BD code v8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PFFP BlueDrop Processing Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Written by: Elise Hummel, ehummel@vt.edu\n",
    "##### Last updated: 1/28/2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description:\n",
    "\n",
    "This code processes raw deceleration readings (.bin files) from the BlueDrop Portable Free-Fall Penetrometer. The ouputs include a summary Excel file and various figures.\n",
    "\n",
    "#### Required Inputs\n",
    "1. BlueDrop Serial Number (1, 2, or 3)\n",
    "2. File Number to process\n",
    "3. Path to .bin Files\n",
    "4. Soil type (if known)\n",
    "5. Area calculation to be performed (mantle or projected)\n",
    "6. Penetrometer tip type (conical, parabolic, or blunt)\n",
    "7. Drop Type (air or water)\n",
    "8. Drop Finding Method (manual or automatic)\n",
    "9. Path for output table\n",
    "10. Path for output figures \n",
    "\n",
    "#### Outputs\n",
    "\n",
    "| Output | Sands | Clays |\n",
    "| :- | -: | :-: |\n",
    "| Penetration Depth (cm) | Y | Y\n",
    "| Maximum Deceleration (m/s^2) | Y | Y\n",
    "| Impact Velocity (m/s) | Y | Y\n",
    "| Depth of Max QSBC (cm) | Y | Y\n",
    "| Max Qdyn (kPa) | Y | Y\n",
    "| Max QSBC (kPa) | Y | Y\n",
    "| Firmness Factor (m-1) | Y | Y\n",
    "| Penetration Time (ms) | Y | Y\n",
    "| Relative Density (%) | Y | \n",
    "| Friction Angle (deg.) | Y | \n",
    "| Undrained shear strength | | Y | \n",
    "\t\t"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "import math\n",
    "from math import pi\n",
    "from math import sqrt\n",
    "import statistics\n",
    "import scipy.integrate\n",
    "from scipy.signal import find_peaks\n",
    "from scipy.signal import argrelmin\n",
    "from numpy import trapz\n",
    "from scipy.integrate import cumtrapz\n",
    "from scipy.integrate import dblquad"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "User Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SETUP VARIABLES - USER INPUTS\n",
    "BD = 3 #Bluedrop file is from \n",
    "fileNum = '01F3' # write the bin file number you want to analyze (do not include 'bLog' or '.bin')\n",
    "soiltype = \"c\" #s = sand, c=clay, m=mixed, u=unknown\n",
    "tiptype = 'c'  # c = cone, p = parabolic, b = blunt\n",
    "truncate_at_cone = \"n\" #\"y\" to end analysis at a penetration depth equal to the cone length, \"n\" to consider full cone length\n",
    "droptype = \"w\" #w = water, #a = air\n",
    "unit_wt = \"uk\" #enter an effective unit weight value in kg/m^3 or \"uk\" if unknown \n",
    "Nkt = 10 #default value for cone factor used in su calculations\n",
    "drop_finding = \"m\" #\"m\" for manual drop picking, \"a\" for automatic drop picking\n",
    "\n",
    "# paste the filepath to the folder where the BD data is stored\n",
    "binFilepath = Path(\"C:/Users/elise/Desktop/bd_manual/sample_drops\")\n",
    "\n",
    "# paste the filepath to an excel file that the analysis results will be printed in\n",
    "outputPath = Path(\"C:/Users/elise/Desktop/bd_manual/sample_drops/Figures/Summary_Excel.xlsx\")\n",
    "\n",
    "# paste the filepath to the folder where the output figures will be saved\n",
    "plotPath = Path(\"C:/Users/elise/Desktop/bd_manual/sample_drops/Figures\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define Functions for use in analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find the penetrated area\n",
    "\n",
    "def areafind(): #finds the embedded area based on the penetration depth, area type, and the tip\n",
    "    global area_m #mantle area\n",
    "    global area_p #projected area\n",
    "    global trunc_index\n",
    "    a_m = list() #placeholder for the penetrated area at that specific depth - mantle area\n",
    "    a_p = list() #placeholder for the penetrated area at that specific depth - projected area\n",
    "    d_m = dep*100 #penetration depth array, in cm - mantle area calculations\n",
    "    d_p = dep*100 #penetration depth array, in cm - projected area calculations\n",
    "    r_m = list() #placeholder for the radius at that specific depth - mantle area calculations\n",
    "    r_p = list() #placeholder for the radius at that specific depth - mantle area calculations\n",
    "\n",
    "    #define calculations for the cone penetrometer tip\n",
    "    if tiptype == 'c': \n",
    "\n",
    "        #perform math for mantle area type\n",
    "        for k in range(0,len(d_m)):\n",
    "            if d_m[k]<length: #if full cone hasn't penetrated yet\n",
    "                #r_m.append(.22+d_m[k]*((4.375-.22)/7.55)) #this is a modification for a specific tip's measurements\n",
    "                r_m.append(d_m[k]*np.tan(30*pi/180)) #generic form of radius calc for a given depth\n",
    "                a_m.append(pi*r_m[k]*(sqrt((r_m[k]**2)+(d_m[k]**2)))) #area calc for a given depth\n",
    "            elif d_m[k]>=length: #if the full cone has penetrated\n",
    "                r_m.append(4.375)\n",
    "                a_m.append(pi*r_m[k]*(sqrt((r_m[k]**2)+(length**2))))\n",
    "            a_m[k] = a_m[k]/10000 #converts from cm^2 to m^2\n",
    "        area_m = a_m\n",
    "        #print(r)\n",
    "        #print(np.column_stack((d_m,r_m, area_m)))\n",
    "        \n",
    "        #perform math for projected area type\n",
    "        for k in range(0,len(d_p)): \n",
    "            if d_p[k]<length:\n",
    "                r_p.append(.22+d_p[k]*((4.375-.22)/7.55))\n",
    "                # r_p.append(d_p[k]*np.tan(30*pi/180))\n",
    "                a_p.append(pi*(r_p[k])**2)\n",
    "            elif d_p[k]>=length:\n",
    "                r_p.append(4.375)\n",
    "                #if r_p[k-1]<r_p[k]:\n",
    "                    #trunc_index = r_p.index(r_p[k-1])+jindex+dropstart #performs analysis only on readings beyond depth of cone height'''\n",
    "                a_p.append(pi*(r_p[k])**2)\n",
    "            a_p[k] = a_p[k]/10000\n",
    "        area_p = a_p\n",
    "\n",
    "        print(\"area_m = \", area_m, \" area_p = \", area_p)\n",
    "\n",
    "    #define calculations for the blunt penetrometer tip\n",
    "    elif tiptype == 'b':\n",
    "\n",
    "        #mantle area type calcs\n",
    "        for k in range(0,len(d_m)): \n",
    "            if d_m[k]<length:\n",
    "                r_m.append(4.375)\n",
    "                a_m.append(pi*r_m[k]**2 + 2*pi*r_m[k]*d_m[k])\n",
    "            if d_m[k]>=length:\n",
    "                r_m.append(4.375)\n",
    "                a_m.append(pi*r_m[k]**2 + 2*pi*r_m[k]*length)\n",
    "            a_m[k]=a_m[k]/10000\n",
    "        area_m = a_m\n",
    "\n",
    "        #projected area type calcs\n",
    "        for k in range(0, len(d_m)): \n",
    "            a_p.append(pi*4.375**2)\n",
    "            a_p[k]=a_p[k]/10000\n",
    "        area_p = a_p\n",
    "\n",
    "    #define calculations for the parabolic penetrometer tip\n",
    "    elif tiptype == \"p\":\n",
    "        #mantle area calcs\n",
    "\n",
    "        for k in range(0,len(d_m)):\n",
    "            if d_m[k]<length:\n",
    "                    r_m.append(sqrt(2.4184*d_m[k]))\n",
    "                    #a_m.append(2*pi*1.03675*d_m[k]**(3/2)*((d_m[k]+0.6046)/d_m[k])**(3/2))\n",
    "                    a_m.append(2*pi*((2/(3*2.4184))*(2.4184*d_m[k]+2.4184**2/4)**(3/2)-(2/(3*2.4184))*(2.4184**2/4)**3/2))\n",
    "\n",
    "            else:\n",
    "                r_m.append(4.375)\n",
    "\n",
    "                a_m.append(2*pi*((2/(3*2.4184))*(2.4184*length+2.4184**2/4)**(3/2)-(2/(3*2.4184))*(2.4184**2/4)**3/2))\n",
    "   \n",
    "            a_m[k]= a_m[k]/10000\n",
    "        area_m = a_m\n",
    "\n",
    "        '''area_df = pd.DataFrame({\"Radius\":r_m, \"Area\":a_m})\n",
    "        with pd.option_context('display.max_rows', None):\n",
    "            display(area_df)\n",
    "\n",
    "        print(area_m)'''\n",
    "\n",
    "        # projected area calcs\n",
    "        for k in range(0,len(d_p)):\n",
    "            if d_p[k]<length:\n",
    "                    r_p.append(sqrt(2.4184*d_p[k]))\n",
    "                    a_p.append(pi*r_p[k]**2)\n",
    "            else:\n",
    "                r_p.append(4.375)\n",
    "                a_p.append(pi*4.375**2)\n",
    "            a_p[k]= a_p[k]/10000\n",
    "        area_p = a_p\n",
    "                \n",
    "\n",
    "def qdynfun(acc): # calculates dynamic bearing capacity \n",
    "    global qdyn_m\n",
    "    global qdyn_p\n",
    "    global maxbcdep\n",
    "    global maxqdyn_m\n",
    "    global maxqdyn_p\n",
    "    global qdyntable\n",
    "    global maxbc_index\n",
    "\n",
    "    buoy = 1020*0.002473\n",
    "    if droptype == \"w\": #water drops\n",
    "        Fbe = (mass-buoy)*acc #drop force\n",
    "\n",
    "    elif droptype ==\"a\": #air drops\n",
    "        Fbe = mass*acc\n",
    "\n",
    "    qdyn_m = (Fbe/area_m)/1000 #Dynamic bearing capacity using mantle area (kPa)\n",
    "    qdyn_p = (Fbe/area_p)/1000\n",
    "    qdyntable = pd.DataFrame()\n",
    "    qdyntable[\"Mantle Qdyn\"] = qdyn_m\n",
    "    qdyntable[\"Projected Qdyn\"] = qdyn_p\n",
    "    qdyntable[\"Depth\"] = dep\n",
    "    if truncate_at_cone == \"y\":\n",
    "        qdyntable = qdyntable.truncate(before=trunc_index)\n",
    "    \n",
    "    print(qdyntable)\n",
    "    threecm_index = qdyntable[qdyntable[\"Depth\"]>=.03].index.values.min()\n",
    "    zeroth_index = qdyntable[\"Depth\"].idxmin()\n",
    "\n",
    "    #print(\"3cm index\", threecm_index)\n",
    "    #print(\"0th index\", zeroth_index)\n",
    "    #print(type(zeroth_index))\n",
    "\n",
    "    qdyntable = qdyntable[threecm_index-zeroth_index:]\n",
    "    print(qdyntable)\n",
    "\n",
    "    maxqdyn_m = max(qdyntable[\"Mantle Qdyn\"]) #gives maximum qdyn below 3cm\n",
    "    maxqdyn_p = qdyntable[\"Projected Qdyn\"].max()\n",
    "    print(\"maxqdyn:\", maxqdyn_m)\n",
    "    maxbcdep = qdyntable.loc[qdyntable[\"Mantle Qdyn\"]==maxqdyn_m, 'Depth'].item() #depth of max mantle and max projected area should be the same\n",
    "    maxbc_index = qdyntable[qdyntable[\"Mantle Qdyn\"]==maxqdyn_m].index.values\n",
    "\n",
    "    maxqdyn_m = round(maxqdyn_m,1)\n",
    "    maxqdyn_p = round(maxqdyn_p,1)\n",
    "    maxbcdep = round(maxbcdep,4)\n",
    "    #print(\"maxbcdep = \", maxbcdep) #finds the penetration depth corresponding to the maximum qdyn\n",
    "\n",
    "\n",
    "    \"\"\"bcmin = argrelmin(qdyn)\n",
    "    peaksArray = np.array(bcmin)\n",
    "    print(\"Peaks Array = \", peaksArray)\"\"\"\n",
    "\n",
    "#define the QSBC function\n",
    "def qsbcfun(acc): #calculates quasi-static bearing capacity\n",
    "    global bctable_m #table of bearing capacity data using mantle areas\n",
    "    global bctable_p #table of bearing capacity data using projected areas\n",
    "    global qsbc_m #mantle qsbc\n",
    "    global qsbc_p #projected qsbc\n",
    "    global maxHqsbc_m #maximum qsbc averaged from the 2 highest strain rate factors and mantle area\n",
    "    global maxHqsbc_p #maximum qsbc averaged from the 2 highest strain rate factors and projected area\n",
    "    global maxLqsbc_m #maximum qsbc averaged from the 2 lowest strain rate factors and mantle area\n",
    "    global maxLqsbc_p #maximum qsbc averaged from the 2 lowest strain rate factors and projected area\n",
