Datawhale 智慧海洋建设-Task2 数据分析

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此部分为智慧海洋建设竞赛的数据分析模块,通过数据分析,可以熟悉数据,为后面的特征工程做准备,欢迎大家后续多多交流。

赛题:智慧海洋建设1 L" v3 r2 _- P+ R

数据分析的目的:

( N: r% J3 |: H: ^8 v EDA的主要价值在于熟悉整个数据集的基本情况(缺失值、异常值),来确定所获得数据集可以用于接下来的机器学习或者深度学习使用。了解特征之间的相关性、分布,以及特征与预测值之间的关系。为进行特征工程提供理论依据。

项目地址:https://github.com/datawhalechina/team-learning-data-mining/tree/master/wisdomOcean比赛地址:https://tianchi.aliyun.com/competition/entrance/231768/introduction?spm=5176.12281957.1004.8.4ac63eafE1rwsY

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2.1 学习目标

学习如何对数据集整体概况进行分析,包括数据集的基本情况(缺失值、异常值)学习了解变量之间的相互关系、变量与预测值之间的存在关系。完成相应学习打卡任务

2.2 内容介绍

数据总体了解读取数据集并了解数据集的大小,原始特征维度;通过info了解数据类型;粗略查看数据集中各特征的基本统计量缺失值和唯一值查看数据缺失值情况查看唯一值情况

数据特性和特征分布

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三类渔船轨迹的可视化坐标序列可视化三类渔船速度和方向序列可视化三类渔船速度和方向的数据分布

作业一:剔除异常点后画图

import pandas as pd

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import geopandas as gpd

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from pyproj import Proj

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from keplergl import KeplerGl

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from tqdm import tqdm

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import os

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import matplotlib.pyplot as plt

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import shapely

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import numpy as np

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from datetime import datetime

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import warnings

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warnings.filterwarnings(ignore)

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plt.rcParams[font.sans-serif] = [SimSun] # 指定默认字体为新宋体。

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plt.rcParams[axes.unicode_minus] = False # 解决保存图像时 负号- 显示为方块和报错的问题。

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#获取文件夹中的数据

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def get_data(file_path,model):

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assert model in [train, test], {} Not Support this type of file.format(model)

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paths = os.listdir(file_path)

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# print(len(paths))

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tmp = []

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for t in tqdm(range(len(paths))):

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p = paths[t]

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with open({}/{}.format(file_path, p), encoding=utf-8) as f:

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next(f)

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for line in f.readlines():

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tmp.append(line.strip().split(,))

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tmp_df = pd.DataFrame(tmp)

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if model == train:

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tmp_df.columns = [ID, lat, lon, speed, direction, time, type]

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else:

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tmp_df[type] = unknown

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tmp_df.columns = [ID, lat, lon, speed, direction, time, type]

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tmp_df[lat] = tmp_df[lat].astype(float)

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tmp_df[lon] = tmp_df[lon].astype(float)

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tmp_df[speed] = tmp_df[speed].astype(float)

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tmp_df[direction] = tmp_df[direction].astype(int)#如果该行代码运行失败,请尝试更新pandas的版本

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return tmp_df

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# 平面坐标转经纬度,供初赛数据使用

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# 选择标准为NAD83 / California zone 6 (ftUS) (EPSG:2230),查询链接:CS2CS - Transform Coordinates On-line - MyGeodata Cloud

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def transform_xy2lonlat(df):

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x = df[lat].values

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y = df[lon].values

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p=Proj(+proj=lcc +lat_1=33.88333333333333 +lat_2=32.78333333333333 +lat_0=32.16666666666666 +lon_0=-116.25 +x_0=2000000.0001016 +y_0=500000.0001016001 +datum=NAD83 +units=us-ft +no_defs )

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df[lon], df[lat] = p(y, x, inverse=True)

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return df

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#修改数据的时间格式

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def reformat_strtime(time_str=None, START_YEAR="2019"):

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"""Reformat the strtime with the form 08 14 to START_YEAR-08-14 """

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time_str_split = time_str.split(" ")

