def build_trees_from_mst(emst, edges_u, edges_v, alpha, coordinate_scale,
offset, voxel_size):
trees = nx.DiGraph()
ndims = len(voxel_size)
for edge, u, v in zip(np.array(emst), np.array(edges_u),
np.array(edges_v)):
if edge[2] > alpha:
continue
pos_u = daisy.Coordinate((0, ) * (3 - ndims) +
tuple((u[-ndims:] / coordinate_scale) +
(offset / voxel_size)))
pos_v = daisy.Coordinate((0, ) * (3 - ndims) +
tuple((v[-ndims:] / coordinate_scale) +
(offset / voxel_size)))
if edge[0] not in trees.nodes:
trees.add_node(edge[0], pos=pos_u)
else:
assert trees.nodes[
edge[0]]["pos"] == pos_u, "locations don't match"
if edge[1] not in trees.nodes:
trees.add_node(edge[1], pos=pos_v)
else:
assert trees.nodes[
edge[1]]["pos"] == pos_v, "locations don't match"
trees.add_edge(edge[0], edge[1], d=edge[2])
return trees
def build_trees(node_ids, locations, edges, node_attrs=None, edge_attrs=None):
if node_attrs is None:
node_attrs = {}
if edge_attrs is None:
edge_attrs = {}
node_to_index = {n: i for i, n in enumerate(node_ids)}
trees = nx.Graph()
pbs = {
int(node_id): node_location
for node_id, node_location in zip(node_ids, locations)
}
for i, row in enumerate(edges):
u = node_to_index.get(int(row[0]), -1)
v = node_to_index.get(int(row[-1]), -1)
e_attrs = {attr: values[i] for attr, values in edge_attrs.items()}
if u == -1 or v == -1:
continue
pos_u = daisy.Coordinate(tuple(pbs[node_ids[u]]))
pos_v = daisy.Coordinate(tuple(pbs[node_ids[v]]))
if node_ids[u] not in trees.nodes:
u_attrs = {attr: values[u] for attr, values in node_attrs.items()}
trees.add_node(node_ids[u], location=pos_u, **u_attrs)
if node_ids[v] not in trees.nodes:
v_attrs = {attr: values[v] for attr, values in node_attrs.items()}
trees.add_node(node_ids[v], location=pos_v, **v_attrs)
trees.add_edge(node_ids[u], node_ids[v], **e_attrs)
return trees
def solve(predict_config, worker_config, data_config, graph_config,
solve_config, num_block_workers, block_size, roi_offset, roi_size,
context, solve_block, base_dir, experiment, train_number,
predict_number, graph_number, solve_number, queue,
singularity_container, mount_dirs, **kwargs):
source_roi = daisy.Roi(daisy.Coordinate(roi_offset),
daisy.Coordinate(roi_size))
solve_setup_dir = os.path.join(
os.path.join(base_dir, experiment),
"04_solve/setup_t{}_p{}_g{}_s{}".format(train_number, predict_number,
graph_number, solve_number))
block_write_roi = daisy.Roi((0, 0, 0), block_size)
block_read_roi = block_write_roi.grow(context, context)
total_roi = source_roi.grow(context, context)
logger.info("Solving in %s", total_roi)
daisy.run_blockwise(
total_roi,
block_read_roi,
block_write_roi,
process_function=lambda:
start_worker(predict_config, worker_config, data_config, graph_config,
solve_config, queue, singularity_container, mount_dirs,
solve_block, solve_setup_dir),
num_workers=num_block_workers,
fit='shrink')
logger.info("Finished solving, parameters id is %s", solve_number)
def get_source_roi(data_dir, sample):
sample_path = os.path.join(data_dir, sample)
# get absolute paths
if os.path.isfile(sample_path) or sample.endswith((".zarr", ".n5")):
sample_dir = os.path.abspath(
os.path.join(data_dir, os.path.dirname(sample)))
else:
sample_dir = os.path.abspath(os.path.join(data_dir, sample))
if os.path.isfile(os.path.join(sample_dir, 'attributes.json')):
with open(os.path.join(sample_dir, 'attributes.json'), 'r') as f:
attributes = json.load(f)
voxel_size = daisy.Coordinate(attributes['resolution'])
shape = daisy.Coordinate(attributes['shape'])
offset = daisy.Coordinate(attributes['offset'])
