beam_postprocessing.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of the OBS WikiHouse beams\n",
    "The code does the following:\n",
    "1) it loads the experimental data of the test done at Imperial\n",
    "2) It cleans the data by removing the initial offsets (if needed)\n",
    "3) It finds parameters of interest such peak force, stiffness etc\n",
    "4) it plots the data and saves the figures"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data loading and cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import useful libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a class object for the single test\n",
    "class Structural_test():\n",
    "    def __init__(self,test_label,data_raw,data_raw_path):\n",
    "        self.data_raw=data_raw; # assign the raw data\n",
    "        self.data_raw_path=data_raw_path; # assign the data raw path\n",
    "        self.test_label=test_label; # assign the test label\n",
    "        \n",
    "    def assign_span(self,L):\n",
    "        self.L=L; # assign span\n",
    "    \n",
    "    def remove_offset(self,channel_indices,i_offset=0):\n",
    "        self.data_offset_removed=self.data_raw.copy(); # initialize the dataset;\n",
    "        for i in channel_indices:\n",
    "            self.data_offset_removed.iloc[:,i]=self.data_offset_removed.iloc[:,i]-self.data_offset_removed.iloc[i_offset,i]; # offset the initial values\n",
    "        return self.data_offset_removed;\n",
    "    \n",
    "    # this method interpolate the values of the actuator between force value F_1 and force value F_2\n",
    "    def adjust_pump(self,force_header,F_1,F_2):\n",
    "        Forces=self.data_offset_removed[force_header].copy(); # extract the force time series\n",
    "        i_F1=next(x[0] for x in enumerate(Forces) if x[1] >= F_1); # find the indeces correspnding to  F_1 and assign it\n",
    "        i_F2=next(x[0] for x in enumerate(Forces) if x[1] >= F_2); # find the indeces correspnding to  F_2 and assign it\n",
    "        Forces[i_F1:i_F2]=np.nan; # subsitute the np.nan values\n",
    "        self.data_offset_removed[force_header]=Forces.interpolate();\n",
    "    \n",
    "    def find_maxF_and_stiffness(self,data,force_header,displacement_header):\n",
    "        self.i_F_max=np.argmax(data[force_header]); # find the index corresponding to the max value of force and assign it\n",
    "        self.F_max=data[force_header][self.i_F_max]; # find the max force value  and assign it\n",
    "        self.d_max=data[displacement_header][self.i_F_max]; # find the displacement corresponding to the max force value  and assign it     \n",
    "        F_10=0.1*self.F_max; # calculate 10% of the max force\n",
    "        F_40=0.4*self.F_max; # calculate 40% of the max force\n",
    "        self.i_10=next(x[0] for x in enumerate(data[force_header]) if x[1] >= F_10); # find the indeces correspnding to  F_10 and assign it\n",
    "        self.i_40=next(x[0] for x in enumerate(data[force_header]) if x[1] >= F_40); # find the indeces correspnding to  F_40 and assign it\n",
    "        self.d_10=data[displacement_header][self.i_10]; # displacement value corresponding to i_10 index\n",
    "        self.F_10=data[force_header][self.i_10]; # force value corresponding to i_10 index\n",
    "        self.d_40=data[displacement_header][self.i_40]; # displacement value corresponding to i_40 index\n",
    "        self.F_40=data[force_header][self.i_40]; # force value corresponding to i_40 index\n",
    "        self.stiffness=(self.F_40-self.F_10)/(self.d_40-self.d_10); # calculate teh elastic stiffness bewtween the 40% and 10% points\n",
    "        self.slip_modulus=self.F_40/(4/3*(self.d_40-self.d_10)); # calculate the slip modulus\n",