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time_str_reformat = START_YEAR + "-" + time_str_split[0][:2] + "-" + time_str_split[0][2:4]

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time_str_reformat = time_str_reformat + " " + time_str_split[1]

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# time_reformat=datetime.strptime(time_str_reformat,%Y-%m-%d %H:%M:%S)

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return time_str_reformat

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#计算两个点的距离

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def haversine_np(lon1, lat1, lon2, lat2):

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lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])

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dlon = lon2 - lon1

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dlat = lat2 - lat1

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a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2

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c = 2 * np.arcsin(np.sqrt(a))

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km = 6367 * c

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return km * 1000

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def compute_traj_diff_time_distance(traj=None):

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"""Compute the sampling time and the coordinate distance."""

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# 计算时间的差值

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time_diff_array = (traj["time"].iloc[1:].reset_index(drop=True) - traj[

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"time"].iloc[:-1].reset_index(drop=True)).dt.total_seconds() / 60

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# 计算坐标之间的距离

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dist_diff_array = haversine_np(traj["lon"].values[1:], # lon_0

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traj["lat"].values[1:], # lat_0

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traj["lon"].values[:-1], # lon_1

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traj["lat"].values[:-1] # lat_1

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# 填充第一个值

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time_diff_array = [time_diff_array.mean()] + time_diff_array.tolist()

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dist_diff_array = [dist_diff_array.mean()] + dist_diff_array.tolist()

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traj.loc[list(traj.index),time_array] = time_diff_array

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traj.loc[list(traj.index),dist_array] = dist_diff_array

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return traj

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#对轨迹进行异常点的剔除

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def assign_traj_anomaly_points_nan(traj=None, speed_maximum=23,

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time_interval_maximum=200,

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coord_speed_maximum=700):

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"""Assign the anomaly points in traj to np.nan."""

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def thigma_data(data_y,n):

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data_x =[i for i in range(len(data_y))]

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ymean = np.mean(data_y)

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ystd = np.std(data_y)

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threshold1 = ymean - n * ystd

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threshold2 = ymean + n * ystd

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judge=[]

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for data in data_y:

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if (data < threshold1)|(data> threshold2):

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judge.append(True)

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else:

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judge.append(False)

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return judge

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# Step 1: The speed anomaly repairing

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is_speed_anomaly = (traj["speed"] > speed_maximum) | (traj["speed"] < 0)

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traj["speed"][is_speed_anomaly] = np.nan

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# Step 2: 根据距离和时间计算速度

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is_anomaly = np.array([False] * len(traj))

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traj["coord_speed"] = traj["dist_array"] / traj["time_array"]

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# Condition 1: 根据3-sigma算法剔除coord speed以及较大时间间隔的点

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is_anomaly_tmp = pd.Series(thigma_data(traj["time_array"],3)) | pd.Series(thigma_data(traj["coord_speed"],3))

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is_anomaly = is_anomaly | is_anomaly_tmp

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is_anomaly.index=traj.index

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# Condition 2: 轨迹点的3-sigma异常处理

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traj = traj[~is_anomaly].reset_index(drop=True)

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is_anomaly = np.array([False] * len(traj))

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if len(traj) != 0:

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lon_std, lon_mean = traj["lon"].std(), traj["lon"].mean()

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lat_std, lat_mean = traj["lat"].std(), traj["lat"].mean()

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lon_low, lon_high = lon_mean - 3 * lon_std, lon_mean + 3 * lon_std

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lat_low, lat_high = lat_mean - 3 * lat_std, lat_mean + 3 * lat_std

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is_anomaly = is_anomaly | (traj["lon"] > lon_high) | ((traj["lon"] < lon_low))

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is_anomaly = is_anomaly | (traj["lat"] > lat_high) | ((traj["lat"] < lat_low))

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traj = traj[~is_anomaly].reset_index(drop=True)

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return traj, [len(is_speed_anomaly) - len(traj)]

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df=get_data(rC:\Users\admin\hy_round1_train_20200102,train)

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#对轨迹进行异常点剔除,对nan值进行线性插值

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ID_list=list(pd.DataFrame(df[ID].value_counts()).index)

