def read_ais_data(self):
for file, color in zip(self.data_paths, self.colors):
one_MMSI = []
st_MMSI = []
parse.get_data_ST(file, st_MMSI)
shipdata = st_MMSI[0]
shipMMSI = shipdata['MMSI']
Length = int(shipdata['StaticInfo'][0]['Length'])
Width = int(shipdata['StaticInfo'][0]['Width'])
Draught = int(shipdata['StaticInfo'][0]['Draught'])
Dun = Length * Width * Draught
parse.get_data_DY(file, one_MMSI)
x_list = []
y_list = []
date_list = []
Dun_list = []
for shipdata in one_MMSI:
shipMMSI = shipdata['MMSI']
x = shipdata['DynamicInfo'][0]['Longitude'] # jingdu
y = shipdata['DynamicInfo'][0]['Latitude'] # weidu
datetime = shipdata['DynamicInfo'][0]['LastTime']
date_list.append(datetime)
x_list.append(x)
y_list.append(y)
Dun_list.append(Dun)
date = np.array(date_list)
x = np.array(x_list)
for i in range((x.shape)[0]):
if x[i] <= 0:
x[i] = 360 + x[i] # correct longtitude
y = np.array(y_list)
table = pd.DataFrame(columns=['Date', 'X', 'Y', 'Dun'])
table['Date'] = date
table['X'] = x
table['Y'] = y
table['Dun'] = np.array(Dun_list)
table.set_index('Date', drop=False, append=False, inplace=True, verify_integrity=False)
table = table.sort_index()
self.tables.append(table)
def Classification_By_Speed(PathName, Output):
data_paths = [os.path.join(PathName, s) for s in os.listdir(PathName)]
speed_distribution = {}
for file in data_paths:
one_MMSI = []
parse.get_data_DY(file, one_MMSI)
sp = one_MMSI[0]['DynamicInfo'][0]['Speed']
if sp <= 0:
continue
if speed_distribution.has_key(int(sp)):
speed_distribution[int(sp)] += 1
else:
speed_distribution[int(sp)] = 1
print speed_distribution
with open(Output, 'w') as f:
json.dump(speed_distribution, f)
def map_AllLine(DirectoryPath):
def compute_date(date0, date1):
d1 = pdatetime.strptime(date0, '%Y-%m-%d %H:%M:%S')
d2 = pdatetime.strptime(date1, '%Y-%m-%d %H:%M:%S')
return (d2 - d1).days
map = Basemap(projection='mill', lon_0=180, width=12000000, height=9000000)
map.drawcoastlines()
map.drawparallels(np.arange(-90, 90, 30), labels=[1, 0, 0, 0])
map.drawmeridians(np.arange(map.lonmin, map.lonmax + 30, 60), labels=[0, 0, 0, 1])
map.drawmapboundary(fill_color='white')
map.drawcoastlines(linewidth=0.5, color='y')
map.drawcountries(color='y')
map.drawstates(color='y')
map.fillcontinents(color='coral', lake_color='blue')
colors = cycle(allcolors.values())
data_paths = [os.path.join(DirectoryPath, s) for s in os.listdir(DirectoryPath)]
counter = 1
for file,color in zip(data_paths,colors):
one_MMSI = []
st_MMSI = []
parse.get_data_ST(file,st_MMSI)
shipdata = st_MMSI[0]
shipMMSI = shipdata['MMSI']
Length = int(shipdata['StaticInfo'][0]['Length'])
Width = int(shipdata['StaticInfo'][0]['Width'])
Draught = int(shipdata['StaticInfo'][0]['Draught'])
Dun = Length * Width * Draught
parse.get_data_DY(file, one_MMSI)
x_list = []
y_list = []
date_list = []
Dun_list = []
for shipdata in one_MMSI:
shipMMSI = shipdata['MMSI']
x = shipdata['DynamicInfo'][0]['Longitude'] # jingdu
y = shipdata['DynamicInfo'][0]['Latitude'] # weidu
