/b/my_string"] = "value"
print("\nNode to write:")
print(n)
# save to hdf5 file using the path-based api
conduit.relay.io.save(n, "my_output.hdf5")
# inspect and modify with an IOHandle
h = conduit.relay.io.IOHandle()
h.open("my_output.hdf5")
# check for and read a path we are interested in
if h.has_path("a/data"):
nread = conduit.Node()
h.read(nread, "a/data")
print('\nValue at "a/data" = {0}'.format(nread.value()))
# check for and remove a path we don't want
if h.has_path("a/more_data"):
h.remove("a/more_data")
print('\nRemoved "a/more_data"')
# verify the data was removed
if not h.has_path("a/more_data"):
print('\nPath "a/more_data" is no more')
# write some new data
print('\nWriting to "a/c"')
n.set(42.0)
h.write(n, "a/c")
# find the names of the children of "a"
cnames = h.list_child_names("a")
print('\nChildren of "a": {0}'.format(cnames))
nread = conduit.Node()
# read the entire contents
h.read(nread)
print("\nRead Result:")
print(nread)
END_EXAMPLE("py_relay_io_handle")
def extract(self, *args, **kargs):
import conduit
import conduit.relay
if not isinstance(self.feature, list):
features = [self.feature]
else:
features = self.feature
ioh = conduit.relay.io.IOHandle()
ioh.open(self.uri, "sidre_hdf5")
# look for self.feature
bp_idx = conduit.Node()
ioh.read(bp_idx, "root/blueprint_index")
bp_path = conduit.Node()
ndoms = conduit.Node()
ioh.read(bp_path, "root/blueprint_index")
out = []
for feature in features:
# split feature name back to mesh + field
sp = feature.split("/")
mesh = sp[0]
field = "/".join(sp[1:])
# get number of domains for this mesh
ioh.read(
ndoms,
"root/blueprint_index/{}/state/number_of_domains".format(mesh))
ndoms = ndoms.value()
# get the path to the selected field in the bulk data
pth = "root/blueprint_index/{}/fields/{}/path".format(mesh, field)
if self.format == "sidre/path":
out.append([ioh, pth])
continue
ioh.read(bp_path, pth)
bp_path = bp_path.value()
res = None
vals = conduit.Node()
for i in range(ndoms):
dom_path = "%d/" % i
dom_path += bp_path + "/values"
ioh.read(vals, dom_path)
npy_array = vals.value()
if res is not None:
res = numpy.concatenate([res, npy_array])
else:
res = npy_array
out.append(res)
if len(features) == 1:
return out[0]
else:
return out
def test_delete_scene(self):
# exec again, but this time remove a scene
# tests regression related to internal book keeping
# with graph setup
mesh = conduit.Node()
conduit.blueprint.mesh.examples.braid("hexs", 5, 5, 5, mesh)
a = ascent.Ascent()
opts = conduit.Node()
opts["exceptions"] = "forward"
a.open(opts)
a.publish(mesh)
actions = conduit.Node()
add_act = actions.append()
add_act["action"] = "add_pipelines"
pipelines = add_act["pipelines"]
pipelines["pl1/f1/type"] = "contour"
contour_params = pipelines["pl1/f1/params"]
contour_params["field"] = "braid"
iso_vals = np.array([0.2, 0.4], dtype=np.float32)
contour_params["iso_values"].set(iso_vals)
add_act2 = actions.append()
add_act2["action"] = "add_scenes"
scenes = add_act2["scenes"]
scenes["s1/plots/p1/type"] = "pseudocolor"
scenes["s1/plots/p1/pipeline"] = "pl1"
scenes["s1/plots/p1/field"] = "braid"
# set the output file name (ascent will add ".png")
scenes["s1/image_name"] = "out_pipeline_ex1_contour"
scenes["s2/plots/p1/type"] = "pseudocolor"
scenes["s2/plots/p1/pipeline"] = "pl1"
scenes["s2/plots/p1/field"] = "braid"
# set the output file name (ascent will add ".png")
scenes["s2/image_name"] = "out_pipeline_ex1_contour_blah"
# print our full actions tree
print(actions.to_yaml())
# execute the actions
a.execute(actions)
# now exec w/o s1
scenes.remove(path="s1")
# print our full actions tree
print(actions.to_yaml())
# execute the actions
a.execute(actions)
def run(_input, output, schema):
print(WARN)
protocol = cb.determine_protocol(output)
# Faster loader, just read metadata
data_loader = cb.load_node_handle(_input)
first_data = conduit.Node()
data_loader.read(first_data, data_loader.list_child_names()[0])
if schema == "auto":
schema_json = first_data.to_json()