    "    global maxAqsbc_m #maximum qsbc from partially saturated correlation and mantle area\n",
    "    global maxAqsbc_p #maximum qsbc from partially saturated correlation and mantle area\n",
    "    global maxqsbc_default_m #maximum qsbc from the default strain rate factor\n",
    "    global maxqsbc_default_p #maximum qsbc from the default strain rate factor\n",
    "    global srfk\n",
    "\n",
    "    if droptype == \"w\":\n",
    "        srcv = np.log10(vel/0.02) #Velocity portion of the strain rate correction. \n",
    "        srfk = [0.1, 0.2, 0.4, 1, 1.5] #list of strain rate factors to run; must be in increasing order [.1 and 1 must be in this list!]\n",
    "\n",
    "        #mantle area calcs\n",
    "        bctable_m = pd.DataFrame()\n",
    "        for i in range(0,len(srfk)):\n",
    "            fsr = 1+srfk[i]*srcv\n",
    "            qsbc_m = qdyn_m/fsr\n",
    "            bctable_m.insert(i, \"qsbc for K = \"+str(srfk[i]), qsbc_m)\n",
    "\n",
    "        #print(bctable_m)\n",
    "\n",
    "        bctable_m_avgs = pd.DataFrame()\n",
    "        bctable_m_avgs[\"High Average Mantle qsbc\"]= (bctable_m.iloc[:,0]+bctable_m.iloc[:,1])/2 #Average of the lowest 2 strain rate factors\n",
    "        bctable_m_avgs[\"Low Average Mantle qsbc\"] = (bctable_m.iloc[:,int(len(srfk))-1]+bctable_m.iloc[:,int(len(srfk)-2)])/2 #average of the highest 2 strain rate factors\n",
    "        maxHqsbc_m = bctable_m_avgs[\"High Average Mantle qsbc\"].max()\n",
    "        maxHqsbc_m = round(maxHqsbc_m, 1)\n",
    "        maxLqsbc_m = bctable_m_avgs[\"Low Average Mantle qsbc\"].max()\n",
    "        maxLqsbc_m = round(maxLqsbc_m,1)\n",
    "        maxAqsbc_m = \"-\"\n",
    "\n",
    "        \n",
    "        if soiltype == \"s\":\n",
    "            maxqsbc_default_m = bctable_m[\"qsbc for K = 1\"][maxbc_index].values[0]\n",
    "            #maxqsbc_default_m = maxqsbc_default_m[0]\n",
    "\n",
    "\n",
    "        elif soiltype == \"u\":\n",
    "            if maxdep <= .2:\n",
    "                maxqsbc_default_m = bctable_m[\"qsbc for K = 1\"][maxbc_index].values[0]\n",
    "                #maxqsbc_default_m = maxqsbc_default_m[0]\n",
    "                \n",
    "            else:\n",
    "                maxqsbc_default_m = bctable_m[\"qsbc for K = 0.1\"][maxbc_index].values[0]\n",
    "                #maxqsbc_default_m = maxqsbc_default_m[0]\n",
    "                \n",
    "        elif soiltype == \"c\":\n",
    "            maxqsbc_default_m = bctable_m[\"qsbc for K = 0.1\"][maxbc_index].values[0]\n",
    "\n",
    "        #projected area calcs\n",
    "        bctable_p = pd.DataFrame()\n",
    "        for i in range(0,len(srfk)):\n",
    "            fsr = 1+srfk[i]*srcv\n",
    "            qsbc_p = qdyn_p/fsr\n",
    "            bctable_p.insert(i, \"qsbc for K = \"+str(srfk[i]), qsbc_p)\n",
    "\n",
    "        bctable_p_avgs = pd.DataFrame()\n",
    "        bctable_p_avgs[\"High Average Projected qsbc\"]= (bctable_p.iloc[:,0]+bctable_p.iloc[:,1])/2 #Average of the lowest 2 strain rate factors\n",
    "        bctable_p_avgs[\"Low Average Projected qsbc\"] = (bctable_p.iloc[:,int(len(srfk))-1]+bctable_p.iloc[:,int(len(srfk)-2)])/2 #average of the highest 2 strain rate factors\n",
    "        maxHqsbc_p = bctable_p_avgs[\"High Average Projected qsbc\"].max()\n",
    "        maxHqsbc_p = round(maxHqsbc_p, 1)\n",
    "        maxLqsbc_p = bctable_p_avgs[\"Low Average Projected qsbc\"].max()\n",
    "        maxLqsbc_p = round(maxLqsbc_p,1)\n",
    "        maxAqsbc_p = \"-\"\n",
    "\n",
    "        if soiltype == \"s\":\n",
    "            maxqsbc_default_p = bctable_p[\"qsbc for K = 1\"][maxbc_index].values[0]\n",
    "\n",
    "        elif soiltype == \"u\":\n",
    "            if maxdep <= .2:\n",
    "                maxqsbc_default_p = bctable_p[\"qsbc for K = 1\"][maxbc_index].values[0]\n",
    "                \n",
    "            else:\n",
    "                maxqsbc_default_p = bctable_p[\"qsbc for K = 0.1\"][maxbc_index].values[0]\n",
    "                \n",
    "        elif soiltype == \"c\":\n",
    "            maxqsbc_default_p = bctable_p[\"qsbc for K = 0.1\"][maxbc_index].values[0]\n",
    "\n",
    "        print(\"bctable_m:\", bctable_m)\n",
    "        \n",
    "\n",
    "    elif droptype == \"a\":\n",
    "        srcv = np.log10(vel/0.02) #Velocity portion of the strain rate correction. \n",
    "        srfk = 0.31*maxvel\n",
    "        fsr = 1+srfk*srcv\n",
    "        qsbc_m = qdyn_m/fsr\n",
    "        qsbc_p = qdyn_p/fsr\n",
    "        maxHqsbc_m = \"-\"\n",
    "        maxHqsbc_p = \"-\"\n",
    "        maxLqsbc_m = \"-\"\n",
    "        maxLqsbc_p = \"-\"\n",
    "        maxAqsbc_m = qsbc_m.max()\n",
    "        maxAqsbc_m = round(maxAqsbc_m,1)\n",
    "        maxAqsbc_p = qsbc_p.max()\n",
    "        maxAqsbc_p = round(maxAqsbc_p,1)\n",
    "        maxqsbc_default_m = qsbc_m[maxbc_index] \n",
    "        #maxqsbc_default_m = maxqsbc_default_m[0]\n",
    "        maxqsbc_default_p = qsbc_p[maxbc_index] \n",
    "        #maxqsbc_default_p = maxqsbc_default_p[0]\n",
    "\n",
    "    return [bctable_m, bctable_p]\n",
    "\n",
    "# FUNCTIONS FOR USE IN ANALYSIS\n",
    "def masslength(tiptype): #sets the mass and length of the pentrometer based on the tip\n",
    "    global mass\n",
    "    global length #lengths are in cm\n",
    "    if tiptype == 'c':\n",
    "        mass = 7.71 #kg\n",
    "        length = 8.48-0.93 #originally 7.87, 7.57 for perfect 60 deg, 8.48 measured - .93 measured 90 deg\n",
    "    elif tiptype == 'p':\n",
    "        mass = 9.15\n",
    "        length = 7.915\n",
    "    elif tiptype == 'b':\n",
    "        mass = 10.30\n",
    "        length = 8.57\n",
    "\n",
    "def dropstartend(peak): #after locating the peaks, this function chops the minute long file into a smaller segment immediately before and after the peak\n",
    "    global dropstart\n",
    "    global dropend\n",
    "    if peak <= 1500:\n",
    "        dropstart = 1\n",
    "        dropend = peak + 500\n",
    "    elif peak > 119500:\n",
    "        dropstart = peak - 1500\n",
    "        dropend = 120000\n",
    "    else:\n",
    "        dropstart = peak - 1500\n",
    "        dropend = peak + 500\n",
    "\n",
    "def accPick(dg, d): #this function picks the smallest accelerometer that's not maxed out to perform the integration on\n",
    "    maxAcc = dg[\"250g (g)\"].max()\n",
    "\n",
    "    global acc\n",
    "    global accName\n",
    "    global accNameg\n",
    "    global accg\n",
    "    if maxAcc < 5: \n",
    "        if dg[\"2g (g)\"].max() < 1.8:   # does an extra check for the 2g because of noise\n",
    "            acc = dfCal[\"2g (m/s^2)\"]\n",
    "            accg = dfCalg[\"2g (g)\"]\n",
    "            accName = \"2g (m/s^2)\"\n",
    "            accNameg = \"2g (g)\"\n",
    "        else:\n",
    "            acc = dfCal[\"18g (m/s^2)\"]\n",
    "            accg = dfCalg[\"18g (g)\"]\n",
    "            accName = \"18g (m/s^2)\"\n",
    "            accNameg = \"18g (g)\"\n",
    "    elif maxAcc < 18:\n",
    "        acc = dfCal[\"18g (m/s^2)\"]\n",
    "        accg = dfCalg[\"18g (g)\"]\n",
    "        accName = \"18g (m/s^2)\"\n",
    "        accNameg = \"18g (g)\"\n",
    "    elif maxAcc < 50: \n",
    "        acc = dfCal[\"50g (m/s^2)\"]\n",
    "        accg = dfCalg[\"50g (g)\"]\n",
    "        accName = \"50g (m/s^2)\"\n",
    "        accNameg = \"50g (g)\"\n",
    "    else:\n",
    "        acc = dfCal[\"250g (m/s^2)\"]\n",
    "        accg = dfCalg[\"50g (g)\"]\n",
    "        accName = \"250g (m/s^2)\"\n",
    "        accNameg = \"250g (g)\"\n",
    "\n",
    "def findchangepts(): #This function picks the moment that the BD impacts the ground (not in use currently)\n",
    "    global drop\n",
    "    jlist = list()\n",
    "    global jindex\n",
    "    print(\"finding start of drop...\")\n",
    "    for i in range(4,len(accg)-4):\n",
    "        p1 = 1\n",
    "        #print(p1)\n",
    "        p2 = i\n",
    "        #print(p2)\n",
    "        p3 = len(accg)\n",
    "        #print(p3)\n",
    "        sample1 = list(accg[p1:p2-1])\n",
    "        #print(sample1)\n",
    "        sample2 = list(accg[p2:p3])\n",
    "        #print(sample2)\n",
    "        stat1 = math.log(statistics.variance(sample1))\n",
    "        stat2 = math.log(statistics.variance(sample2))\n",
    "        #print(stat1)\n",
    "\n",
    "        j1 = (i-1)*stat1\n",
    "        j2 = ((len(accg)-1)-i+1)*stat2\n",
    "        j = j1+j2\n",
    "        #print(j)\n",
    "        jlist.append(j)\n",
    "\n",
    "    drop = min(jlist)\n",
    "    #print(\"drop is\")\n",
    "    #print(drop)\n",
    "    jlist = np.array(jlist)\n",
    "    #print(jlist)\n",
    "    #print(jlist.size)\n",
    "    jlist = np.insert(jlist, 0, (0,0,0,0)) #reshape to match up with dataframe d\n",
    "    jlist = np.append(jlist, (0,0,0,0)) #reshape to match up with dataframe d\n",
    "    #print(jlist.size) #should be 2000\n",
    "    jindex = np.where(jlist==drop) #finds the index of the drop start\n",
    "    jindex = int(jindex[0]) #converts the index into a number from a tuple\n",
    "    print(\"Jlist: \", jlist)\n",
    "\n",
    "def finddropend(n): #finds the location after the peak where the deceleration is 1 \n",
    "    global penend\n",
    "    below0list = list()\n",
    "    #for i in range(dropstart+jindex, dropend, 1):\n",
    "    for i in range(peak, dropend, 1):\n",
    "        if accg[i] < 1: #the offset is applied later in the code\n",
    "            penend = i \n",
    "            below0list = np.append(below0list, penend)\n",
    "    penend=int(min(below0list))\n",
    "\n",
    "def integration(d, accint): #integrates the deceleration data to solve for velocity and penetration depth\n",
    "    global vel\n",
    "    global maxvel\n",
    "    global dep\n",
    "    global maxdep\n",
    "    #accint = acc[penstart:penend]\n",
    "    #print(len(d))\n",
    "    #print(len(accint))\n",
    "    vel = scipy.integrate.cumtrapz(accint, x=d[\"Time (s)\"])  # solves for velocity\n",
    "    vel = np.array(vel)\n",
    "    vel = np.insert(vel, 0, 0) #not sure what to insert here, but it makes it the right size\n",
    "    vel = np.flip(vel)\n",
    "    maxvel = vel.max()\n",
    "    maxvel = round(maxvel,1)\n",
    "    dep = scipy.integrate.cumtrapz(vel, x=d[\"Time (s)\"])  # solves for penetration depth\n",
    "    dep = np.insert(dep, 0, 0)  # not sure what to insert here, but it makes it the right size\n",
    "    maxdep = dep.max()\n",
    "    maxdep = round(maxdep,4)\n",
    "    d.insert(9, \"Velocity (m/s)\", vel)\n",
    "    d.insert(10, \"Penetration Depth (m)\", dep)\n",
    "\n",
    "def peakpick():\n",
    "    global penstart\n",
    "    matplotlib.use('TkAgg')\n",
    "    fig, (ax1) = plt.subplots(1)\n",
    "    plt.plot(dfCalg['Count'], dfCalg[accNameg], label=accNameg)\n",
    "    plt.plot(dfCalg['Count'], dfCalg['2g (g)'], label='2g')\n",
    "\n",
    "    ax1.set_xlim(left=dropstart, right=dropend)\n",
    "    ax1.legend()\n",
    "    ax1.set(ylabel=\"Deceleration (g)\")\n",
    "    ax1.set(xlabel=\"Time\")\n",
    "    ax1.set_title('Double-click start of drop #' + str(n))\n",
