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Colors

Handling the colors in the calendar plots.

This module provides some helper function and some defaults. However, they might not be the best of all purposes.

Color maps here take floats to a color string. Usually a hex string.

Example

Create a color map for count data.

cmap = create_default_cmap(max_value=10)

cmap(0)  # '#ffffe5'
cmap(5)  # '#379e54'
cmap(10) # '#004529'

ColorMap

Bases: ScalarMappable

This supports colorbar for a figure from matplotlib.

Parameters:

Name Type Description Default
norm

matplotlib.colors.Normalize

 required
cmap

matplotlib.cm.ScalarMappable

 required
default_cm CM

matplotlib.cm.ScalarMappable

 required
Source code in latent_calendar/plot/colors.py 
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class ColorMap(ScalarMappable):
    """This supports colorbar for a figure from matplotlib.

    Args:
        norm: matplotlib.colors.Normalize
        cmap: matplotlib.cm.ScalarMappable
        default_cm: matplotlib.cm.ScalarMappable

    """

    def __init__(self, norm, cmap, default_cm: CM) -> None:
        cmap = cmap if cmap is not None else default_cm
        super().__init__(norm=norm, cmap=cmap)

    def __call__(self, x: float) -> str:
        return rgb2hex(self.cmap(self.norm(x)))

    def add_colorbar(self, ax=None) -> None:
        """Add the colorbar to axis or axes.

        Args:
            ax: single or np.ndarray of Axes

        """
        fig = plt.gcf()

        fig.colorbar(self, ax=ax, ticks=self.norm.ticks)

add_colorbar(ax=None)

Add the colorbar to axis or axes.

Parameters:

Name Type Description Default
ax

single or np.ndarray of Axes

 None
Source code in latent_calendar/plot/colors.py 
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def add_colorbar(self, ax=None) -> None:
    """Add the colorbar to axis or axes.

    Args:
        ax: single or np.ndarray of Axes

    """
    fig = plt.gcf()

    fig.colorbar(self, ax=ax, ticks=self.norm.ticks)

create_cmap(max_value, min_value=0.0, cm=None)

Create color map function.

Parameters:

Name Type Description Default
max_value float

maximum value for the color map

 required
min_value float

minimum value for the color map

 0.0
cm CM | None

function that takes a value and returns a color

 None
Source code in latent_calendar/plot/colors.py 
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def create_cmap(max_value: float, min_value: float = 0.0, cm: CM | None = None) -> CMAP:
    """Create color map function.

    Args:
        max_value: maximum value for the color map
        min_value: minimum value for the color map
        cm: function that takes a value and returns a color

    """
    norm = Normalize(vmin=min_value, vmax=max_value)
    norm.ticks = [norm.vmin, norm.vmax]
    return ColorMap(norm=norm, cmap=cm, default_cm=plt.cm.YlGn)

create_diverge_cmap(center_value, range, cm=None)

Create color map function to emphasize a center value and deviation from that center.

Might be good for values that are relative to some baseline

Source code in latent_calendar/plot/colors.py 
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def create_diverge_cmap(
    center_value: float, range: float, cm: CM | None = None
) -> CMAP:
    """Create color map function to emphasize a center value and deviation from that center.

    Might be good for values that are relative to some baseline

    """
    half_range = range / 2
    norm = Normalize(vmin=center_value - half_range, vmax=center_value + half_range)
    norm.ticks = [norm.vmin, 0, norm.vmax]
    return ColorMap(norm=norm, cmap=cm, default_cm=plt.cm.coolwarm)

create_relative_cmap(range)

Good for relative scales.

Source code in latent_calendar/plot/colors.py 
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def create_relative_cmap(range: float) -> CMAP:
    """Good for relative scales."""
    return create_diverge_cmap(center_value=1.0, range=range)

settle_data_and_cmap(data, divergent)

Return a tuple of transformed data and cmap for displaying that data.

Source code in latent_calendar/plot/colors.py 
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def settle_data_and_cmap(data, divergent: bool) -> tuple[np.ndarray, CMAP]:
    """Return a tuple of transformed data and cmap for displaying that data."""
    if divergent:
        # Comparing the values to random rate
        EVEN_PROBABILITY = 1 / data.shape[0]
        data = data / EVEN_PROBABILITY
        return data, create_default_divergent_cmap()

    return data, create_default_cmap(value=data.max())

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