source_roi = daisy.Roi(offset, shape * voxel_size)
return voxel_size, source_roi
elif os.path.isdir(os.path.join(sample_dir, 'timelapse.zarr')):
a = daisy.open_ds(os.path.join(sample_dir, 'timelapse.zarr'),
'volumes/raw')
return a.voxel_size, a.roi
else:
raise RuntimeError(
"Can't find attributes.json or timelapse.zarr in %s" % sample_dir)
def read_data_config(data_config):
config = configparser.ConfigParser()
config.read(data_config)
cfg_dict = {}
# Data
cfg_dict["sample"] = config.get("Data", "sample")
# Don't think I need these
offset = config.get("Data", "roi_offset")
size = config.get("Data", "roi_size")
cfg_dict["roi_offset"] = daisy.Coordinate(
tuple(int(x) for x in offset.split(", ")) if not offset == "None" else [None] * 3
)
cfg_dict["roi_size"] = daisy.Coordinate(
tuple(int(x) for x in size.split(", ")) if not size == "None" else [None] * 3
)
cfg_dict["roi"] = daisy.Roi(cfg_dict["roi_offset"], cfg_dict["roi_size"])
cfg_dict["location_attr"] = config.get("Data", "location_attr")
cfg_dict["penalty_attr"] = config.get("Data", "penalty_attr")
cfg_dict["target_edge_len"] = int(config.get("Data", "target_edge_len"))
# Database
cfg_dict[
"consensus_db"
] = f"mouselight-{cfg_dict['sample']}-{config.get('Data', 'consensus_db')}"
cfg_dict[
"subdivided_db"
] = f"mouselight-{cfg_dict['sample']}-{config.get('Data', 'subdivided_db')}"
cfg_dict["db_host"] = config.get("Data", "db_host")
return cfg_dict
def build_trees(node_ids, locations, edges, voxel_size):
trees = nx.DiGraph()
pbs = {
int(node_id): node_location
for node_id, node_location in zip(node_ids, locations)
}
for row in edges:
u = int(row[0])
v = int(row[-1])
if u == -1 or v == -1:
continue
pos_u = daisy.Coordinate(tuple(pbs[u])) / voxel_size
pos_v = daisy.Coordinate(tuple(pbs[v])) / voxel_size
if u not in trees.nodes:
trees.add_node(u, pos=pos_u)
else:
assert trees.nodes[u]["pos"] == pos_u, "locations don't match"
if v not in trees.nodes:
trees.add_node(v, pos=pos_v)
else:
assert trees.nodes[v]["pos"] == pos_v, "locations don't match"
trees.add_edge(u, v, d=np.linalg.norm(pos_u - pos_v))
return trees
def test_load_klb(self):
self.write_test_klb()
data = daisy.open_ds(self.klb_file, None)
self.assertEqual(data.roi.get_offset(), daisy.Coordinate((0, 0, 0, 0)))
self.assertEqual(data.roi.get_shape(), daisy.Coordinate(
(1, 20, 10, 10)))
self.assertEqual(data.voxel_size, daisy.Coordinate((1, 2, 1, 1)))
self.remove_test_klb()
def extract_edges_blockwise(db_host,
db_name,
sample,
edge_move_threshold,
block_size,
num_workers,
frames=None,
frame_context=1,
data_dir='../01_data',
use_pv_distance=False,
**kwargs):
voxel_size, source_roi = get_source_roi(data_dir, sample)
# limit to specific frames, if given
if frames:
begin, end = frames
begin -= frame_context
end += frame_context
crop_roi = daisy.Roi((begin, None, None, None),
(end - begin, None, None, None))
source_roi = source_roi.intersect(crop_roi)
# block size in world units
block_write_roi = daisy.Roi((0, ) * 4, daisy.Coordinate(block_size))
pos_context = daisy.Coordinate((0, ) + (edge_move_threshold, ) * 3)
neg_context = daisy.Coordinate((1, ) + (edge_move_threshold, ) * 3)
logger.debug("Set neg context to %s", neg_context)
input_roi = source_roi.grow(neg_context, pos_context)
block_read_roi = block_write_roi.grow(neg_context, pos_context)
print("Following ROIs in world units:")
print("Input ROI = %s" % input_roi)
print("Block read ROI = %s" % block_read_roi)
print("Block write ROI = %s" % block_write_roi)
print("Output ROI = %s" % source_roi)
print("Starting block-wise processing...")
