BDcodev4-jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "34b268be",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "matplotlib.use('TkAgg')\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "\n",
    "import scipy.integrate\n",
    "from scipy.signal import find_peaks\n",
    "from numpy import trapz\n",
    "from scipy.integrate import cumtrapz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "456bff57",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  SETUP VARIABLES - USER INPUTS\n",
    "BD = 3\n",
    "atype = 'p'  # m = mantle area, p = projected area\n",
    "tiptype = 'c'  # c = cone, p = parabolic, b = blunt\n",
    "# paste the filepath to the folder where the bd data is stored\n",
    "binFilepath = Path(\"H:\\My Drive\\CEE 5904 - Project & Report\\FRF Data/test data\")\n",
    "# write the bin file number you want to analyze (do not include 'bLog' or '.bin')\n",
    "fileNum = '02F4'\n",
    "outputFile = 'data.xlsx'  #this currently doesn't do anything, but eventually all data will be printed out into an excel sheet\n",
    "outputPath = Path(\"H:\\My Drive\\CEE 5904 - Project & Report\\FRF Data/test data\") #  Path for new files\n",
    "offset = 1 # this value is subtracted from the accelerometer readings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3909e0dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SETUP FUNCTIONS\n",
    "\n",
    "#def tellme(s):\n",
    " #   #print(s)\n",
    "  #  ax1.set(title=s)\n",
    "   # plt.draw()\n",
    "\n",
    "def accPick(dg, d):\n",
    "    # each of the following are the same; if an accelerometer breaks on a BD, can edit that section\n",
    "    # the 200g accelerometer is ignored in all cases\n",
    "    maxAcc = dg[\"250g (g)\"].max()\n",
    "    global acc\n",
    "    global accName\n",
    "    global accNameg\n",
    "    if maxAcc < 5 - offset:\n",
    "        if dg[\"2g (g)\"].max() < 1.8 - offset:  # does an extra check for the 2g because of noise\n",
    "            acc = d[\"2g (m/s^2)\"]\n",
    "            accName = \"2g (m/s^2)\"\n",
    "            accNameg = \"2g (g)\"\n",
    "        else:\n",
    "            acc = d[\"18g (m/s^2)\"]\n",
    "            accName = \"18g (m/s^2)\"\n",
    "            accNameg = \"18g (g)\"\n",
    "    elif maxAcc < 18 - offset:\n",
    "        acc = d[\"18g (m/s^2)\"]\n",
    "        accName = \"18g (m/s^2)\"\n",
    "        accNameg = \"18g (g)\"\n",
    "    elif maxAcc < 50 - offset:\n",
    "        acc = d[\"50g (m/s^2)\"]\n",
    "        accName = \"50g (m/s^2)\"\n",
    "        accNameg = \"50g (g)\"\n",
    "    else:\n",
    "        acc = d[\"250g (m/s^2)\"]\n",
    "        accName = \"250g (m/s^2)\"\n",
    "        accNameg = \"250g (g)\"\n",
    "\n",
    "'''def peakpicker(dnum, dse):\n",
    "    global acc\n",
    "    #global dnum\n",
    "    #global dse\n",
    "    pts = plt.ginput(2)\n",
    "    (x0, y0), (x1, y1) = pts\n",
    "    xmin, xmax = sorted([x0, x1])\n",
    "    dropTimes[dnum, dse] = xmin\n",
    "    xmin = round(xmin)\n",
    "    print(xmin)\n",
    "    dse = dse + 1\n",
    "    dropTimes[dnum, dse] = xmax\n",
    "    xmax = round(xmax)\n",
    "    print(xmax)\n",
    "    print(dropTimes)\n",
    "    d = dfCal[xmin:xmax]\n",
    "    acc = d[accName]\n",
    "    print(d)'''\n",
    "\n",
    "def integration(d):\n",
    "    global vel\n",
    "    global maxvel\n",
    "    global dep\n",
    "    global maxdep\n",
    "    vel = scipy.integrate.cumtrapz(acc, x=d[\"Time (s)\"])  # solves for velocity\n",
    "    vel = np.array(vel)\n",
    "    vel = numpy.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",
    "    dep = scipy.integrate.cumtrapz(vel, x=d[\"Time (s)\"])  # solves for penetration depth\n",
    "    dep = numpy.insert(dep, 0, 0)  # not sure what to insert here, but it makes it the right size\n",