datetime = shipdata['DynamicInfo'][0]['LastTime']
date_list.append(datetime)
x_list.append(x)
y_list.append(y)
Dun_list.append(Dun)
date = np.array(date_list)
x = np.array(x_list)
for i in range((x.shape)[0]):
if x[i] <= 0:
x[i] = 360 + x[i] # correct longtitude
y = np.array(y_list)
table = pd.DataFrame(columns=['Date', 'X', 'Y' , 'Dun'])
table['Date'] = date
table['X'] = x
table['Y'] = y
table['Dun'] = np.array(Dun_list)
table.set_index('Date', drop=False, append=False, inplace=True, verify_integrity=False)
table = table.sort_index()
# table.to_csv('look.csv')
# table = table.iloc[608:989]
print table
splite = [0]
for idx in range(len(table)-1):
if compute_date(str(table.iloc[idx]['Date']),str(table.iloc[idx+1]['Date'])) > 3:
splite.append(idx)
splite.append(len(table))
for idx in range(len(splite)-1):
tmp = table[splite[idx]+1:splite[idx+1]-1]
X = tmp['X'].values
Y = tmp['Y'].values
if X !=[] and Y!=[]:
lon, lat = map(X, Y)
fn = ('segment%d-%d.csv') % (shipMMSI,counter)
tmp.to_csv(fn)
counter += 1
current_label = ('%s %s %s t\n%s') % (shipMMSI,getNationFlag(shipMMSI),str(tmp['Dun'][0]),
str(tmp['Date'][0]))
map.plot(lon, lat,c=sorted_names[np.random.randint(100,len(sorted_names))],linestyle='-',marker='D',
markersize=1,
linewidth=2)
plt.text(lon[0], lat[0],current_label)
map.scatter(lon[0], lat[0], 60, c='green', marker='*', edgecolors='none', zorder=10) # start
map.scatter(lon[-1], lat[-1], 60,c='red',marker='*',edgecolors='none', zorder=10) # end
# print X[0],Y[0],X[-1],Y[-1]
plt.title('All ShipLine')
plt.show()
def map_AllPoint(DirectoryPath):
def _DBSCAN(X, Y, color):
center_x = []
center_y = []
X = X.reshape(-1, 1)
Y = Y.reshape(-1, 1)
data = np.hstack((X, Y))
model = DBSCAN(eps=0.015, min_samples=2)
model.fit(data)
y_hat = model.labels_
core_indices = np.zeros_like(y_hat, dtype=bool)
core_indices[model.core_sample_indices_] = True
y_unique = np.unique(y_hat)
clrs = plt.cm.Spectral(np.linspace(0, 0.8, y_unique.size))
data[:, 0], data[:, 1] = map(data[:, 0], data[:, 1])
for k, clr in zip(y_unique, clrs):
cur = (y_hat == k)
if k == -1:
map.scatter(data[cur, 0], data[cur, 1], s=1,
c='black') # nosie
continue
map.scatter(data[cur, 0], data[cur, 1], s=40, c=color, marker='*')
center_x.extend(data[cur, 0])
center_y.extend(data[cur, 1])
map.plot(center_x,
center_y,
c=sorted_names[np.random.randint(100, len(sorted_names))],
linestyle='-',
markersize=3,
linewidth=1)
map = Basemap(projection='mill', lon_0=180, width=12000000, height=9000000)
map.drawcoastlines()
map.drawparallels(np.arange(-90, 90, 30), labels=[1, 0, 0, 0])
map.drawmeridians(np.arange(map.lonmin, map.lonmax + 30, 60),
labels=[0, 0, 0, 1])
map.drawmapboundary(fill_color='white')
map.drawcoastlines(linewidth=0.5, color='y')
map.drawcountries(color='y')
map.drawstates(color='y')
map.fillcontinents(color='coral', lake_color='blue')
colors = cycle(allcolors.values())
data_paths = [
os.path.join(DirectoryPath, s) for s in os.listdir(DirectoryPath)
]
for file, color in zip(data_paths, colors):
table = pd.DataFrame(columns=['Date', 'X', 'Y', 'Course'])
