Removed joblib.cached-sphinx workaround / Caching fix

- sphinx detection is not reliable, e.g. jupyter notebooks have sphinx
  imported at startup -> no caching from notebooks
- explicitly added cached functions via autofunction to docs
- some functions do not support memory cache, because memory cache can't
  handle non-hashable parameters -> introduced diskcacheNoFallback
  decorator for these functions

cache.py

@@ -15,36 +15,9 @@ try:
     else:
         cacheDir = user_cache_dir('pystencils')
     diskcache = Memory(cachedir=cacheDir, verbose=False).cache
+    diskcacheNoFallback = diskcache
 except ImportError:
     # fallback to in-memory caching if joblib is not available
     diskcache = memorycache(maxsize=64)
+    diskcacheNoFallback = lambda o: o
 
-
-# joblibs Memory decorator does not play nicely with sphinx autodoc (decorated functions do not occur in autodoc)
-# -> if this script is imported by sphinx we use functools instead
-import sys
-calledBySphinx = 'sphinx' in sys.modules
-if calledBySphinx:
-    diskcache = memorycache(maxsize=64)
-
-
-# ------------------------ Helper classes to JSON serialize sympy objects ----------------------------------------------
-
-
-class SympyJSONEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, sp.Basic):
-            return {"_type": "sp", "str": str(obj)}
-        else:
-            super(SympyJSONEncoder, self).default(obj)
-
-
-class SympyJSONDecoder(json.JSONDecoder):
-    def __init__(self, *args, **kwargs):
-        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
-    def object_hook(self, obj):
-        if '_type' in obj:
-            return sp.sympify(obj['str'])
-        else:
-            return obj
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