    "    startendpt = plt.ginput(2, 0)\n",
    "    pentimes = []\n",
    "    for t in startendpt:\n",
    "        for x in t:\n",
    "            pentimes.append(x)\n",
    "\n",
    "    penstart = int(pentimes[0])\n",
    "    print(\"start of penetration: \", penstart)\n",
    "\n",
    "def endpick():\n",
    "    global penend\n",
    "    matplotlib.use('TkAgg')\n",
    "    fig, (ax1) = plt.subplots(1)\n",
    "    plt.plot(dfCalg['Count'], dfCalg[accNameg], label=accNameg)\n",
    "    plt.plot(dfCalg['Count'], dfCalg['2g (g)'], label='2g')\n",
    "    plt.hlines(1, 0, 120000, linestyles=\"--\", color = \"k\")\n",
    "\n",
    "    ax1.set_xlim(left=dropstart, right=dropend)\n",
    "    ax1.legend()\n",
    "    ax1.set(ylabel=\"Deceleration (g)\")\n",
    "    ax1.set(xlabel=\"Time\")\n",
    "    ax1.set_title('Double-click end of drop #' + str(n))\n",
    "    startendpt = plt.ginput(2, 0)\n",
    "    pentimes = []\n",
    "    for t in startendpt:\n",
    "        for x in t:\n",
    "            pentimes.append(x)\n",
    "\n",
    "    penend = int(pentimes[0])\n",
    "\n",
    "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\n",
    "\n",
    "    global jindex\n",
    "    global num1\n",
    "    \n",
    "    xvalues=np.linspace(0,len(dg)-1,len(dg))\n",
    "    plt.figure(num=1)\n",
    "    temp_dg=dg['250g (g)']\n",
    "    plt.plot(xvalues,temp_dg)\n",
    "    plt.grid(visible=True,which='both')\n",
    "    plt.show()\n",
    "    print ('The start of the drop is', jindex)\n",
    "    print ('The end of the drop is', num1)\n",
    "    print('Are the start and end of the drop correct?(y/n)')\n",
    "    drop_input=input()\n",
    "    if drop_input=='n':   \n",
    "        peakpick()\n",
    "        jindex = penstart\n",
    "        num1 = penend\n",
    "\n",
    "def dr(accg): #Albatal 2019 method for calculating relative density (Duck, NC specific!)\n",
    "    maxacc = accg.max() #maximum deceleration, in g\n",
    "    print(\"Max deceleration = \", maxacc)\n",
    "    Dr = -2.18*10**-4*maxacc**3+1.29*10**-2*maxacc**2+1.61*maxacc-13.09 #Albatal 2019\n",
    "    Dr = round(Dr, 1)\n",
    "    return Dr\n",
    "    #print(\"Relative density is \", dr)\n",
    "\n",
    "def ignore_drop():\n",
    "    global keep_drop\n",
    "    print('Keep Drop? (y/n)')\n",
    "    keep_drop=input()\n",
    "\n",
    "def duncan_correlation(Dr): #duncan correlation for caluclating friction angle\n",
    "    global sign\n",
    "    #coefficients A, B, C, and D used in the Duncan Correlation. Duncan[34,10,3,2], Albatal[34,10,2,5]\n",
    "    A = 34\n",
    "    B = 10\n",
    "    C = 3\n",
    "    D = 2\n",
    "    pa = 101.325 #kPa\n",
    "    print(\"max penetration depth = \", maxdep)\n",
    "    if unit_wt == \"uk\":\n",
    "        sign = 1120*maxdep #1120 = 70pcf in kg/m^3\n",
    "    else:\n",
    "        sign = unit_wt*maxdep \n",
    "\n",
    "\n",
    "    phi = A + B*Dr/100-(C+D*Dr/100)*math.log(sign/pa)\n",
    "    phi = round(phi, 1)\n",
    "    print(\"phi =\", phi)\n",
    "    return phi\n",
    "\n",
    "def firmnessfactor(acc, drop):\n",
    "    global maxacc\n",
    "    global ff\n",
    "    maxacc = acc.max()[0]\n",
    "    g = 9.80665\n",
    "    tp = len(drop)/2000\n",
    "\n",
    "    ff = maxacc/(g*tp*maxvel)\n",
    "    ff = round(ff,1)\n",
    "    #ff = ff[0]\n",
    "    print(\"Firmness factor = \", ff)\n",
    "\n",
    "def bctruncate(dropg, drop, acc, qdyn):\n",
    "    dropg = pd.DataFrame(dropg)\n",
    "    drop = pd.DataFrame(drop)\n",
    "    qdyn = pd.DataFrame(qdyn)\n",
    "    acc = pd.DataFrame(acc)\n",
    "    dropg = dropg.truncate(before=trunc_index)\n",
    "    drop = drop.truncate(before=trunc_index)\n",
    "    acc = acc.truncate(before=trunc_index)\n",
    "    qdyn = qdyn.truncate(before=trunc_index)\n",
    "\n",
    "def manual_peak_pick():\n",
    "    x = np.array(g250g)\n",
    "    matplotlib.use('TkAgg')\n",
    "\n",
    "    plt.plot(x)\n",
    "    plt.title(\"Select Drops (Enter to Exit)\")\n",
    "    plt.xlabel(\"Count\")\n",
    "    plt.ylabel(\"Deceleration (g)\")\n",
    "    #plt.legend(\"Deceleration (g)\", \"Pore Pressure (kPa)\")\n",
    "    plt.grid(True)\n",
    "    #plt.show()\n",
    "    global peaksArray\n",
    "    global nDrops\n",
    "    \n",
    "    peak_approx_list = []\n",
    "\n",
    "    peak_approx = plt.ginput(n=-1, show_clicks=True)\n",
    "    for t in peak_approx:\n",
    "        for x in t:\n",
    "            peak_approx_list.append(x)\n",
    "    #user_input = input(\"Enter a value (press Enter to stop): \")\n",
    "    plt.close()\n",
    "\n",
    "    nDrops = int(len(peak_approx_list)/2)\n",
    "    #print(num_drops, \"drops in file\")\n",
    "\n",
    "    approx_peak_index_list = peak_approx_list[::2]\n",
    "    for a in range(0,len(approx_peak_index_list)):\n",
    "        approx_peak_index_list[a] = int(approx_peak_index_list[a])\n",
    "    #print(\"Coordinates of Approximate peaks (index, acceleration):\", peak_approx_list)\n",
    "    #print(\"Approx Peak Indexes:\", approx_peak_index_list) \n",
    "\n",
    "    plot_start_list = []\n",
    "    plot_end_list = [] \n",
    "    index_list = []\n",
    "    for a in range(0, nDrops):\n",
    "        peak = approx_peak_index_list[a]\n",
    "        if peak <= 3000:\n",
    "            plotstart = 1\n",
    "            plotend = peak + 500\n",
    "            \n",
    "        elif peak > 119500:\n",
    "            plotstart = peak - 3000\n",
    "            plotend = 120000\n",
    "        else:\n",
    "            plotstart = peak - 3000\n",
    "            plotend = peak + 500\n",
    "        plot_start_list.append(plotstart)\n",
    "        plot_end_list.append(plotend)\n",
    "        index_list.append(a+1)\n",
    "    plot_ranges = {'Start':plot_start_list, \"End\":plot_end_list}\n",
    "    plot_range_df = pd.DataFrame(data=plot_ranges, index = index_list)\n",
    "    #print(plot_range_df)\n",
    "\n",
    "    #finds the actual maximum deceleration (not just the approximated one)\n",
    "    #print(plot_range_df.at[1,'Start'])\n",
    "    max_dec_list = []\n",
    "    for a in range(1, nDrops+1):\n",
    "        max_dec = max(g250g[plot_range_df.at[a,'Start']:plot_range_df.at[a,'End']])\n",
    "        max_dec_list.append(max_dec)\n",
    "        #print(max_dec) #these should match the maximum values shown in cell []\n",
    "    #print(max_dec_list)\n",
    "\n",
    "    #makes a dataframe of only deceleration values\n",
    "    index_df = pd.DataFrame(data={'Acc':g250g}, index = range(0,len(g250g)))\n",
    "    #print(index_df)\n",
    "\n",
    "    max_index_list = []\n",
    "    #finds the index of the maximum deceleration values\n",
    "    for a in range(1, nDrops+1):\n",
    "        drop_range = index_df[plot_range_df.at[a,'Start']:plot_range_df.at[a,'End']]\n",
    "        #print(drop_range)\n",
    "        #print(drop_range.idxmax().values)\n",
    "        max_index = drop_range.idxmax().values\n",
    "        max_index = int(max_index)\n",
    "        max_index_list.append(max_index)\n",
    "        #print(max_index)\n",
    "    #print(max_index_list)\n",
    "    peaksArray = np.array(max_index_list)\n",
    "\n",
    "#Exporting Functions\n",
    "def exporttoexcel():\n",
    "    with pd.ExcelWriter(outputPath, mode=\"a\", if_sheet_exists='replace') as writer:\n",
    "        output_table.to_excel(writer, sheet_name = fileNum, index=False)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [],
   "source": [
    "#QSBC plots\n",
    "\n",
    "def dep_qsbc_comboplot(dropg, drop, accNameg, qdyn, qsbc, bctable):\n",
    "    #matplotlib.use('TkAgg')\n",
    "    %matplotlib inline\n",
    "    fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "    fig.set_size_inches(7, 7)\n",
    "    ax1.plot(dropg[accNameg], drop[\"Penetration Depth (m)\"]*100, color = \"k\", linestyle = \"solid\") #marker = 11\n",
    "    ax1.plot(drop[\"Velocity (m/s)\"], drop[\"Penetration Depth (m)\"]*100, color = \"k\", linestyle = \"dashed\")\n",
    "    ax1.plot(drop[\"X55g (m/s^2)\"], drop[\"Penetration Depth (m)\"]*100, color = \"b\", linestyle = \"dashed\")\n",
    "    ax1.plot(drop[\"Y55g (m/s^2)\"], drop[\"Penetration Depth (m)\"]*100, color = \"r\", linestyle = \"dashed\")\n",
    "    ax1.set(xlabel=\"Deceleration (g) and Velocity (m/s)\", ylabel=\"Penetration Depth (cm)\")\n",
    "    ax1.invert_yaxis()\n",
    "    ax1.legend([\"Deceleration (g)\", \"Velocity (m/s)\", \"X tilt (g)\", \"Y tilt (g)\"], loc = \"upper right\")\n",
    "    ax1.set_title(\"BD file \"+fileNum)\n",
    "    ax1.grid()\n",
    "\n",
    "    if droptype == \"a\": #for partial saturation, only one qsbc curve\n",
    "        ax2.plot(qdyn, drop[\"Penetration Depth (m)\"]*100, label=\"Qdyn\") #color = \"k\", marker = 11 #Plots Qdyn\n",
    "        ax2.plot(qsbc, drop[\"Penetration Depth (m)\"]*100, label=\"QSBC\") #plots QSBC\n",
    "        ax2.set(xlabel=\"Bearing Capacity (kPa)\")\n",
    "        ax2.set_xlim(0,800)\n",
    "        ax2.invert_yaxis()\n",
    "        ax2.legend([\"Qdyn\", \"QSBC\"], loc = \"upper right\")\n",
    "        ax2.set_title(\"Drop #\"+str(n))\n",
    "        ax2.grid()\n",
    "\n",
    "    else: #for saturated drops, use a range of strain-rate factors\n",
    "        n_srf = len(bctable.columns) #number of strain rate factors checked \n",
    "        ax2.plot(qdyn, drop[\"Penetration Depth (m)\"]*100, label=\"Qdyn\") #color = \"k\", marker = 11 #Plots Qdyn\n",
    "        ax2.plot(bctable.iloc[:,0], drop[\"Penetration Depth (m)\"]*100, label=str(bctable.columns[0])) #plots QSBC with smallest srf\n",
    "        ax2.plot(bctable.iloc[:,n_srf-1], drop[\"Penetration Depth (m)\"]*100, label=str(bctable.columns[n_srf-1])) #plots QSBC with largest srf\n",
    "        for i in range(1,n_srf-1): #plots qsbc for all other srfs\n",
    "            ax2.plot(bctable.iloc[:,i], drop[\"Penetration Depth (m)\"]*100, label=str(bctable.columns[i]), color = '.6')\n",
    "        ax2.set(xlabel=\"Bearing Capacity (kPa)\")\n",
    "        ax2.set_xlim(0,)\n",
    "        ax2.invert_yaxis()\n",
    "        ax2.legend([\"Qdyn\", str(bctable.columns[0]), str(bctable.columns[n_srf-1])], loc = \"upper right\")\n",
    "        ax2.set_title(\"Drop #\"+str(n))\n",
    "        ax2.grid()\n",
    "    \n",
    "    fig.subplots_adjust(bottom=.1, left = .1)\n",
    "\n",
    "def final_dep_qsbc_comboplot(dropg, drop, accNameg, qdyn, qsbc, bctable):\n",
    "    %matplotlib inline\n",
    "    fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "    fig.set_size_inches(7, 7)\n",
    "    ax1.plot(dropg[accNameg], drop[\"Penetration Depth (m)\"]*100, color = \"k\", linestyle = \"solid\") #marker = 11\n",
    "    ax1.plot(drop[\"Velocity (m/s)\"], drop[\"Penetration Depth (m)\"]*100, color = \"k\", linestyle = \"dashed\")\n",
    "    #ax1.plot(drop[\"X55g (m/s^2)\"], drop[\"Penetration Depth (m)\"]*100, color = \"b\", linestyle = \"dashed\")\n",
    "    #ax1.plot(drop[\"Y55g (m/s^2)\"], drop[\"Penetration Depth (m)\"]*100, color = \"r\", linestyle = \"dashed\")\n",
    "    ax1.set(xlabel=\"Deceleration (g) and Velocity (m/s)\", ylabel=\"Penetration Depth (cm)\")\n",
    "    ax1.invert_yaxis()\n",
    "    ax1.legend([\"Deceleration (g)\", \"Velocity (m/s)\"])\n",
    "    ax1.set_title(\"BD file \"+fileNum)\n",
