def on_http_reload_enlighten(request):
gcp_logger = init_gcp_logger()
request_args = request.args
gcp_logger.info('on_http_reload_enlighten(), args=%s', request_args)
year = request_args[
'year'] if request_args and 'year' in request_args else datetime.now(
).year
storage_client = init_storage_client()
bucket = storage_client.get_bucket(GCP_STORAGE_BUCKET_ID)
gcp_logger = init_gcp_logger()
already_fetched = [
x.get('name') for x in get_already_fetched(
storage_client, bucket, ENLIGHTEN_STORAGE_PATH_PREFIX, 1024)
if f"/{str(year)}/" in x.get('name')
]
dfs = [
_blob_to_df(bucket, gcp_logger, blob_name)
for blob_name in already_fetched
]
df_all_dates = pd.concat(dfs)
merge_df_to_db(NMI, df_all_dates, 'sites', gcp_logger)
return ('', 200)
def on_http_reload_nem12(request):
gcp_logger = init_gcp_logger()
gcp_logger.info('on_http_reload_nem12(), args=%s', request.args)
storage_client = init_storage_client()
blob_name = f"nem12/merged/nem12_{NMI}.csv"
bucket = storage_client.get_bucket(GCP_STORAGE_BUCKET_ID)
gcp_logger = init_gcp_logger()
handle_nem12_blob_merged(None, None, storage_client, bucket, blob_name,
'sites', gcp_logger)
return ('', 200)
def on_http_reload_lems(request):
gcp_logger = init_gcp_logger()
request_args = request.args
gcp_logger.info('on_http_reload_lems(), args=%s', request_args)
year = request_args[
'year'] if request_args and 'year' in request_args else datetime.now(
).year
storage_client = init_storage_client()
bucket = storage_client.get_bucket(GCP_STORAGE_BUCKET_ID)
already_fetched = [
x.get('name') for x in get_already_fetched(
storage_client, bucket, LEMS_STORAGE_PATH_PREFIX, 1024)
]
file_name_mask = f"lems_data_{str(year)}"
to_reload = [af for af in already_fetched if file_name_mask in af]
dfs = [
_blob_to_df(bucket, gcp_logger, blob_name) for blob_name in to_reload
]
df_all_dates = pd.concat(dfs)
df_unique = df_all_dates.loc[~df_all_dates.index.duplicated(keep='first')]
merge_df_to_db(NMI, df_unique, 'sites', gcp_logger)
return ('', 200)
def on_http_fetch_daily_temperatures(request):
gcp_logger = init_gcp_logger()
gcp_logger.info('on_http_get_enlighten_data(), args=%s', request.args)
daily_temperatures_to_db('sites')
return ('', 200)
def on_http_fetch_dailies(request):
gcp_logger = init_gcp_logger()
gcp_logger.info('on_http_fetch_dailies(), args=%s', request.args)
storage_client = init_storage_client()
fdb = init_firestore_client()
columns = [
'meter_consumptions_kwh', 'meter_generations_kwh',
'solar_generations_kwh', 'solar_mean_powrs_kw',
'solar_devices_reportings', 'capacities_kw', 'charge_quantities_kwh',
'deterioration_states_pct', 'discharge_quantities_kwh',
'power_at_charges_kw', 'residual_capacities_pct',
'total_charge_quantities_kwh', 'total_discharge_quantities_kwh',
'min_temperature_c', 'max_temperature_c'
]
df_all_dailies = pd.DataFrame(index=pd.DatetimeIndex([]), columns=columns)
missing_values = np.zeros(48)
for doc in fdb.collection(f"sites/{NMI}/dailies").order_by(
'interval_date', direction='ASCENDING').stream():
doc_fields = list(doc.to_dict().keys())
if 'interval_date' not in doc_fields:
gcp_logger.info('Missing field interval_date')
continue
gcp_logger.info('Processing interval_date=%s',
doc.get('interval_date'))
doc_dict = {}
for column in columns:
doc_dict[column] = np.array(
doc.get(column)) if column in doc_fields else missing_values
df_all_dailies.loc[doc.get('interval_date')] = doc_dict
pkl_file_name = f"dailies_{NMI}.pkl"
pkl_file_path = f"/tmp/{pkl_file_name}"
df_all_dailies.to_pickle(f"/tmp/{pkl_file_name}")
bucket = storage_client.get_bucket(GCP_STORAGE_BUCKET_ID)
blob = Blob(pkl_file_name, bucket)
with open(pkl_file_path, "rb") as pkl_file:
blob.upload_from_file(pkl_file)
os.remove(pkl_file_path)
return ('', 200)
def on_storage_blob(data, context):
"""Background Cloud Function to be triggered by Cloud Storage.