    "    maxdep = dep.max()\n",
    "    d.insert(9, \"Velocity (m/s)\", vel)\n",
    "    d.insert(10, \"Penetration Depth (m)\", dep)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "32343298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H:\\My Drive\\CEE 5904 - Project & Report\\FRF Data\\test data\\bLog02F4.bin\n",
      "(120000, 10)\n"
     ]
    }
   ],
   "source": [
    "# READ BD DATA IN\n",
    "\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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9050842",
   "metadata": {},
   "outputs": [],
   "source": [
    "# APPLY CALIBRATION FACTORS\n",
    "\n",
    "if BD == 3:  # calibration factors from July 2019\n",
    "    g2g = (df['g2g']-34426.5)/1615925.8 - offset# accelerometers are in g\n",
    "    g18g = (df['g18g']+12322.1)/163530.7 - offset\n",
    "    g50g = (df['g50g']-237384.9)/63651 - 0.1120 - offset\n",
    "    ppm = ((df['ppm']+62496.7)/20583.0) * 6.89475729 # converts to kPa\n",
    "    g200g = ((df['g200g'] -248943.7)/39009.4)+0.5518 - offset\n",
    "    gX55g = (df['gX55g']-59093.7)/66674.3 - offset #check if lateral accelerometers also need to be offset\n",
    "    gY55g = (df['gY55g']-140224.6)/66674.3 - offset\n",
    "    g250g = (df['g250g']-40536.1)/13631.6 - offset\n",
    "\n",
    "if BD == 2: # calibration factors from Aug 26, 2021\n",
    "    g2g = (df['g2g']+31384.7)/1624987.2-0.035 - offset# accelerometers are in g\n",
    "    g18g = (df['g18g']-26631.0)/159945.4 - offset\n",
    "    g50g = (df['g50g']+92987.0)/63783.5 - offset\n",
    "    ppm = ((df['ppm']-35170.6)/12922.9) * 6.89475729 # converts to kPa\n",
    "    g200g = (df['g200g']-16264.8)/26042.8 -0.277 - offset\n",
    "    gX55g = (df['gX55g']+89890.3)/63897.1 - offset\n",
    "    gY55g = (df['gY55g']+14993.0)/64118.0 - offset\n",
    "    g250g = (df['g250g']+17362.1)/13533.5+0.0656 - offset\n",
    "\n",
    "if BD == 1: # calibration factors from July 2020\n",
    "    g2g = (df['g2g']+277743.2)/1637299.6 - offset # accelerometers are in g\n",
    "    g18g = (df['g18g']-3755.9)/159932.2 - offset\n",
    "    g50g = (df['g50g']+92817.6)/63237.1 - offset\n",
    "    ppm = ((df['ppm']-33154.0)/14763.5) * 6.89475729 # this is kPa\n",
    "    g200g = (df['g200g'] -1155309.9)/28368.5 - 1.464 - offset\n",
    "    gX55g = (df['gX55g'] +97138.4)/62023.7 - offset\n",
    "    gY55g = (df['gY55g']-9921.7)/62669.2 - offset\n",
    "    g250g = (df['g250g']+59211.3)/13276.9 - offset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2b9d0290",
   "metadata": {},
   "outputs": [],
   "source": [
    "#CREATE CALIBRATED DATAFRAMES\n",
    "\n",
    "time = (df['Count']-df['Count'].iloc[0]+1)/2000 # gives time in s\n",
    "count = df[\"Count\"]\n",
    "\n",
    "# make a new dataframe of the calibrated values in units of g\n",
    "dfCalg = pd.DataFrame([time, g2g, g18g, g50g, g200g, g250g, gX55g, gY55g, ppm])\n",
    "dfCalg = dfCalg.T\n",
    "dfCalg.columns = ['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([time, g2g, g18g, g50g, g200g, g250g, gX55g, gY55g, ppm])\n",
    "dfCal = dfCal.T\n",
    "dfCal.columns = ['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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6072d29f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [1], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#AUTOMATIC PEAK FINDING\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39marray(g250g)  \u001b[38;5;66;03m# what accelerometer to get the peaks from\u001b[39;00m\n\u001b[0;32m      3\u001b[0m peaks, _ \u001b[38;5;241m=\u001b[39m find_peaks(x, height \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m, distance\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10000\u001b[39m)  \u001b[38;5;66;03m# finds the largest peaks more than 5g spaced at least 10000 counts apart\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# CREATION OF INDIVIDUAL DROP FILES\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "#AUTOMATIC PEAK FINDING\n",