    "        \n",
    "    def extract_deflected_shape(self,data,potentiomer_headers,force_header,force_values):\n",
    "        deflected_shape=[]; # initialize the list\n",
    "        deflected_shape_forces=[]; # initialize the list\n",
    "        \n",
    "        for force in force_values:\n",
    "            i_F=next(x[0] for x in enumerate(data[force_header]) if x[1] >= force); # find the indeces correspnding to  force and assign it\n",
    "            deflected_shape.append(data.loc[i_F,potentiomer_headers].to_list()); # extract the values corresponding to i_F into L_j list\n",
    "            deflected_shape_forces.append(force); # append the actual force\n",
    "        \n",
    "        self.deflected_shape=deflected_shape; # save the variables\n",
    "        self.deflected_shape_forces=deflected_shape_forces; # save the variables      \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a class to calculate the deflected shape of a beam using rotational springs\n",
    "class beam_with_rotsprings():\n",
    "    def __init__(self,E,I,L,L_j,k_r,F,b):\n",
    "        self.F=F; # force on the specimen in N\n",
    "        self.E=E; # elastic modulus of the specimen\n",
    "        self.I=I; # second moment of inertia of the specimen\n",
    "        self.L=L; # span of the specimen in mm\n",
    "        self.L_j= L_j; # distance between the support and the castellated joint\n",
    "        self.b= b; # distance between the support and the load application point\n",
    "        self.k_r=k_r; # stiffness of the rotational spring \n",
    "        dL=1; # discreatization of the beam span \n",
    "    \n",
    "    \n",
    "        x_1=np.arange(0,L_j,dL);    # coordinates of the beam from the support to the castellated joint\n",
    "        x_2=np.arange(L_j,b,dL); # coordinates on the beam from the castellated joint to the point of application of the force\n",
    "        x_3=np.arange(b,0.5*L+dL,dL); # coordinates on the beam from the point of application of the fprce to the midspan\n",
    "        \n",
    "        \n",
    "        # define the integration constants\n",
    "        C_2=0;\n",
    "        E_1=F*b*L/(2*E*I);\n",
    "        D_1=F*b*L/(2*E*I)-F*b*b/(2*E*I);\n",
    "        C_1=D_1+F*L_j/k_r;\n",
    "        D_2=(C_1-D_1)*L_j;\n",
    "        E_2=-F*pow(b,3)/(6*E*I)+b*D_1+D_2+F*pow(b,3)/(2*E*I)-E_1*b;\n",
    "        \n",
    "        v_1=-F/(6*E*I)*np.power(x_1,3)+C_1*x_1+C_2; # vertical displacement from the support to the castellated joint\n",
    "        v_2=-F/(6*E*I)*np.power(x_2,3)+D_1*x_2+D_2; # verical displacement from the castellated joint to the point of application of the force\n",
    "        v_3=-F*b/(2*E*I)*np.power(x_3,2)+E_1*x_3+E_2; # vertical displacement from the point of application of the fprce to the midspan\n",
    "        \n",
    "        v_half=np.concatenate((v_1, v_2,v_3), axis=None); # concatenate the displacement vectors\n",
    "        self.v=-np.concatenate((v_half, v_half[:-1][::-1]), axis=None); #concatenate the displacement vectors\n",
    "        self.x=np.arange(0,L+dL,dL); # coordinates of the beam\n",
    "        self.x_wrong=np.concatenate((x_1, x_2,x_3), axis=None); # concatenate the displacement vectors"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the raw data\n",
    "input_file_paths=[\"S/S_1.CSV\",\"S/S-2_synch.CSV\",\"S/S_3.CSV\",\"XXS/XXS-1.CSV\",\"XXS/XXS-2.CSV\",\"XXS/XXS-3.CSV\",\"L/L-1.CSV\",\"L/L-2.CSV\",\"L/L-3.CSV\"]; # strucutre of the input file\n",
    "test_labels=[\"S-1\",\"S-2\",\"S-3\",\"XXS-1\",\"XXS-2\",\"XXS-3\",\"L-1\",\"L-2\",\"L-3\"]; # labels for the tests\n",
