def get_all_organisation_monitoring_sites(tenant):
"""
Gets all the monitoring sites for the specified organisation.
Args:
tenant: the name of the organisation whose monitoring sites are to be returned.
Returns:
A list of the monitoring sites associated with the specified organisation name.
"""
records = []
MonitoringSite = monitoring_site.MonitoringSite(tenant)
results = MonitoringSite.get_all()
for result in results:
if 'LatestHourlyMeasurement' in result:
w = result['LatestHourlyMeasurement']
last_hour_pm25_value = int(w[-1]['last_hour_pm25_value'])
last_hour = convert_date.date_to_formated_str(w[-1]['last_hour'])
else:
last_hour_pm25_value = 0
last_hour = ''
obj = {
"DeviceCode": result['DeviceCode'],
'Parish': result['Parish'],
'Division': result['Division'],
'Last_Hour_PM25_Value': last_hour_pm25_value,
'Latitude': result['Latitude'],
'Longitude': result['Longitude'],
'LocationCode': result['LocationCode'],
'LastHour': last_hour,
'_id': str(result['_id'])
}
records.append(obj)
return records
def calculate_average_daily_measurements_for_last_28_days():
ms = monitoring_site.MonitoringSite()
gr = graph.Graph()
monitoring_sites = list(
app.mongo.db.monitoring_site.find({}, {
"DeviceCode": 1,
"Parish": 1,
"LocationCode": 1,
"Division": 1,
"_id": 0
}))
devices_historical_records = []
for monitoring_site_device in monitoring_sites:
print(monitoring_site_device)
code = monitoring_site_device['DeviceCode']
historical_results = []
records = []
pm25_daily_values = []
average_pm25 = 0
if code: #check if code is not empty
print(code)
parish = monitoring_site_device['Parish']
division = monitoring_site_device['Division']
location_code = monitoring_site_device['LocationCode']
created_at = helpers.str_to_date(
helpers.date_to_str(datetime.now()))
endtime = helpers.date_to_str(datetime.now())
starttime = helpers.date_to_str(datetime.now() -
timedelta(days=28))
monitoring_site_measurements_cursor = gr.get_filtered_data(
code, starttime, endtime, 'daily', 'PM 2.5')
#monitoring_site_measurements_cursor = ms.get_device_past_28_days_measurements(code)
for site in monitoring_site_measurements_cursor:
record = {
'pm2_5_value': int(site['pollutant_value']),
'time': site["time"]
}
records.append(record)
pm25_daily_values.append(int(site['pollutant_value']))
historical_results.append(site)
if len(pm25_daily_values) > 0:
average_pm25 = np.mean(pm25_daily_values)
historical_record = {
'deviceCode': code,
'average_pm25': average_pm25,
'historical_records': records,
'Parish': parish,
'Division': division,
'LocationCode': location_code,
'created_at': created_at
}
devices_historical_records.append(historical_record)
mongo_helpers.save_device_daily_historical_averages(
devices_historical_records)
return jsonify({'response': 'all new hourly measurements saved'}), 200
def get_device_past_28_days_measurements():
ms = monitoring_site.MonitoringSite()
gr = graph.Graph()
if request.method == 'GET':
device_code = request.args.get('device_code')
if device_code:
historical_results = []
records = []
pm25_daily_values = []
endtime = helpers.date_to_str(datetime.now())
starttime = helpers.date_to_str(datetime.now() -
timedelta(days=28))
monitoring_site_measurements_cursor = gr.get_filtered_data(
device_code, starttime, endtime, 'daily', 'PM 2.5')
#monitoring_site_measurements_cursor = ms.get_device_past_28_days_measurements(device_code)
for site in monitoring_site_measurements_cursor:
record = {
'pm2_5_value': int(site['pollutant_value']),
'time': site["time"]
}
records.append(record)
pm25_daily_values.append(int(site['pollutant_value']))
historical_results.append(site)
return jsonify({
"historical_measurements": historical_results,
"records": records,
"pm25_values": pm25_daily_values
})
else:
return jsonify({
"error msg":
"device code wasn't supplied in the query string parameter."
