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BD code v7.py
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BD code v7.py
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import numpy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pathlib import Path
import math
from math import pi
from math import sqrt
import statistics
import scipy.integrate
from scipy.signal import find_peaks
from scipy.signal import argrelmin
from numpy import trapz
from scipy.integrate import cumtrapz
# SETUP VARIABLES - USER INPUTS
BD = 3 #Bluedrop file is from
fileNum = '009B' # write the bin file number you want to analyze (do not include 'bLog' or '.bin')
soiltype = "u" #s = sand, c=clay, m=mixed, u=unknown
atype = 'm' # m = mantle area (best for sands), p = projected area (best for clays), b = both
tiptype = 'c' # c = cone, p = parabolic, b = blunt
#offset = 1 # this value is subtracted from the accelerometer readings
droptype = 'a' #w = water, #a = air
sign = "uk" #enter an effective unit weight value in kg/m^3 or "uk" if unknown
# paste the filepath to the folder where the BD data is stored
binFilepath = Path("H:/.shortcut-targets-by-id/1-AJMoJ7sju0bbgBxYYfsq9uMyeECS_MX/Quinhagak June 2023/Demo data collection/PFFP/June 19, 2023/Drops Only")
#paste the filepath to an excel file that the analysis results will be printed in
outputPath = Path("H:/.shortcut-targets-by-id/1-AJMoJ7sju0bbgBxYYfsq9uMyeECS_MX/Quinhagak June 2023/Demo data collection/PFFP/June 19, 2023/Data Analysis 6-19-23.xlsx") # Path to pre-existing Excel File
plotPath = Path("H:/.shortcut-targets-by-id/1-AJMoJ7sju0bbgBxYYfsq9uMyeECS_MX/Quinhagak June 2023/Demo data collection/PFFP/June 19, 2023/Analysis Figures")
#if applicable, paste the filepath to an excel file that troubleshooting data will be printed in
troubleshootingPath = Path("H:\My Drive\CEE 5904 - Project & Report/2021 FRF Data\Bluedrop\October/14 October 2021 AM\Troubleshooting.xlsx")
# FUNCTIONS FOR USE IN ANALYSIS
def masslength(tiptype): #sets the mass and length of the pentrometer based on the tip
global mass
global length
if tiptype == 'c':
mass = 7.71 #kg
length = 8.48-0.93 #originally 7.87, 7.57 for perfect 60 deg, 8.48 measured - .93 measured 90 deg
elif tiptype == 'p':
mass = 9.15
length = 8.26
elif tiptype == 'b':
mass = 10.30
length = 8.57
def dropstartend(peak): #after locating the peaks, this function chops the minute long file into a smaller segment immediately before and after the peak
global dropstart
global dropend
if peak <= 1500:
dropstart = 1
dropend = peak + 500
elif peak > 119500:
dropstart = peak - 1500
dropend = 120000
else:
dropstart = peak - 1500
dropend = peak + 500
def accPick(dg, d): #this function picks the smallest accelerometer that's not maxed out to perform the integration on
maxAcc = dg["250g (g)"].max()
global acc
global accName
global accNameg
global accg
if maxAcc < 5: #- offset:
if dg["2g (g)"].max() < 1.8: #offset: # does an extra check for the 2g because of noise
acc = dfCal["2g (m/s^2)"]
accg = dfCalg["2g (g)"]
accName = "2g (m/s^2)"
accNameg = "2g (g)"
else:
acc = dfCal["18g (m/s^2)"]
accg = dfCalg["18g (g)"]
accName = "18g (m/s^2)"
accNameg = "18g (g)"
elif maxAcc < 18: #- offset
acc = dfCal["18g (m/s^2)"]
accg = dfCalg["18g (g)"]
accName = "18g (m/s^2)"
accNameg = "18g (g)"
elif maxAcc < 50: #- offset:
acc = dfCal["50g (m/s^2)"]
accg = dfCalg["50g (g)"]
accName = "50g (m/s^2)"
accNameg = "50g (g)"
else:
acc = dfCal["250g (m/s^2)"]
accg = dfCalg["50g (g)"]
accName = "250g (m/s^2)"
accNameg = "250g (g)"
def findchangepts(): #This function picks the moment that the BD impacts the ground (not in use currently)
global drop
jlist = list()
global jindex
print("finding start of drop...")
for i in range(4,len(accg)-4):
p1 = 1
#print(p1)
p2 = i
#print(p2)
p3 = len(accg)
#print(p3)
sample1 = list(accg[p1:p2-1])
#print(sample1)
sample2 = list(accg[p2:p3])
#print(sample2)
stat1 = math.log(statistics.variance(sample1))
stat2 = math.log(statistics.variance(sample2))
#print(stat1)
j1 = (i-1)*stat1
j2 = ((len(accg)-1)-i+1)*stat2
j = j1+j2
#print(j)
jlist.append(j)
drop = min(jlist)
#print("drop is")
#print(drop)
jlist = np.array(jlist)
#print(jlist)
#print(jlist.size)
jlist = np.insert(jlist, 0, (0,0,0,0)) #reshape to match up with dataframe d
jlist = np.append(jlist, (0,0,0,0)) #reshape to match up with dataframe d
#print(jlist.size) #should be 2000
jindex = np.where(jlist==drop) #finds the index of the drop start
jindex = int(jindex[0]) #converts the index into a number from a tuple
print("Jlist: ", jlist)
def finddropend(n): #finds the location after the peak where the deceleration is 1
global penend
below0list = list()
#for i in range(dropstart+jindex, dropend, 1):
for i in range(peak, dropend, 1):
if accg[i] < 1: #the offset is applied later in the code
penend = i
below0list = np.append(below0list, penend)
penend=int(min(below0list))
def integration(d, accint): #integrates the deceleration data to solve for velocity and penetration depth
global vel
global maxvel
global dep
global maxdep
#accint = acc[penstart:penend]
#print(len(d))
#print(len(accint))
vel = scipy.integrate.cumtrapz(accint, x=d["Time (s)"]) # solves for velocity