    "    ax1.grid()\n",
    "\n",
    "    if droptype == \"a\": #for partial saturation, only one qsbc curve\n",
    "        y = drop[\"Penetration Depth (m)\"]*100\n",
    "        ax2.plot(qdyn[10:], y[10:], label=\"Qdyn\") #color = \"k\", marker = 11 #Plots Qdyn, chops off first 10 readings\n",
    "        ax2.plot(qsbc[10:-20], y[10:-20], label=\"QSBC\") #plots QSBC; chops off last 20 points which go off towards infinity (can adjust as needed)\n",
    "        ax2.set(xlabel=\"Bearing Capacity (kPa)\",)\n",
    "        ax2.set_xlim(0,)\n",
    "        ax2.invert_yaxis()\n",
    "        ax2.legend([\"Qdyn\", \"QSBC\"], loc = \"upper right\")\n",
    "        ax2.set_title(\"Drop #\"+str(n))\n",
    "        ax2.grid()\n",
    "\n",
    "    else: #for saturated drops, use a range of strain-rate factors\n",
    "        n_srf = len(bctable.columns) #number of strain rate factors checked \n",
    "        y = drop[\"Penetration Depth (m)\"]*100 #[changed this]\n",
    "        upper_bound = 10\n",
    "        lower_bound = -60\n",
    "        ax2.plot(qdyn[upper_bound:lower_bound], y[upper_bound:lower_bound], label=\"Qdyn\") #color = \"k\", marker = 11 #Plots Qdyn\n",
    "        ax2.plot(bctable.iloc[upper_bound:lower_bound,0], y[upper_bound:lower_bound], label=str(bctable.columns[0])) #plots QSBC with smallest srf\n",
    "        ax2.plot(bctable.iloc[upper_bound:lower_bound,len(bctable.columns)-1], y[upper_bound:lower_bound], label=str(bctable.columns[len(bctable.columns)-1])) #plots QSBC with largest srf\n",
    "        for i in range(1,n_srf-1): #plots qsbc for all other srfs\n",
    "            ax2.plot(bctable.iloc[upper_bound:lower_bound,i], y[upper_bound:lower_bound], label=str(bctable.columns[i]), color = '.6')\n",
    "            #bctablet.iloc[:,i].pop()\n",
    "        ax2.set(xlabel=\"Bearing Capacity (kPa)\")\n",
    "        ax2.set_xlim(0,) #//**// can change as needed\n",
    "        ax2.invert_yaxis()\n",
    "        ax2.legend([\"Qdyn\", str(bctable.columns[0]), str(bctable.columns[n_srf-1])], loc = \"upper right\")\n",
    "        ax2.set_title(\"Drop #\"+str(n))\n",
    "        ax2.grid()\n",
    "\n",
    "    fig.subplots_adjust(bottom=.1, left = .1)\n",
    "    #plotName = fileNum+\" \"+str(n)+\" BC\"\n",
    "    #plt.savefig(plotPath / plotName)\n",
    "    #plt.show()\n",
    "\n",
    "def duck_dep_qsbc_comboplot(dropg, drop, dropt, accNameg, qdynt):\n",
    "    fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "    fig.set_size_inches(7, 7)\n",
    "    ax1.plot(dropg[accNameg], drop[\"Penetration Depth (m)\"]*100, color = \"k\", linestyle = \"solid\") #marker = 11\n",
    "    ax1.plot(drop[\"Velocity (m/s)\"], drop[\"Penetration Depth (m)\"]*100, color = \"k\", linestyle = \"dashed\")\n",
    "    #ax1.plot(drop[\"X55g (m/s^2)\"], drop[\"Penetration Depth (m)\"]*100, color = \"b\", linestyle = \"dashed\")\n",
    "    #ax1.plot(drop[\"Y55g (m/s^2)\"], drop[\"Penetration Depth (m)\"]*100, color = \"r\", linestyle = \"dashed\")\n",
    "    ax1.set(xlabel=\"Deceleration (g) and Velocity (m/s)\", ylabel=\"Penetration Depth (cm)\")\n",
    "    ax1.invert_yaxis()\n",
    "    ax1.legend([\"Deceleration (g)\", \"Velocity (m/s)\"])\n",
    "    ax1.set_title(\"BD file \"+fileNum)\n",
    "    ax1.grid()\n",
    "\n",
    "    if droptype == \"a\": #for partial saturation, only one qsbc curve\n",
    "        y = dropt[\"Penetration Depth (m)\"]*100\n",
    "        ax2.plot(qdynt[10:], y[10:], label=\"Qdyn\") #color = \"k\", marker = 11 #Plots Qdyn, chops off first 40 readings\n",
    "        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)\n",
    "        ax2.set(xlabel=\"Bearing Capacity (kPa)\",)\n",
    "        ax2.set_xlim(0,qdyn.max())\n",
    "        ax2.invert_yaxis()\n",
    "        ax2.legend([\"Qdyn\", \"QSBC\"], loc = \"upper right\")\n",
    "        ax2.set_title(\"Drop #\"+str(n))\n",
    "        ax2.grid()\n",
    "\n",
    "    else: #for saturated drops, use a range of strain-rate factors\n",
    "        y = list(dropt[\"Penetration Depth (m)\"]*100)\n",
    "        ax2.plot(qdynt[10:-20], y[10:-20], label=\"Qdyn\") #color = \"k\", marker = 11 #Plots Qdyn\n",
    "        ax2.plot(bctablet.iloc[10:-20,0], y[10:-20], label=str(bctablet.columns[0])) #plots QSBC with smallest srf\n",
    "        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\n",
    "        for i in range(1,len(bctablet.columns)-1): #plots qsbc for all other srfs\n",
    "            ax2.plot(bctablet.iloc[10:-20,i], y[10:-20], label=str(bctablet.columns[i]), color = '.6')\n",
    "            #bctablet.iloc[:,i].pop()\n",
    "        ax2.set(xlabel=\"Bearing Capacity (kPa)\")\n",
    "        ax2.set_xlim(0,)\n",
    "        ax2.invert_yaxis()\n",
    "        ax2.legend([\"Qdyn\", str(bctablet.columns[0]), str(bctablet.columns[len(bctablet.columns)-1])], loc = \"upper right\")\n",
    "        ax2.set_title(\"Drop #\"+str(n))\n",
    "        ax2.grid()\n",
    "\n",
    "    fig.subplots_adjust(bottom=.1, left = .1)\n",
    "    plotName = fileNum+\" \"+str(n)+\" BC\"\n",
    "    plt.savefig(plotPath / plotName)\n",
    "    plt.show()\n",
    "\n",
    "#Plots for exporting\n",
    "def overviewplot(): #Plot showing all accellerometers and pore pressure readings\n",
    "    %matplotlib inline\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(3)\n",
    "    fig.set_size_inches(14, 7)\n",
    "    #plt.tight_layout()\n",
    "    ax1.plot(time, g250g, label=\"250g\")\n",
    "    ax1.plot(time, g50g, label=\"50g\")\n",
    "    ax1.plot(time, g18g, label=\"18g\")\n",
    "    ax1.plot(time, g2g, label=\"2g\")\n",
    "    ax1.legend(loc = \"upper right\")\n",
    "    ax1.set(ylabel=\"Deceleration (g)\")\n",
    "    ax1.set_title(\"BD file \"+fileNum)\n",
    "    ax1.grid()\n",
    "\n",
    "    ax2.plot(time, ppm, label=\"Pore Pressure\")\n",
    "    ax2.set(ylabel=\"Pore Pressure (kPa)\")\n",
    "    ax2.grid()\n",
    "\n",
    "    ax3.plot(time, gX55g, label=\"X 55g\")\n",
    "    ax3.plot(time, gY55g, label=\"Y 55g\")\n",
    "    ax3.legend(loc = \"upper right\")\n",
    "    ax3.set(ylabel=\"Deceleration (g)\")\n",
    "    ax3.set(xlabel=\"Time (s)\")\n",
    "    ax3.grid()\n",
    "\n",
    "    fig.subplots_adjust(bottom=.1, left = .1)\n",
    "    plotName = fileNum+\" Overview.png\"\n",
    "    plt.savefig(plotPath / plotName)\n",
    "    plt.show()\n",
    "\n",
    "def integplot(drop, accName): #Deceleration,Velocity,and penetration depth vs time plots\n",
    "    %matplotlib inline\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(3)\n",
    "    fig.set_size_inches(7, 7)\n",
    "    #resize to 7,7, 7,14\n",
    "    #plt.tight_layout()\n",
    "    timereset = drop[\"Time (s)\"]- drop[\"Time (s)\"].iloc[0]\n",
    "    ax1.plot(timereset, drop[accName], color = \"k\" ) #,marker = 11\n",
    "    ax1.set(ylabel=\"Deceleration (m/s^2)\")\n",
    "    ax1.set_title(\"BD file \"+fileNum+ \" Drop #\"+str(n))\n",
    "    ax1.grid()\n",
    "\n",
    "    ax2.plot(timereset, drop['Velocity (m/s)'] , color = \"k\" )\n",
    "    ax2.set(ylabel=\"Velocity (m/s)\")\n",
    "    ax2.grid()\n",
    "\n",
    "    ax3.plot(timereset, drop[\"Penetration Depth (m)\"] , color = \"k\")\n",
    "    ax3.set(ylabel=\"Penetration Depth (m)\", xlabel=\"Time(s)\")\n",
    "    ax3.grid()\n",
    "\n",
    "    fig.subplots_adjust(bottom=.1, left = .1)\n",
    "    plotName = fileNum+\" \"+str(n)+\" Integ\"\n",
    "    plt.savefig(plotPath / plotName)\n",
    "    plt.show()\n",
    "\n",
    "#plots for troubleshooting \n",
    "def depthplot(dropg, drop, accNameg): #Velocity and develeration vs. penetration depth \n",
    "    %matplotlib inline\n",
    "    fig, (ax1) = plt.subplots(1)\n",
    "    ax1.plot(dropg[accNameg], drop[\"Penetration Depth (m)\"]*100, color = \"k\", linestyle = \"solid\") #marker = 11\n",
    "    ax1.plot(drop[\"Velocity (m/s)\"], drop[\"Penetration Depth (m)\"]*100, color = \"k\", linestyle = \"dashed\")\n",
    "    ax1.set(xlabel=\"Deceleration (g) and Velocity (m/s)\", ylabel=\"Penetration Depth (cm)\")\n",
    "    ax1.invert_yaxis()\n",
    "    ax1.legend([\"Deceleration (g)\", \"Velocity (m/s)\"])\n",
    "    ax1.set_title(\"BD file \"+fileNum)\n",
    "    plt.show()\n",
    "\n",
    "def peakplot(): # Plot showing peak deceleration\n",
    "    %matplotlib inline\n",
    "    fig, (ax1) = plt.subplots(1)\n",
    "    x=np.array(g250g)\n",
    "    plt.plot(x)\n",
    "    plt.plot(peaksArray, x[peaksArray], \"x\")\n",
    "    ax1.set(xlabel=\"Count\", ylabel=\"Deceleration (g)\")\n",
    "    ax1.set_title(\"Drop Locations\")\n",
    "    plt.show()\n",
    "\n",
    "def qdynplot(drop, qdyn): #Plot showing dynamic bearing capacity vs depth\n",
    "    %matplotlib inline\n",
    "    #print(len(drop))\n",
    "    #print(len(qdyn))\n",
    "    #print((bctable.iloc[:,0]))\n",
    "    #print(len(bctable.iloc[:,len(bctable.columns)-1]))\n",
    "    fig, (ax1) = plt.subplots(1)\n",
    "\n",
    "    if droptype == \"a\": #for partial saturation, only one qsbc curve\n",
    "        ax1.plot(qdyn, drop[\"Penetration Depth (m)\"]*100, label=\"Qdyn\") #color = \"k\", marker = 11 #Plots Qdyn\n",
    "        ax1.plot(qsbc, drop[\"Penetration Depth (m)\"]*100, label=\"QSBC\") #plots QSBC\n",
    "        ax1.set(xlabel=\"Bearing Capacity (kPa)\")\n",
    "        ax1.set_xlim(0,)\n",
    "        ax1.invert_yaxis()\n",
    "        ax1.legend([\"Qdyn\", \"QSBC\"])\n",
    "        ax1.set_title(\"Bearing Capacity- \"+fileNum+ \" \"+str(n))\n",
    "        ax1.grid()\n",
    "\n",
    "    else:\n",
    "        ax1.plot(qdyn, drop[\"Penetration Depth (m)\"]*100, label=\"Qdyn\") #color = \"k\", marker = 11 #Plots Qdyn\n",
    "        ax1.plot(bctable.iloc[:,0], drop[\"Penetration Depth (m)\"]*100, label=str(bctable.columns[0])) #plots QSBC with smallest srf\n",
    "        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\n",
    "        for i in range(1,len(bctable.columns)-1): #plots qsbc for all other srfs\n",
    "            ax1.plot(bctable.iloc[:,i], drop[\"Penetration Depth (m)\"]*100, label=str(bctable.columns[i]), color = \"k\")\n",
    "        ax1.set(xlabel=\"Bearing Capacity (kPa)\", ylabel=\"Penetration Depth (cm)\")\n",
    "        ax1.set_xlim(0,)\n",
    "        ax1.invert_yaxis()\n",
    "        ax1.legend([\"Qdyn\", str(bctable.columns[0]), str(bctable.columns[len(bctable.columns)-1])])\n",
    "        ax1.set_title(\"Bearing Capacity- \"+fileNum+ \" \"+str(n))\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "def tiltplot():\n",
    "    %matplotlib inline\n",
    "    fig, (ax1) = plt.subplots(1)\n",
    "    plt.plot(time, gX55g, label=\"2g\")\n",
    "    plt.plot(time, gY55g, label=\"2g\")\n",
    "    ax1.legend()\n",
    "    ax1.set(ylabel=\"Deceleration (g)\")\n",
    "    ax1.set(xlabel=\"Time (s)\")\n",
    "    ax1.set_title(\"BD file \"+fileNum)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "def su_plot(drop, su_type):\n",
    "    %matplotlib inline\n",
    "    upper_bound = 10\n",
    "    lower_bound = -60\n",
    "    fig, ax1 = plt.subplots(1)\n",
    "    fig.set_size_inches(7, 7)\n",
    "    ax1.plot(su_type[upper_bound:lower_bound], drop[\"Penetration Depth (m)\"][upper_bound:lower_bound]*100, color = \"k\", linestyle = \"solid\") #marker = 11\n",
