def test_get_col_spec(self):
r = Raster()
assert r.get_col_spec() == 'raster'
def test_get_col_spec(self):
from geoalchemy2 import Raster
r = Raster()
eq_(r.get_col_spec(), 'raster')
def test_get_col_spec(self):
r = Raster()
assert r.get_col_spec() == 'raster'
def columnsFromDataframe(self, df):
"""Returns a list of columns from a dataframe)"""
Map = {
str:
String,
np.dtype(int):
Integer,
np.dtype(float):
Float,
dict:
JSON,
np.int64:
BIGINT,
float:
Float,
np.float64:
Float,
"string":
String,
"integer":
Integer,
"floating":
Float,
xr.DataArray:
OutDBZarrType(defaultZstore=self.outdbArchiveName(),
modifyUri=self.conf.generalize_path),
xr.Dataset:
OutDBZarrType(defaultZstore=self.outdbArchiveName(),
modifyUri=self.conf.generalize_path),
"datetime64":
DateTime,
np.dtype('<M8[ns]'):
datetime64Type
}
cols = [Column('id', Integer, primary_key=True)]
for name, col in df.iteritems():
if name == "id":
#already added
continue
elif name == self.geoinfo.rastname:
cType = Raster(spatial_index=False)
elif name == self.geoinfo.geoname:
if self.geoinfo.srid == 4326:
cType = Geography(geometry_type=self.geoinfo.geomtype,
srid=self.geoinfo.srid,
spatial_index=True,
dimension=self.geoinfo.dims)
else:
cType = Geometry(geometry_type=self.geoinfo.geomtype,
srid=self.geoinfo.srid,
spatial_index=True,
dimension=self.geoinfo.dims)
else:
dtype = pd.api.types.infer_dtype(col, skipna=True)
if dtype == "mixed":
val = col.loc[col.first_valid_index()]
if type(val) == np.ndarray:
#wrap the type in an array
cType = ARRAY(Map[val.dtype], dimensions=val.ndim)
else:
cType = Map[type(val)]
else:
cType = Map[dtype]
cols.append(Column(name, cType))
return cols