This generic function logs relevant data when a file is changed.
Args:
data (dict): The Cloud Functions event payload.
context (google.cloud.functions.Context): Metadata of triggering event.
Returns:
None; the output is written to Stackdriver Logging
"""
gcp_logger = init_gcp_logger()
gcp_logger.info('on_http_get_lems_data()')
storage_client = init_storage_client()
event_id = context.event_id
event_type = context.event_type
blob_created = data.get('timeCreated')
blob_updated = data.get('updated')
blob_name = data.get('name')
bucket_name = data.get('bucket')
gcp_logger.info(
'event_id=%s, event_type=%s, bucket=%s, name=%s, metageneration=%s, created=%s, updated=%s',
event_id, event_type, bucket_name, blob_name,
data.get('metageneration'), blob_created, blob_updated)
bucket = storage_client.get_bucket(bucket_name)
if blob_name.startswith(NEM12_STORAGE_PATH_IN):
handle_nem12_blob_in(data, context, storage_client, bucket, blob_name,
gcp_logger)
elif blob_name.startswith(NEM12_STORAGE_PATH_MERGED):
handle_nem12_blob_merged(data, context, storage_client, bucket,
blob_name, 'sites', gcp_logger)
elif blob_name.startswith(ENLIGHTEN_STORAGE_PATH_PREFIX):
handle_enlighten_blob(data, context, storage_client, bucket, blob_name,
'sites', gcp_logger)
elif blob_name.startswith(LEMS_STORAGE_PATH_PREFIX):
handle_lems_blob(data, context, storage_client, bucket, blob_name,
'sites', gcp_logger)
else:
gcp_logger.debug('Skipping storage event event_id=%s, event_type=%s',
context.event_id, context.event_type)
return ('', 200)
def on_http_get_lems_data(request):
"""Responds to any HTTP request.
Args:
request (flask.Request): HTTP request object.
Returns:
The response text or any set of values that can be turned into a
Response object using
`make_response <http://flask.pocoo.org/docs/1.0/api/#flask.Flask.make_response>`.
"""
gcp_logger = init_gcp_logger()
gcp_logger.info('on_http_get_lems_data(), args=%s', request.args)
storage_client = init_storage_client()
bucket = storage_client.get_bucket(GCP_STORAGE_BUCKET_ID)
min_date = datetime.combine(date.fromisoformat(LEMS_DATA_MIN_DATE),
datetime.min.time())
yesterday = datetime.combine(date.today(),
datetime.min.time()) - timedelta(days=1)
already_fetched = get_already_fetched(
storage_client, bucket, LEMS_STORAGE_PATH_PREFIX,
ALREADY_FETCHED_SIZE_THRESHOLD_BYTES)
for as_of_date in idate_range(min_date, yesterday):
blob_name = f"{LEMS_STORAGE_PATH_PREFIX}/{str(as_of_date.year)}/lems_data_{as_of_date.strftime('%Y%m%d')}.csv"
blob_exists = next(
(i for i in already_fetched if (i.get('name') == blob_name)),
None) is not None
if not blob_exists:
gcp_logger.info('blob %s not exists, downloading.', blob_name)
resp = get_lems_data_resp(LEMS_USER, LEMS_PASSWORD,
LEMS_BATTERY_ID, as_of_date)
dfm = pd.DataFrame(resp.json())
new_blob = bucket.blob(blob_name)
new_blob.upload_from_string(dfm.to_csv())
else:
gcp_logger.debug('blob %s already exists, skipping.', blob_name)
return ('', 200)
def on_http_get_enlighten_data(request):
"""Responds to any HTTP request.
