# import the libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt import geopandas as gpd import seaborn as sns import datetime as dt import folium import random from branca.element import Figure # It rezise the map, It is a spinoff from Folium

df = pd.read_csv('vehicle_clean.csv')

df1 = df.drop(columns=['VEHICLE_LOCATION_ID','VEHICLE_MESSAGE_ID', 'CITY','STATE'])

#uploading the random sample of 50 trucks sample= pd.read_csv('sample.csv', header=None ) sample= sample[0].tolist()

#subseting the dataframe for the sample vehicles= df1[df1['VEHICLE_ID'].isin(sample)] vehicles.info()

df2= pd.read_csv('faults_clean.csv')

collist=['VEHICLE_ID','LONGITUDE', 'LATITUDE','MESSAGE_DATE_TIME', 'MALFUNCTION_LAMP_STATUS','RED_LAMP_STATUS','YELLOW_LAMP_STATUS', 'PROTECT_LAMP_STATUS']

df3=df2.loc[:,(collist)]

df3.replace(to_replace= {'on':1,'off':0, 'notAvailable':0}, inplace=True) df3.fillna(0, inplace=True)

coldic = {'MESSAGE_DATE_TIME':'DATE','MALFUNCTION_LAMP_STATUS':'MALFUNCTION', 'RED_LAMP_STATUS':'RED','YELLOW_LAMP_STATUS': 'YELLOW', 'PROTECT_LAMP_STATUS': 'PROTECT'}

df3.rename(columns =coldic, inplace= True)

df3['DATE']= pd.to_datetime(df3['DATE']) df3['MONTH']= df3['DATE'].dt.month df3['WEEK']= df3['DATE'].dt.isocalendar().week

# Creating a column that counts all allarms that are not red or yellow df3['OTHER']= np.where((df3['RED']== 1)|(df3['YELLOW']== 1), 0, 1)

df3.head() df3.groupby(['WEEK']).sum()

#import the shape file of US states into a gdf us49 = gpd.read_file('us49/us_49.shp')

sns.reset_defaults()

fig = plt.figure(figsize=(10, 6)) ax = us49.plot(facecolor='LightGray', edgecolor='k', linewidth=.2) x, y = vehicles['LONGITUDE'].values, vehicles['LATITUDE'].values ax.scatter(x,y, color='black', alpha=0.7, s=0.5) plt.show()

STATES =['CA', 'NV', 'UT', 'AZ']

df2 = df[(df['STATE'].isin(STATES))& (df['VEHICLE_ID'].isin(sample))]

# ploting subset database fig = plt.figure(figsize=(10, 6)) ax = us49.plot(facecolor='LightGray', edgecolor='k', linewidth=.2) x, y = df2['LONGITUDE'].values, df2['LATITUDE'].values ax.scatter(x,y, color='black', alpha=0.7, s=0.5) plt.show()

vehicles_w15= vehicles[vehicles['WEEK']==15]

#selecting a week fig = plt.figure(figsize=(10, 6)) ax = us49.plot(facecolor='LightGray', edgecolor='k', linewidth=.2) x, y = vehicles_w15['LONGITUDE'].values, vehicles_w15['LATITUDE'].values ax.scatter(x,y, color='green', alpha=0.7, s=0.5) plt.show()

def select_path(v,w): """Create a subset from a dataframe as a list of tupples""" sub = df1.loc[(df1['VEHICLE_ID']==v)&(df1['WEEK']==w), ('LATITUDE','LONGITUDE')] return list(sub.to_records(index=False))

def select_alarm(v,w,f): """Create a subset from a dataframe as a list of tupples""" """v= VEHICLE_ID, W= week number, F=lamp_status column""" sub2 = df3.loc[(df3['VEHICLE_ID']==v)&(df3['WEEK']==w)&(df3[f]==1), ('LATITUDE','LONGITUDE')] return list(sub2.to_records(index=False))

