py2/h2o_ray.py
def frames(self, key=None, timeoutSecs=60, **kwargs):
    if not (key is None or isinstance(key, (basestring, Key))):
        raise Exception("frames: key should be string or Key type %s %s" % (type(key), key))

    params_dict = {
        'find_compatible_models': 0,
        'row_offset': 0, # is offset working yet?
        'row_count': 5,
    }
    '''
    Return a single Frame or all of the Frames in the h2o cluster.  The
    frames are contained in a list called "frames" at the top level of the
    result.  Currently the list is unordered.
    TODO:
    When find_compatible_models is implemented then the top level 
    dict will also contain a "models" list.
    '''
    h2o_methods.check_params_update_kwargs(params_dict, kwargs, 'frames', False)
    
    # key can be type Key? (from h2o_xl) str(key) should return
    if key:
        if isinstance(key, Key):
            keyStr = key.frame
        else:
            keyStr = key
        result = self.do_json_request('3/Frames.json/' + keyStr, timeout=timeoutSecs, params=params_dict)
    else:
        result = self.do_json_request('3/Frames.json', timeout=timeoutSecs, params=params_dict)
    return result
def frames(self, key=None, timeoutSecs=60, **kwargs):
    if not (key is None or isinstance(key, (basestring, Key))):
        raise Exception("frames: key should be string or Key type %s %s" % (type(key), key))

    params_dict = {
        'find_compatible_models': 0,
        'row_offset': 0, # is offset working yet?
        'row_count': 5,
    }
    '''
    Return a single Frame or all of the Frames in the h2o cluster.  The
    frames are contained in a list called "frames" at the top level of the
    result.  Currently the list is unordered.
    TODO:
    When find_compatible_models is implemented then the top level 
    dict will also contain a "models" list.
    '''
    h2o_methods.check_params_update_kwargs(params_dict, kwargs, 'frames', False)
    
    # key can be type Key? (from h2o_xl) str(key) should return
    if key:
        if isinstance(key, Key):
            keyStr = key.frame
        else:
            keyStr = key
        result = self.do_json_request('3/Frames.json/' + keyStr, timeout=timeoutSecs, params=params_dict)
    else:
        result = self.do_json_request('3/Frames.json', timeout=timeoutSecs, params=params_dict)
    return result
def frames ( self , key = None , timeoutSecs = 60 , * * kwargs ) : if not ( key is None or isinstance ( key , ( basestring , Key ) ) ) : raise Exception ( "frames: key should be string or Key type %s %s" % ( type ( key ) , key ) ) params_dict = { 'find_compatible_models' : 0 , 'row_offset' : 0 , # is offset working yet? 'row_count' : 5 , } h2o_methods . check_params_update_kwargs ( params_dict , kwargs , 'frames' , False ) # key can be type Key? (from h2o_xl) str(key) should return if key : if isinstance ( key , Key ) : keyStr = key . frame else : keyStr = key result = self . do_json_request ( '3/Frames.json/' + keyStr , timeout = timeoutSecs , params = params_dict ) else : result = self . do_json_request ( '3/Frames.json' , timeout = timeoutSecs , params = params_dict ) return result
Return a single Frame or all of the Frames in the h2o cluster.  The
    frames are contained in a list called "frames" at the top level of the
    result.  Currently the list is unordered.
    TODO:
    When find_compatible_models is implemented then the top level 
    dict will also contain a "models" list.
Return a single Frame or all of the Frames in the h2o cluster . The frames are contained in a list called frames at the top level of the result . Currently the list is unordered . TODO : When find_compatible_models is implemented then the top level dict will also contain a models list .