    "Channels_of_interest=[1,2,3,4,5,6,7,8,9,16]; # only load these channels from the file\n",
    "header_names=['Force_old (kN)','Disp_actuator (mm)','Pot_4 (mm)','Pot_5 (mm)','Pot_6 (mm)','Pot_7 (mm)','Pot_8 (mm)','Pot_9 (mm)','Pot_10 (mm)','Force (kN)']; # headers and units of each experimental channel\n",
    "experiment_dictionary={}; # create the empty dictionary\n",
    "\n",
    "for i,(data_path,label) in enumerate(zip(input_file_paths,test_labels)):\n",
    "    if label!='L-1':\n",
    "        data_raw=pd.read_table(data_path,sep=',',skiprows=1,usecols=Channels_of_interest,names=header_names); # read the data\n",
    "    else:\n",
    "        data_raw=pd.read_table(data_path,sep=',',skiprows=464,usecols=Channels_of_interest,names=header_names); # read the data\n",
    "    test=Structural_test(label,data_raw,data_path); # create the structural test object and add the raw data\n",
    "    experiment_dictionary[label]=test; # add the data to the dictionary\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data analysis\n",
    "The followings operations are made:\n",
    "1) Data are offset with respect to their initial value\n",
    "2) Max force, stiffness and slip modulus are calculated by simple math operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data analysis \n",
    "force_header='Force (kN)'; # extract the header associated to forces\n",
    "displacement_header='Pot_7 (mm)'; # extract the header associated to displacements\n",
    "\n",
    "for test in experiment_dictionary.values():\n",
    "    test.remove_offset(range(len(header_names))); # remove offsets on all channels\n",
    "    if test.test_label=='S-1':\n",
    "        test.adjust_pump(force_header,F_1=3.7,F_2=7.7);\n",
    "        \n",
    "    test.find_maxF_and_stiffness(data=test.data_offset_removed,force_header=force_header,displacement_header=displacement_header); # find the max force and stiffness by passing the column correscponding to force and the column corresponding to displacement\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data plotting\n",
    "Plot the data and generate meaningful visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 425.197x283.465 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the force displacement \n",
    "output_folder=\"Figures/\";\n",
    "x_label_plot='Displacement (mm)'; # labelling of the x axis\n",
    "y_label_plot='Force (kN)'; # labelling of the y axis\n",
    "fig_h=10; # fig height in cm\n",
    "fig_w=15; # fig with in cm\n",
    "x_lim_plot=[0,60]; # limits in the plot\n",
    "y_lim_plot=[0,20]; # limits in the plot\n",
    "\n",
    "test_to_plot='L-3'; # name of the test to plot\n",
    "test=experiment_dictionary[test_to_plot]; # select the test to plot\n",
    "fig,ax=plt.subplots()\n",
    "ax.plot(test.data_offset_removed[displacement_header],test.data_offset_removed[force_header],color='k',linestyle='-',label=test.test_label)\n",
    "ax.plot(test.d_max,test.F_max,'r^',label=r'$F_{max}$')\n",
    "ax.plot([test.d_10,test.d_40],[test.F_10,test.F_40],'ro--',label=r'$k_{el}$')\n",
    "\n",
    "ax.grid()\n",
    "ax.legend(loc=\"upper left\",framealpha=0.0)\n",
    "ax.set_xlim(x_lim_plot)\n",
    "ax.set_ylim(y_lim_plot)\n",
    "ax.set_xlabel(x_label_plot)\n",
    "ax.set_ylabel(y_label_plot)\n",
    "fig.set_size_inches(fig_w/2.54,fig_h/2.54)\n",
    "fig.tight_layout()\n",
    "name=output_folder+test_to_plot+'.png'\n",
    "fig.savefig(name,dpi=400)   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1275.59x283.465 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(1,3)\n",
    "for test in experiment_dictionary.values():\n",
    "    if 'X' in test.test_label[0]:\n",