X = []
Y = []
one_MMSI = []
parse.get_data_DY(file, one_MMSI)
x_list = []
y_list = []
date_list = []
course_list = []
speed_list = []
for shipdata in one_MMSI:
shipMMSI = shipdata['MMSI']
x = shipdata['DynamicInfo'][0]['Longitude'] # jingdu
y = shipdata['DynamicInfo'][0]['Latitude'] # weidu
datetime = shipdata['DynamicInfo'][0]['LastTime']
course = shipdata['DynamicInfo'][0]['course']
speed = shipdata['DynamicInfo'][0]['Speed']
date_list.append(datetime)
course_list.append(course)
speed_list.append(speed)
x_list.append(x)
y_list.append(y)
date = np.array(date_list)
x = np.array(x_list)
for i in range((x.shape)[0]):
if x[i] <= 0:
x[i] = 360 + x[i] # correct longtitude
y = np.array(y_list)
tab = pd.DataFrame(columns=['Date', 'X', 'Y', 'Course', 'Speed'])
tab['Date'] = date
tab['X'] = x
tab['Y'] = y
tab['Course'] = np.array(course_list)
tab['Speed'] = np.array(speed_list)
table = pd.concat([table, tab])
table.set_index('Date',
drop=False,
append=False,
inplace=True,
verify_integrity=False)
table = table.sort_index()
print table
# table.to_csv('look.csv')
west_east_splite = []
if table.iloc[0]['Course'] > 180:
west_east_splite.append(0)
else:
west_east_splite.append(0)
for idx in range(len(table) - 1):
if float(table.iloc[idx]['Course']) > 180 and float(
table.iloc[idx + 1]['Course']) < 180:
west_east_splite.append(idx)
# elif float(table.iloc[idx]['Course']) < 180 and float(table.iloc[idx+1]['Course']) > 180:
# west2east.append(idx)
west_east_splite.append(len(table) - 1)
X = table['X'].values
Y = table['Y'].values
if X != [] and Y != []:
_DBSCAN(X, Y, color)
lon, lat = map(X, Y)
# map.scatter(lon, lat,c='b',marker='D',s=1)
plt.title('Japan ShipLine')
plt.show()
table.iloc[idx + 1]['Course']) < 180:
west_east_splite.append(idx)
# elif float(table.iloc[idx]['Course']) < 180 and float(table.iloc[idx+1]['Course']) > 180:
# west2east.append(idx)
west_east_splite.append(len(table) - 1)
X = table['X'].values
Y = table['Y'].values
if X != [] and Y != []:
_DBSCAN(X, Y, color)
lon, lat = map(X, Y)
# map.scatter(lon, lat,c='b',marker='D',s=1)
plt.title('Japan ShipLine')
plt.show()
if __name__ == "__main__":
all_MMSI = []
one_MMSI = []
all_MMSI_DY = []
path = "/media/xxxx/xx/AISProject/Hawaii/tanker"
data_paths = [os.path.join(path, s) for s in os.listdir(path)]
for file in data_paths:
one_MMSI = []
parse.get_data_DY(file, one_MMSI)
mapline(one_MMSI)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import ais_parse as parse
import ais_draw as draw
if __name__ == "__main__":
all_MMSI_cargo = []
all_MMSI_tanker = []
cargo_path = "xxx"
tanker_path = "xxx"
tmp_cargo = [os.path.join(cargo_path + os.sep, s) for s in os.listdir(cargo_path + os.sep)]
tmp_tanker = [os.path.join(tanker_path + os.sep, s) for s in os.listdir(tanker_path + os.sep)]
for file in tmp_cargo:
parse.get_data_DY(file, all_MMSI_cargo)
for file in tmp_tanker:
parse.get_data_DY(file, all_MMSI_tanker)
# print all_MMSI_cargo,all_MMSI_tanker
draw.mapscatter(all_MMSI_cargo,all_MMSI_tanker)