def setup_class(cls):
cls.base_dir = os.path.abspath(os.path.dirname(__file__))
path = os.path.join(cls.base_dir, "data/vector")
path = os.path.join(path, "bay_area_counties.geojson")
cls.counties, cls.counties_df = vt.read_geojson(path, index="NAME")
clu_path = "data/vector/clu/clu_public_a_il189.shp"
cls.clus, _ = vt.read_layer(os.path.join(cls.base_dir, clu_path))
il_path = os.path.join(cls.base_dir, "data/vector/il.geojson")
cls.il, cls.il_df = vt.read_geojson(il_path, index="name")
def test_intersects():
shape, _ = vt.read_geojson(get_path('test_shape.json'))
with open(get_path('test_soils.json')) as f:
soils = f.read()
soils = vt.read_geojson(soils)[0]
soils.intersects(shape[0])
assert (shape[0].IsValid())
def test_intersects():
shape, _ = vt.read_geojson(get_path('test_shape.json'))
with open(get_path('test_soils.json')) as f:
soils = f.read()
soils = vt.read_geojson(soils)[0]
soils.intersects(shape[0])
assert (shape[0].IsValid())
def setup_class(cls):
cls.base_dir = os.path.abspath(os.path.dirname(__file__))
path = os.path.join(cls.base_dir, "data/vector")
path = os.path.join(path, "bay_area_counties.geojson")
cls.counties, cls.counties_df = vt.read_geojson(path, index="NAME")
clu_path = "data/vector/clu/clu_public_a_il189.shp"
cls.clus, _ = vt.read_layer(os.path.join(cls.base_dir, clu_path))
il_path = os.path.join(cls.base_dir, "data/vector/il.geojson")
cls.il, cls.il_df = vt.read_geojson(il_path, index="name")
def read_catalog(dataset_catalog_filename_or_handle, workdir=None):
"""Take a catalog file and create a raster dataset
Parameters
----------
dataset_catalog_filename_or_handle : str or opened file handle
if str : Path to catalog file for the dataset. May be relative or absolute.
Catalog files are in json format, and usually represent a type of data
(e.g. CDL) and a year (e.g. 2014).
Returns
-------
RasterDataset
See Also
--------
scripts/create_catalog.py : How to create a catalog file for a dataset.
raster_query_test.py : Simple examples of exercising RasterQuery on tiled
and untiled datasets, and computing stats from results.
vector.py : Details of VectorLayer."""
if isinstance(dataset_catalog_filename_or_handle, string_types):
with open(dataset_catalog_filename_or_handle) as catalog_file:
decoded = json.load(catalog_file)
else:
decoded = json.load(dataset_catalog_filename_or_handle)
size = [int(x) for x in decoded["Size"]]
coordinate_system = str(decoded["CoordinateSystem"])
transform = decoded["GeoTransform"]
# Get the projection for the raster
proj = osr.SpatialReference()
proj.ImportFromWkt(coordinate_system)
if workdir is None:
path = decoded["Path"]
else:
path = os.path.join(workdir, decoded["Path"])
grid_size = decoded.get("GridSize", None)
index = None
tile_structure = None
if "Index" in decoded:
index, index_df = read_geojson(json.dumps(decoded["Index"]),
index="location")
index = index.transform(proj)
if "Tile_structure" in decoded:
tile_structure = decoded["Tile_structure"]
return RasterDataset(path,
size[0],
size[1],
geo_transform=transform,
proj=proj,
grid_size=grid_size,
index=index,
tile_structure=tile_structure)
def test_vector_json_io_gs():
filepath = 'data/vector/test_shape.json'
gs_path = 'gs://%s/%s' % (gs_bucket_name, filepath)
# write a vector json file to gs
shape, _ = vt.read_geojson(os.path.join(base, filepath))
shape.to_json(path=gs_path, precision=15)
# read the vector json file from gs
shape_json = vt.fetch_geojson(gs_path)
geojson, _ = vt.read_geojson(shape_json)
# ensure it is valid
assert geojson[0].IsValid()
# clean up
delete_gs_key(filepath)
def __test_read():
path = ("http://www2.census.gov/geo/tiger/GENZ2014/shp/"
"cb_2014_us_state_500k.zip")
assert len(vt.read_layer(path)[0]) == 56
path = get_path("cb_2014_us_state_500k.zip")
assert len(vt.read_layer(path)[0]) == 56
paths = ["http://eric.clst.org/wupl/Stuff/gz_2010_us_040_00_500k.json",
get_path("gz_2010_us_040_00_500k.json")]
for path in paths:
assert len(vt.read_geojson(path)[0]) == 52
geojson_str = open(paths[1]).read()
assert len(vt.read_geojson(geojson_str)[0]) == 52
def __test_read():
path = ("http://www2.census.gov/geo/tiger/GENZ2014/shp/"
"cb_2014_us_state_500k.zip")
assert len(vt.read_layer(path)[0]) == 56
path = get_path("cb_2014_us_state_500k.zip")
assert len(vt.read_layer(path)[0]) == 56
paths = ["http://eric.clst.org/wupl/Stuff/gz_2010_us_040_00_500k.json",
get_path("gz_2010_us_040_00_500k.json")]
for path in paths:
assert len(vt.read_geojson(path)[0]) == 52
geojson_str = open(paths[1]).read()
assert len(vt.read_geojson(geojson_str)[0]) == 52
def read_catalog(dataset_catalog_filename_or_handle, workdir=None):
"""Take a catalog file and create a raster dataset
Parameters
----------
dataset_catalog_filename_or_handle : str or opened file handle
if str : Path to catalog file for the dataset. May be relative or absolute.
Catalog files are in json format, and usually represent a type of data
(e.g. CDL) and a year (e.g. 2014).
Returns
-------
RasterDataset
See Also
--------
scripts/create_catalog.py : How to create a catalog file for a dataset.
raster_query_test.py : Simple examples of exercising RasterQuery on tiled
and untiled datasets, and computing stats from results.
vector.py : Details of VectorLayer."""
if isinstance(dataset_catalog_filename_or_handle, string_types):
with open(dataset_catalog_filename_or_handle) as catalog_file:
decoded = json.load(catalog_file)
else:
decoded = json.load(dataset_catalog_filename_or_handle)
size = [int(x) for x in decoded["Size"]]
coordinate_system = str(decoded["CoordinateSystem"])
transform = decoded["GeoTransform"]
# Get the projection for the raster
proj = osr.SpatialReference()
proj.ImportFromWkt(coordinate_system)
if workdir is None:
path = decoded["Path"]
else:
path = os.path.join(workdir, decoded["Path"])
grid_size = decoded.get("GridSize", None)
index = None
tile_structure = None
if "Index" in decoded:
index, index_df = read_geojson(json.dumps(decoded["Index"]),
index="location")
index = index.transform(proj)
if "Tile_structure" in decoded:
tile_structure = decoded["Tile_structure"]
return RasterDataset(path, size[0], size[1],
geo_transform=transform,
proj=proj,
grid_size=grid_size,
index=index,
tile_structure=tile_structure)
def setup_class(cls):
path1 = get_path("clu/four_shapes_2il_2ca.geojson")
path2 = get_path("gz_2010_us_040_00_500k.json")
path3 = get_path("bay_area_counties.geojson")
path4 = get_path("bay_area_zips.geojson")
cls.vl1, cls.df1 = vt.read_geojson(path1)
cls.vl2, cls.df2 = vt.read_geojson(path2)
cls.counties, cls.df3 = vt.read_geojson(path3, index="NAME")
cls.sf = "San Francisco"
proj = projection_from_epsg()
rect.AssignSpatialReference(proj)
farallon.AssignSpatialReference(proj)
cls.counties[cls.sf] = cls.counties[cls.sf].Difference(farallon)
cls.zips, cls.df4 = vt.read_geojson(path4, index="ZCTA5CE10")
p = get_path("clu/four_shapes_2il_2ca.p")
cls.df = pickle.load(open(p))
assert isinstance(cls.counties, vt.VectorLayer)
assert isinstance(cls.counties["San Francisco"], ogr.Geometry)
def setup_class(cls):
path1 = get_path("clu/four_shapes_2il_2ca.geojson")
path2 = get_path("gz_2010_us_040_00_500k.json")
path3 = get_path("bay_area_counties.geojson")
path4 = get_path("bay_area_zips.geojson")
cls.vl1, cls.df1 = vt.read_geojson(path1)
cls.vl2, cls.df2 = vt.read_geojson(path2)
cls.counties, cls.df3 = vt.read_geojson(path3, index="NAME")
cls.sf = "San Francisco"
proj = projection_from_epsg()
rect.AssignSpatialReference(proj)
farallon.AssignSpatialReference(proj)
cls.counties[cls.sf] = cls.counties[cls.sf].Difference(farallon)
cls.zips, cls.df4 = vt.read_geojson(path4, index="ZCTA5CE10")
p = get_path("clu/four_shapes_2il_2ca.p")
cls.df = pickle.load(open(p))
assert isinstance(cls.counties, vt.VectorLayer)
assert isinstance(cls.counties["San Francisco"], ogr.Geometry)
def test_shapes_outside_raster_should_be_filtered(self):
