otp.agg.generic#

generic(query_fun, bucket_delimiter=False, bucket_interval=0, bucket_units=None, bucket_time='end', bucket_end_condition=None, running=False, group_by=None, end_condition_per_group=False, boundary_tick_bucket='new')#

Generic aggregation. Aggregation logic is provided in query_fun parameter and this logic is applied for ticks in each bucket. Currently, this aggregation can be used only with .apply() method.

Note, that query_fun should return a Source object, assuming that resulted query have only one tick per bucket.

Also, query_fun could have additional parameters, which will be passed to query_fun during aggregation. Those parameters should be specified in .apply() as keyword arguments, ex: .apply(src, additional_param=1).

Parameters

query_fun (Callable) – Function that takes Source as a parameter, applies some aggregation logic to it and returns Source as a result. Note that currently only methods that support dynamic symbol change could be used in the provided function. For example, rename() can’t be used. If you try to use such methods here, you will get an error during runtime.
bucket_delimiter (bool, default=False) – When set to True an extra tick is created after each bucket. Also, an additional column, called DELIMITER, is added to output ticks. The extra tick has values of all fields set to the defaults (0,NaN,””), except the delimiter field, which is set to “D” All other ticks have the DELIMITER set to string zero “0”.
bucket_interval (int or Operation or OnetickParameter or symbol parameter, default=0) –
Determines the length of each bucket (units depends on bucket_units).

If Operation of bool type is passed, acts as bucket_end_condition.

Bucket interval can also be set with integer OnetickParameter or symbol parameter.
bucket_units (Optional[Literal['seconds', 'ticks', 'days', 'months', 'flexible']], default=None) –
Set bucket interval units.

By default, if bucket_units and bucket_end_condition not specified, set to seconds. If bucket_end_condition specified, then bucket_units set to flexible.

If set to flexible then bucket_end_condition must be set.

Note that seconds bucket unit doesn’t take into account daylight-saving time of the timezone, so you may not get expected results when using, for example, 24 * 60 * 60 seconds as bucket interval. In such case use days bucket unit instead. See example in onetick.py.agg.sum().
bucket_time (Literal['start', 'end'], default=end) –
Control output timestamp.
- start
  
  the timestamp assigned to the bucket is the start time of the bucket.
- end
  
  the timestamp assigned to the bucket is the end time of the bucket.
bucket_end_condition (condition, default=None) –
An expression that is evaluated on every tick. If it evaluates to “True”, then a new bucket is created. This parameter is only used if bucket_units is set to “flexible”.

Also can be set via bucket_interval parameter by passing Operation object.
running (bool, default=False) –
Aggregation will be calculated as sliding window. running and bucket_interval parameters determines when new buckets are created.
- running = True
  
  aggregation will be calculated in a sliding window.
  - bucket_interval = N (N > 0)
    
    Window size will be N. Output tick will be generated when tick “enter” window (arrival event) and when “exit” window (exit event)
  - bucket_interval = 0
    
    Left boundary of window will be bound to start time. For each tick aggregation will be calculated in [start_time; tick_t].
- running = False
  
  buckets partition the [query start time, query end time) interval into non-overlapping intervals of size bucket_interval (with the last interval possibly of a smaller size). If bucket_interval is set to 0 a single bucket for the entire interval is created.
  
  Note that in non-running mode OneTick unconditionally divides the whole time interval into specified number of buckets. It means that you will always get this specified number of ticks in the result, even if you have less ticks in the input data.
Default: False
group_by (list, str or expression, default=None) – When specified, each bucket is broken further into additional sub-buckets based on specified field values. If Operation is used then GROUP_{i} column is added. Where i is index in group_by list. For example, if Operation is the only element in group_by list then GROUP_0 field will be added.
end_condition_per_group (bool, default=False) –
Controls application of bucket_end_condition in groups.
- end_condition_per_group = True
  
  bucket_end_condition is applied only to the group defined by group_by
- end_condition_per_group = False
  
  bucket_end_condition applied across all groups
This parameter is only used if bucket_units is set to “flexible”.

When set to True, applies to all bucketing conditions. Useful, for example, if you need to specify group_by, and you want to group items first, and create buckets after that.
boundary_tick_bucket (Literal['new', 'previous'], default=new) –
Controls boundary tick ownership.
- previous
  
  A tick on which bucket_end_condition evaluates to “true” belongs to the bucket being closed.
- new
  
  tick belongs to the new bucket.
This parameter is only used if bucket_units is set to “flexible”

Note

Some functions may be not supported in query_fun. For example, join() and rename().

Examples

The simplest case, just copying some other aggregation logic:

>>> data = otp.Ticks({'A': [1, 2, 3]})
>>> def agg_fun(source):
...     return source.agg({'X': otp.agg.count()})
>>> data = otp.agg.generic(agg_fun).apply(data)
>>> otp.run(data)
        Time  X
0 2003-12-04  3

Passing parameters to aggregation function:

>>> data = otp.Ticks({'A': [1, 2, 1]})
>>> def count_values(source, value):
...     values, _ = source[source['A'] == value]
...     return values.agg({'count': otp.agg.count()})
>>> data = otp.agg.generic(count_values).apply(data, value=1)
>>> otp.run(data)
        Time  count
0 2003-12-04  2

Getting first 3 ticks from 5 milliseconds buckets:

>>> data = otp.Ticks({'A': list(range(10))})
>>> def agg_fun(source, n):
...     return source.first(n)
>>> data = otp.agg.generic(agg_fun, bucket_interval=0.005).apply(data, n=3)
>>> otp.run(data, start=otp.config.default_start_time, end=otp.config.default_start_time + otp.Milli(10))
                     Time  A
0 2003-12-01 00:00:00.005  0
1 2003-12-01 00:00:00.005  1
2 2003-12-01 00:00:00.005  2
3 2003-12-01 00:00:00.010  5
4 2003-12-01 00:00:00.010  6
5 2003-12-01 00:00:00.010  7