work around change in pandas 0.20

* tests/core/ltlcross4.test: Replace describe() by agg() to work
around a backward incompatible change in pandas 0.20.

tests/core/ltlcross4.test

 #!/bin/sh
 # -*- coding: utf-8 -*-
-# Copyright (C) 2012, 2013, 2014 Laboratoire de Recherche et
+# Copyright (C) 2012-2014, 2017 Laboratoire de Recherche et
 # Développement de l'Epita (LRDE).
 #
 # This file is part of Spot, a model checking library.
@@ -40,49 +40,35 @@ cat >test.py <<EOF
 import sys
 try:
   import pandas
+  import numpy as np
 except ImportError:
   sys.exit(77)
 
 x = pandas.read_csv("output.csv")
+# We used to call describe() instead of agg(),
+# but the output of this function was changed
+# in pandas 0.20.
 print(x.filter(('formula', 'tool',
-                'states', 'transitions')).groupby('tool').describe())
+                'states', 'transitions')).\
+        groupby('tool').\
+        agg([np.mean, np.std, np.min, np.max]))
 EOF
 
 # will exit 77 if panda is not installed
 $PYTHON test.py >out.1
 
 # remove trailing whitespace from pandas' output,
-# and limit to 26 lines, because Pandas 0.13 adds
+# and limit to 6 lines, because Pandas 0.13 adds
 # the size of the dataframe afterwards.
-sed 's/[ \t]*$//g;26q' <out.1 > py.out
+sed 's/[ \t]*$//g;6q' <out.1 > py.out
 
 cat >expected <<EOF
-                      states  transitions
+             states                     transitions
+               mean       std amin amax        mean       std amin amax
 tool
-ltl2tgba any count  4.000000     4.000000
-             mean   2.250000    10.000000
-             std    0.957427     5.163978
-             min    1.000000     4.000000
-             25%    1.750000     7.000000
-             50%    2.500000    10.000000
-             75%    3.000000    13.000000
-             max    3.000000    16.000000
-ltl2tgba det count  4.000000     4.000000
-             mean   2.250000     9.250000
-             std    0.957427     4.573474
-             min    1.000000     4.000000
-             25%    1.750000     6.250000
-             50%    2.500000     9.500000
-             75%    3.000000    12.500000
-             max    3.000000    14.000000
-ltl2tgba sma count  4.000000     4.000000
-             mean   2.250000     9.250000
-             std    0.957427     4.573474
-             min    1.000000     4.000000
-             25%    1.750000     6.250000
-             50%    2.500000     9.500000
-             75%    3.000000    12.500000
-             max    3.000000    14.000000
+ltl2tgba any   2.25  0.957427    1    3       10.00  5.163978    4   16
+ltl2tgba det   2.25  0.957427    1    3        9.25  4.573474    4   14
+ltl2tgba sma   2.25  0.957427    1    3        9.25  4.573474    4   14
 EOF
 
 diff py.out expected