def inv_energy__gen():
print('inv_energy__gen')
# Getting the form values
filter_date = None
filter_location_list = None
filter_type = 'DAY'
filter_type_nev = None
try:
filter_date = request.form['filter_date']
filter_location_list = request.form.getlist('filter_location_list[]')
filter_type = request.form['filter_type']
filter_type_nev = request.form['filter_type_nev']
except Exception as err:
print(str(err))
req_param = {
'filter_date': filter_date,
'filter_location_list': filter_location_list
}
# Changing to the time format
try:
start_time = datetime.strptime(filter_date, '%d/%m/%Y')
end_time = start_time + timedelta(days=int(1))
except Exception as err:
print(str(err))
start_time = None
end_time = None
if start_time is None and start_time != "":
start_time = DateUtil.get_current_day()
if end_time is None and end_time != "":
end_time = start_time + timedelta(days=int(1))
# Change the table and time condition based on the query
curr_day = DateUtil.get_current_day()
if filter_type_nev == "NEXT":
curr_day = session['last_end_time'] + timedelta(days=int(1))
start_time = curr_day
end_time = curr_day + timedelta(days=int(1))
elif filter_type_nev == "PREV":
curr_day = session['last_start_date'] - timedelta(days=int(1))
start_time = curr_day
end_time = curr_day + timedelta(days=int(1))
time_axis_format = '%d-%b(%H:%M)'
_table = TABLE.IOT_AGG_1_HOUR_SUMMARY_TABLE
if filter_type == "WEEK":
start_time, end_time = DateUtil.week_range(curr_day)
_table = TABLE.IOT_AGG_1_HOUR_SUMMARY_TABLE
time_axis_format = '%d-%b(%H:%M)'
elif filter_type == "MONTH":
start_time, end_time = DateUtil.month_range(curr_day)
_table = TABLE.IOT_AGG_1_HOUR_SUMMARY_TABLE
time_axis_format = '%d-%b-%Y'
elif filter_type == "YEAR":
start_time, end_time = DateUtil.year_range(curr_day)
_table = TABLE.IOT_AGG_1_HOUR_SUMMARY_TABLE
time_axis_format = '%d-%b(%H:%M)'
session['last_start_date'] = start_time
session['last_end_time'] = end_time
_condition = {
"sender_timestamp": {
"$gte": start_time,
"$lte": end_time
},
'sensor_type': "INVERTER",
"irr_value": {
"$exists": 'true'
}
}
if filter_location_list and "ALL" not in filter_location_list:
_condition.__setitem__('resource_path', {'$in': filter_location_list})
_projection = {
"sender_timestamp": 1,
'resource_path': 1,
'sensor_id': 1,
'AGGREGATE_VALUE': 1,
'performance_ratio': 1,
'expected_value': 1
}
print("search criteria ", _condition)
print("Projection ", _projection)
result = iot_dao.getrecord_from_table(
_table, _condition, _projection,
_sortby='resource_path').reset_index()
try:
result['AGGREGATE_VALUE'] = result['AGGREGATE_VALUE'].replace(
[np.NaN, ''], 0).astype(np.float64).fillna(0.0)
result['AGGREGATE_VALUE'] = round(result['AGGREGATE_VALUE'], 2)
result['performance_ratio'] = result['performance_ratio'].replace(
[np.NaN, ''], 0).astype(np.float64).fillna(0.0)
result['performance_ratio'] = round(result['performance_ratio'], 2)
except Exception as err:
print(str(err))
agg = {"AGGREGATE_VALUE": 'sum', "performance_ratio": 'mean'}
location_detail = uti_service.get_location_details()
data_lst = []
graph_set = []
common_x = []
max_y = 10
if not result.empty:
#result_4_grp = result[result['AGGREGATE_VALUE'] > 0]
result_4_grp = result.groupby(['sender_timestamp',
'sensor_id']).agg(agg).reset_index()
result_4_grp = result_4_grp.sort_values(['sender_timestamp'],
ascending=[True])
sensor_id_list = []
summary = []
for sensor_id in result_4_grp['sensor_id'].unique():
sensor_id_list.append(sensor_id)
x = []
y = []
result_4_grp_tmp = result_4_grp[result_4_grp['sensor_id'] ==
sensor_id]
print("sensor_id ", sensor_id)
summary_info = {}
max = 0
sum = 0
min = 1000
for index, row in result_4_grp_tmp.iterrows():
print(index)