    "        axes[0].plot(test.data_offset_removed[displacement_header],test.data_offset_removed[force_header],color='k',linestyle='-',label=test.test_label)\n",
    "        axes[0].plot(test.d_max,test.F_max,'r^')\n",
    "        axes[0].plot([test.d_10,test.d_40],[test.F_10,test.F_40],'ro--')  \n",
    "        if '3' in test.test_label[-1]:\n",
    "            axes[0].plot(test.d_max,test.F_max,'r^',label=r'$F_{max}$')\n",
    "            axes[0].plot([test.d_10,test.d_40],[test.F_10,test.F_40],'ro--',label=r'$k_{el}$')  \n",
    "            \n",
    "    if 'S' in test.test_label[0]:\n",
    "        axes[1].plot(test.data_offset_removed[displacement_header],test.data_offset_removed[force_header],color='k',linestyle='-',label=test.test_label)\n",
    "        axes[1].plot(test.d_max,test.F_max,'r^')\n",
    "        axes[1].plot([test.d_10,test.d_40],[test.F_10,test.F_40],'ro--')\n",
    "        if '3' in test.test_label[-1]:\n",
    "            axes[1].plot(test.d_max,test.F_max,'r^',label=r'$F_{max}$')\n",
    "            axes[1].plot([test.d_10,test.d_40],[test.F_10,test.F_40],'ro--',label=r'$k_{el}$')  \n",
    "            \n",
    "    if 'L' in test.test_label[0]:\n",
    "        axes[2].plot(test.data_offset_removed[displacement_header],test.data_offset_removed[force_header],color='k',linestyle='-',label=test.test_label)\n",
    "        axes[2].plot(test.d_max,test.F_max,'r^')\n",
    "        axes[2].plot([test.d_10,test.d_40],[test.F_10,test.F_40],'ro--')\n",
    "        if '3' in test.test_label[-1]:\n",
    "            axes[2].plot(test.d_max,test.F_max,'r^',label=r'$F_{max}$')\n",
    "            axes[2].plot([test.d_10,test.d_40],[test.F_10,test.F_40],'ro--',label=r'$k_{el}$')  \n",
    "       \n",
    "for ax in axes:\n",
    "    ax.grid()\n",
    "    ax.legend(loc=\"upper left\",framealpha=0.0)\n",
    "    ax.set_xlim(x_lim_plot)\n",
    "    ax.set_ylim(y_lim_plot)\n",
    "    ax.set_xlabel(x_label_plot)\n",
    "    ax.set_ylabel(y_label_plot)\n",
    "fig.set_size_inches(fig_w*3/2.54,fig_h/2.54)\n",
    "fig.tight_layout()\n",
    "name=output_folder+'fd_all.png'\n",
    "fig.savefig(name,dpi=400)  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 425.197x283.465 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_results=pd.DataFrame()\n",
    "fig,ax=plt.subplots()\n",
    "for i,test in enumerate(experiment_dictionary.values()):\n",
    "    \n",
    "    if 'S' in test.test_label[0]:\n",
    "        test.assign_span(4.5); # assign span in m\n",
    "        Moment_capacity=test.F_max*test.L/6; # calculate moment capacity\n",
    "        ax.plot(2,Moment_capacity,'^k')       \n",
    "        dic={'Specimen': [test.test_label],'version':'S','F_max':[test.F_max],'k':[test.stiffness],'L (m)':[test.L],'M': [Moment_capacity]};\n",
    "            \n",
    "    if 'X' in test.test_label[0]:\n",
    "        test.assign_span(3.3); # assign span in m\n",
    "        Moment_capacity=test.F_max*test.L/6; # calculate moment capacity\n",
    "        ax.plot(1,Moment_capacity,'^k')    \n",
    "        dic={'Specimen': [test.test_label],'version':'XXS','F_max':[test.F_max],'k':[test.stiffness],'L (m)':[test.L],'M': [Moment_capacity]};\n",
    "        \n",
    "    if 'L' in test.test_label[0]:\n",
    "        test.assign_span(5.7); # assign span in m\n",
    "        Moment_capacity=test.F_max*test.L/6; # calculate moment capacity\n",
    "        ax.plot(3,Moment_capacity,'^k')    \n",
    "        dic={'Specimen': [test.test_label],'version':'L','F_max':[test.F_max],'k':[test.stiffness],'L (m)':[test.L],'M': [Moment_capacity]};\n",
    "    \n",
    "    test_results=pd.concat([test_results,pd.DataFrame(dic)]);\n",
    "\n",