    "x = np.array(g250g)  # what accelerometer to get the peaks from\n",
    "peaks, _ = find_peaks(x, height = 2, distance=10000)  # finds the largest peaks more than 5g spaced at least 10000 counts apart\n",
    "\n",
    "# CREATION OF INDIVIDUAL DROP FILES\n",
    "peaksArray = np.array(peaks)  # prints a list of the count where the peaks occur\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",
    "a = 0\n",
    "n = 1\n",
    "\n",
    "dropTimes = np.array([[0]*2]*nDrops)\n",
    "dnum = 0 #row index for drop; 0 = drop 1, etc\n",
    "dse = 0 #column index for drop 0 = start, 1 = end\n",
    "\n",
    "    # MAKE INDIVIDUAL DATAFRAMES FOR EACH DROP AND PLOT DECELERATION VS TIME\n",
    "while n <= nDrops :\n",
    "    b = int(peaksArray[a])  # count at the ath drop\n",
    "    dropstart = b - 100  # offset in counts before impact to include in sliced df\n",
    "    dropend = b + 100  # offset in counts after impact to include in sliced df\n",
    "\n",
    "    if n == 1 :\n",
    "        drop1 = dfCal[dropstart:dropend] #start and end points of the drop in m/s^2\n",
    "        drop1g = dfCalg[dropstart:dropend] #start and end points of the drop in g\n",
    "        drop1 = pd.DataFrame(drop1) # makes dataframe including all data within the start and end points of the drop\n",
    "        drop1g = pd.DataFrame(drop1g)\n",
    "        dg = drop1g # chooses what accelerometer to use based on the max g\n",
    "        d = drop1\n",
    "        accPick(dg, d)  # chooses what accelerometer to use\n",
    "        acc1 = acc\n",
    "        acc1Name = accName\n",
    "        acc1Nameg = accNameg\n",
    "        drop1.plot(y=accName, ylabel=\"Deceleration (g)\", title='Select start and end of drop #' + str(n))\n",
    "        #plt.show()\n",
    "        #plt.set(title = 'Select start and end of drop #' + str(n))\n",
    "        #peakpicker(dnum, dse)\n",
    "        pts = plt.ginput(2)\n",
    "        (x0, y0), (x1, y1) = pts\n",
    "        xmin, xmax = sorted([x0, x1])\n",
    "        dropTimes[dnum, dse] = xmin\n",
    "        xmin = round(xmin)\n",
    "        print(xmin)\n",
    "        dse = dse + 1\n",
    "        dropTimes[dnum, dse] = xmax\n",
    "        xmax = round(xmax)\n",
    "        print(xmax)\n",
    "        print(dropTimes)\n",
    "        d = dfCal[xmin:xmax]\n",
    "        acc = d[accName]\n",
    "        print(d)\n",
    "        integration(d)\n",
    "        drop1 = d\n",
    "        dnum = dnum + 1\n",
    "        dse = dse - 1\n",
    "\n",
    "    if n == 2 :\n",
    "        drop2 = dfCal[dropstart:dropend]\n",
    "        drop2g = dfCalg[dropstart:dropend]\n",
    "        drop2 = pd.DataFrame(drop2)\n",
    "        drop2g = pd.DataFrame(drop2g)\n",
    "        dg = drop2g\n",
    "        d = drop2\n",
    "        accPick(dg, d)  # chooses what accelerometer to use\n",
    "        acc2 = acc\n",
    "        acc2Name = accName\n",
    "        acc2Nameg = accNameg\n",
    "        drop2.plot(y=accName, ylabel=\"Deceleration (g)\", title='Select start and end of drop #' + str(n))\n",
    "        #plt.show()\n",
    "        pts = plt.ginput(2)\n",
    "        (x0, y0), (x1, y1) = pts\n",
    "        xmin, xmax = sorted([x0, x1])\n",
    "        dropTimes[dnum, dse] = xmin\n",
    "        xmin = round(xmin)\n",
    "        print(xmin)\n",
    "        dse = dse + 1\n",
    "        dropTimes[dnum, dse] = xmax\n",
    "        xmax = round(xmax)\n",
    "        print(xmax)\n",
    "        print(dropTimes)\n",
    "        d = dfCal[xmin:xmax]\n",
    "        acc = d[accName]\n",
    "        print(d)\n",
    "        drop2 = d\n",
    "        integration(d)\n",