vel = np.array(vel)
vel = numpy.insert(vel, 0, 0) #not sure what to insert here, but it makes it the right size
vel = np.flip(vel)
maxvel = vel.max()
maxvel = round(maxvel,1)
dep = scipy.integrate.cumtrapz(vel, x=d["Time (s)"]) # solves for penetration depth
dep = numpy.insert(dep, 0, 0) # not sure what to insert here, but it makes it the right size
maxdep = dep.max()
maxdep = round(maxdep,4)
d.insert(9, "Velocity (m/s)", vel)
d.insert(10, "Penetration Depth (m)", dep)
def peakpick():
global penstart
fig, (ax1) = plt.subplots(1)
plt.plot(dfCalg['Count'], dfCalg[accNameg], label=accNameg)
plt.plot(dfCalg['Count'], dfCalg['2g (g)'], label='2g')
ax1.set_xlim(left=dropstart, right=dropend)
ax1.legend()
ax1.set(ylabel="Deceleration (g)")
ax1.set(xlabel="Time")
ax1.set_title('Double-click start of drop #' + str(n))
startendpt = plt.ginput(2, 0)
pentimes = []
for t in startendpt:
for x in t:
pentimes.append(x)
penstart = int(pentimes[0])
print("start of penetration: ", penstart)
def CheckingFunction(): # (to be used with auto peak finding) This function is added to check the values of the start and end of the drop if the autofinding function doesn't work
global jindex
global num1
xvalues=np.linspace(0,len(dg)-1,len(dg))
plt.figure(num=1)
temp_dg=dg['250g (g)']
plt.plot(xvalues,temp_dg)
plt.grid(visible=True,which='both')
plt.show()
print ('The start of the drop is', jindex)
print ('The end of the drop is', num1)
print('Are the start and end of the drop correct?(y/n)')
drop_input=input()
if drop_input=='n':
peakpick()
jindex = penstart
num1 = penend
def areafind(): #finds the embedded area based on the penetration depth, area type, and the tip
global area
global trunc_index
a1 = list() #placeholder for the penetrated area at that specific depth
d1 = dep*100 #penetration depth array, in cm
#print(len(d1))
r = list() #placeholder for the radius at that specific depth
if tiptype == 'c':
if atype == 'm':
for k in range(0,len(d1)):
if d1[k]<length:
r.append(.22+d1[k]*((4.375-.22)/7.55))
#r.append(d1[k]*np.tan(30*pi/180)) this is from the original matlabcode
a1.append(pi*r[k]*(sqrt((r[k]**2)+(d1[k]**2))))
elif d1[k]>=length:
r.append(4.375)
a1.append(pi*r[k]*(sqrt((r[k]**2)+(length**2))))
a1[k] = a1[k]/10000
area = a1
#print(r)
print(np.column_stack((d1,r, area)))
elif atype == 'p':
for k in range(0,len(d1)):
if d1[k]<length:
r.append(.22+d1[k]*((4.375-.22)/7.55))
# r.append(d1[k]*np.tan(30*pi/180))
a1.append(pi*(r[k])**2)
elif d1[k]>=length:
r.append(4.375)
'''if r[k-1]<r[k]:
trunc_index = r.index(r[k-1])+jindex+dropstart #performs analysis only on readings beyond depth of cone height'''
a1.append(pi*(r[k])**2)
a1[k] = a1[k]/10000
area = a1
elif tiptype == 'b':
if atype =='m':
for k in range(0,len(d1)):
if d1[k]<length:
r.append(4.375)
a1.append(pi*r[k]**2 + 2*pi*r[k]*d1[k])
if d1(k)>=length:
r.append(4.375)
a1.append(pi*r[k]**2 + 2*pi*r[k]*length)
a1[k]=a1[k]/10000
area = a1
elif atype == 'p':
for k in range(0,len(d1)):
a1.append(pi*4.375**2)
a1[k]=a1[k]/10000
area = a1
'''elif tiptype == "p":
if atype == 'm':
for k in range(1,len(d)):
if d1[k]<length:
r[k]=sqrt(2.4184*d1[k])
polarfun = @(theta,r) r.*sqrt(0.745*r.^2 + 1);
A1(k)= integral2(polarfun,0,2*pi,0,r(k));
elif atype == 'p':
for k in range(1,len(d)):'''
def qdynfun(acc): # calculates dynamic bearing capacity
global qdyn
global maxbcdep
global maxqdyn
global qdyntable
buoy = 1020*0.002473
if droptype == "w": #water drops
Fbe = (mass-buoy)*acc #drop force
elif droptype =="a": #air drops
Fbe = mass*acc
qdyn = (Fbe/area)/1000 #Dynamic bearing capacity (kPa)
qdyntable = pd.DataFrame()
qdyntable["Qdyn"] = qdyn
qdyntable["Depth"] = dep
qdyntablet = qdyntable#.truncate(before=trunc_index)
qdynt = qdyntablet["Qdyn"]
#print(qdyntable)
maxqdyn = qdynt.max() #gives maximum qdyn
maxbcdep = qdyntablet.loc[qdyntable["Qdyn"]==maxqdyn, 'Depth'].item()
maxqdyn = round(maxqdyn,1)
maxbcdep = round(maxbcdep,4)
#print("maxbcdep = ", maxbcdep) #finds the penetration depth corresponding to the maximum qdyn
"""bcmin = argrelmin(qdyn)
peaksArray = np.array(bcmin)
print("Peaks Array = ", peaksArray)"""
def qsbcfun(acc): #calculates quasi-static bearing capacity
global bctable
global qsbc
global maxHqsbc
global maxLqsbc
global maxAqsbc
if droptype == "w":
srcv = np.log10(vel/0.02) #Velocity portion of the strain rate correction.
srfk = [0.2, 0.4, 1, 1.5] #list of strain rate factors to run; must be in increasing order
bctable = pd.DataFrame()
for i in range(0,len(srfk)):
fsr = 1+srfk[i]*srcv
qsbc = qdyn/fsr
bctable.insert(i, "qsbc for srf = "+str(srfk[i]), qsbc)
bctable_avgs = pd.DataFrame()
bctable_avgs["High Average qsbc"]= (bctable.iloc[:,0]+bctable.iloc[:,1])/2 #Average of the lowest 2 strain rate factors
bctable_avgs["Low Average qsbc"] = (bctable.iloc[:,int(len(srfk))-1]+bctable.iloc[:,int(len(srfk)-2)])/2 #average of the highest 2 strain rate factors
maxHqsbc = bctable_avgs["High Average qsbc"].max()
maxHqsbc = round(maxHqsbc, 1)
maxLqsbc = bctable_avgs["Low Average qsbc"].max()
maxLqsbc = round(maxLqsbc,1)
maxAqsbc = "-"
#print(bctable)
elif droptype == "a":
srcv = np.log10(vel/0.02) #Velocity portion of the strain rate correction.