# process block-wise
daisy.run_blockwise(input_roi,
block_read_roi,
block_write_roi,
process_function=lambda b: extract_edges_in_block(
db_name,
db_host,
edge_move_threshold,
b,
use_pv_distance=use_pv_distance),
check_function=lambda b: check_function(
b, 'extract_edges', db_name, db_host),
num_workers=num_workers,
processes=True,
read_write_conflict=False,
fit='shrink')
def ingest_consensus(sample: str, consensus_dir: Path, transform_file: Path,
url: str):
"""
Storing data in MongoDB using psuedo world coords (1,.3,.3) microns rather than the
slightly off floats found in the transform.txt file.
"""
mongo_graph_provider = daisy.persistence.MongoDbGraphProvider(
f"mouselight-{sample}-consensus", url, directed=True, mode="w")
graph = mongo_graph_provider.get_graph(
daisy.Roi(daisy.Coordinate([None, None, None]),
daisy.Coordinate([None, None, None])))
consensus_graphs = []
for consensus_neuron in tqdm(consensus_dir.iterdir(),
"Consensus neurons: "):
if (not consensus_neuron.is_dir()
or not (consensus_neuron / "consensus.swc").exists()):
continue
consensus_graph = parse_consensus(
consensus_neuron / "consensus.swc",
consensus_neuron / "dendrite.swc",
transform,
offset=np.array([0, 0, 0]),
resolution=np.array([300, 300, 1000]),
transpose=[2, 1, 0],
)
for node in consensus_graph.nodes:
consensus_graph.nodes[node]["position"] = consensus_graph.nodes[
node]["location"].tolist()
del consensus_graph.nodes[node]["location"]
consensus_graphs.append(consensus_graph)
logger.info("Consolidating consensus graphs!")
consensus_graph = nx.disjoint_union_all(consensus_graphs)
data = {}
for node_id, attrs in consensus_graph.nodes.items():
node_id = int(np.int64(node_id))
node_ids = data.setdefault("id", [])
node_ids.append(node_id)
for key, value in attrs.items():
dlist = data.setdefault(key, [])
dlist.append(value)
logger.info(
f"Writing {len(consensus_graph.nodes)} nodes and {len(consensus_graph.edges)} edges!"
)
bulk_write_nodes(url, f"mouselight-{sample}-consensus", "nodes", data)
bulk_write_edges(
url,
f"mouselight-{sample}-consensus",
"edges",
("u", "v"),
list(consensus_graph.edges),
True,
)
def __init__(self, positions, dataset, size):
self.dataset = dataset
self.data = dataset.open_daisy()
self.voxel_size = daisy.Coordinate(dataset.voxel_size)
self.size = daisy.Coordinate(size)
self.size_nm = self.size * self.voxel_size
self.positions = self.filter_positions(positions)
self.transform = None
def test_overwrite_attrs(self):
self.write_test_klb()
data = daisy.open_ds(self.klb_file,
None,
attr_filename=self.attrs_file)
self.assertEqual(data.roi.get_offset(),
daisy.Coordinate((0, 100, 10, 0)))
self.assertEqual(data.roi.get_shape(), daisy.Coordinate(
(1, 10, 20, 20)))
self.assertEqual(data.voxel_size, daisy.Coordinate((1, 1, 2, 2)))
self.remove_test_klb()
def get_raw(locs, size, voxel_size, data_container, data_set):
"""
Get raw crops from the specified
dataset.