    "    ax1.set(xlabel=\"Undrained Strength (kPa)\", ylabel=\"Penetration Depth (cm)\")\n",
    "    ax1.invert_yaxis()\n",
    "    ax1.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [],
   "source": [
    "def su_plot_with_range(dropg, drop, accNameg, qsbc, bctable): \n",
    "\n",
    "    %matplotlib inline\n",
    "    fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "    fig.set_size_inches(7, 7)\n",
    "    ax1.plot(dropg[accNameg], drop[\"Penetration Depth (m)\"]*100, color = \"k\", linestyle = \"solid\") #marker = 11\n",
    "    ax1.plot(drop[\"Velocity (m/s)\"], drop[\"Penetration Depth (m)\"]*100, color = \"k\", linestyle = \"dashed\")\n",
    "    #ax1.plot(drop[\"X55g (m/s^2)\"], drop[\"Penetration Depth (m)\"]*100, color = \"b\", linestyle = \"dashed\")\n",
    "    #ax1.plot(drop[\"Y55g (m/s^2)\"], drop[\"Penetration Depth (m)\"]*100, color = \"r\", linestyle = \"dashed\")\n",
    "    ax1.set(xlabel=\"Deceleration (g) and Velocity (m/s)\", ylabel=\"Penetration Depth (cm)\")\n",
    "    ax1.invert_yaxis()\n",
    "    ax1.legend([\"Deceleration (g)\", \"Velocity (m/s)\"])\n",
    "    ax1.set_title(\"BD file \"+fileNum)\n",
    "    ax1.grid()\n",
    "\n",
    "    if droptype == \"a\": #for partial saturation, only one qsbc curve\n",
    "        y = drop[\"Penetration Depth (m)\"]*100\n",
    "        ax2.plot(qsbc[10:-20]/Nkt, y[10:-20]) #plots QSBC; chops off last 20 points which go off towards infinity (can adjust as needed)\n",
    "        ax2.set(xlabel=\"Undrained Strength (kPa)\")\n",
    "        ax2.set_xlim(0,)\n",
    "        ax2.invert_yaxis()\n",
    "        ax2.legend([\"su\"], loc = \"upper right\")\n",
    "        ax2.set_title(\"Drop #\"+str(n))\n",
    "        ax2.grid()\n",
    "\n",
    "    else: #for saturated drops, use a range of strain-rate factors\n",
    "        n_srf = len(bctable.columns) #number of strain rate factors checked \n",
    "        y = drop[\"Penetration Depth (m)\"]*100\n",
    "        upper_bound = 10\n",
    "        lower_bound = -60\n",
    "        ax2.plot(bctable.iloc[upper_bound:lower_bound,0]/Nkt, y[upper_bound:lower_bound]) #plots QSBC with smallest srf\n",
    "        ax2.plot(bctable.iloc[upper_bound:lower_bound,len(bctable.columns)-1]/Nkt, y[upper_bound:lower_bound]) #plots QSBC with largest srf\n",
    "        for i in range(1,n_srf-1): #plots qsbc for all other srfs\n",
    "            ax2.plot(bctable.iloc[upper_bound:lower_bound,i]/Nkt, y[upper_bound:lower_bound], color = '.6')\n",
    "            #bctablet.iloc[:,i].pop()\n",
    "        ax2.set(xlabel=\"Undrained Strength (kPa)\")\n",
    "        ax2.set_xlim(0,) #//**// can change as needed\n",
    "        ax2.invert_yaxis()\n",
    "        ax2.legend([\"K = \" + str(srfk[0]), \"K = \" + str(srfk[-1])], loc = \"upper right\")\n",
    "        ax2.set_title(\"Drop #\"+str(n))\n",
    "        ax2.grid()\n",
    "\n",
    "    fig.subplots_adjust(bottom=.1, left = .1)\n",
    "    #plotName = fileNum+\" \"+str(n)+\" BC\"\n",
    "    #plt.savefig(plotPath / plotName)\n",
    "    #plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bring in penetrometer data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set the penetrometer mass and length\n",
    "masslength(tiptype)\n",
    "\n",
    "# READ BD DATA IN\n",
    "data_array = []  # creates an empty array for us to fill with bd data\n",
    "fileName = 'bLog'+fileNum+\".bin\"\n",
    "# print(fileName)\n",
    "newPath = binFilepath / fileName\n",
    "#print(newPath)\n",
    "file = open(newPath, 'rb')  # read file\n",
    "element = file.read(3)  # create a byte list with each element having 3 bytes\n",
    "\n",
    "while element:\n",
    "    # Convert to signed integer before adding to data array\n",
    "    iVAl = int.from_bytes(element, byteorder='big', signed=True)\n",
    "    data_array.append(iVAl)  # adds the reshaped data from the bd file to the data frame\n",
    "    element = file.read(3)\n",
    "\n",
    "np_array = np.array(data_array)  # create numpy array from the list\n",
    "np_array = np.reshape(np_array, (-1, 10))  # convert the 1d array to 2d array with 10 cols\n",
    "\n",
    "#print(np_array.shape)\n",
    "# print(np_array)\n",
    "\n",
    "df = pd.DataFrame(np_array) # Creates a Dataframe in pandas from the bd data\n",
    "df.columns = ['Count', 'no clue', 'g2g', 'g18g', 'g50g', 'ppm', 'g200g', 'gX55g', 'gY55g', 'g250g'] # names columns\n",
    "# print(dfCal)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply Calibration Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "# APPLY CALIBRATION FACTORS\n",
    "if BD == 3:  # calibration factors from Sequim 2021\n",
    "    g2g = (df['g2g']-34426.5)/1615925.8 # accelerometers are in g\n",
    "    g18g = (df['g18g']+12322.1)/163530.7\n",
    "    g50g = (df['g50g']-237384.9)/63651 - 0.1120\n",
    "    ppm = ((df['ppm']+62496.7)/20705) * 6.89475729 -38# converts to kPa\n",
    "    g200g = (df['g200g'] -262332.4)/38888.7 \n",
    "    gX55g = (df['gX55g']-59093.7)/66674.3\n",
    "    gY55g = (df['gY55g']-140224.6)/66674.3\n",
    "    g250g = (df['g250g']-40536.1)/13631.6\n",
    "\n",
    "if BD == 2: # calibration factors from March 2023\n",
    "    g2g = (df['g2g']+36597.6)/1637627.3 # accelerometers are in g\n",
    "    g18g = (df['g18g']-26185.3)/160297.2 \n",
    "    g50g = (df['g50g']-212256.9)/63968.7 \n",
    "    ppm = ((df['ppm']+55518.9)/18981.7) * 6.89475729 # converts to kPa\n",
    "    g200g = (df['g200g']-175499.4)/30583.8\n",
    "    gX55g = (df['gX55g']-53629.9)/68590.9\n",
    "    gY55g = (df['gY55g']-43694.3)/68280.3\n",
    "    g250g = (df['g250g']-39619.9)/13545.8\n",
    "\n",
    "if BD == 1: # calibration factors from March 2023\n",
    "    g2g = (df['g2g']-43727.6)/1625064  # accelerometers are in g\n",
    "    g18g = (df['g18g']-45085.6)/160925.7 \n",
    "    g50g = (df['g50g']-173493.4)/63944.6 \n",
    "    ppm = ((df['ppm']+31776.1)/20679.7) * 6.89475729 # this is kPa\n",
    "    g200g = (df['g200g'] -731734.3)/32192.4  \n",
    "    gX55g = (df['gX55g'] -68837.9)/52137.3\n",
    "    gY55g = (df['gY55g']-68015.9)/28074.9\n",
    "    g250g = (df['g250g']+10580)/13688.7 "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create Calibrated Dataframes for Penetrometer Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = (df['Count']-df['Count'].iloc[0]+1)/2000 # gives time in s\n",
    "count = df[\"Count\"]-df['Count'].iloc[0]\n",
    "\n",
    "# make a new dataframe of the calibrated values in units of g\n",
    "dfCalg = pd.DataFrame([count, time, g2g, g18g, g50g, g200g, g250g, gX55g, gY55g, ppm])\n",
    "dfCalg = dfCalg.T\n",
    "dfCalg.columns = ['Count', 'Time (s)', '2g (g)', '18g (g)', '50g (g)', '200g (g)', '250g (g)', 'X55g (g)', 'Y55g (g)', 'Pore Pressure (kPa)']  # names columns\n",
    "#print(dfCalg)\n",
    "\n",
    "#make a new dataframe of the calibrated values in units of m/s^2\n",
    "dfCal = pd.DataFrame([count, time, g2g, g18g, g50g, g200g, g250g, gX55g, gY55g, ppm])\n",
    "dfCal = dfCal.T\n",
    "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\n",
    "dfCal['2g (m/s^2)'] = dfCal['2g (m/s^2)'] * 9.80665\n",
    "dfCal['18g (m/s^2)'] = dfCal['18g (m/s^2)'] * 9.80665\n",
    "dfCal['50g (m/s^2)'] = dfCal['50g (m/s^2)'] * 9.80665\n",
    "dfCal['200g (m/s^2)'] = dfCal['200g (m/s^2)'] * 9.80665\n",
    "dfCal['250g (m/s^2)'] = dfCal['250g (m/s^2)'] * 9.80665\n",
    "dfCal['X55g (m/s^2)'] = dfCal['X55g (m/s^2)'] * 9.80665\n",
    "dfCal['Y55g (m/s^2)'] = dfCal['Y55g (m/s^2)'] * 9.80665\n",
    "#print(dfCal)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Locate the Drops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Locate the drops\n",
    "if drop_finding == \"a\":\n",
    "    x = np.array(g250g)  # what accelerometer to get the peaks from - use 250 because it will never be maxed out\n",
    "    max250 = max(g250g)\n",
    "\n",
    "    peaks, _ = find_peaks(x, height = 2, distance=1000, prominence=3)  # finds the largest peaks more than 2g spaced at least 10000 counts apart\n",
    "\n",
    "    peaksArray = np.array(peaks)  # prints a list of the count where the peaks occur\n",
    "    \n",
    "    #peaksArray = filter(filterpeak, peaksArray)\n",
    "\n",
    "    #print(peaksArray)\n",
    "    q = (peaksArray.shape) #gives number of peaks\n",
    "    nDrops = int(q[0]) #number of drops in the file\n",
    "    #print(nDrops)\n",
    "\n",
    "if drop_finding == \"m\":\n",
    "    manual_peak_pick()\n",
    "\n",
    "peakplot()\n",
    "overviewplot()\n",
    "#print(peaksArray)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the analysis function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze(drop_n, drop_ng, acc_n, acc_ng, acc_nName, acc_nNameg, qdyn_n_m, qdyn_n_p, output_table_n): # took out \n",
    "        \n",
    "    global output_table\n",
    "\n",
    "    drop_n = dfCal[dropstart:dropend]  # start and end points of the drop in m/s^2\n",
    "    drop_ng = dfCalg[dropstart:dropend]  # start and end points of the drop in g\n",
    "    drop_n = pd.DataFrame(drop_n)  # makes dataframe including all data within the start and end points of the drop\n",
    "    drop_ng = pd.DataFrame(drop_ng)\n",
    "    dg = drop_ng \n",
    "    d = drop_n\n",
    "    accPick(dg, d)  # chooses what accelerometer to use\n",
    "    acc_n = acc\n",
    "    acc_ng = accg\n",
    "    acc_nName = accName\n",
    "    acc_nNameg = accNameg\n",
    "    #findchangepts() #automatically identifies the moment of penetration\n",
    "    peakpick() #interactive graph for user to choose start of penetration\n",
    "    plt.close()\n",
    "    endpick()\n",
    "    plt.close()\n",
    "    print(\"penstart: \", penstart)\n",
    "    print(\"penend: \", penend)\n",
    "    #finddropend(n-1) #automatically find the end of the drop\n",
    "    offset = acc_ng[penstart] #sets the offset to be the acceleration at the moment of impact\n",
    "    print(\"offset: \", offset)\n",
    "    #print(drop)\n",
    "    dg = dfCalg[penstart:penend] #chops the file to only include penetration \n",
    "    d = dfCal[penstart:penend]\n",
    "    acc_ng = accg[penstart:penend]\n",
    "    acc_ng = acc_ng-offset\n",
    "    acc_n = acc[penstart:penend]\n",
    "    offsetms = offset * 9.80665\n",
    "    acc_n = acc_n-offsetms\n",
    "\n",
    "    drop_n = d\n",
    "    drop_ng = dg\n",
    "    \n",
    "    integration(d, acc_n) #double integration to solve for velocity and penetration depth\n",
    "    drop_n = d\n",
    "    areafind() #area calculations for bearing capacity calculations\n",
    "    print(\"Acc_n: \",acc_n.shape)\n",
    "    print(\"area: \", len(area_m))\n",
    "    qdynfun(acc_n) #calculation of dynamic bearing capacity\n",
    "\n",
    "    qsbcfun(acc_n) #calculation of quasi-static bearing capacities\n",
    "\n",
    "    qsbc_table_m = bctable_m #qsbcfun(acc_n)[0]\n",
    "    #print(qsbc_table_m)\n",
    "    qsbc_table_p = bctable_p #qsbcfun(acc_n)[1]\n",
    "    #print(bctable_m)\n",
    "    qdyn_n_m = qdyn_m\n",
    "    qdyn_n_p = qdyn_p\n",
    "    integplot(drop_n,acc_nName)\n",
    "    #qdynplot(drop1, qdyn1)\n",
    "    print(\"Max penetration depth is: \", maxdep)\n",
    "    print(\"Pre-truncation d1\", drop_n)\n",
    "    drop_n = pd.DataFrame(drop_n)\n",
    "    drop_ng = pd.DataFrame(drop_ng)\n",
    "    acc_n = pd.DataFrame(acc_n)\n",
