Added all scripts by @warsitzka_at_ig.cas.cz

FileConversion/ConverstVST_TDMStoTXT.py

+# -*- coding: utf-8 -*-
+"""
+Created on Mon Aug 13 15:13:56 2018
+
+@author: Warsitzka, Michael
+
+This script can be used to convert tdms files of the velocity stepping test into text files and to plot the vst-data.
+"""
+#%%=============IMPORT=====================================================
+import os
+import numpy as np
+from nptdms import TdmsFile
+import pandas as pd
+from scipy import stats 
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+from os.path import splitext
+import itertools
+import csv
+#%%==============PARAMETERS=================================================
+A =     0.022619            # area of shear zone (= surface area of lid) (m^2)
+li =    0.0776              # inner lever (center cell to center of shear zone)
+lo =    0.1250              # outer lever (center cell to hinge point)
+le=     1.611655493         # load point level
+v=      30                  # shear velocity (mm/min)
+#g =     9.81                # acceleration due to gravity
+normalstress= 2000          # normal stress in Pa, only for .txt or asc. files
+#%%==================NAMES=================================================
+path_in =        'input/' #define path of input data
+path_out =        'output/' #define path of input data
+curve_fit=      'logx'      #define type of curve fit for shear velocity-shear stress curve:
+                            #>>'lin' = linear:
+                            # [slope, intercept] = logfit(x,y,'linear')
+                            #            yApprox = (intercept)+(slope)*x  
+                            #>> 'logx' = logarithmic:
+                            # [slope, intercept] = logfit(x,y,'logx')
+                            #            yApprox = (intercept)+(slope)*log10(x)
+                            #>>'logy' = exponential:
+                            # [slope, intercept] = logfit(x,y,'logy')
+                            #            yApprox = (10^intercept)*(10^slope).^x
+                            #>>'loglog' = powerlaw:
+                            # [slope, intercept] = logfit(x,y,'loglog')
+                            #            yApprox = (10^intercept)*x.^(slope)             
+
+#%%==========================READ & CONVERT DATA==============================================  
+files = [i for i in os.listdir(path_in) \
+         if i.endswith(".tdms") and \
+         ('VST' in i or 'vst' in i)]
+n = len(files)
+names = [splitext(files[i])[0] for i in range(0,n)]
+dfvst_raw = np.empty(n, dtype=object)
+dfvst = np.empty(n, dtype=object)
+i=0
+for i in range(0,n):
+    tdms = TdmsFile(path_in + files[i])
+    groups = tdms.groups()                      # get group names
+    channels = tdms.group_channels(groups[0])   #get channel names
+    inc = list((tdms.object(groups[0], 'Velocity').properties).values())[2] # time increment between each point
+    dfvst_raw[i] = tdms.object(groups[0]).as_dataframe()          #load tdms as data frame
+    shearforce = dfvst_raw[i]['Shear Force']      #velocity in [N]
+    normalforce = dfvst_raw[i]['Normal Force']    #velocity in [N]
+    liddispl = dfvst_raw[i]['Lid Displacement']   #velocity in [mm]
+    vel = dfvst_raw[i]['Velocity']                # shear velocity in [mm/s]
+    if filter(lambda x: x < 0, vel) == True:
+        vel[vel < 0] = nan
+    time = np.arange(0, len(vel)*inc, inc)  # time in [s]
+    displ = (np.cumsum(inc*vel))             #cumulative displacement in [mm]
+    sigma = normalforce/A                 #calculate normal stress
+    tau = (shearforce*lo)/(li*A)          #calculate shear stress
+    fric = (tau/sigma)                        #friction []
+    dil = -(liddispl-liddispl[0])           # calculation dilation
+    dfvst[i]= pd.DataFrame.from_dict({'time': time,
+         'displacement': displ,
+         'velocity': vel,
+         'normalstress': sigma,
+         'shearstress': tau,
+         'friction': fric,
+         'dilation':dil}, orient='index').transpose()
