# util module¶

This module contains some small and useful utility functions and classes.

 util.resolutionToBinsize(resolution) Return the bin size from the resolution unit util.binsizeToResolution(binsize) Return the resolution unit from the bin size util.sorted_nicely(inputList) Sorts the given given list in the way that is expected. util.locate_significant_digit_after_decimal(value) Get location at which significant digit start after decimal util.kth_diag_indices(k, a) Get diagonal indices of 2D array ‘a’ offset by ‘k’ util.detectOutliers1D(points[, thresh]) Returns a boolean array with True if points are outliers and False otherwise. util.getRandomName([size, chars]) Random name generator util.MapNotFoundError(value) util.ResolutionNotFoundError(value)

## Exception classes¶

class MapNotFoundError(value)
class ResolutionNotFoundError(value)

## Small utility functions¶

exception MapNotFoundError(value)
exception ResolutionNotFoundError(value)
binsizeToResolution(binsize)

Return the resolution unit from the bin size

It is a convenient function to convert binsize into resolution unit. It has a support of base (b), kilobase (kb), megabase (mb) and gigabase (gb) unit. It also convert binsize to decimal resolution unit as shown below in examples.

Parameters: binsize (int) – bin size resolution – resolution unit str

Examples

>>> binsizeToResolution(1)
'1b'
>>> binsizeToResolution(10)
'10b'
>>> binsizeToResolution(10000)
'10kb'
>>> binsizeToResolution(100000)
'100kb'
>>> binsizeToResolution(125500)
'125.5kb'
>>> binsizeToResolution(1000000)
'1mb'
>>> binsizeToResolution(1634300)
'1.6343mb'

detectOutliers1D(points, thresh=3.5)

Returns a boolean array with True if points are outliers and False otherwise.

Parameters: points (numpy.ndarray) – An numobservations by numdimensions array of observations thresh (float) – The modified z-score to use as a threshold. Observations with a modified z-score (based on the median absolute deviation) greater than this value will be classified as outliers. outBool – A numobservations-length boolean array. numpy.ndarray

References

Boris Iglewicz and David Hoaglin (1993), “Volume 16: How to Detect and Handle Outliers”, The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.

detectOutliersMasked1D(points, thresh=3.5)

Returns a masked array where outliers are masked with preserved input mask.

Parameters: points (numpy.ma.ndarray) – An numobservations by numdimensions array of observations thresh (float) – The modified z-score to use as a threshold. Observations with a modified z-score (based on the median absolute deviation) greater than this value will be classified as outliers. maskArray – A numobservations-length masked array. numpy.ma.ndarray

References

Boris Iglewicz and David Hoaglin (1993), “Volume 16: How to Detect and Handle Outliers”, The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.

getRandomName(size=10, chars='abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789')

Random name generator

Parameters: size (int) – Number of alphabets in the name. name – Randomly generated name. str
kth_diag_indices(k, a)

Get diagonal indices of 2D array ‘a’ offset by ‘k’

Parameters: k (int) – Diagonal offset a (numpy.ndarray) – Input numpy 2D array indices – It contain indences of elements that are at the diagonal offset by ‘k’. tuple of two numpy.ndarray
locate_significant_digit_after_decimal(value)

Get location at which significant digit start after decimal

Parameters: value (float) – Input value value – Number of zeros after which digit start in a small decimal number. int
resolutionToBinsize(resolution)

Return the bin size from the resolution unit

It is a convenient function to convert resolution unit to binsize. It has a support of base (b), kilobase (kb), megabase (mb) and gigabase (gb) unit. It also convert decimal resolution unit as shown below in examples.

Parameters: resolution (str) – resolution in b, kb, mb or gb. binsize – bin size int

Examples

>>> resolutionToBinsize('1b')
1
>>> resolutionToBinsize('10b')
10
>>> resolutionToBinsize('1kb')
1000
>>> resolutionToBinsize('16kb')
16000
>>> resolutionToBinsize('1.23kb')
1230
>>> resolutionToBinsize('1.6mb')
1600000
>>> resolutionToBinsize('1.457mb')
1457000

sorted_nicely(inputList)

Sorts the given given list in the way that is expected.

Parameters: inputList (list) – The input list to be sorted. outputList – The sorted list list