x.append(row['sender_timestamp'].strftime('%d-%m-%Y %H:%M'))
y.append(round(row['AGGREGATE_VALUE'], 2))
sum = sum + round(row['AGGREGATE_VALUE'], 2)
if max < round(row['AGGREGATE_VALUE'], 2):
max = round(row['AGGREGATE_VALUE'], 2)
if row['AGGREGATE_VALUE'] != 0 and min > round(
row['AGGREGATE_VALUE'], 2):
min = row['AGGREGATE_VALUE']
if max_y < row['AGGREGATE_VALUE']:
max_y = round(row['AGGREGATE_VALUE'])
summary_info.__setitem__('sensor_id', sensor_id)
summary_info.__setitem__('max_energy', round(max, 2))
if min == 1000:
min = 0
summary_info.__setitem__('min_energy', round(min, 2))
summary_info.__setitem__('energy_gen', round(sum, 2))
summary.append(summary_info)
if len(common_x) < len(x):
common_x = x
gp = {'X': x, 'Y': y, 'label': sensor_id, 'type': 'line'}
print(gp)
graph_set.append(gp)
asert_info_tmp = iot_dao.getJSON_LISTrecord_from_table(
TABLE.IOT_ASSET_MSTR, {"sensor_id": {
'$in': sensor_id_list
}}, {
"asset_name": 1,
"resource_path": 1,
"asset_capacity": 1,
"asset_location": 1,
"asset_desc": 1,
"sensor_id": 1,
"sensor_type": 1,
"model": 1,
"module_efficiency": 1,
"serial_no": 1,
"total_panel_area": 1,
"vendor_detail": 1
}, None)
asert_info = []
for row in asert_info_tmp:
rw = {}
rw.__setitem__('Device Id', row['sensor_id'])
rw.__setitem__('Location', row['resource_path'])
rw.__setitem__('model', row['model'])
rw.__setitem__('Device Name', row['asset_name'])
rw.__setitem__('Device Desc', row['asset_desc'])
rw.__setitem__('Total PV Area (SQM)', row['total_panel_area'])
rw.__setitem__('Capacity (kWp)', row['asset_capacity'])
asert_info.append(rw)
for index, row in result.iterrows():
rw = {}
rw.__setitem__('SENDER_TIMESTAMP',
row['sender_timestamp'].strftime('%d-%m-%Y %H:%M'))
rw.__setitem__('Location', row['resource_path'])
rw.__setitem__(
'Location Code',
location_detail[row['resource_path']]['location_code'])
rw.__setitem__('sensor_id', row['sensor_id'])
rw.__setitem__('Energy (kWh)', row['AGGREGATE_VALUE'])
rw.__setitem__('Performance Ratio (%)', row['performance_ratio'])
data_lst.append(rw)
loc_list = []
for index, val in location_detail.items():
val['selected'] = 'F'
if filter_location_list:
if (val['resource_path'] in filter_location_list):
val['selected'] = 'Y'
loc_list.append(val)
print(max_y)
return render_template('sub_pages/common_template.html',
pagename="inv_energy_gen_summary",
title="Inverter Energy Generation",
menu="menu_pr",
data=data_lst,
location_detail=loc_list,
req_param=req_param,
filter_type=filter_type,
graph_set=graph_set,
X_axies=common_x,
max_y=max_y,
info=asert_info,
summary=summary)
def inverter_pr():
print('inverter_pr')
# Getting the form values
filter_date = None
filter_location_list = None
filter_type = 'DAY'
filter_type_nev = None
try:
filter_date = request.form['filter_date']
filter_location_list = request.form.getlist('filter_location_list[]')
filter_type = request.form['filter_type']
filter_type_nev = request.form['filter_type_nev']
except Exception as err:
print(str(err))
req_param = {
'filter_date': filter_date,
'filter_location_list': filter_location_list
}
# Changing to the time format
try:
start_time = datetime.strptime(filter_date, '%d/%m/%Y')
end_time = start_time + timedelta(days=int(1))
except Exception as err:
print(str(err))
start_time = None
end_time = None
if start_time is None and start_time != "":
start_time = DateUtil.get_current_day()
if end_time is None and end_time != "":
end_time = start_time + timedelta(days=int(1))
# Change the table and time condition based on the query
curr_day = DateUtil.get_current_day()
if filter_type_nev == "NEXT":
curr_day = session['last_end_time'] + timedelta(days=int(1))
start_time = curr_day
end_time = curr_day + timedelta(days=int(1))
elif filter_type_nev == "PREV":