+    #%%==========FIT SHEAR LOAD CURVE============================================
+    if curve_fit== 'lin': #'linear'
+        logvel=vel
+        logfric=fric
+    elif curve_fit== 'logx': #'logarithmic'
+        logvel=np.log10(vel)
+        logfric=fric
+    elif curve_fit== 'logy': #'exponential'
+        logfric=np.log10(fric) 
+        logvel=vel
+    elif curve_fit== 'loglog': #'powerlaw'
+        logvel=np.log10(vel)
+        logfric=np.log10(fric)
+        
+    # linear fit and evaluation:
+    slope, intercept,r,p,std = stats.linregress(logvel,logfric)
+    fitfric=   np.polyval((slope,intercept),logvel) 
+    # Standard deviations:
+    m=len(logvel)
+    s = np.sum((fric-intercept-slope*logvel)**2)/(m-2)
+    std_slope = np.sqrt(m*s/(m*np.sum(logvel**2)-np.sum(logvel)**2))                    #standard error Coef. friction
+    std_intercept = np.sqrt(np.sum(logvel**2) * s/ (m*np.sum(logvel**2)-np.sum(logvel)**2))       #standard error cohesion
+    
+    # rescale the approximation results for plotting
+    if curve_fit == 'lin':
+        name_fit='y = '+str(round(slope,3))+'*x +'+str(round(intercept,3))
+    elif curve_fit=='logx':
+        name_fit = r'y = ({:.4f}$\pm${:.4f})log10(x) + ({:.4f}$\pm${:.4f})'.format(slope,
+                          std_slope,intercept,std_intercept)        
+    if curve_fit=='logy':
+        fitfric=10**fitfric
+        logfric=10**logfric
+        name_fit = 'y = (10^('+str(round(intercept,3))+')*10^('+str(round(slope,3))+')^x'
+    elif curve_fit=='loglog':
+        ran=10**ran
+        fitfric=10**fitfric
+        logfric=10**logfric 
+        name_fit= 'y = 10^('+str(round(intercept,3))+')*x^('+str(round(slope,3))+')'
+        
+    # Calculate MSE and R2
+    MSE =       np.mean((logfric-fitfric)**2) # mean squared error.
+    Covy =      np.cov(fitfric, fric) # = cov(estimated values, experimental values)
+    R2        = (Covy[1]**2)/(np.var(fitfric)*np.var(fric))  #variance  
+    
+    
+#%%=======================SAVE VST DATA TO TXT FILE================================================= 
+   # data_vst = pd.DataFrame({'Time [s]': time, 'Shear velocity [mm/s]': vel, 'Normal force [N]': normalforce, 'Shear force [N]': shearforce})
+    data_vst = np.array((time, vel, normalforce, shearforce))
+    #data_vst.to_csv(path+names[i] + '.txt',index=None, sep='\t', mode='a')
+    
+    
+    header_vst = 'Time [s]'+'\t' 'Shear velocity [mm s^-1]'+ '\t'+'Normal force [N]'+'\t'+'Shear force [N]'
+    with open(path_out + names[i] + '.txt', 'w') as f:
+        f.write(header_vst + '\n')
+        for row in data_vst.T:
+            csv.writer(f, delimiter='\t', lineterminator='\n').writerow(row)  
+        f.close
+    

FileConversion/ConvertAsciitoTXT.py

+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep 25 14:54:43 2018
+
+@author: analab
+"""
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import csv
+import pylab
+from scipy import stats 
+import os
+import fnmatch
+import glob, shutil
+import pickle
+from os.path import isfile, join, basename, splitext
+from nptdms import TdmsFile
+import collections
+import itertools
+import codecs
+
+
+#%%==========================PARAMETERS & VARIABLES==============================================
+A =  0.022619    # area of shear zone (= surface area of lid) (m^2)
+li = 0.0776     # inner lever (center cell to center of shear zone)
+lo = 0.1250     # outer lever (center cell to hinge point)
+le= 1.611655493 #load point level
+v=  30          #shear velocity (mm/min)
+g = 9.81        # acceleration due to gravity
+prec = 125      # rounding precision for normal load in file names
+velnoise = 1e-2
+stressnoise = 0.025
+smoothwindow = 51 # define window for smooth shear stress curve
+
+
+projectname =   '324-01_QuartzneusandBern_basal'
+TSname=        projectname+'_ts'
+
+path_in =       'input/'
+path_out =      'output/'
+
+#%%==========================READ & CONVERT DATA==============================================
+
+for file in os.listdir(path_in):
+    files,n = glob.glob1(path_in,"*.asc"),len(glob.glob1(path_in,'*.asc'))
+    DF = np.empty(n, dtype=object)
