Source code for holoviews.operation.timeseries

import param
import numpy as np
import pandas as pd

from ..core import Operation, Element
from import PandasInterface
from ..element import Scatter

[docs]class RollingBase(param.Parameterized): """ Parameters shared between `rolling` and `rolling_outlier_std`. """ center = param.Boolean(default=True, doc=""" Whether to set the x-coordinate at the center or right edge of the window.""") min_periods = param.Integer(default=None, doc=""" Minimum number of observations in window required to have a value (otherwise result is NaN).""") rolling_window = param.Integer(default=10, doc=""" The window size over which to operate.""") def _roll_kwargs(self): return {'window': self.p.rolling_window, 'center':, 'min_periods': self.p.min_periods}
[docs]class rolling(Operation,RollingBase): """ Applies a function over a rolling window. """ window_type = param.ObjectSelector(default=None, objects=['boxcar', 'triang', 'blackman', 'hamming', 'bartlett', 'parzen', 'bohman', 'blackmanharris', 'nuttall', 'barthann', 'kaiser', 'gaussian', 'general_gaussian', 'slepian'], doc="The shape of the window to apply") function = param.Callable(default=np.mean, doc=""" The function to apply over the rolling window.""") def _process_layer(self, element, key=None): xdim = element.kdims[0].name df = PandasInterface.as_dframe(element) df = df.set_index(xdim).rolling(win_type=self.p.window_type, **self._roll_kwargs()) if self.p.window_type is None: rolled = df.apply(self.p.function) else: if self.p.function is np.mean: rolled = df.mean() elif self.p.function is np.sum: rolled = df.sum() else: raise ValueError("Rolling window function only supports " "mean and sum when custom window_type is supplied") return element.clone(rolled.reset_index()) def _process(self, element, key=None): return, Element)
[docs]class resample(Operation): """ Resamples a timeseries of dates with a frequency and function. """ closed = param.ObjectSelector(default=None, objects=['left', 'right'], doc="Which side of bin interval is closed") function = param.Callable(default=np.mean, doc=""" Function for computing new values out of existing ones.""") label = param.ObjectSelector(default='right', doc=""" The bin edge to label the bin with.""") rule = param.String(default='D', doc=""" A string representing the time interval over which to apply the resampling""") def _process_layer(self, element, key=None): df = PandasInterface.as_dframe(element) xdim = element.kdims[0].name resample_kwargs = {'rule': self.p.rule, 'label': self.p.label, 'closed': self.p.closed} df = df.set_index(xdim).resample(**resample_kwargs) return element.clone(df.apply(self.p.function).reset_index()) def _process(self, element, key=None): return, Element)
[docs]class rolling_outlier_std(Operation, RollingBase): """ Detect outliers using the standard deviation within a rolling window. Outliers are the array elements outside `sigma` standard deviations from the smoothed trend line, as calculated from the trend line residuals. The rolling window is controlled by parameters shared with the `rolling` operation via the base class RollingBase, to make it simpler to use the same settings for both. """ sigma = param.Number(default=2.0, doc=""" Minimum sigma before a value is considered an outlier.""") def _process_layer(self, element, key=None): ys = element.dimension_values(1) # Calculate the variation in the distribution of the residual avg = pd.Series(ys).rolling(**self._roll_kwargs()).mean() residual = ys - avg std = pd.Series(residual).rolling(**self._roll_kwargs()).std() # Get indices of outliers with np.errstate(invalid='ignore'): outliers = (np.abs(residual) > std * self.p.sigma).values return element[outliers].clone(new_type=Scatter) def _process(self, element, key=None): return, Element)