def from_filelist(cls, filelist, basepath=None):
"""
Instantiate the class using a filelist as a python list.
An adjacency structure is calculated using the lat/lon information in the
input images. Currently only images with this information are supported.
Parameters
----------
filelist : list
A list containing the files (with full paths) to construct an adjacency graph from
Returns
-------
: object
A Network graph object
"""
if isinstance(filelist, str):
filelist = io_utils.file_to_list(filelist)
# TODO: Reject unsupported file formats + work with more file formats
if basepath:
datasets = [
GeoDataset(os.path.join(basepath, f)) for f in filelist
]
else:
datasets = [GeoDataset(f) for f in filelist]
# This is brute force for now, could swap to an RTree at some point.
adjacency_dict = {}
valid_datasets = []
for i in datasets:
adjacency_dict[i.file_name] = []
fp = i.footprint
if fp and fp.IsValid():
valid_datasets.append(i)
else:
warnings.warn(
'Missing or invalid geospatial data for {}'.format(
i.base_name))
# Grab the footprints and test for intersection
for i, j in itertools.permutations(valid_datasets, 2):
i_fp = i.footprint
j_fp = j.footprint
try:
if i_fp.Intersects(j_fp):
adjacency_dict[i.file_name].append(j.file_name)
adjacency_dict[j.file_name].append(i.file_name)
except:
warnings.warn(
'Failed to calculate intersection between {} and {}'.
format(i, j))
return cls.from_adjacency(adjacency_dict)
def from_filelist(cls, filelist, basepath=None):
"""
Instantiate the class using a filelist as a python list.
An adjacency structure is calculated using the lat/lon information in the
input images. Currently only images with this information are supported.
Parameters
----------
filelist : list
A list containing the files (with full paths) to construct an adjacency graph from
Returns
-------
: object
A Network graph object
"""
if isinstance(filelist, str):
filelist = io_utils.file_to_list(filelist)
# TODO: Reject unsupported file formats + work with more file formats
if basepath:
datasets = [GeoDataset(os.path.join(basepath, f)) for f in filelist]
else:
datasets = [GeoDataset(f) for f in filelist]
# This is brute force for now, could swap to an RTree at some point.
adjacency_dict = {}
valid_datasets = []
for i in datasets:
adjacency_dict[i.file_name] = []
fp = i.footprint
if fp and fp.IsValid():
valid_datasets.append(i)
else:
warnings.warn('Missing or invalid geospatial data for {}'.format(i.base_name))
# Grab the footprints and test for intersection
for i, j in itertools.permutations(valid_datasets, 2):
i_fp = i.footprint
j_fp = j.footprint
try:
if i_fp.Intersects(j_fp):
adjacency_dict[i.file_name].append(j.file_name)
adjacency_dict[j.file_name].append(i.file_name)
except:
warnings.warn('Failed to calculated intersection between {} and {}'.format(i, j))
return cls(adjacency_dict)
def from_filelist(cls, filelist, basepath=None):
"""
This methods instantiates a network candidate graph by first parsing
the filelist and adding those images to the database. This method then
dispatches to the from_database cls method to create the network
candidate graph object.
"""
if isinstance(filelist, str):
filelist = io_utils.file_to_list(filelist)
if basepath:
filelist = [(f, os.path.join(basepath, f)) for f in filelist]
else:
filelist = [(os.path.basename(f), f) for f in filelist]
parent = Parent(config)
# Get each of the images added to the DB (duplicates, by PATH, are omitted)
for f in filelist:
n = NetworkNode(image_name=f[0], image_path=f[1], parent=parent)
pathlist = [f[1] for f in filelist]
qs = 'SELECT * FROM public.Images WHERE public.Images.path IN ({})'.format(','.join("'{0}'".format(p) for p in pathlist))
return NetworkCandidateGraph.from_database(query_string=qs)