})
def calculate_average_daily_measurements_for_last_28_days():
tenant = request.args.get('tenant')
if not tenant:
return jsonify({
"message":
"please specify the organization name. Refer to the API documentation for details.",
"success": False
}), 400
MonitoringSiteModel = monitoring_site.MonitoringSite(tenant)
monitoring_sites = MonitoringSiteModel.get_monitoring_sites()
devices_historical_records = []
for monitoring_site_device in monitoring_sites:
print(monitoring_site_device)
code = monitoring_site_device['DeviceCode']
historical_results = []
records = []
pm25_daily_values = []
average_pm25 = 0
if code: # check if code is not empty
parish = monitoring_site_device['Parish']
division = monitoring_site_device['Division']
location_code = monitoring_site_device['LocationCode']
created_at = convert_date.str_to_date(
convert_date.str_to_date(datetime.now()))
endtime = convert_date.str_to_date(datetime.now())
starttime = convert_date.str_to_date(datetime.now() -
timedelta(days=28))
DailyAverage = device_daily_measurements.DailyMeasurements(tenant)
monitoring_site_measurements_cursor = DailyAverage.get_filtered_data(
code, starttime, endtime, 'daily', 'PM 2.5')
for site in monitoring_site_measurements_cursor:
record = {
'pm2_5_value': int(site['pollutant_value']),
'time': site["time"]
}
records.append(record)
pm25_daily_values.append(int(site['pollutant_value']))
historical_results.append(site)
if len(pm25_daily_values) > 0:
average_pm25 = np.mean(pm25_daily_values)
historical_record = {
'deviceCode': code,
'average_pm25': average_pm25,
'historical_records': records,
'Parish': parish,
'Division': division,
'LocationCode': location_code,
'created_at': created_at
}
devices_historical_records.append(historical_record)
# save_device_daily_historical_averages(devices_historical_records)
print(devices_historical_records)
def get_organisation_monitoring_site_locations():
ms = monitoring_site.MonitoringSite()
if request.method == 'GET':
org_name = request.args.get('organisation_name')
if org_name:
monitoring_sites_locations = []
organisation_monitoring_sites_cursor = ms.get_monitoring_site_locations(
org_name)
for site in organisation_monitoring_sites_cursor:
monitoring_sites_locations.append(site)
results = json.loads(json_util.dumps(monitoring_sites_locations))
return jsonify({"airquality_monitoring_sites": results})
else:
return jsonify({"error msg": "organisation name wasn't supplied in the query string parameter."})
def get_pm25categorycount_for_locations():
ms = monitoring_site.MonitoringSite()
d = dashboard.Dashboard()
if request.method == 'GET':
org_name = request.args.get('organisation_name')
if org_name:
# organisation_monitoring_sites_cursor = ms.get_all_organisation_monitoring_sites(org_name)
# results = d.get_pm25_category_count(organisation_monitoring_sites_cursor)
results = d.get_pm25_category_location_count_for_the_lasthour(
org_name)
if results:
return jsonify(results[0]['pm25_categories'])
else:
return jsonify([])
else:
return jsonify({"error msg": "organisation name wasn't supplied in the query string parameter."})
def get_all_device_past_28_days_measurements():
ms = monitoring_site.MonitoringSite()
if request.method == 'GET':
results = []
values = []
labels = []
background_colors = []
monitoring_site_measurements_cursor = ms.get_all_devices_past_28_days_measurements()
for site in monitoring_site_measurements_cursor:
values.append(int(site["average_pm25"]))
labels.append(site["Parish"])
background_colors.append(
helpers.set_pm25_category_background(site["average_pm25"]))
results.append(site)
return jsonify({"results": {"average_pm25_values": values, "labels": labels, "background_colors": background_colors}})
else:
return jsonify({"error msg": "invalid request."})
def get_random_location_hourly_customised_chart_data_2():
ms = monitoring_site.MonitoringSite()
gr = graph.Graph()
device_code = 'A743BPWK'
start_date = '2020-04-09T07:00:00.000000Z'
end_date = '2020-05-12T07:00:00.000000Z'
frequency = 'monthly'
pollutant = 'PM 2.5'
chart_type = 'pie'
organisation_name = 'KCCA'
parish = ' Wandegeya'
location_code = 'KCCA_KWPE_AQ05'
division = 'Kawempe'
custom_chat_data = []
datasets = []
colors = ['#7F7F7F', '#E377C2', '#17BECF',
'#BCBD22', '#3f51b5'] # blue,cyan, olive,
custom_chart_title = 'Mean ' + frequency.capitalize() + ' ' + \
pollutant + ' for '
locations_names = parish
