def diffme(original_file, new_file, new_points_f, deleted_points_f, id_field): original = geojson.load(original_file) new = geojson.load(new_file) # Load all the points into a dict original_layer = loadpoints(original.features, id_field) new_layer = loadpoints(new.features, id_field) # TODO: Check that CRS is identical. # Find all the points that were added original_guids = set(original_layer.keys()) new_guids = set(new_layer.keys()) added_guids = new_guids - original_guids new_points = geojson.FeatureCollection([]) new_points.crs = new.crs if id_field is None: new_points.features = filter((lambda x: hash_coords(*x.geometry.coordinates) in added_guids), new.features) else: new_points.features = filter((lambda x: x.properties[id_field] in added_guids), new.features) geojson.dump(new_points, new_points_f) new_points_f.close() deleted_guids = original_guids - new_guids deleted_points = geojson.FeatureCollection([]) deleted_points.crs = original.crs if id_field is None: deleted_points.features = filter((lambda x: hash_coords(*x.geometry.coordinates) in deleted_guids), original.features) else: deleted_points.features = filter((lambda x: x.properties[id_field] in deleted_guids), original.features) geojson.dump(deleted_points, deleted_points_f) deleted_points_f.close()
def joinme(original_file, new_file, output_f, id_field, exclude_original_only=False, include_new_only=False, original_prefix='old_', new_prefix='new_'):
original = geojson.load(original_file)
new = geojson.load(new_file)
# Load all the points into a dict
original_layer = loadpoints(original.features, id_field)
new_layer = loadpoints(new.features, id_field)
output_layer = geojson.FeatureCollection([])
output_layer.crs = original.crs
for feature in original.features:
if exclude_original_only and feature.properties[id_field] not in new_layer:
# feature is missing from new file.
continue
output_layer.features.append(feature)
old_properties = {}
for k, v in feature.properties.iteritems():
if k == id_field:
old_properties[k] = v
else:
old_properties[original_prefix + k] = v
feature.properties = old_properties
# Add in "new" properties if they exist
if feature.properties[id_field] in new_layer:
for k, v in new_layer[feature.properties[id_field]].properties.iteritems():
if k == id_field:
continue
feature.properties[new_prefix + k] = v
if include_new_only:
for feature in new.features:
if feature.properties[id_field] not in original_layer:
properties = {}
for k, v in feature.properties.iteritems():
properties[new_prefix + k] = v
feature.properties = properties
output_layer.features.append(feature)
# now dump the resulting file
geojson.dump(output_layer, output_f)
output_f.close()
def __init__(self,path,shpfile=False):
self.path = normpath(abspath(path))
self.gobj = {}
self.code = None
if shpfile:
dpath = tmpdir("tmp",basename(path).split(".")[0])
system("cd " + dpath + "; unzip " + self.path)
shpfiles = getfiles(dpath,".shp")
for x in shpfiles:
p = join(dpath,x)
geop = p[:-3] + "geojson"
cmd = "ogr2ogr -f GeoJSON " + geop + " " + p
print "running",cmd
system(cmd)
bs = basename(p).split("-")
f = bs[3]
if self.code is None:
self.code = bs[2]
geo = geojson.load(codecs.open(geop,encoding="shift_jisx0213"))
self.gobj.setdefault(f,[]).extend(geo["features"])
rmall(dpath)
else:
self.file_open()
self.get_code()
def import_lakes2(self):
lakes = geojson.load(open(
'src/iwlearn.project/iwlearn/project/dataimport/missing_lakes.json',
'r'))
parent = self.portal_url.getPortalObject()['iw-projects']['basins']['lakes']
for lake in lakes['features']:
if lake['properties']['TYPE']=='Lake':
new_obj_id = idn.normalize(lake['properties']['GLWD_ID'])
print new_obj_id
self.portal_types.constructContent('Basin', parent, new_obj_id)
new_obj=parent[new_obj_id]
if lake['properties']['LAKE_NAME']:
new_obj.setTitle(lake['properties']['LAKE_NAME'])
new_obj.setDescription("Area: %s; Perimeter: %s; Countries: %s" % (
lake['properties']['AREA_SKM'],
lake['properties']['PERIM_KM'],
lake['properties']['COUNTRY'],
))
new_obj.setBasin_type('Lake')
color='2c80d3'
style = IGeoCustomFeatureStyle(new_obj)
style.geostyles.data['use_custom_styles']=True
style.geostyles.data['polygoncolor']=color
style.geostyles.update(style.geostyles)
geo = IGeoManager(new_obj)
q = asShape(lake['geometry']).simplify(0.1).__geo_interface__
geo.setCoordinates(q['type'], q['coordinates'])
def read_geojson(self, filename, quiet=False):
if not quiet:
log.info("Reading PNwk %s", filename)
with open(filename + self.FILE_EXTENSION, "r") as f:
# needs geojson>1.0.7, for non-ascii property keys
geo = geojson.load(f)
self._parse_geojson(geo)
def osmme(input_f, output_f, name_field, all_tags=False):
output_dom = ET.Element('osm', dict(version='0.5', generator='geojson2osm'))
layer = geojson.load(input_f, use_decimal=True)
# Note: does not check CRS, assumes WGS84!
for gid, point in enumerate(layer.features):