elif "," in schema:
sub_list = schema.split(",")
schema_node = conduit.Node()
for item in sub_list:
schema_node[item] = first_data[item]
schema_json = schema_node.to_json()
else:
with open(schema, "r") as f:
schema_json = f.read()
g = conduit.Generator(schema_json, "json")
schema = conduit.Node()
g.walk_external(schema)
data_paths = []
for path, _ in generate_scalar_path_pairs(schema):
data_paths.append(path)
samples = data_loader.list_child_names()
# Walk through all the samples and create a unified list (ie pack into a
# dictionary of lists)
all_dict = {}
for s in samples:
filtered_node = conduit.Node()
for path in data_paths:
sample_path = "/".join((s, path))
if data_loader.has_path(sample_path):
data_loader.read(filtered_node[path], sample_path)
else:
filtered_node[
sample_path] = np.nan # if a value is missing, that could be a problem
make_data_array_dict(all_dict, filtered_node)
for dat in all_dict.keys():
all_dict[dat] = np.vstack(all_dict[dat])
# Save according to output extension, either numpy or conduit-compatible
if protocol == "npz":
np.savez(output, **all_dict)
else:
n = cb.pack_conduit_node_from_dict(all_dict)
cb.dump_node(n, output)
def pack_conduit_node_from_dict(d):
"""
If d is a dict, returns a conduit node, unpacked recursively using the
dictionary to create the conduit hierarchy.
If d is not a dict, simply returns d, to avoid extra conduit nodes being
created. The intent is that the return value is then assigned to a conduit
node by the caller and conduit will handle packing it appropriately.
Conduit currently supports following basic types:
int32, uint32, int64, uint64, float32, float64, numpy arrays of (uint32,
uin64, float32, float64), strings.
There is a bug in the current version of conduit that prevents usage of
numpy arrays of signed ints.
Notably, lists, dicts, tuples, and unicode strings can not be assigned
directly as a conduit node. numpy is your friend. This method will auto
convert tuples into numpy arrays of float64, will iterate over lists and
give them each a separate entry in the hierarchy, labeled by their index,
and assume any dicts are meant as part of the hierarchy description so the
dict hierarchy ends up in the conduit hierarchy.
"""
if isinstance(d, dict):
node = conduit.Node()
for k in d:
try:
node[str(k)] = pack_conduit_node_from_dict(d[k])
except TypeError:
try:
if isinstance(d[k], list):
node[str(k)] = create_conduit_node_from_list(d[k])
elif isinstance(d[k], tuple):
node[str(k)] = np.array(list(d[k]), dtype="float64")
else:
LOG.error("Conduit does not support following value", k, d[k])
except TypeError:
LOG.error("Conduit does not support following value", k, d[k])
return node
else:
if isinstance(d, (list, tuple, np.ndarray)):
d_a = np.asarray(d)
node = conduit.Node()
node["data"] = d_a
if len(d_a.shape) > 1:
node["metadata/shape"] = d_a.shape
return node
if d is None:
return "None"
return d
def saveDataByName(args):
hdf5path = os.path.abspath(args.infile)
hdf5pathOut = os.path.abspath(args.outfile)
n = conduit.Node()
relay.io.load(n, hdf5path)
cmpdKey = "rec/" + args.savename
nOut = conduit.Node()
nOut[cmpdKey] = n[cmpdKey]
relay.io.save(nOut, hdf5pathOut)
def saveSDFtoHDF(args):
if os.path.isfile(args.sdffile):
with open(args.sdffile, 'r') as f:
nSDF = conduit.Node()
nSDF['SDF'] = f.read()
hdf5path = os.path.abspath(args.outfile)
conduit.relay.io.save_merged(nSDF, hdf5path)
def PPL4toCDT4(args):
nHeader = conduit.Node()
nHeader[
'date'] = "Created by PPL4toCDT4hdf5.py at " + datetime.datetime.now(
).strftime("%m-%d-%Y %H:%M:%S")
print("Created by PPL4toCDT4hdf5.py at " +
datetime.datetime.now().strftime("%m-%d-%Y %H:%M:%S"))
if not os.path.exists(args.outfile):
os.makedirs(args.outfile)
hdf5pathDir = os.path.abspath(args.outfile)
hdf5path = os.path.join(hdf5pathDir, "gbsa_proc1.hdf5")
print(hdf5path)
conduit.relay.io.save_merged(nHeader, hdf5path)
comDirPath = os.path.abspath(args.scrDir + "/com")
print(comDirPath)
os.chdir(comDirPath)
dirs = os.listdir(".")
