Allow more numpy and pandas in sandbox

product/Wendelin/__init__.py

@@ -29,7 +29,7 @@
     Wendelin is a product containing general purpose tools to handle big 
     data manipulations.
 """
-from AccessControl import allow_module, allow_type
+from AccessControl import allow_module, allow_type, allow_class
 
 # we neeed to allow access to numpy's internal types
 import numpy as np
@@ -37,10 +37,21 @@ allow_module('numpy')
 for dtype in ('int8', 'int16', 'int32', 'int64', \
               'uint8', 'uint16', 'uint32', 'uint64', \
               'float16', 'float32', 'float64', \
-              'complex64', 'complex128',):
+              'complex64', 'complex128'):
   z = np.array([0,], dtype = dtype)
   allow_type(type(z[0]))
   allow_type(type(z))
+  
+  sz = np.array([(0,)], dtype = [('f0', dtype)])
+  allow_type(type(sz[0]))
+  allow_type(type(sz))
+  
+  rz = np.rec.array(np.array([(0,)], dtype = [('f0', dtype)]))
+  allow_type(type(rz[0]))
+  allow_type(type(rz))
+  
+sz = np.array([('2017-07-12T12:30:20',)], dtype=[('date', 'M8[s]')])
+allow_type(type(sz[0]['date']))
 
 allow_module('sklearn')
 allow_module('scipy')
@@ -53,13 +64,20 @@ allow_type(type(np.c_))
 
 import pandas as pd
 allow_type(pd.Series)
+allow_type(pd.Timestamp)
 allow_type(pd.DatetimeIndex)
 allow_type(pd.DataFrame)
 
+allow_class(pd.DataFrame)
+allow_class(pd.core.groupby.DataFrameGroupBy)
+
 # Modify 'safetype' dict in full_write_guard function
 # of RestrictedPython (closure) directly To allow
 # write access to ndarray and ZBigArray objects
 from RestrictedPython.Guards import full_write_guard
 full_write_guard.func_closure[1].cell_contents.__self__[np.ndarray] = True
+full_write_guard.func_closure[1].cell_contents.__self__[np.core.records.recarray] = True
 from wendelin.bigarray.array_zodb import ZBigArray
 full_write_guard.func_closure[1].cell_contents.__self__[ZBigArray] = True
+
+allow_type(ZBigArray)
\ No newline at end of file