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

RSTpicking/RST_Func.py

@@ -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):

RSTpicking/RST_main.py

@@ -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============================================