def upload_shapefile_to_services(client):
# Upload to incore services and put under commresilience space
# It assumes the shapefile is in the utils directory
datasvc = DataService(client)
dataset_prop = {
"title": "Joplin Population Dislocation For Heatmap Plotting",
"description":
"Contains only dislocated numprec for Joplin playbook plotting usage",
"contributors": [],
"dataType": "incore:popdislocationShp",
"storedUrl": "",
"format": "shapefile"
}
response = datasvc.create_dataset(dataset_prop)
dataset_id = response['id']
files = [
'joplin-pop-disl-numprec.shp', 'joplin-pop-disl-numprec.dbf',
'joplin-pop-disl-numprec.shx', 'joplin-pop-disl-numprec.prj'
]
datasvc.add_files_to_dataset(dataset_id, files)
# add to space
spacesvc = SpaceService(client)
spacesvc.add_dataset_to_space("5f99ba8b0ace240b22a82e00",
dataset_id=dataset_id) # commresilience
print(dataset_id +
" successfully uploaded and move to commresilience space!")
def test_insecure_client():
"""
test insecure client
"""
client = InsecureIncoreClient(username="******")
data_svc = DataService(client)
try:
r = data_svc.get_datasets()
assert len(r) != 0
except requests.HTTPError:
assert False
def pytest_sessionstart(session):
"""
Called after the Session object has been created and
before performing collection and entering the run test loop.
"""
try:
with open(
os.path.join(os.path.dirname(__file__), "pyincore/.incorepw"),
'r') as f:
cred = f.read().splitlines()
except EnvironmentError:
assert False
credentials = jwt.decode(cred[0], cred[1])
monkeypatch = MonkeyPatch()
monkeypatch.setattr("builtins.input", lambda x: credentials["username"])
monkeypatch.setattr("getpass.getpass", lambda y: credentials["password"])
client = IncoreClient(service_url=pyglobals.INCORE_API_DEV_URL,
token_file_name=".incrtesttoken")
pytest.client = client
pytest.datasvc = DataService(client)
pytest.fragilitysvc = FragilityService(client)
pytest.repairsvc = RepairService(client)
pytest.restorationsvc = RestorationService(client)
pytest.hazardsvc = HazardService(client)
pytest.spacesvc = SpaceService(client)
print(
f"Successfully initialized Incore client and services. Using {pyglobals.INCORE_API_DEV_URL}"
)
def create_result_dataset(datasvc: DataService, parentid: str,
result_files: List[str], title: str,
output_metadata: Dict[str, str]):
# Result metadata
properties = output_metadata
properties["title"] = title
properties["sourceDataset"] = parentid
# Create child dataset with parent dataset as sourceDataset
result_dataset = datasvc.create_dataset(properties)
result_dataset_id = result_dataset["id"]
# Attach files to result dataset
datasvc.add_files_to_dataset(result_dataset_id, result_files)
return result_dataset_id
def get_mapped_result_from_dataset_id(client,
inventory_id: str,
dmg_result_id: str,
bldg_func_id,
archetype_mapping_id: str,
groupby_col_name: str = "max_state"):
"""Use this if your damage results are already stored in the data service and you have their dataset ids.
All the inputs (except groupby_col_name) are dataset ids.
Args:
client: Service client with authentication info
inventory_id: Inventory dataset id
dmg_result_id: Damage result dataset id
bldg_func_id: Incore dataset for building func id
archetype_mapping_id: Mapping id dataset for archetype
groupby_col_name: column name to group by, default to max_state
Returns:
dmg_ret_json: JSON of the damage state results ordered by cluster and category.
func_ret_json: JSON of the building functionality results ordered by cluster and category.
max_state_df: Dataframe of max damage state
"""
bldg_inv = Dataset.from_data_service(inventory_id, DataService(client))
inventory = pd.DataFrame(gpd.read_file(bldg_inv.local_file_path))
dmg_result_dataset = Dataset.from_data_service(dmg_result_id,
DataService(client))
dmg_result = dmg_result_dataset.get_dataframe_from_csv()
bldg_func_dataset = Dataset.from_data_service(bldg_func_id,
DataService(client))
bldg_func_df = bldg_func_dataset.get_dataframe_from_csv()
bldg_func_df.rename(columns={'building_guid': 'guid'}, inplace=True)
archtype_mapping_dataset = Dataset.from_data_service(
archetype_mapping_id, DataService(client))
arch_mapping = archtype_mapping_dataset.get_dataframe_from_csv()
max_state_df = DataProcessUtil.get_max_damage_state(dmg_result)
dmg_ret_json = DataProcessUtil.create_mapped_dmg_result(
inventory, max_state_df, arch_mapping, groupby_col_name)
func_ret_json = DataProcessUtil.create_mapped_func_result(
inventory, bldg_func_df, arch_mapping)
return dmg_ret_json, func_ret_json, max_state_df
def get_mapped_result_from_analysis(client,
inventory_id: str,
dmg_result_dataset,
bldg_func_dataset,
archetype_mapping_id: str,
groupby_col_name: str = "max_state"):
"""Use this if you want to load results directly from the output files of the analysis, than storing the results
to data service and loading from there using ids.
