From bac6d6f5ece8cbbea8e08f394b6b265a6cd614ef Mon Sep 17 00:00:00 2001
From: Darius Arnold <Darius.Arnold@ruhr-uni-bochum.de>
Date: Sat, 26 Aug 2017 17:22:28 +0200
Subject: [PATCH] [add] jackknife safety factor no longer hardcoded, GUI
 parameter added

---
 pylot/core/io/default_parameters.py | 8 +++++++-
 pylot/core/pick/autopick.py         | 3 ++-
 pylot/core/pick/utils.py            | 6 +++---
 3 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/pylot/core/io/default_parameters.py b/pylot/core/io/default_parameters.py
index a8e648b5..4a6e75b7 100644
--- a/pylot/core/io/default_parameters.py
+++ b/pylot/core/io/default_parameters.py
@@ -348,6 +348,11 @@ defaults = {'rootpath': {'type': str,
                              'value': 1.0,
                              'namestring': 'Wadati tolerance'},
 
+            'jackfactor': {'type': float,
+                             'tooltip': 'pick is removed if the variance of the subgroup with the pick removed is larger than the mean variance of all subgroups times safety factor',
+                             'value': 5.0,
+                             'namestring': 'Jackknife safety factor'},
+
             'WAscaling': {'type': (float, float, float),
                           'tooltip': 'Scaling relation (log(Ao)+Alog(r)+Br+C) of Wood-Anderson amplitude Ao [nm] \
                           If zeros are set, original Richter magnitude is calculated!',
@@ -481,5 +486,6 @@ settings_special_pick = {
         'minpercent',
         'zfac',
         'mdttolerance',
-        'wdttolerance']
+        'wdttolerance',
+        'jackfactor'],
 }
diff --git a/pylot/core/pick/autopick.py b/pylot/core/pick/autopick.py
index 0b60f4f7..5b8c4d96 100644
--- a/pylot/core/pick/autopick.py
+++ b/pylot/core/pick/autopick.py
@@ -41,6 +41,7 @@ def autopickevent(data, param, iplot=0, fig_dict=None, fig_dict_wadatijack=None,
     # parameter input file (usually autoPyLoT.in).
     wdttolerance = param.get('wdttolerance')
     mdttolerance = param.get('mdttolerance')
+    jackfactor = param.get('jackfactor')
     apverbose = param.get('apverbose')
     for n in range(len(data)):
         station = data[n].stats.station
@@ -80,7 +81,7 @@ def autopickevent(data, param, iplot=0, fig_dict=None, fig_dict_wadatijack=None,
 
     # quality control
     # median check and jackknife on P-onset times
-    jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, 1, fig_dict_wadatijack)
+    jk_checked_onsets = checkPonsets(all_onsets, mdttolerance, jackfactor, 1, fig_dict_wadatijack)
     #return jk_checked_onsets
     # check S-P times (Wadati)
     wadationsets = wadaticheck(jk_checked_onsets, wdttolerance, 1, fig_dict_wadatijack)
diff --git a/pylot/core/pick/utils.py b/pylot/core/pick/utils.py
index 3a5e2892..049d2d44 100644
--- a/pylot/core/pick/utils.py
+++ b/pylot/core/pick/utils.py
@@ -803,7 +803,7 @@ def checksignallength(X, pick, TSNR, minsiglength, nfac, minpercent, iplot=0, fi
     return returnflag
 
 
-def checkPonsets(pickdic, dttolerance, iplot=0, fig_dict=None):
+def checkPonsets(pickdic, dttolerance, jackfactor, iplot=0, fig_dict=None):
     '''
     Function to check statistics of P-onset times: Control deviation from
     median (maximum adjusted deviation = dttolerance) and apply pseudo-
@@ -840,9 +840,9 @@ def checkPonsets(pickdic, dttolerance, iplot=0, fig_dict=None):
         return
     # get pseudo variances smaller than average variances
     # (times safety factor), these picks passed jackknife test
-    ij = np.where(PHI_pseudo <= 5 * xjack)
+    ij = np.where(PHI_pseudo <= jackfactor * xjack)
     # these picks did not pass jackknife test
-    badjk = np.where(PHI_pseudo > 5 * xjack)
+    badjk = np.where(PHI_pseudo > jackfactor * xjack)
     badjkstations = np.array(stations)[badjk]
     print("checkPonsets: %d pick(s) did not pass jackknife test!" % len(badjkstations))
     print(badjkstations)