Better manual picks #7:

 - Now manual picks on filtered data
 - Data is now filtered while reading

rstevaluation/rstevaluation/files.py

@@ -12,7 +12,9 @@ import shutil
 import nptdms
 import numpy as np
 import uncertainties as unc
+from scipy.signal import medfilt
 from tqdm import tqdm
+from uncertainties import unumpy as unp
 
 from rstevaluation import data as rstdat
 
@@ -66,6 +68,19 @@ def convert(path, file_in, config):
     data['displacement'] = np.cumsum(data['time'][1]*data['velocity'])
 
     data['name'] = file_in
+    data['shear_smth_nom'] = medfilt(
+        data['shearstress'],
+        config.getint('parameters', 'smoothwindow')
+    )
+    data['shear_smth'] = unp.uarray(
+        data['shear_smth_nom'],
+        np.full_like(
+            data['shear_smth_nom'],
+            # estimated accuracy of machine (+-10 Pa)
+            # and error of sensor (0.5%)
+            np.sqrt(10**2 + (data['shear_smth_nom']*0.005)**2)
+        )
+    )
 
     print(file_in+' read')
     return data

rstevaluation/rstevaluation/picking.py

@@ -10,8 +10,6 @@ import matplotlib as mpl
 import matplotlib.pyplot as plt
 import numpy as np
 import uncertainties as unc
-from scipy.signal import medfilt
-from uncertainties import unumpy as unp
 
 
 # %%==================EVALUATION SHEAR CURVE DATA==============================
@@ -73,21 +71,6 @@ def eval_shearstress(cur_dat, config, review=None):
 
 def _auto_pick(cur_dat, config):
     """Helper function to automatically pick the points"""
-    # smoothing function: 1st#: window size, 2nd#: polynomial order
-    shear_smth = medfilt(
-        cur_dat['shearstress'],
-        config.getint('parameters', 'smoothwindow')
-    )
-    shear_smth = unp.uarray(
-        shear_smth,
-        np.full_like(
-            shear_smth,
-            # estimated accuracy of machine (+-10 Pa)
-            # and error of sensor (0.5%)
-            np.sqrt(10**2 + (shear_smth*0.005)**2)
-        )
-    )
-
     # Searching for velocity changes to define data range
     vel_above = np.nonzero(
         cur_dat['velocity'] > config.getfloat('parameters', 'velnoise')
@@ -98,16 +81,16 @@ def _auto_pick(cur_dat, config):
     start_2 = end_1+np.max(np.diff(vel_above))+10
     end_2 = vel_above[0][-1]
     # Peak friction
-    i_peak = np.argmax(shear_smth[start_1:end_1])+start_1
-    tau_peak = shear_smth[i_peak]
+    i_peak = np.argmax(cur_dat['shear_smth'][start_1:end_1])+start_1
+    tau_peak = cur_dat['shear_smth'][i_peak]
 
     # Dynamic friction
-    i_dyn = np.argmin(shear_smth[i_peak:end_1])+i_peak
-    tau_dyn = shear_smth[i_dyn]
+    i_dyn = np.argmin(cur_dat['shear_smth'][i_peak:end_1])+i_peak
+    tau_dyn = cur_dat['shear_smth'][i_dyn]
 
     # Static friction (reactivation)
-    i_stat = np.argmax(shear_smth[start_2:end_2])+start_2
-    tau_stat = shear_smth[i_stat]
+    i_stat = np.argmax(cur_dat['shear_smth'][start_2:end_2])+start_2
+    tau_stat = cur_dat['shear_smth'][i_stat]
 
     # Store picks in dictionary
     picks = dict()
@@ -153,7 +136,7 @@ def _manual_pick(cur_dat, config,
         while not goodPick:
             try:
                 (sel_dat, sel) = _select_data(cur_dat)
-                pdata = pick_func[pick](cur_dat['shearstress'][sel_dat])
+                pdata = pick_func[pick](cur_dat['shear_smth_nom'][sel_dat])
                 picked_val = unc.ufloat(
                     pdata, np.sqrt(10**2+pdata*.005**2)
                 )
@@ -163,7 +146,7 @@ def _manual_pick(cur_dat, config,
 
         ax.plot(
             cur_dat['displacement'][sel_dat],
-            cur_dat['shearstress'][sel_dat],
+            cur_dat['shear_smth_nom'][sel_dat],
             color=pick_col[pick]
         )
         plt.pause(0.001)
@@ -261,12 +244,9 @@ def _pick_base_plot(cur_dat, config):
         alpha=.5,
         label='2$\sigma$ accuracy'
     )
-    shear_smth = medfilt(
-        cur_dat['shearstress'], config.getint('parameters', 'smoothwindow')
-    )
     ax.plot(
         cur_dat['displacement'],
-        shear_smth,
+        cur_dat['shear_smth_nom'],
         ':',
         label='filtered data',
         color='C1'