locs(``list of tuple of ints``):
list of centers of location of interest
size(``tuple of ints``):
size of cropout in voxel
voxel_size(``tuple of ints``):
size of a voxel
data_container(``string``):
path to data container (e.g. zarr file)
data_set(``string``):
corresponding data_set name, (e.g. raw)
"""
raw = []
size = daisy.Coordinate(size)
voxel_size = daisy.Coordinate(voxel_size)
size_nm = (size * voxel_size)
dataset = daisy.open_ds(data_container, data_set)
for loc in locs:
offset_nm = loc - (size / 2 * voxel_size)
roi = daisy.Roi(offset_nm, size_nm).snap_to_grid(voxel_size,
mode='closest')
if roi.get_shape()[0] != size[0]:
roi.set_shape(size_nm)
if not dataset.roi.contains(roi):
logger.WARNING("Location %s is not fully contained in dataset" %
loc)
return
raw.append(dataset[roi].to_ndarray())
raw = np.stack(raw)
raw = raw.astype(np.float32)
raw_normalized = raw / 255.0
raw_normalized = raw_normalized * 2.0 - 1.0
return raw, raw_normalized
def extract_edges(
db_host,
db_name,
soft_mask_container,
soft_mask_dataset,
roi_offset,
roi_size,
distance_threshold,
block_size,
num_block_workers,
graph_number,
**kwargs):
# Define Rois:
source_roi = daisy.Roi(roi_offset, roi_size)
block_write_roi = daisy.Roi(
(0,) * 3,
daisy.Coordinate(block_size))
pos_context = daisy.Coordinate((distance_threshold,)*3)
neg_context = daisy.Coordinate((distance_threshold,)*3)
logger.debug("Set pos context to %s", pos_context)
logger.debug("Set neg context to %s", neg_context)
input_roi = source_roi.grow(neg_context, pos_context)
block_read_roi = block_write_roi.grow(neg_context, pos_context)
logger.info("Following ROIs in world units:")
logger.info("Input ROI = %s" % input_roi)
logger.info("Block read ROI = %s" % block_read_roi)
logger.info("Block write ROI = %s" % block_write_roi)
logger.info("Output ROI = %s" % source_roi)
logger.info("Starting block-wise processing...")
# process block-wise
daisy.run_blockwise(
input_roi,
block_read_roi,
block_write_roi,
process_function=lambda b: extract_edges_in_block(
db_name,
db_host,
soft_mask_container,
soft_mask_dataset,
distance_threshold,
graph_number,
b),
num_workers=num_block_workers,
processes=True,
read_write_conflict=False,
fit='shrink')
def test_none(self):
a = daisy.Coordinate((None, 1, 2))
b = daisy.Coordinate((3, 4, None))
assert a + b == (None, 5, None)
assert a - b == (None, -3, None)
assert a / b == (None, 0, None)
assert a // b == (None, 0, None)
assert b / a == (None, 4, None)
assert b // a == (None, 4, None)
assert abs(a) == (None, 1, 2)
assert abs(-a) == (None, 1, 2)
def visualize_npy(npy_file: Path, voxel_size):
voxel_size = daisy.Coordinate(voxel_size)
viewer = neuroglancer.Viewer()
with viewer.txn() as s:
v = np.load(npy_file)
m = daisy.Array(
v,
daisy.Roi(daisy.Coordinate([0, 0, 0]), daisy.Coordinate(v.shape)),
daisy.Coordinate([1, 1, 1]),
)
add_layer(s, m, f"npy array")
print(viewer)
input("Hit ENTER to quit!")
def get_raw_parallel(locs, size, voxel_size, data_container, data_set):
"""
Get raw crops from the specified
dataset.
