def test_choropleth_multiple_dataset(client):
social_vulnerability_census_block_group = '5a284f57c7d30d13bc08254c'
dislocation_census_block_group = '5a284f58c7d30d13bc082566'
dataset1 = Dataset.from_data_service(
social_vulnerability_census_block_group, DataService(client))
dataset2 = Dataset.from_data_service(dislocation_census_block_group,
DataService(client))
viz.plot_choropleth_multiple_dataset([dataset1, dataset2],
['tot_hh', 'p_16pyr'])
assert True
def test_multiple_vector_visualization(client):
centerville_model_tornado = '60c917b498a93232884f367d'
centerville_epn_link = '5b1fdc2db1cf3e336d7cecc9'
tornado_metadata = HazardService(client).get_tornado_hazard_metadata(
centerville_model_tornado)
dataset1 = Dataset.from_data_service(centerville_epn_link,
DataService(client))
dataset2 = Dataset.from_data_service(tornado_metadata["datasetId"],
DataService(client))
viz.plot_multiple_vector_dataset([dataset1, dataset2])
assert True
def test_seaside_bridges(client):
trns_brdg_dataset_id = "5d251172b9219c0692cd7523"
trns_brdg_dataset = Dataset.from_data_service(trns_brdg_dataset_id,
DataService(client))
viz.plot_map(trns_brdg_dataset, column=None, category=False, basemap=True)
assert True
def test_plot_map_table_dataset(client):
building_damage_id = '5a296b53c7d30d4af5378cd5'
dataset = Dataset.from_data_service(building_damage_id,
DataService(client))
map = viz.plot_table_dataset(dataset, client, 'meandamage')
assert True
def test_visualize_raster_file(client):
galvaston_wave_height_id = '5f11e503feef2d758c4df6db'
dataset = Dataset.from_data_service(galvaston_wave_height_id,
DataService(client))
map = viz.plot_raster_from_path(dataset.get_file_path('tif'))
assert True
def test_seaside_roadways(client):
trns_brdg_dataset_id = "5ee7af50772cf80008577ae3"
trns_brdg_dataset = Dataset.from_data_service(trns_brdg_dataset_id,
DataService(client))
viz.plot_map(trns_brdg_dataset, column=None, category=False, basemap=True)
assert True
def test_heatmap(client):
shelby_hospital_inv_id = "5a284f0bc7d30d13bc081a28"
dataset = Dataset.from_data_service(shelby_hospital_inv_id,
DataService(client))
map = viz.plot_heatmap(dataset, "str_prob")
assert True
def test_choropleth_sinlge_dataset(client):
social_vulnerability_census_block_group = '5a284f57c7d30d13bc08254c'
dataset = Dataset.from_data_service(
social_vulnerability_census_block_group, DataService(client))
viz.plot_choropleth_multiple_fields_from_single_dataset(
dataset, ['tot_hh', 'totpop'])
assert True
def test_visualize_network(client):
centerville_epn_network_id = "5d25fb355648c40482a80e1c"
dataset = Dataset.from_data_service(centerville_epn_network_id,
DataService(client))
network_dataset = NetworkDataset(dataset)
viz.plot_network_dataset(network_dataset)
assert True
def test_visualize_earthquake(client):
eq_hazard_id = "5b902cb273c3371e1236b36b"
eq_metadata = HazardService(client).get_earthquake_hazard_metadata(
eq_hazard_id)
eq_dataset_id = eq_metadata['rasterDataset']['datasetId']
eq_dataset = Dataset.from_data_service(eq_dataset_id, DataService(client))
viz.plot_earthquake(eq_hazard_id, client)
assert True
def test_visualize_inventory(client):
shelby_hospital_inv_id = "5a284f0bc7d30d13bc081a28"
shelby_road_id = "5a284f2bc7d30d13bc081eb6"
# get shelvy building inventory and road
sh_bldg_inv = Dataset.from_data_service(shelby_hospital_inv_id,
DataService(client))
sh_road = Dataset.from_data_service(shelby_road_id, DataService(client))
# visualize building inventory
viz.plot_map(sh_bldg_inv,
column="struct_typ",
category=False,
basemap=True)
# visualize building inventory from geoserver
viz.get_wms_map([sh_bldg_inv, sh_road])
viz.get_gdf_map([sh_bldg_inv, sh_road])
viz.get_gdf_wms_map([sh_bldg_inv], [sh_road])
assert True
def test_visualize_joplin_tornado_building(client):
# testing datasets
tornado_hazard_id = "5dfa32bbc0601200080893fb"
joplin_bldg_inv_id = "5df7d0de425e0b00092d0082"
viz.plot_tornado(tornado_hazard_id, client, basemap=False)
tornado_dataset_id = HazardService(client).get_tornado_hazard_metadata(
tornado_hazard_id)['datasetId']
tornado_dataset = Dataset.from_data_service(tornado_dataset_id,
DataService(client))
viz.get_gdf_map([tornado_dataset])
# get joplin building inventory
joplin_bldg_inv = Dataset.from_data_service(joplin_bldg_inv_id,
DataService(client))
# using wms layer for joplin building inv. gdf will crash the browser
viz.get_gdf_wms_map([tornado_dataset], [joplin_bldg_inv])
assert True
def test_plot_table_dataset_list_from_single_source(client):
seaside_building_polygon_id = '5f7c95d681c8dd4d309d5a46'
dataset_id_list = ['5f7c9b4f81c8dd4d309d5b62', '5f7c9af781c8dd4d309d5b5e']
dataset_list = []
for dataset_id in dataset_id_list:
dataset_list.append(
Dataset.from_data_service(dataset_id, DataService(client)))
map = viz.plot_table_dataset_list_from_single_source(
client, dataset_list, 'failure_probability',
seaside_building_polygon_id)
assert True
def plot_table_dataset_list_from_single_source(client,
dataset_list=list,
column=str,
in_source_dataset_id=None):
"""Creates map window with a list of table dataset and source dataset
Args:
client (Client): pyincore service Client Object
dataset_list (list): list of table dataset
column (str): column name to be plot
in_source_dataset_id (str): source dataset id, the dafault is None
Returns:
GeoUtil.map (ipyleaflet.Map): ipyleaflet Map object
"""
source_dataset_id = None
if in_source_dataset_id is None:
joined_df, dataset_id_list, source_dataset_id = \
GeoUtil.merge_table_dataset_with_field(dataset_list, column)
else:
joined_df, dataset_id_list, source_dataset_id = \
GeoUtil.merge_table_dataset_with_field(dataset_list, column, in_source_dataset_id)
if source_dataset_id is None:
raise Exception("There is no sourceDataset id.")
