def insert_to_postgres(sensor_list: List, stored_sensor_ids: List) -> None:
"""Insert sensor list details to local Postgres DB"""
with UseDatabase(aurora_creds) as cursor:
sql_create = """CREATE TABLE IF NOT EXISTS all_sensor_info(
sensor_id varchar(8),
days_active integer,
start_date timestamp,
end_date timestamp,
latitude numeric,
longitude numeric,
address varchar(140),
owner_id varchar(36));"""
cursor.execute(sql_create)
for i in sensor_list:
if i[0] in stored_sensor_ids:
cursor.execute("""UPDATE all_sensor_info
SET days_active = %s,
start_date = %s,
end_date = %s
WHERE sensor_id = %s""",
(i[1], i[2], i[3], i[0]))
# VALUES(%s, %s, %s, %s, %s, %s, %s, %s)""",
# (i[0], i[1], i[2], i[3], i[4], i[5], i[6], i[7]))
else:
cursor.execute("""INSERT INTO all_sensor_info
VALUES(%s, %s, %s, %s, %s, %s, %s, %s)""",
(i[0], i[1], i[2], i[3], i[4], i[5], i[6], i[7]))
def faulty_stats() -> 'JSON':
"""Return most recent faulty GROW data"""
with UseDatabase(aurora_creds) as cursor:
owner = request.args.get('owner_id')
sql_faulty = sql.SQL("""SELECT sensor_id
FROM all_sensor_info
WHERE sensor_id IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies
WHERE days_since_anomaly < 2)
AND sensor_id IN
(SELECT sensor_id
FROM all_sensor_info
WHERE owner_id = {});""").format(
sql.Literal(owner))
cursor.execute(sql_faulty)
faulty_sensors = [x[0] for x in cursor.fetchall()]
faulty_data = []
for i in faulty_sensors:
sql_select = sql.SQL("""SELECT sensor_id,
battery_level,
soil_moisture,
light,
air_temperature,
datetime
FROM {}
WHERE datetime = (SELECT MAX(datetime)
FROM {})""").format(
sql.Identifier(f'grow_data_{i}'),
sql.Identifier(f'grow_data_{i}'))
cursor.execute(sql_select)
results = cursor.fetchall()
faulty_data.append(results)
return jsonify(faulty_data)
def filter_for_new_sensor_updates(new_sensor_list: List) -> List:
"""Compare sensor info to Postgres table to see if the individual sensor
info is already in the table. If not present, keep the sensor info for further
processing and eventual insert into Postgres table"""
with UseDatabase(aurora_creds) as cursor:
sql_check = """SELECT EXISTS (SELECT 1 FROM pg_tables
WHERE tablename = 'all_sensor_info');"""
cursor.execute(sql_check)
response = cursor.fetchone()
if response[0] == True:
sql_collect = """SELECT row_to_json(all_sensor_info)
FROM all_sensor_info;"""
cursor.execute(sql_collect)
all_stored_sensors = cursor.fetchall()
else:
all_stored_sensors = []
stored_sensor_ids = []
for i in all_stored_sensors:
stored_sensor_ids.append(i[0]['sensor_id'])
new_sensor_info = []
for i in new_sensor_list:
if i[0] not in stored_sensor_ids:
new_sensor_info.append(i)
else:
for stored_sensor in all_stored_sensors:
if stored_sensor[0]['sensor_id'] == i[0] and \
stored_sensor[0]['end_date'].replace('-','').replace(':','') != i[3]:
new_sensor_info.append(i)
return new_sensor_info, stored_sensor_ids
def insert_anomalies(soil_anomalies: List, light_anomalies: List,
air_anomalies: List, table_name: str, aurora_creds: dict,
analyse_datetime: str) -> None:
"""Insert anomalous datetimes into AWS Aurora grow_anomalies table"""
with UseDatabase(aurora_creds) as cursor:
for anom in soil_anomalies:
sql_insert = sql.SQL("""INSERT INTO grow_anomalies
(grow_table, soil_date, last_analysed)
VALUES({},{},{})""") \
.format(sql.Literal(table_name),
sql.Literal(str(anom[1])),
sql.Literal(analyse_datetime))
cursor.execute(sql_insert)
for anom in light_anomalies:
sql_insert = sql.SQL("""INSERT INTO grow_anomalies
(grow_table, light_date, last_analysed)
VALUES({},{},{})""") \
.format(sql.Literal(table_name),
sql.Literal(str(anom[1])),
sql.Literal(analyse_datetime))
cursor.execute(sql_insert)
for anom in air_anomalies:
sql_insert = sql.SQL("""INSERT INTO grow_anomalies
(grow_table, air_date, last_analysed)
VALUES({},{},{})""") \
.format(sql.Literal(table_name),
sql.Literal(str(anom[1])),
sql.Literal(analyse_datetime))
cursor.execute(sql_insert)
def recovered_stats() -> 'JSON':
with UseDatabase(aurora_creds) as cursor:
# healthy_sensors = request.args.get('healthy_sensors')
# print(healthy_sensors)
owner = request.args.get('owner_id')
sql_recovered = sql.SQL("""SELECT sensor_id
FROM all_sensor_info
WHERE sensor_id IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies
WHERE days_since_anomaly >= 2)
AND sensor_id IN
(SELECT sensor_id
FROM all_sensor_info
WHERE owner_id = {});""").format(
sql.Literal(owner))
cursor.execute(sql_recovered)
recovered_sensors = [x[0] for x in cursor.fetchall()]
recovered_data = []
for i in recovered_sensors:
sql_select = sql.SQL("""SELECT sensor_id,
battery_level,
soil_moisture,
light,
air_temperature,
datetime
FROM {}
WHERE datetime = (SELECT MAX(datetime)
FROM {})""").format(
sql.Identifier(f'grow_data_{i}'),
sql.Identifier(f'grow_data_{i}'))
cursor.execute(sql_select)
results = cursor.fetchall()
recovered_data.append(results)
return jsonify(recovered_data)
def main():
"""Connects to Aurora Database, calculates the delta between
most recent GROW anomaly and most recent GROW recorded date.
Inserts the delta as 'days_since_anomaly' column in
'grow_anomalies' Aurora table.
"""
aurora_secret = get_aurora_secret()
aurora_creds = {
'host': aurora_secret['host'],
'port': aurora_secret['port'],
'dbname': aurora_secret['engine'],
'user': aurora_secret['username'],
'password': aurora_secret['password']
}
with UseDatabase(aurora_creds) as cursor:
sql_anomaly = """SELECT grow_table,
MAX(GREATEST(soil_date, light_date, air_date))
FROM grow_anomalies
GROUP BY grow_table;"""
cursor.execute(sql_anomaly)
anomaly_dates = cursor.fetchall()
all_deltas = []
for i in anomaly_dates:
sql_select = sql.SQL("""SELECT MAX(datetime)
FROM {}""").format(sql.Identifier(i[0]))
cursor.execute(sql_select)
result_datetime = cursor.fetchone()
all_deltas.append([i[0], result_datetime[0] - i[1]])
for i in all_deltas:
sql_update = sql.SQL("""UPDATE public.grow_anomalies
SET days_since_anomaly = {}
WHERE grow_table = {}""").format(
sql.Literal(i[1].days), sql.Literal(i[0]))
cursor.execute(sql_update)
def all_grow_map() -> 'html':
"""Renders GROW map page with four statistics from SQL queries"""
with UseDatabase(aurora_creds) as cursor:
sql_select_sensors = """select count(*) from all_sensor_info ;"""
cursor.execute(sql_select_sensors)
total_sensors = cursor.fetchone()[0]
