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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Eclipse SUMO, Simulation of Urban MObility; see https://eclipse.dev/sumo
# Copyright (C) 2021-2023 German Aerospace Center (DLR) and others.
# This program and the accompanying materials are made available under the
# terms of the Eclipse Public License 2.0 which is available at
# https://www.eclipse.org/legal/epl-2.0/
# This Source Code may also be made available under the following Secondary
# Licenses when the conditions for such availability set forth in the Eclipse
# Public License 2.0 are satisfied: GNU General Public License, version 2
# or later which is available at
# https://www.gnu.org/licenses/old-licenses/gpl-2.0-standalone.html
# SPDX-License-Identifier: EPL-2.0 OR GPL-2.0-or-later
# @file macrOutput.py
# @author Amirhosein Karbasi
# @date 2021-04-20
from __future__ import absolute_import
from __future__ import print_function
import os
import sys
import matplotlib.pyplot as plt
import pandas_read_xml as pdx
import pandas as pd
import numpy as np
from collections import Counter
if "SUMO_HOME" in os.environ:
sys.path.append(os.path.join(os.environ['SUMO_HOME'], 'tools'))
import sumolib # noqa
def main(args):
ap = sumolib.options.ArgumentParser()
ap.add_argument("file", dest="file", category="input", type=ap.file, required=True, help="An XML input file")
options = ap.parse_args(args=args)
if args is None or len(args) < 2:
print("Error: An xml file must be given as input")
sys.exit(1)
df = pdx.read_xml(options.file, ['meandata'])
df = pdx.flatten(df)
df = df.pipe(pdx.flatten)
df = df.pipe(pdx.flatten)
df = df.pipe(pdx.flatten)
df = df.rename({'interval|@begin': 'begin', 'interval|@end': 'end',
'interval|edge|@sampledSeconds': 'sampledSeconds', 'interval|edge|@density': 'density',
'interval|edge|@laneDensity': 'laneDensity', 'interval|edge|@speed': 'speed'}, axis=1)
df['begin'] = df['begin'].astype(float)
df['end'] = df['end'].astype(float)
df["sampledSeconds"] = df["sampledSeconds"].astype(float)
df["density"] = df["density"].astype(float)
df["laneDensity"] = df["laneDensity"].astype(float)
df["speed"] = df["speed"].astype(float)
df = df.replace(np.NaN, 0)
df['begin'] = df['begin'].astype(int)
# calculation time interval
bft = df.begin.iloc[0]
eft = df.end.iloc[0]
time_interval = eft - bft
time_interval = int(time_interval)
# The end time of the last interval
_lastsimulationperiod_ = (df.end.iat[-1]).astype(int)
# creating a list of all end time intervals
_beginvalues_ = list(range(time_interval, _lastsimulationperiod_+time_interval, time_interval))
# detecting number of segments
counter1 = Counter(df.begin)
_seg = counter1[0]
# calculating total length of network
length = df['sampledSeconds'] / (df['end']-df['begin']) / df['density']
df['Length'] = length.replace(np.NaN, 0).replace(np.inf, 0)
i = 0
j = 0
__net = []
while _beginvalues_[i] < (_lastsimulationperiod_):
_net = sum(df.Length.iloc[j:j+_seg])
__net.append(_net)
i = i+1
j = j+_seg
# calculating meandensity,meanflow,meanspeed (density=density)
i = 0
j = 0
MD = []
MS = []
MF = []
while _beginvalues_[i] < (_lastsimulationperiod_):
numofveh = (1/time_interval)*(sum(df.sampledSeconds.iloc[j:j+_seg]))
speedznumofveh = (1/time_interval)*(sum(df.sampledSeconds.iloc[j:j+_seg]*df.speed.iloc[j:j+_seg]))
if numofveh > 0:
meanspeed_ = 3.6*speedznumofveh/numofveh
else:
meanspeed_ = 0
meandensity_ = (sum(df.density.iloc[j:j+_seg]*df.Length.iloc[j:j+_seg]))/_net
meanflow_ = (sum(df.density.iloc[j:j+_seg]*df.Length.iloc[j:j+_seg]*df.speed.iloc[j:j+_seg]*3.6))/_net
MD.append(meandensity_)
MS.append(meanspeed_)
MF.append(meanflow_)
i = i+1
j = j+_seg
# plot
plt.scatter(MD, MS)
plt.xlabel("Density (Veh/km)")
plt.ylabel("Speed (Km/hr)")
plt.show()
plt.scatter(MD, MF)
plt.xlabel("Density (Veh/km)")
plt.ylabel("Flow (Veh/hr)")
plt.show()
plt.scatter(MS, MF)
plt.xlabel("Speed (Km/hr)")
plt.ylabel("Flow (Veh/hr)")
plt.show()
# calculating meandensity,meanflow,meanspeed (density=laneDensity)
i = 0
j = 0
lMD = []
lMS = []
lMF = []
while _beginvalues_[i] <= (_lastsimulationperiod_ - time_interval):
numofveh = (1/time_interval)*(sum(df.sampledSeconds.iloc[j:j+_seg]))
speedznumofveh = (1/time_interval)*(sum(df.sampledSeconds.iloc[j:j+_seg]*df.speed.iloc[j:j+_seg]))
if numofveh > 0:
meanspeed_ = 3.6*speedznumofveh/numofveh
else:
meanspeed_ = 0
meandensity_ = (sum(df.laneDensity.iloc[j:j+_seg]*df.Length.iloc[j:j+_seg]))/_net
meanflow_ = (sum(df.laneDensity.iloc[j:j+_seg]*df.Length.iloc[j:j+_seg]*df.speed.iloc[j:j+_seg]*3.6))/_net
lMD.append(meandensity_)
lMS.append(meanspeed_)
lMF.append(meanflow_)
i = i+1
j = j+_seg
# plot
plt.scatter(lMD, lMS)
plt.xlabel("Density (Veh/km)")
plt.ylabel("Speed (Km/hr)")
plt.show()
plt.scatter(lMD, lMF)
plt.xlabel("Density (Veh/km)")
plt.ylabel("Flow (Veh/hr)")
plt.show()
plt.scatter(lMS, lMF)
plt.xlabel("Speed (Km/hr)")
plt.ylabel("Flow (Veh/hr)")
plt.show()
# Build a csv file
Macro_Features = {'Density': MD,
'Speed': MS,
'Flow': MF,
}
df = pd.DataFrame(Macro_Features, columns=['Density', 'Speed', 'Flow'])
df.to_csv('Macro_density.csv')
# Build a csv file
Macro_Features = {'Density': lMD,
'Speed': lMS,
'Flow': lMF,
}
df = pd.DataFrame(Macro_Features, columns=['Density', 'Speed', 'Flow'])
df.to_csv('Macro_lanedensity.csv')
if __name__ == "__main__":
sys.exit(main())
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