def test_insert_risk(self):
dycast_parameters = test_helper_functions.get_dycast_parameters()
risk_service = risk_service_module.RiskService(dycast_parameters)
session = database_service.get_sqlalchemy_session()
gridpoints = geography_service.generate_grid(dycast_parameters)
point = geography_service.get_shape_from_sqlalch_element(gridpoints[0])
risk = Risk(risk_date=datetime.date(int(2016), int(3), int(25)),
number_of_cases=5,
lat=point.x,
long=point.y,
close_pairs=3,
close_space=2,
close_time=1,
cumulative_probability=0.032)
session.query(Risk.risk_date).filter(Risk.risk_date == risk.risk_date,
Risk.lat == risk.lat,
Risk.long == risk.long) \
.delete()
risk_service.insert_risk(session, risk)
session.commit()
session.query(Risk.risk_date).filter(Risk.risk_date == risk.risk_date,
Risk.lat == risk.lat,
Risk.long == risk.long) \
.one()
def test_close_time_only(self):
dycast_parameters = test_helper_functions.get_dycast_parameters()
risk_service = risk_service_module.RiskService(dycast_parameters)
comparative_test_service = comparative_test_service_module.ComparativeTestService(dycast_parameters)
session = database_service.get_sqlalchemy_session()
riskdate = datetime.date(int(2016), int(3), int(25))
gridpoints = geography_service.generate_grid(dycast_parameters)
clusters_per_point_query = risk_service.get_clusters_per_point_query(session, gridpoints, riskdate)
clusters_per_point = risk_service.get_clusters_per_point_from_query(clusters_per_point_query)
risk_service.enrich_clusters_per_point_with_close_space_and_time(clusters_per_point)
# Compare to old query
daily_cases_query = comparative_test_service.get_daily_cases_query(session,
riskdate)
for point in gridpoints:
cases_in_cluster_query = comparative_test_service.get_cases_in_cluster_query(daily_cases_query,
point)
count_old = comparative_test_service.get_close_time_only(cases_in_cluster_query)
for cluster in clusters_per_point:
if cluster.point.equals(geography_service.get_shape_from_sqlalch_element(point)):
self.assertEquals(cluster.close_in_time, count_old)
def test_get_clusters_per_point_query(self):
dycast_parameters = test_helper_functions.get_dycast_parameters(large_dataset=True)
risk_service = risk_service_module.RiskService(dycast_parameters)
comparative_test_service = comparative_test_service_module.ComparativeTestService(dycast_parameters)
session = database_service.get_sqlalchemy_session()
riskdate = datetime.date(int(2016), int(3), int(25))
gridpoints = geography_service.generate_grid(dycast_parameters)
clusters_per_point_query = risk_service.get_clusters_per_point_query(session, gridpoints, riskdate)
clusters_per_point = risk_service.get_clusters_per_point_from_query(clusters_per_point_query)
daily_cases_query = comparative_test_service.get_daily_cases_query(session, riskdate)
for cluster in clusters_per_point:
point_wkt_element = geography_service.get_wktelement_from_wkt(cluster.point.wkt)
cases_in_cluster_query = comparative_test_service.get_cases_in_cluster_query(daily_cases_query,
point_wkt_element)
vector_count_new = cluster.get_case_count()
vector_count_old = database_service.get_count_for_query(cases_in_cluster_query)
self.assertEqual(vector_count_new, vector_count_old)
def export_risk(self, dycast_parameters):
session = database_service.get_sqlalchemy_session()
startdate = dycast_parameters.startdate
enddate = dycast_parameters.enddate
export_directory = dycast_parameters.export_directory
export_prefix = dycast_parameters.export_prefix
export_format = dycast_parameters.export_format
# Quick and dirty solution
if export_format != "tsv" and export_format != "csv":
logging.error("Incorrect export format: %s", export_format)
return 1
else:
separator = self.get_separator(export_format)
if export_directory is None:
export_directory = CONFIG.get("system", "export_directory")