curr_day = session['last_start_date'] - timedelta(days=int(1))
start_time = curr_day
end_time = curr_day + timedelta(days=int(1))
time_axis_format = '%d-%b(%H:%M)'
_table = TABLE.IOT_AGG_1_HOUR_SUMMARY_TABLE
if filter_type == "WEEK":
start_time, end_time = DateUtil.week_range(curr_day)
_table = TABLE.IOT_AGG_1_HOUR_SUMMARY_TABLE
time_axis_format = '%d-%b(%H:%M)'
elif filter_type == "MONTH":
start_time, end_time = DateUtil.month_range(curr_day)
_table = TABLE.IOT_AGG_1_HOUR_SUMMARY_TABLE
time_axis_format = '%d-%b-%Y'
elif filter_type == "YEAR":
start_time, end_time = DateUtil.year_range(curr_day)
_table = TABLE.IOT_AGG_1_HOUR_SUMMARY_TABLE
time_axis_format = '%d-%b(%H:%M)'
session['last_start_date'] = start_time
session['last_end_time'] = end_time
_condition = {
"sender_timestamp": {
"$gte": start_time,
"$lte": end_time
},
'sensor_type': "INVERTER",
"irr_value": {
"$exists": 'true'
}
}
if filter_location_list and "ALL" not in filter_location_list:
_condition.__setitem__('resource_path', {'$in': filter_location_list})
_projection = {
"sender_timestamp": 1,
'resource_path': 1,
'sensor_id': 1,
'AGGREGATE_VALUE': 1,
'performance_ratio': 1,
'expected_value': 1
}
print("search criteria ", _condition)
print("Projection ", _projection)
result = iot_dao.getrecord_from_table(
_table, _condition, _projection,
_sortby='resource_path').reset_index()
try:
result['AGGREGATE_VALUE'] = result['AGGREGATE_VALUE'].replace(
[np.NaN, ''], 0).astype(np.float64).fillna(0.0)
result['AGGREGATE_VALUE'] = round(result['AGGREGATE_VALUE'], 2)
result['performance_ratio'] = result['performance_ratio'].replace(
[np.NaN, ''], 0).astype(np.float64).fillna(0.0)
result['performance_ratio'] = round(result['performance_ratio'], 2)
except Exception as err:
print(str(err))
agg = {"AGGREGATE_VALUE": 'sum', "performance_ratio": 'mean'}
location_detail = uti_service.get_location_details()
data_lst = []
x = []
y1 = []
y2 = []
if not result.empty:
result_4_grp = result[result['AGGREGATE_VALUE'] > 0]
result_4_grp = result_4_grp.groupby(['sensor_id'
]).agg(agg).reset_index()
result_4_grp = result_4_grp.sort_values(['AGGREGATE_VALUE'],
ascending=[True])
for index, row in result_4_grp.iterrows():
x.append(row['sensor_id'])
if row['performance_ratio'] > 100:
row['performance_ratio'] = 100
y1.append(round(row['performance_ratio']))
y2.append(round(row['AGGREGATE_VALUE'], 2))
for index, row in result.iterrows():
rw = {}
rw.__setitem__('SENDER_TIMESTAMP',
row['sender_timestamp'].strftime('%d-%m-%Y %H:%M'))
rw.__setitem__('Location', row['resource_path'])
rw.__setitem__(
'Location Code',
location_detail[row['resource_path']]['location_code'])
rw.__setitem__('sensor_id', row['sensor_id'])
rw.__setitem__('Energy (kWh)', row['AGGREGATE_VALUE'])
rw.__setitem__('Performance Ratio (%)', row['performance_ratio'])
data_lst.append(rw)
loc_list = []
for index, val in location_detail.items():
val['selected'] = 'F'
if filter_location_list:
if (val['resource_path'] in filter_location_list):
val['selected'] = 'Y'
loc_list.append(val)
plot_data, layout = polt_dashboard.rander_simplegraph(
x_axies=x,
y_axies_01=y1,
y_label_01="PR (%)",
y_axies_02=y2,
y_label_02="Energy (kWh)",
title="Time Vs performance_ratio",
type_01="Scatter",
type_02="Bar")
print(plot_data)
print(layout)
graphs = dict(data=plot_data, layout=layout)
graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)
return render_template('sub_pages/common_template.html',
pagename="inv_performance_ratio_summary",
title="Inverter Performance Ratio",
menu="menu_pr",
data=data_lst,
location_detail=loc_list,
req_param=req_param,
filter_type=filter_type,
graphJSON=graphJSON,
X=x,
Y1=y1,
Y2=y2,
y1_lable="Energy (kWh)",
y2_lable="Performance Ratio (%)")