# Only supports points!
if point.geometry.type != 'Point':
continue
node = ET.SubElement(output_dom, 'node', dict(
id=unicode(-gid),
visible='true',
lat=unicode(point.geometry.coordinates[1]),
lon=unicode(point.geometry.coordinates[0])
))
ET.SubElement(node, 'tag', dict(k='name', v=unicode(point.properties[name_field])))
ET.SubElement(node, 'tag', dict(k='note', v=unicode(point.properties[name_field])))
# so that this doesn't show up in the output again.
del point.properties[name_field]
if all_tags:
# write out other properties as custom tags
for k, v in point.properties.iteritems():
ET.SubElement(node, 'tag', dict(k=u'custom:' + k, v=unicode(v)))
# write XML
output_f.write("<?xml version='1.0' encoding='UTF-8'?>\n")
output_f.write(ET.tostring(output_dom, encoding='utf-8'))
output_f.write('\n')
output_f.close()
def importer(self, config_entity, db_entity, **kwargs):
"""
Creates various GeojsonFeature classes by importing geojson and saving it to the database via a dynamic subclass of GeojsonFeature
:schema: The optional schema to use for the dynamic subclass's meta db_table attribute, which will allow the class's table to be saved in the specified schema. Defaults to public
:data: Optional python dict data to use instead of loading from the db_entity.url
:return: a list of lists. Each list is a list of features of distinct subclass of GeoJsonFeature that is created dynamically. To persist these features, you must first create the subclass's table in the database using create_table_for_dynamic_class(). You should also register the table as a DbEntity.
"""
if self.seed_data:
data = geojson.loads(jsonify(self.seed_data), object_hook=geojson.GeoJSON.to_instance)
else:
fp = open(db_entity.url.replace('file://', ''))
data = geojson.load(fp, object_hook=geojson.GeoJSON.to_instance)
feature_class_creator = FeatureClassCreator(config_entity, db_entity)
# find all unique properties
feature_class_configuration = feature_class_creator.feature_class_configuration_from_geojson_introspection(data)
feature_class_creator.update_db_entity(feature_class_configuration)
feature_class = feature_class_creator.dynamic_model_class(base_only=True)
# Create our base table. Normally this is done by the import, but we're just importing into memory
create_tables_for_dynamic_classes(feature_class)
# Now write each feature to our newly created table
for feature in map(lambda feature: self.instantiate_sub_class(feature_class, feature), data.features):
feature.save()
# Create the rel table too
rel_feature_class = feature_class_creator.dynamic_model_class()
create_tables_for_dynamic_classes(rel_feature_class)
# PostGIS 2 handles this for us now
# if InformationSchema.objects.table_exists(db_entity.schema, db_entity.table):
# # Tell PostGIS about the new geometry column or the table
# sync_geometry_columns(db_entity.schema, db_entity.table)
# Create association classes and tables and populate them with data
create_and_populate_relations(config_entity, db_entity)
def split(input_file, file_1, file_2, no_in_first_file):
'''
Split a geojson in two separate files.
Args:
input_file (str): Input filename.
file_1 (str): Output file name 1.
file_2 (str): Output file name 2.
no_features (int): Number of features in input_file to go to file_1.
output_file (str): Output file name.
'''
# get feature collection
with open(input_file) as f:
feat_collection = geojson.load(f)
features = feat_collection['features']
feat_collection_1 = geojson.FeatureCollection(features[0:no_in_first_file])
feat_collection_2 = geojson.FeatureCollection(features[no_in_first_file:])
with open(file_1, 'w') as f:
geojson.dump(feat_collection_1, f)
with open(file_2, 'w') as f:
geojson.dump(feat_collection_2, f)
def store_file(self, path, **kwargs):
abs_path = os.path.abspath(path)
fh = open(abs_path, 'r')
feature = geojson.load(fh)
props = feature.get('properties', {})
id = props.get('wof:id', None)
if id == None:
raise Exception, "E_INSUFFICIENT_WHOSONFIRST"
rel_path = utils.id2relpath(id)
if kwargs.get("prefix", None):
rel_path = os.path.join(kwargs['prefix'], rel_path)
logging.debug("copy %s to %s:%s" % (abs_path, self.bucket, rel_path))
k = Key(self.bucket)
k.key = rel_path
k.set_contents_from_filename(abs_path)
acl = kwargs.get('acl', 'public-read')
k.set_acl(acl)
# print k.generate_url(expires_in=0, query_auth=False)
return True
def filter_by_property(input_file, output_file, property_name, values):
'''
Create a file containing only features with specified property value(s) from
input_file.
INPUT input_file (str): File name.
output_file (str): Output file name.
property_name (str): Name of the feature property to filter by.
values (list): Value(s) a feature may have for property_name if it is to be
included in output_file.
'''
filtered_feats = []
if not output_file.endswith('.geojson'):
output_file += '.geojson'
# Load feature list
with open(input_file) as f:
feature_collection = geojson.load(f)
# Filter feats by property_name
for feat in feature_collection['features']:
if feat['properties'][property_name] in values:
filtered_feats.append(feat)
feature_collection['features'] = filtered_feats
# Save filtered file
with open(output_file, 'wb') as f:
geojson.dump(f)
def get(self, request, geocodigo, disease):
"""
:param kwargs:
:return:
"""
geocode = geocodigo
# load shapefile
for path in (settings.STATIC_ROOT, settings.STATICFILES_DIRS[0]):
if not os.path.isdir(path):
continue
geojson_path = os.path.join(path, 'geojson', '%s.json' % geocode)
hex_color = get_last_color_alert(geocode, disease, color_type='hex')
with open(geojson_path) as f:
geojson_data = geojson.load(f)
geojson_data['features'][0]['properties']['fill'] = hex_color
result = geojson.dumps(geojson_data)
response = HttpResponse(
result, content_type='application/force-download'
)
response['Content-Disposition'] = (
'attachment; filename=%s.json' % geocode
)
return response
def uniform_chip_generator(input_file, batch_size=32, **kwargs):
'''
Generate batches of uniformly-sized pixel intensity arrays from image strips using a
geojson file. Output will be in the same format as get_data_from_polygon_list.