+    len_m = np.zeros(n)
+    Sigma_avg = np.zeros(m)
+    for i in range(0,len(files)):
+        if files[i].endswith('.asc'):                  # loop over all asc files
+            name = splitext(files[i])[0]                # extract names of asc files
+            with codecs.open(path_in + files[i], encoding='utf-8-sig') as f:
+                data_load = np.loadtxt(f)         # load file for further calculations
+             # write data to new varables with meaningfull names
+            time = data_load[:,0]
+            shearload = data_load[:,1] #in kg
+            velocity = data_load[:,2]
+            dilation = data_load[:,3]
+            normalload = data_load[:,4] #in kg
+        # Corrections
+        if time[0] >= 0:
+            time = time - time[0]                       # set starting time to 0
+        else:
+            time = time  
+        dilation = -(dilation-dilation[0])              # set initial dilation to 0
+        shearforce = shearload *g       #in N
+        normalforce = normalload *g     #in N
+        sigma = normalforce/A                     # kg to normal stress in [Pa]
+        tau = (shearforce*lo)/(li*A)         # kg to shear stress in [Pa]
+        velocity = velocity/60                          # transform to [mm/s]
+        velocity[velocity < 0] = 0
+        inc = time[1]-time[0]                              # timesteps, first one = 0
+        #incdisp = np.diff(time)*velocity[:-1]                      # incremental displacement in mm
+        displacement = np.cumsum(inc*velocity[:-1])   # cummulative displacement in mm
+        Sigma_avg[i] = np.average(sigma[sigma>0])  # only for the file name
+        DF[i]= pd.DataFrame.from_dict({'time': time, 
+                                  'displacement': displacement,
+                                  'velocity': velocity,
+                                  'normalforce': normalforce,
+                                  'normalstress': sigma,
+                                  'shearforce': shearforce,
+                                  'shearstress': tau,
+                                  'dilation':dilation}, orient='index').transpose()
+#        open(path+ str(name)+'_'+str(int(avgsigma))+'.txt', 'w').close()   #write and save calculated data in new txt file
+#        with open(path+ str(name)+'_'+str(int(avgsigma))+'.txt', 'r+') as output_set:
+#            write_data = csv.writer(output_set, delimiter='\t')
+#            write_data.writerow(['Time', 'Shear stress', 'Velocity', 'Dilation','Normal stress', 'Displacement'])          
+#            write_data.writerows(zip(time,tau,velocity,dilation,sigma,displacement))
+#            output_set.closed
+#        current_header= np.genfromtxt((path_in+str(files_txt[i])), 
+#                                      delimiter='\t', 
+#                                      dtype='unicode')[0]
+        len_m[i] = len(time)
+        print(name +' processed')
+    break
+
+        
+    TS =  np.zeros((n+1,maxlen_df))#np.zeros((n+1, maxlen_df))     #array of zeros for time series of all files
+#%%==========================EVALUATE STRESSES==============================================
+for i in range(0,len(DF)):
+    current_set = DF[i]
+    #create df of time series for txt file:
+    current_ts = np.concatenate(([pd.Series(current_set.time).values], 
+            [pd.Series(current_set.shearforce).values]))
+#    ts_dspl = np.zeros(ts.shape)
+#    for l in range(0, current_ts.shape[0]):
+#        ts_dspl[l,0] = np.copy(ts[l,0] * vel/60)
+#        ts_dspl[l,1] = np.copy(ts[l,1])
+#        l = l+1
+    #convert load (kg) to stress (Pa)
+#    for k in range(1,current_ts.shape[1]):
+#        for l in range(0,current_ts.shape[0]):
+#            ts_dspl[l,k] = ts[l,k]*9.81*loadlev/A
+#            l = l+1
+#        k= k+1
+    current_ts = np.asarray([np.pad(a, (0, maxlen_df - len(a)), 'constant', constant_values=np.NaN) for a in current_ts])  
+    TS[i+1] = current_ts[1]
+    if i == len_m_max_ind:
+        TS[0] = current_ts[0]
+        
