def get_data(sensor):
sensor = sensor.replace("%20", " ")
# CHeck it is a valid request
logger.debug(f"API request STARTING for sensor {sensor}")
if sensor not in DataCollector.get_sensors():
raise NameError('Invaid sensor name')
data_collector = DataCollector()
unit = data_collector.sensors[sensor].get_unit()
logger.debug(f"Unit fetched and is {unit}")
df = data_collector.get_data(sensor)
graph_json = build_graph(df, sensor, unit)
logger.debug(f"API request SUCCESSFUL for sensor {sensor}")
logger.debug(f"GRAPH JSON looks like {graph_json}")
return graph_json
class TestCollectData(unittest.TestCase):
test_bucket = 'example-bucket-whitew1994'
def setUp(self) -> None:
self.s3 = boto3.resource('s3')
self.bucket = self.s3.Bucket(self.test_bucket)
self.data_collector = DataCollector()
# Delete contents of test bucket prior to testing
def empty_bucket(self):
bucket_versioning = self.s3.BucketVersioning(self.test_bucket)
if bucket_versioning.status == 'Enabled':
self.bucket.object_versions.delete()
else:
self.bucket.objects.all().delete()
def test_collect_data(self):
self.empty_bucket()
rain_rate = random.randint(1, 100)
self.data_collector.collect_data(rain_rate, 100)
last_added = self.data_collector.df.iloc[-1:]
self.assertGreater(len(self.data_collector.df.index), 0)
print(last_added)
self.assertEqual(last_added['Rain Rate'].iloc[0], rain_rate)
self.assertIn('Shower Volume', list(last_added.columns))
self.assertIsNotNone(last_added['Shower Volume'].iloc[0])
def test_save_current_data(self):
self.empty_bucket()
sensors_rand_walk = {field: 50 for field in self.data_collector.get_sensors()}
for day in range(-5, 0):
test_data = {field: [] for field in self.data_collector.get_sensors()}
test_data['Timestamp'] = []
for minutes in range((24*60) // 10):
test_data['Timestamp'].append(pd.Timestamp.now() + pd.Timedelta(day, unit='day') \
+ pd.Timedelta(minutes * 10, unit='minutes'))
for sensor in sensors_rand_walk.keys():
sensors_rand_walk[sensor] = sensors_rand_walk[sensor] + random.randint(-5, 5)
sensors_rand_walk[sensor] = max(sensors_rand_walk[sensor], 0)
test_data[sensor].append(sensors_rand_walk[sensor])
self.data_collector.df = pd.DataFrame(test_data)
previous_day = pd.Timestamp.now() + pd.Timedelta(days=day)
self.data_collector.save_current_data(previous_day=previous_day)
bucket_file_name = 's3://' + self.test_bucket + '/year=' + str(previous_day.year) + '/month=' + str(previous_day.month) + '/' + \
str(previous_day.day) + '.csv'
test_df = pd.read_csv(bucket_file_name)
self.data_collector.df = pd.DataFrame(test_data)
test_df_dict = test_df.to_dict()
dc_dict = self.data_collector.df.to_dict()
del test_df_dict['Timestamp'], dc_dict['Timestamp']
self.assertEqual(test_df_dict, dc_dict)
def test_get_data(self):
sensor = "Temperature"
df = self.data_collector.get_data(sensor)
self.assertIsNotNone(df['Timestamp'].values)
self.assertIsNotNone(df['Sensor'].values)
self.assertGreater(len(df['Timestamp']), 1)
self.assertGreater(len(df['Sensor']), 1)