sql_select_healthy = """SELECT count(*)
FROM all_sensor_info
WHERE sensor_id NOT IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies);"""
cursor.execute(sql_select_healthy)
healthy_sensors = cursor.fetchone()[0]
sql_select_recovered = """SELECT count(*)
FROM all_sensor_info
WHERE sensor_id IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies
WHERE days_since_anomaly >= 2);"""
cursor.execute(sql_select_recovered)
recovered_sensors = cursor.fetchone()[0]
sql_select_faulty = """SELECT count(*)
FROM all_sensor_info
WHERE sensor_id IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies
WHERE days_since_anomaly < 2);"""
cursor.execute(sql_select_faulty)
faulty_sensors = cursor.fetchone()[0]
return render_template('new_grow_map.html',
healthy_sensors=healthy_sensors,
recovered_sensors=recovered_sensors,
faulty_sensors=faulty_sensors,
number_sensors=total_sensors)
def match_wow_site(sensor_id: str) -> str:
"""Find closest WOW site to select grow sensor"""
with UseDatabase(db_creds) as cursor:
cursor.execute("""SELECT site_id
FROM grow_to_wow_mapping_0625
WHERE sensor_id = %s;""", (sensor_id,))
site_id = cursor.fetchone() # ('c1d3cfdd-4829-e911-9462-0003ff59610a',)
# site_id = cursor.fetchall() # [('c1d3cfdd-4829-e911-9462-0003ff59610a',)]
return site_id
def match_wow_site(sensor_id: str) -> str:
"""Find closest WOW site to select grow sensor"""
with UseDatabase(aurora_creds) as cursor:
cursor.execute(
"""SELECT site_id, distance
FROM grow_to_wow_mapping
WHERE sensor_id = %s;""", (sensor_id, ))
site_id = cursor.fetchone()
return site_id
def fetch_all_json() -> 'JSON':
with UseDatabase(aurora_creds) as cursor:
resp_list = []
SQL = """SELECT row_to_json(all_sensor_info)
FROM all_sensor_info;"""
cursor.execute(SQL)
response = cursor.fetchall()
for i in response:
resp_list.append(i[0])
return jsonify(resp_list)
def autocomplete():
with UseDatabase(aurora_creds) as cursor:
search = request.args.get('q')
search_str = f'%{search}%'
sql_select = sql.SQL("""SELECT address
FROM all_sensor_info
WHERE address LIKE {}""").format(
sql.Literal(search_str))
cursor.execute(sql_select)
results = [i[0] for i in cursor.fetchall()]
return jsonify(matching_results=results)
def grow_by_address() -> 'JSON':
with UseDatabase(aurora_creds) as cursor:
address = request.args.get('address')
resp_list = []
sql_select = sql.SQL("""SELECT row_to_json(all_sensor_info)
FROM all_sensor_info
WHERE address = {};""").format(sql.Literal(address))
cursor.execute(sql_select)
response = cursor.fetchall()
for i in response:
resp_list.append(i[0])
return jsonify(resp_list)
def grow_by_owner() -> 'JSON':
"""Fetch all sensor info by owner"""
with UseDatabase(aurora_creds) as cursor:
owner = request.args.get('owner_id')
resp_list = []
sql_select = sql.SQL("""SELECT row_to_json(all_sensor_info)
FROM all_sensor_info
WHERE owner_id = {};""").format(sql.Literal(owner))
cursor.execute(sql_select)
response = cursor.fetchall()
for i in response:
resp_list.append(i[0])
return jsonify(resp_list)
def owner_stats() -> 'DataTable':
"""Return count of healthy, recovered, & faulty GROW sensors
per GROW owner.
