A tool package for MITS AIS users.
pip install aisUtils
git clone https://github.com/aisUtils.git
The package can satisfy fundamental demand for the use of AIS, which is what I usually do.
Suppose we start with AIS messages. We need to decode, crawl static information and perform filtering operations.
The decoder process requires the package—libais.
Parameters include source file path, destination file path and original timestamp, no return value. If you want to decode multiple files, it is easy to use a loop outside the call.
from aisUtils import Preprocessing as pp
sourcePath = r'D:/data/2021-08-28_CST.txt' # source file path
desPath = r'D:/data/2021-08-28_output.csv' # destination file path
timestr = '2021-08-28:00:00:00;' # AIS initial timestamp
pp.decode(sourcePath, desPath, timestr)If you need some static data such as length, ship type, crawl them from a mmsi list. Then you will get a static data table.
from aisUtils import Preprocessing as pp
sourcePath = r'D:/python/data' # a directory stored all ais files
mmsiPath = r'D:/python/mmsiall.csv' # store mmsilist
staticDataPath = r'D:/python/staticData.csv' # store ais static data
# get the mmsilist
mmsiList = pp.getMmsiList(sourcePath)
# write the mmsilist
pp.writeMmsiList(mmsiPath, mmsiList)
# crawl
pp.crawl(mmsiPath, staticDataPath)Match the static data — length and ship type with the ais files.
from aisUtils import Preprocessing as pp
staticDataPath = r'D:/python/staticData.csv'
aisPath = r'D:/data/2021-08-28_output.csv'
desPath = r'D:/data/2021-08-28_matching.csv'
pp.match(staticDataPath, aisPath, desPath)This part includes area, speed and ship type filtering.
from aisUtils import Preprocessing as pp
sourcePath = r'D:/data/2021-08-28_matching.csv'
desPath = r'D:/data/2021-08-28_prep.csv'
max_Lon = 122.757
min_Lon = 122.145
max_Lat = 30.749
min_Lat = 30.409
speed = [2, 20]
shiptype_list = [6, 7, 8]
pp.filtering(sourcePath, desPath, max_Lon, min_Lon, max_Lat, min_Lat, speed, shiptype_list)if you want to read the file, you can use the code block, which returns a array type.
import pandas as pd
data = pd.read_csv(r'D:/data/2021-08-28_prep.csv')
data_p = data.values
#print(type(data_p)) #numpy.ndarrayPackage scipy has powerful statistical tools. CSDN link
from scipy import interpolate as inter
from scipy.interpolate import lagrang
import numpy as np
# sort the data according the index
# data = data[np.lexsort((data[:,1], data[:,0]))]
xli = np.arange(0,constants.pi*2,0.1) # Interval points
yli = inter.interp1d(x,y,kind ="linear")(xli) # Linear interpolation
ycub = inter.interp1d(x,y,kind ="cubic")(xli) # Cubic spline interpolation
yquadratic = inter.interp1d(x,y,kind ="quadratic")(xli) # Quadratic spline interpolation
ynear = inter.interp1d(x,y,kind ="nearest")(xli) # Nearest neighbor interpolation
# Lagrange interpolation
def lagrangePoly(poly, x, length):
result = []
for i in range(len(x)):
subresult = 0
for j in range(length):
subresult += poly[j] * (x[i] ** j)
result.append(subresult)
return result
x = np.linspace(0,constants.pi*2,4)
y = np.cos(x**2/3+4)
poly = lagrange(x, y)
length = len(x)
ylagrange = lagrangePoly(poly, xli, length)from haversine import haversine
lyon=(45.7597,4.8422)# (lat, lon)
paris=(48.8567,2.3508)
haversine(lyon,paris)from scipy.spatial.distance import cdist
cdist(x1, x2, metric = ' euclidean') # Euclidean distance
cdist(x1, x2, metric = 'minkowski')
cdist(x1, x2, metric = ' jaccard')Some codes for project, including section flow statistics, ship types statistics and track point drawing.
Although I have created many functions in the package according my own ideas, there are still many possible requirements that can be improved later. I am tired. Thanks to senior fellow apprentice Chunshen for supporting the development of the crawl function.
*** 最后写依赖的时候整理一下
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