def test_during_search(self):
"""Function to test the temporal during search query.
"""
for e in self.testVector["entity_ids"]:
self.entity = Entity(e, token=self.config["headers"]["token"])
for time_param in self.testVector["temporal_params"]:
df = self.entity.during_search(
start_time=time_param["start_time"],
end_time=time_param["end_time"]
)
self.assertNotEqual(df.shape[0], 0)
self.assertGreaterEqual(df.shape[0], 1)
self.assertIsNotNone(df)
self.assertIsInstance(df, pd.DataFrame)
print("*"*60 + "\n" + "*"*60)
print(
f"DataFrame has {df.shape[0]} rows and {df.shape[1]} columns.",
end="\n-----------\n"
)
print(f"Columns in DataFrame:\n{df.columns}", end="\n-----------\n")
print(df.head)
print("*"*60)
def test_batches_resourcegroup(self):
e = Entity(
"datakaveri.org/04a15c9960ffda227e9546f3f46e629e1fe4132b/rs.iudx.org.in/pune-env-aqm",
token_obj=self.token_obj)
df = e.during_search("2021-12-18T00:00:00Z", "2021-12-31T00:00:00Z")
df["observationDateTime"] = pd.to_datetime(df["observationDateTime"])
print(df["observationDateTime"].max())
print(df["observationDateTime"].min())
def test_batches_resource(self):
e = Entity(
"datakaveri.org/04a15c9960ffda227e9546f3f46e629e1fe4132b/rs.iudx.org.in/pune-env-aqm/83cdf03d-5787-7052-08aa-143cfbfb807d",
token_obj=self.token_obj)
query = ResourceQuery().add_entity(
"datakaveri.org/04a15c9960ffda227e9546f3f46e629e1fe4132b/rs.iudx.org.in/pune-env-aqm/83cdf03d-5787-7052-08aa-143cfbfb807d"
).during_search("2021-12-18T00:00:00Z", "2021-12-29T00:00:00Z")
batch_queries = []
e.make_query_batches(query, batch_queries)
for q in batch_queries:
print(q.get_query())
def test_date_bins(self):
e = Entity("datakaveri.org/04a15c9960ffda227e9546f3f46e629e1fe4132b/rs.iudx.org.in/pune-env-aqm/83cdf03d-5787-7052-08aa-143cfbfb807d", token_obj=self.token_obj)
query = ResourceQuery().add_entity(
"datakaveri.org/04a15c9960ffda227e9546f3f46e629e1fe4132b/rs.iudx.org.in/pune-env-aqm/83cdf03d-5787-7052-08aa-143cfbfb807d").during_search("2021-12-18T00:00:00Z", "2021-12-29T00:00:00Z")
date_bins = []
start_time = datetime.strptime("2021-12-16T00:00:00Z", self.time_format)
end_time = datetime.strptime("2021-12-27T00:00:00Z", self.time_format)
e.make_date_bins(start_time, end_time, date_bins)
for i in range(0, len(date_bins)-1):
print(date_bins[i], date_bins[i+1])
print(date_bins)
def test_download(self):
"""Function to test the downloading of file in csv format.
"""
file_name = "test_download_file"
for e in self.testVector["entity_ids"]:
self.entity = Entity(e, token=self.config["headers"]["token"])
for time_param in self.testVector["temporal_params"]:
df = self.entity.during_search(
start_time=time_param["start_time"],
end_time=time_param["end_time"]
)
self.assertNotEqual(df.shape[0], 0)
self.assertGreaterEqual(df.shape[0], 1)
self.assertIsNotNone(df)
self.assertIsInstance(df, pd.DataFrame)
for file_type in self.testVector["file_types"]:
self.entity.download(file_name, file_type)
zf = zipfile.ZipFile(f"{file_name}.zip")
if file_type == "csv":
df_csv = pd.read_csv(zf.open(f"{file_name}.{file_type}"))
self.assertIsNotNone(df_csv)
self.assertIsInstance(df_csv, pd.DataFrame)
os.remove(f"{file_name}.zip")
elif file_type == "json":
df_json = pd.read_json(zf.open(f"{file_name}.{file_type}"), orient='records')
self.assertIsNotNone(df_json)
self.assertIsInstance(df_json, pd.DataFrame)
os.remove(f"{file_name}.zip")
else:
raise RuntimeError(f"File type '{file_type}' is not supported.")
def test_latest(self):
"""Function to test the latest search query.
