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Stream Stats

Particula-beta Index / Particula Beta / Data / Stream Stats

Auto-generated documentation for particula_beta.data.stream_stats module.

average_std

Show source in stream_stats.py:33

Calculate the average and standard deviation of data within a given 'stream' object over specified intervals.

This function takes a 'stream' object, which should contain time-series data, and computes the average and standard deviation of the data at intervals specified by 'average_interval'. If data.time is in seconds then the units of the interval are seconds (hour in hours etc). The results are returned as a new 'StreamAveraged' object containing the processed data.

Arguments

  • stream (object): The input stream object containing 'time' and 'data' arrays along with other associated metadata.
  • average_interval (float|int, optional): The time interval over which the averaging is to be performed.
  • new_time_array (np.ndarray, optional): An optional array of time points at which the average and standard deviation are computed. If not provided, a new time array is generated based on the start and end times within the 'stream.time' object.

Returns

  • StreamAveraged (object): An object of type 'StreamAveraged' containing the averaged data, time array, start and stop times, the standard deviation of the averaged data, and other metadata from the original 'stream' object.

The function checks for an existing 'new_time_array' and generates one if needed. It then calculates the average and standard deviation for each interval and constructs a 'StreamAveraged' object with the results and metadata from the original 'stream' object.

Signature

def average_std(
    stream: Stream,
    average_interval: Union[float, int] = 60,
    new_time_array: Optional[np.ndarray] = None,
) -> StreamAveraged: ...

See also

drop_masked

Show source in stream_stats.py:13

Drop rows where mask is false, and return data stream.

Args

stream : object data stream object mask : np.ndarray mask to apply to data stream

Returns

object stream object

Signature

def drop_masked(stream: Stream, mask: ignore) -> Stream: ...

See also

filtering

Show source in stream_stats.py:103

Filters the data of the given 'stream' object based on the specified bounds or specific value. The filtered data can be either dropped or replaced with a specified value. Note, not all parameters need to be specified, but at least one must be provided (top, bottom, value)

Arguments

  • stream (Stream): The input stream object containing 'data' and 'time' attributes.
  • bottom (float, optional): The lower bound for filtering data. Defaults to None.
  • top (float, optional): The upper bound for filtering data. Defaults to None.
  • value (float, optional): Specific value to filter from data. Defaults to None.
  • invert (bool): If True, inverts the filter criteria. Defaults to False.
  • clone (bool): If True, returns a copy of the 'stream' object, with filtered data. If False, modifies the 'stream' object in-place. Defaults to True.
  • replace_with (float|int, optional): Value to replace filtered-out data. Defaults to None.
  • drop (bool, optional): If True, filtered-out data points are dropped from the dataset. Defaults to False.
  • header (list, optional): The header of the data to filter on. This can same as calling Stream['header'] Defaults to None.

Returns

  • - Stream - The 'stream' object with data filtered as specified.

If 'drop' is True, 'replace_with' is ignored and filtered data points are removed from the 'stream' object. Otherwise, filtered data points are replaced with 'replace_with' value.

add specific data row to filter on

Signature

def filtering(
    stream: Stream,
    bottom: Optional[float] = None,
    top: Optional[float] = None,
    value: Optional[float] = None,
    invert: Optional[bool] = False,
    clone: Optional[bool] = True,
    replace_with: Optional[Union[float, int]] = None,
    drop: Optional[bool] = False,
    header: Optional[Union[list, int, str]] = None,
) -> Stream: ...

See also

remove_time_window

Show source in stream_stats.py:176

Remove a time window from a stream object.

Arguments

  • stream - The input stream object containing 'data' and 'time' attributes.
  • epoch_start - The start time of the time window to be removed.
  • epoch_end - The end time of the time window to be removed. If not provided, the time window is the closest time point to 'epoch_start'.

Returns

  • Stream - The 'stream' object with the specified time window removed.

Signature

def remove_time_window(
    stream: Stream,
    epoch_start: Union[float, int],
    epoch_end: Optional[Union[float, int]] = None,
) -> Stream: ...

See also

select_time_window

Show source in stream_stats.py:211

Keep only a specified time window in a stream object and remove all other data.

Arguments

  • stream - The input stream object containing 'data' and 'time' attributes.
  • epoch_start - The start time of the time window to be kept.
  • epoch_end - The end time of the time window to be kept. If not provided, only the closest time point to 'epoch_start' will be kept.

Returns

  • Stream - The stream object with only the specified time window retained.

Signature

def select_time_window(
    stream: Stream,
    epoch_start: Union[float, int],
    epoch_end: Optional[Union[float, int]] = None,
    clone: Optional[bool] = True,
) -> Stream: ...

See also

time_derivative_of_stream

Show source in stream_stats.py:250

Calculate the rate of change of the concentration PMF over time and return a new stream.

Uses a linear regression model to fit the slope over a time window. The edge cases are handled by using a smaller window size.

Arguments

  • pmf_fitted_stream - Stream object containing the fitted concentration PMF data.
  • window_size - Size of the time window for fitting the slope.

Returns

  • rate_of_change_stream - Stream object containing the rate of change of the concentration PMF.

Signature

def time_derivative_of_stream(
    stream: Stream, liner_slope_window_size: int = 12
) -> Stream: ...

See also