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    # Miscellaneous
    A collection of unrelated quality check functions.
    ## Index
    - [flagRange](#flagrange)
    - [flagSeasonalRange](#flagseasonalrange)
    - [flagIsolated](#flagisolated)
    - [flagPattern](#flagpattern)
    - [flagMissing](#flagmissing)
    - [clearFlags](#clearflags)
    - [forceFlags](#forceflags)
    ## flagRange
    ```
    flagRange(min, max)
    ```
    | parameter | data type | default value | description |
    | --------- | --------- | ------------- | ----------- |
    | min | float | | The upper bound for valid values |
    | max | float | | The lower bound for valid values |
    The function flags all values outside the closed interval
    $`[`$`min`, `max`$`]`$.
    ## flagSeasonalRange
    ```
    flagSeasonalRange(min, max, startmonth=1, endmonth=12, startday=1, endday=31)
    ```
    | parameter | data type | default value | description |
    | --------- | ----------- | ---- | ----------- |
    | min | float | | The upper bound for valid values |
    | max | float | | The lower bound for valid values |
    | startmonth | integer | `1` | The interval start month |
    | endmonth | integer | `12` | The interval end month |
    | startday | integer | `1` | The interval start day |
    | endday | integer | `31` | The interval end day |
    The function does the same as `flagRange`, but only if the timestamp of the
    data-point lies in a defined interval, which is build from days and months only.
    In particular, the *year* is not considered in the Interval.
    The left
    boundary is defined by `startmonth` and `startday`, the right boundary by `endmonth`
    and `endday`. Both boundaries are inclusive. If the left side occurs later
    in the year than the right side, the interval is extended over the change of
    year (e.g. an interval of [01/12, 01/03], will flag values in December,
    January and February).
    NOTE: Only works for time-series-like datasets.
    ## flagIsolated
    ```
    flagIsolated(window, gap_window, group_window)
    ```
    | parameter | data type | default value | description |
    |--------------|---------------------------------------------------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------|
    | gap_window | [offset string](docs/ParameterDescriptions.md#offset-strings) | | The minimum size of the gap before and after a group of valid values, which makes this group considered as isolated. See condition (2) and (3) |
    | group_window | [offset string](docs/ParameterDescriptions.md#offset-strings) | | The maximum size of an isolated group of valid data. See condition (1). |
    The function flags arbitrary large groups of values, if they are surrounded by sufficiently
    large data gaps. A gap is defined as group of missing and/or flagged values.
    A continuous group of values
    $`x_{k}, x_{k+1},...,x_{k+n}`$ with timestamps $`t_{k}, t_{k+1}, ..., t_{k+n}`$
    is considered to be isolated, if:
    1. $` t_{k+n} - t_{k} \le `$ `group_window`
    2. None of the values $` x_i, ..., x_{k-1} `$, with $`t_{k-1} - t_{i} \ge `$ `gap_window` is valid or unflagged
    3. None of the values $` x_{k+n+1}, ..., x_{j} `$, with $`t_{j} - t_{k+n+1} \ge `$ `gap_window` is valid or unflagged
    ## flagMissing
    ```
    flagMissing(nodata=NaN)
    ```
    | parameter | data type | default value | description |
    | --------- | ---------- | -------------- | ----------- |
    | nodata | any | `NAN` | A value that defines missing data |
    The function flags all values indicating missing data.
    ## flagPattern
    ```
    flagPattern(ref_datafield, sample_freq = '15 Min', method = 'dtw', min_distance = None)
    ```
    | parameter | data type | default value | description |
    |-----------------------|---------------------------------------------------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------|
    | ref_datafield | string | |Name of the reference datafield = "pattern" |
    | sample_freq | string | `"15 Min"` |Sample frequency to harmonize the data |
    | method | string | `"dtw "` |"dtw" for Dynamic Time Warping (DTW), "wavelet" for Wavelet Pattern Recognition Algorithm |
    | min_distance | float | `None` |For DTW - alogrithm: the minimum distance of two graphs in order to be classified as "different" |
    Implementation of the pattern recognition algorithms introduced in [Pattern Recognition](https://git.ufz.de/rdm-software/saqc/-/wikis/Pattern-Recognition).
    ## clearFlags
    ```
    clearFlags()
    ```
    The funcion removes all previously set flags.
    ## forceFlags
    ```
    forceFlags(flag)
    ```
    | parameter | data type | default value | description |
    | --------- | ----------- | ---- | ----------- |
    | flag | float/[flagging constant](docs/ParameterDescriptions.md#flagging-constants) | GOOD | The flag that is set unconditionally |
    The functions overwrites all previous set flags with the given flag.
    ## flagDummy
    ```
    flagDummy()
    ```
    Identity function, i.e. the function does nothing.
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