# dates are objects, not strings
startdate_string = conversion_service.get_string_from_date_object(
startdate)
enddate_string = conversion_service.get_string_from_date_object(
enddate)
export_time = strftime("%Y-%m-%d__%H-%M-%S")
filename = "exported_{0}__risk_{1}--{2}.{3}".format(
export_time, startdate_string, enddate_string, export_format)
if export_prefix:
filename = export_prefix + filename
filepath = os.path.join(export_directory, filename)
logging.info("Exporting risk for: %s - %s", startdate_string,
enddate_string)
risk_query = self.get_risk_query(session, startdate, enddate)
risk_count = database_service.get_count_for_query(risk_query)
if risk_count == 0:
logging.info("No risk found for the provided dates: %s - %s",
startdate_string, enddate_string)
return
risk_collection = risk_query.all()
table_content = file_service.TableContent()
header = self.get_header_as_string(separator)
table_content.set_header(header)
body = self.get_rows_as_string(risk_collection, separator)
table_content.set_body(body)
file_service.save_file(table_content.get_content(), filepath)
return filepath
def test_load_case_data_error(self):
session = database_service.get_sqlalchemy_session()
import_service = import_service_module.ImportService()
dycast_model = dycast_parameters.DycastParameters()
dycast_model.srid_of_cases = 3857
location_type = enums.Location_type.LAT_LONG
line_incorrect_date = "9998\t30/09/16\t1832445.278\t2118527.399"
with self.assertRaises(DataError):
import_service.load_case(session, dycast_model, line_incorrect_date, location_type)
def load_case_file(self, dycast_parameters, filename):
session = database_service.get_sqlalchemy_session()
lines_read = 0
lines_processed = 0
lines_loaded = 0
lines_skipped = 0
location_type = ""
try:
input_file = file_service.read_file(filename)
except Exception:
logging.exception("Could not read file: %s", filename)
sys.exit(1)
for line_number, line in enumerate(input_file):
line = remove_trailing_newline(line)
if line_number == 0:
header_count = line.count("\t") + 1
if header_count == 4:
location_type = enums.Location_type.LAT_LONG
elif header_count == 3:
location_type = enums.Location_type.GEOMETRY
else:
logging.error("Incorrect column count: %s, exiting...",
header_count)
sys.exit(1)
logging.info("Loading cases as location type: %s",
enums.Location_type(location_type).name)
else:
lines_read += 1
result = 0
try:
result = self.load_case(session, dycast_parameters, line,
location_type)
except Exception:
raise
# If result is a case ID or -1 (meaning duplicate) then:
lines_processed += 1
if result == -1:
lines_skipped += 1
else:
lines_loaded += 1
try:
session.commit()
except SQLAlchemyError, e:
session.rollback()
logging.exception("Couldn't insert cases")
logging.exception(e)
raise
def test_get_daily_cases_query_old(self):
dycast_parameters = test_helper_functions.get_dycast_parameters()
comparative_test_service = comparative_test_service_module.ComparativeTestService(dycast_parameters)
session = database_service.get_sqlalchemy_session()
riskdate = datetime.date(int(2016), int(3), int(25))
daily_cases_query = comparative_test_service.get_daily_cases_query(session, riskdate)
count = database_service.get_count_for_query(daily_cases_query)
self.assertGreater(count, 0)
def insert_test_cases():
import_service = import_service_module.ImportService()
dycast_model = dycast_parameters.DycastParameters()
dycast_model.srid_of_cases = '3857'
dycast_model.files_to_import = get_test_cases_import_files_latlong()
session = database_service.get_sqlalchemy_session()
case_query = session.query(Case)
case_count = database_service.get_count_for_query(case_query)
if case_count == 0:
import_service.load_case_files(dycast_model)
def test_get_exact_match_distribution_margin(self):
dycast_parameters = test_helper_functions.get_dycast_parameters()