srfk = 0.31*maxvel
fsr = 1+srfk*srcv
qsbc = qdyn/fsr
maxHqsbc = "-"
maxLqsbc = "-"
maxAqsbc = qsbc.max()
maxAqsbc = round(maxAqsbc,1)
def dr(accg): #Albatal 2019 method for calculating relative density (Duck, NC specific!)
global Dr
maxacc = accg.max() #maximum deceleration, in g
#print("Max deceleration = ", maxacc)
Dr = -2.18*10**-4*maxacc**3+1.29*10**-2*maxacc**2+1.61*maxacc-13.09 #Albatal 2019
Dr = round(Dr, 1)
#print("Relative density is ", dr)
def ignore_drop():
global keep_drop
print('Keep Drop? (y/n)')
keep_drop=input()
def duncan_correlation(): #duncan correlation for caluclating friction angle
global phi
global sign
#coefficients A, B, C, and D used in the Duncan Correlation. Duncan[34,10,3,2], Albatal[34,10,2,5]
A = 34
B = 10
C = 3
D = 2
pa = 101.325 #kPa
print("max penetration depth = ", maxdep)
if sign == "uk":
sign = 1120*maxdep #1120 = 70pcf in kg/m^3
phi = A + B*Dr/100-(C+D*Dr/100)*math.log(sign/pa)
phi = round(phi, 1)
print("phi =", phi)
def firmnessfactor(acc, drop):
global maxacc
global ff
maxacc = acc.max()
g = 9.80665
tp = len(drop)/2000
ff = maxacc/(g*tp*maxvel)
ff = round(ff,1)
print("Firmness factor = ", ff)
def bctruncate(dropg, drop, acc, qdyn):
dropg = pd.DataFrame(dropg)
drop = pd.DataFrame(drop)
qdyn = pd.DataFrame(qdyn)
acc = pd.DataFrame(acc)
dropg = dropg.truncate(before=trunc_index)
drop = drop.truncate(before=trunc_index)
acc = acc.truncate(before=trunc_index)
qdyn = qdyn.truncate(before=trunc_index)
#Plots for exporting
def overviewplot(): #Plot showing all accellerometers and pore pressure readings
fig, (ax1, ax2, ax3) = plt.subplots(3)
fig.set_size_inches(14, 7)
#plt.tight_layout()
ax1.plot(time, g250g, label="250g")
ax1.plot(time, g50g, label="50g")
ax1.plot(time, g18g, label="18g")
ax1.plot(time, g2g, label="2g")
ax1.legend(loc = "upper right")
ax1.set(ylabel="Deceleration (g)")
ax1.set_title("BD file "+fileNum)
ax1.grid()
ax2.plot(time, ppm, label="Pore Pressure")
ax2.set(ylabel="Pore Pressure (kPa)")
ax2.grid()
ax3.plot(time, gX55g, label="X 55g")
ax3.plot(time, gY55g, label="Y 55g")
ax3.legend(loc = "upper right")
ax3.set(ylabel="Deceleration (g)")
ax3.set(xlabel="Time (s)")
ax3.grid()
fig.subplots_adjust(bottom=.1, left = .1)
plotName = fileNum+" Overview.png"
#plt.savefig(plotPath / plotName)
plt.show()
def integplot(drop, accName): #Deceleration,Velocity,and penetration depth vs time plots
fig, (ax1, ax2, ax3) = plt.subplots(3)
fig.set_size_inches(7, 7)
#resize to 7,7, 7,14
#plt.tight_layout()
timereset = drop["Time (s)"]- drop["Time (s)"].iloc[0]
ax1.plot(timereset, drop[accName], color = "k" ) #,marker = 11
ax1.set(ylabel="Deceleration (m/s^2)")
ax1.set_title("BD file "+fileNum+ " Drop #"+str(n))
ax1.grid()
ax2.plot(timereset, drop['Velocity (m/s)'] , color = "k" )
ax2.set(ylabel="Velocity (m/s)")
ax2.grid()
ax3.plot(timereset, drop["Penetration Depth (m)"] , color = "k")
ax3.set(ylabel="Penetration Depth (m)", xlabel="Time(s)")
ax3.grid()
fig.subplots_adjust(bottom=.1, left = .1)
plotName = fileNum+" "+str(n)+" Integ"
plt.savefig(plotPath / plotName)
plt.show()
def dep_qsbc_comboplot(dropg, drop, accNameg, qdyn):
fig, (ax1, ax2) = plt.subplots(1,2)
fig.set_size_inches(7, 7)
ax1.plot(dropg[accNameg], drop["Penetration Depth (m)"]*100, color = "k", linestyle = "solid") #marker = 11
ax1.plot(drop["Velocity (m/s)"], drop["Penetration Depth (m)"]*100, color = "k", linestyle = "dashed")
ax1.plot(drop["X55g (m/s^2)"], drop["Penetration Depth (m)"]*100, color = "b", linestyle = "dashed")
ax1.plot(drop["Y55g (m/s^2)"], drop["Penetration Depth (m)"]*100, color = "r", linestyle = "dashed")
ax1.set(xlabel="Deceleration (g) and Velocity (m/s)", ylabel="Penetration Depth (cm)")
ax1.invert_yaxis()
ax1.legend(["Deceleration (g)", "Velocity (m/s)", "X tilt (g)", "Y tilt (g)"], loc = "upper right")
ax1.set_title("BD file "+fileNum)
ax1.grid()
if droptype == "a": #for partial saturation, only one qsbc curve
ax2.plot(qdyn, drop["Penetration Depth (m)"]*100, label="Qdyn") #color = "k", marker = 11 #Plots Qdyn
ax2.plot(qsbc, drop["Penetration Depth (m)"]*100, label="QSBC") #plots QSBC
ax2.set(xlabel="Bearing Capacity (kPa)")
ax2.set_xlim(0,800)
ax2.invert_yaxis()
ax2.legend(["Qdyn", "QSBC"], loc = "upper right")
ax2.set_title("Drop #"+str(n))
ax2.grid()
else: #for saturated drops, use a range of strain-rate factors
ax2.plot(qdyn, drop["Penetration Depth (m)"]*100, label="Qdyn") #color = "k", marker = 11 #Plots Qdyn
ax2.plot(bctable.iloc[:,0], drop["Penetration Depth (m)"]*100, label=str(bctable.columns[0])) #plots QSBC with smallest srf
ax2.plot(bctable.iloc[:,len(bctable.columns)-1], drop["Penetration Depth (m)"]*100, label=str(bctable.columns[len(bctable.columns)-1])) #plots QSBC with largest srf