    "ax.plot([1.8,2.2],[test_results[test_results.version=='S'].M.mean(),test_results[test_results.version=='S'].M.mean()],'r--');\n",
    "ax.plot([0.8,1.2],[test_results[test_results.version=='XXS'].M.mean(),test_results[test_results.version=='XXS'].M.mean()],'r--');\n",
    "ax.plot([2.8,3.2],[test_results[test_results.version=='L'].M.mean(),test_results[test_results.version=='L'].M.mean()],'r--');\n",
    "\n",
    "ax.grid()\n",
    "ax.set_xlim(0,4)\n",
    "ax.set_ylim(0,20)\n",
    "ax.set_ylabel(r'$M_{Rd}$ (kNm)')\n",
    "ax.set_xlabel('Specimens')\n",
    "ax.set_xticks([1,2,3])\n",
    "ax.set_xticklabels(['XXS','S','L'])\n",
    "fig.set_size_inches(fig_w/2.54,fig_h/2.54)\n",
    "fig.tight_layout()\n",
    "name=output_folder+'capacity.png'\n",
    "fig.savefig(name,dpi=400)  \n",
    "        \n",
    "    \n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results.to_csv('table_results.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 637.795x283.465 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the deflected shape\n",
    "force_ratios_to_plot=[.2,0.4,0.9]; # ratios of the force values (with respect to the capacity) to plot the deflected shapes\n",
    "plot_analytical_solution=True; # choose if plotting the analytical solution or not\n",
    "test_to_plot='L-3'; # name of the test to plot\n",
    "force_values_to_plot=np.array(force_ratios_to_plot)*experiment_dictionary[test_to_plot].F_max; # force values to use for plotting the deflected shape\n",
    "force_values_percentage=[]; # force values in percentage to be used for plotting\n",
    "\n",
    "\n",
    "# set the dictionaris with the values to calculate the analyical solution\n",
    "#potentiometers_location_numeric_old=[0,1100,1578.33,2367.5,3156.67,3635,4735]; # location of the potentiometers in numbers\n",
    "beam_dictionary_200S={\"E\":4930,\"I\":126920000,\"L\":4500,\"L_j\":1080,\"k_r\":1713000000,\"offset\":118}; # preset values for the S specimen\n",
    "potentiometers_location_numeric_200S=[0,1196,1618,2368,3118,3540,4736]; # location of the potentiometers in numbers\n",
    "beam_dictionary_150XXS={\"E\":4930,\"I\":62132400,\"L\":3300,\"L_j\":726,\"k_r\":1350000000,\"offset\":118}; # preset values for the L specimen\n",
    "potentiometers_location_numeric_150XXS=[0,896,1218,1768,2318,2640,3536]; # location of the potentiometers in numbers\n",
    "beam_dictionary_250L={\"E\":4930,\"I\":287650000,\"L\":5738,\"L_j\":1700,\"k_r\":2295000000,\"offset\":149}; # preset values for the XXS specimen\n",
    "potentiometers_location_numeric_250L=[0,1853,2062,3018,3974,4187,6036]; # location of the potentiometers in numbers\n",
    "potentiometers_location_labels=[r'$-L_c/2$',r'$L_j$',r'$L/3$',r'$L/2$',r'$2L/3$',r'$L_j$',r'$L+L_c/2$']; # labels in symbols\n",
    "potentiometers_location_labels_meters=[]; # labels is meters\n",
    "\n",
    "\n",
    "\n",
    "if 'S' in experiment_dictionary[test_to_plot].test_label[0]:\n",
    "        beam_dictionary=beam_dictionary_200S; # select whic dictionary to use\n",
    "        potentiometers_location_numeric=potentiometers_location_numeric_200S; # assign numeric values\n",
    "        \n",
    "elif 'X' in experiment_dictionary[test_to_plot].test_label[0]:\n",
    "        beam_dictionary=beam_dictionary_150XXS; # select whic dictionary to use\n",
    "        potentiometers_location_numeric=potentiometers_location_numeric_150XXS; # assign numeric values\n",
    "\n",
    "elif 'L' in experiment_dictionary[test_to_plot].test_label[0]:\n",
    "        beam_dictionary=beam_dictionary_250L; # select whic dictionary to use\n",
    "        potentiometers_location_numeric=potentiometers_location_numeric_250L; # assign numeric values\n",
    "        \n",
    "else:\n",