    "        dnum = dnum + 1\n",
    "        dse = dse - 1\n",
    "\n",
    "    if n == 3 :\n",
    "        drop3 = dfCal[dropstart:dropend] #start and end points of the drop in m/s^2\n",
    "        drop3g = dfCalg[dropstart:dropend] #start and end points of the drop in g\n",
    "        drop3 = pd.DataFrame(drop3) # makes dataframe including all data within the start and end points of the drop\n",
    "        drop3g = pd.DataFrame(drop3g)\n",
    "        dg = drop3g # chooses what accelerometer to use based on the max g\n",
    "        d = drop3\n",
    "        accPick(dg, d)  # chooses what accelerometer to use\n",
    "        acc3 = acc\n",
    "        acc3Name = accName\n",
    "        acc3Nameg = accNameg\n",
    "        drop3.plot(y=accName, ylabel=\"Deceleration (g)\", title='Select start and end of drop #' + str(n))\n",
    "        pts = plt.ginput(2)\n",
    "        (x0, y0), (x1, y1) = pts\n",
    "        xmin, xmax = sorted([x0, x1])\n",
    "        dropTimes[dnum, dse] = xmin\n",
    "        xmin = round(xmin)\n",
    "        print(xmin)\n",
    "        dse = dse + 1\n",
    "        dropTimes[dnum, dse] = xmax\n",
    "        xmax = round(xmax)\n",
    "        print(xmax)\n",
    "        print(dropTimes)\n",
    "        d = dfCal[xmin:xmax]\n",
    "        acc = d[accName]\n",
    "        print(d)\n",
    "        #drop3.plot(x=\"Time (s)\", y=accName, ylabel=\"Deceleration (g)\")\n",
    "        #plt.show()\n",
    "        drop3 = d\n",
    "        integration(d)\n",
    "        dnum = dnum + 1\n",
    "        dse = dse - 1\n",
    "\n",
    "    if n == 4 :\n",
    "        drop4 = dfCal[dropstart:dropend] #start and end points of the drop in m/s^2\n",
    "        drop4g = dfCalg[dropstart:dropend] #start and end points of the drop in g\n",
    "        drop4 = pd.DataFrame(drop4) # makes dataframe including all data within the start and end points of the drop\n",
    "        drop4g = pd.DataFrame(drop4g)\n",
    "        dg = drop4g # chooses what accelerometer to use based on the max g\n",
    "        d = drop4\n",
    "        accPick(dg, d)  # chooses what accelerometer to use\n",
    "        acc4 = acc\n",
    "        #drop4.plot(x=\"Time (s)\", y=accName, ylabel=\"Deceleration (g)\")\n",
    "        #plt.show()\n",
    "        integration(d)\n",
    "\n",
    "    if n == 5 :\n",
    "        drop5 = dfCal[dropstart:dropend] #start and end points of the drop in m/s^2\n",
    "        drop5g = dfCalg[dropstart:dropend] #start and end points of the drop in g\n",
    "        drop5 = pd.DataFrame(drop5) # makes dataframe including all data within the start and end points of the drop\n",
    "        drop5g = pd.DataFrame(drop5g)\n",
    "        dg = drop5g # chooses what accelerometer to use based on the max g\n",
    "        d = drop5\n",
    "        accPick(dg, d)  # chooses what accelerometer to use\n",
    "        acc5 = acc\n",
    "        #drop5.plot(x=\"Time (s)\", y=accName, ylabel=\"Deceleration (g)\")\n",
    "        #plt.show()\n",
    "        integration(d)\n",
    "\n",
    "    if n == 6 :\n",
    "        drop6 = dfCal[dropstart:dropend] #start and end points of the drop in m/s^2\n",
    "        drop6g = dfCalg[dropstart:dropend] #start and end points of the drop in g\n",
    "        drop6 = pd.DataFrame(drop6) # makes dataframe including all data within the start and end points of the drop\n",
    "        drop6g = pd.DataFrame(drop6g)\n",
    "        dg = drop6g # chooses what accelerometer to use based on the max g\n",
    "        d = drop6\n",
    "        accPick(dg, d)  # chooses what accelerometer to use\n",
    "        acc6 = acc\n",
    "        #drop6.plot(x=\"Time (s)\", y=accName, ylabel=\"Deceleration (g)\")\n",
    "        #plt.show()\n",
    "        integration(d)\n",
    "\n",
    "    n = n + 1\n",
