breaks - Partitioning a scale for readability

All scales have a means by which the values that are mapped onto the scale are interpreted. Numeric digital scales put out numbers for direct interpretation, but most scales cannot do this. What they offer is named markers/ticks that aid in assessing the values e.g. the common odometer will have ticks and values to help gauge the speed of the vehicle.

The named markers are what we call breaks. Properly calculated breaks make interpretation straight forward. These functions provide ways to calculate good(hopefully) breaks.

class mizani.breaks.breaks_log(n: int = 5, base: float = 10)

Integer breaks on log transformed scales

Parameters:

nint

Desired number of breaks

baseint

Base of logarithm

Examples

>>> x = np.logspace(3, 6)
>>> limits = min(x), max(x)
>>> breaks_log()(limits)
array([     1000,    10000,   100000,  1000000])
>>> breaks_log(2)(limits)
array([  1000, 100000])
>>> breaks_log()([0.1, 1])
array([0.1, 0.3, 1. , 3. ])

__call__(limits: TupleFloat2) → NDArrayFloat

Compute breaks

Parameters:

limitstuple

Minimum and maximum values

Returns:

outarray_like

Sequence of breaks points

class mizani.breaks.breaks_symlog

Breaks for the Symmetric Logarithm Transform

Parameters:

nint

Desired number of breaks

baseint

Base of logarithm

Examples

>>> limits = (-100, 100)
>>> breaks_symlog()(limits)
array([-100,  -10,    0,   10,  100])

__call__(limits: TupleFloat2) → NDArrayFloat

Call self as a function.

class mizani.breaks.minor_breaks(n: int = 1)

Compute minor breaks

This is the naive method. It does not take into account the transformation.

Parameters:

nint

Number of minor breaks between the major breaks.

Examples

>>> major = [1, 2, 3, 4]
>>> limits = [0, 5]
>>> minor_breaks()(major, limits)
array([0.5, 1.5, 2.5, 3.5, 4.5])
>>> minor_breaks()([1, 2], (1, 2))
array([1.5])

More than 1 minor break.

>>> minor_breaks(3)([1, 2], (1, 2))
array([1.25, 1.5 , 1.75])
>>> minor_breaks()([1, 2], (1, 2), 3)
array([1.25, 1.5 , 1.75])

__call__(major: FloatArrayLike, limits: TupleFloat2 | None = None, n: int | None = None) → NDArrayFloat

Minor breaks

Parameters:

majorarray_like

Major breaks

limitsarray_like | None

Limits of the scale. If array_like, must be of size 2. If None, then the minimum and maximum of the major breaks are used.

nint

Number of minor breaks between the major breaks. If None, then self.n is used.

Returns:

outarray_like

Minor beraks

class mizani.breaks.minor_breaks_trans(trans: Trans, n: int = 1)

Compute minor breaks for transformed scales

The minor breaks are computed in data space. This together with major breaks computed in transform space reveals the non linearity of of a scale. See the log transforms created with log_trans() like log10_trans.

Parameters:

transtrans or type

Trans object or trans class.

nint

Number of minor breaks between the major breaks.

Examples

>>> from mizani.transforms import sqrt_trans
>>> major = [1, 2, 3, 4]
>>> limits = [0, 5]
>>> t1 = sqrt_trans()
>>> t1.minor_breaks(major, limits)
array([1.58113883, 2.54950976, 3.53553391])

# Changing the regular minor_breaks method

>>> t2 = sqrt_trans()
>>> t2.minor_breaks = minor_breaks()
>>> t2.minor_breaks(major, limits)
array([0.5, 1.5, 2.5, 3.5, 4.5])

More than 1 minor break

>>> major = [1, 10]
>>> limits = [1, 10]
>>> t2.minor_breaks(major, limits, 4)
array([2.8, 4.6, 6.4, 8.2])

__call__(major: FloatArrayLike, limits: TupleFloat2 | None = None, n: int | None = None) → NDArrayFloat

Minor breaks for transformed scales

Parameters:

majorarray_like

Major breaks

limitsarray_like | None

Limits of the scale. If array_like, must be of size 2. If None, then the minimum and maximum of the major breaks are used.

nint

Number of minor breaks between the major breaks. If None, then self.n is used.

Returns:

outarray_like

Minor breaks

class mizani.breaks.breaks_date(n: int = 5, width: str | None = None)

Regularly spaced dates

Parameters:

n

Desired number of breaks.

widthstr | None

An interval specification. Must be one of [second, minute, hour, day, week, month, year] If None, the interval automatic.

Examples

>>> from datetime import datetime
>>> limits = (datetime(2010, 1, 1), datetime(2026, 1, 1))

Default breaks will be regularly spaced but the spacing is automatically determined

>>> breaks = breaks_date(9)
>>> [d.year for d in breaks(limits)]
[2010, 2012, 2014, 2016, 2018, 2020, 2022, 2024, 2026]

Breaks at 4 year intervals

>>> breaks = breaks_date('4 year')
>>> [d.year for d in breaks(limits)]
[2010, 2014, 2018, 2022, 2026]

__call__(limits: TupleT2[datetime]) → Sequence[datetime]

Compute breaks

Parameters:

limitstuple

Minimum and maximum datetime.datetime values.

Returns:

outarray_like

Sequence of break points.

class mizani.breaks.breaks_timedelta(n: int = 5, Q: Sequence[float] = (1, 2, 5, 10))

Timedelta breaks

Returns:

outcallable() f(limits)

A function that takes a sequence of two datetime.timedelta values and returns a sequence of break points.

Examples

>>> from datetime import timedelta
>>> breaks = breaks_timedelta()
>>> x = [timedelta(days=i*365) for i in range(25)]
>>> limits = min(x), max(x)
>>> major = breaks(limits)
>>> [val.total_seconds()/(365*24*60*60)for val in major]
[0.0, 5.0, 10.0, 15.0, 20.0, 25.0]

__call__(limits: tuple[Timedelta, Timedelta]) → NDArrayTimedelta

Compute breaks

Parameters:

limitstuple

Minimum and maximum datetime.timedelta values.

Returns:

outarray_like

Sequence of break points.

class mizani.breaks.breaks_extended(n: int = 5, Q: Sequence[float] = (1, 5, 2, 2.5, 4, 3), only_inside: bool = False, w: Sequence[float] = (0.25, 0.2, 0.5, 0.05))

An extension of Wilkinson's tick position algorithm

Parameters:

nint

Desired number of breaks

Qlist

List of nice numbers

only_insidebool

If True, then all the breaks will be within the given range.

wlist

Weights applied to the four optimization components (simplicity, coverage, density, and legibility). They should add up to 1.

References

• Talbot, J., Lin, S., Hanrahan, P. (2010) An Extension of Wilkinson's Algorithm for Positioning Tick Labels on Axes, InfoVis 2010.

Additional Credit to Justin Talbot on whose code this implementation is almost entirely based.

Examples

>>> limits = (0, 9)
>>> breaks_extended()(limits)
array([  0. ,   2.5,   5. ,   7.5,  10. ])
>>> breaks_extended(n=6)(limits)
array([  0.,   2.,   4.,   6.,   8.,  10.])

__call__(limits: TupleFloat2) → NDArrayFloat

Calculate the breaks

Parameters:

limitsarray

Minimum and maximum values.

Returns:

outarray_like

Sequence of break points.