for i in range(1,len(bctable.columns)-1): #plots qsbc for all other srfs
ax2.plot(bctable.iloc[:,i], drop["Penetration Depth (m)"]*100, label=str(bctable.columns[i]), color = '.6')
ax2.set(xlabel="Bearing Capacity (kPa)")
ax2.set_xlim(0,)
ax2.invert_yaxis()
ax2.legend(["Qdyn", str(bctable.columns[0]), str(bctable.columns[len(bctable.columns)-1])], loc = "upper right")
ax2.set_title("Drop #"+str(n))
ax2.grid()
fig.subplots_adjust(bottom=.1, left = .1)
def duck_dep_qsbc_comboplot(dropg, drop, dropt, accNameg, qdynt):
fig, (ax1, ax2) = plt.subplots(1,2)
fig.set_size_inches(7, 7)
ax1.plot(dropg[accNameg], drop["Penetration Depth (m)"]*100, color = "k", linestyle = "solid") #marker = 11
ax1.plot(drop["Velocity (m/s)"], drop["Penetration Depth (m)"]*100, color = "k", linestyle = "dashed")
#ax1.plot(drop["X55g (m/s^2)"], drop["Penetration Depth (m)"]*100, color = "b", linestyle = "dashed")
#ax1.plot(drop["Y55g (m/s^2)"], drop["Penetration Depth (m)"]*100, color = "r", linestyle = "dashed")
ax1.set(xlabel="Deceleration (g) and Velocity (m/s)", ylabel="Penetration Depth (cm)")
ax1.invert_yaxis()
ax1.legend(["Deceleration (g)", "Velocity (m/s)"])
ax1.set_title("BD file "+fileNum)
ax1.grid()
if droptype == "a": #for partial saturation, only one qsbc curve
y = dropt["Penetration Depth (m)"]*100
ax2.plot(qdynt[10:], y[10:], label="Qdyn") #color = "k", marker = 11 #Plots Qdyn, chops off first 40 readings
ax2.plot(qsbc[10:-20], y[10:-20], label="QSBC") #plots QSBC; chops off last 10 points which go off towards infinity (can adjust as needed)
ax2.set(xlabel="Bearing Capacity (kPa)",)
ax2.set_xlim(0,qdyn.max())
ax2.invert_yaxis()
ax2.legend(["Qdyn", "QSBC"], loc = "upper right")
ax2.set_title("Drop #"+str(n))
ax2.grid()
else: #for saturated drops, use a range of strain-rate factors
y = list(dropt["Penetration Depth (m)"]*100)
ax2.plot(qdynt[10:-20], y[10:-20], label="Qdyn") #color = "k", marker = 11 #Plots Qdyn
ax2.plot(bctablet.iloc[10:-20,0], y[10:-20], label=str(bctablet.columns[0])) #plots QSBC with smallest srf
ax2.plot(bctablet.iloc[10:-20,len(bctable.columns)-1], y[10:-20], label=str(bctable.columns[len(bctable.columns)-1])) #plots QSBC with largest srf
for i in range(1,len(bctablet.columns)-1): #plots qsbc for all other srfs
ax2.plot(bctablet.iloc[10:-20,i], y[10:-20], label=str(bctablet.columns[i]), color = '.6')
#bctablet.iloc[:,i].pop()
ax2.set(xlabel="Bearing Capacity (kPa)")
ax2.set_xlim(0,)
ax2.invert_yaxis()
ax2.legend(["Qdyn", str(bctablet.columns[0]), str(bctablet.columns[len(bctablet.columns)-1])], loc = "upper right")
ax2.set_title("Drop #"+str(n))
ax2.grid()
fig.subplots_adjust(bottom=.1, left = .1)
plotName = fileNum+" "+str(n)+" BC"
plt.savefig(plotPath / plotName)
plt.show()
#plots for troubleshooting
def depthplot(dropg, drop, accNameg): #Velocity and develeration vs. penetration depth
fig, (ax1) = plt.subplots(1)
ax1.plot(dropg[accNameg], drop["Penetration Depth (m)"]*100, color = "k", linestyle = "solid") #marker = 11
ax1.plot(drop["Velocity (m/s)"], drop["Penetration Depth (m)"]*100, color = "k", linestyle = "dashed")
ax1.set(xlabel="Deceleration (g) and Velocity (m/s)", ylabel="Penetration Depth (cm)")
ax1.invert_yaxis()
ax1.legend(["Deceleration (g)", "Velocity (m/s)"])
ax1.set_title("BD file "+fileNum)
plt.show()
def peakplot(): # Plot showing peak deceleration
peakplot = plt.plot(x)
peakplot = plt.plot(peaks, x[peaks], "x")
plt.show()
def qdynplot(drop, qdyn): #Plot showing dynamic bearing capacity vs depth
#print(len(drop))
#print(len(qdyn))
#print((bctable.iloc[:,0]))
#print(len(bctable.iloc[:,len(bctable.columns)-1]))
fig, (ax1) = plt.subplots(1)
if droptype == "a": #for partial saturation, only one qsbc curve
ax1.plot(qdyn, drop["Penetration Depth (m)"]*100, label="Qdyn") #color = "k", marker = 11 #Plots Qdyn
ax1.plot(qsbc, drop["Penetration Depth (m)"]*100, label="QSBC") #plots QSBC
ax1.set(xlabel="Bearing Capacity (kPa)")
ax1.set_xlim(0,)
ax1.invert_yaxis()
ax1.legend(["Qdyn", "QSBC"])
ax1.set_title("Bearing Capacity- "+fileNum+ " "+str(n))
ax1.grid()
else:
ax1.plot(qdyn, drop["Penetration Depth (m)"]*100, label="Qdyn") #color = "k", marker = 11 #Plots Qdyn
ax1.plot(bctable.iloc[:,0], drop["Penetration Depth (m)"]*100, label=str(bctable.columns[0])) #plots QSBC with smallest srf
ax1.plot(bctable.iloc[:,len(bctable.columns)-1], drop["Penetration Depth (m)"]*100, label=str(bctable.columns[len(bctable.columns)-1])) #plots QSBC with largest srf
for i in range(1,len(bctable.columns)-1): #plots qsbc for all other srfs
ax1.plot(bctable.iloc[:,i], drop["Penetration Depth (m)"]*100, label=str(bctable.columns[i]), color = "k")
ax1.set(xlabel="Bearing Capacity (kPa)", ylabel="Penetration Depth (cm)")
ax1.set_xlim(0,)
ax1.invert_yaxis()
ax1.legend(["Qdyn", str(bctable.columns[0]), str(bctable.columns[len(bctable.columns)-1])])