    "    a = a + 1\n",
    "\n",
    "\n",
    "\n",
    "#Deceleration,Velocity,and penetration depth vs time plots\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c6e4e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot showing peak deceleration\n",
    "peakplot = plt.plot(x)\n",
    "peakplot = plt.plot(peaks, x[peaks], \"x\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "12f9524b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Plot showing all accelerometers and pore pressure readings\n",
    "fig, (ax1, ax2) = plt.subplots(2)\n",
    "\n",
    "ax1.plot(time, g2g, label=\"2g\" )\n",
    "ax1.plot(time, g18g, label=\"18g\")\n",
    "ax1.plot(time, g50g, label=\"50g\")\n",
    "#plt.plot(time, ppm)\n",
    "#ax1.plot(time, g200g, label=\"200g\")\n",
    "#plt.plot(time, gX55g, label=\"2g\")\n",
    "#plt.plot(time, gY55g, label=\"2g\")\n",
    "ax1.plot(time, g250g, label=\"250g\")\n",
    "ax1.legend()\n",
    "ax1.set(ylabel=\"Deceleration (g)\")\n",
    "ax1.set(xlabel=\"Time (s)\")\n",
    "\n",
    "ax2.plot(time, ppm, label=\"Pore Pressure\")\n",
    "ax2.set(ylabel=\"Pore Pressure (kPa)\")\n",
    "ax2.set(xlabel=\"Time (s)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d668bd2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deceleration,Velocity,and penetration depth vs time plots - Drop #1\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3)\n",
    "ax1.plot(drop1[\"Time (s)\"], drop1[acc1Name], marker = 11, color = \"k\")\n",
    "ax1.set(ylabel=\"Deceleration (m/s^2)\", xlabel=\"Time(s)\",title=\"Drop 1\")\n",
    "ax2.plot(drop1[\"Time (s)\"], drop1['Velocity (m/s)'], marker = 11, color = \"k\")\n",
    "ax2.set(ylabel=\"Velocity (m/s)\", xlabel=\"Time(s)\")\n",
    "ax3.plot(drop1[\"Time (s)\"], drop1[\"Penetration Depth (m)\"], marker = 11, color = \"k\")\n",
    "ax3.set(ylabel=\"Penetration Depth (m)\", xlabel=\"Time(s)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d4de205",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deceleration,Velocity,and penetration depth vs time plots - Drop #2\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3)\n",
    "ax1.plot(drop2[\"Time (s)\"], drop2[acc2Name], marker = 11, color = \"k\")\n",
    "ax1.set(ylabel=\"Deceleration (m/s^2)\", xlabel=\"Time(s)\",title=\"Drop 2\")\n",
    "ax2.plot(drop2[\"Time (s)\"], drop2['Velocity (m/s)'], marker = 11, color = \"k\")\n",
    "ax2.set(ylabel=\"Velocity (m/s)\", xlabel=\"Time(s)\")\n",
    "ax3.plot(drop2[\"Time (s)\"], drop2[\"Penetration Depth (m)\"], marker = 11, color = \"k\")\n",
    "ax3.set(ylabel=\"Penetration Depth (m)\", xlabel=\"Time(s)\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1aed80ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deceleration,Velocity,and penetration depth vs time plots - Drop #3\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3)\n",
    "ax1.plot(drop3[\"Time (s)\"], drop3[acc3Name], marker = 11, color = \"k\")\n",
    "ax1.set(ylabel=\"Deceleration (m/s^2)\", xlabel=\"Time(s)\", title=\"Drop 3\")\n",
    "ax2.plot(drop3[\"Time (s)\"], drop3['Velocity (m/s)'], marker = 11, color = \"k\")\n",
    "ax2.set(ylabel=\"Velocity (m/s)\", xlabel=\"Time(s)\")\n",
    "ax3.plot(drop3[\"Time (s)\"], drop3[\"Penetration Depth (m)\"], marker = 11, color = \"k\")\n",
