def test_compare_voronoi_polygons(self):
files = ['up_in_3_thie_join.shp', 'up_in_11_thie_join.shp']
out = tempfile.mkdtemp(prefix='voronoi_')
for f in files:
voronoi_shp_file = res_mgr.get_resource_path('test/shp/%s' % f)
area_shp_file = res_mgr.get_resource_path(
'extraction/shp/kub-wgs84/kub-wgs84.shp')
shape_df = gpd.GeoDataFrame.from_file(voronoi_shp_file)
points = {}
for i in range(len(shape_df)):
points[shape_df['OBJECTID'][i]] = [
shape_df['x'][i], shape_df['y'][i]
]
result = get_voronoi_polygons(points,
area_shp_file, ['OBJECTID', 1],
output_shape_file=os.path.join(
out, '%s_out.shp' % f))
for i in range(len(shape_df)):
self.assertAlmostEqual(result['area'][i],
shape_df['Shape_Area'][i],
places=4)
def test_get_voronoi_polygons_kub(self):
points = {
'Colombo': [79.8653, 6.898158],
'IBATTARA3': [79.86, 6.89],
'Isurupaya': [79.92, 6.89],
'Daraniyagala': [80.33805556, 6.924444444],
'Glencourse': [80.20305556, 6.978055556],
'Hanwella': [80.08166667, 6.909722222],
'Holombuwa': [80.26480556, 7.185166667],
'Kitulgala': [80.41777778, 6.989166667],
'Borella': [
79.86,
6.93,
],
'Kompannaveediya': [79.85, 6.92],
}
shp = res_mgr.get_resource_path(
'extraction/shp/kelani-upper-basin.shp')
out = tempfile.mkdtemp(prefix='voronoi_')
print(out)
result = get_voronoi_polygons(points,
shp, ['OBJECTID', 1],
output_shape_file=os.path.join(
out, 'out.shp'))
print(result)
def get_thessian_polygon_from_gage_points(shape_file, gage_points):
shape = res_mgr.get_resource_path(shape_file)
# calculate the voronoi/thesian polygons w.r.t given station points.
thessian_df = get_voronoi_polygons(gage_points, shape, ['OBJECTID', 1],
output_shape_file=os.path.join(SUB_CATCHMENT_SHAPE_FILE_DIR,
'sub_catchment.shp'))
return thessian_df
def get_voronoi_polygons_kub(points):
shp = res_mgr.get_resource_path('kub/kelani-upper-basin.shp')
result = get_voronoi_polygons(points,
shp, ['OBJECTID', 1],
output_shape_file=os.path.join(
'/home/hasitha/QGis/test', 'out.shp'))
print(result.iloc[0])
def get_thessian_polygon_from_gage_points(shape_file, gage_points):
shape = res_mgr.get_resource_path(shape_file)
# calculate the voronoi/thesian polygons w.r.t given station points.
thessian_df = get_voronoi_polygons(
gage_points,
shape, ['OBJECTID', 1],
output_shape_file=os.path.join(
PROJECT_PATH,
datetime.date.today().strftime('%Y-%m-%d') + '.shp'))
return thessian_df
def test_is_inside_polygon(self):
points = {
'Colombo': [79.8653, 6.898158],
'IBATTARA3': [79.86, 6.89],
'Isurupaya': [79.92, 6.89],
'Borella': [
79.86,
6.93,
],
'Kompannaveediya': [79.85, 6.92],
}
shp = res_mgr.get_resource_path(
'extraction/shp/klb-wgs84/klb-wgs84.shp')
result = get_voronoi_polygons(points, shp, ['OBJECTID', 1])
print(result)
for k in points.keys():
pp = is_inside_geo_df(result, points[k][0], points[k][1])
print(points[k], pp)
self.assertEqual(pp, k)
def test_get_voronoi_polygons(self):
points = {
'Colombo': [79.8653, 6.898158],
'IBATTARA3': [79.86, 6.89],
'Isurupaya': [79.92, 6.89],
'Borella': [
79.86,
6.93,
],
'Kompannaveediya': [79.85, 6.92],
}
shp = res_mgr.get_resource_path(
'extraction/shp/klb-wgs84/klb-wgs84.shp')
out = tempfile.mkdtemp(prefix='voronoi_')
result = get_voronoi_polygons(points,
shp, ['OBJECTID', 1],
output_shape_file=os.path.join(
out, 'out.shp'))
print(result)
from resources import manager as res_mgr
import geopandas as gpd
if __name__ == '__main__':
shape_file = res_mgr.get_resource_path('kub-wgs84/kub-wgs84.shp')
shape_df = gpd.GeoDataFrame.from_file(shape_file)
print('shape_df : ', shape_df)
shape_polygon = shape_df['geometry']
print('shape_polygon : ', shape_df['geometry'])
area = shape_polygon.area
print('kub area : ', area)
sub_shape_file = res_mgr.get_resource_path('sub_catchments/sub_subcatchments.shp')
catchment_df = gpd.GeoDataFrame.from_file(sub_shape_file)
for i, catchment_polygon in enumerate(catchment_df['geometry']):
print('sub area : ', catchment_polygon.area)
def get_gage_points():
gage_csv = res_mgr.get_resource_path('gages/CurwRainGauges.csv')
gage_df = pd.read_csv(gage_csv)[['name', 'longitude', 'latitude']]
gage_dict = gage_df.set_index('name').T.to_dict('list')
return gage_dict
def get_catchment_area(catchment_file):
shape = res_mgr.get_resource_path(catchment_file)
catchment_df = gpd.GeoDataFrame.from_file(shape)
return catchment_df