"""
for e in self.testVector["entity_ids"]:
self.entity = Entity(e)
for time_param in self.testVector["temporal_params"]:
df = self.entity.latest()
self.assertNotEqual(df.shape[0], 0)
self.assertGreaterEqual(df.shape[0], 1)
self.assertIsNotNone(df)
self.assertIsInstance(df, pd.DataFrame)
print("*"*60 + "\n" + "*"*60)
print(
f"DataFrame has {df.shape[0]} rows and {df.shape[1]} columns.",
end="\n-----------\n"
)
print(f"Columns in DataFrame:\n{df.columns}", end="\n-----------\n")
print(df.head)
print("*"*60)
def test_get_static_data(self):
iid = "varanasismartcity.gov.in/62d1f729edd3d2a1a090cb1c6c89356296963d55/rs.iudx.org.in/varanasi-point-of-interests/smartpole-locations"
e = Entity(iid, token_obj=self.token_obj)
df = e.property_search("id", iid, operation="==")
print(df.shape)
class EntityTest(unittest.TestCase):
"""Test different scenarios for the Entity class.
"""
def __init__(self, *args, **kwargs):
"""EntityTest base class constructor.
"""
super(EntityTest, self).__init__(*args, **kwargs)
self.config = {}
with open("./config.json", "r") as f:
self.config = json.load(f)
self.testVector = {}
with open("./tests/entity/testVector_Entity.json", "r") as f:
self.testVector = json.load(f)
def test_latest(self):
"""Function to test the latest search query.
"""
for e in self.testVector["entity_ids"]:
self.entity = Entity(e)
for time_param in self.testVector["temporal_params"]:
df = self.entity.latest()
self.assertNotEqual(df.shape[0], 0)
self.assertGreaterEqual(df.shape[0], 1)
self.assertIsNotNone(df)
self.assertIsInstance(df, pd.DataFrame)
print("*"*60 + "\n" + "*"*60)
print(
f"DataFrame has {df.shape[0]} rows and {df.shape[1]} columns.",
end="\n-----------\n"
)
print(f"Columns in DataFrame:\n{df.columns}", end="\n-----------\n")
print(df.head)
print("*"*60)
def test_during_search(self):
"""Function to test the temporal during search query.
"""
for e in self.testVector["entity_ids"]:
self.entity = Entity(e, token=self.config["headers"]["token"])
for time_param in self.testVector["temporal_params"]:
df = self.entity.during_search(
start_time=time_param["start_time"],
end_time=time_param["end_time"]
)
self.assertNotEqual(df.shape[0], 0)
self.assertGreaterEqual(df.shape[0], 1)
self.assertIsNotNone(df)
self.assertIsInstance(df, pd.DataFrame)
print("*"*60 + "\n" + "*"*60)
print(
f"DataFrame has {df.shape[0]} rows and {df.shape[1]} columns.",
end="\n-----------\n"
)
print(f"Columns in DataFrame:\n{df.columns}", end="\n-----------\n")
print(df.head)
print("*"*60)
def test_download(self):
"""Function to test the downloading of file in csv format.
"""
file_name = "test_download_file"
for e in self.testVector["entity_ids"]:
self.entity = Entity(e, token=self.config["headers"]["token"])
for time_param in self.testVector["temporal_params"]:
df = self.entity.during_search(
start_time=time_param["start_time"],
end_time=time_param["end_time"]
)
self.assertNotEqual(df.shape[0], 0)
self.assertGreaterEqual(df.shape[0], 1)
self.assertIsNotNone(df)
self.assertIsInstance(df, pd.DataFrame)
for file_type in self.testVector["file_types"]:
self.entity.download(file_name, file_type)
zf = zipfile.ZipFile(f"{file_name}.zip")
if file_type == "csv":
df_csv = pd.read_csv(zf.open(f"{file_name}.{file_type}"))
self.assertIsNotNone(df_csv)
self.assertIsInstance(df_csv, pd.DataFrame)
os.remove(f"{file_name}.zip")
elif file_type == "json":
df_json = pd.read_json(zf.open(f"{file_name}.{file_type}"), orient='records')
self.assertIsNotNone(df_json)
self.assertIsInstance(df_json, pd.DataFrame)
os.remove(f"{file_name}.zip")
else:
raise RuntimeError(f"File type '{file_type}' is not supported.")