custom_chart_title = custom_chart_title + locations_names + ' Between ' + helpers.convert_date_to_formated_str(helpers.str_to_date(
start_date), frequency) + ' and ' + helpers.convert_date_to_formated_str(helpers.str_to_date(end_date), frequency)
values = []
labels = []
device_results = {}
filtered_data = gr.get_filtered_data(
device_code, start_date, end_date, frequency, pollutant)
if filtered_data:
for data in filtered_data:
values.append(data['pollutant_value'])
labels.append(data['time'])
device_results = {'pollutant_values': values, 'labels': labels}
color = colors.pop()
dataset = {'data': values, 'label': parish + ' ' + pollutant,
'borderColor': color, 'backgroundColor': color, 'fill': False}
datasets.append(dataset)
custom_chat_data.append({'start_date': start_date, 'end_date': end_date, 'division': division,
'parish': parish, 'frequency': frequency, 'pollutant': pollutant,
'location_code': location_code, 'chart_type': chart_type, 'chart_data': device_results})
return jsonify({'results': custom_chat_data, 'datasets': datasets, 'custom_chart_title': custom_chart_title})
def download_customised_data():
# create an instance of the MonitoringSite class
ms = monitoring_site.MonitoringSite()
# create an instance of the Graph class
gr = graph.Graph()
if request.method != 'POST':
return {'message': 'Method not allowed. The method is not allowed for the requested URL'}, 400
else:
json_data = request.get_json()
download_type = request.args.get('type')
if not json_data:
return {'message': 'No input data provided'}, 400
if not download_type:
return {'message': 'Please specify the type of data you wish to download'}, 400
if download_type not in ['csv', 'json']:
return {'message': 'Invalid data type. Please specify csv or json'}, 400
locations = json_data["locations"]
start_date = json_data["startDate"]
end_date = json_data["endDate"]
frequency = json_data["frequency"]
# select multiple pollutants
pollutants = json_data["pollutants"]
file_type = json_data["fileType"]
degree_of_cleanness = json_data["degreeOfClean"]
organisation_name = json_data["organisation_name"]
custom_chat_data = []
# datasets = {} # displaying multiple locations
datasets = []
locations_devices = []
for location in locations:
devices = ms.get_location_devices_code(
organisation_name, location['label'])
# datasets[location['label']] = {}
for device in devices:
device_code = device['DeviceCode']
division = device['Division']
parish = device['Parish']
location_code = device['LocationCode']
values = []
labels = []
pollutant_list = []
device_results = {}
data_to_download = {}
channel_ref = []
# control how some of the data is accessed
flag = 0
# loop through pollutants selected by the user
for pollutant in pollutants:
filtered_data = gr.get_filtered_data(
device_code, start_date, end_date, frequency, pollutant['value'])
data_to_download[pollutant['value']] = []
if filtered_data:
for data in filtered_data:
values.append(data['pollutant_value'])
if flag == 0:
labels.append(data['time'])
channel_ref.append(device_code)
pollutant_list.append(pollutant['value'])
data_to_download[pollutant['value']].append(
data['pollutant_value'])
flag = 1
data_to_download['channel_ref'] = channel_ref
data_to_download['device_code'] = device_code
data_to_download['division'] = division
data_to_download['parish'] = parish
data_to_download['time'] = labels
data_to_download['location_code'] = location_code
data_to_download['start_date'] = start_date
data_to_download['end_date'] = end_date
data_to_download['frequency'] = frequency
data_to_download['file_type'] = file_type
data_to_download['owner'] = organisation_name
data_to_download['location'] = location['label']
# datasets[location['label']] = data_to_download
datasets.append(data_to_download)
# downloading json
if download_type == 'json':
return jsonify({'results': datasets})
# downloading csv
if download_type == 'csv':
# print(json.dumps(datasets, indent=1))
# json normalization to pandas datafrome
tempp = pd.DataFrame()
for location in locations:
temp = pd.json_normalize(datasets, 'time', ['owner'])
tempp['datetime'] = temp[0]
for pollutant in pollutants:
temp = pd.json_normalize(datasets, pollutant['label'], [
'owner', 'location', 'parish', 'division', 'frequency', 'location_code'])
tempp['owner'] = temp['owner']
tempp['location'] = temp['location']
