pyhampel/src/filtering_with_dev.py
def hampel_filter_dev_list_of_ts(ts_list: list, vals_col: str, time_col=None,
                             win_size: int = 30, num_dev: int = 3, center_win: bool = True):
    """
    Function returns a list of filtered dataframes, each consisting of original columns along with
    the Hampel filtered data, outlier values and boolean flags where outliers found.

    Parameters
    ----------
    ts_list: list
        List of time-series dataframes
    vals_col: str
        Single column name that contains values that need to be filtered.
    time_col: str
        Name of column that contains dates or timestamps
    win_size: int
        Size of sliding window for filtering.  Essentially the number of time steps to be considered when filtering.
    num_dev: int
        Number of standard deviations to consider when detecting values that would be considered outliers.
    center_win: Boolean
        Boolean value that determines whether the window is centered about the point being filtered?  Default=True.
        If False, point is at the leading edge (i.e. right side) of window  calculation.

    Returns
    -------
    Returns a list of filtered dataframes, each consisting of original columns along with
    the Hampel filtered data, outlier values and boolean flags where outliers found.
    """
    filtered_ts = []

    for ts in ts_list:
        filtered_ts.append(hampel_filter_with_dev_df(ts, vals_col, time_col, win_size, num_dev, center_win))
    return filtered_ts

    Function returns a list of filtered dataframes, each consisting of original columns along with
    the Hampel filtered data, outlier values and boolean flags where outliers found.

    Parameters
    ----------
    ts_list: list
        List of time-series dataframes
    vals_col: str
        Single column name that contains values that need to be filtered.
    time_col: str
        Name of column that contains dates or timestamps
    win_size: int
        Size of sliding window for filtering.  Essentially the number of time steps to be considered when filtering.
    num_dev: int
        Number of standard deviations to consider when detecting values that would be considered outliers.
    center_win: Boolean
        Boolean value that determines whether the window is centered about the point being filtered?  Default=True.
        If False, point is at the leading edge (i.e. right side) of window  calculation.

    Returns
    -------
    Returns a list of filtered dataframes, each consisting of original columns along with
    the Hampel filtered data, outlier values and boolean flags where outliers found.
    
Function returns a list of filtered dataframes, each consisting of original columns along with
the Hampel filtered data, outlier values and boolean flags where outliers found.

Parameters

list
List of time-series dataframes
vals_col: str
Single column name that contains values that need to be filtered.
time_col: str
Name of column that contains dates or timestamps
win_size: int
Size of sliding window for filtering.  Essentially the number of time steps to be considered when filtering.
num_dev: int
Number of standard deviations to consider when detecting values that would be considered outliers.
center_win: Boolean
Boolean value that determines whether the window is centered about the point being filtered.  Default=True.
If False, point is at the leading edge  of window  calculation.

Returns

Returns a list of filtered dataframes, each consisting of original columns along with
the Hampel filtered data, outlier values and boolean flags where outliers found.
Function returns a list of filtered dataframes each consisting of original columns along with the Hampel filtered data outlier values and boolean flags where outliers found . Parameters list List of time - series dataframes vals_col : str Single column name that contains values that need to be filtered . time_col : str Name of column that contains dates or timestamps win_size : int Size of sliding window for filtering . Essentially the number of time steps to be considered when filtering . num_dev : int Number of standard deviations to consider when detecting values that would be considered outliers . center_win : Boolean Boolean value that determines whether the window is centered about the point being filtered . Default = True . If False point is at the leading edge of window calculation . Returns Returns a list of filtered dataframes each consisting of original columns along with the Hampel filtered data outlier values and boolean flags where outliers found .
def hampel_filter_dev_list_of_ts(ts_list: list, vals_col: str, time_col=None,
                             win_size: int = 30, num_dev: int = 3, center_win: bool = True):
    filtered_ts = []
    for ts in ts_list:
        filtered_ts.append(hampel_filter_with_dev_df(ts, vals_col, time_col, win_size, num_dev, center_win))
    return filtered_ts
def hampel_filter_dev_list_of_ts ( ts_list : list , vals_col : str , time_col = None , win_size : int = 30 , num_dev : int = 3 , center_win : bool = True ) : filtered_ts = [ ] for ts in ts_list : filtered_ts . append ( hampel_filter_with_dev_df ( ts , vals_col , time_col , win_size , num_dev , center_win ) ) return filtered_ts
Function returns a list of filtered dataframes, each consisting of original columns along with
the Hampel filtered data, outlier values and boolean flags where outliers found.
Function returns a list of filtered dataframes each consisting of original columns along with the Hampel filtered data outlier values and boolean flags where outliers found .
"""
    Function returns a list of filtered dataframes, each consisting of original columns along with
    the Hampel filtered data, outlier values and boolean flags where outliers found.

    Parameters
    ----------
    ts_list: list
        List of time-series dataframes
    vals_col: str
        Single column name that contains values that need to be filtered.
    time_col: str
        Name of column that contains dates or timestamps
    win_size: int
        Size of sliding window for filtering.  Essentially the number of time steps to be considered when filtering.
    num_dev: int
        Number of standard deviations to consider when detecting values that would be considered outliers.
    center_win: Boolean
        Boolean value that determines whether the window is centered about the point being filtered?  Default=True.
        If False, point is at the leading edge (i.e. right side) of window  calculation.

    Returns
    -------
    Returns a list of filtered dataframes, each consisting of original columns along with
    the Hampel filtered data, outlier values and boolean flags where outliers found.
    """
ts_list
list
vals_col
str
time_col
win_size
int
num_dev
int
center_win
bool