ax1.set_title("Bearing Capacity- "+fileNum+ " "+str(n))
plt.show()
def tiltplot():
fig, (ax1) = plt.subplots(1)
plt.plot(time, gX55g, label="2g")
plt.plot(time, gY55g, label="2g")
ax1.legend()
ax1.set(ylabel="Deceleration (g)")
ax1.set(xlabel="Time (s)")
ax1.set_title("BD file "+fileNum)
#Exporting Functions
def exporttoexcel():
with pd.ExcelWriter(outputPath, mode="a", if_sheet_exists='replace') as writer:
output_table.to_excel(writer, sheet_name = fileNum, index=False)
def troubleshooting_export():
with pd.ExcelWriter(troubleshootingPath, mode="a", if_sheet_exists = 'new') as writer:
drop1.to_excel(writer, sheet_name = str(n), index = False)
qdyntable.to_excel(writer, sheet_name = "qdyn", index = False)
#Set the penetrometer mass and length
masslength(tiptype)
# READ BD DATA IN
data_array = [] # creates an empty array for us to fill with bd data
fileName = 'bLog'+fileNum+".bin"
# print(fileName)
newPath = binFilepath / fileName
#print(newPath)
file = open(newPath, 'rb') # read file
element = file.read(3) # create a byte list with each element having 3 bytes
while element:
# Convert to signed integer before adding to data array
iVAl = int.from_bytes(element, byteorder='big', signed=True)
data_array.append(iVAl) # adds the reshaped data from the bd file to the data frame
element = file.read(3)
np_array = np.array(data_array) # create numpy array from the list
np_array = np.reshape(np_array, (-1, 10)) # convert the 1d array to 2d array with 10 cols
#print(np_array.shape)
# print(np_array)
df = pd.DataFrame(np_array) # Creates a Dataframe in pandas from the bd data
df.columns = ['Count', 'no clue', 'g2g', 'g18g', 'g50g', 'ppm', 'g200g', 'gX55g', 'gY55g', 'g250g'] # names columns
# print(dfCal)
# APPLY CALIBRATION FACTORS
if BD == 3: # calibration factors from March 2023
g2g = (df['g2g']-33570.1)/1614577.9 #- offset# accelerometers are in g
g18g = (df['g18g']+13495)/163387.2 #- offset
g50g = (df['g50g']-238817.4)/63779.5 #- offset
ppm = ((df['ppm']-139040.1)/20705) * 6.89475729 # converts to kPa
g200g = (df['g200g'] -262332.4)/38888.7 #- offset
gX55g = (df['gX55g']-70406)/59754.3
gY55g = (df['gY55g']-69421.1)/141871.5
g250g = (df['g250g']-39077.4)/13746.9 #- offset
if BD == 2: # calibration factors from March 2023
g2g = (df['g2g']+36597.6)/1637627.3 #- offset# accelerometers are in g
g18g = (df['g18g']-26185.3)/160297.2 #- offset
g50g = (df['g50g']-212256.9)/63968.7 #- offset
ppm = ((df['ppm']+55518.9)/18981.7) * 6.89475729 # converts to kPa
g200g = (df['g200g']-175499.4)/30583.8 #- offset
gX55g = (df['gX55g']-53629.9)/68590.9
gY55g = (df['gY55g']-43694.3)/68280.3
g250g = (df['g250g']-39619.9)/13545.8 #- offset
if BD == 1: # calibration factors from March 2023
g2g = (df['g2g']-43727.6)/1625064 #- offset # accelerometers are in g
g18g = (df['g18g']-45085.6)/160925.7 #- offset
g50g = (df['g50g']-173493.4)/63944.6 #- offset
ppm = ((df['ppm']+31776.1)/20679.7) * 6.89475729 # this is kPa
g200g = (df['g200g'] -731734.3)/32192.4 #- offset
gX55g = (df['gX55g'] -68837.9)/52137.3
gY55g = (df['gY55g']-68015.9)/28074.9
g250g = (df['g250g']+10580)/13688.7 #- offset
time = (df['Count']-df['Count'].iloc[0]+1)/2000 # gives time in s
count = df["Count"]-df['Count'].iloc[0]
# make a new dataframe of the calibrated values in units of g
dfCalg = pd.DataFrame([count, time, g2g, g18g, g50g, g200g, g250g, gX55g, gY55g, ppm])
dfCalg = dfCalg.T
dfCalg.columns = ['Count', 'Time (s)', '2g (g)', '18g (g)', '50g (g)', '200g (g)', '250g (g)', 'X55g (g)', 'Y55g (g)', 'Pore Pressure (kPa)'] # names columns
#print(dfCalg)
#make a new dataframe of the calibrated values in units of m/s^2
dfCal = pd.DataFrame([count, time, g2g, g18g, g50g, g200g, g250g, gX55g, gY55g, ppm])
dfCal = dfCal.T
dfCal.columns = ['Count','Time (s)', '2g (m/s^2)', '18g (m/s^2)', '50g (m/s^2)', '200g (m/s^2)', '250g (m/s^2)', 'X55g (m/s^2)', 'Y55g (m/s^2)', 'Pore Pressure (kPa)'] # names columns
dfCal['2g (m/s^2)'] = dfCal['2g (m/s^2)'] * 9.80665
dfCal['18g (m/s^2)'] = dfCal['18g (m/s^2)'] * 9.80665
dfCal['50g (m/s^2)'] = dfCal['50g (m/s^2)'] * 9.80665
dfCal['200g (m/s^2)'] = dfCal['200g (m/s^2)'] * 9.80665
dfCal['250g (m/s^2)'] = dfCal['250g (m/s^2)'] * 9.80665
dfCal['X55g (m/s^2)'] = dfCal['X55g (m/s^2)'] * 9.80665
dfCal['Y55g (m/s^2)'] = dfCal['Y55g (m/s^2)'] * 9.80665
#print(dfCal)
#Locate the drops
x = np.array(g250g) # what accelerometer to get the peaks from - use 250 because it will never be maxed out
max250 = max(g250g)
peaks, _ = find_peaks(x, height = 2, distance=1000, prominence=3) # finds the largest peaks more than 2g spaced at least 10000 counts apart
peaksArray = np.array(peaks) # prints a list of the count where the peaks occur
#peaksArray = filter(filterpeak, peaksArray)