It takes the static inputs: inventory & archetypes as dataset ids. The result inputs are taken as
Dataset class objects, which are created by reading the output result files.
Args:
client: Service client with authentication info
inventory_id: Inventory dataset id
dmg_result_dataset: Incore dataset for damage result
bldg_func_dataset: Incore dataset for building func dataset
archetype_mapping_id: Mapping id dataset for archetype
Returns:
dmg_ret_json: JSON of the damage state results ordered by cluster and category.
func_ret_json: JSON of the building functionality results ordered by cluster and category.
mapped_df: Dataframe of max damage state
"""
bldg_inv = Dataset.from_data_service(inventory_id, DataService(client))
inventory = pd.DataFrame(gpd.read_file(bldg_inv.local_file_path))
dmg_result = dmg_result_dataset.get_dataframe_from_csv()
bldg_func_df = bldg_func_dataset.get_dataframe_from_csv()
bldg_func_df.rename(columns={'building_guid': 'guid'}, inplace=True)
arch_mapping = Dataset.from_data_service(
archetype_mapping_id,
DataService(client)).get_dataframe_from_csv()
max_state_df = DataProcessUtil.get_max_damage_state(dmg_result)
dmg_ret_json = DataProcessUtil.create_mapped_dmg_result(
inventory, max_state_df, arch_mapping, groupby_col_name)
func_ret_json = DataProcessUtil.create_mapped_func_result(
inventory, bldg_func_df, arch_mapping)
return dmg_ret_json, func_ret_json, max_state_df
def cache_files(self, data_service: DataService):
"""Get the set of fragility data, curves.
Args:
data_service (obj): Data service.
Returns:
str: A path to the local file.
"""
if self.local_file_path is not None:
return
self.local_file_path = data_service.get_dataset_blob(self.id)
return self.local_file_path
def test_get_mapped_result_from_analysis(client):
bldg_dataset_id = "5f9091df3e86721ed82f701d"
bldg_dmg_dataset_id = "5f9868c00ace240b22a7f2a5" # legacy DS_name
# bldg_dmg_dataset_id = "602d96e4b1db9c28aeeebdce" # new DS_name
dmg_result_dataset = Dataset.from_data_service(bldg_dmg_dataset_id,
DataService(client))
archetype_id = "5fca915fb34b193f7a44059b"
bldg_func_dataset_id = "60674c89c57ada48e492b97b"
bldg_func_dataset = Dataset.from_data_service(bldg_func_dataset_id,
DataService(client))
dmg_ret_json, func_ret_json, max_state_df = util.get_mapped_result_from_analysis(
client, bldg_dataset_id, dmg_result_dataset, bldg_func_dataset,
archetype_id)
assert "by_cluster" in dmg_ret_json.keys(
) and "by_category" in dmg_ret_json.keys()
assert "by_cluster" in func_ret_json.keys(
) and "by_category" in func_ret_json.keys()
assert "max_state" in max_state_df._info_axis.values
def from_data_service(cls, id: str, data_service: DataService):
"""Get Dataset from Data service, get metadata as well.
Args:
id (str): ID of the Dataset.
data_service (obj): Data service.
Returns:
obj: Dataset from Data service.
"""
metadata = data_service.get_dataset_metadata(id)
instance = cls(metadata)
instance.cache_files(data_service)
return instance
def __init__(self, incore_client):
self.spec = self.get_spec()
self.client = incore_client
self.data_service = DataService(self.client)
# initialize parameters, input_datasets, output_datasets, etc
self.parameters = {}
for param in self.spec['input_parameters']:
self.parameters[param['id']] = {'spec': param, 'value': None}
self.input_datasets = {}
for input_dataset in self.spec['input_datasets']:
self.input_datasets[input_dataset['id']] = {'spec': input_dataset, 'value': None}
self.output_datasets = {}
for output_dataset in self.spec['output_datasets']:
self.output_datasets[output_dataset['id']] = {'spec': output_dataset, 'value': None}
def join_table_dataset_with_source_dataset(dataset, client):
"""Creates geopandas geodataframe by joining table dataset and its source dataset
Args:
dataset (Dataset): pyincore dataset object
client (Client): pyincore service client object
Returns:
gpd.Dataset: Geopandas geodataframe object.