INPUT input_file (str): File name
batch_size (int): Number of chips to yield per iteration
kwargs:
-------
See get_data_from_polygon_list docstring for other input params. Do not use
the num_chips arg.
OUTPUT chips (array): Uniformly sized chips with the following dimensions:
(num_chips, num_channels, max_side_dim, max_side_dim)
ids (list): Feature ids corresponding to chips.
labels (array): One-hot encoded labels for chips with the follwoing
dimensions: (num_chips, num_classes)
'''
# Load features from input_file
with open(input_file) as f:
feature_collection = geojson.load(f)['features']
# Produce batches using get_data_from_polygon_list
for batch_ix in range(0, len(feature_collection), batch_size):
this_batch = feature_collection[batch_ix: batch_ix + batch_size]
yield get_data_from_polygon_list(this_batch, **kwargs)
def crawl(source, **kwargs):
validate = kwargs.get('validate', False)
inflate = kwargs.get('inflate', False)
for (root, dirs, files) in os.walk(source):
for f in files:
path = os.path.join(root, f)
path = os.path.abspath(path)
ret = path
if not path.endswith('geojson'):
continue
if validate or inflate:
try:
fh = open(path, 'r')
data = geojson.load(fh)
except Exception, e:
logging.error("failed to load %s, because %s" % (path, e))
continue
if not inflate:
ret = path
else:
ret = data
yield ret
def get_uniform_chips(input_file, num_chips=None, **kwargs):
'''
Get uniformly-sized pixel intensity arrays from image strips using a geojson file.
Output will be in the same format as get_data_from_polygon_list.
INPUT input_file (str): File name. This file should be filtered for polygon size
num_chips (int): Maximum number of chips to return. If None will return all
chips in input_file. Defaults to None
kwargs:
-------
See get_data_from_polygon_list docstring for other input params
OUTPUT chips (array): Uniformly sized chips with the following dimensions:
(num_chips, num_channels, max_side_dim, max_side_dim)
ids (list): Feature ids corresponding to chips.
labels (array): One-hot encoded labels for chips with the follwoing
dimensions: (num_chips, num_classes)
'''
# Load features from input_file
with open(input_file) as f:
feature_collection = geojson.load(f)['features']
if num_chips:
feature_collection = feature_collection[: num_chips]
return get_data_from_polygon_list(feature_collection, num_chips=num_chips, **kwargs)
def get_proportion(self, property_name, property):
'''
Helper function to get the proportion of polygons with a given property in a
shapefile
INPUT shapefile (string): name of the shapefile containing the polygons
property_name (string): name of the property to search for exactly as it
appears in the shapefile properties (ex: image_id)
property (string): the property of which to get the proportion of in the
shapefile (ex: '1040010014800C00')
OUTPUT proportion (float): proportion of polygons that have the property of interest
'''
total, prop = 0,0
# open shapefile, get polygons
with open(self.shapefile) as f:
data = geojson.load(f)['features']
# loop through features, find property count
for polygon in data:
total += 1
try:
if str(polygon['properties'][property_name]) == property:
prop += 1
except:
continue
return float(prop) / total
def import_oceans(self):
oceans = geojson.load(open(
'src/iwlearn.project/iwlearn/project/dataimport/oceans.json',
'r'))
parent = self.portal_url.getPortalObject()['iw-projects']['basins']['oceans']
for ocean in oceans['features']:
mpoly = []
geom = asShape(ocean['geometry'])
for g in geom.geoms:
if g.area > 5:
mpoly.append(g)
mp = MultiPolygon(mpoly).simplify(0.2)
q = mp.__geo_interface__
new_obj_id = idn.normalize(ocean['properties']['NAME'])
print new_obj_id
self.portal_types.constructContent('Basin', parent, new_obj_id)
new_obj=parent[new_obj_id]
new_obj.setTitle(ocean['properties']['NAME'])
new_obj.setBasin_type('Ocean')
color='aa22ff'
style = IGeoCustomFeatureStyle(new_obj)
style.geostyles.data['use_custom_styles']=True
style.geostyles.data['polygoncolor']=color
style.geostyles.update(style.geostyles)
geo = IGeoManager(new_obj)
geo.setCoordinates(q['type'], q['coordinates'])
def get_file(request, waitLaneStrId, which, fileType): waitLaneId = str(waitLaneStrId) attributeName = which+'File' try: waitLane = WaitLane.objects.get(id=waitLaneId) fileField = getattr(waitLane, attributeName) if fileType == "wkt": wrapper = None response = None with open(fileField.path, 'r') as fileObject: geoJsonFile = File(fileObject) geoJsonObject = geojson.load(geoJsonFile) temp = tempfile.TemporaryFile() for feature in geoJsonObject['features']: s = shape(feature['geometry']) wktGeometry = dump(s, temp) #send content of temporary file wrapper = FileWrapper(temp) response = HttpResponse(wrapper, content_type='application/wkt') #response['Content-Disposition'] = 'attachment; filename=something.wkt' response['Content-Length'] = temp.tell() temp.seek(0) #pdb.set_trace() return response else: wrapper = FileWrapper(fileField.file) response = HttpResponse(wrapper, content_type='application/json') response['Content-Length'] = os.path.getsize(fileField.path) #pdb.set_trace() return response except ObjectDoesNotExist: #FIXME should send json instead return HttpResponseNotFound()