locs(``list of tuple of ints``):
list of centers of location of interest
size(``tuple of ints``):
size of cropout in voxel
voxel_size(``tuple of ints``):
size of a voxel
data_container(``string``):
path to data container (e.g. zarr file)
data_set(``string``):
corresponding data_set name, (e.g. raw)
"""
pool = Pool(processes=len(locs))
raw = []
size = daisy.Coordinate(size)
voxel_size = daisy.Coordinate(voxel_size)
size_nm = (size * voxel_size)
dataset = daisy.open_ds(data_container, data_set)
raw_workers = [
pool.apply_async(fetch_from_ds,
(dataset, loc, voxel_size, size, size_nm))
for loc in locs
]
raw = [w.get(timeout=60) for w in raw_workers]
pool.close()
pool.join()
raw = np.stack(raw)
raw = raw.astype(np.float32)
raw_normalized = raw / 255.0
raw_normalized = raw_normalized * 2.0 - 1.0
return raw, raw_normalized
def run_test_graph_write_roi(self, provider_factory):
graph_provider = provider_factory('w')
graph = graph_provider[daisy.Roi((0, 0, 0), (10, 10, 10))]
graph.add_node(2, comment="without position")
graph.add_node(42, position=(1, 1, 1))
graph.add_node(23, position=(5, 5, 5), swip='swap')
graph.add_node(57, position=daisy.Coordinate((7, 7, 7)), zap='zip')
graph.add_edge(42, 23)
graph.add_edge(57, 23)
graph.add_edge(2, 42)
write_roi = daisy.Roi((0, 0, 0), (6, 6, 6))
graph.write_nodes(roi=write_roi)
graph.write_edges(roi=write_roi)
graph_provider = provider_factory('r')
compare_graph = graph_provider[daisy.Roi((0, 0, 0), (10, 10, 10))]
nodes = sorted(list(graph.nodes()))
nodes.remove(2) # node 2 has no position and will not be queried
nodes.remove(57) # node 57 is outside of the write_roi
compare_nodes = compare_graph.nodes(data=True)
compare_nodes = [
node_id for node_id, data in compare_nodes if len(data) > 0
]
compare_nodes = sorted(list(compare_nodes))
edges = sorted(list(graph.edges()))
edges.remove((2, 42)) # node 2 has no position and will not be queried
compare_edges = sorted(list(compare_graph.edges()))
self.assertEqual(nodes, compare_nodes)
self.assertEqual(edges, compare_edges)
def get_site_fragment_lut(fragments, sites, roi):
#Get the fragment IDs of all the sites that are contained in the given ROI
sites = list(sites)
if len(sites) == 0:
logging.info(f"No sites in {roi}, skipping")
return None, None
logging.info(
f"Getting fragment IDs for {len(sites)} synaptic sites in {roi}...")
# for a few sites, direct lookup is faster than memory copies
if len(sites) >= 15:
logging.info("Copying fragments into memory...")
fragments = fragments[roi]
fragments.materialize()
logging.info(f"Getting fragment IDs for synaptic sites in {roi}...")
fragment_ids = np.array([
fragments[daisy.Coordinate((site['z'], site['y'], site['x']))]
for site in sites
])
site_ids = np.array([site['id'] for site in sites], dtype=np.uint64)
fg_mask = fragment_ids != 0
fragment_ids = fragment_ids[fg_mask]
site_ids = site_ids[fg_mask]
lut = np.array([site_ids, fragment_ids])
return lut, (fg_mask == 0).sum()
def downscale_block(in_array, out_array, factor, block):
dims = len(factor)
in_data = in_array.to_ndarray(block.read_roi, fill_value=0)
in_shape = daisy.Coordinate(in_data.shape[-dims:])
assert in_shape.is_multiple_of(factor)
n_channels = len(in_data.shape) - dims
if n_channels >= 1:
factor = (1, ) * n_channels + factor
if in_data.dtype == np.uint64:
slices = tuple(slice(k // 2, None, k) for k in factor)
out_data = in_data[slices]
else:
out_data = skimage.measure.block_reduce(in_data, factor, np.mean)
try:
out_array[block.write_roi] = out_data
except Exception:
print("Failed to write to %s" % block.write_roi)
raise
return 0
def parallel_lsd_agglomerate(lsds, fragments, rag_provider, lsd_extractor,
block_size, context, num_workers):
'''Agglomerate fragments in parallel using only the shape descriptors.
Args:
lsds (`class:daisy.Array`):
An array containing the LSDs.
fragments (`class:daisy.Array`):
An array containing fragments.
rag_provider (`class:SharedRagProvider`):
A RAG provider to read nodes from and write found edges to.
lsd_extractor (``LsdExtractor``):
The local shape descriptor object used to compute the difference
between the segmentation and the target LSDs.
block_size (``tuple`` of ``int``):
The size of the blocks to process in parallel, in world units.
context (``tuple`` of ``int``):
The context to consider for agglomeration, in world units.
num_workers (``int``):
The number of parallel workers.
