Time Required for Running 5-50 Addresses.

There are a few conclusions we may draw from this information:

1. Server POST time is reliably the slowest part of finding a route. This is determined by the speed of the internet connection. Choosing a hosting service with fast throughput will be essential.
2. The BpTspSolver.js code is probably fast enough, without modification, to serve a future site. That means we can use existing code without doing more work like writing compiled C to increase site speed.
3. Recall that there are three address sets. Set 1 (always plotted as run 1) is only local Boulder addresses. Set 3 (always plotted as run 3) contains Denver metro area addresses including Longmont and Boulder. This means that the third set is of higher complexity. However, this does not increase the time needed for the site to process the addresses. That means that input complexity is not a significant factor in how long the site takes to return a route.

Python Code

# Routing Data Reformatting Tool
# Eric Johnson -- eric@focusNumeric.net

import numpy as np
import pandas as pd

# NOTE:  The blank rows printed to the end of this file are causing problems!  Fix it!

inputFilename = 'RoutingTestRuns.xlsx'
destinationFilename = 'RoutingTestRunsReformatted.xlsx'
# Required -- how many sheets are there?
numSheets = 8

# Initialize excel writer
writer = pd.ExcelWriter(destinationFilename)

for sheetIndex in range(0,numSheets):
    thisSheet = pd.read_excel(inputFilename, sheetname=sheetIndex)
    # pulls sheets.  Sheet 9 only has two runs.
    thisRunData1 = pd.Series(thisSheet['Set1'],dtype=str)
    thisRunData2 = pd.Series(thisSheet['Set2'],dtype=str)
    thisRunData3 = pd.Series(thisSheet['Set3'],dtype=str)
    # Perform the remainder of the processing on run 1
    for index in range(0, len(thisRunData1)):
        thisLine = thisRunData1[index]
        if not thisLine.startswith('h'):
            #print("Does not start with h")
            thisRunData1[index] = thisRunData1[index]
            # print(set1_5[index])
        if thisLine.startswith('h'):
            #print("starts with h")
            thisRunData1[index - 1] = thisRunData1[index - 1] + " " + thisRunData1[index]
            thisRunData1[index] = 'nan'
            # print(set1_5[index-1])
    # Now pull the lines that is not 'nan'
    thisRunData1 = thisRunData1.replace('nan', np.nan)
    thisRunData1 = thisRunData1.dropna()
    print(thisRunData1)
    thisRunData1 = pd.DataFrame(thisRunData1)
    sheet = str(5+5*sheetIndex) + str(" Run 1")
    thisRunData1.to_excel(writer, sheet_name=sheet)
    # Perform the remainder of the processing on run 2
    for index in range(0, len(thisRunData2)):
        thisLine = thisRunData2[index]
        if not thisLine.startswith('h'):
            # print("Does not start with h")
            thisRunData2[index] = thisRunData2[index]
            # print(set1_5[index])
        if thisLine.startswith('h'):
            # print("starts with h")
            thisRunData2[index - 1] = thisRunData2[index - 1] + " " + thisRunData2[index]
            thisRunData2[index] = 'nan'
            # print(set1_5[index-1])
    # Now pull the lines that is not 'nan'
    thisRunData2 = thisRunData2.replace('nan', np.nan)
    thisRunData2 = thisRunData2.dropna()
    print(thisRunData2)
    thisRunData2 = pd.DataFrame(thisRunData2)
    sheet = str(5+5*sheetIndex) + str(" Run 2")
    thisRunData2.to_excel(writer, sheet_name=sheet)
    # Perform the remainder of the processing on run 3
    for index in range(0, len(thisRunData3)):
        thisLine = thisRunData3[index]
        if not thisLine.startswith('h'):
            # print("Does not start with h")
            thisRunData3[index] = thisRunData3[index]
            # print(set1_5[index])
        if thisLine.startswith('h'):
            # print("starts with h")
            thisRunData3[index - 1] = thisRunData3[index - 1] + " " + thisRunData3[index]
            thisRunData3[index] = 'nan'
            # print(set1_5[index-1])
    # Now pull the lines that is not 'nan'
    thisRunData3 = thisRunData3.replace('nan', np.nan)
    thisRunData3 = thisRunData3.dropna()
    print(thisRunData3)
    thisRunData3 = pd.DataFrame(thisRunData3)
    sheet = str(5+5*sheetIndex) + str(" Run 3")
    thisRunData3.to_excel(writer, sheet_name=sheet)

writer.save()

\end{verbatim}
\begin{center}
\textbf{This is the 5 address plotting code:}
\end{center}
\begin{verbatim}
# Routing Performance Data Plotting
# Eric Johnson -- eric@focusNumeric.net

import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
import matplotlib.dates as mdates
import matplotlib.dates as dates
from matplotlib import gridspec


inputFilename = 'RoutingTestRunsReformatted.xlsx'
destinationFilename = ''

