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AnchorageDiscovery.py
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AnchorageDiscovery.py
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"""
OPTICS_kafka_multiprocess_v01.py
"""
""" OPTICStool"""
###########################################################################################################
""" Import python files """
###########################################################################################################
sys.path.append(os.path.join(os.path.dirname(__file__), 'lib'))
import OPTICS_config as cfg
###########################################################################################################
""" Import libraries """
###########################################################################################################
from kafka import KafkaConsumer, KafkaProducer, TopicPartition
import csv
import json
import pandas
import time
import logging
import multiprocessing
import numpy
from kafka.admin import KafkaAdminClient, NewTopic
import os
import datetime
from sklearn.cluster import OPTICS
import pyproj
from shapely.geometry import Point
import shapely
import geopandas as gpd
###########################################################################################################
""" OPTICS """
###########################################################################################################
def fun_optics_model(data, min_samples, max_eps):
model = OPTICS(min_samples=min_samples, max_eps=max_eps)
model = model.fit(data[['lon', 'lat']])
predicted_clusters = model.labels_
# print("predictions:", predicted_clusters)
return predicted_clusters
###########################################################################################################
""" OPTICS """
###########################################################################################################
def fun_optics_model2(min_samples, max_eps):
startt = time.time()
df = pandas.read_csv(cfg.CSV_FILE_FOR_READ, header=0, delimiter=',', index_col=None)
print(df.columns)
#print(df)
#print(df['timestamp'])
print(df)
df['id'] = df['mmsi'].copy()
df['timestamp'] = (pandas.to_datetime(df['timestamp']).astype(int) / 10 ** 6).astype(int)
df.sort_values(by=['id', 'timestamp'], ascending=[True, True], inplace=True); df.reset_index(drop=True, inplace=True)
df.drop_duplicates(keep='first', inplace=True) # Drop duplicates
df.drop_duplicates(subset=['id', 'timestamp'], inplace=True) # Check if there are points with the same timestamp
myProj = pyproj.Proj("+proj=utm +zone=35S, +ellps=WGS84 +datum=WGS84 +units=m +no_defs")
lon2, lat2 = myProj(df['lon'].values, df['lat'].values)
df['UTMlon'] = lon2
df['UTMlat'] = lat2
df['dt'] = (df['timestamp'].groupby(df['id']).diff()).values # Diff Time in seconds
df['dlon'] = (df['UTMlon'].groupby(df['id']).diff()).values # Diff Longitude in meters
df['dlat'] = (df['UTMlat'].groupby(df['id']).diff()).values # Diff Latitude in meters
df['dist_m'] = numpy.sqrt(df.dlon.values ** 2 + df.dlat.values ** 2) # euclidean distance in meters
df['my_speed'] = df['dist_m'] / df['dt'] # calculate speed m/s
CFG_THRESHOLD_SPEED_MIN = cfg.CFG_THRESHOLD_SPEED_MIN*0.514444444
CFG_THRESHOLD_SPEED_MAX = cfg.CFG_THRESHOLD_SPEED_MAX*0.514444444
df = df.drop(df[df['my_speed'] < CFG_THRESHOLD_SPEED_MIN].index) # Drop points with limited speed
df = df.drop(df[df['my_speed'] > CFG_THRESHOLD_SPEED_MAX].index) # Drop points with high speed
print("Start OPTICS....")
