@@ -19,9 +19,9 @@ associated with "missing" data. The missing data indicator (`np.nan` by default)
, can be altered to any other value by passing this new value to the
parameter `nodata`.
| parameter | description |
| ------ | ------ |
| nodata | Value. (Default = np.nan). Any value, that shall indicate missing data in the passed dataseries. |
| parameter | data format | description |
| ------ | ------ | ------ |
| nodata | any Value. (Default = np.nan). | Any value, that shall indicate missing data in the passed dataseries. (If not np.nan, evaluation will be performed by `nodata == data`) |
## sesonalRange
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@@ -93,18 +93,18 @@ outliers, but also plateau-ish value courses.
The implementation is a time-window based version of an outlier test from the
UFZ Python library, that can be found [here](https://git.ufz.de/chs/python/blob/master/ufz/level1/spike.py).
| parameter | description |
| ------ | ------ |
| thresh | Float. <br/> Minimum jump margin for spikes. See condition (1). |
| tolerance | Float. <br/> Range of area, containing al "valid return values". See condition (2). |
| window_size | Offset String. <br/> An offset string, denoting the maximal length of "spikish" value courses. See condition (3). |
| parameter | data format | description |
| ------ | ------ | ------ |
| thresh | Float. | Minimum jump margin for spikes. See condition (1). |
| tolerance | Float. | Range of area, containing al "valid return values". See condition (2). |
| window_size | Offset String. | An offset string, denoting the maximal length of "spikish" value courses. See condition (3). |
3. The dataset, $`X_k`$, surrounding $`x_{k}`$, within `noise_window_size` range,
3. The dataset, $`X_k`$, surrounding $`x_{k}`$, within `noise_window_range` range,
but excluding $`x_{k}`$, is not too noisy. Wheras the noisyness gets measured
by `noise_statistic`:
*`noise_statistic`$`(X_k) <`$ `noise_barrier`
NOTE, that the derivative is calculated after applying a savitsky-golay filter
to $`x`$.
This Function is a generalization of the Spectrum based Spike flagging
mechanism as presented in:
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@@ -139,13 +141,15 @@ doi:10.2136/vzj2012.0097.
All parameters default to the values given there.
| parameter | description |
| ------ | ------ |
| raise_factor | Float. (Default=0.15). <br/> Minimum change margin for a datapoint to become a candidate for a spike. See condition (1). |
| dev_cont_factor | Float. (Default=0.2). <br/> See condition (2). |
| noise_barrier| Float. (Default=1). <br/> Upper bound for noisyness of data surrounding potential spikes. See condition (3).|
| noise_window_size| Offset String. (Default='12h'). <br/> Size of the timewindow of the "surrounding" data of a potential spike. See condition (3). |
| noise_statistic| String. (Default="CoVar"). <br/> Operator to calculate noisyness of data, surrounding potential spike. Either "Covar" (=Coefficient od Variation) or "rvar" (=relative Variance).|
| parameter | data format | description |
| ------ | ------ | ------ |
| raise_factor | Float. (Default=0.15). | Minimum change margin for a datapoint to become a candidate for a spike. See condition (1). |
| dev_cont_factor | Float. (Default=0.2). | See condition (2). |
| noise_barrier| Float. (Default=1). | Upper bound for noisyness of data surrounding potential spikes. See condition (3).|
| noise_window_range| Offset String. (Default='12h'). | Range of the timewindow of the "surrounding" data of a potential spike. See condition (3). |
| noise_statistic| String. (Default="CoVar"). | Operator to calculate noisyness of data, surrounding potential spike. Either "Covar" (=Coefficient od Variation) or "rvar" (=relative Variance).|
| smooth_poly_order| Integer. | Order of the polynomial fit, applied for smoothing|
| filter_window_size | Offset String. (Default=None) | Range of the smoothing window. The default value (='None') results in a window range, equalling 3 times the sampling rate and thus including always 3 values in a smoothing window. |
