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 get_exceedances():
gr = graph.Graph()
if request.method == 'POST':
json_data = request.get_json()
if not json_data:
return {'message': 'No input data provided'}, 400
pollutant = json_data["pollutant"]
standard = json_data["standard"]
exceedances_data = gr.get_all_devices_past_28_days_exceedences(
pollutant, standard)
return jsonify(exceedances_data[0]['exceedences'])
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})
mnode.add_edge('8', 20)
mnode.add_edge('2', 15)
mnode.add_edge('10', 10)
nodes.append(mnode)
mnode = node.Node('10', {})
mnode.add_edge('2', 5)
mnode.add_edge('3', 15)
nodes.append(mnode)
return nodes
#init
nodes = CreateNodes()
mygraph1 = graph.Graph('x', nodes)
existnode = CreateexistNodewithnewedge()
mygraph1.add_node(existnode)
nodes2 = CreateNodes2()
mygraph2 = graph.Graph('Y', nodes2)
# Magic functions:
print('\n__str__ - print (mygraph1)')
print(mygraph1)
print('\n__len__ - nodes number :{}\n'.format(len(mygraph1)))
print('\n__add__ - new_graph = Y+X :'.format())
new_graph = mygraph1 + mygraph2
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})
# Build dict holds avg = time spent on path / number of users go throw this path
for k,v in timedict_we.items():
avgdict_we[k] = int(round(v/countdict_we[k]))
# Build dict holds avg = time spent on path / number of users go throw this path
for kew,vew in timedict_ew.items():
avgdict_ew[kew] = int(round(v/countdict_ew[kew]))
# Create first Graph
nodes=[]
# k exmple - north_east:1233
for k,v in avgdict_we.items():
mnode = node.Node(k.split('_')[0], {})
mnode.add_edge(k.split('_')[1], v)
nodes.append(mnode)
graph1= graph.Graph('r1', nodes)
print(graph1)
# Create second Graph
nodes=[]
for k,v in avgdict_ew.items():
mnode = node.Node(k.split('_')[0], {})
mnode.add_edge(k.split('_')[1], v)
nodes.append(mnode)
graph2= graph.Graph('r2', nodes)
print(graph2)
graph3 = graph1+graph2
print(graph3)
def creatgraphs():
nodes = []
mnode = node.Node('1', {})
mnode.add_edge('5', 20)
mnode.add_edge('6', 5)
mnode.add_edge('7', 15)
mnode.add_edge('2', 10)
mnode.add_edge('4', 20)
nodes.append(mnode)
mnode = node.Node('2', {})
mnode.add_edge('4', 10)
mnode.add_edge('3', 20)
nodes.append(mnode)
mnode = node.Node('3', {})
mnode.add_edge('4', 5)
mnode.add_edge('2', 15)
nodes.append(mnode)
mnode = node.Node('4', {})
mnode.add_edge('5', 10)
nodes.append(mnode)
graph1 = graph.Graph('1', nodes)
nodes = []
mnode = node.Node('5', {})
mnode.add_edge('6', 5)
nodes.append(mnode)
mnode = node.Node('6', {})
nodes.append(mnode)
mnode = node.Node('7', {})
mnode.add_edge('6', 10)
nodes.append(mnode)
graph2 = graph.Graph('2', nodes)
nodes = []
mnode = node.Node('8', {})
mnode.add_edge('1', 5)
mnode.add_edge('2', 20)
mnode.add_edge('7', 5)
nodes.append(mnode)
mnode = node.Node('9', {})
mnode.add_edge('8', 20)
mnode.add_edge('2', 15)
mnode.add_edge('10', 10)
nodes.append(mnode)
mnode = node.Node('10', {})
mnode.add_edge('2', 5)
mnode.add_edge('3', 15)
nodes.append(mnode)
graph3 = graph.Graph('3', nodes)
return graph1, graph2, graph3