+#%%=======================PLOT  TS=================================================
+        
+plt.rcParams['figure.figsize'] =        (10, 8) 
+linecolor= ['','red', 'orange','gold', 'green', 'royalblue', 'k']
+t = int(m/3)
+fig3=plt.figure()
+for i in range(0,t):
+    sigma_avg = int((data_peak[:,0][i]+data_peak[:,0][i+t]++data_peak[:,0][i+2*t])/3)
+    plt.plot(TS.T[:,0], np.zeros(len(TS.T[:,0])), linewidth= 0.5, color=linecolor[i+1], label=str(int(sigma_avg))+' Pa')
+    plt.plot(TS.T[:,0], TS.T[:,i*3+1:(i+1)*3+1], linewidth= 0.5, color=linecolor[i+1])
+plt.legend(fontsize=8, 
+         facecolor='w', 
+         edgecolor='k', 
+         framealpha=1,
+         loc= 'upper right',
+         title="Normal stress")
+
+plt.xlabel('Shear displacement [mm]')
+plt.ylabel(r'Shear stress $\tau$ [Pa]')
+plt.xlim(0, max(TS.T[:,0]))
+fig3.suptitle(rstname, y=0.92)
+plt.savefig(path_out + TSname, bbox_inches='tight', edgecolor='w')
+plt.close()      
+        
+#%% Save ts data to txt files:   
+sigma_str = "\t".join([str(int(x.round(-1))) for x in Sigma_avg])#.split(',')
+header_ts = 'Time [s]'+'\t'+'Normal stress [Pa]:'+'\t'+sigma_str  
+with open(path_out + TSname + '.txt', 'w') as f:
+    f.write(header_ts + '\n')
+    for row in TS.T:
+        csv.writer(f, delimiter='\t', lineterminator='\n').writerow(row)  
+    f.close
\ No newline at end of file

FileConversion/ConvertTDMStoTXT.py

+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep 25 14:54:43 2018
+
+@author: analab
+"""
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import csv
+import pylab
+from scipy import stats 
+import os
+import fnmatch
+import glob, shutil
+import pickle
+from os.path import isfile, join, basename, splitext
+from nptdms import TdmsFile
+import collections
+import itertools
+
+
+#%%==========================PARAMETERS & VARIABLES==============================================
+A =  0.022619    # area of shear zone (= surface area of lid) (m^2)
+li = 0.0776     # inner lever (center cell to center of shear zone)
+lo = 0.1250     # outer lever (center cell to hinge point)
+le= 1.611655493 #load point level
+v=  30          #shear velocity (mm/min)
+g = 9.81        # acceleration due to gravity
+prec = 125      # rounding precision for normal load in file names
+velnoise = 1e-2
+stressnoise = 0.025
+smoothwindow = 51 # define window for smooth shear stress curve
+params_conv =   [A,li,lo,g,prec]
+params_eval =     [velnoise, stressnoise, A, v, le]
+
+
+projectname =   '393-01_01_Foamglass100'
+TSname=        projectname+'_ts'
+
+path_in =       'input/'
+path_out =      'output/'
+
+#%%==========================READ & CONVERT DATA==============================================
+for file in os.listdir(path_in):
+    files,n = glob.glob1(path_in,"*.tdms"), len(glob.glob1(path_in,'*.tdms'))
+    DF = np.empty(n, dtype=object)
+    len_m = np.zeros(n)
+    for i in range(0,n):
+        name = splitext(files[i])[0]
+        tdms = TdmsFile(path + files[i])
+        groups = tdms.groups()
+        channels = tdms.group_channels(groups[0])
+        # time increment between each point:
+        inc = list((tdms.object(groups[0], 'Velocity').properties).values())[2]
+        df_raw=tdms.object(groups[0]).as_dataframe()
+        Length[i] = df_raw.shape[0]
+        shearforce = df_raw['Shear Force'] #Shear force in [N]
+        normalforce = df_raw['Normal Force'] #normal forcein [N]
+        liddispl = df_raw['Lid Displacement'] #velocity in [mm]
+        velocity = df_raw['Velocity'] #velocity in [mm/s]
+        #convert colums:
+        time = np.arange(0, Length[i]*inc, inc) # time in [s]
+        displacement = np.cumsum(inc*velocity)        #cumulative displacement in [mm]
+        sigma = normalforce/A
+        tau = (shearforce*lo)/(li*A)
+        dilation = -(liddispl-liddispl[0])
+        df[i]= pd.DataFrame.from_dict({'time': time, 
+                      'displacement': displacement,
+                      'velocity': velocity,
+                      'normalforce': normalforce,
+                      'normalstress': sigma,
+                      'shearforce': shearforce,