"""
with UseDatabase(aurora_creds) as cursor:
owner = request.args.get('owner_id')
sql_healthy = sql.SQL("""SELECT sensor_id
FROM all_sensor_info
WHERE sensor_id NOT IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies)
AND sensor_id IN
(SELECT sensor_id
FROM all_sensor_info
WHERE owner_id = {});""").format(
sql.Literal(owner))
cursor.execute(sql_healthy)
healthy_sensors = [x[0] for x in cursor.fetchall()]
sql_recovered = sql.SQL("""SELECT sensor_id
FROM all_sensor_info
WHERE sensor_id IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies
WHERE days_since_anomaly >= 2)
AND sensor_id IN
(SELECT sensor_id
FROM all_sensor_info
WHERE owner_id = {});""").format(
sql.Literal(owner))
cursor.execute(sql_recovered)
recovered_sensors = [x[0] for x in cursor.fetchall()]
sql_faulty = sql.SQL("""SELECT sensor_id
FROM all_sensor_info
WHERE sensor_id IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies
WHERE days_since_anomaly < 2)
AND sensor_id IN
(SELECT sensor_id
FROM all_sensor_info
WHERE owner_id = {});""").format(
sql.Literal(owner))
cursor.execute(sql_faulty)
faulty_sensors = [x[0] for x in cursor.fetchall()]
sensor_dict = dict()
sensor_dict['owner_id'] = owner
sensor_dict['healthy'] = healthy_sensors
sensor_dict['recovered'] = recovered_sensors
sensor_dict['faulty'] = faulty_sensors
print(jsonify(sensor_dict))
return jsonify(sensor_dict)
def check_most_recent_grow_data(aurora_creds: dict, sensor_id: str,
start_date: str,
end_date: str) -> Tuple[str, List]:
"""Check to see if the most recent sensor reading is already stored in AWS Aurora.
If it is not already stored, calculate the delta time interval between last
stored reading and last recorded reading, and calculate 10 day intervals
that add up to the delta interval. This is done because the GROW API
only allows query ranges to be 10 days maximum.
"""
with UseDatabase(aurora_creds) as cursor:
table_name = f"grow_data_{sensor_id}"
try:
# Get the most recent sensor recording datetime
cursor.execute(
sql.SQL("SELECT MAX(datetime) FROM {}").format(
sql.Identifier(table_name)))
stored_end_date = cursor.fetchone()[0]
if stored_end_date == None:
stored_end_date = datetime.datetime.strptime(
start_date, '%Y%m%d%H%M%S')
except psycopg2.ProgrammingError:
# If table does not exist
stored_end_date = datetime.datetime.strptime(
start_date, '%Y%m%d%H%M%S')
end_dt = datetime.datetime.strptime(end_date, '%Y%m%d%H%M%S')
delta = end_dt - stored_end_date
print('delta', delta, 'stored_end_date', stored_end_date, 'sensor',
sensor_id)
if delta == datetime.timedelta(0):
# If the stored end date and most recent end date are the same, no updates need to be made
sensor_start_end_intervals = []
else:
sensor_start_end_intervals = []
start = stored_end_date
while delta > datetime.timedelta(0):
if delta < datetime.timedelta(days=9):
interval = []
end = end_dt
interval.append(start.strftime('%Y%m%d%H%M%S'))
interval.append(end.strftime('%Y%m%d%H%M%S'))
sensor_start_end_intervals.append(interval)
break
else:
interval = []
end = start + datetime.timedelta(days=9)
interval.append(start.strftime('%Y%m%d%H%M%S'))
interval.append(end.strftime('%Y%m%d%H%M%S'))
sensor_start_end_intervals.append(interval)
delta -= datetime.timedelta(days=9)
start = end
return sensor_id, sensor_start_end_intervals
def check_faulty_grow() -> List:
"""Fetch most recent anomaly date for specific GROW sensor"""
sensor_id = request.args.get('sensor_id', None)
grow_table = f'grow_data_{sensor_id}'
with UseDatabase(aurora_creds) as cursor:
sql_select = sql.SQL("""SELECT days_since_anomaly,
MAX(GREATEST(soil_date, light_date, air_date))
FROM grow_anomalies
WHERE grow_table = {}
GROUP BY days_since_anomaly;""").format(
sql.Literal(grow_table))
cursor.execute(sql_select)
response = cursor.fetchall()
return jsonify(response)
def fetch_all_healthy_json() -> 'JSON':
"""Fetch all healthy GROW sensor info as JSON"""
with UseDatabase(aurora_creds) as cursor:
resp_list = []
SQL = """SELECT row_to_json(all_sensor_info)
FROM all_sensor_info
WHERE sensor_id NOT IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies);"""
cursor.execute(SQL)
response = cursor.fetchall()
for i in response:
resp_list.append(i[0])
return jsonify(resp_list)
def grab_data(sensor_id: str, start_end_interval: List) -> List:
with UseDatabase(aurora_creds) as cursor:
table_name = f'grow_data_{sensor_id}'
cursor.execute(
sql.SQL("""SELECT soil_moisture, light, air_temperature, datetime
FROM {}
WHERE sensor_id = {}
AND datetime >= {}
AND datetime <= {}""").format(
sql.Identifier(table_name), sql.Literal(sensor_id),
sql.Literal(start_end_interval[0]),
sql.Literal(start_end_interval[1])))
response = cursor.fetchall()
return response
def entry() -> 'html':
"""Renders Entry page with two statistics from SQL queries"""
with UseDatabase(aurora_creds) as cursor:
sql_select_sensors = """select count(*) from all_sensor_info ;"""
cursor.execute(sql_select_sensors)
sensors = cursor.fetchone()[0]
sql_select_owners = """SELECT COUNT(DISTINCT(owner_id))
FROM all_sensor_info;"""
cursor.execute(sql_select_owners)
owners = cursor.fetchone()[0]
return render_template('login.html',
number_sensors=sensors,
number_owners=owners)
def autocomplete():
"""Accepts HTML input and searces text in SQL statement,
returns all results matching the HTML input text.