def crawl(source, **kwargs):
validate = kwargs.get('validate', False)
inflate = kwargs.get('inflate', False)
ensure = kwargs.get('ensure_placetype', [])
skip = kwargs.get('skip_placetype', [])
for (root, dirs, files) in os.walk(source):
for f in files:
path = os.path.join(root, f)
path = os.path.abspath(path)
ret = path
if not path.endswith('geojson'):
continue
if path.endswith('-alt.geojson'):
continue
if validate or inflate or len(skip) or len(ensure):
try:
fh = open(path, 'r')
data = geojson.load(fh)
except Exception, e:
logging.error("failed to load %s, because %s" % (path, e))
continue
if len(ensure):
props = data['properties']
pt = props.get('wof:placetype', None)
if not pt in ensure:
logging.debug("skipping %s because it is a %s" % (path, pt))
continue
elif len(skip):
props = data['properties']
pt = props.get('wof:placetype', None)
if pt in skip:
logging.debug("skipping %s because it is a %s" % (path, pt))
continue
if not pt:
logging.error("can not determine placetype for %s" % path)
if not inflate:
ret = path
else:
ret = data
yield ret
def dump_file(path, **kwargs):
try:
fh = open(path, 'r')
feature = geojson.load(fh)
except Exception, e:
logging.error("failed to load %s, because %s" % (path, e))
return None
def geojson2geobuf(layer):
"""Geojson to Geobuf conversion."""
with open('{layer}.geojson'.format(layer=layer), 'r') as json:
with open('{layer}.pbf'.format(layer=layer), 'wb') as buf:
data = geojson.load(json)
pbf = geobuf.encode(data)
buf.write(pbf)
return 'Successfully wrote geobuf.'
def publish_file(self, path):
path = os.path.abspath(path)
fh = open(path, 'r')
feature = geojson.load(fh)
fh.close()
return self.publish_feature(feature)
def load_file(self, f):
try:
fh = open(f, 'r')
return geojson.load(fh)
except Exception, e:
logging.error("failed to open %s, because %s" % (f, e))
raise Exception, e
def filter_polygon_size(shapefile, output_file, min_polygon_hw=30, max_polygon_hw=224):
'''
Creates a geojson file containing only acceptable side dimensions for polygons.
INPUT (1) string 'shapefile': name of shapefile with original samples
(2) string 'output_file': name of file in which to save selected polygons.
This should end in '.geojson'
(3) int 'min_polygon_hw': minimum acceptable side length (in pixels) for
given polygon
(4) int 'max_polygon_hw': maximum acceptable side length (in pixels) for
given polygon
OUTPUT (1) a geojson file (output_file.geojson) containing only polygons of
acceptable side dimensions
'''
# load polygons
with open(shapefile) as f:
data = geojson.load(f)
total = float(len(data['features']))
# find indicies of acceptable polygons
ix_ok, ix = [], 0
print 'Extracting image ids...'
img_ids = find_unique_values(shapefile, property_name='image_id')
print 'Filtering polygons...'
for img_id in img_ids:
print '... for image {}'.format(img_id)
img = geoio.GeoImage(img_id + '.tif')
# cycle thru polygons
for chip, properties in img.iter_vector(vector=shapefile,
properties=True,
filter=[{'image_id': img_id}],
mask=True):
chan,h,w = np.shape(chip)
if chip is None or min(h, w) < min_polygon_hw or max(h, w) > max_polygon_hw:
ix += 1
# add percent complete to stdout
sys.stdout.write('\r%{0:.2f}'.format(100 * ix / total) + ' ' * 20)
sys.stdout.flush()
continue
ix_ok.append(ix)
ix += 1
# add percent complete to stdout
sys.stdout.write('\r%{0:.2f}'.format(100 * ix / total) + ' ' * 20)
sys.stdout.flush()
print 'Saving...'
ok_polygons = [data['features'][i] for i in ix_ok]
np.random.shuffle(ok_polygons)
filtrate = {data.keys()[0]: data.values()[0],
data.keys()[1]: ok_polygons}
# save new geojson
with open(output_file, 'wb') as f:
geojson.dump(filtrate, f)
print 'Saved {} polygons to {}'.format(str(len(ok_polygons)), output_file)
def mergeme(inputs, output, no_dupe_handling, id_field, id_property):
if no_dupe_handling:
assert (not id_field) and (not id_property)
else:
assert (not id_field) or (not id_property)
assert not (id_field and id_property)
known_ids = set()
crs = None
output_layer = geojson.FeatureCollection([])
# Flatten the list of inputs
inputs = list(itertools.chain.from_iterable(inputs))
for i, layer_f in enumerate(inputs):
print "Processing input file #%d..." % i
layer = geojson.load(layer_f)
# FIXME: this requires the CRS be specified on a "layer" level. GeoJSON allows this to be ommitted, and this should include a check to ensure it is ommitted for all in this case.