Returns:
True, if all tasks succeeded.
'''
assert fragments.data.dtype == np.uint64
shape = lsds.shape[1:]
context = daisy.Coordinate(context)
total_roi = lsds.roi.grow(context, context)
read_roi = daisy.Roi((0, ) * lsds.roi.dims(),
block_size).grow(context, context)
write_roi = daisy.Roi((0, ) * lsds.roi.dims(), block_size)
return daisy.run_blockwise(
total_roi,
read_roi,
write_roi,
lambda b: agglomerate_in_block(lsds, fragments, rag_provider,
lsd_extractor, b),
lambda b: block_done(b, rag_provider),
num_workers=num_workers,
read_write_conflict=False,
fit='shrink')
def relabel_connected_components(array_in, array_out, block_size, num_workers):
'''Relabel connected components in an array in parallel.
Args:
array_in (``daisy.Array``):
The array to relabel.
array_out (``daisy.Array``):
The array to write to. Should initially be empty (i.e., all zeros).
block_size (``daisy.Coordinate``):
The size of the blocks to relabel in, in world units.
num_workers (``int``):
The number of workers to use.
'''
block_size = daisy.Coordinate(block_size)
segment_blockwise(
array_in,
array_out,
block_size=block_size,
context=array_in.voxel_size,
num_workers=num_workers,
segment_function=label_connected_components)
def overlay_segmentation(db_host,
db_name,
roi_offset,
roi_size,
selected_attr,
solved_attr,
edge_collection,
segmentation_container,
segmentation_dataset,
segmentation_number,
voxel_size=(40, 4, 4)):
graph_provider = MongoDbGraphProvider(db_name,
db_host,
directed=False,
position_attribute=['z', 'y', 'x'],
edges_collection=edge_collection)
graph_roi = daisy.Roi(roi_offset, roi_size)
segmentation = daisy.open_ds(segmentation_container, segmentation_dataset)
intersection_roi = segmentation.roi.intersect(graph_roi).snap_to_grid(
voxel_size)
nx_graph = graph_provider.get_graph(intersection_roi,
nodes_filter={selected_attr: True},
edges_filter={selected_attr: True})
for node_id, data in nx_graph.nodes(data=True):
node_position = daisy.Coordinate((data["z"], data["y"]), data["x"])
nx_graph.nodes[node_id]["segmentation_{}".format(
segmentation_number)] = segmentation[node_position]
graph_provider.write_nodes(intersection_roi)
def run_test_graph_write_attributes(self, provider_factory):
graph_provider = provider_factory('w')
graph = graph_provider[daisy.Roi((0, 0, 0), (10, 10, 10))]
graph.add_node(2, comment="without position")
graph.add_node(42, position=(1, 1, 1))
graph.add_node(23, position=(5, 5, 5), swip='swap')
graph.add_node(57, position=daisy.Coordinate((7, 7, 7)), zap='zip')
graph.add_edge(42, 23)
graph.add_edge(57, 23)
graph.add_edge(2, 42)
graph.write_nodes(attributes=['position', 'swip'])
graph.write_edges()
graph_provider = provider_factory('r')
compare_graph = graph_provider[daisy.Roi((0, 0, 0), (10, 10, 10))]
nodes = []
for node, data in graph.nodes(data=True):
if node == 2:
continue
if 'zap' in data:
del data['zap']
data['position'] = list(data['position'])
nodes.append((node, data))
compare_nodes = compare_graph.nodes(data=True)
compare_nodes = [(node_id, data) for node_id, data in compare_nodes
if len(data) > 0]
self.assertCountEqual(nodes, compare_nodes)
def test_graph_multiple_separate_collections(self):
attributes = {'3': ['selected'], '4': ['swip']}
graph_provider = self.get_mongo_graph_provider('w', attributes,
attributes)
roi = daisy.Roi((0, 0, 0), (10, 10, 10))
graph = graph_provider[roi]