# 2 shapes in IL that should be in range,
# plus 2 shapes in CA that should be out of range.
p = get_path("vector/clu/four_shapes_2il_2ca.geojson")
vl_outside, vl_df = read_geojson(p)
assert (len(vl_outside.keys()) == 4)
dataset_catalog_file = get_path("../catalog/cdl_2014.json")
rd = read_catalog(dataset_catalog_file)
df = self.make_dataframe(rd.query(vl_outside))
assert (len(df.index) == 4)
sums = df.sum(axis=1).map(int)
assert df[sums < 1e-6].shape[0] == 2
assert df[(sums - 1).map(np.abs) < 1e-6].shape[0] == 2
def test_shapes_outside_raster_should_be_filtered(self):
# 2 shapes in IL that should be in range,
# plus 2 shapes in CA that should be out of range.
p = get_path("vector/clu/four_shapes_2il_2ca.geojson")
vl_outside, vl_df = read_geojson(p)
assert (len(vl_outside.keys()) == 4)
dataset_catalog_file = get_path("../catalog/cdl_2014.json")
rd = read_catalog(dataset_catalog_file)
df = self.make_dataframe(rd.query(vl_outside))
assert (len(df.index) == 4)
sums = df.sum(axis=1).map(int)
assert df[sums < 1e-6].shape[0] == 2
assert df[(sums - 1).map(np.abs) < 1e-6].shape[0] == 2
def read_catalog(dataset_catalog_file):
"""Take a catalog file and create a raster dataset
Parameters
----------
dataset_catalog_file : str
Path to catalog file for the dataset. May be relative or absolute.
Catalog files are in json format, and usually represent a type of data
(e.g. CDL) and a year (e.g. 2014).
Returns
-------
RasterDataset
See Also
--------
scripts/create_catalog.py : How to create a catalog file for a dataset.
raster_query_test.py : Simple examples of exercising RasterQuery on tiled
and untiled datasets, and computing stats from results.
vector.py : Details of VectorLayer."""