tempp['location_code'] = temp['location_code']
tempp['parish'] = temp['parish']
tempp['division'] = temp['division']
tempp['frequency'] = temp['frequency']
tempp[pollutant['label']] = temp[0]
# FIELDS = ["location", "PM 2.5", "start_date"]
# df = pd.json_normalize(datasets, max_level=1)
# print(df)
for location in locations:
temp = pd.json_normalize(datasets, 'channel_ref', ['owner'])
tempp['channel_ref'] = temp[0]
print(tempp)
final_data = tempp.to_json(orient='records')
# buid a dataframe from data_to_download
# df_new = pd.DataFrame(columns=['PM 2.5', 'PM 10', 'channel_ref'])
# for item in datasets:
# df_new[item] = datasets[item]
# print(df_new)
# print(df_new.to_json(orient='columns'))
# return jsonify({'results': datasets})
return final_data
def generate_customised_chart_data():
ms = monitoring_site.MonitoringSite()
gr = graph.Graph()
if request.method == 'POST':
json_data = request.get_json()
if not json_data:
return {'message': 'No input data provided'}, 400
# input_data, errors = validate_inputs(input_data=json_data) //add server side validation
# if not errors:
locations = json_data["locations"]
start_date = json_data["startDate"]
end_date = json_data["endDate"]
frequency = json_data["frequency"]
pollutant = json_data["pollutant"]
chart_type = json_data["chartType"]
organisation_name = json_data["organisation_name"]
custom_chat_data = []
datasets = [] #displaying multiple locations
locations_devices =[]
colors =['#7F7F7F','#E377C2', '#17BECF', '#BCBD22','#3f51b5']
custom_chart_title= 'Mean ' + frequency.capitalize() + ' '+ pollutant + ' for '
locations_names = ','.join([str(location['label']) for location in locations])
custom_chart_title = custom_chart_title + locations_names
custom_chart_title_second_section = ' Between ' + helpers.convert_date_to_formated_str(helpers.str_to_date(start_date),frequency) + ' and ' + helpers.convert_date_to_formated_str(helpers.str_to_date(end_date),frequency)
for location in locations:
devices = ms.get_location_devices_code( organisation_name, location['label'])
for device in devices:
device_code = device['DeviceCode']
division = device['Division']
parish = device['Parish']
location_code= device['LocationCode']
values =[]
labels =[]
background_colors= []
device_results={}
if chart_type == 'pie':
filtered_data = gr.get_piechart_data(
device_code, start_date, end_date, frequency, pollutant)
if filtered_data:
for data in filtered_data:
values.append(data['category_count'])
labels.append(data['category_name'])
background_colors.append(
helpers.assign_color_to_pollutant_category(data['category_name']))
device_results = {
'pollutant_values': values, 'labels': labels}
color = colors.pop()
dataset = {'data': values, 'label': parish + ' ' +
pollutant, 'backgroundColor': background_colors}
datasets.append(dataset)
custom_chat_data.append({'start_date':start_date, 'end_date':end_date, 'division':division,
'parish':parish,'frequency':frequency, 'pollutant':pollutant,
'location_code':location_code, 'chart_type':chart_type,'chart_data':device_results, 'datasets':datasets, 'custom_chart_title':custom_chart_title, 'custom_chart_title_second_section':custom_chart_title_second_section})
else:
filtered_data = gr.get_filtered_data(device_code, start_date, end_date, frequency, pollutant)
if filtered_data:
for data in filtered_data:
values.append(data['pollutant_value'])
labels.append(data['time'])
device_results = {
'pollutant_values': values, 'labels': labels}
color = colors.pop()
dataset = {'data': values, 'label': parish + ' ' + pollutant,
'borderColor': color, 'backgroundColor': color, 'fill': False}
datasets.append(dataset)
measurement_units = '(µg/m3)'
if pollutant == 'NO2':
measurement_units = ' Concentration'
chart_label = pollutant + measurement_units
custom_chat_data.append({'start_date':start_date, 'end_date':end_date, 'division':division,
'parish':parish,'frequency':frequency, 'pollutant':pollutant,
'location_code':location_code, 'chart_type':chart_type,'chart_data':device_results,
'datasets':datasets, 'custom_chart_title':custom_chart_title, 'chart_label':chart_label, 'custom_chart_title_second_section':custom_chart_title_second_section})
locations_devices.append(devices)
return jsonify({'results':custom_chat_data, 'datasets':datasets, 'custom_chart_title':custom_chart_title, 'custom_chart_title_second_section':custom_chart_title_second_section})