plotnine.scales.scale_x_continuous

class plotnine.scales.scale_x_continuous(**kwargs)[source]

Continuous x position

Parameters:
breaks : array_like or callable, optional

Major break points. Alternatively, a callable that takes a tuple of limits and returns a list of breaks. Default is to automatically calculate the breaks.

expand : array_like, optional

Multiplicative and additive expansion constants that determine how the scale is expanded. If specified must of of length 2 or 4. Specifically the the values are of this order:

(mul, add)
(mul_low, add_low, mul_high, add_high)

If not specified, suitable defaults are chosen.

name : str, optional

Name used as the label of the scale. This is what shows up as the axis label or legend title. Suitable defaults are chosen depending on the type of scale.

labels : list or callable, optional

List of str. Labels at the breaks. Alternatively, a callable that takes an array_like of break points as input and returns a list of strings.

limits : array_like, optional

Limits of the scale. Most commonly, these are the min & max values for the scales. For scales that deal with categoricals, these may be a subset or superset of the categories.

na_value : scalar

What value to assign to missing values. Default is to assign np.nan.

palette : callable, optional

Function to map data points onto the scale. Most scales define their own palettes.

aesthetics : list, optional

list of str. Aesthetics covered by the scale. These are defined by each scale and the user should probably not change them. Have fun.

trans : str | function

Name of a trans function or a trans function. See mizani.transforms for possible options.

oob : function

Function to deal with out of bounds (limits) data points. Default is to turn them into np.nan, which then get dropped.

minor_breaks : list-like or int or callable or None

If a list-like, it is the minor breaks points. If an integer, it is the number of minor breaks between any set of major breaks. If a function, it should have the signature func(limits) and return a list-like of consisting of the minor break points. If None, no minor breaks are calculated. The default is to automatically calculate them.

rescaler : function, optional

Function to rescale data points so that they can be handled by the palette. Default is to rescale them onto the [0, 1] range. Scales that inherit from this class may have another default.

Examples

In [1]:
import numpy as np
import pandas as pd
from plotnine import *
from mizani.transforms import trans

Guitar Neck

Using a transformed x-axis to visualise guitar chords

The x-axis is transformed to resemble the narrowing width of frets on a 25.5 inch Strat. To do that we create custom transformation.

The key parts of any transform object are the transform and inverse functions.

In [2]:
class frets_trans(trans):
    """
    Frets Transformation
    """
    number_of_frets = 23               # Including fret 0
    domain = (0, number_of_frets-1)

    @staticmethod
    def transform(x):
        x = np.asarray(x)
        return 25.5 - (25.5 / (2 ** (x/12)))

    @staticmethod
    def inverse(x):
        x = np.asarray(x)
        return 12 * np.log2(25.5/(25.5-x))

    @classmethod
    def breaks_(cls, limits):
        # Fixed major breaks
        return cls.domain

    @classmethod
    def minor_breaks(cls, major, limits):
        # The major breaks as passed to this method are in transformed space.
        # The minor breaks are calculated in data space to reveal the
        # non-linearity of the scale.
        _major = cls.inverse(major)
        minor = cls.transform(np.linspace(*_major, cls.number_of_frets))
        return minor

The above transform is different from most in that, breaks and minor breaks do not change. This is common of very specialized scales. It can also be a key requirement when creating graphics for demontration purposes.

Some chord Data

In [3]:
# Notes: the 0 fret is an open strum, all other frets are played half-way between fret bars.
# The strings are 1:low E, 2: A, 3: D, 4: G, 5: B, 6: E
c_chord = pd.DataFrame({
    'Fret':   [0, 2.5, 1.5, 0, 0.5, 0],
    'String': [1, 2, 3, 4, 5, 6]
})

# Sequence based on the number of notes in the chord
c_chord['Sequence'] = list(range(1, 1+len(c_chord['Fret'])))

# Standard markings for a Stratocaster
markings = pd.DataFrame({
    'Fret':   [2.5, 4.5, 6.5, 8.5, 11.5, 11.5, 14.5, 16.5, 18.5, 20.5],
    'String': [3.5, 3.5, 3.5, 3.5, 2, 5, 3.5, 3.5, 3.5, 3.5]
})

Visualizing the chord

In [4]:
# Look and feel of the graphic
neck_color = '#FFDDCC'
fret_color = '#998888'
string_color = '#AA9944'

neck_theme = theme(
    figure_size=(10, 2),
    panel_background=element_rect(fill=neck_color),
    panel_grid_major_y=element_line(color=string_color, size=2.2),
    panel_grid_major_x=element_line(color=fret_color, size=2.2),
    panel_grid_minor_x=element_line(color=fret_color, size=1)
)

# The plot
(ggplot(c_chord, aes('Fret', 'String'))
 + geom_path(aes(color='Sequence'), size=3)
 + geom_point(aes(color='Sequence'), fill='#FFFFFF', size=3)
 + geom_point(data=markings, fill='#000000', size=4)
 + scale_x_continuous(trans=frets_trans)
 + scale_y_continuous(breaks=range(0, 7), minor_breaks=[])
 + guides(color=False)
 + neck_theme
)
../_images/scale_x_continuous_8_0.png
Out[4]:
<ggplot: (97654321012345679)>

Credit: This example was motivated by Jonathan Vitale who wanted to create graphics for a guitar scale trainer.