    "    raise Exception('Select the beam properties')\n",
    "        \n",
    "for el in potentiometers_location_numeric:\n",
    "    potentiometers_location_labels_meters.append(\"{:.1f}\".format(el/1000))\n",
    "    \n",
    "            \n",
    "beams_theoretical=[]; # list with the analytical solution for the deflected shape of beams\n",
    "for F,r in zip(force_values_to_plot,force_ratios_to_plot):        \n",
    "    beams_theoretical.append(beam_with_rotsprings(*list(beam_dictionary.values())[0:-1],F=F*1000/2,b=int(beam_dictionary['L']/3))); # calculate the deflected shape\n",
    "    force_values_percentage.append(\"{:.0%}\".format(r)); # append the string value in percentage\n",
    "\n",
    "\n",
    "test=experiment_dictionary[test_to_plot]; # retrieve the test data\n",
    "potentiometer_headers=header_names[2:9]; # retrieve only from Pot 5 to pot 9\n",
    "if 'L' in experiment_dictionary[test_to_plot].test_label[0]:\n",
    "    potentiometer_headers[1], potentiometer_headers[2] = potentiometer_headers[2], potentiometer_headers[1];\n",
    "    potentiometer_headers[4], potentiometer_headers[5] = potentiometer_headers[5], potentiometer_headers[4];\n",
    "\n",
    "test.extract_deflected_shape(data=test.data_offset_removed,\n",
    "                             potentiomer_headers=potentiometer_headers,\n",
    "                             force_header=force_header\n",
    "                             ,force_values=force_values_to_plot); # extract the deflected shape\n",
    "\n",
    "#potentiometers_location_labels=[0,r'$L_j$',r'$L/3$',r'$L/2$',r'$2L/3$',r'$L-L_j$','L']\n",
    "color_list=['k']*4;\n",
    "line_list=['--']*4;\n",
    "symbol_list=['s','o','^','d'];\n",
    "fig,ax=plt.subplots()\n",
    "for i,(shape,F,c,l,s,s_theory,F_per) in enumerate(zip(test.deflected_shape,test.deflected_shape_forces,color_list,line_list,symbol_list,beams_theoretical,force_values_percentage)):  \n",
    "    ax.plot(potentiometers_location_numeric,-np.array(shape),c+l+s,label='F='+F_per+r'$F_{max}$')\n",
    "    if plot_analytical_solution:\n",
    "        if i!=len(test.deflected_shape_forces)-1:\n",
    "            ax.plot(s_theory.x+beam_dictionary['offset'],s_theory.v,'r--')\n",
    "        else:\n",
    "            ax.plot(s_theory.x+beam_dictionary['offset'],s_theory.v,'r--',label='Theoretical')\n",
    "\n",
    "x_delta_plot=100;\n",
    "ax.grid()\n",
    "ax.legend(loc=\"lower left\",framealpha=0.0)\n",
    "ax.set_xlim([potentiometers_location_numeric[0]-x_delta_plot,potentiometers_location_numeric[-1]+x_delta_plot])\n",
    "ax.set_xticks(potentiometers_location_numeric)\n",
    "ax.set_xticklabels(potentiometers_location_labels_meters)\n",
    "\n",
    "secax = ax.secondary_xaxis('top')\n",
    "secax.set_xticks(potentiometers_location_numeric)\n",
    "secax.set_xticklabels(potentiometers_location_labels)\n",
    "\n",
    "ax.set_ylim([-50,10])\n",
    "ax.set_xlabel('Position (m)')\n",
    "ax.set_ylabel('Displacement (mm)')\n",
    "fig.set_size_inches(fig_w*1.5/2.54,fig_h/2.54)\n",
    "fig.tight_layout()\n",
    "name=output_folder+test_to_plot+'_deflected_shape.png'\n",
    "fig.savefig(name,dpi=400)   \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "1.9\n",
      "2.1\n",
      "3.0\n",
      "4.0\n",
      "4.2\n",
      "6.0\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pot_4 (mm)',\n",
       " 'Pot_5 (mm)',\n",
       " 'Pot_6 (mm)',\n",
       " 'Pot_7 (mm)',\n",
       " 'Pot_8 (mm)',\n",
       " 'Pot_9 (mm)',\n",
       " 'Pot_10 (mm)']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "potentiometer_headers=header_names[2:9]\n",
    "potentiometer_headers"
   ]
  }
 ],
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