def _sub(self, data):
import datetime
if data.get("aa", False):
from ambry.geo.analysisarea import get_analysis_area
aa = get_analysis_area(self.bundle.library, **data["aa"])
aa_d = dict(aa.__dict__)
aa_d["aa_name"] = aa_d["name"]
del aa_d["name"]
data = dict(data.items() + aa_d.items())
data["bundle_name"] = self.bundle.identity.name
data["date"] = datetime.datetime.now().date().isoformat()
data["query"] = data.get("query", "").format(**data)
data["extract_where"] = data.get("extract_where", "").format(**data)
data["title"] = data.get("title", "").format(**data)
data["description"] = data.get("description", "").format(**data)
data["name"] = data.get("name", "").format(**data)
data["layer_name"] = data.get("layer_name", "").format(**data)
data["path"] = self.bundle.filesystem.path("extracts", format(data["name"]))
data["done_if"] = data.get("done_if", "os.path.exists(path)").format(**data)
return data
def make_hdf(self):
import ambry.geo as dg
from ambry.geo.analysisarea import get_analysis_area
from osgeo.gdalconst import GDT_Float32
import numpy as np
scale = 10
k = dg.DistanceKernel(21)
k.matrix *= scale * 100 # Measured in cm: meters * 100 cm / m.
k.matrix = np.array(k.matrix, dtype=int) # Intify
p = self.partitions.find(table='streetlights', format='geo')
if not p:
raise Exception("Didn't find partition for streetlights")
raster = self.partitions.find_or_new_hdf(table='distance')
for cityrow in p.query("SELECT count(*) AS count, city FROM streetlights group BY city"):
city = cityrow['city']
if not city or city == '-':
continue;
if cityrow['count'] < 100:
continue # Most of the cityies have a, probably spurious, small number of lamps
self.log("Making HDF for city {}".format(city))
lr = self.init_log_rate()
aa = get_analysis_area(self.library, place=city, scale=scale)
trans = aa.get_translator()
a = aa.new_array(dtype = np.int16)
a = a + 12000 # Set the baseline to a max at 120 meters.
for row in p.query("""SELECT * FROM streetlights WHERE city = ?""", city):
k.apply_min(a, trans(row['lon'], row['lat']))
lr("Distance raster point for {}".format(city))
raster.database.put_geo(city, a, aa)
raster.database.flush()
aa.write_geotiff( self.filesystem.path('extracts', city+"-dist"), a[:])
def test_sfschema(self):
from ambry.geo.sfschema import TableShapefile
from ambry.geo.analysisarea import get_analysis_area
_, communities = self.bundle.library.dep('communities')
csrs = communities.get_srs()
gp = self.bundle.partitions.new_geo_partition(table='geot2')
with gp.database.inserter(source_srs=csrs) as ins:
for row in communities.query("""
SELECT *,
X(Transform(Centroid(geometry), 4326)) AS lon,
Y(Transform(Centroid(geometry), 4326)) as lat,
AsText(geometry) as wkt,
AsBinary(geometry) as wkb
FROM communities"""):
r = {'name':row['cpname'], 'lat': row['lat'], 'lon': row['lon'], 'wkt': row['wkt']}
ins.insert(r)
return
aa = get_analysis_area(self.bundle.library, geoid = 'CG0666000')
path1 = '/tmp/geot1.kml'
if os.path.exists(path1): os.remove(path1)
sfs1 = TableShapefile(self.bundle, path1, 'geot1' )
path2 = '/tmp/geot2.kml'
if os.path.exists(path2): os.remove(path2)
sfs2 = TableShapefile(self.bundle, path2, 'geot2', source_srs=communities.get_srs())
print sfs1.type, sfs2.type
for row in communities.query("""
SELECT *,
X(Transform(Centroid(geometry), 4326)) AS lon,
Y(Transform(Centroid(geometry), 4326)) as lat,
AsText(geometry) as wkt,
AsBinary(geometry) as wkb
FROM communities"""):
sfs1.add_feature( {'name':row['cpname'], 'lat': row['lat'], 'lon': row['lon'], 'wkt': row['wkt']})
sfs2.add_feature( {'name':row['cpname'], 'lat': row['lat'], 'lon': row['lon'], 'wkt': row['wkt']})
sfs1.close()
sfs2.close()
def evari_extract_image(self, data):
import ambry.geo as dg
from ambry.geo.analysisarea import get_analysis_area
from osgeo.gdalconst import GDT_Float32
aa = get_analysis_area(self.library, geoid='CG0666000')
trans = aa.get_translator()
a_old = aa.new_array()
a_new = aa.new_masked_array()
a_nip = aa.new_array()
a_total = aa.new_masked_array()
k = dg.GaussianKernel(21,7)
p = self.partitions.find(table='lights')
for row in p.query("""SELECT * FROM lights """):
p = trans(row['lon'], row['lat'])
status = row['status']
if status == 'not in project':
k.apply_add(a_nip, p)
elif status == 'converted':
k.apply_add(a_new, p)
k.apply_add(a_total, p)
elif status == 'not converted':
k.apply_add(a_old, p)
k.apply_add(a_total, p)
def fnp(prefix):
return self.filesystem.path('extracts','{}-{}'.format(prefix,data['name']))
aa.write_geotiff(fnp('old'), a_old, data_type=GDT_Float32)
aa.write_geotiff(fnp('new'), a_new, data_type=GDT_Float32)
aa.write_geotiff(fnp('nip'), a_nip, data_type=GDT_Float32)
aa.write_geotiff(fnp('total'), a_total, data_type=GDT_Float32)
pct = 1 - (a_new / a_total)
aa.write_geotiff(fnp('pct'), pct, data_type=GDT_Float32)
return fnp('total')
def x_test_basic(self):
from ambry.geo.analysisarea import get_analysis_area, draw_edges
from ambry.geo import Point
from ambry.geo.kernel import GaussianKernel
aa = get_analysis_area(self.bundle.library, geoid = 'CG0666000')
a = aa.new_array()
#draw_edges(a)
print a.shape, a.size
gaussian = GaussianKernel(11,6)
for i in range(0,400, 20):
p = Point(100+i,100+i)
gaussian.apply_add(a,p)
aa.write_geotiff('/tmp/box.tiff', a, data_type=GDT_Float32)