for recid in dirs:
recPath = os.path.join(comDirPath, recid + "/gbsa")
if os.path.isdir(recPath):
os.chdir(recPath)
print(os.getcwd())
if args.iszip:
ligWorkFlowZip(recPath, recid, hdf5path)
else:
ligWorkFlow(recPath, recid, hdf5path)
def test_002_io_handle_sidre(self):
import conduit.relay
if conduit.relay.io.about()["protocols/hdf5"] != "enabled":
return
BEGIN_EXAMPLE("py_relay_io_handle_sidre")
import conduit
import conduit.relay.io
# this example reads a sample hdf5 sidre style file
input_fname = relay_test_data_path(
"texample_sidre_basic_ds_demo.sidre_hdf5")
# open our sidre file for read with an IOHandle
h = conduit.relay.io.IOHandle()
h.open(input_fname, "sidre_hdf5")
# find the names of the children at the root
cnames = h.list_child_names()
print('\nChildren at root {0}'.format(cnames))
nread = conduit.Node()
# read the entire contents
h.read(nread)
print("Read Result:")
print(nread)
END_EXAMPLE("py_relay_io_handle_sidre")
def make_dummy_node():
x = conduit.Node()
data_types = [1, 1.0, "hi", "c"]
for d in data_types:
key = str(type(d))
x[key] = d
return x
def test_001_basics_very_basic(self):
BEGIN_EXAMPLE("py_basics_very_basic")
import conduit
n = conduit.Node()
n["my"] = "data"
print(n)
END_EXAMPLE("py_basics_very_basic")
def test_009_basics_fetch_exist(self):
BEGIN_EXAMPLE("py_basics_fetch_exist")
# setup a node with a leaf array
n = conduit.Node()
data = numpy.zeros((5, ), dtype=numpy.float64)
n["my/path/to/data"] = data
# access via fetch existing
# first fetch the node
n_data = n.fetch_existing("my/path/to/data")
# then the value
my_data = n_data.value()
print("== this will be an ndarray == ")
print("data: ", my_data)
print("repr: ", repr(my_data))
print()
# using fetch_existing,
# if the path doesn't exist - we will get an Exception
try:
n_data = n.fetch_existing("my/path/TYPO/data")
except Exception as e:
print("Here is what went wrong:")
print(e)
END_EXAMPLE("py_basics_fetch_exist")
def getDataByligID(args, id=None):
if not id:
id = args.ligid
hdf5path = os.path.abspath(args.infile)
dirpath = os.path.dirname(hdf5path)
ligpath = os.path.join(dirpath, 'lig/' + id)
if not os.path.exists(ligpath):
os.makedirs(ligpath)
os.chdir(ligpath)
n = conduit.Node()
relay.io.load(n, hdf5path)
cmpdKey = "lig/" + id
print("Ligand ID", id)
print("Ligand Name", n[cmpdKey + '/meta/name'])
print("status", n[cmpdKey + '/status'])
print("Mesg", n[cmpdKey + '/meta/Mesg'])
print("GBSA", n[cmpdKey + '/meta/GBEN'])
print("Ligand Old path", n[cmpdKey + '/meta/LigPath'])
print("Ligand data path", os.getcwd())
#fileList = ['LIG.inpcrd', 'LIG.lib', 'LIG.prmtop', 'LIG_min.rst', 'LIG_minGB.out', 'ligand.frcmod', 'LIG_min.pdbqt']
itr = n[cmpdKey + "/file"].children()
for fileItr in itr:
with open(fileItr.name(), 'w') as f:
f.write(fileItr.node().value())
def test_load_handle():
base = "_dummy"
exts = (".h5", ".hdf5", ".json", ".yaml", ".cbin")
x = make_dummy_node()
y = conduit.Node()
y["x"] = x
y["z"] = "z"
save_node_many(y, base=base, exts=exts)
just_handles = load_node_many(base=base, exts=exts, path=None)
just_x = load_node_many(base=base, exts=exts, path="x")
bad_path1 = load_node_many(base=base, exts=exts, path="bogus")
bad_path2 = load_node_many(base=base, exts=exts, path="")
all_data = load_node_many(base=base, exts=exts, path="/")
for h in just_handles:
assert h.has_path("x")
assert h.has_path("z")
h.close()
for h in just_x:
assert nodes_equal(h, x)
for h in bad_path1:
assert h is None
for h in bad_path2:
assert h is None
for h in all_data:
assert nodes_equal(h, y)
delete_data(base=base, exts=exts)
def make_schema_compatible(original_node, add_uuid):
node = conduit.Node()
if add_uuid:
node[str(uuid4())] = original_node
else:
node = original_node
return node
def dump_node(
conduit_node,
fname,
dump_options=(
("hdf5/chunking/threshold", 2000),
("hdf5/chunking/chunk_size", 2000),
),
):
"""
Saves a conduit node to disk. Protocol determined by fname extension.