#print(peaksArray)
q = (peaksArray.shape) #gives number of peaks
nDrops = int(q[0]) #number of drops in the file
#print(nDrops)
# For each drop, find the start and end points and integrate to solve for velocity and acceleration
#peakplot()
a = 0 #index variable for drop analysis
n = 1 #index variable for drop analysis
peakplot()
overviewplot()
while n <= nDrops :
peak = int(peaksArray[a]) # count at the ath drop
print("peak index:" , peak)
dropstartend(peak) #zooms in the drop file to only consider 500 counts before and 1500 counts after the peak deceleration
#print(dropstart, dropend)
if n == 1 :
drop1 = dfCal[dropstart:dropend] # start and end points of the drop in m/s^2
drop1g = dfCalg[dropstart:dropend] # start and end points of the drop in g
drop1 = pd.DataFrame(drop1) # makes dataframe including all data within the start and end points of the drop
drop1g = pd.DataFrame(drop1g)
dg = drop1g
d = drop1
accPick(dg, d) # chooses what accelerometer to use
acc1 = acc
acc1g = accg
acc1Name = accName
acc1Nameg = accNameg
#findchangepts() #automatically identifies the moment of penetration
peakpick() #interactive graph for user to choose start of penetration
print("penstart: ", penstart)
finddropend(n-1) #automatically find the end of the drop
print("penend: ", penend)
offset = acc1g[penstart] #sets the offset to be the acceleration at the moment of impact
print("offset: ", offset)
#print(drop)
dg = dfCalg[penstart:penend] #chops the file to only include penetration
d = dfCal[penstart:penend]
acc1g = accg[penstart:penend]
acc1g = acc1g-offset
acc1 = acc[penstart:penend]
offsetms = offset * 9.80665
acc1 = acc1-offsetms
drop1 = d
drop1g = dg
integration(d, acc1) #double integration to solve for velocity and penetration depth
drop1 = d
areafind() #area calculations for bearing capacity calculations
qdynfun(acc1) #calculation of dynamic bearing capacity
qsbcfun(acc1) #calculation of quasi-static bearing capacities
qdyn1 = qdyn
integplot(drop1,acc1Name)
#qdynplot(drop1, qdyn1)
print("Max penetration depth is: ", maxdep)
print("Pre-truncation d1", drop1)
drop1 = pd.DataFrame(drop1)
drop1g = pd.DataFrame(drop1g)
acc1 = pd.DataFrame(acc1)
qdyn1 = pd.DataFrame(qdyn1)
if droptype == "w":
bctable = pd.DataFrame(bctable)
drop1t = drop1 #.truncate(before=trunc_index) #this truncates each df such that only depths below cone height are considered
drop1gt = drop1g #.truncate(before=trunc_index)
acc1t = acc1 #.truncate(before=trunc_index)
acc1t = np.array(acc1t)
acc1gt = acc1g #.truncate(before=trunc_index)
acc1gt = np.array(acc1gt)
drop1t = drop1 #truncate(before=trunc_index)
qdyn1t = qdyn1#.truncate(before=trunc_index)
if droptype == "w":
bctablet = bctable#.truncate(before=trunc_index)
#bctruncate(drop1g, drop1, acc1, qdyn1)
firmnessfactor(acc1t, drop1) #calculates the firmness factor
if soiltype == "s":
dr(acc1gt) #calculates relative density
duncan_correlation() #calculates friction angle
elif soiltype == "u":
if maxdep <= .2:
dr(acc1gt)
duncan_correlation()
pentime=(penend - penstart)/2 #ms #calculates the time of penetration
dep_qsbc_comboplot(drop1g,drop1,acc1Nameg, qdyn1)
plt.show()
ignore_drop()
if keep_drop == "y":
duck_dep_qsbc_comboplot(drop1g, drop1, drop1t, acc1Nameg, qdyn1t)
print("pentime= ", pentime)
output_table1 = pd.DataFrame([BD, fileNum, n, soiltype, atype, tiptype, droptype, maxdep*100, maxacc, maxvel, maxbcdep*100, maxqdyn, maxHqsbc, maxLqsbc, maxAqsbc, ff, pentime, Dr, phi])
output_table1 = output_table1.T
output_table = output_table1
output_table.columns = ["BlueDrop", "BD File", "Drop No.", "Soil Type", "Area Type", "Tip Type", "Drop Type", "Penetration Depth (cm)", "Maximum Deceleration (m/s^2)", "Impact Velocity (m/s)", "Depth of Max QSBC (cm)", "Max Qdyn (kPa)", "Max QSBC-Upper Bounds (kPa)", "Max QSBC-Lower Bounds (kPa)", "Max QSBC-Average (kPa)", "Firmness Factor (m-1)", "Penetration Time (ms)", "Relative Density (%)", "Friction Angle (deg.)"] # Creates an empty Dataframe in pandas from the output data
else:
dep_qsbc_comboplot(drop1g,drop1,acc1Nameg, qdyn1)
plotName = fileNum+" "+str(n)+" BC - REJECTED"
plt.savefig(plotPath / plotName)
output_table1 = pd.DataFrame([BD, fileNum, n, soiltype, atype, tiptype, droptype,'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN'])
output_table1 = output_table1.T
output_table = output_table1
output_table.columns = ["BlueDrop", "BD File", "Drop No.", "Soil Type", "Area Type", "Tip Type", "Drop Type", "Penetration Depth (cm)", "Maximum Deceleration (m/s^2)", "Impact Velocity (m/s)", "Depth of Max QSBC (cm)", "Max Qdyn (kPa)", "Max QSBC-Upper Bounds (kPa)", "Max QSBC-Lower Bounds (kPa)", "Max QSBC-Average (kPa)", "Firmness Factor (m-1)", "Penetration Time (ms)", "Relative Density (%)", "Friction Angle (deg.)"] # Creates an empty Dataframe in pandas from the output data
