otp.agg.min#

min(column, running=False, all_fields=False, bucket_interval=0, bucket_time='end', bucket_units=None, bucket_end_condition=None, end_condition_per_group=False, boundary_tick_bucket='new', group_by=None, time_series_type='event_ts', large_ints=False, null_int_val=0)#

Return minimum value of input column

Parameters

column (str or Column) – column to be aggregated
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
all_fields (Union[bool, str], default=False) –
- all_fields = True
  
  output ticks include all fields from the input ticks
  - running = True
  an output tick is created only when a tick enters the sliding window
  - running = False
  fields of first tick in bucket will be used
- all_fields = False and running = True
  
  output ticks are created when a tick enters or leaves the sliding window.
- all_fields = “when_ticks_exit_window” and running = True
  
  output ticks are generated only for exit events, but all attributes from the exiting tick are copied over to the output tick and the aggregation is added as another attribute.
bucket_interval (int or Operation or OnetickParameter or symbol parameter or datetime offset object, 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 be set via datetime offset objects like otp.Second, otp.Minute, otp.Hour, otp.Day, otp.Month. In this case you could omit setting bucket_units parameter.

Bucket interval can also be set with integer OnetickParameter or symbol parameter.
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_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_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.
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”
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.
time_series_type (Literal['event_ts', 'state_ts'], default=event_ts) –
Controls initial value for each bucket
- event_ts
  
  only ticks from current bucket used for calculations
- state_ts
  - if there is a tick in bucket with timestamp = bucket start
    
    only ticks in bucket used for calculation max value
  - else
    
    latest tick from previous bucket included in current bucket
large_ints (bool or onetick.py.adaptive, default=False) –
This parameter should be set if the input field of this aggregation may contain integer values that consist of 15 digits or more.

Such large integer values cannot be represented by the double type without precision errors and thus require special handling.

If set to True , the input field is expected to be a 64-bit integer number. The output field will also have 64-bit integer type. When no tick belongs to a given time bucket, the output value is set to a minimum of 64-bit integer.

When this parameter set to onetick.py.adaptive, the aggregation behaves the same way as when this parameter is set to True when the input field is a 64-bit integer type, and the same way as when this parameter is set to False when the input field is not a 64-bit integer type.
null_int_val (int, default=0) – The value of this parameter is considered to be the equivalent of NaN when large_ints is set to True or when large_ints is set to onetick.py.adaptive and the input field is a 64-bit integer type.

Examples

>>> data = otp.Ticks(X=[1, 2, 3, 4])
>>> data = data.agg({'RESULT': otp.agg.min('X')})
>>> otp.run(data)
        Time  RESULT
0 2003-12-04       1