with open(dataset_catalog_file) as catalog_file:
decoded = json.load(catalog_file)
size = map(int, decoded["Size"])
coordinate_system = str(decoded["CoordinateSystem"])
transform = decoded["GeoTransform"]
# Get the projection for the raster
proj = osr.SpatialReference()
proj.ImportFromWkt(coordinate_system)
path = decoded["Path"]
grid_size = decoded.get("GridSize", None)
index = None
tile_regex = None
if "Index" in decoded:
index, index_df = read_geojson(json.dumps(decoded["Index"]),
index="location")
index = index.transform(proj)
tile_regex = re.compile('([0-9]+)_([0-9]+)\.tif')
return RasterDataset(path, size[0], size[1], transform, proj,
grid_size=grid_size, index=index,
tile_regex=tile_regex)
def test_co_ne_border(self):
p = get_path("vector/clu/clu_public_a_co095.shp")
vl, _ = read_layer(p)
p = get_path("vector/co_ne_border.geojson")
co_ne_border, df = read_geojson(p)
vl = vl.within(co_ne_border.bbox())
rd = read_catalog(get_path("../catalog/co_soil.json"))
for r in rd.query(vl):
r
#compute_stats(r.values, r.weights)
rd = read_catalog(get_path("../catalog/co_soil_bad.json"))
failed = False
try:
for r in rd.query(vl):
r
#compute_stats(r.values, r.weights)
except IndexError:
failed = True
assert failed
def test_co_ne_border(self):
p = get_path("vector/clu/clu_public_a_co095.shp")
vl, _ = read_layer(p)
p = get_path("vector/co_ne_border.geojson")
co_ne_border, df = read_geojson(p)
vl = vl.within(co_ne_border.bbox())
rd = read_catalog(get_path("../catalog/co_soil.json"))
for r in rd.query(vl):
r
#compute_stats(r.values, r.weights)
rd = read_catalog(get_path("../catalog/co_soil_bad.json"))
failed = False
try:
for r in rd.query(vl):
r
#compute_stats(r.values, r.weights)
except IndexError:
failed = True
assert failed
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import os
from pyspatial.vector import read_geojson, to_geometry
from pyspatial.raster import read_raster
import numpy as np
from numpy.testing import assert_array_almost_equal
from test_raster_query import compute_stats
base_dir = os.path.abspath(os.path.dirname(__file__))
get_path = lambda x: os.path.join(base_dir, "data", x)
vl, vldf = read_geojson(get_path("vector/small_polygon.geojson"))
rd = read_raster(get_path("raster/95000_45000.tif"))
shp = vl[0]
def test_small_polygon():
bboxes = vl.boundingboxes()
shp_px = rd.to_pixels(bboxes)[0]
grid = rd.to_geometry_grid(*shp_px.bounds)
areas = {}
for i, b in grid.iteritems():
diff = b.Intersection(to_geometry(shp, proj=rd.proj))
areas[i] = diff.GetArea()
def test_to_json():
shape, _ = vt.read_geojson(get_path('test_shape.json'))
# This shape requires more precision when serializing
j = shape.to_json(precision=15)
jj, _ = vt.read_geojson(j)
assert jj[0].IsValid()
def test_to_json():
shape, _ = vt.read_geojson(get_path('test_shape.json'))
# This shape requires more precision when serializing
j = shape.to_json(precision=15)
jj, _ = vt.read_geojson(j)
assert jj[0].IsValid()
if len(tiles) == 0:
raise ValueError("%s is empty" % args.tile_path)
tile = os.path.join(args.tile_path, tiles[0])
ds = gdal.OpenShared(tile)
if ds is None:
raise ValueError("Unable to open file: %s" % tile)
xsize = ds.RasterXSize
ysize = ds.RasterYSize
if xsize != ysize:
raise ValueError("tiles must have same X and Y size")
catalog["GridSize"] = xsize
catalog["Path"] = args.tile_path
else:
raise ValueError("tiles path does not exist: %s" % args.tile_path)
if args.index_path is not None:
if args.index_path.endswith("json"):
index, index_df = read_geojson(args.index_path)
else:
index = read_layer(args.index_path)
catalog["Index"] = index.transform(projection_from_string()).to_dict()
if args.dest is not None:
with open(args.dest, "w+b") as outf:
outf.write(json.dumps(catalog, indent=4, sort_keys=True))
else:
print json.dumps(catalog, indent=4, sort_keys=True)
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import os
from pyspatial.vector import read_geojson, to_geometry
from pyspatial.raster import read_raster
import numpy as np
from numpy.testing import assert_array_almost_equal
from test_raster_query import compute_stats
base_dir = os.path.abspath(os.path.dirname(__file__))
get_path = lambda x: os.path.join(base_dir, "data", x)
vl, vldf = read_geojson(get_path("vector/small_polygon.geojson"))
rd = read_raster(get_path("raster/95000_45000.tif"))
shp = vl[0]
def test_small_polygon():
bboxes = vl.boundingboxes()
shp_px = rd.to_pixels(bboxes)[0]
grid = rd.to_geometry_grid(*shp_px.bounds)
areas = {}
for i, b in grid.iteritems():
diff = b.Intersection(to_geometry(shp, proj=rd.proj))
areas[i] = diff.GetArea()
