Commit 4260ec45 by Michael Rudolf

### Removed unnecessary modules and fixed small bug due to missing ','

parent dc779628
 ... ... @@ -10,16 +10,12 @@ import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import pandas as pd import glob import shutil import codecs import csv from os.path import isfile, join, basename, splitext import os from nptdms import TdmsFile import matplotlib.pyplot as plt from scipy import signal from pylab import * from scipy import stats ... ... @@ -105,7 +101,6 @@ def eval_shearstress(R, var): # %===================SMOOTHING FUCTION======================================== def savitzky_golay(y, window_size, order, deriv=0, rate=1): import numpy as np from math import factorial try: ... ... @@ -122,7 +117,7 @@ def savitzky_golay(y, window_size, order, deriv=0, rate=1): b = np.mat([[k**i for i in ord_rng] for k in range(-half_w, half_w+1)]) m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv) # pad the signal at the extremes with values taken from the signal itself firstvals = y[0] - np.abs(y[1:half_w+1][::-1] - y[0]) firstvals = y[0]-np.abs(y[1:half_w+1][::-1] - y[0]) lastvals = y[-1]+np.abs(y[-half_w-1:-1][::-1] - y[-1]) y = np.concatenate((firstvals, y, lastvals)) return np.convolve(m[::-1], y, mode='valid') ... ... @@ -141,11 +136,11 @@ def rst_analmut(x, y): M[k, j] = (y[k+j]-y[k])/(x[k+j]-x[k]) # calculate slope/ friction j += 1 k += 1 M[M == inf] = NaN # set inf to Nan M[M == -inf] = NaN # set -inf to Nan M[M == 0] = NaN # set 0 to Nan M[M < 0] = NaN # set <0 to Nan M[M > 1] = NaN # set 0 to Nan M[M == np.inf] = np.nan # set inf to Nan M[M == -np.inf] = np.nan # set -inf to Nan M[M == 0] = np.nan # set 0 to Nan M[M < 0] = np.nan # set <0 to Nan M[M > 1] = np.nan # set 0 to Nan M_avg, M_std = stats.norm.fit(M[~np.isnan(M)]) # mean and standard deviation for k in range(0, n-1): ... ... @@ -154,9 +149,9 @@ def rst_analmut(x, y): j = j+1 k = k+1 # calculation of cohesions (y axis intercept): C[C == inf] = NaN # set inf to Nan C[C == -inf] = NaN # set -inf to Nan C[C == 0.0] = NaN # set 0 to Nan C[C == np.inf] = np.nan # set inf to Nan C[C == -np.inf] = np.nan # set -inf to Nan C[C == 0.0] = np.nan # set 0 to Nan C_avg, C_std = stats.norm.fit(C[~np.isnan(C)]) # mean and standard deviation fric_mut = (M_avg, M_std, C_avg, C_std) data_mut = (M, C) ... ... @@ -267,7 +262,7 @@ def plothist(path, name, strength, data_mut): # ==============FRICTION COEFFICIENT======================== axrow[0].hist(coef[~np.isnan(coef)], bins=nbins, normed=True, density=True, color='royalblue', edgecolor='black') lnspc = np.linspace(np.nanmin(coef), np.nanmax(coef), len(coef)) ... ... @@ -292,7 +287,7 @@ def plothist(path, name, strength, data_mut): # ==============COHESION================================ axrow[1].hist(coh[~np.isnan(coh)], bins=nbins, normed=True, density=True, color='royalblue', edgecolor='black') statscoh = stats.norm.fit(coh[~np.isnan(coh)]) ... ... @@ -339,12 +334,12 @@ def plotts(path, name, ts, sigma_sort, var): for i in range(0, t): sigma_legend = int(np.sum(sigma_sort[i*3:(i*3)+3])/3) plt.plot(ts.iloc[:, 0], np.zeros(len(ts.iloc[:, 0])), linewidth=0.5 color=linecolor[i+1] linewidth=0.5, color=linecolor[i+1], label=str(sigma_legend)+' Pa') plt.plot(ts.iloc[:, 0], ts.iloc[:, i*3+1:(i+1)*3+1] linewidth=0.5 ts.iloc[:, i*3+1:(i+1)*3+1], linewidth=0.5, color=linecolor[i+1]) plt.legend(fontsize=8, facecolor='w', ... ... @@ -371,7 +366,7 @@ def saveTS(path, name, ts): index=None, sep='\t', mode='w', na_rep='NaN') # write to txt file na_rep='np.nan') # write to txt file def saveStrength(path, name, strength): ... ...
 ... ... @@ -8,20 +8,7 @@ Created on Mon Jul 23 14:32:17 2018 # %%===========================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 import shutil import pickle from nptdms import TdmsFile import collections import itertools from operator import itemgetter import RST_Func # %%==========================NAMES============================================ ... ...
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