# read from file
dataSheet = pd.read_excel(inputFilename, sheetname=0)
runData1 = pd.Series(dataSheet['Set1'], dtype=str)
runData1 = runData1.sort_values()
dataSheet = pd.read_excel(inputFilename, sheetname=1)
runData2 = pd.Series(dataSheet['Set2'], dtype=str)
runData2 = runData2.sort_values()
dataSheet = pd.read_excel(inputFilename, sheetname=2)
runData3 = pd.Series(dataSheet['Set3'], dtype=str)
runData3 = runData3.sort_values()

# Normalize all timestamp info - keep track in timeList()

# Time format - required for datetime
FMT = '%H:%M:%S.%f'
# For data part 1
# Time list - to keep track of timeElapsed
timeList1 = list()
# Data definitions
firstLine1 = runData1[0]
firstLine1 = list(firstLine1.split(" "))
timeZero1 = firstLine1[0]
# Normalize time
for thisRow1 in runData1:
    thisList1 = list(thisRow1.split(" "))
    timeElapsed1 = str(datetime.strptime(thisList1[0], FMT) - datetime.strptime(timeZero1, FMT))
    thisList1[0] = timeElapsed1
    thisRow1 = thisList1
    timeList1.append(timeElapsed1)
# Add milliseconds to first element
timeList1[0] += ".000000"

print(timeList1)

events1 = range(0,len(timeList1))
print(len(events1))
print(len(timeList1))

# For data part 2
# Time list - to keep track of timeElapsed
timeList2 = list()
# Data definitions
firstLine2 = runData2[0]
firstLine2 = list(firstLine2.split(" "))
timeZero2 = firstLine2[0]
# Normalize time
for thisRow2 in runData2:
    thisList2 = list(thisRow2.split(" "))
    timeElapsed2 = str(datetime.strptime(thisList2[0], FMT) - datetime.strptime(timeZero2, FMT))
    thisList2[0] = timeElapsed2
    thisRow2 = thisList2
    timeList2.append(timeElapsed2)
# Add milliseconds to first element
timeList2[0] += ".000000"

print(timeList2)

events2 = range(0,len(timeList2))
print(len(events2))
print(len(timeList2))

# For data part 3
# Time list - to keep track of timeElapsed
timeList3 = list()
# Data definitions
firstLine3 = runData3[0]
firstLine3 = list(firstLine3.split(" "))
timeZero3 = firstLine3[0]
# Normalize time
for thisRow3 in runData3:
    thisList3 = list(thisRow3.split(" "))
    timeElapsed3 = str(datetime.strptime(thisList3[0], FMT) - datetime.strptime(timeZero3, FMT))
    thisList3[0] = timeElapsed3
    thisRow3 = thisList3
    timeList3.append(timeElapsed3)
# Add milliseconds to first element
timeList3[0] += ".000000"

print(timeList3)

events3 = range(0,len(timeList3))
print(len(events3))
print(len(timeList3))

# Now plot
x1 = dates.datestr2num(timeList1)
x2 = dates.datestr2num(timeList2)
x3 = dates.datestr2num(timeList3)
y1 = events1
y2 = events2
y3 = events3
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 3])
ax1 = plt.subplot(gs[0])
ax1.axis('on')
ax1.axes.get_xaxis().set_ticks([])
ax1.axes.get_yaxis().set_ticks([])
ax1.axvspan(xmin=0, xmax=.2, ymin=.05, ymax=.11, facecolor='#1f77b4', alpha=0.125)
ax1.text(.07, .073, 'Initialize', fontsize=15)
ax1.axvspan(xmin=0, xmax=.2, ymin=.13, ymax=.25, facecolor='#ff7f0e', alpha=0.125)
ax1.text(.025, .185, 'GET Server Requests', fontsize=15)
ax1.axvspan(xmin=0, xmax=.2, ymin=.255, ymax=.85, facecolor='#2ca02c', alpha=0.125)
ax1.text(.013, .55, 'BpTspSolver.js Executions', fontsize=15)
ax1.axvspan(xmin=0, xmax=.2, ymin=.875, ymax=.95, facecolor='#1f77b4', alpha=0.125)
ax1.text(.08, .9, 'Paint', fontsize=15)

txt = ax1.text(0.5, 0.5, "test", size=30, ha="center", color="w")
ax2 = plt.subplot(gs[1])
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S.%f'))
plt.plot(x1,y1,label="Run 1", color='#1f77b4')
plt.plot(x2,y2,label="Run 2", color='#ff7f0e')
plt.plot(x3,y3,label="Run 3", color='#2ca02c')
plt.title("5 Address Velocity Tests", fontsize=20, color='#1f77b4', fontstyle='oblique')
plt.xlabel("Execution Time: {hrs:min:sec.ms}", fontsize=15)
plt.ylabel("Server and JavaScript Events", fontsize=15)
plt.legend(loc="upper left", bbox_to_anchor=[0, 1], ncol=1, shadow=True, title="Legend", fancybox=True)
plt.gcf().autofmt_xdate()
plt.show()