import sys import numpy as np import matplotlib.pyplot as plt # from scipy import signal processing function. Not all used. from scipy.signal import butter, lfilter, freqz, filtfilt import pandas as pd # these are just for finding file paths import os import ntpath # 0 = script.py # argv1 = 'C:\Users\User\Documents\Multis\CMULTIS002-1_UL_AC_S_0_T\oksdata.txt' # argv1 = 'C:\Users\User\Documents\Multis\CurrentData\data.txt' # dir = '/Users/schimmt/Multis/app/TissueTesting/Data/' # input_filename = 'C:\Users\User\Documents\Multis\RubberSuccess\test.txt' files = ['data.txt', 'data1.txt'] threshold = 50.0 # threshold ####### adjust accordingly to sample type # files = ['\data.txt','\data1.txt'] fig2, (ax3, ax4) = plt.subplots(nrows=2, ncols=1, figsize=(18, 9), dpi=90) fig3, (ax5, ax6) = plt.subplots(nrows=1, ncols=2, figsize=(18, 8), dpi=90) #load v disp and stress v strain at ramp before and after filecount=0 for file in files: input_filename = dir+file infile = open(input_filename, 'r') if file == files[0]: #first test specs init_length = 25.4935 # length width = 4 elif file== files[1]: #second test specs init_length = 27.4065 else: #third test specs init_length = 27.767 area = float(np.pi*width**2) #area # -------------------- # pick all data for move relative and wait commands ' rampcount = 0 ramplist = [] for line in infile: # read each line if line[1:5] == 'Move': rampcount += 1 ramplist.append(line) print(line) if line[1:4] == 'Sin': rampcount += 1 ramplist.append(line) print(line) infile.close() infile = open(input_filename, 'r') ramps = [[] for x in range(rampcount)] sins = [] i = -1 f = -1 sn = -1 for line in infile: # read each line if line[1:5] == 'Move': i += 1 for _ in xrange(6): line = infile.next() a = line[0] while a.isdigit(): # line = line.rstrip(',,,\n') ramps[i].append(line) line = infile.next() a = line[0] if line[1:4] == 'Sin': sn += 1 for _ in xrange(8): line = infile.next() b = line[0] while b.isdigit(): # line = line.rstrip(',,,\n') sins.append(line) line = infile.next() b = line[0] ramps = ramps[0:-1] rampcount = len(ramps) infile.close() vel = [] infile = open(input_filename, 'r') for line in infile: if line[0:8] == 'Velocity': vel.append(line[16:22]) # print vel line = infile.next() infile.close() print('----------') print('Expected rate, mm/s =', vel[-2]) # separate time, disp and load from all move relative and wait command data r_time = [[] for x in range(rampcount)] r_disp = [[] for x in range(rampcount)] r_load = [[] for x in range(rampcount)] sin_disp = [] sin_load = [] sin_time = [] loadindex=-1 for j in range(0, rampcount): for line in ramps[j]: line = line.split() line = map(float, line) r_time[j].append(line[0]) r_disp[j].append(abs(line[1])) r_load[j].append(abs(line[loadindex])) line = infile.next for line in sins: line = line.split() line = map(float, line) sin_time.append(line[0]) sin_disp.append(abs(line[1])) sin_load.append(abs(line[loadindex])) line = infile.next # ------------------------------------------- # low pass butterworth filter, 3rd order, 100 hz cutoff freq def butter_lowpass(cutoff, fs, order=3): nyq = 0.5 * fs normal_cutoff = cutoff / nyq b, a = butter(order, normal_cutoff, btype='low', analog=False) return b, a def butter_lowpass_filter(data, cutoff, fs, order): b, a = butter_lowpass(cutoff, fs, order=order) # y = lfilter(b, a, data) y = filtfilt(b, a, data, method="gust") return y # Filter order = 3 fs = 2.5 * 1000 # sample rate, Hz cutoff = 20 # desired cutoff frequency of the filter, Hz # filter coefficients to check its frequency response b, a = butter_lowpass(cutoff, fs, order) filtered_r_load = [[] for x in range(rampcount)] filtered_r_disp = [[] for x in range(rampcount)] # filtered_w_disp=[[] for x in range(waitcount)] filtered_sin_load = [] print(rampcount) # Apply the filter. for r in range(0, rampcount): print(r) filtered_r_load[r] = butter_lowpass_filter(r_load[r], cutoff, fs, order) filtered_r_disp[r] = butter_lowpass_filter(r_disp[r], cutoff, fs, order) filtered_sin_load = butter_lowpass_filter(sin_load, cutoff, fs, order) sin_disp_orig = sin_disp sin_disp = [((x - r_disp[2][0]) - 0.3) for x in sin_disp] # accommodate 300 micron buffer def concotenateTime(r_time): time = [[] for x in range(len(r_time))] for i in range(len(r_time)): # print i if i==0: time[0]=np.array(r_time[i]) if i>0: time[i]= np.array(r_time[i])+np.array(time[i-1][-1]) return time time = concotenateTime(r_time) fig1, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(18, 9), dpi=90) fig4, (ax7) = plt.subplots(nrows=1, ncols=1, figsize=(18, 9), dpi=90) # load v time during preconditioning # fig2, (ax3, ax4) = plt.subplots(nrows=2, ncols=1, figsize=(18, 9), dpi=90) ax1.set_title(file) ax3.set_title(file) ax7.set_title(file) fig1.tight_layout(pad=4, w_pad=.5, h_pad=2) fig2.tight_layout(pad=4, w_pad=.5, h_pad=2) #fig1 ax1.set_xlabel('time,s') ax1.set_ylabel('load,gf') ax2.set_xlabel('time,s') ax2.set_ylabel('disp,mm') #fig2 ax3.set_xlabel('time,s') ax3.set_ylabel('load,gf') ax4.set_xlabel('time,s') ax4.set_ylabel('disp,mm') #fig3 ax5.set_xlabel('disp,mm') ax5.set_ylabel('load,fg') ax6.set_xlabel('strain') ax6.set_ylabel('stress') #fig4 ax7.set_xlabel('time,s') ax7.set_ylabel('load,gf') ax7.plot(sin_time, filtered_sin_load) disp_adjustment = [] cnt = 0 redo=[] for i in filtered_r_load: # i = np.array(i) # print max(i) result = np.where(i 1: not finished # rmsload[r] = filtered_r_load[r][startindex:(np.int(-(.1)*len(norm_f_disp[r])))] # masterdisp.append()x a=2 b=3 strain = [] stress = [] cnt=0 for i in range(a,b): ## fix strain.append([c / float(init_length) for c in norm_f_disp[i]]) stress.append([(c*.0098) / float(area) for c in filtered_r_load[i]]) # stress = [c * 0.0098 for c in stress[cnt]] # gf to N cnt+=1 aa=6 bb=7 cnt=0 for i in range(aa,bb): strain.append([c / float(init_length) for c in norm_f_disp[i]]) stress.append([(c*.0098) / float(area) for c in filtered_r_load[i]]) # stress = [c * 0.0098 for c in stress[cnt]] # gf to N cnt+=1 norm_r_disp = [[] for x in range(rampcount)] #for left figure force v disp for r in range(0, rampcount): norm_r_disp[r] = [x - float(r_disp[2][0]+0.0) for x in r_disp[r]] if file==files[0]: ax5.plot(norm_r_disp[2], filtered_r_load[2],c='r',label=file) ax5.plot(norm_r_disp[3], filtered_r_load[3],c='orange',label=file) ax5.plot(norm_r_disp[aa], filtered_r_load[aa],c='g',label=file) ax5.plot(norm_r_disp[bb], filtered_r_load[bb],c='b',label=file) else: ax5.plot(norm_r_disp[2], filtered_r_load[2], c='r',ls='--',label=file) ax5.plot(norm_r_disp[3], filtered_r_load[3], c='orange',ls='--',label=file) ax5.plot(norm_r_disp[aa], filtered_r_load[aa], c='green',ls='--',label=file) ax5.plot(norm_r_disp[bb], filtered_r_load[bb], c='b',ls='--',label=file) for i in range(len(stress)): if i==1: #added the if statement to remove ramps before preconditioning if file==files[0]: ax6.plot(strain[i],stress[i],label=file) else: ax6.plot(strain[i],stress[i],ls='--',label=file) # time1=time[2:] for i in range(len(time)): if i>=2 and i<4: ax3.plot(time[i], filtered_r_load[i],lw=4) if i>5 and i<=7: ax3.plot(time[i], filtered_r_load[i],lw=4) if i>=2: ax3.plot(time[i], filtered_r_load[i]) ax4.plot(time[i], filtered_r_disp[i]) A = 5 totaltime = r_time totaltime.insert(A,list(sin_time)) totaltime = concotenateTime(totaltime) allLoads = filtered_r_load allLoads.insert(A,list(sin_load)) allDisp = filtered_r_disp allDisp.insert(A,list(sin_disp_orig)) # print len(totaltime) # exit() leg1=ax6.legend(loc=2) leg2=ax5.legend(loc=2) for i in range(len(totaltime)): ax1.plot(totaltime[i], allLoads[i]) for i in range(len(totaltime)): ax2.plot(totaltime[i], allDisp[i]) filecount+=1 # plt.figure(3) # plt.plot(sin_disp, filtered_sin_load) # plt.xlabel('disp,mm') # plt.ylabel('load,gf') # # plt.figure(4) # plt.plot(sin_time, filtered_sin_load) # plt.xlabel('time,s') # plt.ylabel('load,gf') filtered_r_load filtered_r_disp plt.show()