# Some broken GeoJSON files do weird things...
if isinstance(layer.crs, list):
layer.crs = layer.crs[0]
if isinstance(layer.crs.properties, list):
newprops = {}
for x in range(len(layer.crs.properties)/2):
newprops[layer.crs.properties[x*2]] = layer.crs.properties[(x*2) + 1]
layer.crs.properties = newprops
# We don't care about per-geometry CRS, these can mingle
if i == 0:
# first file sets the CRS!
crs = layer.crs.properties['name']
output_layer.crs = layer.crs
else:
assert layer.crs.properties['name'] == crs, ('CRS of files must match. File has CRS %r, expected %r' % (layer.crs.properties['name'], crs))
# We have a matching CRS, start merging geometries.
for geometry in layer.features:
if not no_dupe_handling:
if id_property and geometry.properties[id_property] in known_ids:
# Geometry is already present, skip
continue
elif id_field and geometry.id in known_ids:
# Geometry is already present, skip
continue
# Geometry is new, add to list
output_layer.features.append(geometry)
if id_property:
known_ids.add(geometry.properties[id_property])
elif id_field:
known_ids.add(geometry.id)
print "OK! (%d total geometries written, %d read from this file)" % (len(output_layer.features), len(layer.features))
# all files complete
geojson.dump(output_layer, output)
print "Files merged!"
def load_allegheny_munis(allegheny_munis_file):
munis = geojson.load(open(allegheny_munis_file))['features']
for muni in munis:
muni['shape'] = shapely.geometry.asShape(muni['geometry'])
global allegheny_outline
allegheny_outline = munis[0]['shape']
for muni in munis:
allegheny_outline = allegheny_outline.union(muni['shape'])
return munis
def load(root, id, **kwargs):
path = id2abspath(root, id, **kwargs)
if not os.path.exists(path):
raise Exception, "%s does not exist" % path
fh = open(path, 'r')
return geojson.load(fh)
def _get_shapes(self):
scale = str(self.scale).zfill(2)
filename = NESTED_FILE.format(year=self.year, scale=scale)
url = ZIP_URL.format(year=self.year, scale=scale)
r = requests.get(url, verify=True)
r.raise_for_status()
with ZipFile(BytesIO(r.content)) as zipfile:
with zipfile.open(filename) as f:
shapes = geojson.load(f)
return shapes
def open_json(input_file):
"""otevre vstupni geojson
vstup: input_file: soubor json
výstup: načtnená data vstupního souboru"""
with open(input_file, encoding='utf-8') as f:
gj = geojson.load(f)
#adress = gj['features'][0]
#print(adress)
return (gj)
def read_geojson(path,*key_path):
""" read geojson file
path<str>: path to geojson file
*key_path: keys to go to in object
"""
with open(path,'rb') as file:
obj=geojson.load(file)
for k in key_path:
obj=obj[k]
return obj
def get_geojson():
global app
geo = getattr(flask.g, '_goesbrowse_geojson', None)
if geo is None:
geo = {}
for k, v in GEOJSON_FILES.items():
with app.open_resource(v, mode='r') as f:
geo[k] = geojson.load(f)
flask.g._goesbrowse_geojson = geo
return geo
def deserialize(filename):
"""Loads results of a search from a geojson file.
:param filename: A filename containing a search result encoded as a geojson
:type filename: str
:returns: The search results encoded in `filename`
:rtype: :class:`~eodag.api.search_result.SearchResult`
"""
with open(filename, "r") as fh:
return SearchResult.from_geojson(geojson.load(fh))
def load_nghds():
neighborhoods = geojson.load(open('static/neighborhoods.json'))
nghds = neighborhoods['features']
for nghd in nghds:
nghd['shape'] = shapely.geometry.asShape(nghd.geometry)
global pittsburgh_outline
pittsburgh_outline = nghds[0]['shape']
for nghd in nghds:
pittsburgh_outline = pittsburgh_outline.union(nghd['shape'])
return nghds
def load_nghds(json_file="neighborhoods.json"):
neighborhoods = geojson.load(open(json_file))
nghds = neighborhoods['features']
for nghd in nghds:
nghd['shape'] = shapely.geometry.asShape(nghd.geometry)
global pittsburgh_outline # TODO ugh globals
pittsburgh_outline = nghds[0]['shape']
for nghd in nghds:
pittsburgh_outline = pittsburgh_outline.union(nghd['shape'])
return nghds
def bbox(inputs, output):
crs = None
output_layer = geojson.FeatureCollection([])
# Flatten the list of inputs
inputs = list(itertools.chain.from_iterable(inputs))
for i, layer_f in enumerate(inputs):
print "Processing input file #%d..." % i
layer = geojson.load(layer_f)
# FIXME: this requires the CRS be specified on a "layer" level. GeoJSON allows this to be ommitted, and this should include a check to ensure it is ommitted for all in this case.