    "    qdyn_n_m = pd.DataFrame(qdyn_n_m)\n",
    "    qdyn_n_p = pd.DataFrame(qdyn_n_p)\n",
    "    #if droptype == \"w\":\n",
    "        #bctable_m = pd.DataFrame(bctable_m)\n",
    "        #bctable_p = pd.DataFrame(bctable_p)\n",
    "\n",
    "    '''if truncate_at_cone == \"y\": #if terminating analysis at the cone-stem interface\n",
    "        drop_n = drop_n.truncate(before=trunc_index) #this truncates each df such that only depths below cone height are considered\n",
    "        drop_ng = drop_ng.truncate(before=trunc_index)\n",
    "        acc_n = acc_n.truncate(before=trunc_index)\n",
    "        acc_n = np.array(acc_n)\n",
    "        acc_ng = acc_ng.truncate(before=trunc_index)\n",
    "        acc_ng = np.array(acc_ng)\n",
    "        drop_n = drop_n.truncate(before=trunc_index)\n",
    "        qdyn_n_m = qdyn_n_m.truncate(before=trunc_index)\n",
    "        qdyn_n_p = qdyn_n_p.truncate(before=trunc_index)\n",
    "\n",
    "        if droptype == \"w\":\n",
    "            qsbc_table_m = bctable_m.truncate(before=trunc_index)\n",
    "            qsbc_table_p = bctable_p.truncate(before=trunc_index)'''\n",
    "\n",
    "\n",
    "    firmnessfactor(acc_n, drop_n) #calculates the firmness factor\n",
    "    \n",
    "    if soiltype == \"s\":\n",
    "        Dr = dr(acc_ng) #calculates relative density\n",
    "        phi = duncan_correlation(Dr) #calculates friction angle\n",
    "        su_m = 'NaN'\n",
    "        su_p = 'NaN'\n",
    "        max_su_m = 'NaN'\n",
    "        max_su_p = 'NaN'\n",
    "\n",
    "    elif soiltype == \"u\":\n",
    "        if maxdep <= .2:\n",
    "            Dr = dr(acc_ng)\n",
    "            phi = duncan_correlation()\n",
    "            su_m = 'NaN'\n",
    "            su_p = 'NaN'\n",
    "            max_su_m = 'NaN'\n",
    "            max_su_p = 'NaN'\n",
    "        else:\n",
    "            Dr = 'NaN'\n",
    "            phi = \"NaN\"\n",
    "            su_m = qsbc_m/Nkt #need to add this still //*//\n",
    "            su_p = qsbc_p/Nkt\n",
    "            max_su_m = maxqsbc_default_m / Nkt\n",
    "            max_su_p = maxqsbc_default_p / Nkt\n",
    "\n",
    "\n",
    "    elif soiltype == \"c\":\n",
    "        Dr = 'NaN'\n",
    "        phi = \"NaN\"\n",
    "        su_m = qsbc_m/Nkt #need to add this still //*//\n",
    "        su_p = qsbc_p/Nkt\n",
    "        max_su_m = maxqsbc_default_m / Nkt\n",
    "\n",
    "        print(max_su_m)\n",
    "        print(type(max_su_m))\n",
    "        max_su_p = maxqsbc_default_p / Nkt\n",
    "\n",
    "    pentime=(penend - penstart)/2 #ms #calculates the time of penetration\n",
    "\n",
    "    #print(bctable_m) # = None\n",
    "    #bctable_p = None\n",
    "\n",
    "    dep_qsbc_comboplot(drop_ng,drop_n,acc_nNameg, qdyn_n_m, qsbc_m, qsbc_table_m)\n",
    "    plt.show()\n",
    "    ignore_drop()\n",
    "    \n",
    "    if n == 1:\n",
    "        if keep_drop == \"y\":\n",
    "\n",
    "            final_dep_qsbc_comboplot(drop_ng, drop_n, acc_nNameg, qdyn_n_m, qsbc_m, qsbc_table_m)\n",
    "            plotName = fileNum+\" \"+str(n)+\" - m\"\n",
    "            plt.savefig(plotPath / plotName) \n",
    "\n",
    "            final_dep_qsbc_comboplot(drop_ng, drop_n, acc_nNameg, qdyn_n_p, qsbc_p, qsbc_table_p)\n",
    "            plotName = fileNum+\" \"+str(n)+\" - p\"\n",
    "            plt.savefig(plotPath / plotName)\n",
    "            print(\"pentime= \", pentime)\n",
    "\n",
    "            if max_su_m == 'NaN':\n",
    "                placeholder = 0 #do nothing\n",
    "            else:\n",
    "                \n",
    "                # uncomment the below plot for a single value of strain rate factor K\n",
    "                '''su_plot(drop_n, su_m)\n",
    "                plotName = fileNum+\" \"+str(n)+\" Undrained Strength -m\"\n",
    "                plt.savefig(plotPath / plotName)'''\n",
    "\n",
    "                su_plot_with_range(drop_ng, drop_n, acc_nNameg, qsbc_m, qsbc_table_m)\n",
    "                plotName = fileNum+\" \"+str(n)+\" Undrained Strength - Range -m\"\n",
    "                plt.savefig(plotPath / plotName)\n",
    "\n",
    "                '''su_plot(drop_n, su_p)\n",
    "                plotName = fileNum+\" \"+str(n)+\" Undrained Strength -p\"\n",
    "                plt.savefig(plotPath / plotName)'''\n",
    "\n",
    "                su_plot_with_range(drop_ng, drop_n, acc_nNameg, qsbc_p, qsbc_table_p)\n",
    "                plotName = fileNum+\" \"+str(n)+\" Undrained Strength - Range -p\"\n",
    "                plt.savefig(plotPath / plotName)\n",
    "\n",
    "\n",
    "            output_table_n = pd.DataFrame([BD, fileNum, n, soiltype, tiptype, droptype, maxdep*100, maxacc, maxvel, \\\n",
    "                maxbcdep*100, maxqdyn_m, maxqsbc_default_m, maxqdyn_p, maxqsbc_default_p, ff, pentime, Dr, phi, max_su_m, max_su_p]) \\\n",
    "                    #old version that give upper and lower bounds: pd.DataFrame([BD, fileNum, n, soiltype, tiptype, droptype, maxdep*100, maxacc, maxvel, maxbcdep*100, maxqdyn_m, maxHqsbc_m, maxLqsbc_m, maxAqsbc_m, maxqdyn_p, maxHqsbc_p, maxLqsbc_p, maxAqsbc_p, ff, pentime, Dr, phi])\n",
    "            output_table_n = output_table_n.T\n",
    "            output_table = output_table_n\n",
    "            output_table.columns = [\"BlueDrop\", \"BD File\", \"Drop No.\", \"Soil Type\", \"Tip Type\", \"Drop Type\", \\\n",
    "                \"Penetration Depth (cm)\", \"Maximum Deceleration (m/s^2)\", \"Impact Velocity (m/s)\", \"Depth of Max QSBC (cm)\", \\\n",
    "                    \"Max Qdyn (mantle) (kPa)\", \"Max QSBC (mantle) (kPa)\", \"Max Qdyn (projected) (kPa)\", \"Max QSBC (projected) (kPa)\", \\\n",
    "                         \"Firmness Factor (m-1)\", \"Penetration Time (ms)\", \"Relative Density (%)\", \"Friction Angle (deg.)\", \\\n",
    "                            \"Max Su (mantle) (kPa)\", \"Max Su (projected) (kPa)\"]\\\n",
    "                            #old version that give upper and lower bounds:[\"BlueDrop\", \"BD File\", \"Drop No.\", \"Soil Type\", \"Tip Type\", \"Drop Type\", \"Penetration Depth (cm)\", \"Maximum Deceleration (m/s^2)\", \"Impact Velocity (m/s)\", \"Depth of Max QSBC (cm)\", \"Max Qdyn (mantle) (kPa)\", \"Max QSBC-Upper Bounds (mantle) (kPa)\", \"Max QSBC-Lower Bounds (mantle) (kPa)\", \"Max QSBC-Average (kPa) (mantle)\", \"Max Qdyn (projected) (kPa)\", \"Max QSBC-Upper Bounds (projected) (kPa)\", \"Max QSBC-Lower Bounds (projected) (kPa)\", \"Max QSBC-Average (kPa) (projected)\", \"Firmness Factor (m-1)\", \"Penetration Time (ms)\", \"Relative Density (%)\", \"Friction Angle (deg.)\"]  # Creates an empty Dataframe in pandas from the output data\n",
    "        else:\n",
    "            dep_qsbc_comboplot(drop_ng,drop_n,acc_nNameg, qdyn_n_m, qsbc_m, qsbc_table_m)\n",
    "            plotName = fileNum+\" \"+str(n)+\" BC - REJECTED\"\n",
    "            plt.savefig(plotPath / plotName)\n",
    "            output_table1 = pd.DataFrame([BD, fileNum, n, soiltype, tiptype, droptype,'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN'])\n",
    "            output_table1 = output_table1.T\n",
    "            output_table = output_table1\n",
    "            output_table.columns = [\"BlueDrop\", \"BD File\", \"Drop No.\", \"Soil Type\", \"Tip Type\", \"Drop Type\", \\\n",
    "                \"Penetration Depth (cm)\", \"Maximum Deceleration (m/s^2)\", \"Impact Velocity (m/s)\", \"Depth of Max QSBC (cm)\", \\\n",
    "                    \"Max Qdyn (mantle) (kPa)\", \"Max QSBC (mantle) (kPa)\", \"Max Qdyn (projected) (kPa)\", \"Max QSBC (projected) (kPa)\", \\\n",
    "                         \"Firmness Factor (m-1)\", \"Penetration Time (ms)\", \"Relative Density (%)\", \"Friction Angle (deg.)\", \\\n",
    "                            \"Max Su (mantle) (kPa)\", \"Max Su (projected) (kPa)\"]\\\n",
    "                            #old version that give upper and lower bounds:[\"BlueDrop\", \"BD File\", \"Drop No.\", \"Soil Type\", \"Tip Type\", \"Drop Type\", \"Penetration Depth (cm)\", \"Maximum Deceleration (m/s^2)\", \"Impact Velocity (m/s)\", \"Depth of Max QSBC (cm)\", \"Max Qdyn (mantle) (kPa)\", \"Max QSBC-Upper Bounds (mantle) (kPa)\", \"Max QSBC-Lower Bounds (mantle) (kPa)\", \"Max QSBC-Average (kPa) (mantle)\", \"Max Qdyn (projected) (kPa)\", \"Max QSBC-Upper Bounds (projected) (kPa)\", \"Max QSBC-Lower Bounds (projected) (kPa)\", \"Max QSBC-Average (kPa) (projected)\", \"Firmness Factor (m-1)\", \"Penetration Time (ms)\", \"Relative Density (%)\", \"Friction Angle (deg.)\"]  # Creates an empty Dataframe in pandas from the output data\n",
    "    else:\n",
    "        if keep_drop == \"y\":\n",
    "            final_dep_qsbc_comboplot(drop_ng, drop_n, acc_nNameg, qdyn_n_m, qsbc_m, qsbc_table_m)\n",
    "            plotName = fileNum+\" \"+str(n)+\" Bearing Capacity - m\"\n",
    "            plt.savefig(plotPath / plotName) \n",
    "            final_dep_qsbc_comboplot(drop_ng, drop_n, acc_nNameg, qdyn_n_p, qsbc_p, qsbc_table_p)\n",
    "            plotName = fileNum+\" \"+str(n)+\" Bearing Capacity - p\"\n",
    "            plt.savefig(plotPath / plotName) \n",
    "            print(\"pentime= \", pentime)\n",
    "\n",
    "            if max_su_m == 'NaN':\n",
    "                placeholder = 0\n",
    "            else:\n",
    "\n",
    "                # uncomment the below plot for a single value of strain rate factor K\n",
    "                '''su_plot(drop_n, su_m)\n",
    "                plotName = fileNum+\" \"+str(n)+\" Undrained Strength -m\"\n",
    "                plt.savefig(plotPath / plotName)'''\n",
    "\n",
    "                su_plot_with_range(drop_ng, drop_n, acc_nNameg, qsbc_m, qsbc_table_m)\n",
    "                plotName = fileNum+\" \"+str(n)+\" Undrained Strength - Range -m\"\n",
    "                plt.savefig(plotPath / plotName)\n",
    "\n",
    "                '''su_plot(drop_n, su_p)\n",
    "                plotName = fileNum+\" \"+str(n)+\" Undrained Strength -p\"\n",
    "                plt.savefig(plotPath / plotName)'''\n",
    "\n",
    "                su_plot_with_range(drop_ng, drop_n, acc_nNameg, qsbc_p, qsbc_table_p)\n",
    "                plotName = fileNum+\" \"+str(n)+\" Undrained Strength - Range -p\"\n",
    "                plt.savefig(plotPath / plotName)\n",
    "\n",
    "            output_table_n = pd.DataFrame([BD, fileNum, n, soiltype, tiptype, droptype, maxdep*100, maxacc, maxvel, \\\n",
    "                maxbcdep*100, maxqdyn_m, maxqsbc_default_m, maxqdyn_p, maxqsbc_default_p, ff, pentime, Dr, phi, max_su_m, max_su_p]) \n",
    "            output_table_n = output_table_n.T\n",
    "            output_table_n.columns = [\"BlueDrop\", \"BD File\", \"Drop No.\", \"Soil Type\", \"Tip Type\", \"Drop Type\", \\\n",
    "                \"Penetration Depth (cm)\", \"Maximum Deceleration (m/s^2)\", \"Impact Velocity (m/s)\", \"Depth of Max QSBC (cm)\", \\\n",
    "                    \"Max Qdyn (mantle) (kPa)\", \"Max QSBC (mantle) (kPa)\", \"Max Qdyn (projected) (kPa)\", \"Max QSBC (projected) (kPa)\", \\\n",
    "                         \"Firmness Factor (m-1)\", \"Penetration Time (ms)\", \"Relative Density (%)\", \"Friction Angle (deg.)\", \\\n",
    "                            \"Max Su (mantle) (kPa)\", \"Max Su (projected) (kPa)\"]\\\n",