risk_service = risk_service_module.RiskService(dycast_parameters)
session = database_service.get_sqlalchemy_session()
cluster = Cluster()
cluster.case_count = 2,
cluster.close_space_and_time = 1,
cluster.close_in_space = 1,
cluster.close_in_time = 1,
cumulative_probability = risk_service.get_exact_match_cumulative_probability(session, cluster)
self.assertGreater(cumulative_probability, 0)
def test_get_cumulative_probability(self):
dycast_parameters = test_helper_functions.get_dycast_parameters()
risk_service = risk_service_module.RiskService(dycast_parameters)
session = database_service.get_sqlalchemy_session()
cluster = Cluster()
cluster.case_count = 10
cluster.close_space_and_time = 3
cluster.close_in_space = 5
cluster.close_in_time = 27
risk_service.get_cumulative_probability_for_cluster(session, cluster)
self.assertGreater(cluster.cumulative_probability, 0)
def test_get_nearest_close_in_time_distribution_margin_query(self):
dycast_parameters = test_helper_functions.get_dycast_parameters()
risk_service = risk_service_module.RiskService(dycast_parameters)
session = database_service.get_sqlalchemy_session()
cluster = Cluster()
cluster.case_count = 30
cluster.close_space_and_time = 1
cluster.close_in_space = 2
cluster.close_in_time = 10
nearest_close_in_time_query = risk_service.get_nearest_close_in_time_distribution_margin_query(session, cluster)
result = session.query(nearest_close_in_time_query).first()
self.assertIsNotNone(result)
self.assertGreaterEqual(result, 0)
def test_get_cumulative_probability_by_nearest_close_in_time_and_space(self):
dycast_parameters = test_helper_functions.get_dycast_parameters()
risk_service = risk_service_module.RiskService(dycast_parameters)
session = database_service.get_sqlalchemy_session()
cluster = Cluster()
cluster.case_count = 30
cluster.close_space_and_time = 1
cluster.close_in_space = 2
cluster.close_in_time = 10
query_result = risk_service.get_cumulative_probability_by_nearest_close_in_time_and_space(session,
cluster)
cumulative_probability = query_result.cumulative_probability
self.assertIsNotNone(cumulative_probability)
self.assertGreater(cumulative_probability, 0)
def test_get_cases_in_cluster_query_old(self):
dycast_parameters = test_helper_functions.get_dycast_parameters()
comparative_test_service = comparative_test_service_module.ComparativeTestService(dycast_parameters)
session = database_service.get_sqlalchemy_session()
riskdate = datetime.date(int(2016), int(3), int(25))
gridpoints = geography_service.generate_grid(dycast_parameters)
point = gridpoints[0]
daily_cases_query = comparative_test_service.get_daily_cases_query(session, riskdate)
cases_in_cluster_query = comparative_test_service.get_cases_in_cluster_query(daily_cases_query, point)
vector_count = database_service.get_count_for_query(cases_in_cluster_query)
self.assertGreater(vector_count, 0)
def test_load_case_correct(self):
session = database_service.get_sqlalchemy_session()
import_service = import_service_module.ImportService()
dycast_model = dycast_parameters.DycastParameters()
dycast_model.srid_of_cases = 3857
line_correct = "99999\t03/09/16\t1832445.278\t2118527.399"
location_type = enums.Location_type.LAT_LONG
import_service.load_case(session, dycast_model, line_correct, location_type)
session.commit()
query = session.query(Case).filter(Case.id == '99999')
count = database_service.get_count_for_query(query)
self.assertEquals(count, 1)
session.delete(query.first())
session.commit()
def generate_risk(self):
session = database_service.get_sqlalchemy_session()
logging_service.display_current_parameter_set(self.dycast_parameters)
case_threshold = self.dycast_parameters.case_threshold
gridpoints = geography_service.generate_grid(self.dycast_parameters)
day = self.dycast_parameters.startdate
delta = datetime.timedelta(days=1)
while day <= self.dycast_parameters.enddate:
start_time = time.time()
logging.info("Starting daily_risk for %s", day)
points_above_threshold = 0
clusters_per_point_query = self.get_clusters_per_point_query(
session, gridpoints, day)
clusters_per_point = self.get_clusters_per_point_from_query(
clusters_per_point_query)
for cluster in clusters_per_point:
vector_count = cluster.get_case_count()
if vector_count >= case_threshold:
points_above_threshold += 1
self.get_close_space_and_time_for_cluster(cluster)
self.get_cumulative_probability_for_cluster(
session, cluster)
risk = Risk(
risk_date=day,
number_of_cases=vector_count,
lat=cluster.point.y,
long=cluster.point.x,
close_pairs=cluster.close_space_and_time,
close_space=cluster.close_in_space,
close_time=cluster.close_in_time,
cumulative_probability=cluster.cumulative_probability)
self.insert_risk(session, risk)
session.commit()
logging.info("Finished daily_risk for %s: done %s points", day,
len(gridpoints))
logging.info("Total points above threshold of %s: %s",
case_threshold, points_above_threshold)
logging.info("Time elapsed: %.0f seconds",
time.time() - start_time)
day += delta
try:
session.commit()
except SQLAlchemyError, e:
session.rollback()
logging.exception(
"There was a problem committing the risk data session")
logging.exception(e)
raise
def generate_risk(self):
session = database_service.get_sqlalchemy_session()
logging_service.display_current_parameter_set(self.dycast_parameters)
case_threshold = self.dycast_parameters.case_threshold
gridpoints = geography_service.generate_grid(self.dycast_parameters)
day = self.dycast_parameters.startdate
delta = datetime.timedelta(days=1)
while day <= self.dycast_parameters.enddate:
daily_cases_query = self.get_daily_cases_query(session, day)
daily_case_count = database_service.get_count_for_query(
daily_cases_query)
if daily_case_count >= case_threshold:
start_time = time.time()
logging.info("Starting daily_risk for %s", day)
points_above_threshold = 0
clusters_per_point = self.get_clusters_per_point_query(
session, gridpoints, day)
for cluster in clusters_per_point:
vector_count = len(cluster.case_array)
if vector_count >= case_threshold:
points_above_threshold += 1
point = geography_service.get_shape_from_sqlalch_element(
cluster.point)
risk = Risk(risk_date=day,
number_of_cases=vector_count,
lat=point.x,
long=point.y)
for point in gridpoints:
cases_in_cluster_query = self.get_cases_in_cluster_query(
daily_cases_query, point)
vector_count = database_service.get_count_for_query(
cases_in_cluster_query)
if vector_count >= case_threshold:
points_above_threshold += 1
risk = Risk(risk_date=day,
number_of_cases=vector_count,
lat=point.x,
long=point.y)
risk.close_pairs = self.get_close_space_and_time(
cases_in_cluster_query)
risk.close_space = self.get_close_space_only_old(
cases_in_cluster_query) - risk.close_pairs
risk.close_time = self.get_close_time_only(
cases_in_cluster_query) - risk.close_pairs
risk.cumulative_probability = self.get_cumulative_probability(
session, risk.number_of_cases, risk.close_pairs,
risk.close_space, risk.close_time)
self.insert_risk(session, risk)
logging.info("Finished daily_risk for %s: done %s points", day,
len(gridpoints))
logging.info("Total points above threshold of %s: %s",
case_threshold, points_above_threshold)
logging.info("Time elapsed: %.0f seconds",
time.time() - start_time)
else:
logging.info(
"Amount of cases for %s lower than threshold %s: %s, skipping.",
day, case_threshold, daily_case_count)
day += delta
try:
session.commit()
except SQLAlchemyError, e:
session.rollback()
logging.exception(
"There was a problem committing the risk data session")
logging.exception(e)
raise
def test_can_get_cases(self):
session = database_service.get_sqlalchemy_session()
cases = session.query(Case.id).all()
case_count = len(cases)
self.assertGreater(case_count, 0)