# Some broken GeoJSON files do weird things...
if isinstance(layer.crs, list):
layer.crs = layer.crs[0]
if isinstance(layer.crs.properties, list):
newprops = {}
for x in range(len(layer.crs.properties)/2):
newprops[layer.crs.properties[x*2]] = layer.crs.properties[(x*2) + 1]
layer.crs.properties = newprops
# We don't care about per-geometry CRS, these can mingle
if i == 0:
# first file sets the CRS!
crs = layer.crs.properties['name']
output_layer.crs = layer.crs
else:
assert layer.crs.properties['name'] == crs, ('CRS of files must match. File has CRS %r, expected %r' % (layer.crs.properties['name'], crs))
# We have a matching CRS, start processing the file
assert len(layer.bbox) == 4, 'File must have a bounding box'
output_layer.features.append(
geojson.Feature(
geometry=geojson.Polygon(
coordinates=[[
[layer.bbox[0], layer.bbox[1]],
[layer.bbox[0], layer.bbox[3]],
[layer.bbox[2], layer.bbox[3]],
[layer.bbox[2], layer.bbox[1]],
[layer.bbox[0], layer.bbox[1]]
]]
),
properties=dict(
id=i,
filename=layer_f.name
),
id=i
)
)
# all files complete
geojson.dump(output_layer, output)
print "Bounding boxes drawn!"
def _parse_geojson(self, json_dir):
"""Iterate through each label file in a geojson dir and extract the
feature locations
Args
----
json_dir (self) : the directory of the json files
Returns
-------
dic of form {label: [ [feature coor] ]
"""
features = {}
# convert form dstl coors to pixels
def coor_to_px(coor):
return (coor[0] / self._xmax * self._W,
coor[1] / self._ymin * self._H)
# track number of errors
e_count = 0
# For each feature type (separate file) in the image's json directory,
# store the feature locations
for fname in glob.glob(json_dir + "/*.geojson"):
# Load the file with geojson, this is the same as json.load()
with open(fname) as f:
raw_json = geojson.load(f)
# parse the mask (geojson polygon) for each object in a feature
for feature in raw_json['features']:
try:
dstl_label = feature['properties']['LABEL']
except KeyError:
self._FLAGS['errors']['KeyError'] = e_count
else:
label = MAP_TO_LOCAL_LABELS[dstl_label]
if label:
coors = feature['geometry']['coordinates'][0]
try:
features.setdefault(label, []).append(
list(map(coor_to_px, coors)))
except TypeError:
e_count += 1
self._FLAGS['errors']['TypeError'] = e_count
else:
# There were no files in that json directory
print(json_dir)
if not features:
print("No files in {} found".format(json_dir))
return features
def invoke(self):
# Get inputs
lat = self.get_input_string_port('lat')
lon = self.get_input_string_port('lon')
dtg = self.get_input_string_port('dtg')
meta = self.get_input_string_port('meta')
minsize = self.get_input_string_port('minsize', default='1000.0')
smooth = self.get_input_string_port('smooth', default='1.0')
img = self.get_input_data_port('image')
vector_dir = self.get_output_data_port('vector')
os.makedirs(vector_dir)
raster_dir = self.get_output_data_port('raster')
os.makedirs(raster_dir)
tide = tide_coordination(float(lat), float(lon), dtg)
result = {'metadata': json.loads(meta), 'tides': tide}
# with open(os.path.join(output_dir, 'tides.json'), 'w') as f:
# json.dump(result, f)
img1 = glob2.glob('%s/**/*_B1.TIF' % img)
img2 = glob2.glob('%s/**/*_B5.TIF' % img)
os.system(
'bfalg-ndwi -i %s -i %s --outdir %s --basename bf --minsize %s --smooth %s'
% (img1[0], img2[0], vector_dir, minsize, smooth))
os.rename(os.path.join(vector_dir, 'bf_ndwi.tif'),
os.path.join(raster_dir, 'bf_ndwi.tif'))
# Okay, so we need to open the output bf.geojson here, and iterate
# through the features, added result to properties for each and every
# one.
with open(os.path.join(vector_dir, 'bf.geojson')) as f:
data = geojson.load(f)
feature_collection = data['features']
valid_feats = []
for feat in feature_collection:
feat['properties'] = result
valid_feats.append(feat)
data['features'] = valid_feats
with open(os.path.join(vector_dir, 'bf.geojson'), 'wb') as f:
geojson.dump(data, f)
self.reason = 'Successfully created shoreline GeoJSON'
def __init__(self, geojson_filename, velocity_meters_per_s):
"""
:param geojson_file: A geojson file that contains a Feature with a LineString
:param velocity_meters_per_s: Object velocity in meters/sec
"""
with open(geojson_filename) as geojson_file:
geo_feature = geojson.load(geojson_file)
self.geojson_path = geojson.utils.coords(geo_feature)
self.velocity_meters_per_s = float(velocity_meters_per_s)
def load_neighborhoods(filename):
"""Load a geojson file. Also puts the value 'name' in the properties.
So for anything you get back from here you can say nghd['properties']['name'].
"""
nghds = geojson.load(open(filename))
for nghd in nghds['features']:
if 'neighborho' in nghd['properties']:
nghd['properties']['name'] = nghd['properties']['neighborho']
if 'hood' in nghd['properties']:
nghd['properties']['name'] = nghd['properties']['hood']
return nghds['features']
def asserts(input_dir: Path, output_dir: Path, quicklook_dir: Path, logger):
# Print out bbox of one tile
geojson_path = output_dir / "data.json"
with open(str(geojson_path)) as f:
feature_collection = geojson.load(f)
out_path = feature_collection.features[0]["properties"]["up42.data_path"]
logger.info(out_path)
logger.info("e2e test successful")
def generate_intersection_points(street_network_file, street_edge_name_file,
intersection_points_file):
"""
Find all the points that are intersections between two DIFFERENT streets.
This is what we classify as a street intersection for calculating proximity.