"""
with UseDatabase(aurora_creds) as cursor:
search = request.args.get('q')
search_str = f'%{search}%'
sql_select = sql.SQL("""SELECT address
FROM all_sensor_info
WHERE address LIKE {}""").format(
sql.Literal(search_str))
cursor.execute(sql_select)
results = [i[0] for i in cursor.fetchall()]
return jsonify(matching_results=results)
def get_all_grow_tables(aurora_creds: dict) -> np.ndarray:
"""Return all GROW table names from AWS Aurora DB, subtract those
that have already been stored in grow_anomalies table. Result is
array of GROW tables that have not been analysed yet for anomalies.
"""
# all_tables_array = pd.read_sql(sql_all, conn).values
# anom_tables_array = pd.read_sql(sql_anom, conn).values
with UseDatabase(aurora_creds) as cursor:
# Fetch all grow data table names
sql_all = """SELECT table_name
FROM information_schema.tables
WHERE table_name
LIKE 'grow_data_%%';"""
cursor.execute(sql_all)
all_tables_array = cursor.fetchall()
grow_tables = []
for i in all_tables_array:
# Fetch most recent observation date recorded per grow table
sql_grow = sql.SQL(
"""SELECT CONCAT('grow_data_', sensor_id), datetime
FROM {}
WHERE datetime = (SELECT MAX(datetime) FROM {})
""").format(sql.Identifier(i[0]),
sql.Identifier(i[0]))
cursor.execute(sql_grow)
result_array = cursor.fetchall()
grow_tables.append(result_array)
# Fetch most recently analysed grow table & date
sql_anom = """SELECT DISTINCT(grow_table), last_analysed
FROM public.grow_anomalies
WHERE last_analysed =
(SELECT MAX(last_analysed) FROM public.grow_anomalies);"""
cursor.execute(sql_anom)
anom_tables_array = cursor.fetchall()
# Find grow tables that have not been analysed yet, and
# grow tables that have new data that needs to be analysed
tables_to_analyse = []
for i in grow_tables:
# If grow table has not been analysed yet
if i[0][0][0] not in [x[0] for x in anom_tables_array]:
tables_to_analyse.append(i[0][0][0])
continue
for tab in anom_tables_array:
# If grow table has more recent data than that already analysed
if i[0][0][0] == tab[0] and i[0][0][1] > tab[1]:
tables_to_analyse.append(i[0][0][0])
return tables_to_analyse
def login() -> 'html':
with UseDatabase(aurora_creds) as cursor:
sql_select_sensors = """SELECT COUNT(tablename)
FROM pg_tables
WHERE tablename LIKE ('grow_data_%');"""
cursor.execute(sql_select_sensors)
sensors = cursor.fetchone()[0]
sql_select_owners = """SELECT COUNT(DISTINCT(owner_id))
FROM all_sensor_info;"""
cursor.execute(sql_select_owners)
owners = cursor.fetchone()[0]
return render_template('login.html',
number_sensors=sensors,
number_owners=owners)
def insert_df_to_aurora(sensor_id: str) -> None:
"""Create table in AWS Aurora and insert GROW data"""
with UseDatabase(aurora_creds) as cursor:
table_name = f"grow_data_{sensor_id}"
sql_create = sql.SQL("""CREATE TABLE IF NOT EXISTS {}(
sensor_id varchar(8),
datetime timestamp,
soil_moisture numeric,
light numeric,
air_temperature numeric
)""").format(sql.Identifier(table_name))
cursor.execute(sql_create)
with open(f'temp_csvs/grow_data_{sensor_id}.csv') as csv:
next(csv)