    "                            #old version that give upper and lower bounds:[\"BlueDrop\", \"BD File\", \"Drop No.\", \"Soil Type\", \"Tip Type\", \"Drop Type\", \"Penetration Depth (cm)\", \"Maximum Deceleration (m/s^2)\", \"Impact Velocity (m/s)\", \"Depth of Max QSBC (cm)\", \"Max Qdyn (mantle) (kPa)\", \"Max QSBC-Upper Bounds (mantle) (kPa)\", \"Max QSBC-Lower Bounds (mantle) (kPa)\", \"Max QSBC-Average (kPa) (mantle)\", \"Max Qdyn (projected) (kPa)\", \"Max QSBC-Upper Bounds (projected) (kPa)\", \"Max QSBC-Lower Bounds (projected) (kPa)\", \"Max QSBC-Average (kPa) (projected)\", \"Firmness Factor (m-1)\", \"Penetration Time (ms)\", \"Relative Density (%)\", \"Friction Angle (deg.)\"]  # Creates an empty Dataframe in pandas from the output data\n",
    "        else:\n",
    "            dep_qsbc_comboplot(drop_ng,drop_n,acc_nNameg, qdyn_n_m, qsbc_m, bctable_m)\n",
    "            plotName = fileNum+\" \"+str(n)+\" BC - REJECTED\"\n",
    "            plt.savefig(plotPath / plotName)\n",
    "            output_table_n = pd.DataFrame([BD, fileNum, n, soiltype, tiptype, droptype,'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN', 'NaN'])\n",
    "            output_table_n = output_table_n.T\n",
    "            output_table_n.columns = [\"BlueDrop\", \"BD File\", \"Drop No.\", \"Soil Type\", \"Tip Type\", \"Drop Type\", \\\n",
    "                \"Penetration Depth (cm)\", \"Maximum Deceleration (m/s^2)\", \"Impact Velocity (m/s)\", \"Depth of Max QSBC (cm)\", \\\n",
    "                    \"Max Qdyn (mantle) (kPa)\", \"Max QSBC (mantle) (kPa)\", \"Max Qdyn (projected) (kPa)\", \"Max QSBC (projected) (kPa)\", \\\n",
    "                         \"Firmness Factor (m-1)\", \"Penetration Time (ms)\", \"Relative Density (%)\", \"Friction Angle (deg.)\", \\\n",
    "                            \"Max Su (mantle) (kPa)\", \"Max Su (projected) (kPa)\"]\\\n",
    "                            #old version that give upper and lower bounds:[\"BlueDrop\", \"BD File\", \"Drop No.\", \"Soil Type\", \"Tip Type\", \"Drop Type\", \"Penetration Depth (cm)\", \"Maximum Deceleration (m/s^2)\", \"Impact Velocity (m/s)\", \"Depth of Max QSBC (cm)\", \"Max Qdyn (mantle) (kPa)\", \"Max QSBC-Upper Bounds (mantle) (kPa)\", \"Max QSBC-Lower Bounds (mantle) (kPa)\", \"Max QSBC-Average (kPa) (mantle)\", \"Max Qdyn (projected) (kPa)\", \"Max QSBC-Upper Bounds (projected) (kPa)\", \"Max QSBC-Lower Bounds (projected) (kPa)\", \"Max QSBC-Average (kPa) (projected)\", \"Firmness Factor (m-1)\", \"Penetration Time (ms)\", \"Relative Density (%)\", \"Friction Angle (deg.)\"]  # Creates an empty Dataframe in pandas from the output data\n",
    "\n",
    "        output_table = pd.concat([output_table, output_table_n])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(bctable_m.columns[:])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run Preliminary Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak index: 25311\n",
      "start of penetration:  24888\n",
      "penstart:  24888\n",
      "penend:  25436\n",
      "offset:  1.0543592120623222\n",
      "Acc_n:  (548,)\n",
      "area:  548\n",
      "       Mantle Qdyn  Projected Qdyn     Depth\n",
      "24888     0.000000        0.000000  0.000000\n",
      "24889    -0.031337       -0.033740  0.001677\n",
      "24890    -0.042234       -0.048710  0.003354\n",
      "24891     0.092509        0.113786  0.005031\n",
      "24892     0.062448        0.081597  0.006708\n",
      "...            ...             ...       ...\n",
      "25431     0.409227        2.012464  0.347499\n",
      "25432     0.349813        1.720282  0.347499\n",
      "25433     0.304657        1.498217  0.347499\n",
      "25434     0.244549        1.202623  0.347499\n",
      "25435     0.170404        0.837997  0.347499\n",
      "\n",
      "[548 rows x 3 columns]\n",
      "3cm index 24906\n",
      "0th index 24888\n",
      "<class 'int'>\n",
      "       Mantle Qdyn  Projected Qdyn     Depth\n",
      "24906     0.303091        0.720255  0.030108\n",
      "24907     0.301474        0.739336  0.031771\n",
      "24908     0.311619        0.787890  0.033433\n",
      "24909     0.321806        0.838074  0.035094\n",
      "24910     0.318577        0.853819  0.036752\n",
      "...            ...             ...       ...\n",
      "25431     0.409227        2.012464  0.347499\n",
      "25432     0.349813        1.720282  0.347499\n",
      "25433     0.304657        1.498217  0.347499\n",
      "25434     0.244549        1.202623  0.347499\n",
      "25435     0.170404        0.837997  0.347499\n",
      "\n",
      "[530 rows x 3 columns]\n",
      "maxqdyn: 5.741187120419532\n",
      "bctable_m:        qsbc for K = 0.1  qsbc for K = 0.2  qsbc for K = 0.4  qsbc for K = 1  \\\n",
      "24888          0.000000          0.000000          0.000000        0.000000   \n",
      "24889         -0.025635         -0.021688         -0.016582       -0.009718   \n",
      "24890         -0.034548         -0.029229         -0.022348       -0.013098   \n",
      "24891          0.075675          0.064025          0.048952        0.028690   \n",
      "24892          0.051085          0.043220          0.033046        0.019368   \n",
      "...                 ...               ...               ...             ...   \n",
      "25431          0.572799          0.954202         -2.876571       -0.220530   \n",
      "25432               NaN               NaN               NaN             NaN   \n",
      "25433               NaN               NaN               NaN             NaN   \n",
      "25434               NaN               NaN               NaN             NaN   \n",
      "25435         -0.000000         -0.000000         -0.000000       -0.000000   \n",
      "\n",
      "       qsbc for K = 1.5  \n",
      "24888          0.000000  \n",
      "24889         -0.007226  \n",
      "24890         -0.009738  \n",
      "24891          0.021332  \n",
      "24892          0.014400  \n",
      "...                 ...  \n",
      "25431         -0.124632  \n",
      "25432               NaN  \n",
      "25433               NaN  \n",
      "25434               NaN  \n",
      "25435         -0.000000  \n",
      "\n",
      "[548 rows x 5 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\elise\\AppData\\Local\\Temp\\ipykernel_2356\\377958712.py:172: RuntimeWarning: divide by zero encountered in log10\n",
      "  srcv = np.log10(vel/0.02) #Velocity portion of the strain rate correction.\n",
      "C:\\Users\\elise\\AppData\\Local\\Temp\\ipykernel_2356\\377958712.py:172: RuntimeWarning: invalid value encountered in log10\n",
      "  srcv = np.log10(vel/0.02) #Velocity portion of the strain rate correction.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max penetration depth is:  0.3475\n",
      "Pre-truncation d1          Count  Time (s)  2g (m/s^2)  18g (m/s^2)  50g (m/s^2)  200g (m/s^2)  \\\n",
      "24888  24888.0   12.4445   10.311738    10.339732    10.231447     15.655255   \n",
      "24889  24889.0   12.4450   10.296372    10.313646    10.341915     15.715777   \n",
      "24890  24890.0   12.4455   10.332639    10.302072    10.337909     15.721072   \n",
      "24891  24891.0   12.4460   10.386074    10.427705    10.329281     15.747802   \n",
      "24892  24892.0   12.4465   10.434909    10.402818    10.215424     15.797733   \n",
      "...        ...       ...         ...          ...          ...           ...   \n",
      "25431  25431.0   12.7160   11.850961    11.895668    11.377105     17.388687   \n",
      "25432  25432.0   12.7165   11.616768    11.669768    10.913819     17.103985   \n",
      "25433  25433.0   12.7170   11.453489    11.498078    10.953415     16.872491   \n",
      "25434  25434.0   12.7175   11.226517    11.269539    11.223652     16.606953   \n",
      "25435  25435.0   12.7180   10.986850    10.987629    11.317326     16.383529   \n",
      "\n",
      "       250g (m/s^2)  X55g (m/s^2)  Y55g (m/s^2)  Velocity (m/s)  \\\n",
      "24888     10.260811      0.291268      0.321435        3.354863   \n",
      "24889     11.442794      0.490860      0.201562        3.354469   \n",
      "24890     11.564374      0.463944      0.461605        3.353947   \n",
      "24891     10.339945      0.309948      0.644135        3.353325   \n",
      "24892      9.965854      0.000191      0.873584        3.352603   \n",
      "...             ...           ...           ...             ...   \n",
      "25431     12.503198     -0.037168      0.312022        0.000028   \n",
      "25432     12.742041      0.165512      0.536323       -0.000010   \n",
      "25433     12.426222      0.307447      0.778569       -0.000022   \n",
      "25434     11.247835     -0.180868      0.403066       -0.000007   \n",
      "25435     11.030575     -0.255586      0.371884        0.000000   \n",
      "\n",
      "       Penetration Depth (m)  Pore Pressure (kPa)  \n",
      "24888               0.000000           251.929023  \n",
      "24889               0.001677           251.939346  \n",
      "24890               0.003354           251.641977  \n",
      "24891               0.005031           251.043910  \n",
      "24892               0.006708           251.211742  \n",
      "...                      ...                  ...  \n",
      "25431               0.347499           259.924677  \n",
      "25432               0.347499           259.814787  \n",
      "25433               0.347499           259.756512  \n",
      "25434               0.347499           259.523079  \n",
      "25435               0.347499           259.477791  \n",
      "\n",
      "[548 rows x 12 columns]\n",
      "Firmness factor =  2.4\n",
      "0.5270453529694842\n",
      "<class 'numpy.float64'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keep Drop? (y/n)\n",
      "pentime=  274.0\n",
      "  BlueDrop BD File Drop No. Soil Type Tip Type Drop Type  \\\n",
      "0        3    01F3        1         c        b         w   \n",
      "\n",
      "  Penetration Depth (cm) Maximum Deceleration (m/s^2) Impact Velocity (m/s)  \\\n",
      "0                  34.75                    21.828743                   3.4   \n",
      "\n",
      "  Depth of Max QSBC (cm) Max Qdyn (mantle) (kPa) Max QSBC (mantle) (kPa)  \\\n",
      "0                  34.46                     5.7                5.270454   \n",
      "\n",
      "  Max Qdyn (projected) (kPa) Max QSBC (projected) (kPa) Firmness Factor (m-1)  \\\n",
      "0                       28.2                  25.918585                   2.4   \n",
      "\n",
      "  Penetration Time (ms) Relative Density (%) Friction Angle (deg.)  \\\n",
      "0                 274.0                  NaN                   NaN   \n",
      "\n",
      "  Max Su (mantle) (kPa) Max Su (projected) (kPa)  \n",
      "0              0.527045                 2.591858  \n",
      "Run Complete\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = 0 #index variable for drop analysis\n",
    "n = 1 #index variable for drop analysis\n",
    "\n",
    "while n <= nDrops :\n",
    "    peak = int(peaksArray[a])  # count at the ath drop\n",
    "    print(\"peak index:\" , peak)\n",
    "    dropstartend(peak) #zooms in the drop file to only consider 500 counts before and 1500 counts after the peak deceleration\n",
    "    #print(dropstart, dropend)\n",
    "\n",
    "    if n == 1 :\n",
    "        drop1 = None\n",