+                      'shearstress': tau,
+                      'dilation':dilation}, orient='index').transpose()
+        print(name +' processed')
+        len_m[i] = len(time)
+    break
+    len_m_max = int(max(len_m))
+    len_m_max_ind = next(i for i, j in enumerate(len_m) if j == max(len_m))
+        
+        
+        
+    TS =  np.zeros((n+1,maxlen_df))#np.zeros((n+1, maxlen_df))     #array of zeros for time series of all files
+#%%==========================EVALUATE STRESSES==============================================
+Sigma_avg = np.zeros(m)
+for i in range(0,len(DF)):
+    current_set = DF[i]
+    #create df of time series for txt file:
+    current_ts = np.concatenate(([pd.Series(current_set.time).values], 
+            [pd.Series(current_set.shearforce).values]))
+#    ts_dspl = np.zeros(ts.shape)
+#    for l in range(0, current_ts.shape[0]):
+#        ts_dspl[l,0] = np.copy(ts[l,0] * vel/60)
+#        ts_dspl[l,1] = np.copy(ts[l,1])
+#        l = l+1
+    #convert load (kg) to stress (Pa)
+#    for k in range(1,current_ts.shape[1]):
+#        for l in range(0,current_ts.shape[0]):
+#            ts_dspl[l,k] = ts[l,k]*9.81*loadlev/A
+#            l = l+1
+#        k= k+1
+    sigma = pd.Series(current_set.shearstress).values
+    Sigma_avg[i] = np.average(sigma[sigma>0])
+    current_ts = np.asarray([np.pad(a, (0, maxlen_df - len(a)), 'constant', constant_values=np.NaN) for a in current_ts])  
+    TS[i+1] = current_ts[1]
+    if i == len_m_max_ind:
+        TS[0] = current_ts[0]
+        
+#%%=======================PLOT  TS=================================================
+        
+plt.rcParams['figure.figsize'] =        (10, 8) 
+linecolor= ['','red', 'orange','gold', 'green', 'royalblue', 'k']
+t = int(m/3)
+fig3=plt.figure()
+for i in range(0,t):
+    normalstress_avg = int((data_peak[:,0][i]+data_peak[:,0][i+t]++data_peak[:,0][i+2*t])/3)
+    plt.plot(TS.T[:,0], np.zeros(len(TS.T[:,0])), linewidth= 0.5, color=linecolor[i+1], label=str(int(normalstress_avg))+' Pa')
+    plt.plot(TS.T[:,0], TS.T[:,i*3+1:(i+1)*3+1], linewidth= 0.5, color=linecolor[i+1])
+plt.legend(fontsize=8, 
+         facecolor='w', 
+         edgecolor='k', 
+         framealpha=1,
+         loc= 'upper right',
+         title="Normal stress")
+
+plt.xlabel('Shear displacement [mm]')
+plt.ylabel(r'Shear stress $\tau$ [Pa]')
+plt.xlim(0, max(TS.T[:,0]))
+fig3.suptitle(rstname, y=0.92)
+plt.savefig(path_out + TSname, bbox_inches='tight', edgecolor='w')
+plt.close()      
+        
+        
+  
+       
+
+
+#%% Save ts data to txt files:   
+sigma_str = "\t".join([str(int(x.round(-1))) for x in Sigma_avg])#.split(',')
+header_ts = 'Time [s]'+'\t'+'Normal stress [Pa]:'+'\t'+sigma_str  
+with open(path_out + TSname + '.txt', 'w') as f:
+    f.write(header_ts + '\n')
+    for row in TS.T:
+        csv.writer(f, delimiter='\t', lineterminator='\n').writerow(row)  
+    f.close
\ No newline at end of file

FileConversion/Convert_TS_TDMStoTXT.py

+# -*- coding: utf-8 -*-
+"""
+Created on Mon Oct 22 10:16:04 2018
+
+@author: analab
+"""
+
+#%%===========================IMPORT==========================================
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import csv
+import pylab
+from scipy import stats 
+import os
+import fnmatch
+import glob, shutil
+import pickle
+from nptdms import TdmsFile
+import collections
+import itertools
+import RST_convert_old
+
+projectname =   '419-01_Quartzsand_Zaragosa_L-7080S'
+path_in =       'input/'
+path_out =      'output/'
+name_ts=        projectname+'_ts'
+
+#%%==========================PARAMETERS & VARIABLES==============================================
+A =  0.022619    # area of shear zone (= surface area of lid) (m^2)
+li = 0.0776     # inner lever (center cell to center of shear zone)
+lo = 0.1250     # outer lever (center cell to hinge point)
+v=  30          #shear velocity (mm/min)
+prec = 125      # rounding precision for normal load in file names
+params_conv =   [A,li,lo,prec]
+
+#%%==========================READ & CONVERT DATA==============================================