source_dataset = Dataset.from_data_service(source_dataset_id,
DataService(client))
inventory_df = PlotUtil.inventory_to_geodataframe(source_dataset)
inventory_df = PlotUtil.remove_null_inventories(inventory_df, 'guid')
# merge inventory dataframe and joined table dataframe
inventory_df = inventory_df.merge(joined_df, on='guid')
# keep only necessary fields
keep_list = ['guid', 'geometry']
for dataset_id in dataset_id_list:
# dataset_id will be used as a column name to visualize the values in the field
keep_list.append(dataset_id)
inventory_df = inventory_df[keep_list]
# create base map
map = table_list_map()
map.create_basemap_ipylft(inventory_df, dataset_id_list)
return map.map
def plot_tornado(tornado_id, client, category=False, basemap=True):
"""Plot a tornado path
Args:
tornado_id (str): ID of tornado hazard
client (Client): pyincore service Client Object
category (boolean): turn on/off category option
basemap (boolean): turn on/off base map (e.g. openstreetmap)
"""
# it needs descartes pakcage for polygon plotting
# getting tornado dataset should be part of Tornado Hazard code
tornado_dataset_id = HazardService(client).get_tornado_hazard_metadata(
tornado_id)['datasetId']
tornado_dataset = Dataset.from_data_service(tornado_dataset_id,
DataService(client))
tornado_gdf = gpd.read_file(tornado_dataset.local_file_path)
GeoUtil.plot_gdf_map(tornado_gdf, 'ef_rating', category, basemap)
def plot_earthquake(earthquake_id, client):
"""Plot earthquake raster data
Args:
earthquake_id (str): ID of tornado hazard
client (Client): pyincore service Client Object
"""
eq_metadata = HazardService(client).get_earthquake_hazard_metadata(
earthquake_id)
eq_dataset_id = eq_metadata['rasterDataset']['datasetId']
eq_dataset = Dataset.from_data_service(eq_dataset_id,
DataService(client))
raster_file_path = Path(eq_dataset.local_file_path).joinpath(
eq_dataset.metadata['fileDescriptors'][0]['filename'])
raster = rasterio.open(raster_file_path)
rasterio.plot.show(raster)
def test_plot_map_dataset_list(client):
galveston_roadway_id = '5f0dd5ecb922f96f4e962caf'
galvaston_wave_height_id = '5f11e503feef2d758c4df6db'
shelvy_building_damage_id = '5a296b53c7d30d4af5378cd5'
dataset_id_list = [
galveston_roadway_id, galvaston_wave_height_id,
shelvy_building_damage_id
]
dataset_list = []
for dataset_id in dataset_id_list:
dataset_list.append(
Dataset.from_data_service(dataset_id, DataService(client)))
# table dataset plot map
map = viz.plot_maps_dataset_list(dataset_list, client)
assert True
def join_table_dataset_with_source_dataset(dataset, client):
"""Creates geopandas dataframe by joining table dataset and its source dataset
Args:
dataset (Dataset): pyincore dataset object
client (Client): pyincore service client object
Returns:
joined_dbf (geodataframe): geopandas geodataframe object
"""
is_source_dataset = False
source_dataset = None
# check if the given dataset is table dastaset
if dataset.metadata['format'] != 'table' and dataset.metadata[
'format'] != 'csv':
print("The given dataset is not a table dataset")
return None
# check if source dataset exists
try:
source_dataset = dataset.metadata['sourceDataset']
is_source_dataset = True
except Exception:
print("There is no source dataset for the give table dataset")
if is_source_dataset:
# merge dataset and source dataset
geodataset = Dataset.from_data_service(source_dataset,
DataService(client))
joined_gdf = GeoUtil.join_datasets(geodataset, dataset)
else:
return None
return joined_gdf