Protocol can be conduit_bin, hdf5, json,
silo, json64, and probably others. Will turn on compression for hdf5.
"""
protocol = determine_protocol(fname)
# If hdf5, turn on compression.
if protocol == "hdf5":
save_options = conduit.Node()
for opt in dump_options:
save_options[opt[0]] = opt[1]
try:
conduit.relay.io.save(conduit_node, fname, options=save_options)
except TypeError: # Conduit version needs to be updated.
LOG.error(
"Unable to customize save: please upgrade conduit to "
"expose save options!"
)
conduit.relay.io.save(conduit_node, fname)
else:
conduit.relay.io.save(conduit_node, fname)
def test_inception(self):
flow.Workspace.clear_supported_filter_types()
flow.Workspace.register_builtin_filter_types()
flow.Workspace.register_filter_type(flow.wrap_function(src))
w = flow.Workspace()
w.graph().add_filter("src", "s")
#
py_params = conduit.Node()
py_params["source"] = py_script
w.graph().add_filter("python_script", "py", py_params)
w.graph().connect("s", "py", "in")
print(w)
w.execute()
print("Reg from Py")
print(w.registry())
v = w.registry().fetch("py")
self.assertEqual(v, 42)
def process_args(args):
print(WARN)
files = args.infiles
nfiles = len(files)
if not args.chunk_size:
chunk_size = nfiles
else:
chunk_size = args.chunk_size
fileno = 0
results = []
for group in grouper(files, chunk_size):
result = conduit.Node()
for path in group:
if not path:
continue
try:
subnode = cb.load_node(path)
subnode = make_schema_compatible(subnode, args.add_uuid)
for top_path in subnode.child_names():
if top_path in results:
print("Error! Already in results: " + top_path)
new_path = "-".join((top_path, str(uuid4())))
print("Renaming duplicate to node to: " + new_path)
else:
new_path = top_path
result[new_path] = subnode[top_path]
results.append(top_path)
except:
print("Unable to load " + path)
cb.dump_node(result, savename(fileno, args))
fileno = fileno + 1
def getMetaData(args):
hdf5path = os.path.abspath(args.infile)
n = conduit.Node()
relay.io.load(n, hdf5path)
recItr = n['rec'].children()
outfh = open(args.meta, "w")
outfh.write(
"Rec_Name, Status, GBSA, Volume, Cent_x, Cent_y, Cent_z, Dim_x, Dim_y, Dim_Z\n"
)
for rec in recItr:
nrec = rec.node()
recName = rec.name()
status = nrec['status']
gbsa = nrec['meta/GBEN']
vol = nrec['meta/Site/Volume']
cent_x = nrec['meta/Site/Centroid/X']
cent_y = nrec['meta/Site/Centroid/Y']
cent_z = nrec['meta/Site/Centroid/Z']
dim_x = nrec['meta/Site/Dimension/X']
dim_y = nrec['meta/Site/Dimension/Y']
dim_z = nrec['meta/Site/Dimension/Z']
outfh.write("{}, {}, {}, {}, {}, {}, {}, {}, {}, {}\n".format(
recName, status, gbsa, vol, cent_x, cent_y, cent_z, dim_x, dim_y,
dim_z))
def extractScoreOnly(args):
outfh = open(args.scoreonly, "w")
outfh.write("rec, lig, Scoreonly\n")
dirpath = os.path.abspath("scratch")
hdf5pathDir = os.path.abspath(args.indir)
os.chdir(hdf5pathDir)
h5files = glob.glob('dock_proc*.hdf5')
for h5f in h5files:
n = conduit.Node()
conduit.relay.io.load(n, h5f)
itrRec = n["dock"].children()
for rec in itrRec:
recid = rec.name()
nrec = rec.node()
itrLig = nrec.children()
for lig in itrLig:
ligid = lig.name()
entryKey = "dock/" + recid + "/" + ligid
ndata = n[entryKey]
if ndata.has_path("file/scores.log"):
scoreslog = ndata["file/scores.log"]
strs = scoreslog.split()
if strs[0] == "Affinity:":
outfh.write(recid + ", " + ligid + ", " + strs[1] +
"\n")