holoviews.operation.timeseries module#

class holoviews.operation.timeseries.RollingBase(*, center, min_periods, rolling_window, name)[source]#

Bases: Parameterized

Parameters shared between rolling and rolling_outlier_std.

Parameter Definitions

---

center = Boolean(default=True, label='Center')

Whether to set the x-coordinate at the center or right edge of the window.

min_periods = Integer(allow_None=True, inclusive_bounds=(True, True), label='Min periods')

Minimum number of observations in window required to have a value (otherwise result is NaN).

rolling_window = Integer(default=10, inclusive_bounds=(True, True), label='Rolling window')

The window size over which to operate.

class holoviews.operation.timeseries.resample(*, closed, function, label, rule, dynamic, group, input_ranges, link_inputs, streams, name)[source]#

Bases: Operation

Resamples a timeseries of dates with a frequency and function.

Parameter Definitions

---

Parameters inherited from:

holoviews.core.operation.Operation: group, dynamic, input_ranges, link_inputs, streams

closed = Selector(allow_None=True, label='Closed', names={}, objects=['left', 'right'])

Which side of bin interval is closed

function = Callable(default=<function mean at 0x10881a1f0>, label='Function')

Function for computing new values out of existing ones.

label = Selector(default='right', label='Label', names={}, objects=['right'])

The bin edge to label the bin with.

rule = String(default='D', label='Rule')

A string representing the time interval over which to apply the resampling

function(axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>)[source]#

Compute the arithmetic mean along the specified axis.

Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs.

Parameters#

aarray_like

Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.

axisNone or int or tuple of ints, optional

Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.

If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before.

dtypedata-type, optional

Type to use in computing the mean. For integer inputs, the default is float64; for floating point inputs, it is the same as the input dtype.

outndarray, optional

Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details. See ufuncs-output-type for more details.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

If the default value is passed, then keepdims will not be passed through to the mean method of sub-classes of ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.

wherearray_like of bool, optional

Elements to include in the mean. See ~numpy.ufunc.reduce for details.

Added in version 1.20.0.

Returns#

mndarray, see dtype parameter above

If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned.

See Also#

average : Weighted average std, var, nanmean, nanstd, nanvar

Notes#

The arithmetic mean is the sum of the elements along the axis divided by the number of elements.

Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue.

By default, float16 results are computed using float32 intermediates for extra precision.

Examples#

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> np.mean(a)
2.5
>>> np.mean(a, axis=0)
array([2., 3.])
>>> np.mean(a, axis=1)
array([1.5, 3.5])

In single precision, mean can be inaccurate:

>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.mean(a)
np.float32(0.54999924)

Computing the mean in float64 is more accurate:

>>> np.mean(a, dtype=np.float64)
0.55000000074505806 # may vary

Computing the mean in timedelta64 is available:

>>> b = np.array([1, 3], dtype="timedelta64[D]")
>>> np.mean(b)
np.timedelta64(2,'D')

Specifying a where argument:

>>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]])
>>> np.mean(a)
12.0
>>> np.mean(a, where=[[True], [False], [False]])
9.0

class holoviews.operation.timeseries.rolling(*, function, window_type, dynamic, group, input_ranges, link_inputs, streams, center, min_periods, rolling_window, name)[source]#

Bases: Operation, RollingBase

Applies a function over a rolling window.

Parameter Definitions

---

Parameters inherited from:

holoviews.operation.timeseries.RollingBase: center, min_periods, rolling_window

holoviews.core.operation.Operation: group, dynamic, input_ranges, link_inputs, streams

window_type = Selector(allow_None=True, label='Window type', names={}, objects=['boxcar', 'triang', 'blackman', 'hamming', 'bartlett', 'parzen', 'bohman', 'blackmanharris', 'nuttall', 'barthann', 'kaiser', 'gaussian', 'general_gaussian', 'slepian'])

The shape of the window to apply

function = Callable(default=<function mean at 0x10881a1f0>, label='Function')

The function to apply over the rolling window.

function(axis=None, dtype=None, out=None, keepdims=<no value>, *, where=<no value>)[source]#

Compute the arithmetic mean along the specified axis.

Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs.

Parameters#

aarray_like

Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted.

axisNone or int or tuple of ints, optional

Axis or axes along which the means are computed. The default is to compute the mean of the flattened array.

If this is a tuple of ints, a mean is performed over multiple axes, instead of a single axis or all the axes as before.

dtypedata-type, optional

Type to use in computing the mean. For integer inputs, the default is float64; for floating point inputs, it is the same as the input dtype.

outndarray, optional

Alternate output array in which to place the result. The default is None; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See ufuncs-output-type for more details. See ufuncs-output-type for more details.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

If the default value is passed, then keepdims will not be passed through to the mean method of sub-classes of ndarray, however any non-default value will be. If the sub-class’ method does not implement keepdims any exceptions will be raised.

wherearray_like of bool, optional

Elements to include in the mean. See ~numpy.ufunc.reduce for details.

Added in version 1.20.0.

Returns#

mndarray, see dtype parameter above

If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned.

See Also#

average : Weighted average std, var, nanmean, nanstd, nanvar

Notes#

The arithmetic mean is the sum of the elements along the axis divided by the number of elements.

Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for float32 (see example below). Specifying a higher-precision accumulator using the dtype keyword can alleviate this issue.

By default, float16 results are computed using float32 intermediates for extra precision.

Examples#

>>> import numpy as np
>>> a = np.array([[1, 2], [3, 4]])
>>> np.mean(a)
2.5
>>> np.mean(a, axis=0)
array([2., 3.])
>>> np.mean(a, axis=1)
array([1.5, 3.5])

In single precision, mean can be inaccurate:

>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.mean(a)
np.float32(0.54999924)

Computing the mean in float64 is more accurate:

>>> np.mean(a, dtype=np.float64)
0.55000000074505806 # may vary

Computing the mean in timedelta64 is available:

>>> b = np.array([1, 3], dtype="timedelta64[D]")
>>> np.mean(b)
np.timedelta64(2,'D')

Specifying a where argument:

>>> a = np.array([[5, 9, 13], [14, 10, 12], [11, 15, 19]])
>>> np.mean(a)
12.0
>>> np.mean(a, where=[[True], [False], [False]])
9.0

class holoviews.operation.timeseries.rolling_outlier_std(*, sigma, dynamic, group, input_ranges, link_inputs, streams, center, min_periods, rolling_window, name)[source]#

Bases: 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.

Parameter Definitions

---

Parameters inherited from:

holoviews.operation.timeseries.RollingBase: center, min_periods, rolling_window

holoviews.core.operation.Operation: group, dynamic, input_ranges, link_inputs, streams

sigma = Number(default=2.0, inclusive_bounds=(True, True), label='Sigma')

Minimum sigma before a value is considered an outlier.