cursor.copy_from(csv, table_name, columns=('datetime','soil_moisture','light','air_temperature','sensor_id'), sep=',')
def insert_to_db(aurora_creds: dict, mappings_and_distance: List) -> None:
"""Insert the GROW/WOW sensor/site mappings to AWS Aurora DB"""
with UseDatabase(aurora_creds) as cursor:
sql_create = """CREATE TABLE IF NOT EXISTS grow_to_wow_mapping(
sensor_id varchar(8),
grow_lat numeric,
grow_lon numeric,
site_id varchar(36),
wow_lat numeric,
wow_lon numeric,
distance numeric);"""
cursor.execute(sql_create)
for i in mappings_and_distance:
cursor.execute("""INSERT INTO grow_to_wow_mapping
VALUES(%s, %s, %s, %s, %s, %s, %s)""",
(i[0], i[1], i[2], i[3][0], i[3][1], i[3][2], i[4]))
def grab_data(sensor_id: str, variable: str) -> List:
with UseDatabase(aurora_creds) as cursor:
table_name = f'grow_data_{sensor_id}'
cursor.execute(
sql.SQL("""SELECT datetime, {}
FROM {}
WHERE sensor_id = {}""").format(
sql.Identifier(variable), sql.Identifier(table_name),
sql.Literal(sensor_id)))
response = cursor.fetchall()
dataframe = pd.DataFrame({
'date': [x[0] for x in response],
'value': [float(x[1]) for x in response]
})
dataframe.set_index('date', inplace=True)
data_1d = dataframe.iloc[:, 0].values
return data_1d
def grow_sensors_to_insert(grow_current_sensors: List) -> List:
with UseDatabase(aurora_creds) as cursor:
sql_table_check = """SELECT EXISTS (SELECT 1 FROM pg_tables
WHERE tablename = 'grow_to_wow_mapping');"""
cursor.execute(sql_table_check)
response = cursor.fetchone()
if response[0] == True:
sql_collect = """SELECT sensor_id
FROM grow_to_wow_mapping;"""
cursor.execute(sql_collect)
all_stored_sensors = cursor.fetchall()
else:
all_stored_sensors = []
sensors_to_insert = []
for i in grow_current_sensors:
if i[0] not in [x[0] for x in all_stored_sensors]:
sensors_to_insert.append(i)
return sensors_to_insert
def grab_data(sensor_id: str, variable: str) -> List:
with UseDatabase(aurora_creds) as cursor:
table_name = f'grow_data_{sensor_id}'
cursor.execute(
sql.SQL("""SELECT {} FROM {}
WHERE sensor_id = {}""").format(
sql.Identifier(variable), sql.Identifier(table_name),
sql.Literal(sensor_id)))
response = cursor.fetchall()
cursor.execute(
sql.SQL("""SELECT MIN(datetime),
MAX(datetime)
FROM {}
WHERE sensor_id = {}""").format(
sql.Identifier(table_name), sql.Literal(sensor_id)))
dates = cursor.fetchall()
start_date = dates[0][0].strftime('%Y-%m-%d %H:%M:%S')
end_date = dates[0][1].strftime('%Y-%m-%d %H:%M:%S')
return response, start_date, end_date, variable
def insert_anomalies(soil_anomalies: List, light_anomalies: List,
air_anomalies: List, table_name: str,
aurora_creds: dict, analyse_datetime: str) -> None:
"""Insert anomalous datetimes into AWS Aurora grow_anomalies table"""
with UseDatabase(aurora_creds) as cursor:
# If grow sensor table is already in grow_anomalies, delete the rows
# so fresh data can be inserted in its place
sql_check = sql.SQL("""SELECT * FROM grow_anomalies
WHERE grow_table = {}
LIMIT 1;""").format(sql.Literal(table_name))
cursor.execute(sql_check)
results = cursor.fetchall()
