foundryts.functions.time_range（foundryts.functions.time_range）¶

foundryts.functions.time_range(start=None, end=None)¶

Returns a function that filters a single time series to the specified time-range.

Each time-range acts as an individual series. Setting a time-range for your query makes them more efficient as your query will only read the points in the time-range, instead of all the points in the time series. This is also useful for doing operations on intervals of time series.

Parameters:
start (int , str , datetime.datetime , optional) – Inclusive starting point of the time-range. Integers are interpreted as the number of nanoseconds. For more human-readable durations, you can provide a datetime.timedelta object or a string that will be parsed as a pandas.Timedelta. String inputs should follow the format recognized by pandas.to_timedelta, such as ‘1 day’, ‘1 hour’, ‘10 minutes’, or ’42s’. (default is the first point of the time series)
end (int , str , datetime.datetime , optional) – Exclusive end point of the time-range. Integers are interpreted as the number of nanoseconds. For more human-readable durations, you can provide a datetime.timedelta object or a string that will be parsed as a pandas.Timedelta. String inputs should follow the format recognized by pandas.to_timedelta, such as ‘1 day’, ‘1 hour’, ‘10 minutes’, or ’42s’. (default is the last point of the time series)
Returns: A function that accepts a single time series as input and returns the filtered time-range.
Return type: (FunctionNode) -> FunctionNode

Dataframe schema¶

Column name	Type	Description
timestamp	pandas.Timestamp	Timestamp of the point
value	float	str

Examples¶

>>> series = F.points((1, 1.0), (101, 2.0), (200, 4.0), (201, 8.0), name="series")
>>> series.to_pandas()
                    timestamp  value
0 1970-01-01 00:00:00.000000001    1.0
1 1970-01-01 00:00:00.000000101    2.0
2 1970-01-01 00:00:00.000000200    4.0
3 1970-01-01 00:00:00.000000201    8.0

>>> time_range = F.time_range(start=200, end=202)(series)
>>> time_range.to_pandas()
                      timestamp  value
0 1970-01-01 00:00:00.000000200    4.0
1 1970-01-01 00:00:00.000000201    8.0

>>> smaller_time_range = F.time_range(start=200, end=201)(series)
>>> smaller_time_range.to_pandas()
                    timestamp  value
0 1970-01-01 00:00:00.000000200    4.0

>>> unbounded_start_range = F.time_range(end=201)(series)
>>> unbounded_start_range.to_pandas()
                      timestamp  value
0 1970-01-01 00:00:00.000000001    1.0
1 1970-01-01 00:00:00.000000101    2.0
2 1970-01-01 00:00:00.000000200    4.0

>>> unbounded_end_range = F.time_range(start=101)(series)
>>> unbounded_end_range.to_pandas()
                      timestamp  value
0 1970-01-01 00:00:00.000000101    2.0
1 1970-01-01 00:00:00.000000200    4.0
2 1970-01-01 00:00:00.000000201    8.0

中文翻译¶

foundryts.functions.time_range¶

foundryts.functions.time_range(start=None, end=None)¶

返回一个函数，用于将单个时间序列（time series）过滤到指定的时间范围（time-range）。

每个时间范围作为一个独立的序列。为查询设置时间范围可以提高效率，因为查询只会读取该时间范围内的数据点，而非时间序列中的所有数据点。这对于对时间序列的区间执行操作也非常有用。

参数：
start (int , str , datetime.datetime , 可选) – 时间范围的起始点（包含）。整数被解释为纳秒数。如需更易读的持续时间，可提供 datetime.timedelta 对象或将被解析为 pandas.Timedelta 的字符串。字符串输入应遵循 pandas.to_timedelta 识别的格式，例如 '1 day'、'1 hour'、'10 minutes' 或 '42s'。（默认为时间序列的第一个数据点）
end (int , str , datetime.datetime , 可选) – 时间范围的结束点（不包含）。整数被解释为纳秒数。如需更易读的持续时间，可提供 datetime.timedelta 对象或将被解析为 pandas.Timedelta 的字符串。字符串输入应遵循 pandas.to_timedelta 识别的格式，例如 '1 day'、'1 hour'、'10 minutes' 或 '42s'。（默认为时间序列的最后一个数据点）
返回： 一个函数，接受单个时间序列作为输入，并返回过滤后的时间范围。
返回类型： (FunctionNode) -> FunctionNode

数据框模式（Dataframe schema）¶

列名	类型	描述
timestamp	pandas.Timestamp	数据点的时间戳
value	float	str

示例¶

>>> series = F.points((1, 1.0), (101, 2.0), (200, 4.0), (201, 8.0), name="series")
>>> series.to_pandas()
                    timestamp  value
0 1970-01-01 00:00:00.000000001    1.0
1 1970-01-01 00:00:00.000000101    2.0
2 1970-01-01 00:00:00.000000200    4.0
3 1970-01-01 00:00:00.000000201    8.0

>>> time_range = F.time_range(start=200, end=202)(series)
>>> time_range.to_pandas()
                      timestamp  value
0 1970-01-01 00:00:00.000000200    4.0
1 1970-01-01 00:00:00.000000201    8.0

>>> smaller_time_range = F.time_range(start=200, end=201)(series)
>>> smaller_time_range.to_pandas()
                    timestamp  value
0 1970-01-01 00:00:00.000000200    4.0

>>> unbounded_start_range = F.time_range(end=201)(series)
>>> unbounded_start_range.to_pandas()
                      timestamp  value
0 1970-01-01 00:00:00.000000001    1.0
1 1970-01-01 00:00:00.000000101    2.0
2 1970-01-01 00:00:00.000000200    4.0

>>> unbounded_end_range = F.time_range(start=101)(series)
>>> unbounded_end_range.to_pandas()
                      timestamp  value
0 1970-01-01 00:00:00.000000101    2.0
1 1970-01-01 00:00:00.000000200    4.0
2 1970-01-01 00:00:00.000000201    8.0