# resistics.time.clean module¶

resistics.time.clean.groupConsecutive(vals: numpy.ndarray, stepsize: int = 1)[source]

Takes an array of values and splits it into consecutive sections of stepsize

In general, the stepsize is 1.

Parameters
valsnp.ndarray

A set of values to split into consecutive sections

stepsizeint

The stepsize between values that means they are consecutive

Examples

An array of [1,2,3,5,6,7,10,12,13] would be split into consecutive sections [1,2,3], [5,6,7], [10], [12,13]

resistics.time.clean.removeNans(data: Dict)[source]

Remove NaNs in the data

This function finds NaNs in the data and tries to fill them in with better data i.e. interpolated data or some such.

Parameters

Dictionary of data with channel as key and a np.ndarray as value

Returns
Dict

Dictionary of data with channel as key and a np.ndarray as value (with zero stretches removed)

resistics.time.clean.removeNansSingle(data)[source]

Remove NaNs in a data array

This function finds NaNs in the np.ndarray and tries to fill them in with better data i.e. interpolated data or some such.

Parameters
datanp.ndarray

Array of data

Returns
np.ndarray

Array of data with zeros removed

resistics.time.clean.removeZeros(data: Dict)[source]

Remove a stretch of zeros in the data

This function finds a stretch of zeros and tries to fill them in with better data i.e. interpolated data or some such.

Parameters

Dictionary of data with channel as key and a np.ndarray as value

Returns
Dict

Dictionary of data with channel as key and a np.ndarray as value (with zero stretches removed)

resistics.time.clean.removeZerosSingle(data: numpy.ndarray) → numpy.ndarray[source]

Remove a stretch of zeros in a data array

This function finds a stretch of zeros and tries to fill them in with better data i.e. interpolated data or some such.

Parameters
datanp.ndarray

Array of data

Returns
np.ndarray

Array of data with zeros removed