"""
is_source_dataset = False
source_dataset = None
# check if the given dataset is table dataset
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 = DatasetUtil.join_datasets(geodataset, dataset)
else:
return None
return joined_gdf
import os import pandas as pd import geopandas as gpd from pyincore import DataService, IncoreClient, Dataset client = IncoreClient() datasvc = DataService(client) # reading in Nathanael's building inventory building_inventory_pop_id = '5d5433edb9219c0689b98344' dataset = Dataset.from_data_service(building_inventory_pop_id, datasvc) df = dataset.get_dataframe_from_csv() # reading in dataframe seaside_building_shapefile_id = '5df40388b9219c06cf8b0c80' dataset = Dataset.from_data_service(seaside_building_shapefile_id, datasvc) gdf = dataset.get_dataframe_from_shapefile() # merging Nathanael's building inventory CSV with OSU's shapefile gdf_new = pd.merge(gdf, df[['strctid', 'guid']], how='left', left_on='guid', right_on='guid') print(len(df), len(gdf), len(gdf_new)) # checking lengths # writing to shapefile file_new = os.path.join(os.getcwd(), 'seaside_bldg.shp') gdf_new.to_file(file_new)
shp_file = input("Is shape file created?[y/n]: ")
if shp_file=='y':
shp_loc = input("shapefile package location + filename without extension ")
print("Including Files - \n ")
print(shp_loc+'.shp')
print(shp_loc+'.shx')
print(shp_loc+'.prj')
print(shp_loc+'.dbf')
proceed = input("\n Ready to upload to incore. Proceed?[y/n] ")
if proceed=='y':
print("\n Uploading dataset to incore")
print("================================")
client = IncoreClient()
data_services = DataService(client)
space_services = SpaceService(client)
dataset_metadata = {
"title":"MMSA All Building Inventory",
"description": "Shelby building inventory containing strctid, longitude, latitude and block id",
"dataType": "ergo:buildingInventoryVer5",
"format": "shapefile"
}
created_dataset = data_services.create_dataset(dataset_metadata)
dataset_id = created_dataset['id']
print('dataset is created with id ' + dataset_id)
files = [shp_loc+'.shp', shp_loc+'.shx', shp_loc+'.prj', shp_loc+'.dbf']
full_dataset = data_services.add_files_to_dataset(dataset_id, files)
def __init__(self, incore_client):
self.hazardsvc = HazardService(incore_client)
self.fragilitysvc = FragilityService(incore_client)
self.datasetsvc = DataService(incore_client)
self.fragility_tower_id = '5b201b41b1cf3e336de8fa67'
self.fragility_pole_id = '5b201d91b1cf3e336de8fa68'
# this is for deciding to use indpnode field. Not using this could be safer for general dataset
self.use_indpnode = False
self.nnode = 0
self.highest_node_num = 0
self.EF = 0
self.nint = []
self.indpnode = []
self.mcost = 1435 # mean repair cost for single distribution pole
self.vcost = (0.1 * self.mcost)**2
self.sigmad = math.sqrt(math.log(
self.vcost / (self.mcost**2) +
1)) # convert to gaussian Std Deviation to be used in logncdf
self.mud = math.log(
(self.mcost**2) / math.sqrt(self.vcost + self.mcost**2))
self.mcost = 400000 # mean repair cost for single transmission pole
self.vcost = (0.1 * self.mcost)**2
self.sigmat = math.sqrt(math.log(
self.vcost / (self.mcost**2) +
1)) # convert to gaussian Std Deviation to be used in logncdf
self.mut = math.log(
(self.mcost**2) / math.sqrt(self.vcost + self.mcost**2))
self.tmut = 72 # mean repairtime for transmission tower in hrs
self.tsigmat = 36 # std dev
self.tmud = 5 # mean repairtime for poles in hrs
self.tsigmad = 2.5
self.totalcost2repairpath = []
self.totalpoles2repair = []
self.tornado_sim_field_name = 'SIMULATION'
self.tornado_ef_field_name = 'EF_RATING'
# tornado number of simulation and ef_rate
self.nmcs = 0
self.tornado_ef_rate = 0
self.pole_distance = 38.1
# node variables
self.nodenwid_fld_name = "NODENWID"
self.indpnode_fld_name = "INDPNODE"
self.guid_fldname = 'GUID'
# link variables
self.tonode_fld_name = "TONODE"
self.fromnode_fld_name = "FROMNODE"
self.linetype_fld_name = "LINETYPE"
# line type variable
self.line_transmission = "transmission"
self.line_distribution = "distribution"
super(TornadoEpnDamage, self).__init__(incore_client)
def datasvc(monkeypatch):
client = IncoreClient(service_url=pyglobals.INCORE_API_DEV_URL,
token_file_name=".incrtesttoken")
return DataService(client)