"""
with open(street_network_file) as f:
streets_gj = geojson.load(f)
# Read streets into a list of street edge id->coordinates mapping
streets_list = streets_gj['features']
edge_id_to_coords_list = {}
# load all edges
for street in streets_list:
edge_id, coords_list = extract_street_coords_from_geojson(street)
edge_id_to_coords_list[edge_id] = coords_list
# compute which points are intersections of at least two different streets
points_to_streets = dict()
edge_to_name = pd.read_csv(street_edge_name_file)
edge_to_name.set_index('street_edge_id', inplace=True)
for edge_id, coords_list in edge_id_to_coords_list.items():
try:
street_name = edge_to_name.loc[edge_id].street_name
except KeyError:
continue
for float_point in coords_list:
point_lng, point_lat = int(float_point[0] * multiplier), int(
float_point[1] * multiplier)
# print(float_point)
if (point_lng, point_lat) not in points_to_streets:
points_to_streets[point_lng, point_lat] = set()
if type(street_name) == str:
# Add a street to the intersection
points_to_streets[point_lng, point_lat].add(street_name)
elif pd.isna(street_name):
# Unnamed street, just represent it as an empty string
points_to_streets[point_lng, point_lat].add('')
# filter all points that aren't on > 1 streets
intersection_points = dict()
for point, street_names in points_to_streets.items():
if len(street_names) > 1:
intersection_points[point] = street_names
with open(intersection_points_file, 'wb') as f:
pickle.dump(intersection_points, f)
def prep_test_data(file_name):
path = make_test_data_file(file_name)()
with open(path) as gjs:
# records is a list of maps
records = geojson.load(gjs)
return [{'flight_id': record['flight_id'],
'lats': record['horizontal_path']['lats'],
'lons': record['horizontal_path']['lons'],
'min_alt': min(record['altitude_profile']['altitudes']),
'max_alt': max(record['altitude_profile']['altitudes'])}
for record in records['data']]
def load_geometry(cls, gjson_path):
# LOAD THE FIELDS FOR WHICH THE TIMESERIES
# SHOULD BE EXTRACTED FOR THE CROP CALENDARS
with open(gjson_path) as f:
gj = geojson.load(f)
### Buffer the fields 10 m inwards before requesting the TS from OpenEO
polygons_inw_buffered, poly_too_small_buffer = prepare_geometry(gj)
#TODO this is a bit confusing: I woud expect to continue with polygons_inw_buffered here
gj = remove_small_poly(gj, poly_too_small_buffer)
return gj,polygons_inw_buffered
def import_geojson_to_sheet(geojson_path):
"""WARNING: will clear existing spreadsheet!!!
pushes a geojson file to the google spreadsheet."""
with open(geojson_path) as f:
data = geojson.load(f)
try:
parsed_list = []
for feature in data.features:
if feature.geometry is not None \
and feature.geometry.type == "Point":
lon, lat = list(geojson.coords(feature))[0]
verified = feature.properties.get('Verified Status',
'unknown')
if (verified == 'unknown'):
# try to use the 'verified' field instead
verified = feature.properties.get(
'verified', 'unknown')
marking = feature.properties.get('Pedestrian Markings',
'unknown')
signal = feature.properties.get('Crossing Signal',
'unknown')
features = feature.properties.get('Other Features', '')
notes = feature.properties.get('Notes', '')
parsed_list.append(
[lon, lat, verified, marking, signal, features, notes])
# if we got here, we built a full parsed list.
# clear the spreadsheet and push what we have up
try:
worksheet = backend.worksheet("gis_dataset")
except gspread.exceptions.WorksheetNotFound:
worksheet = backend.add_worksheet("gis_dataset", 1, 1)
worksheet.clear()
row_header = [
"Longitude", "Latitude", "Verified Status",
"Pedestrian Markings", "Crossing Signal", "Other Features",
"Notes"
]
# create enough rows and columns to batch an update
worksheet.resize(rows=len(parsed_list) + 1, cols=len(row_header))
batched_cells = worksheet.range(1, 1,
len(parsed_list) + 1,
len(row_header))
for cel in batched_cells:
if cel.row == 1:
cel.value = row_header[cel.col - 1]
else:
cel.value = parsed_list[cel.row - 2][cel.col - 1]
worksheet.update_cells(batched_cells)
except AttributeError as e:
print("was expecting a file with one FeatureCollection, " +
"where each feature is a point!")
