''' Snap4city Computing HEATMAP.
Copyright (C) 2024 DISIT Lab http://www.disit.org - University of Florence
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
from datetime import datetime
from flask import Flask, request, jsonify, make_response
from heatmap import heatmapIDW
from helper import parse_from_date, is_date
app = Flask(__name__)
@app.route('/')
def hello_word():
return 'hello_word'
@app.route('/heatmap', methods=['POST'])
def heat_map():
data = request.json
date_format = '%Y-%m-%dT%H:%M:%S'
required_fields = {
'city': str,
'long_min': float,
'long_max': float,
'lat_min': float,
'lat_max': float,
'epsg_projection': int,
'value_types': list,
'subnature': str,
'scenario': str,
'color_map': str,
'from_date_time': str,
'to_date_time': str,
'token': str,
'heat_map_model_name': str,
}
missing_fields = [field for field in required_fields if field not in data]
if missing_fields:
return make_response(jsonify({
"response": "error",
"error": f"Missing fields: {', '.join(missing_fields)}"
}), 400)
for field, field_type in required_fields.items():
if not isinstance(data.get(field), field_type):
return make_response(jsonify({
"response": "error",
"message": f"Invalid data type for {field}. Expected {field_type.__name__}."
}), 400)
city = data.get('city')
long_min = float(data.get('long_min'))
long_max = float(data.get('long_max'))
lat_min = float(data.get('lat_min'))
lat_max = float(data.get('lat_max'))
epsg_projection = int(data.get('epsg_projection'))
value_types = data.get('value_types')
# TODO handle value unit
value_types = [value.split('-')[0] for value in value_types]
subnature = data.get('subnature')
scenario = data.get('scenario')
color_map = data.get('color_map')
from_date_time = data.get('from_date_time')
to_date_time = data.get('to_date_time')
token = data.get('token')
heat_map_model_name = data.get('heat_map_model_name')
model = data.get('model')
clustered = int(data.get('clustered', 0))
file = int(data.get('file', 1))
broker = data.get('broker')
if not (-180 <= long_min <= 180 and -180 <= long_max <= 180):
return make_response(jsonify({
"response": "error",
"message": "Longitude values (long_min, long_max) must be between -180 and 180."
}), 400)
if not (-90 <= lat_min <= 90 and -90 <= lat_max <= 90):
return make_response(jsonify({
"response": "error",
"message": "Latitude values (lat_min, lat_max) must be between -90 and 90."
}), 400)
if long_max <= long_min:
return make_response(jsonify({
"response": "error",
"message": "Please insert 'long_max' and 'long_min' parameters correctly. 'long_max' must be greater than 'long_min'."
}), 400)
if lat_max <= lat_min:
return make_response(jsonify({
"response": "error",
"message": "Please insert 'lat_max' and 'lat_min' parameters correctly. 'lat_max' must be greater than 'lat_min'."
}), 400)
parsed_from_date = parse_from_date(from_date_time, to_date_time)
if parsed_from_date == None and is_date(from_date_time) == False:
return make_response(jsonify({
"response": "error",
"error": "Please insert 'fromDateTime' parameter correctly and inject the node again. "
"The parameter has to be in a string format (n-hour or n-day) or in a date and time format (yyyy-mm-ddThh:mm:ss)"
}), 400)
if parsed_from_date == None:
parsed_from_date = datetime.strptime(from_date_time, "%Y-%m-%dT%H:%M:%S")
if is_date(to_date_time) == False:
return make_response(jsonify({
"response": "error",
"error": "Please insert 'to_date_time' parameter correctly and inject the node again. The parameter has to be in a date and time format (yyyy-mm-ddThh:mm:ss)"
}), 400)
parsed_to_date = datetime.strptime(to_date_time, date_format)
if parsed_to_date <= parsed_from_date:
return make_response(jsonify({
"response": "error",
"message": "Please insert 'from_date_time' and 'to_date_time' parameters correctly. 'to_date_time' must be later than 'from_date_time'."
}), 400)
if model not in ['IDW']:
return make_response(jsonify({
"response": "error",
"message": "Invalid AI model. Choose either 'IDW'."
}), 400)
if model == 'IDW':
response = heatmapIDW(
city=city,
long_min=long_min,
long_max=long_max,
lat_min=lat_min,
lat_max=lat_max,
epsg_projection=epsg_projection,
value_types=value_types,
subnature=subnature,
scenario=scenario,
color_map=color_map,
from_date_time=from_date_time,
to_date_time=to_date_time,
token=token,
heat_map_model_name=heat_map_model_name,
clustered=clustered,
file=file,
broker=broker
)
return response
if __name__ == '__main__':
app.run(host='0.0.0.0', port=8085)
-------------------------
from https://github.com/disit/snap4city/blob/master/Computing/predictions/heatmap_service.py