# list of all vehicles with red alarm in 2020 in the southwest all_red= [100139268, 100222721, 100228008, 100229375, 100299676, 100314180, 100256311, 100262353, 100347615, 100350264, 100233932, 100254004, 100254536, 100254962, 100258247, 100269163, 100270479, 100293104, 100309951, 100322706, 100334205, 100369400, 100229625, 100231342, 100231648, 100238360, 100250699, 100267705, 100270284, 100307132, 100308279, 100309928, 100311028, 100383265, 100260140, 100323525, 100237621, 100248663, 100268680, 100309471, 100266778, 100287723, 100300068, 100208091, 100230714, 100232478, 100233334, 100234028, 100247982, 100257964, 100276636, 100280244, 100288872, 100301943, 100305030, 100310845, 100315213, 100316098, 100326189, 100338103, 100340040, 100241773, 100287419, 100242661, 100272854, 100314940, 100235108, 100280727, 100282535, 100296157, 100279283, 100354895, 100298153]

# Setting a random selction of vehicles, and choose the week samples=random.choices(all_red, k=10) week= 51

# Here you can adjust the size and where it is centered fig01=Figure(height=400,width=850) map_01 = folium.Map(location=[38, -95], zoom_start=4, tiles='cartodbpositron')

df3.info()

# A: adding markers for the red alarm for i in range(len(samples)): b = (select_alarm(samples[i], week, 'RED')) if len(b)>0: for each in b: folium.Marker(each,icon=folium.Icon(color='red')).add_to(map_01)

# B: adding markers for the yellow alarm for i in range(len(samples)): c = (select_alarm(samples[i], week, 'YELLOW')) if len(c)>0: for each in c: folium.Marker(each,icon=folium.Icon(color='orange')).add_to(map_01)

# C: marker for other faults for i in range(len(samples)): d = (select_alarm(samples[i], week, 'OTHER')) if len(d)>0: for each in d: folium.Marker(each,icon=folium.Icon(color='lightgray')).add_to(map_01)

# A: converting the location of vehicles (coordinates) into a list of tuples point_01= select_path(samples[0], week) point_02= select_path(samples[1], week) point_03= select_path(samples[2], week) point_04= select_path(samples[3], week) point_05= select_path(samples[4], week) point_06= select_path(samples[5], week) point_07= select_path(samples[6], week) point_08= select_path(samples[7], week) point_09= select_path(samples[8], week) point_10= select_path(samples[9], week)

# B: Creating lines for the vehicles path f1= folium.PolyLine(point_01, color="pink", weight=2.5, opacity=1).add_to(map_01) f2= folium.PolyLine(point_02, color="darkred", weight=2.5, opacity=1).add_to(map_01) f3= folium.PolyLine(point_03, color="red", weight=2.5, opacity=1).add_to(map_01) f4= folium.PolyLine(point_04, color="gold", weight=2.5, opacity=1).add_to(map_01) f5= folium.PolyLine(point_05, color="yellow", weight=2.5, opacity=1).add_to(map_01) f6= folium.PolyLine(point_06, color="lightgreen", weight=2.5, opacity=1).add_to(map_01) f5= folium.PolyLine(point_07, color="cyan", weight=2.5, opacity=1).add_to(map_01) f8= folium.PolyLine(point_08, color="blue", weight=2.5, opacity=1).add_to(map_01) f9= folium.PolyLine(point_09, color="cadetblue", weight=2.5, opacity=1).add_to(map_01) f10= folium.PolyLine(point_10, color="purple", weight=2.5, opacity=1).add_to(map_01)

# Step 4: Ploting the map fig01.add_child(map_01)

# You dont neeed to run the lines bellow, here just to show how to do it # Adding lines to the different feature groups line_1=folium.vector_layers.PolyLine(point_01,tooltip='Vehicle_1',color='green',weight=1).add_to(f1) line_2=folium.vector_layers.PolyLine(point_02,popup='<b>Path of Vehicle_2</b>',tooltip='Vehicle_2',color='blue',weight=1).add_to(f2) line_3= ... # Creating feature groups f1=folium.FeatureGroup("Vehicle 1") f2=folium.FeatureGroup("Vehicle 2") f3=folium.FeatureGroup("Vehicle 3") # Adding to the map and a layer control f1.add_to(map_01) f2.add_to(map_01) f3.add_to(map_01) folium.LayerControl().add_to(map_01) map_01