print(e)
def setUp(self):
with open('testing.json', 'r') as f:
self.result = json.load(f)
with open('testing_input.json', 'r') as f:
self.input = json.load(f)
self.start_coord = (53.2815126, -6.2341631)
self.end_coord = (53.3881208173444, -6.2659470210)
self.graph_location = '../assets/graph.json'
with open(self.graph_location, 'rb') as jfile:
graph_json = geojson.load(jfile)
self.G = nx.node_link_graph(graph_json)
def remove_sensor(url, sn):
print('starter remove')
with open(url, 'r') as data_file:
data = geojson.load(data_file)
print(data)
print(len(data['features']))
data['features'] = [element for element in data['features'] if not int(element['properties']['Sn']) == int(sn)]
print(len(data['features']))
print(int(data['features'][0]['properties']['Sn'])
def from_geojson(cls, filename, index=0):
json = geojson.load(open(filename))
try:
coords = json["coordinates"]
except KeyError:
try:
coords = json["geometry"]["coordinates"]
except KeyError:
coords = json["features"][index]["geometry"]["coordinates"]
return cls.from_lng_lat_tuples(coords)
def get_lat_long_list_from_file(file_name):
final_list = []
with open(file_name) as json_file:
feature_collection = geojson.load(json_file)
coords = list(geojson.utils.coords(feature_collection))
for x in coords:
final_list.append({"lat": x[1], "long": x[0]})
return final_list
def LoadPolygon(eo1FileName):
base_dir = os.path.dirname(eo1FileName)
footstepsFileName = os.path.join(base_dir, "geojson", "footsteps.tif.json")
with open(footstepsFileName) as json_data:
jsond = geojson.load(json_data)
json_data.close()
feature = jsond['features'][0]
coords = feature['geometry']['coordinates']
poly = coords[0]
return poly
def _prepare_data(self):
with open(JSON_OUTPUT_PATH, 'r') as f:
self.trees_geojson = json.load(f)
with open(BUS_LINES_PATH, 'r') as f:
self.bus_geojson = geojson.load(f)
if DEBUG:
self.bus_df_co2 = pd.read_csv('./data/calculated_co2.csv')
else:
self._prepare_bus_df()
self._calc_carbon_emissions()
def find_intersecting_polygons(all_polygons_file, bounding_polygon,
output_file, id_prefix):
# Load all polygons into a feature set
with open(all_polygons_file, mode="r") as in_file:
all_polygons = geojson.load(in_file)
total_area = 0
num_polys = 0
intersecting_polygons = []
# Find the intersecting polygons
for feature in all_polygons["features"]:
polygon = geometry.shape(feature["geometry"])
if not polygon.is_valid:
if DEBUG:
print("Attempting to fix invalid polygon")
polygon = polygon.buffer(0)
# If it's still not valid, just skip it
if not polygon.is_valid:
if DEBUG:
print("Fix was unsuccessful. Skipping polygon")
continue
intersection = bounding_polygon.intersection(polygon)
# If polygon overlaps with bounds, we want to include it
if intersection.area > 0:
num_polys += 1
# Construct new geojson polygon for intersection area
if intersection.geom_type == 'MultiPolygon':
new_polygon = geojson.MultiPolygon(
geometry.mapping(intersection)["coordinates"])
else:
new_polygon = geojson.Polygon(
geometry.mapping(intersection)["coordinates"])
#Create feature and add to list
new_feature = geojson.Feature(geometry=new_polygon)
new_feature["properties"]["id"] = id_prefix + str(uuid.uuid4())
# Add to list of features to return
intersecting_polygons.append(new_feature)
# Add all features to a feature set
new_feature_collection = geojson.FeatureCollection(intersecting_polygons)
# Output to a file
with open(output_file, mode="w") as out_file:
geojson.dump(new_feature_collection, out_file, indent=4)
def merge_geojson_feature_collections(filepaths):
"""Merge all GeoJSON feature collections into a single, nation-wide file."""
all_features = []
for fp in filepaths:
with open(fp, 'r') as f:
json_blob = geojson.load(f)
new_features = json_blob['features']
all_features.extend(new_features)
crs = json_blob['crs']
full_feature_collection = geojson.FeatureCollection(features=all_features, crs=crs)
return full_feature_collection
def invoke(self):
'''
Execute task
'''
# Load list of features
with open(self.geoj) as f:
info = geojson.load(f)['features']
poly_ct = len(info)
# Load trained model
if self.classes:
m = VggNet(classes=self.classes, model_name='model')
else:
m = VggNet(model_name='model')
m.model.load_weights(self.weights)
# Format input_shape and max_side_dim
inp_shape = m.input_shape[-3:]
if not self.max_side_dim:
self.max_side_dim = inp_shape[-1]
# Check all imgs have correct bands
bands = inp_shape[0]
for img in self.imgs:
img_bands = geoio.GeoImage(img).shape[0]
if bands != img_bands:
raise Exception('Make sure the model was trained on an image with the ' \
'same number of bands as all input images.')
# Filter shapefile
de.filter_polygon_size(self.geoj,
output_file=self.geoj,
max_side_dim=self.max_side_dim,
min_side_dim=self.min_side_dim)
# Numerical vs string classes
out_name, num_classes = 'classified.geojson', True
if self.classes:
num_classes = False
# Classify file
m.classify_geojson(self.geoj,
output_name=out_name,
numerical_classes=num_classes,
max_side_dim=self.max_side_dim,
min_side_dim=self.min_side_dim,
chips_in_mem=1000,
bit_depth=self.bit_depth)
# Write output
move(out_name, self.output_dir)
def handle(self, *args, **kwargs):
city = kwargs['city']
file_data = kwargs['data']
if Grid.objects.filter(city_name=city).exists():
print(f'Город {city} уже есть в базе')
return
try:
geojson.load(file_data)
except json.decoder.JSONDecodeError:
print(f'Файл {file_data.name} некорректен')
return
file_data.close()
city_grid = Grid.objects.create(city_name=city)
rows_cnt = int(abs(GRID_END_LOC[0] - GRID_START_LOC[0]) / GRID_STEP)
cols_cnt = int(abs(GRID_END_LOC[1] - GRID_START_LOC[1]) / GRID_STEP)
print(rows_cnt, cols_cnt, flush=True)
output_dir = f'{BASE_DIR}/output'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(f'{output_dir}/result.csv', 'w') as out:
lat = GRID_START_LOC[0]
for i in range(rows_cnt):
lon = GRID_START_LOC[1]
for j in range(cols_cnt):
cell = Cell.objects.create(parent_grid=city_grid,
latitude=lat,
longitude=lon)
out.write(f"{cell.id},{lat},{lon}\n")
lon += GRID_STEP
lat -= GRID_STEP