    "ax3.set(ylabel=\"Penetration Depth (m)\", xlabel=\"Time(s)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a318b707",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deceleration,Velocity,and penetration depth vs time plots - Drop #4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "55e7ab96",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deceleration,Velocity,and penetration depth vs time plots - Drop #5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9243da4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Deceleration,Velocity,and penetration depth vs time plots - Drop #6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "857522b5",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (200,) and (46,)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [17], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#Deceleration vs. Penetration Depth, Velocity vs. Penetration Depth\u001b[39;00m\n\u001b[0;32m      3\u001b[0m fig, (ax1) \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m \u001b[43max1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdrop3g\u001b[49m\u001b[43m[\u001b[49m\u001b[43macc3Nameg\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdrop3\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPenetration Depth (m)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmarker\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m11\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mk\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m ax1\u001b[38;5;241m.\u001b[39mplot(drop3[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mVelocity (m/s)\u001b[39m\u001b[38;5;124m\"\u001b[39m], drop3[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPenetration Depth (m)\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m, marker \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m11\u001b[39m, color \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m      6\u001b[0m ax1\u001b[38;5;241m.\u001b[39mset(xlabel\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDeceleration (g) and Velocity (m/s)\u001b[39m\u001b[38;5;124m\"\u001b[39m, ylabel\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPenetration Depth (cm)\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\axes\\_axes.py:1635\u001b[0m, in \u001b[0;36mAxes.plot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1393\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1394\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[0;32m   1395\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1632\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[0;32m   1633\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1634\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[1;32m-> 1635\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[0;32m   1636\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[0;32m   1637\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\axes\\_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[1;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m    310\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[0;32m    311\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[1;32m--> 312\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\matplotlib\\axes\\_base.py:498\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[1;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[0;32m    495\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[0;32m    497\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m--> 498\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    499\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    500\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[0;32m    501\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    502\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (200,) and (46,)"
     ]
    }
   ],
   "source": [
    "#Deceleration vs. Penetration Depth, Velocity vs. Penetration Depth\n",
    "\n",
    "fig, (ax1) = plt.subplots(1)\n",
    "ax1.plot(drop3g[acc3Nameg], drop3[\"Penetration Depth (m)\"]*100, marker = 11, color = \"k\")\n",
    "ax1.plot(drop3[\"Velocity (m/s)\"], drop3[\"Penetration Depth (m)\"]*100, marker = 11, color = \"k\")\n",
    "ax1.set(xlabel=\"Deceleration (g) and Velocity (m/s)\", ylabel=\"Penetration Depth (cm)\")\n",
    "ax1.invert_yaxis()\n",
    "plt.show()\n"
   ]
  }
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