if results:
sql_delete = sql.SQL("""DELETE FROM grow_anomalies
WHERE grow_table = {}""").format(sql.Literal(table_name))
cursor.execute(sql_delete)
for anom in soil_anomalies:
sql_insert = sql.SQL("""INSERT INTO grow_anomalies
(grow_table, soil_date, last_analysed)
VALUES({},{},{})""") \
.format(sql.Literal(table_name),
sql.Literal(str(anom[1])),
sql.Literal(analyse_datetime))
cursor.execute(sql_insert)
for anom in light_anomalies:
sql_insert = sql.SQL("""INSERT INTO grow_anomalies
(grow_table, light_date, last_analysed)
VALUES({},{},{})""") \
.format(sql.Literal(table_name),
sql.Literal(str(anom[1])),
sql.Literal(analyse_datetime))
cursor.execute(sql_insert)
for anom in air_anomalies:
sql_insert = sql.SQL("""INSERT INTO grow_anomalies
(grow_table, air_date, last_analysed)
VALUES({},{},{})""") \
.format(sql.Literal(table_name),
sql.Literal(str(anom[1])),
sql.Literal(analyse_datetime))
cursor.execute(sql_insert)
def all_grow_map() -> 'html':
with UseDatabase(aurora_creds) as cursor:
# sql_select_sensors = """SELECT COUNT(tablename)
# FROM pg_tables
# WHERE tablename LIKE ('grow_data_%');"""
sql_select_sensors = """select count(*) from all_sensor_info ;"""
cursor.execute(sql_select_sensors)
total_sensors = cursor.fetchone()[0]
sql_select_healthy = """SELECT count(*)
FROM all_sensor_info
WHERE sensor_id NOT IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies);"""
cursor.execute(sql_select_healthy)
healthy_sensors = cursor.fetchone()[0]
sql_select_recovered = """SELECT count(*)
FROM all_sensor_info
WHERE sensor_id IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies
WHERE days_since_anomaly >= 2);"""
cursor.execute(sql_select_recovered)
recovered_sensors = cursor.fetchone()[0]
sql_select_faulty = """SELECT count(*)
FROM all_sensor_info
WHERE sensor_id IN
(SELECT SUBSTRING(grow_table, 11, 8)
FROM grow_anomalies
WHERE days_since_anomaly < 2);"""
cursor.execute(sql_select_faulty)
faulty_sensors = cursor.fetchone()[0]
return render_template('new_grow_map.html',
healthy_sensors=healthy_sensors,
recovered_sensors=recovered_sensors,
faulty_sensors=faulty_sensors,
number_sensors=total_sensors)
for a in sensor_readings:
if a[0] == json_object['Data'][0]['LocationCode'] and a[1] == edit_datetime:
a.append('temperature')
a.append(reading['Value'])
elif i['VariableCode'].endswith('temperature'):
indiv_reading = []
datetime = reading['DateTime']
edit_datetime = datetime[:8] + 'T' + datetime[8:]
indiv_reading.append(json_object['Data'][0]['LocationCode'])
indiv_reading.append(edit_datetime)
indiv_reading.append('air_temperature')
indiv_reading.append(reading['Value'])
sensor_readings.append(indiv_reading)
with UseDatabase(db_creds) as cursor:
for i in sensor_readings:
cursor.execute("""INSERT INTO combined_variables
VALUES(%s, %s, %s, %s, %s)""",
(i[0], i[1], i[3], i[5], i[7]))
# extracts three variables, but they exist in a list separately
# sensor_id | datetime | soil_moisture | light | air_temp
# -----------+---------------------+---------------+-------+----------
# 02krq5q5 | 2018-11-05 23:51:25 | 39.01 | |