model = OPTICS(min_samples=min_samples, max_eps=max_eps, n_jobs=-1)
data = df[['UTMlon', 'UTMlat']].copy()
#data = data.iloc[:1000].copy()
model = model.fit(data)
p_cl = model.labels_
print("predictions:", p_cl)
df['oo'] = p_cl
df.to_csv('optics_clusters.csv')
print("ELAPSED TIME:", time.time() - startt)
return p_cl, df
###########################################################################################################
""" FIND POLYGONS """
###########################################################################################################
def fun_convex_hull(df):
startt = time.time()
df.sort_values(by=['oo'], ascending=[True], inplace=True)
df.reset_index(drop=True, inplace=True)
df['geometry'] = [shapely.geometry.Point(xy) for xy in zip(df['lon'], df['lat'])]
anchs_clusters = pandas.DataFrame()
anchs_clusters_gdf = gpd.GeoDataFrame()
gb = df.groupby(df['oo'])
for y in gb.groups:
df0 = gb.get_group(y).copy()
point_collection = shapely.geometry.MultiPoint(list(df0['geometry']))
# point_collection.envelope
convex_hull_polygon = point_collection.convex_hull
anchs_clusters = anchs_clusters.append(pandas.DataFrame(data={'anchorage_id':[y],'geom':[convex_hull_polygon]}))
anchs_clusters_gdf = anchs_clusters_gdf.append(gpd.GeoDataFrame({'anchorage_id':[y],'geometry':[convex_hull_polygon]}))
anchs_clusters.reset_index(inplace=True)
anchs_clusters_gdf.reset_index(inplace=True)
anchs_clusters_gdf.crs = 'epsg:4326'
anchs_clusters.to_csv('anchs_clusters.csv')
print("ELAPSED TIME:", time.time() - startt)
return anchs_clusters, anchs_clusters_gdf
###########################################################################################################
""" INTERSECT """
###########################################################################################################
def fun_intersect(anchs_clusters, anchs_clusters_gdf):
startt = time.time()
anchs_buffer = pandas.read_csv(cfg.CSV_FILE_FOR_READ_anchs, header=0, delimiter=';', index_col=None)
print(anchs_buffer.columns)
anchs_buffer_gdf = gpd.GeoDataFrame()
anchs_buffer_gdf['cd'] = anchs_buffer['cd']
anchs_buffer_gdf['geometry'] = [shapely.wkb.loads(xy, hex='True') for xy in anchs_buffer['var_buffer']]
anchs_buffer_gdf.crs = 'epsg:2100'
anchs_buffer_gdf = anchs_buffer_gdf.copy().to_crs('epsg:4326')
anchs_intersect_gdf = anchs_buffer_gdf.copy()
anchs_intersect_gdf['intersect_num_clusters'] = -10
anchs_intersect_gdf['intersect_clusters_ids'] = '-'
anchs_intersect_gdf['intersect_clusters_ids'] = anchs_intersect_gdf['intersect_clusters_ids'].astype(object)
anchs_intersect_gdf['intersect_clusters_geom'] = anchs_intersect_gdf['intersect_clusters_ids'].copy()
for i in range(anchs_buffer_gdf['geometry'].shape[0]):
anchs_intersect0 = anchs_clusters_gdf['geometry'].intersects(anchs_buffer_gdf['geometry'][i])
# print(anchs_intersect.sum().copy())
anchs_intersect_gdf['intersect_num_clusters'][i] = anchs_intersect0.sum()
if anchs_intersect0.sum()>0:
anchs_intersect_gdf['intersect_clusters_ids'][i] = anchs_clusters_gdf[anchs_intersect0==True]['anchorage_id'].to_list()
anchs_intersect_gdf['intersect_clusters_geom'][i] = anchs_clusters_gdf[anchs_intersect0==True]['geometry'].to_list()
anchs_intersect = pandas.DataFrame(anchs_intersect_gdf)
anchs_intersect.to_csv('anchs_intersect.csv')
print("ELAPSED TIME:", time.time() - startt)
return anchs_intersect, anchs_intersect_gdf
###########################################################################################################
""" MAIN """
###########################################################################################################
if __name__ == "__main__":
print('[Stage 1 - Validity Check] Preprocessing Input AIS/VMS Data...')
print('optics'); p_cl, df = fun_optics_model2(cfg.MIN_SAMPLES, cfg.MAX_EPS)
#print('load csv'); df = pandas.read_csv('optics_clusters.csv', delimiter=',')
print('[Stage 2 - Data Clustering] Clustering with OPTICS...')
print('convex'); anchs_clusters, anchs_clusters_gdf = fun_convex_hull(df)
print('[Stage 3 - Anchorage Discovery] Create Polygons from Convex Hulls of OPTICS\' Clusters...')
print('intersect'); anchs_intersect, anchs_intersect_gdf = fun_intersect(anchs_clusters, anchs_clusters_gdf)
print("END")