#troubleshooting_export()
if n == 2 :
drop2 = dfCal[dropstart:dropend] # start and end points of the drop in m/s^2
drop2g = dfCalg[dropstart:dropend] # start and end points of the drop in g
drop2 = pd.DataFrame(drop2) # makes dataframe including all data within the start and end points of the drop
drop2g = pd.DataFrame(drop2g)
dg = drop2g
d = drop2
accPick(dg, d) # chooses what accelerometer to use
acc2 = acc
acc2g = accg
acc2Name = accName
acc2Nameg = accNameg
#findchangepts() #automatically identifies the moment of penetration
peakpick() #interactive graph for user to choose start of penetration
print("penstart: ", penstart)
finddropend(n-1) #automatically find the end of the drop
print("penend: ", penend)
offset = acc2g[penstart] #sets the offset to be the acceleration at the moment of impact
print("offset: ", offset)
#print(drop)
dg = dfCalg[penstart:penend] #chops the file to only include penetration
d = dfCal[penstart:penend]
acc2g = accg[penstart:penend]
acc2g = acc2g-offset
acc2 = acc[penstart:penend]
offsetms = offset * 9.80665
acc2 = acc2-offsetms
drop2 = d
drop2g = dg
integration(d, acc2) #double integration to solve for velocity and penetration depth
drop2 = d
areafind() #area calculations for bearing capacity calculations
qdynfun(acc2) #calculation of dynamic bearing capacity
qsbcfun(acc2) #calculation of quasi-static bearing capacities
qdyn2 = qdyn
integplot(drop2,acc2Name)
#qdynplot(drop1, qdyn1)
print("Max penetration depth is: ", maxdep)
drop2 = pd.DataFrame(drop2)
drop2g = pd.DataFrame(drop2g)
acc2 = pd.DataFrame(acc2)
qdyn2 = pd.DataFrame(qdyn2)
if droptype == "w":
bctable = pd.DataFrame(bctable)
drop2t = drop2 #.truncate(before=trunc_index) #this truncates each df such that only depths below cone height are considered
drop2gt = drop2g #.truncate(before=trunc_index)
acc2t = acc2 #.truncate(before=trunc_index)
acc2t = np.array(acc2t)
acc2gt = acc2g #.truncate(before=trunc_index)
acc2gt = np.array(acc2gt)
drop2t = drop2 #truncate(before=trunc_index)
qdyn2t = qdyn2#.truncate(before=trunc_index)
if droptype == "w":
bctablet = bctable#.truncate(before=trunc_index)
#bctruncate(drop1g, drop1, acc1, qdyn1)
firmnessfactor(acc2t, drop2) #calculates the firmness factor
if soiltype == "s":
dr(acc2gt) #calculates relative density
duncan_correlation() #calculates friction angle
elif soiltype == "u":
if maxdep <= .2:
dr(acc2gt)
duncan_correlation()
pentime=(penend - penstart)/2 #ms #calculates the time of penetration
dep_qsbc_comboplot(drop2g,drop2,acc2Nameg, qdyn2)
plt.show()
ignore_drop()
if keep_drop == "y":
duck_dep_qsbc_comboplot(drop2g, drop2, drop2t, acc2Nameg, qdyn2t)
print("pentime= ", pentime)
output_table2 = pd.DataFrame([BD, fileNum, n, soiltype, atype, tiptype, droptype, maxdep*100, maxacc, maxvel, maxbcdep*100, maxqdyn, maxHqsbc, maxLqsbc, maxAqsbc, ff, pentime, Dr, phi])
output_table2 = output_table2.T
output_table2.columns = ["BlueDrop", "BD File", "Drop No.", "Soil Type", "Area Type", "Tip Type", "Drop Type", "Penetration Depth (cm)", "Maximum Deceleration (m/s^2)", "Impact Velocity (m/s)", "Depth of Max QSBC (cm)", "Max Qdyn (kPa)", "Max QSBC-Upper Bounds (kPa)", "Max QSBC-Lower Bounds (kPa)", "Max QSBC-Average (kPa)", "Firmness Factor (m-1)", "Penetration Time (ms)", "Relative Density (%)", "Friction Angle (deg.)"] # Creates an empty Dataframe in pandas from the output data
else:
dep_qsbc_comboplot(drop2g,drop2,acc2Nameg, qdyn2)
plotName = fileNum+" "+str(n)+" BC - REJECTED"
plt.savefig(plotPath / plotName)
output_table2 = pd.DataFrame([BD, fileNum, n, soiltype, atype, tiptype, droptype,'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN'])
output_table2 = output_table2.T
output_table2.columns = ["BlueDrop", "BD File", "Drop No.", "Soil Type", "Area Type", "Tip Type", "Drop Type", "Penetration Depth (cm)", "Maximum Deceleration (m/s^2)", "Impact Velocity (m/s)", "Depth of Max QSBC (cm)", "Max Qdyn (kPa)", "Max QSBC-Upper Bounds (kPa)", "Max QSBC-Lower Bounds (kPa)", "Max QSBC-Average (kPa)", "Firmness Factor (m-1)", "Penetration Time (ms)", "Relative Density (%)", "Friction Angle (deg.)"] # Creates an empty Dataframe in pandas from the output data
output_table = pd.concat([output_table, output_table2])
if n == 3 :
drop3 = dfCal[dropstart:dropend] # start and end points of the drop in m/s^2
drop3g = dfCalg[dropstart:dropend] # start and end points of the drop in g
drop3 = pd.DataFrame(drop3) # makes dataframe including all data within the start and end points of the drop
drop3g = pd.DataFrame(drop3g)
dg = drop3g
d = drop3
accPick(dg, d) # chooses what accelerometer to use
acc3 = acc
acc3g = accg
acc3Name = accName
acc3Nameg = accNameg
