def read_hospitals(self, filename):
# Read hospitals from a headered CSV into a pandas dataframe
hospital_headers = ["latitude", "longitude"]
hospitals_df = parse_headered_csv(filename, hospital_headers)
# Generate list of models from dataframe
latitudes = []
longitudes = []
for index, row in hospitals_df.iterrows():
latitudes.append(row["latitude"])
longitudes.append(row["longitude"])
return latitudes, longitudes
def read_cases(self):
# Read cases from CSV into a pandas dataframe
cases_df = parse_headered_csv(self.filename, self.headers)
# TODO -- Pass in dict or find another way to generalize ordering of headers
id_key = self.headers[0]
timestamp_key = self.headers[1]
latitude_key = self.headers[2]
longitude_key = self.headers[3]
priority_key = self.headers[4] if len(self.headers) > 3 else None
# Generate list of models from dataframe
cases = []
for index, row in cases_df.iterrows():
case = RandomCase(id=row[id_key],
date_recorded=datetime.strptime(row[timestamp_key], '%Y-%m-%d %H:%M:%S.%f'),
incident_location=Point(row[latitude_key], row[longitude_key]),
event_generator=self.event_generator,
priority=row[priority_key] if priority_key is not None else None)
cases.append(case)
def read(self, filename):
# Read ambulance from a headered CSV into a pandas dataframe
ambulances_df = parse_headered_csv(filename, self.headers)
# TODO -- generalize
id_key = self.headers[0]
b_latitude_key = self.headers[1]
b_longitude_key = self.headers[2]
capability_key = self.headers[3]
# Generate list of models from dataframe
a = []
for index, row in ambulances_df.iterrows():
base = Point(row[b_latitude_key],
row[b_longitude_key])
ambulance = Ambulance(id=row[id_key],
base=base,
capability=Capability[row[capability_key]])
a.append(ambulance)
return a
def read_cases(self):
# Read cases from CSV into a pandas dataframe
case_headers = ["id", "latitud", "longitud", "fecha", "dia_semana", "hora_llamada", "vprioridad"]
cases_df = parse_headered_csv(self.filename, case_headers)
# Aggregates columms 'date' and 'time' to produce a column for datetime objects
# TODO -- SLOW OPERATION - find a better way to parse date and time to datetime object
cases_df["datetime"] = pd.to_datetime(cases_df.fecha + ' ' + cases_df.hora_llamada)
# Sorts all cases by their datetimes (REQUIRED BY SIMULATOR)
cases_df = cases_df.sort_values('datetime', ascending=True)
cases_df = cases_df[:100]
# Generate list of models from dataframe
cases = []
for index, row in cases_df.iterrows():
case = RandomCase(id=row["id"],
date_recorded=row["datetime"],
incident_location=Point(row["latitud"], row["longitud"]),
event_generator=self.event_generator,
priority=row["vprioridad"])
cases.append(case)
def read_cases(self, filename):
# Read cases from CSV into a pandas dataframe
case_headers = [
"Fecha", "No_unidad", "Dia_semana", "Latitud salida",
"Longitud salida", "Hora_salida", "Latitud llegada incidente",
"Longitud llegada incidente", "Hora_llegada incidente",
"Latitud salida al hospital", "Longitud salida al hospital",
"Hora_salida al hospital", "Latitud arribo al hospital",
"Longitud arribo al hospital", "Hora_arribo al hospital",
"Tiempo_base_incidente", "Tiempo dando atencion pre-hospitalaria ",
"Tiempo_incidente_hospital", "tiempo_total base-hospital"
]
cases_df = parse_headered_csv(filename, case_headers)
# Datetime format
dt_format = "%m/%d/%Y %H:%M:%S"
# Generate list of models from dataframe
cases = []
for index, row in cases_df.iterrows():
### Datetimes
# Create strings for dates; used to convert to datetimes
base_depart_dt_str = row["Fecha"] + " " + row["Hora_salida"]
incident_arrival_dt_str = row["Fecha"] + " " + row[
"Hora_llegada incidente"]
incident_depart_dt_str = row["Fecha"] + " " + row[
"Hora_salida al hospital"]
hospital_arrival_dt_str = row["Fecha"] + " " + row[
"Hora_arribo al hospital"]
# Generate datetimes
base_depart_dt = datetime.strptime(base_depart_dt_str, dt_format)
incident_arrival_dt = datetime.strptime(incident_arrival_dt_str,
dt_format)
incident_depart_dt = datetime.strptime(incident_depart_dt_str,
dt_format)
hospital_arrival_dt = datetime.strptime(hospital_arrival_dt_str,
dt_format)
### Events
events = []
# Event capturing ambulance travelling from base to incident
base_to_incident_event = Event(
destination=Point(latitude=row["Latitud llegada incidente"],
longitude=row["Longitud llegada incidente"]),
event_type=EventType.TO_INCIDENT,
duration=incident_arrival_dt - base_depart_dt)
events.append(base_to_incident_event)
if row["Latitud salida al hospital"] != 0:
# Event capturing patient pickup
# TODO -- When there is no patient to bring to hospital, no information provided on incident departure
at_incident_event = Event(destination=Point(
latitude=row["Latitud llegada incidente"],
longitude=row["Longitud llegada incidente"]),
event_type=EventType.AT_INCIDENT,
duration=incident_depart_dt -
incident_arrival_dt)
events.append(at_incident_event)
# Event capturing ambulance travelling from incident to hospital
incident_to_hospital_event = Event(
destination=Point(
latitude=row["Latitud arribo al hospital"],
longitude=row['Longitud arribo al hospital']),
event_type=EventType.TO_HOSPITAL,
duration=hospital_arrival_dt - incident_depart_dt)
events.append(incident_to_hospital_event)
# Event capturing ambulance travelling from hospital to base
hospital_to_base_event = Event(destination=Point(
latitude=row["Latitud salida"],
longitude=row["Longitud salida"]),
event_type=EventType.TO_BASE)
events.append(hospital_to_base_event)
else:
# Event capturing ambulance travelling from incident to base
incident_to_base_event = Event(destination=Point(
latitude=row["Latitud salida"],
longitude=row["Longitud salida"]),
event_type=EventType.TO_BASE)
events.append(incident_to_base_event)
# Generate a case from events
case = DefinedCase(
id=index,
date_recorded=base_depart_dt,
incident_location=Point(
latitude=row["Latitud llegada incidente"],
longitude=row["Longitud llegada incidente"]),
events=events)
cases.append(case)
# Sort all cases by the departure time from base
cases.sort(key=lambda case: case.date_recorded)
return cases