Args:
request (flask.Request): HTTP request object.
Returns:
The response text or any set of values that can be turned into a
Response object using
`make_response <http://flask.pocoo.org/docs/1.0/api/#flask.Flask.make_response>`.
"""
gcp_logger = init_gcp_logger()
gcp_logger.info('on_http_get_enlighten_data(), args=%s', request.args)
storage_client = init_storage_client()
bucket = storage_client.get_bucket(GCP_STORAGE_BUCKET_ID)
min_date = datetime.combine(date.fromisoformat(
ENLIGHTEN_DATA_MIN_DATE), datetime.min.time())
yesterday = datetime.combine(
date.today(), datetime.min.time()) - timedelta(days=1)
already_fetched = get_already_fetched(
storage_client, bucket, ENLIGHTEN_STORAGE_PATH_PREFIX, ALREADY_FETCHED_SIZE_THRESHOLD_BYTES)
fetched_counter = 0
for as_of_date in idate_range(min_date, yesterday):
if fetched_counter >= MAX_FETCHED_BATCH_SIZE:
break
blob_name = f"{ENLIGHTEN_STORAGE_PATH_PREFIX}/{str(as_of_date.year)}/enlighten_stats_{as_of_date.strftime('%Y%m%d')}.json"
blob_exists = next((i for i in already_fetched if (
i.get('name') == blob_name)), None) is not None
if not blob_exists:
gcp_logger.info('blob %s not exists, downloading.', blob_name)
resp = get_enlighten_stats_resp(
ENLIGHTEN_API_KEY, ENLIGHTEN_USER_ID, ENLIGHTEN_SYSTEM_ID, as_of_date)
new_blob = bucket.blob(blob_name)
new_blob.upload_from_string(resp.text)
fetched_counter += 1
else:
gcp_logger.debug('blob %s already exists, skipping.', blob_name)
return ('', 200)
def on_fdb_dailies_write(event, context):
"""Triggered by a change to a Firestore document.
Args:
event (dict): Event payload.
context (google.cloud.functions.Context): Metadata for the event.
"""
gcp_logger = init_gcp_logger()
gcp_logger.info('on_fdb_dailies_write()')
event_id = context.event_id
event_type = context.event_type
resource = context.resource
resource = 'projects/firefire-gcp-1/databases/(default)/documents/test_sites/6408091979/dailies/20190331'
doc_path = resource.split('/documents/')[1]
day_id = doc_path.split('/')[-1]
gcp_logger.info(
'event_id=%s, event_type=%s, resource=%s, context=%s, event=%s, day_id=%s',
event_id, event_type, resource, context, event, day_id)
return ('', 200)
df_result = df_min.join(df_max)
return df_result
# %%
scoresby_min_filename = 'SCORESBY_MIN_TEMP_IDCJAC0011_086104_1800_Data.csv'
scoresby_max_filename = 'SCORESBY_MAX_TEMP_IDCJAC0010_086104_1800_Data.csv'
viewbank_min_filename = 'VIEWBANK_MIN_TEMP_IDCJAC0011_086068_1800_Data.csv'
viewbank_max_filename = 'VIEWBANK_MAX_TEMP_IDCJAC0010_086068_1800_Data.csv'
storage_client = init_storage_client()
bucket = storage_client.get_bucket(GCP_STORAGE_BUCKET_ID)
nem12_min_date = datetime.strptime(NEM12_DATA_MIN_DATE, '%Y-%m-%d')
df_viewbank = _min_max_storage_csv_to_df(
(viewbank_min_filename, viewbank_max_filename), bucket, nem12_min_date)
df_scoresby = _min_max_storage_csv_to_df(
(scoresby_min_filename, scoresby_max_filename), bucket, nem12_min_date)
df_viewbank['min_temperature_c'].fillna(df_scoresby['min_temperature_c'],
inplace=True)
df_viewbank['max_temperature_c'].fillna(df_scoresby['max_temperature_c'],
inplace=True)
df_viewbank
# %%
merge_df_to_db(NMI, df_viewbank, 'sites', init_gcp_logger())
# %%