graph.add_node(2, position=(2, 2, 2), swip='swap')
graph.add_node(42, position=(1, 1, 1), selected=False, swip='swim')
graph.add_node(23, position=(5, 5, 5), selected=True)
graph.add_node(57, position=daisy.Coordinate((7, 7, 7)), selected=True)
graph.add_edge(42, 23)
graph.add_edge(57, 23, selected=True, swip='swap')
graph.add_edge(2, 42, selected=True)
graph.add_edge(42, 2, swip='swim')
graph.write_nodes()
graph.write_edges()
graph_provider = self.get_mongo_graph_provider('r', attributes,
attributes)
compare_graph = graph_provider[roi]
self.assertFalse('selected' in compare_graph.nodes[2])
self.assertEqual('swap', compare_graph.nodes[2]['swip'])
self.assertEqual(False, compare_graph.nodes[42]['selected'])
self.assertEqual('swim', compare_graph.nodes[42]['swip'])
self.assertFalse('swip' in compare_graph.nodes[57])
self.assertEqual(True, compare_graph.edges[2, 42]['selected'])
self.assertFalse('swip' in compare_graph.edges[2, 42])
self.assertFalse('selected' in compare_graph.edges[42, 23])
self.assertEqual('swim', compare_graph.edges[42, 2]['swip'])
def run_test_graph_io(self, provider_factory):
graph_provider = provider_factory('w')
graph = graph_provider[daisy.Roi((0, 0, 0), (10, 10, 10))]
graph.add_node(2, comment="without position")
graph.add_node(42, position=(1, 1, 1))
graph.add_node(23, position=(5, 5, 5), swip='swap')
graph.add_node(57, position=daisy.Coordinate((7, 7, 7)), zap='zip')
graph.add_edge(42, 23)
graph.add_edge(57, 23)
graph.add_edge(2, 42)
graph.write_nodes()
graph.write_edges()
graph_provider = provider_factory('r')
compare_graph = graph_provider[daisy.Roi((0, 0, 0), (10, 10, 10))]
nodes = sorted(list(graph.nodes()))
nodes.remove(2) # node 2 has no position and will not be queried
compare_nodes = sorted(list(compare_graph.nodes()))
edges = sorted(list(graph.edges()))
edges.remove((2, 42)) # node 2 has no position and will not be queried
compare_edges = sorted(list(compare_graph.edges()))
self.assertEqual(nodes, compare_nodes)
self.assertEqual(edges, compare_edges)
def test_graph_read_unbounded_roi(self):
graph_provider = self.get_mongo_graph_provider('w')
roi = daisy.Roi((0, 0, 0), (10, 10, 10))
unbounded_roi = daisy.Roi((None, None, None), (None, None, None))
graph = graph_provider[roi]
graph.add_node(2, position=(2, 2, 2), selected=True, test='test')
graph.add_node(42, position=(1, 1, 1), selected=False, test='test2')
graph.add_node(23, position=(5, 5, 5), selected=True, test='test2')
graph.add_node(57,
position=daisy.Coordinate((7, 7, 7)),
selected=True,
test='test')
graph.add_edge(42, 23, selected=False, a=100, b=3)
graph.add_edge(57, 23, selected=True, a=100, b=2)
graph.add_edge(2, 42, selected=True, a=101, b=3)
graph.write_nodes()
graph.write_edges()
graph_provider = self.get_mongo_graph_provider('r+')
limited_graph = graph_provider.get_graph(unbounded_roi,
node_attrs=['selected'],
edge_attrs=['c'])
seen = []
for node, data in limited_graph.nodes(data=True):
self.assertFalse('test' in data)
self.assertTrue('selected' in data)
data['selected'] = True
seen.append(node)
self.assertCountEqual(seen, [2, 42, 23, 57])
def run_test_graph_connected_components(self, provider_factory):
graph_provider = provider_factory('w')
graph = graph_provider[daisy.Roi((0, 0, 0), (10, 10, 10))]
graph.add_node(2, comment="without position")
graph.add_node(42, position=(1, 1, 1))
graph.add_node(23, position=(5, 5, 5), swip='swap')