    "        drop1g = None\n",
    "        acc1 = None\n",
    "        acc1g = None\n",
    "        acc1Name = None\n",
    "        acc1Nameg = None\n",
    "        qdyn1_m = None\n",
    "        qdyn1_p = None\n",
    "        output_table1 = None\n",
    "        analyze(drop1, drop1g, acc1, acc1g, acc1Name, acc1Nameg, qdyn1_m, qdyn1_p, output_table1)\n",
    "\n",
    "    if n == 2 :\n",
    "        drop2 = None\n",
    "        drop2g = None\n",
    "        acc2 = None\n",
    "        acc2g = None\n",
    "        acc2Name = None\n",
    "        acc2Nameg = None\n",
    "        qdyn2_m = None\n",
    "        qdyn2_p = None\n",
    "        output_table2 = None\n",
    "        analyze(drop2, drop2g, acc2, acc2g, acc2Name, acc2Nameg, qdyn2_m, qdyn2_p, output_table2)\n",
    "\n",
    "    if n == 3 :\n",
    "        drop3 = None\n",
    "        drop3g = None\n",
    "        acc3 = None\n",
    "        acc3g = None\n",
    "        acc3Name = None\n",
    "        acc3Nameg = None\n",
    "        qdyn3_m = None\n",
    "        qdyn3_p = None\n",
    "        output_table3 = None\n",
    "        analyze(drop3, drop3g, acc3, acc3g, acc3Name, acc3Nameg, qdyn3_m, qdyn3_p, output_table3)\n",
    "\n",
    "    if n == 4 :\n",
    "        drop4 = None\n",
    "        drop4g = None\n",
    "        acc4 = None\n",
    "        acc4g = None\n",
    "        acc4Name = None\n",
    "        acc4Nameg = None\n",
    "        qdyn4_m = None\n",
    "        qdyn4_p = None\n",
    "        output_table4 = None\n",
    "        analyze(drop4, drop4g, acc4, acc4g, acc4Name, acc4Nameg, qdyn4_m, qdyn4_p, output_table4)\n",
    "\n",
    "    if n == 5 :\n",
    "        drop5 = None\n",
    "        drop5g = None\n",
    "        acc5 = None\n",
    "        acc5g = None\n",
    "        acc5Name = None\n",
    "        acc5Nameg = None\n",
    "        qdyn5_m = None\n",
    "        qdyn5_p = None\n",
    "        output_table5 = None\n",
    "        analyze(drop5, drop5g, acc5, acc5g, acc5Name, acc5Nameg, qdyn5_m, qdyn5_p, output_table5)\n",
    "\n",
    "    if n == 6 :\n",
    "        drop6 = None\n",
    "        drop6g = None\n",
    "        acc6 = None\n",
    "        acc6g = None\n",
    "        acc6Name = None\n",
    "        acc6Nameg = None\n",
    "        qdyn6_m = None\n",
    "        qdyn6_p = None\n",
    "        output_table6 = None\n",
    "        analyze(drop6, drop6g, acc6, acc6g, acc6Name, acc6Nameg, qdyn6_m, qdyn6_p, output_table6)\n",
    "\n",
    "    if n == 7 :\n",
    "        drop7 = None\n",
    "        drop7g = None\n",
    "        acc7 = None\n",
    "        acc7g = None\n",
    "        acc7Name = None\n",
    "        acc7Nameg = None\n",
    "        qdyn7_m = None\n",
    "        qdyn7_p = None\n",
    "        output_table7 = None\n",
    "        analyze(drop7, drop7g, acc7, acc7g, acc7Name, acc7Nameg, qdyn7_m, qdyn7_p, output_table7)\n",
    "\n",
    "    if n == 8 :\n",
    "        drop8 = None\n",
    "        drop8g = None\n",
    "        acc8 = None\n",
    "        acc8g = None\n",
    "        acc8Name = None\n",
    "        acc8Nameg = None\n",
    "        qdyn8_m = None\n",
    "        qdyn8_p = None\n",
    "        output_table8 = None\n",
    "        analyze(drop8, drop8g, acc8, acc8g, acc8Name, acc8Nameg, qdyn8_m, qdyn8_p, output_table8)\n",
    "\n",
    "    if n == 9 :\n",
    "        drop9 = None\n",
    "        drop9g = None\n",
    "        acc9 = None\n",
    "        acc9g = None\n",
    "        acc9Name = None\n",
    "        acc9Nameg = None\n",
    "        qdyn9_m = None\n",
    "        qdyn9_p = None\n",
    "        output_table9 = None\n",
    "        analyze(drop9, drop9g, acc9, acc9g, acc9Name, acc9Nameg, qdyn9_m, qdyn9_p, output_table9)\n",
    "\n",
    "    if n == 10 :\n",
    "        drop10 = None\n",
    "        drop10g = None\n",
    "        acc10 = None\n",
    "        acc10g = None\n",
    "        acc10Name = None\n",
    "        acc10Nameg = None\n",
    "        qdyn10_m = None\n",
    "        qdyn10_p = None\n",
    "        output_table10 = None\n",
    "        analyze(drop10, drop10g, acc10, acc10g, acc10Name, acc10Nameg, qdyn10_m, qdyn10_p, output_table10)\n",
    "\n",
    "    n = n + 1\n",
    "    a = a + 1\n",
    "    \n",
    "print(output_table)\n",
    "\n",
    "exporttoexcel()\n",
    "\n",
    "print(\"Run Complete\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nHISTORICAL CALIBRATION FACTORS\\n\\nblueDrop #1 calibration factors:\\n- for data collected from July 2020 onward, use the following calibration factors:\\n            g2g=(df['g2g']-42590.9)/1626361.1; %accelerometers are in g\\n            g18g=(df['g18g']-44492.9)/161125.5;\\n            g50g=(df['g50g']-171656.1)/64020.3;\\n            ppm=(df['ppm']+31776.1)/20679.7;  % this is psi\\n            g200g=(df['g200g']-723404.8)/32209.7);\\n            gX55g=(df['gX55g']-54881.1)/64858.6);\\n            gY55g=(df['gY55g']-28735.5)/63839.9;\\n            g250g=(df['g250g']+13299.7)/13697.1;\\n            ppm=ppm*6.89475729; % convert into kPa\\n\\nblueDrop #2 calibration factors:\\n\\n- for data collected between July 2019 and September 2021, use the following calibration factors:\\n   g2g=((double(F(:,3)))+31683)/1624691; %accelerometers are in g\\n            g18g=((double(F(:,4)))-27002.4)/159942.4;\\n            g50g=((double(F(:,5)))+91943.2)/63865;\\n            ppm=((double(F(:,6)))+37971.1)/12982;  % this is psi\\n            g200g=((double(F(:,7)))-17936.6)/26226.6;\\n            gX55g=((double(F(:,8)))+89249)/66161.6;\\n            gY55g=((double(F(:,9)))+11088.8)/64773.6;\\n            g250g=((double(F(:,10)))+18888.6)/13518.9;\\n            ppm=ppm*6.89475729; % convert into kPa\\n\\n- for data collected following September 2021, use the following calibration factors:\\n   g2g=((double(F(:,3)))+37242.2)/1639250.2; %accelerometers are in g\\n            g18g=((double(F(:,4)))-26867.0)/160460.5;\\n            g50g=((double(F(:,5)))-213923.3)/64080.7;\\n            ppm=((double(F(:,6)))+55518.9)/18981.7;  % this is psi\\n            g200g=((double(F(:,7)))-171448.6)/30334.2;\\n            gX55g=((double(F(:,8)))-54242.6)/64767.7;\\n            gY55g=((double(F(:,9)))-40574.2)/66343.1;\\n            g250g=((double(F(:,10)))-40614.9)/13654.6;\\n\\nblueDrop #3 calibration factors:\\n\\n- for data collected before July 2019, use the following calibration factors:\\n   g2g=((double(F(:,3)))-38285.6)/1615800.9; %accelerometers are in g\\n   g18g=((double(F(:,4)))+13738)/163516.8;\\n     g50g=((double(F(:,5)))-238520.6)/63666.0;\\n   ppm=((double(F(:,6)))-139040.1)/20705.0;  % this is psi\\n   g200g=((double(F(:,7)))+12142.6)/27751.9;\\n   gX55g=((double(F(:,8)))-90237.0)/65351.5;\\n   gY55g=((double(F(:,9)))-57464.2)/65545.5;\\n   g250g=((double(F(:,10)))-40420.3)/13636.9;\\n\\n- for data collected after July 2019, use the following calibration factors:\\n   g2g=((double(F(:,3)))-38285.6)/1615800.9; %accelerometers are in g\\n            g18g=((double(F(:,4)))+13738)/163516.8;\\n            g50g=((double(F(:,5)))-238520.6)/63666;\\n            ppm=((double(F(:,6)))-139040.1)/20705;  % this is psi\\n            g200g=(((double(F(:,7)))+12142.6)/27751.9);\\n            gX55g=((double(F(:,8)))-90237)/65351.5;\\n            gY55g=((double(F(:,9)))-57464.2)/65545.5;\\n            g250g=((double(F(:,10)))-40420.3)/13636.9;\\n            g2g(end) = []; g2g = [1;g2g];\\n            g200g(end) = []; g200g = [1;g200g];\\n            g18g(end) = []; g18g = [1;g18g];\\n            g200g(end) = []; g200g = [1;g200g];\\n            ppm=ppm*6.89475729; % convert into kPa\""
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "HISTORICAL CALIBRATION FACTORS\n",
    "\n",
    "blueDrop #1 calibration factors:\n",
    "- for data collected from July 2020 onward, use the following calibration factors:\n",
    "            g2g=(df['g2g']-42590.9)/1626361.1; %accelerometers are in g\n",
    "            g18g=(df['g18g']-44492.9)/161125.5;\n",
    "            g50g=(df['g50g']-171656.1)/64020.3;\n",
    "            ppm=(df['ppm']+31776.1)/20679.7;  % this is psi\n",
    "            g200g=(df['g200g']-723404.8)/32209.7);\n",
    "            gX55g=(df['gX55g']-54881.1)/64858.6);\n",
    "            gY55g=(df['gY55g']-28735.5)/63839.9;\n",
    "            g250g=(df['g250g']+13299.7)/13697.1;\n",
    "            ppm=ppm*6.89475729; % convert into kPa\n",
    "\n",
    "blueDrop #2 calibration factors:\n",
    "\n",
    "- for data collected between July 2019 and September 2021, use the following calibration factors:\n",
    "   g2g=((double(F(:,3)))+31683)/1624691; %accelerometers are in g\n",
    "            g18g=((double(F(:,4)))-27002.4)/159942.4;\n",
    "            g50g=((double(F(:,5)))+91943.2)/63865;\n",
    "            ppm=((double(F(:,6)))+37971.1)/12982;  % this is psi\n",
    "            g200g=((double(F(:,7)))-17936.6)/26226.6;\n",
    "            gX55g=((double(F(:,8)))+89249)/66161.6;\n",
    "            gY55g=((double(F(:,9)))+11088.8)/64773.6;\n",
    "            g250g=((double(F(:,10)))+18888.6)/13518.9;\n",
    "            ppm=ppm*6.89475729; % convert into kPa\n",
    "\n",
    "- for data collected following September 2021, use the following calibration factors:\n",
    "   g2g=((double(F(:,3)))+37242.2)/1639250.2; %accelerometers are in g\n",
    "            g18g=((double(F(:,4)))-26867.0)/160460.5;\n",
    "            g50g=((double(F(:,5)))-213923.3)/64080.7;\n",
    "            ppm=((double(F(:,6)))+55518.9)/18981.7;  % this is psi\n",
    "            g200g=((double(F(:,7)))-171448.6)/30334.2;\n",
    "            gX55g=((double(F(:,8)))-54242.6)/64767.7;\n",
    "            gY55g=((double(F(:,9)))-40574.2)/66343.1;\n",
    "            g250g=((double(F(:,10)))-40614.9)/13654.6;\n",
    "\n",
    "blueDrop #3 calibration factors:\n",
    "\n",
    "- for data collected before July 2019, use the following calibration factors:\n",
    "   g2g=((double(F(:,3)))-38285.6)/1615800.9; %accelerometers are in g\n",
    "   g18g=((double(F(:,4)))+13738)/163516.8;\n",
    "     g50g=((double(F(:,5)))-238520.6)/63666.0;\n",
    "   ppm=((double(F(:,6)))-139040.1)/20705.0;  % this is psi\n",
    "   g200g=((double(F(:,7)))+12142.6)/27751.9;\n",
    "   gX55g=((double(F(:,8)))-90237.0)/65351.5;\n",
    "   gY55g=((double(F(:,9)))-57464.2)/65545.5;\n",
    "   g250g=((double(F(:,10)))-40420.3)/13636.9;\n",
    "\n",
    "- for data collected after July 2019, use the following calibration factors:\n",
    "   g2g=((double(F(:,3)))-38285.6)/1615800.9; %accelerometers are in g\n",
    "            g18g=((double(F(:,4)))+13738)/163516.8;\n",
    "            g50g=((double(F(:,5)))-238520.6)/63666;\n",
    "            ppm=((double(F(:,6)))-139040.1)/20705;  % this is psi\n",
    "            g200g=(((double(F(:,7)))+12142.6)/27751.9);\n",
    "            gX55g=((double(F(:,8)))-90237)/65351.5;\n",
    "            gY55g=((double(F(:,9)))-57464.2)/65545.5;\n",
    "            g250g=((double(F(:,10)))-40420.3)/13636.9;\n",
    "            g2g(end) = []; g2g = [1;g2g];\n",
    "            g200g(end) = []; g200g = [1;g200g];\n",
    "            g18g(end) = []; g18g = [1;g18g];\n",
    "            g200g(end) = []; g200g = [1;g200g];\n",
    "            ppm=ppm*6.89475729; % convert into kPa'''"
   ]
  }
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