#findchangepts() #automatically identifies the moment of penetration
peakpick() #interactive graph for user to choose start of penetration
print("penstart: ", penstart)
finddropend(n-1) #automatically find the end of the drop
print("penend: ", penend)
offset = acc3g[penstart] #sets the offset to be the acceleration at the moment of impact
print("offset: ", offset)
#print(drop)
dg = dfCalg[penstart:penend] #chops the file to only include penetration
d = dfCal[penstart:penend]
acc3g = accg[penstart:penend]
acc3g = acc3g-offset
acc3 = acc[penstart:penend]
offsetms = offset * 9.80665
acc3 = acc3-offsetms
drop3 = d
drop3g = dg
integration(d, acc3) #double integration to solve for velocity and penetration depth
drop3 = d
areafind() #area calculations for bearing capacity calculations
qdynfun(acc3) #calculation of dynamic bearing capacity
qsbcfun(acc3) #calculation of quasi-static bearing capacities
qdyn3 = qdyn
integplot(drop3,acc3Name)
print("Max penetration depth is: ", maxdep)
drop3 = pd.DataFrame(drop3)
drop3g = pd.DataFrame(drop3g)
acc3 = pd.DataFrame(acc3)
qdyn3 = pd.DataFrame(qdyn3)
if droptype == "w":
bctable = pd.DataFrame(bctable)
drop3t = drop3 #.truncate(before=trunc_index) #this truncates each df such that only depths below cone height are considered
drop3gt = drop3g #.truncate(before=trunc_index)
acc3t = acc3 #.truncate(before=trunc_index)
acc3t = np.array(acc3t)
acc3gt = acc3g #.truncate(before=trunc_index)
acc3gt = np.array(acc3gt)
drop3t = drop3 #truncate(before=trunc_index)
qdyn3t = qdyn3#.truncate(before=trunc_index)
if droptype == "w":
bctablet = bctable#.truncate(before=trunc_index)
#bctruncate(drop1g, drop1, acc1, qdyn1)
firmnessfactor(acc3t, drop3) #calculates the firmness factor
if soiltype == "s":
dr(acc3gt) #calculates relative density
duncan_correlation() #calculates friction angle
elif soiltype == "u":
if maxdep <= .2:
dr(acc3gt)
duncan_correlation()
pentime=(penend - penstart)/2 #ms #calculates the time of penetration
dep_qsbc_comboplot(drop3g,drop3,acc3Nameg, qdyn3)
plt.show()
ignore_drop()
if keep_drop == "y":
duck_dep_qsbc_comboplot(drop3g, drop3, drop3t, acc3Nameg, qdyn3t)
print("pentime= ", pentime)
output_table3 = pd.DataFrame([BD, fileNum, n, soiltype, atype, tiptype, droptype, maxdep*100, maxacc, maxvel, maxbcdep*100, maxqdyn, maxHqsbc, maxLqsbc, maxAqsbc, ff, pentime, Dr, phi])
output_table3 = output_table3.T
output_table3.columns = ["BlueDrop", "BD File", "Drop No.", "Soil Type", "Area Type", "Tip Type", "Drop Type", "Penetration Depth (cm)", "Maximum Deceleration (m/s^2)", "Impact Velocity (m/s)", "Depth of Max QSBC (cm)", "Max Qdyn (kPa)", "Max QSBC-Upper Bounds (kPa)", "Max QSBC-Lower Bounds (kPa)", "Max QSBC-Average (kPa)", "Firmness Factor (m-1)", "Penetration Time (ms)", "Relative Density (%)", "Friction Angle (deg.)"] # Creates an empty Dataframe in pandas from the output data
else:
dep_qsbc_comboplot(drop3g,drop3,acc3Nameg, qdyn3)
plotName = fileNum+" "+str(n)+" BC - REJECTED"
plt.savefig(plotPath / plotName)
output_table3 = pd.DataFrame([BD, fileNum, n, soiltype, atype, tiptype, droptype,'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN'])
output_table3 = output_table3.T
output_table3.columns = ["BlueDrop", "BD File", "Drop No.", "Soil Type", "Area Type", "Tip Type", "Drop Type", "Penetration Depth (cm)", "Maximum Deceleration (m/s^2)", "Impact Velocity (m/s)", "Depth of Max QSBC (cm)", "Max Qdyn (kPa)", "Max QSBC-Upper Bounds (kPa)", "Max QSBC-Lower Bounds (kPa)", "Max QSBC-Average (kPa)", "Firmness Factor (m-1)", "Penetration Time (ms)", "Relative Density (%)", "Friction Angle (deg.)"] # Creates an empty Dataframe in pandas from the output data
output_table = pd.concat([output_table, output_table3])
if n == 4 :
drop4 = dfCal[dropstart:dropend] # start and end points of the drop in m/s^2
drop4g = dfCalg[dropstart:dropend] # start and end points of the drop in g
drop4 = pd.DataFrame(drop4) # makes dataframe including all data within the start and end points of the drop
drop4g = pd.DataFrame(drop4g)
dg = drop4g
d = drop4
accPick(dg, d) # chooses what accelerometer to use
acc4 = acc
acc4g = accg
acc4Name = accName
acc4Nameg = accNameg
#findchangepts() #automatically identifies the moment of penetration
peakpick() #interactive graph for user to choose start of penetration
print("penstart: ", penstart)
finddropend(n-1) #automatically find the end of the drop
print("penend: ", penend)
offset = acc4g[penstart] #sets the offset to be the acceleration at the moment of impact
print("offset: ", offset)
#print(drop)
dg = dfCalg[penstart:penend] #chops the file to only include penetration
d = dfCal[penstart:penend]
acc4g = accg[penstart:penend]
acc4g = acc4g-offset
acc4 = acc[penstart:penend]
offsetms = offset * 9.80665
acc4 = acc4-offsetms
drop4 = d