+
+def convert_tdms(path,params):
+    files,N = glob.glob1(path,"*.tdms"), len(glob.glob1(path,'*.tdms'))
+    A,li,lo,prec = params[0],params[1],params[2],params[3]  # values for calculation
+    DF = np.empty(N, dtype=object)
+    Length = np.zeros(N)
+    Sigma_avg = np.zeros(N) 
+    for i in range(0,N):
+        name = splitext(files[i])[0]
+        tdms = TdmsFile(path + files[i])
+        groups = tdms.groups()
+        channels = tdms.group_channels(groups[0])
+        # time increment between each point:
+        inc = list((tdms.object(groups[0], 'Velocity').properties).values())[2]
+        df_raw=tdms.object(groups[0]).as_dataframe()
+        Length[i] = df_raw.shape[0]
+        shearforce = df_raw['Shear Force'] #Shear force in [N]
+        normalforce = df_raw['Normal Force'] #normal forcein [N]
+        liddispl = df_raw['Lid Displacement'] #velocity in [mm]
+        velocity = df_raw['Velocity'] #velocity in [mm/s]
+        #convert colums:
+        time = np.arange(0, Length[i]*inc, inc) # time in [s]
+        displacement = np.cumsum(inc*velocity)        #cumulative displacement in [mm]
+        sigma = normalforce/A
+        tau = (shearforce*lo)/(li*A)
+        dilation = -(liddispl-liddispl[0])
+        DF[i]= pd.DataFrame.from_dict({'time': time, 
+                      'displacement': displacement,
+                      'velocity': velocity,
+                      'normalforce': normalforce,
+                      'normalstress': sigma,
+                      'shearforce': shearforce,
+                      'shearstress': tau,
+                      'dilation':dilation}, orient='index').transpose()
+        print(name +' processed')
+    len_N_max = int(max(len_m))
+    
+    len_N_max_ind = next(i for i, j in enumerate(len_m) if j == max(len_m)) 
+    DFTS =  np.zeros((N+1,len_N_max))#np.zeros((n+1, maxlen_df))     #array of zeros for time series of all files
+    for i in range(0,len(DF)):
+        current_set = DF[i]
+        current_ts = np.concatenate(([pd.Series(current_set.time).values], 
+                [pd.Series(current_set.shearforce).values]))
+        current_ts = np.asarray([np.pad(a, (0, len_N_max- len(a)), 'constant', constant_values=np.NaN) for a in current_ts])  
+        DFTS[i+1] = current_ts[1]
+        if i == len_N_max_ind:
+            DFTS[0] = current_ts[0]
+    return DFTS
+
+#%%=======================PLOT  DFTS=================================================
+def RST_plot(out_path, TSname, DFTS):
+#    plt.rcParams['savefig.format'] =         'pdf'
+#    plt.rcParams['figure.figsize'] =        (14, 10)
+#    plt.rcParams['font.size'] =             10
+#    plt.rcParams['font.family'] =           'Arial Unicode MS'
+#    plt.rcParams['savefig.dpi'] =           1000
+#    plt.rcParams['figure.figsize'] =        (10, 8) 
+#    for k in range(0,DFTS.shape[0]):
+#        plt.plot(DFTS.T[:,0], DFTS.T[:,k], linewidth= 0.5, color='k')
+#        k= k+1
+#    plt.xlabel('Shear displacement [mm]')
+#    plt.ylabel(r'Shear stress [Pa] ')
+#    plt.xlim(0, max(DFTS[0]))
+#    plt.ylim(0, 40)
+#    plt.savefig(out_path + TSname, bbox_inches='tight', edgecolor='w')
+#    plt.close()
+    Sigma = [500,1000,2000,4000,8000,16000,500,1000,2000,4000,8000,16000, 500,1000,2000,4000,8000,16000]
+    sigma_str = "\t".join([str(int(x)) for x in Sigma])#.split(',')
+    header_ts = 'Time [s]'+'\t'+'Normal stress [Pa]:'+'\t'+sigma_str  
+    with open(out_path + TSname + '.txt', 'w') as f:
+        f.write(header_ts + '\n')
+        for row in DFTS.T:
+            csv.writer(f, delimiter='\t', lineterminator='\n').writerow(row)  
+        f.close
+
+def main():    
+    DFTS = convert_tdms(path_in,params_conv) 
+    RST_plot(path_out, name_ts, DFTS)
+
+main()
+

RSTanalysis/RST/Data files/example_dynamic.txt

+Normal stress [Pa]	Shear strength [Pa]
+500.0	384.19
+1000.0	670.06
+2000.0	1244.05
+4000.0	2395.66
+8000.0	4641.83
+16000.0	9163.69
+500.0	373.07
+1000.0	667.8
+2000.0	1244.02
+4000.0	2387.42
+8000.0	4646.12
+16000.0	9112.94
+500.0	377.95
+1000.0	671.29