graph.add_node(57, position=daisy.Coordinate((7, 7, 7)), zap='zip')
graph.add_edge(57, 23)
graph.add_edge(2, 42)
components = graph.get_connected_components()
self.assertEqual(len(components), 2)
c1, c2 = components
n1 = sorted(list(c1.nodes()))
n2 = sorted(list(c2.nodes()))
compare_n1 = [2, 42]
compare_n2 = [23, 57]
if 2 in n2:
temp = n2
n2 = n1
n1 = temp
self.assertCountEqual(n1, compare_n1)
self.assertCountEqual(n2, compare_n2)
def test_graph():
graph_provider = daisy.persistence.MongoDbGraphProvider(
'test_daisy_graph',
'10.40.4.51',
nodes_collection='nodes',
edges_collection='edges',
mode='w')
graph = graph_provider[daisy.Roi((0, 0, 0), (10, 10, 10))]
graph.add_node(2, comment="without position")
graph.add_node(42, position=(1, 1, 1))
graph.add_node(23, position=(5, 5, 5), swip='swap')
graph.add_node(57, position=daisy.Coordinate((7, 7, 7)), zap='zip')
graph.add_edge(42, 23)
for i in range(10000):
graph.add_node(i + 100,
position=(random.randint(0, 10), random.randint(0, 10),
random.randint(0, 10)))
start = time.time()
graph.write_nodes()
graph.write_edges()
print("Wrote graph in %.3fs" % (time.time() - start))
start = time.time()
graph = graph_provider[daisy.Roi((0, 0, 0), (10, 10, 10))]
print("Read graph in %.3fs" % (time.time() - start))
def test_graph_separate_collection_simple(self):
attributes = {'1': ['selected']}
graph_provider = self.get_mongo_graph_provider('w', attributes,
attributes)
roi = daisy.Roi((0, 0, 0), (10, 10, 10))
graph = graph_provider[roi]
graph.add_node(2, position=(2, 2, 2), selected=True)
graph.add_node(42, position=(1, 1, 1), selected=False)
graph.add_node(23, position=(5, 5, 5), selected=True)
graph.add_node(57, position=daisy.Coordinate((7, 7, 7)), selected=True)
graph.add_edge(42, 23, selected=False)
graph.add_edge(57, 23, selected=True)
graph.add_edge(2, 42, selected=True)
graph.write_nodes()
graph.write_edges()
graph_provider = self.get_mongo_graph_provider('r', attributes,
attributes)
compare_graph = graph_provider[roi]
self.assertEqual(True, compare_graph.nodes[2]['selected'])
self.assertEqual(False, compare_graph.nodes[42]['selected'])
self.assertEqual(True, compare_graph.edges[2, 42]['selected'])
self.assertEqual(False, compare_graph.edges[42, 23]['selected'])
def get_crops(self, center_positions, size):
"""
Args:
center_positions (list of tuple of ints): [(z,y,x)] in nm
size (tuple of ints): size of the crop, in voxels
"""
crops = []
size = daisy.Coordinate(size)
voxel_size = daisy.Coordinate(self.voxel_size)
size_nm = (size * voxel_size)
dataset = daisy.open_ds(self.container, self.dataset)
dataset_resolution = None
try:
dataset_resolution = dataset.voxel_size
except AttributeError:
pass
if dataset_resolution is not None:
if not np.all(dataset.voxel_size == self.voxel_size):
raise ValueError(
f"Dataset {dataset} resolution missmatch {dataset.resolution} vs {self.voxel_size}"
)
for position in center_positions:
position = daisy.Coordinate(tuple(position))
offset_nm = position - ((size / 2) * voxel_size)
roi = daisy.Roi(offset_nm, size_nm).snap_to_grid(voxel_size,
mode='closest')
if roi.get_shape()[0] != size_nm[0]:
roi.set_shape(size_nm)
if not dataset.roi.contains(roi):
raise Warning(
f"Location {position} is not fully contained in dataset")
return
crops.append(dataset[roi].to_ndarray())
crops_batched = np.stack(crops)
crops_batched = crops_batched.astype(np.float32)
return crops_batched