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DF_NEW=[]

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Anomaly_count=[]

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for ID in tqdm(ID_list):

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df_id=compute_traj_diff_time_distance(df[df[ID]==ID])

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df_new,count=assign_traj_anomaly_points_nan(df_id)

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df_new["speed"] = df_new["speed"].interpolate(method="linear", axis=0)

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df_new = df_new.fillna(method="bfill")

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df_new = df_new.fillna(method="ffill")

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df_new["speed"] = df_new["speed"].clip(0, 23)

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Anomaly_count.append(count)#统计每个id异常点的数量有多少

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DF_NEW.append(df_new)

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#将数据写入到pkl格式

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load_save = Load_Save_Data()

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load_save.save_data(DF_NEW,"C:/Users/admin/wisdomOcean/data_tmp1/total_data.pkl")

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#### 三类渔船速度和方向可视化

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# 把训练集的所有数据,根据类别存放到不同的数据文件中

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def get_diff_data():

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Path = "C:/Users/admin/wisdomOcean/data_tmp1/total_data.pkl"

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with open(Path,"rb") as f:

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total_data = pickle.load(f)

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load_save = Load_Save_Data()

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kind_data = ["刺网","围网","拖网"]

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file_names = ["ciwang_data.pkl","weiwang_data.pkl","tuowang_data.pkl"]

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for i,datax in enumerate(kind_data):

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data_type = [data for data in total_data if data["type"].unique()[0] == datax]

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load_save.save_data(data_type,"C:/Users/admin/wisdomOcean/data_tmp1/" + file_names[i])

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get_diff_data()

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#对轨迹进行异常点剔除,对nan值进行线性插值

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ID_list=list(pd.DataFrame(df[ID].value_counts()).index)

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DF_NEW=[]

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Anomaly_count=[]

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for ID in tqdm(ID_list):

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df_id=compute_traj_diff_time_distance(df[df[ID]==ID])

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df_new,count=assign_traj_anomaly_points_nan(df_id)

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df_new["speed"] = df_new["speed"].interpolate(method="linear", axis=0)

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df_new = df_new.fillna(method="bfill")

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df_new = df_new.fillna(method="ffill")

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df_new["speed"] = df_new["speed"].clip(0, 23)

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Anomaly_count.append(count)#统计每个id异常点的数量有多少

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DF_NEW.append(df_new)

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# 每类轨迹,随机选取某个渔船,可视化速度序列和方向序列

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def visualize_three_traj_speed_direction():

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fig,axes = plt.subplots(nrows=3,ncols=2,figsize=(20,15))

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plt.subplots_adjust(wspace=0.3,hspace=0.3)

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# 随机选出刺网的三条轨迹进行可视化

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file_types = ["ciwang_data","weiwang_data","tuowang_data"]

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speed_types = ["ciwang_speed","weiwang_speed","tuowang_speed"]

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doirections = ["ciwang_direction","weiwang_direction","tuowang_direction"]

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colors = [pink, lightblue, lightgreen]

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for i,file_name in tqdm(enumerate(file_types)):

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datax = get_random_one_traj(type=file_name)

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x_data = datax["速度"].loc[-1:].values

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y_data = datax["方向"].loc[-1:].values

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axes[i][0].plot(range(len(x_data)), x_data, label=speed_types[i], color=colors[i])

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axes[i][0].grid(alpha=2)

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axes[i][0].legend(loc="best")

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axes[i][1].plot(range(len(y_data)), y_data, label=doirections[i], color=colors[i])

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axes[i][1].grid(alpha=2)

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axes[i][1].legend(loc="best")

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plt.show()

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visualize_three_traj_speed_direction()

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作业二:相关性分析。

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data_train.loc[data_train[type]==刺网,type_id]=1

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data_train.loc[data_train[type]==围网,type_id]=2

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data_train.loc[data_train[type]==拖网,type_id]=3

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f, ax = plt.subplots(figsize=(9, 6))

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ax = sns.heatmap(np.abs(df.corr()),annot=True)

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plt.show()

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从图中可以清楚看到,经纬度和速度跟类型相关性比较大。

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